Intelligent working place dust monitoring and early warning system

Abstract

The invention discloses an intelligent working place dust monitoring and early warning system. The system comprises a dust sensor module, a data processing and storage module, a wireless communication module, an alarm module and a visual interface. The dust sensor detects the dust concentration in the air in real time, dust with different particle sizes is included, the data processing module carries out concentration calculation, trend analysis and acceleration monitoring, and dynamic threshold adjustment and trend prediction are supported. The system has a multi-stage alarm function, and when the dust concentration reaches a preset threshold value, early warning signals with different intensities are triggered to remind a user to take protective measures in time. The system not only improves the real-time performance and accuracy of dust monitoring, but also has automatic learning and intelligent early warning functions, is suitable for various high-dust environments such as mines, construction sites, workshops and the like, and remarkably improves the safety management level of workplaces.

Description

Technical Field

[0001] The present invention relates to the technical field of dust monitoring, and in particular to an intelligent workplace dust monitoring and early warning system. Background Art

[0002] In the process of industrial production and modernization, dust pollution is becoming increasingly serious. Especially in the mining, construction, metallurgy, chemical, pharmaceutical, manufacturing and other industries, excessive dust concentration has become a major hidden danger to workers' health. Long-term inhalation of dust particles may cause a series of respiratory diseases and even occupational diseases such as pneumoconiosis, which not only threatens the life and health of workers, but also brings potential economic burden to enterprises. In addition, high-concentration dust may also cause serious accidents such as dust explosions, further exacerbating the necessity of dust control.

[0003] At present, traditional dust monitoring methods mainly rely on manual sampling and laboratory analysis. Although these methods can obtain certain accurate data, they have the following defects:

[0004] Poor real-time performance: Manual sampling usually requires taking air samples back to the laboratory for analysis. The whole process is time-consuming and cannot achieve real-time monitoring and early warning of dust concentration. Due to the volatility of dust concentration, manual sampling can only reflect the dust situation at a certain moment and it is difficult to accurately capture the dynamic changes of dust concentration.

[0005] Complex operation: Traditional monitoring methods require specialized equipment and professional technicians to operate, with high implementation costs, and are difficult to promote to small and medium-sized enterprises, especially in some difficult-to-access operating environments (such as deep in mines or construction sites), where sampling is difficult and risky.

[0006] Insufficient early warning function: Traditional monitoring methods cannot issue an alarm in time when dust concentration exceeds the standard, resulting in the inability to quickly take dust reduction measures, which can easily lead to sudden occupational health problems or accidents. For example, if the high dust concentration in coal mining operations is not controlled in time, it may lead to serious consequences of dust explosions.

[0007] Lack of data analysis: Traditional monitoring methods are mainly used to record instantaneous values ​​or short-term trends of dust concentrations. They lack the ability to accumulate long-term data and perform trend analysis, and are unable to help companies achieve refined management of dust concentrations and risk prediction. In addition, data cannot be viewed remotely or analyzed in real time, resulting in low management efficiency.

[0008] In order to solve the above problems, some dust monitoring solutions based on sensors and wireless communication technologies have been proposed in recent years, and gradually developed in the direction of intelligence. These solutions usually use optical sensors or laser sensors to detect dust concentration in real time, and realize remote transmission of data through wireless communication modules. However, the existing intelligent dust monitoring system still has the following shortcomings:

[0009] Insufficient accuracy: Some cheap sensors have low detection accuracy and it is difficult to distinguish between dust particles of different sizes (such as PM1.0, PM2.5, PM10, etc.), which affects the accuracy of the monitoring results, especially in environments with high dust concentrations, where saturation is prone to occur.

[0010] Limited data analysis capabilities: Most existing systems only provide basic real-time monitoring functions, lack in-depth data analysis and early warning functions, and are unable to effectively predict the changing trend of dust concentration or automatically adjust the alarm threshold. In addition, the system usually lacks the storage and management of historical data, making it difficult to conduct long-term trend analysis and risk assessment.

[0011] Poor adaptability: Existing systems usually alarm based on fixed preset thresholds, lack the function of optimizing thresholds based on actual environments and historical data, and cannot adapt to special needs in different environments. For example, the dust concentration changes in mines, construction sites, and public places vary, and using a unified threshold may lead to false alarms or missed alarms.

[0012] Lack of remote control and user interaction: Many systems only support on-site monitoring, and users cannot view and control system data through remote terminals, nor can they adjust the system's monitoring parameters and alarm thresholds in real time. This results in poor flexibility of the dust monitoring system and makes it difficult to meet the diverse needs of users in different scenarios.

