A factory air environment pollution monitoring device and method for occupational hazards

Abstract

The application discloses a kind of factory air environment pollution monitoring equipment and method of occupational hazards, belong to air environment monitoring technical field, solve the complex pollutant toxicity multiplication formed by quenching oil mist and metal dust in bearing manufacturing plant heat treatment and fine grinding workshop, but because of oil mist interference detection, workshop pollutant retention zone causes sampling distortion, finally make the problem of false detection report compliance.The sensor array module includes a quartz crystal microbalance array module, a multi-wavelength optical sensor array module, a wideband impedance sensor array module, and a surface acoustic wave sensor array module.The application realizes comprehensive and accurate monitoring and risk warning of complex pollutants in the factory environment by synchronously collecting multi-modal sensor data, real-time analysis and processing, quantifying pollutant carrier effect, fusing multi-source information, accurately identifying pollutant components, evaluating health risk level and automatically generating warning signals.

Description

Technical Field

[0001] This invention relates to the field of air environment monitoring technology, and in particular to a factory air environment pollution monitoring device and method for occupational disease hazards. Background Technology

[0002] Occupational hazard air pollution testing in factories needs to be conducted regularly according to the degree of hazard. First, the production process is investigated to determine sampling points. Pollutants such as dust and hazardous chemicals are collected using fixed-point / individual sampling or online monitoring. These samples are sent to the laboratory for analysis using chromatography and other methods to calculate the concentration and compare it with occupational exposure limits. After issuing a report, it is used for job classification, selection of protective equipment, and improvement suggestions when standards are exceeded, ensuring worker health. The core principle is compliance with the GBZ series standards.

[0003] In the heat treatment and precision grinding workshop of a bearing manufacturing plant, quenching oil mist, grinding metal dust, and welding fumes present at the same time form complex mixed pollutants under intense heat and pressure. Among them, oil mist acts as a carrier to adsorb toxic metal particles, forming "composite particles" that can penetrate deeper into the alveoli, greatly enhancing toxicity. However, oil mist can clog sampling filter membranes, causing distortion of dust concentration measurements. Its oily matrix can also interfere with the accurate detection of heavy metals in laboratory analysis. At the same time, the "pollutant retention zone" formed by the high space of the workshop makes fixed-point sampling unable to represent the true exposure level of workers' breathing zones.

[0004] The direct consequence of these problems is the generation of a test report that appears to "meet the standards" but is completely distorted. This report not only conceals the truth that workers have been exposed to highly toxic compound pollutants for a long time, leading to irreversible health damage such as pneumoconiosis and mixed lesions, but also causes companies to miss the critical opportunity to take effective protective measures because they cannot identify the real risks. Ultimately, this creates a management blind spot and an occupational health tragedy where "the data is qualified, but the workers are harmed".

[0005] Therefore, a factory air pollution monitoring device and method for occupational disease hazards is proposed to solve or alleviate the above problems. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by proposing a factory air pollution monitoring device and method for occupational disease hazards.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: An occupational hazard monitoring device for factory air pollution includes a sensor array module and a data processing module. The sensor array module includes a quartz crystal microbalance array module, a multi-wavelength optical sensor array module, a broadband impedance sensor array module, a surface acoustic wave sensor array module, a thermal conductivity detector array module, and an electrochemical sensor array module. The analog signal output terminal of the sensor array module is connected to the digital signal input terminal of the data processing module, and the control signal output terminal of the data processing module is connected to the control signal input terminal of the sensor array module. The data processing module performs a full-process analysis, including feature extraction, carrier effect quantification, multi-source information fusion, pollutant identification and classification, health risk assessment, adaptive calibration optimization, and trend prediction and early warning, and finally outputs the identification results of composite pollutants and the health risk level.

[0008] Preferably, the quartz crystal microbalance array module includes a T-cut quartz crystal, a J310 junction field-effect transistor (JFET), a 74VHC04 inverter, an ADG1434 RF switch, an ADE1 mixer, a first AD8302 phase detector, and a first ADS1278 analog-to-digital converter. The first end of the T-cut quartz crystal is connected to the gate of the J310 JFET, and the second end of the T-cut quartz crystal is grounded. The source of the J310 JFET is grounded through a first resistor, and the drain of the J310 JFET is connected to the input pin of the 74VHC04 inverter. The output pin of the 74VHC04 inverter is fed back to its input pin through a first capacitor. The positive power supply terminal of the 74VHC04 inverter is energized, and the ground terminal of the 74VHC04 inverter is grounded. The first RF input / output terminal of the ADG1434 RF switch is connected to the output pin of the 74VHC04 inverter. The second RF input / output terminal of the ADG1434 RF switch is connected to the local oscillator input terminal of the ADE1 mixer. The first control terminal of the ADG1434 RF switch is connected to the first general-purpose input / output terminal of the microcontroller unit in the data processing module. The second control terminal of the ADG1434 RF switch is connected to the second general-purpose input / output terminal of the microcontroller unit in the data processing module. The intermediate frequency output terminal of the ADE1 mixer is connected to the phase input terminal of the first AD8302 phase detector, with its positive power supply terminal energized and its ground terminal grounded. The output terminal of the first AD8302 phase detector is connected to the field-programmable gate array real-time processing unit in the data processing module through the first ADS1278 analog-to-digital converter.

[0009] Preferably, the multi-wavelength optical sensor array module includes an ultraviolet light-emitting diode (UV LED), a blue light-emitting diode (Blue LED), a red light-emitting diode (Red LED), an infrared light-emitting diode (IR LED), an LM317 constant current driver, a silicon photodiode, an OPA656 transimpedance amplifier, a PGA112 programmable gain amplifier, an LTC1562 anti-aliasing filter, a first ADS8688 analog-to-digital converter (ADC), an avalanche photodiode, a CVA400 high-speed amplifier, and a TDCGP30 time-to-digital converter. The anode of the UV LED is connected to the LM317 via a second resistor. The output terminal of the LM317 constant current driver has its cathode grounded. The adjustment terminal of the LM317 constant current driver is grounded through a third resistor. The input terminal of the LM317 constant current driver is energized. The cathode of the silicon photodiode is connected to the inverting input terminal of the OPA656 transimpedance amplifier, and the anode of the silicon photodiode is energized. The non-inverting input terminal of the OPA656 transimpedance amplifier is grounded. The output terminal of the OPA656 transimpedance amplifier is connected to its inverting input terminal through a fourth resistor. The power supply terminal of the OPA656 transimpedance amplifier is energized. The output of the A656 transimpedance amplifier is connected to the input of the PGA112 programmable gain amplifier. The gain control terminal of the PGA112 programmable gain amplifier's input is connected to the microcontroller unit in the data processing module. The output of the PGA112 programmable gain amplifier is connected to the input of the LTC1562 anti-aliasing filter. The output of the LTC1562 anti-aliasing filter is connected to the first ADS8688 analog-to-digital converter. The output of the first ADS8688 analog-to-digital converter is connected to the field-programmable gate array (FPGA) real-time processing unit in the data processing module. The cathode of the avalanche photodiode is energized, and the anode of the avalanche photodiode is connected to the input of the CVA400 high-speed amplifier. The output of the CVA400 high-speed amplifier is connected to the high-speed channel input of the TDCGP30 time-to-digital converter. The clock input of the TDCGP30 time-to-digital converter is connected to a 25MHz crystal oscillator. The data output of the TDCGP30 time-to-digital converter is connected to the data bus of the FPGA real-time processing unit in the data processing module.

[0010] Preferably, the wideband impedance sensor array module includes an interdigital electrode array, an AD9958 direct digital synthesizer, an OPA564 power amplifier, a first IVC102 current-to-voltage converter, and a second ADS1278 analog-to-digital converter. The output of the AD9958 direct digital synthesizer is connected to the working electrode of the interdigital electrode array. The complementary output of the AD9958 direct digital synthesizer is grounded through a fifth resistor. The serial clock, serial data input / output, and chip select terminals of the AD9958 direct digital synthesizer are respectively connected to the serial peripheral interface clock, serial peripheral interface data, and serial peripheral interface chip select terminals of the microcontroller unit in the data processing module. The non-inverting input of the OPA564 power amplifier is connected to the output of the AD9958 direct digital synthesizer. The inverting input of the OPA564 power amplifier is grounded through a sixth resistor. The output of the 564 power amplifier is connected to the counter electrode of the interdigital electrode array, and its power supply is energized. The inverting input of the first IVC102 current-to-voltage converter is connected to the counter electrode of the interdigital electrode array. The non-inverting input of the first IVC102 current-to-voltage converter is grounded. The output of the first IVC102 current-to-voltage converter is connected to the analog input of the second ADS1278 analog-to-digital converter. A first integrating capacitor is connected between the first capacitor terminal and the second capacitor terminal of the first IVC102 current-to-voltage converter. The analog input of the second ADS1278 analog-to-digital converter is connected to the output of the first IVC102 current-to-voltage converter. The data output, data clock, and data ready terminals of the second ADS1278 analog-to-digital converter are connected to the serial data input, serial clock, and control terminals of the field-programmable gate array real-time processing unit in the data processing module, respectively.

[0011] Preferably, the surface acoustic wave (SAW) sensor array module includes a SAW device, an ADF4351 RF synthesizer, an SKY65111 power amplifier, an HMC773 mixer, a second AD8302 phase detector, and a second ADS8688 analog-to-digital converter. The RF output of the ADF4351 RF synthesizer is connected to the RF input of the SKY65111 power amplifier. The enable, clock, and data terminals of the ADF4351 RF synthesizer are respectively connected to the first serial peripheral interface enable, serial peripheral interface clock, and serial peripheral interface data terminals of the microcontroller unit in the data processing module. The RF output of the SKY65111 power amplifier is connected to the input of the SAW device via a directional coupler, and its positive power supply is connected to the circuit. The RF output of the HMC773 mixer is connected to the input of the SAW device via a directional coupler. The input terminal is connected to the output terminal of the surface acoustic wave device. The local oscillator input terminal of the HMC773 mixer is connected to the RF output terminal of the ADF4351 RF synthesizer. The intermediate frequency output terminal of the HMC773 mixer is connected to the phase input terminal of the second AD8302 phase detector. The voltage output terminal of the second AD8302 phase detector is connected to the analog input terminal of the second ADS8688 analog-to-digital converter, with its positive power supply terminal energized and its ground terminal grounded. The analog input terminal of the second ADS8688 analog-to-digital converter is connected to the voltage output terminal of the second AD8302 phase detector. The serial data output terminal, serial clock terminal, and chip select terminal of the second ADS8688 analog-to-digital converter are respectively connected to the serial data input terminal, serial clock terminal, and chip select terminal of the field-programmable gate array real-time processing unit in the data processing module.

[0012] Preferably, the thermal conductivity detector array module includes a platinum heater, a PT1000 temperature sensor, an IRF7413 field-effect transistor, an ADN8830 proportional-integral-differential controller, a MAX31865 resistance temperature detector reader, a Wheatstone bridge, an INA128 instrumentation amplifier, and a third ADS8688 analog-to-digital converter. The first terminal of the platinum heater is connected to the drain of the IRF7413 field-effect transistor, and the second terminal of the platinum heater is grounded. The gate of the IRF7413 field-effect transistor is connected to the high-side drive of the ADN8830 proportional-integral-differential controller, and the source of the IRF7413 field-effect transistor is grounded. The non-inverting input of the ADN8830 proportional-integral-differential controller is connected to the output of the third ADS8688 analog-to-digital converter, and the inverting input of the ADN8830 proportional-integral-differential controller is connected to the data output of the MAX31865 resistance temperature detector reader, with its positive power supply connected. The positive and negative terminals of the AX31865 resistance temperature detector reader are connected to the output of the PT1000 temperature sensor. The chip select, serial clock, master-slave input, and master-input-slave output terminals of the AX31865 resistance temperature detector reader are respectively connected to the chip select, serial peripheral interface clock, serial peripheral interface data output, and serial peripheral interface data input terminals of the microcontroller unit in the data processing module. The first terminal of the Wheatstone bridge is powered on, the second terminal of the Wheatstone bridge is connected to the inverting input of the INA128 instrumentation amplifier, the third terminal of the Wheatstone bridge is connected to the non-inverting input of the INA128 instrumentation amplifier, and the fourth terminal of the Wheatstone bridge is grounded. The output of the INA128 instrumentation amplifier is connected to the analog input of the third ADS8688 analog-to-digital converter, and its power supply is powered on. The output of the third ADS8688 analog-to-digital converter is connected to the field-programmable gate array real-time processing unit in the data processing module.

[0013] Preferably, the electrochemical sensor array module includes a working electrode, a counter electrode, a reference electrode, an OPA445 potentiostat operational amplifier, a second IVC102 current-to-voltage converter, a DAC8568 digital-to-analog converter, a fourth ADS8688 analog-to-digital converter, and an ADuM1412 digital isolator. The inverting input of the OPA445 potentiostat operational amplifier is connected to the working electrode, the non-inverting input is connected to the output of the DAC8568, the output is connected to the counter electrode, the power supply is connected to the OPA445, and the output of the DAC8568 is connected to the non-inverting input of the OPA445. Its synchronization terminal, serial data input terminal, and serial clock terminal are respectively connected to the data processing module. The microcontroller unit has a serial peripheral interface synchronization terminal, a serial peripheral interface data output terminal, and a serial peripheral interface clock terminal. The inverting input terminal of the second IVC102 current-to-voltage converter is connected to the counter electrode. The output terminal of the second IVC102 current-to-voltage converter is connected to the analog input terminal of the fourth ADS8688 analog-to-digital converter. A second integrating capacitor is connected between the first and second capacitor terminals of the second IVC102 current-to-voltage converter. The analog input terminal of the fourth ADS8688 analog-to-digital converter is connected to the output terminal of the second IVC102 current-to-voltage converter. Its serial data output terminal, serial clock terminal, and chip select terminal are respectively connected to the input data terminal, clock input terminal, and chip select input terminal of the ADuM1412 digital isolator. The output data terminal of the ADuM1412 digital isolator is connected to the data input terminal of the field-programmable gate array real-time processing unit in the data processing module.

[0014] Preferably, the data processing module includes a field-programmable gate array (FPGA) real-time processing unit, a microcontroller unit, DDR3 memory, and an SI5338A clock generator. The microcontroller unit is an STM32H753 microcontroller. The data bus pins of the FPGA real-time processing unit are connected to the data bus of the DDR3 memory. The clock output of the SI5338A clock generator is connected to the global clock pin of the FPGA real-time processing unit and the clock pin of the DDR3 memory, respectively. The configuration interface of the SI5338A clock generator is connected to the integrated circuit bus of the microcontroller unit. The Ethernet media independent interface of the microcontroller unit is connected to a LAN8720 Ethernet physical layer chip.