[0013] To this end, we urgently need to design an intelligent workplace dust monitoring and early warning system to solve the above problems. Summary of the invention

[0014] The purpose of the present invention is to provide an intelligent workplace dust monitoring and early warning system to address the deficiencies of the prior art and to solve the problems raised in the background technology.

[0015] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent workplace dust monitoring and early warning system, including a dust sensor module, a data processing and storage module, a wireless communication module, an alarm module and a visual interface,

[0016] Dust sensor module: used to collect dust particle concentration in the air in real time and transmit the data to the data processing and storage module; the calculation formula of dust concentration C is:

[0017]

[0018] Where k is the proportionality coefficient, I i is the particle light scattering signal intensity, n is the number of particles detected;

[0019] Data processing and storage module: including microprocessor and data storage unit, receiving dust concentration data and calculating the instantaneous value C(t) and average value C of dust concentration avg ,in:

[0020]

[0021] Where T is the number of measurements within a specific time period, C(t) is the instantaneous measurement value of dust concentration, and the data storage unit saves real-time and historical concentration data for subsequent analysis;

[0022] Wireless communication module: supports Wi-Fi, Bluetooth or LoRa communication modes, transmits data to remote terminal devices, and facilitates users to view monitoring results in real time and receive warning threshold parameters set by users;

[0023] Alarm module: When the dust concentration C exceeds the preset threshold C warn When C≥C, the multi-level alarm function is activated, including sound and light alarm or sending early warning signals to the terminal. The early warning condition formula is: if C≥C warn , then start the alarm module

[0024] Visual interface: Displays real-time dust concentration C(t), average concentration C avg The dust concentration trend chart provides intuitive information about dust concentration changes, making it easier for users to take protective measures.

[0025] Preferably, the dust sensor module adopts an optical sensor to detect the dust concentration in the air by an optical scattering method, and can detect the concentration of particles of different particle sizes, including PM1.0, PM2.5, PM10, etc.

[0026] Preferably, the data processing and storage module calculates the peak value of the dust concentration C max And the growth rate ΔC, where the growth rate calculation formula is:

[0027]

[0028] Where Δt is the time interval, and C(t) and C(t-1) are the concentration values ​​at adjacent time points.

[0029] Preferably, the data processing module predicts the future dust concentration based on the historical data trend, and uses the least squares method to establish the trend function f(t) to minimize the error e:

[0030]

[0031] Where f(t) is the prediction function and C(t) is the actual measured concentration.

[0032] Preferably, the wireless communication module supports receiving remote commands, allowing the user to adjust system parameters or warning thresholds through a mobile terminal or computer to achieve remote management.

[0033] Preferably, the alarm module has a multi-level alarm function. When the dust concentration C reaches different thresholds, alarms of different intensities are issued, including yellow alarms, red alarms, etc., to adapt to different dust concentration conditions:

[0034] if C ≥ C critical , a red alarm is issued ifC warn ≤C <C critical , a yellow alarm is issued

[0035] Preferably, the dust concentration historical data recorded in the data processing and storage module is stored in chronological order and managed by day, month and year. The user can query the dust concentration records of any time period through the terminal.

[0036] Preferably, the visual interface has a user interaction function, which supports the user to manually set the alarm threshold C of the dust concentration. warn and C critical , and display real-time concentration, trend forecasts and historical changes through charts.

[0037] Preferably, the system includes an automatic learning module that can automatically optimize the alarm threshold C based on historical dust concentration data. warn and C critical ,The optimization calculation formula is as follows:

[0038]

[0039] Where 0≤α,β≤1 is the learning rate.

[0040] Preferably, the system supports extended environmental monitoring functions, which can be integrated with temperature, humidity and wind speed sensors to comprehensively analyze the environmental influencing factor E of dust concentration, and the calculation formula is:

[0041] E=w1·T+w2·H+w3·V

[0042] Where T is temperature, H is humidity, V is wind speed, and w1, w2, and w3 are corresponding weight coefficients used to calculate the comprehensive impact of environmental factors on dust concentration.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] The intelligent workplace dust monitoring and early warning system of the present invention has real-time and accurate dust monitoring functions. It detects dust concentrations of different particle sizes through high-precision sensors, can capture the dynamic changes of dust concentration in real time, and provide comprehensive monitoring support for various high-dust environments. At the same time, the system is equipped with a multi-level alarm mechanism, which automatically triggers alarms of different intensities when the dust concentration reaches the warning or critical level, ensuring timely response to abnormal changes in dust concentration and protecting the health of workers and environmental safety.