[0015] This invention also provides a method for monitoring factory air pollution caused by occupational hazards, which involves monitoring using the factory air pollution monitoring equipment for occupational hazards as described above, and includes the following steps: Multimodal sensing data is collected synchronously through a sensor array module; The data processing module extracts multi-dimensional features of composite pollutants from the data. These multi-dimensional features include mass loading features, optical scattering features, impedance spectrum features, acoustic wave propagation features, thermal conductivity features, and electrochemical features. The enhancing effect of oil mist carrier effect on the toxicity of metal dust was quantified, and the carrier efficiency and toxicity enhancement coefficient were calculated. By integrating the feature information of each sensor through a multi-sensor data fusion algorithm, feature-level fusion and decision-level fusion can be achieved. Pollutant type identification and quantitative concentration analysis are performed based on the fused features; Assess the health risk levels of multiple pollutants, and calculate exposure doses and risk levels; The system performance is optimized through an adaptive calibration algorithm, and trend prediction and risk warning are performed.

[0016] The present invention has the following beneficial effects: This invention achieves comprehensive and accurate monitoring and risk warning of complex pollutants in the factory environment by simultaneously collecting multimodal sensor data, analyzing and processing it in real time, quantifying the pollutant carrier effect, fusing multi-source information, accurately identifying pollutant components, assessing health risk levels, and automatically generating early warning signals. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a structural block diagram of the present invention; Figure 2 This is a flowchart of the present invention.

[0019] 1. Quartz crystal microbalance array module; 2. Multi-wavelength optical sensor array module; 3. Wideband impedance sensor array module; 4. Surface acoustic wave sensor array module; 5. Thermal conductivity detector array module; 6. Electrochemical sensor array module; 7. Data processing module. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0022] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0023] In the description of this invention, it should be understood that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use, or the orientation or positional relationship commonly understood by those skilled in the art. They are only used to facilitate the description of this invention and to simplify the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0024] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0025] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0026] A factory air pollution monitoring device for occupational disease hazards, such as Figure 1As shown, the system includes a sensor array module and a data processing module 7. The sensor array module includes a quartz crystal microbalance array module 1, a multi-wavelength optical sensor array module 2, a broadband impedance sensor array module 3, a surface acoustic wave sensor array module 4, a thermal conductivity detector array module 5, and an electrochemical sensor array module 6. The analog signal output terminal of the sensor array module is connected to the digital signal input terminal of the data processing module 7, and the control signal output terminal of the data processing module 7 is connected to the control signal input terminal of the sensor array module. The data processing module 7 performs a full-process analysis, including feature extraction, carrier effect quantification, multi-source information fusion, pollutant identification and classification, health risk assessment, adaptive calibration optimization, and trend prediction and early warning, and finally outputs the identification results and health risk levels of composite pollutants.

[0027] The quartz crystal microbalance array module 1 includes a T-cut quartz crystal, a J310 junction field-effect transistor (JFET), a 74VHC04 inverter, an ADG1434 RF switch, an ADE1 mixer, a first AD8302 phase detector, and a first ADS1278 analog-to-digital converter (ADC). The first end of the T-cut quartz crystal is connected to the gate of the J310 JFET, and the second end is grounded. The source of the J310 JFET is grounded through a first resistor, and the drain of the J310 JFET is connected to the input pin of the 74VHC04 inverter. The output pin of the 74VHC04 inverter is fed back to its input pin through a first capacitor. The positive power supply terminal of the 74VHC04 inverter is energized, and the ground terminal of the 74VHC04 inverter is grounded. The ADG1434... The first RF input / output terminal of the type 4 RF switch is connected to the output pin of the 74VHC04 inverter. The second RF input / output terminal of the ADG1434 RF switch is connected to the local oscillator input terminal of the ADE1 mixer. The first control terminal of the ADG1434 RF switch is connected to the first general-purpose input / output terminal of the microcontroller unit in the data processing module 7. The second control terminal of the ADG1434 RF switch is connected to the second general-purpose input / output terminal of the microcontroller unit in the data processing module 7. The intermediate frequency output terminal of the ADE1 mixer is connected to the phase input terminal of the first AD8302 phase detector, with its positive power supply terminal connected to power and its ground terminal grounded. The output terminal of the first AD8302 phase detector is connected to the field-programmable gate array real-time processing unit in the data processing module 7 through the first ADS1278 analog-to-digital converter.

[0028] The multi-wavelength optical sensor array module 2 includes ultraviolet light-emitting diodes (UV LEDs), blue LEDs, red LEDs, infrared LEDs, an LM317 constant current driver, a silicon photodiode, an OPA656 transimpedance amplifier, a PGA112 programmable gain amplifier, an LTC1562 anti-aliasing filter, a first ADS8688 analog-to-digital converter, an avalanche photodiode, a CVA400 high-speed amplifier, and a TDCGP30 time-to-digital converter. The anode of the photodiode is connected to the output of the LM317 constant current driver via a second resistor, and its cathode is grounded. The adjustment terminal of the LM317 constant current driver is grounded via a third resistor. The input terminal of the LM317 constant current driver is energized. The cathode of the silicon photodiode is connected to the inverting input of the OPA656 transimpedance amplifier, and the anode of the silicon photodiode is energized. The non-inverting input of the OPA656 transimpedance amplifier is grounded. The output of the OPA656 transimpedance amplifier is connected to its inverting input via a fourth resistor. The power supply is connected. The output of the OPA656 transimpedance amplifier is connected to the input of the PGA112 programmable gain amplifier. The gain control terminal of the PGA112 programmable gain amplifier's input is connected to the microcontroller unit in data processing module 7. The output of the PGA112 programmable gain amplifier is connected to the input of the LTC1562 anti-aliasing filter. The output of the LTC1562 anti-aliasing filter is connected to the first ADS8688 analog-to-digital converter. The output of the first ADS8688 analog-to-digital converter is connected to the field-programmable gate array (FPGA) real-time processing unit in data processing module 7. The cathode of the avalanche photodiode is connected to power. The anode of the avalanche photodiode is connected to the input of the CVA400 high-speed amplifier. The output of the CVA400 high-speed amplifier is connected to the high-speed channel input of the TDCGP30 time-to-digital converter. The clock input of the TDCGP30 time-to-digital converter is connected to a 25MHz crystal oscillator. The data output of the TDCGP30 time-to-digital converter is connected to the data bus of the FPGA real-time processing unit in data processing module 7.

[0029] The wideband impedance sensor array module 3 includes an interdigital electrode array, an AD9958 direct digital synthesizer, an OPA564 power amplifier, a first IVC102 current-to-voltage converter, and a second ADS1278 analog-to-digital converter. The output of the AD9958 direct digital synthesizer is connected to the working electrode of the interdigital electrode array. The complementary output of the AD9958 direct digital synthesizer is grounded through a fifth resistor. The serial clock, serial data input / output, and chip select terminals of the AD9958 direct digital synthesizer are respectively connected to the serial peripheral interface clock, serial peripheral interface data, and serial peripheral interface chip select terminals of the microcontroller unit in the data processing module 7. The non-inverting input of the OPA564 power amplifier is connected to the output of the AD9958 direct digital synthesizer. The inverting input of the OPA564 power amplifier is grounded through a sixth resistor. The output of the Type 4 power amplifier is connected to the counter electrode of the interdigital electrode array, and its power supply is energized. The inverting input of the first IVC102 current-to-voltage converter is connected to the counter electrode of the interdigital electrode array. The non-inverting input of the first IVC102 current-to-voltage converter is grounded. The output of the first IVC102 current-to-voltage converter is connected to the analog input of the second ADS1278 analog-to-digital converter. A first integrating capacitor is connected between the first capacitor terminal and the second capacitor terminal of the first IVC102 current-to-voltage converter. The analog input of the second ADS1278 analog-to-digital converter is connected to the output of the first IVC102 current-to-voltage converter. The data output, data clock, and data ready terminals of the second ADS1278 analog-to-digital converter are connected to the serial data input, serial clock, and control terminals of the field-programmable gate array real-time processing unit in the data processing module 7, respectively.

[0030] The surface acoustic wave (SAW) sensor array module 4 includes a SAW device, an ADF4351 RF synthesizer, an SKY65111 power amplifier, an HMC773 mixer, a second AD8302 phase detector, and a second ADS8688 analog-to-digital converter. The RF output of the ADF4351 RF synthesizer is connected to the RF input of the SKY65111 power amplifier. The enable, clock, and data terminals of the ADF4351 RF synthesizer are respectively connected to the first serial peripheral interface enable, serial peripheral interface clock, and serial peripheral interface data terminals of the microcontroller unit in the data processing module 7. The RF output of the SKY65111 power amplifier is connected to the input of the SAW device via a directional coupler, and its positive power supply is connected to the circuit. The RF input of the HMC773 mixer... The local oscillator input of the HMC773 mixer is connected to the RF output of the ADF4351 RF synthesizer. The intermediate frequency output of the HMC773 mixer is connected to the phase input of the second AD8302 phase detector. The voltage output of the second AD8302 phase detector is connected to the analog input of the second ADS8688 analog-to-digital converter, with its positive power supply connected to the circuit and its grounding terminal grounded. The analog input of the second ADS8688 analog-to-digital converter is connected to the voltage output of the second AD8302 phase detector. The serial data output, serial clock, and chip select of the second ADS8688 analog-to-digital converter are respectively connected to the serial data input, serial clock, and chip select of the field-programmable gate array real-time processing unit in data processing module 7.

[0031] The thermal conductivity detector array module 5 includes a platinum heater, a PT1000 temperature sensor, an IRF7413 field-effect transistor, an ADN8830 proportional-integral-differential controller, a MAX31865 resistance temperature detector reader, a Wheatstone bridge, an INA128 instrumentation amplifier, and a third ADS8688 analog-to-digital converter. The first terminal of the platinum heater is connected to the drain of the IRF7413 field-effect transistor, and the second terminal is grounded. The gate of the IRF7413 field-effect transistor is connected to the high-side drive of the ADN8830 proportional-integral-differential controller, and the source of the IRF7413 field-effect transistor is grounded. The non-inverting input of the ADN8830 proportional-integral-differential controller is connected to the output of the third ADS8688 analog-to-digital converter, and the inverting input of the ADN8830 proportional-integral-differential controller is connected to the data output of the MAX31865 resistance temperature detector reader, with its positive power supply connected. The positive and negative terminals of the 1865 type resistance temperature detector reader are connected to the output terminal of the PT1000 type temperature sensor. The chip select terminal, serial clock terminal, master-slave input terminal, and master-input-slave output terminal of the MAX31865 type resistance temperature detector reader are respectively connected to the chip select terminal, serial peripheral interface clock terminal, serial peripheral interface data output terminal, and serial peripheral interface data input terminal of the microcontroller unit in data processing module 7. The first terminal of the Wheatstone bridge is powered on, the second terminal of the Wheatstone bridge is connected to the inverting input terminal of the INA128 type instrumentation amplifier, the third terminal of the Wheatstone bridge is connected to the non-inverting input terminal of the INA128 type instrumentation amplifier, and the fourth terminal of the Wheatstone bridge is grounded. The output terminal of the INA128 type instrumentation amplifier is connected to the analog input terminal of the third ADS8688 type analog-to-digital converter, and its power supply terminal is powered on. The output terminal of the third ADS8688 type analog-to-digital converter is connected to the field programmable gate array real-time processing unit in data processing module 7.

[0032] The electrochemical sensor array module 6 includes a working electrode, a counter electrode, a reference electrode, an OPA445 potentiostat operational amplifier, a second IVC102 current-to-voltage converter, a DAC8568 digital-to-analog converter, a fourth ADS8688 analog-to-digital converter, and an ADuM1412 digital isolator. The inverting input of the OPA445 potentiostat operational amplifier is connected to the working electrode, the non-inverting input is connected to the output of the DAC8568, the output is connected to the counter electrode, the power supply is connected to the OPA445, and the output of the DAC8568 isolator is connected to the non-inverting input. Its synchronization terminal, serial data input terminal, and serial clock terminal are respectively connected to the microcontroller in the data processing module 7. The serial peripheral interface synchronization terminal, serial peripheral interface data output terminal, and serial peripheral interface clock terminal of the controller unit are connected to the counter electrode. The inverting input terminal of the second IVC102 current-to-voltage converter is connected to the analog input terminal of the fourth ADS8688 analog-to-digital converter. A second integrating capacitor is connected between the first and second capacitor terminals of the second IVC102 current-to-voltage converter. The analog input terminal of the fourth ADS8688 analog-to-digital converter is connected to the output terminal of the second IVC102 current-to-voltage converter. Its serial data output terminal, serial clock terminal, and chip select terminal are respectively connected to the input data terminal, clock input terminal, and chip select input terminal of the ADuM1412 digital isolator. The output data terminal of the ADuM1412 digital isolator is connected to the data input terminal of the field-programmable gate array real-time processing unit in the data processing module 7.

[0033] Data processing module 7 includes a field-programmable gate array (FPGA) real-time processing unit, a microcontroller unit, DDR3 memory, and an SI5338A clock generator. The microcontroller unit is an STM32H753 microcontroller. The data bus pins of the FPGA real-time processing unit are connected to the data bus of the DDR3 memory. The clock output of the SI5338A clock generator is connected to the global clock pin of the FPGA real-time processing unit and the clock pin of the DDR3 memory, respectively. The configuration interface of the SI5338A clock generator is connected to the integrated circuit bus of the microcontroller unit. The Ethernet media independent interface of the microcontroller unit is connected to a LAN8720 Ethernet physical layer chip.