[0045] The system has a built-in automatic learning algorithm that can dynamically optimize the alarm threshold based on historical data to avoid false alarms or missed alarms, thereby improving the adaptability and reliability of the system. In addition, the system has trend analysis and dust concentration prediction functions, which can analyze and warn of future dust change trends, help users take measures in advance to reduce risks, and provide long-term historical data storage to provide data support for dust management and help companies optimize dust prevention and control strategies.

[0046] The system supports wireless communication and remote control. Users can view dust concentration data and adjust monitoring parameters at any time through mobile devices. It is easy to operate and efficient to manage. The visual interface is user-friendly, and real-time data, historical data and concentration change trends are clearly presented to adapt to different work scenarios and expansion needs. In general, the system of the present invention realizes efficient and intelligent dust monitoring and management, and has a wide range of application value. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a system block diagram of the intelligent workplace dust monitoring and early warning system proposed by the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The following is combined with Figure 1 With reference to the accompanying drawings and a number of embodiments, specific embodiments of the present invention are described in detail.

[0050] 1. System overall structure

[0051] The intelligent workplace dust monitoring and early warning system includes the following five main modules:

[0052] Dust sensor module: used to collect dust concentration data in the air, capable of detecting dust particles of different sizes, including PM1.0, PM2.5 and PM10. The module uses an optical scattering sensor to convert the intensity of the light signal reflected by the dust particles into concentration data.

[0053] Data processing and storage module: The data processing module consists of a microprocessor (such as STM32) and a storage unit. It is responsible for receiving data from the dust sensor, calculating the real-time concentration, average concentration, peak value and growth rate, and storing historical data.

[0054] Wireless communication module: used for remote transmission of data, uploading monitoring data to the cloud via Wi-Fi, LoRa or 5G network, facilitating remote access and control by users.

[0055] Alarm module: It is equipped with a multi-level threshold alarm mechanism. When the dust concentration reaches or exceeds the set value, different levels of alarms are triggered.

[0056] Visualization interface: It is used to display trend charts of real-time and historical data and provide user interaction functions so that users can set thresholds and alarm parameters.

[0057] 2. Key Algorithms and Formulas

[0058] Dust concentration calculation: The dust sensor detects the reflected light signal intensity I of the particles i Converted to dust concentration C. The formula is as follows:

[0059]

[0060] Where k is the proportionality coefficient (obtained by sensor calibration), I i is the light signal intensity of the ith particle, and n is the number of particles. This formula can reflect the real-time dust concentration.

[0061] Average concentration calculation: Average concentration C avg It is used to display the average dust concentration value within a specified time period. The calculation formula is:

[0062]

[0063] Where T is the total number of measurements and C(t) is the dust concentration value measured each time. The average value can smooth short-term fluctuations and reflect the overall trend.

[0064] Dust concentration growth rate: The growth rate ΔC is used to monitor the rapid changes in dust concentration. The calculation formula is:

[0065]

[0066] Where Δt is the time interval between adjacent samples, C(t) and C(t-1) are the current and last dust concentration values, respectively. When the growth rate exceeds the threshold, an early warning signal can be issued.

[0067] Trend prediction: To predict the changing trend of dust concentration, the system uses the least squares method to fit the trend function f(t) to minimize the error e:

[0068]

[0069] Trend forecasting can help users judge possible changes in dust concentration in the future, making it easier to take protective measures in advance.

[0070] Automatic learning threshold update: The system can automatically adjust the alarm threshold based on historical data C warn and C critical , the calculation formula is:

[0071]

[0072] Where α and β are learning rates (ranging from 0 to 1), C max is the peak concentration within the set period.

[0073] 3. Embodiment

[0074] Example 1: Construction site dust monitoring and early warning

[0075] Background: There is a lot of dust at construction sites, especially during earthwork and material cutting, the dust concentration rises sharply, affecting the health of workers.

[0076] Operation process: Sensor layout: Arrange multiple dust sensors at different locations of the construction site (such as material storage area, cutting area, etc.) to collect dust concentration data in real time.

[0077] Data calculation: The system collects data every 10 minutes and calculates the dust concentration C(t) in real time. Calculate the average concentration C avg And monitor the speed increase ΔC:

[0078]

[0079] Alarm threshold setting: Set C warn =150μg / m 3 (Yellow Alert),C critical =300 μg / m 3 (Red Alert).