[0034] This invention also provides a method for monitoring factory air pollution related to occupational hazards, such as... Figure 2 As shown, monitoring of factory air pollution using the above-mentioned occupational hazard monitoring equipment includes the following steps: Multimodal sensing data is synchronously acquired through sensor array modules. More specifically, mass load data is acquired through quartz crystal microbalance array module 1, and frequency offset and impedance change are measured. Multi-wavelength optical sensor array module 2 acquires multi-angle scattering data and absorption spectrum data. Wideband impedance sensor array module 3 acquires impedance amplitude and phase data at multiple frequency points. Surface acoustic wave sensor array module 4 acquires sound wave propagation speed and attenuation data. Thermal conductivity detector array module 5 acquires gas thermal conductivity change data. Electrochemical sensor array module 6 acquires current and voltage characteristic data. All sensor data are synchronized using a unified timestamp. The sampling trigger signal is simultaneously sent to each sensor array module by data processing module 7. The data processing module 7 performs preprocessing and quality control on the acquired sensor data, including signal denoising, sensor drift compensation, and signal quality assessment. More specifically, it uses a wavelet threshold denoising algorithm to eliminate noise in the original sensor data, which includes multi-scale wavelet decomposition of the signal, soft thresholding of wavelet coefficients, and reconstruction of the denoised signal through inverse wavelet transform. It also uses an adaptive Kalman filter algorithm to filter the time-varying signal, including state prediction, covariance prediction, Kalman gain calculation, state update, and covariance update steps. Furthermore, it uses a baseline drift correction algorithm to eliminate the long-term drift effect of the sensor, estimating the baseline drift amount using the exponential weighted moving average method and subtracting it from the original signal. Finally, it uses a temperature compensation model to correct the influence of ambient temperature on sensor readings, performing real-time compensation based on a pre-calibrated temperature coefficient. The module also performs quality assessment on the preprocessed data, calculating the signal-to-noise ratio and data integrity index. Data processing module 7 extracts multi-dimensional features of composite pollutants from the data. These features include mass loading characteristics, optical scattering characteristics, impedance spectrum characteristics, acoustic wave propagation characteristics, thermal conductivity characteristics, and electrochemical characteristics. More specifically, it calculates mass loading characteristics from quartz crystal microbalance data based on the Sauerbrey equation, where the change in mass loading is proportional to the frequency shift and inversely proportional to the harmonic order. It calculates viscoelastic correction characteristics based on the Kanazawa-Gordon equation, where the frequency shift is proportional to the square root of the liquid density and viscosity and inversely proportional to the square root of the crystal density and shear modulus. It inverts particle size distribution characteristics from optical sensor data based on Mie scattering theory, where the scattering intensity is proportional to the particle number concentration and the integral of the Mie scattering intensity function. It extracts relaxation time characteristics from impedance spectrum data based on the Cole-Cole model, where the impedance value is related to low-frequency resistance value, high-frequency resistance value, angular frequency, relaxation time, and distribution parameters. It also extracts interface polarization characteristics based on relaxation time distribution analysis. The enhancement effect of oil mist carrier effect on the toxicity of metal dust was quantified by calculating carrier efficiency and toxicity enhancement coefficient. More specifically, the oil mist carrier efficiency was calculated as the ratio of metal concentration on carrier particles to total metal concentration. The alveolar deposition enhancement factor was calculated as the ratio of deposition fraction of composite particles in the alveolar region to deposition fraction of pure metal particles. The bioavailability correction coefficient was calculated as the ratio of dissolution rate constant of composite particles to dissolution rate constant of pure metal particles. The composite toxicity index was calculated as the sum of the products of each pollutant concentration and toxicity factor multiplied by one plus the difference between the synergistic effect coefficient and the product of carrier efficiency and bioavailability correction coefficient minus one. By integrating the feature information of various sensors through multi-sensor data fusion algorithms, feature-level fusion and decision-level fusion are achieved. More specifically, the multi-sensor features are reduced in dimensionality through principal component analysis, projecting the original features onto a new space defined by the feature vector matrix. Independent component analysis is used to separate the independent components in the mixed signal. The mixing matrix and source signal are solved by maximizing non-Gaussianity. Uncertainty reasoning is performed using Dempster-Shafer evidence theory, where the combined quality function is equal to the sum of the products of the quality functions of all hypotheses with non-empty intersections divided by one minus the conflict coefficient. The imprecise information is processed by the fuzzy logic reasoning system, and the output membership function is equal to the minimum membership of all rule antecedents and the maximum membership of all rule consequents. Based on the fused features, pollutant type identification and concentration quantification are performed. More specifically, pollutant type classification is performed using the support vector machine algorithm, where the classification function is equal to the linear combination of the product of the support vectors and the kernel function plus the sign function of the bias term. Multi-class classification is performed using the random forest algorithm, where the class probability is equal to the average of the class probabilities of all decision trees. Concentration quantification is performed using the multiple linear regression model, where the concentration value is equal to the sum of the products of each feature and the corresponding regression coefficient plus the constant term and the error term. Multicollinearity is handled using the partial least squares regression model, which decomposes the independent and dependent variables into the product of the latent variables and the loading matrix plus the residual matrix, and establishes the relationship by maximizing the covariance of the latent variables. Assess the health risk level of compound pollutants, calculate the exposure dose and risk level, and more specifically, calculate the time-weighted average exposure concentration, which is equal to the sum of the products of the concentration in each time period and the corresponding time divided by the total time; calculate the inhalation exposure in the breathing zone, which is equal to the product of the time-weighted average concentration and the breathing rate, exposure time and the proportion of inhalable substances; and calculate the hazard quotient, which is equal to the ratio of the inhalation exposure to the reference dose. The composite risk index is calculated as the sum of the products of the hazard quotients of each pollutant and their corresponding synergistic factors. Risk levels are classified based on the risk index. A red alert is triggered when the risk index is greater than or equal to the critical risk threshold; an orange alert is triggered when the risk index is greater than or equal to the high risk threshold but less than the critical risk threshold; a yellow alert is triggered when the risk index is greater than or equal to the medium risk threshold but less than the high risk threshold; and a green normal state is indicated when the risk index is less than the medium risk threshold. The system performance is optimized through an adaptive calibration algorithm, which also enables trend prediction and risk warning. More specifically, online parameter calibration is performed using a recursive least squares algorithm, including Kalman gain calculation, parameter estimation and update, and covariance matrix update steps. The Kalman gain is equal to the product of the previous covariance matrix and the regression vector, divided by the forgetting factor, plus the product of the transpose of the regression vector and the covariance matrix. The sensor health status is assessed, with the health status equal to the ratio of the measurement error standard deviation to the nominal standard deviation. Pollutant concentration trend is predicted using an autoregressive integral moving average model, where the differencing time series values ​​are correlated with the autoregressive and moving average terms. Anomaly detection is performed based on the moving average and standard deviation. Anomalies are identified when the absolute difference between a data point and the moving average is greater than a multiple of the standard deviation. Multi-objective optimization is performed based on system performance indicators, minimizing multiple objective functions while satisfying constraints.

[0035] When the factory air pollution monitoring equipment for occupational disease hazards is used to monitor factory air pollution according to the occupational disease hazard monitoring method, multiple AT-cut quartz crystals in the quartz crystal microbalance array module 1 generate stable high-frequency vibrations under the drive of a dual-mode oscillation circuit composed of a J310 junction field-effect transistor and a 74VHC04 inverter. When the quenching oil mist and metal dust compound are deposited on the crystal surface, the ADE1 mixer down-converts the frequency signal and sends it to the first AD8302 phase detector to accurately measure the frequency offset and impedance change. This direct mass detection method avoids the measurement distortion problem caused by oil mist blockage in traditional filter membrane sampling by real-time monitoring of the mass load and viscoelastic changes of the deposits on the crystal surface. At the same time, the multi-harmonic measurement capability enables the differentiation between pure mass effect and viscoelastic effect caused by oil mist.

[0036] In the synchronously operating multi-wavelength optical sensor array module 2, ultraviolet, blue, red, and infrared light-emitting diodes emit beams of specific wavelengths under the constant current drive of an LM317. When the beams pass through the contaminated area, they interact with the composite particles. Silicon photodiodes receive scattered light signals at forward, 90-degree, and backward angles, respectively. An OPA656 transimpedance amplifier converts the weak photocurrent into a voltage signal. A PGA112 programmable gain amplifier automatically adjusts the gain according to the signal strength to ensure the optimal measurement dynamic range. An LTC1562 anti-aliasing filter eliminates high-frequency noise. A first ADS8688 analog-to-digital converter converts the analog signal into a digital signal. The avalanche photodiode is connected to a TDCGP30 time-to-digital converter through a CVA400 high-speed amplifier, forming an ultra-high sensitivity photon counting path. This multi-wavelength, multi-angle optical design, combined with high dynamic range detection and single-photon counting technology, can effectively distinguish the optical characteristics of oil mist droplets and metal particles. It quantifies the oil mist concentration through the absorption characteristics of the ultraviolet band and analyzes the particle size distribution of metal particles through the scattering characteristics of the blue band.

[0037] A wideband impedance sensor array generates a precise sweep frequency signal from 1Hz to 1MHz through an AD9958 direct digital synthesizer. After being driven by an OPA564 power amplifier, the signal is applied to a platinum interdigital electrode array. An IVC102 current-to-voltage converter measures the dielectric response of the composite pollutants in real time, and a second ADS1278 analog-to-digital converter captures complete impedance spectrum data at high resolution. This wideband impedance analysis can distinguish the interfacial polarization of oil mist and the volume conduction effect of metal particles by the dielectric response characteristics at different frequencies. It can directly detect the presence and concentration of metal particles through the oil mist layer, thus solving the problem of interference of the oil mist matrix on metal detection.

[0038] The ADF4351 RF synthesizer in the surface acoustic wave (SAW) sensor array generates RF signals in three frequency bands: 434MHz, 868MHz, and 2.45GHz. These signals are amplified by a SKY65111 power amplifier and then used to excite SAW devices on LiNbO3, LiTaO3, and Quartz substrates. An HMC773 mixer down-converts the returned signals, and a second AD8302 phase detector analyzes the phase and amplitude changes. A second ADS8688 analog-to-digital converter digitizes the signals. This multi-band acoustic wave detection technology is extremely sensitive to changes in oil mist viscosity and density. The 434MHz band is sensitive to changes in oil film thickness, while the 2.45GHz band is sensitive to surface mass load. By comparing and analyzing the multi-band measurement results, it is possible to track changes in oil film thickness in real time and distinguish between oil mist deposition and particulate matter deposition, effectively overcoming the problem of uneven contaminant deposition on the sensor surface under hot-pressing conditions.

[0039] The thermal conductivity detector array precisely maintains the platinum heater at a constant temperature of 200℃ using an ADN8830 proportional-integral-derivative controller. The PT1000 temperature sensor monitors the temperature data in real time using a MAX31865 resistance temperature detector reader. An IRF7413 field-effect transistor acts as a power switch to drive the heater. When oil mist volatiles pass through the detection chamber, the balance formed by the precision resistors in the Wheatstone bridge is broken. An INA128 instrumentation amplifier detects the slight imbalance voltage, and a third ADS8688 analog-to-digital converter acquires the signal. This thermal conductivity detection effectively distinguishes the difference in thermal conduction between oil mist volatiles and air, providing an independent measurement parameter for the concentration of oil mist volatiles and compensating for the shortcomings of other sensors in detecting gaseous components.

[0040] The OPA445 potentiostat operational amplifier in the electrochemical sensor array, controlled by a DAC8568 digital-to-analog converter, applies a precise scanning potential to the electrochemical cell consisting of a glassy carbon working electrode, a platinum wire counter electrode, and an Ag / AgCl reference electrode. A second IVC102 current-to-voltage converter measures the Faraday current generated by the oxidation-reduction of metal ions, and a fourth ADS8688 analog-to-digital converter acquires the signal through an ADuM1412 digital isolator. This electrochemical detection directly quantifies the concentration of soluble metal ions, provides key information on the bioavailability of pollutants, and improves the assessment dimensions of the toxicity of complex pollutants.

[0041] After all sensor data is converted into digital signals by various analog-to-digital converters, it enters the field-programmable gate array (FPGA) real-time processing unit in data processing module 7. The FPGA real-time processing unit uses its internal parallel architecture to synchronously perform data reception and preprocessing, loads configuration information through W25Q256 flash memory, performs high-speed data caching through DDR3 memory, and uses SI5338A clock generator to provide precise timing synchronization for the entire processing device. This hardware parallel processing capability ensures the synchronization and real-time performance of multi-sensor data, providing a computational foundation for subsequent complex algorithms.

[0042] The preprocessed multimodal data is transmitted to the STM32H753 microcontroller unit via a serial peripheral interface. Here, the data processing method of the composite pollutant monitoring system is run, and the entire process analysis is performed in sequence, including signal preprocessing and quality control, multi-dimensional feature extraction, carrier effect quantification, multi-source information fusion, pollutant identification and classification, health risk assessment, adaptive calibration optimization, and trend prediction and early warning. Specifically, it includes calculating mass load characteristics from quartz crystal microbalance data based on the Sauerbrey equation, calculating viscoelastic correction characteristics based on the Kanazawa-Gordon equation, inverting particle size distribution characteristics from optical sensor data based on Mie scattering theory, and extracting relaxation time characteristics from impedance spectrum data based on the Cole-Cole model. Through these multi-dimensional feature extraction algorithms, the characteristics of composite pollutants are fully characterized from different physical dimensions.

[0043] Then, the oil mist carrier efficiency, alveolar deposition enhancement factor, and bioavailability correction coefficient were calculated to quantify the enhancing effect of oil mist on the toxicity of metal dust, revealing the compound toxicity multiplication effect that was overlooked in traditional detection.

[0044] Next, the multi-sensor features are reduced in dimensionality using principal component analysis, the independent components in the mixed signal are separated using independent component analysis, uncertainty reasoning is performed using Dempster-Shafer evidence theory, and imprecise information is processed using a fuzzy logic reasoning system. This multi-level fusion ensures that even if a single sensor is affected by oil mist, accurate measurements can still be obtained through compensation from other sensors.

[0045] Based on the fused feature information, pollutant type classification is performed using the support vector machine algorithm, multi-class classification is performed using the random forest algorithm, and concentration quantification is performed using the multiple linear regression model and the partial least squares regression model, overcoming the shortcomings of single chemical analysis in characterizing complexes.

[0046] Then, the time-weighted average exposure concentration, inhalation exposure in the breathing zone, and hazard quotient are calculated, and combined with the composite risk index to generate the true risk level, thus solving the management blind spot of "data being qualified but hazard persisting".

[0047] Online parameter calibration is performed using a recursive least squares algorithm to assess sensor health, pollutant concentration trends are predicted using an autoregressive integral moving average model, and anomaly detection is performed based on moving average and standard deviation to ensure that the equipment maintains accuracy during long-term operation.

[0048] Ultimately, all monitoring results and early warning information are transmitted to the host computer system through the LAN8720 Ethernet physical layer chip and the FT2232H universal serial bus interface chip.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A factory air pollution monitoring device for occupational disease hazards, characterized in that, The system includes a sensor array module and a data processing module (7). The sensor array module includes a quartz crystal microbalance array module (1), a multi-wavelength optical sensor array module (2), a broadband impedance sensor array module (3), a surface acoustic wave sensor array module (4), a thermal conductivity detector array module (5), and an electrochemical sensor array module (6). The analog signal output terminal of the sensor array module is connected to the digital signal input terminal of the data processing module (7), and the control signal output terminal of the data processing module (7) is connected to the control signal input terminal of the sensor array module. The data processing module (7) sequentially performs feature extraction, carrier effect quantization, and multi-wavelength optical sensor array (6). The entire process of source information fusion, pollutant identification and classification, health risk assessment, adaptive calibration optimization, and trend prediction and early warning is analyzed, and the final output is the identification result and health risk level of composite pollutants; the quartz crystal microbalance array module (1) includes a T-cut quartz crystal, a J310 junction field-effect transistor, a 74VHC04 inverter, an ADG1434 RF switch, an ADE1 mixer, a first AD8302 phase detector, and a first ADS1278 analog-to-digital converter. The first end of the T-cut quartz crystal is connected to the gate of the J310 junction field-effect transistor, and the second end of the T-cut quartz crystal is grounded. The source of the J310 junction field-effect transistor is grounded through a first resistor. The drain of the J310 junction field-effect transistor is connected to the input pin of a 74VHC04 inverter. The output pin of the 74VHC04 inverter is fed back to its input pin through a first capacitor. The positive power supply terminal of the 74VHC04 inverter is powered on, and the ground terminal of the 74VHC04 inverter is grounded. The first RF input / output terminal of the ADG1434 RF switch is connected to the output pin of the 74VHC04 inverter, and the second RF input / output terminal of the ADG1434 RF switch is connected to the local oscillator input of the ADE1 mixer. The first control terminal of the ADG1434 RF switch is connected to the first general-purpose input / output terminal of the microcontroller unit in the data processing module (7), the second control terminal of the ADG1434 RF switch is connected to the second general-purpose input / output terminal of the microcontroller unit in the data processing module (7), the intermediate frequency output terminal of the ADE1 mixer is connected to the phase input terminal of the first AD8302 phase detector, its positive power supply terminal is connected to power, its ground terminal is grounded, and the output terminal of the first AD8302 phase detector is connected to the field programmable gate array real-time processing unit in the data processing module (7) through the first ADS1278 analog-to-digital converter;The multi-wavelength optical sensor array module (2) includes an ultraviolet light-emitting diode, a blue light-emitting diode, a red light-emitting diode, an infrared light-emitting diode, an LM317 constant current driver, a silicon photodiode, an OPA656 transimpedance amplifier, a PGA112 programmable gain amplifier, an LTC1562 anti-aliasing filter, a first ADS8688 analog-to-digital converter, an avalanche photodiode, a high-speed amplifier CVA400, and a TDCGP30 time-to-digital converter. The anode of the ultraviolet light-emitting diode is connected to the LM317 constant current driver via a second resistor. The output terminal of the driver has its cathode grounded. The adjustment terminal of the LM317 constant current driver is grounded through a third resistor. The input terminal of the LM317 constant current driver is energized. The cathode of the silicon photodiode is connected to the inverting input terminal of the OPA656 transimpedance amplifier. The anode of the silicon photodiode is energized. The non-inverting input terminal of the OPA656 transimpedance amplifier is grounded. The output terminal of the OPA656 transimpedance amplifier is connected to its inverting input terminal through a fourth resistor. The power supply terminal of the OPA656 transimpedance amplifier is energized. The output of the transimpedance amplifier is connected to the input of the PGA112 programmable gain amplifier. The gain control terminal of the input of the PGA112 programmable gain amplifier is connected to the microcontroller unit in the data processing module (7). The output of the PGA112 programmable gain amplifier is connected to the input of the LTC1562 anti-aliasing filter. The output of the LTC1562 anti-aliasing filter is connected to the first ADS8688 analog-to-digital converter. The output of the first ADS8688 analog-to-digital converter is connected to the field-programmable gate array real-time processing unit in the data processing module (7). The cathode of the avalanche photodiode is energized. The anode of the avalanche photodiode is connected to the input of the CVA400 high-speed amplifier. The output of the CVA400 high-speed amplifier is connected to the high-speed channel input of the TDCGP30 time-to-digital converter. The clock input of the TDCGP30 time-to-digital converter is connected to a 25MHz crystal oscillator. The data output of the TDCGP30 time-to-digital converter is connected to the data bus of the field-programmable gate array real-time processing unit in the data processing module (7).The wideband impedance sensor array module (3) includes an interdigital electrode array, an AD9958 direct digital synthesizer, an OPA564 power amplifier, a first IVC102 current-to-voltage converter, and a second ADS1278 analog-to-digital converter. The output of the AD9958 direct digital synthesizer is connected to the working electrode of the interdigital electrode array. The complementary output of the AD9958 direct digital synthesizer is grounded through a fifth resistor. The serial clock, serial data input / output, and chip select terminals of the AD9958 direct digital synthesizer are respectively connected to the serial peripheral interface clock, serial peripheral interface data, and serial peripheral interface chip select terminals of the microcontroller unit in the data processing module (7). The non-inverting input of the OPA564 power amplifier is connected to the output of the AD9958 direct digital synthesizer. The inverting input of the OPA564 power amplifier is grounded through a sixth resistor. The output of the 564 power amplifier is connected to the counter electrode of the interdigital electrode array, and its power supply is energized. The inverting input of the first IVC102 current-to-voltage converter is connected to the counter electrode of the interdigital electrode array. The non-inverting input of the first IVC102 current-to-voltage converter is grounded. The output of the first IVC102 current-to-voltage converter is connected to the analog input of the second ADS1278 analog-to-digital converter. A first integrating capacitor is connected between the first capacitor and the second capacitor of the first IVC102 current-to-voltage converter. The analog input of the second ADS1278 analog-to-digital converter is connected to the output of the first IVC102 current-to-voltage converter. The data output, data clock, and data ready terminals of the second ADS1278 analog-to-digital converter are connected to the serial data input, serial clock, and control terminals of the field-programmable gate array real-time processing unit in the data processing module (7), respectively.

2. The factory air pollution monitoring equipment for occupational disease hazards according to claim 1, characterized in that, The surface acoustic wave sensor array module (4) includes a surface acoustic wave device, an ADF4351 RF synthesizer, a SKY65111 power amplifier, an HMC773 mixer, a second AD8302 phase detector, and a second ADS8688 analog-to-digital converter. The RF output terminal of the ADF4351 RF synthesizer is connected to the RF input terminal of the SKY65111 power amplifier. The chip enable terminal, clock terminal, and data terminal of the ADF4351 RF synthesizer are respectively connected to the first serial peripheral interface enable terminal, serial peripheral interface clock terminal, and serial peripheral interface data terminal of the microcontroller unit in the data processing module (7). The RF output terminal of the SKY65111 power amplifier is connected to the input terminal of the surface acoustic wave device through a directional coupler, and its positive power supply terminal is energized. The RF output terminal of the HMC773 mixer is connected to the input terminal of the surface acoustic wave device through a directional coupler. The input terminal is connected to the output terminal of the surface acoustic wave device. The local oscillator input terminal of the HMC773 mixer is connected to the RF output terminal of the ADF4351 RF synthesizer. The intermediate frequency output terminal of the HMC773 mixer is connected to the phase input terminal of the second AD8302 phase detector. The voltage output terminal of the second AD8302 phase detector is connected to the analog input terminal of the second ADS8688 analog-to-digital converter. Its positive power supply terminal is connected to power, and its ground terminal is grounded. The analog input terminal of the second ADS8688 analog-to-digital converter is connected to the voltage output terminal of the second AD8302 phase detector. The serial data output terminal, serial clock terminal, and chip select terminal of the second ADS8688 analog-to-digital converter are respectively connected to the serial data input terminal, serial clock terminal, and chip select terminal of the field programmable gate array real-time processing unit in the data processing module (7).

3. The factory air pollution monitoring equipment for occupational disease hazards according to claim 1, characterized in that, The thermal conductivity detector array module (5) includes a platinum heater, a PT1000 temperature sensor, an IRF7413 field-effect transistor, an ADN8830 proportional-integral-differential controller, a MAX31865 resistance temperature detector reader, a Wheatstone bridge, an INA128 instrumentation amplifier, and a third ADS8688 analog-to-digital converter. The first terminal of the platinum heater is connected to the drain of the IRF7413 field-effect transistor, and the second terminal of the platinum heater is grounded. The gate of the IRF7413 field-effect transistor is connected to the high-side drive of the ADN8830 proportional-integral-differential controller, and the source of the IRF7413 field-effect transistor is grounded. The non-inverting input of the ADN8830 proportional-integral-differential controller is connected to the output of the third ADS8688 analog-to-digital converter, and the inverting input of the ADN8830 proportional-integral-differential controller is connected to the data output of the MAX31865 resistance temperature detector reader, with its positive power supply connected. The positive and negative terminals of the resistance temperature detector of the 1865 type resistance temperature detector reader are connected to the output terminal of the PT1000 type temperature sensor. The chip select terminal, serial clock terminal, master output slave input terminal, and master input slave output terminal of the MAX31865 type resistance temperature detector reader are respectively connected to the chip select terminal, serial peripheral interface clock terminal, serial peripheral interface data output terminal, and serial peripheral interface data input terminal of the microcontroller unit in the data processing module (7). The first terminal of the Wheatstone bridge is powered on. The second terminal of the Wheatstone bridge is connected to the inverting input terminal of the INA128 type instrumentation amplifier. The third terminal of the Wheatstone bridge is connected to the non-inverting input terminal of the INA128 type instrumentation amplifier. The fourth terminal of the Wheatstone bridge is grounded. The output terminal of the INA128 type instrumentation amplifier is connected to the analog input terminal of the third ADS8688 type analog-to-digital converter, and its power supply terminal is powered on. The output terminal of the third ADS8688 type analog-to-digital converter is connected to the field programmable gate array real-time processing unit in the data processing module (7).

4. The factory air pollution monitoring equipment for occupational disease hazards according to claim 1, characterized in that, The electrochemical sensor array module (6) includes a working electrode, a counter electrode, a reference electrode, an OPA445 potentiostat operational amplifier, a second IVC102 current-to-voltage converter, a DAC8568 digital-to-analog converter, a fourth ADS8688 analog-to-digital converter, and an ADuM1412 digital isolator. The inverting input of the OPA445 potentiostat operational amplifier is connected to the working electrode, the non-inverting input of the OPA445 potentiostat operational amplifier is connected to the output of the DAC8568 digital-to-analog converter, the output of the OPA445 potentiostat operational amplifier is connected to the counter electrode, the power supply of the OPA445 potentiostat operational amplifier is connected to power, and the output of the DAC8568 digital-to-analog converter is connected to the non-inverting input of the OPA445 potentiostat operational amplifier. Its synchronization terminal, serial data input terminal, and serial clock terminal are respectively connected to the data processing module (7). The serial peripheral interface synchronization terminal, serial peripheral interface data output terminal, and serial peripheral interface clock terminal of the microcontroller unit; the inverting input terminal of the second IVC102 current-to-voltage converter is connected to the counter electrode; the output terminal of the second IVC102 current-to-voltage converter is connected to the analog input terminal of the fourth ADS8688 analog-to-digital converter; a second integrating capacitor is connected between the first capacitor terminal and the second capacitor terminal of the second IVC102 current-to-voltage converter; the analog input terminal of the fourth ADS8688 analog-to-digital converter is connected to the output terminal of the second IVC102 current-to-voltage converter; its serial data output terminal, serial clock terminal, and chip select terminal are respectively connected to the input data terminal, clock input terminal, and chip select input terminal of the ADuM1412 digital isolator; the output data terminal of the ADuM1412 digital isolator is connected to the data input terminal of the field-programmable gate array real-time processing unit in the data processing module (7).

5. The factory air pollution monitoring equipment for occupational disease hazards according to claim 1, characterized in that, The data processing module (7) includes a field-programmable gate array (FPGA) real-time processing unit, a microcontroller unit, DDR3 memory, and an SI5338A clock generator. The microcontroller unit is an STM32H753 microcontroller. The data bus pin of the FPGA real-time processing unit is connected to the data bus of the DDR3 memory. The clock output terminal of the SI5338A clock generator is connected to the global clock pin of the FPGA real-time processing unit and the clock pin of the DDR3 memory, respectively. The configuration interface of the SI5338A clock generator is connected to the integrated circuit bus of the microcontroller unit. The Ethernet media independent interface of the microcontroller unit is connected to a LAN8720 Ethernet physical layer chip.

6. A method for monitoring factory air pollution hazard caused by occupational disease, using the factory air pollution monitoring equipment for occupational disease hazards as described in any one of claims 1-5, comprising the following steps: Multimodal sensing data is collected synchronously through a sensor array module; The data processing module (7) extracts multi-dimensional features of the composite pollutants from the data. These multi-dimensional features include mass loading characteristics, optical scattering characteristics, impedance spectrum characteristics, acoustic wave propagation characteristics, thermal conductivity characteristics, and electrochemical characteristics. Mass load characteristics were calculated from quartz crystal microbalance data based on the Sauerbrey equation, where the change in mass load is proportional to the frequency shift and inversely proportional to the harmonic order. Viscoelastic correction characteristics were calculated based on the Kanazawa-Gordon equation, where the frequency shift is proportional to the square root of the liquid density and viscosity and inversely proportional to the square root of the crystal density and shear modulus. Particle size distribution characteristics were inverted from optical sensor data based on Mie scattering theory, where the scattering intensity is proportional to the particle number concentration and the integral of the Mie scattering intensity function. Relaxation time characteristics were extracted from impedance spectrum data based on the Cole-Cole model, where the impedance value is related to low-frequency resistance value, high-frequency resistance value, angular frequency, relaxation time, and distribution parameters. Interface polarization characteristics were extracted based on relaxation time distribution analysis. The enhancement effect of oil mist carrier effect on the toxicity of metal dust was quantified by calculating carrier efficiency and toxicity enhancement coefficient. The oil mist carrier efficiency was defined as the ratio of metal concentration on carrier particles to total metal concentration. The alveolar deposition enhancement factor was defined as the ratio of deposition fraction of composite particles in the alveolar region to deposition fraction of pure metal particles. The bioavailability correction coefficient was defined as the ratio of dissolution rate constant of composite particles to dissolution rate constant of pure metal particles. The composite toxicity index was defined as the sum of the products of each pollutant concentration and toxicity factor multiplied by one, plus the difference between the synergistic effect coefficient and the product of carrier efficiency and bioavailability correction coefficient minus one. By integrating the feature information of each sensor through a multi-sensor data fusion algorithm, feature-level fusion and decision-level fusion can be achieved. Pollutant type identification and quantitative concentration analysis are performed based on the fused features; Assess the health risk level of compound pollutants, calculate exposure dose and risk level. Specifically, calculate the time-weighted average exposure concentration (equal to the sum of the products of concentration and corresponding time for each time period, divided by the total time); calculate the inhalation exposure in the breathing zone (equal to the product of time-weighted average concentration, breathing rate, exposure time, and the proportion of inhalable pollutants); calculate the hazard quotient (equal to the ratio of inhalation exposure to reference dose); and calculate the composite risk index (equal to the sum of the products of the hazard quotients of each pollutant and their corresponding synergistic factors). Based on the risk index, risk levels are classified. A red alert is triggered when the risk index is greater than or equal to the critical risk threshold; an orange alert is triggered when the risk index is greater than or equal to the high risk threshold but less than the critical risk threshold; a yellow alert is triggered when the risk index is greater than or equal to the medium risk threshold but less than the high risk threshold; and a green normal state is triggered when the risk index is less than the medium risk threshold. The system performance is optimized through an adaptive calibration algorithm, and trend prediction and risk warning are performed.

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