[0080] When the dust concentration exceeds the yellow threshold, workers are reminded to take measures; when it exceeds the red threshold, work is stopped immediately and dust reduction measures are carried out.

[0081] Visual interface: Managers can view dust concentration charts through mobile phones or computers and export data regularly.

[0082] Effect: The system can promptly remind site managers to implement dust reduction measures such as sprinkling water, thereby reducing the risk of workers inhaling high concentrations of dust.

[0083] Example 2: Dust monitoring and early warning in mine environment

[0084] Background: The dust concentration in mine operations is high and the air circulation is poor, which easily leads to the accumulation of high-concentration dust and threatens the safety of workers.

[0085] Operation process: Sensor layout: Install dust sensors at key locations such as the mine entrance, working surface and ventilation outlet. Data processing: Collect data every 5 minutes to monitor the dust concentration C(t) in real time. Calculate the average concentration C within 1 hour. avg , used to assess short-term concentration levels.

[0086] Trend prediction: The system calculates the trend function f(t) every hour and predicts the dust concentration in the next hour.

[0087] Alarm setting: Set the special alarm threshold of the mine to C warn =200 μg / m 3 and C critical =500 μg / m 3 .

[0088] If the concentration growth rate ΔC exceeds the preset value, the system will issue an early warning to avoid safety risks caused by high dust concentration.

[0089] Effect: The system can issue an early warning when dust concentration rises rapidly, and managers can immediately take measures such as ventilation and dust reduction to ensure the safety of workers.

[0090] Example 3: Dust monitoring and early warning in a manufacturing workshop

[0091] Background: The production process in manufacturing workshops such as metallurgy and chemical industry generates a large amount of dust, and the air quality in the workshop needs to be monitored in real time to ensure the health of workers.

[0092] Operation process: Sensor layout: Sensors are arranged in different areas of the manufacturing workshop (such as production lines, packaging areas, etc.) to monitor dust concentration in real time.

[0093] Data processing: Collect data every 15 minutes to calculate the real-time concentration C(t) and the average concentration C within 1 hour. avgo

[0094] Automatic learning threshold: The system automatically adjusts the alarm threshold based on historical data. Set the initial C warn =100 μg / m3 and C critical =250 μg / m 3 .

[0095] Update the threshold using an automatic learning algorithm:

[0096]

[0097] Alarm module: Once the threshold is reached, the system starts the workshop's exhaust equipment and sends an alarm to the operator.

[0098] Effect: The system can automatically turn on dust reduction equipment when the dust concentration exceeds the standard, ensuring that the air quality in the workshop meets the standard and protecting the health of workers.

[0099] Example 4: Dust monitoring and early warning in public places

[0100] Background: Public places such as airports and subway stations have a large flow of people and high requirements for air quality management.

[0101] Operation process: Sensor layout: Install dust sensors in crowded areas such as waiting areas, passages and security checkpoints to monitor dust concentration.

[0102] Data processing: Collect data every 30 minutes and calculate the current concentration C(t) and the average concentration C avgo

[0103] Trend prediction and alarm: Use the trend prediction function f(t) to analyze future dust change trends. When it is predicted that the dust concentration may approach the warning line, send a warning signal in advance.

[0104] Visual interface and public display: The system displays real-time air quality index (AQI) and dust concentration on public display screens, providing information transparency.

[0105] Effect: The system improves the air quality management level in public places, notifies cleaning staff in time to clean and purify the air, and ensures public health.

[0106] Through the detailed description of the above embodiments, the intelligent dust monitoring and early warning system is suitable for dust monitoring needs in different scenarios. The core algorithms of the system include real-time concentration calculation, growth rate monitoring, trend prediction and automatic threshold adjustment, which can effectively identify and manage changes in dust concentration in the workplace and reduce the harm of dust to human health.

[0107] Example 5: Comprehensive monitoring of environmental factors and analysis of the impact of dust concentration

[0108] In some workplaces with complex environmental conditions, such as metallurgical plants, mines and construction sites, environmental factors such as temperature, humidity and wind speed will affect the generation, diffusion and settlement of dust. By monitoring these environmental factors and incorporating them into the system's dust concentration analysis, the system can more accurately predict changes in dust concentration and provide more accurate early warnings.

[0109] Sensor integration: Based on the dust sensor module, the system integrates temperature, humidity and wind speed sensors. At the main monitoring points (such as mine operation areas, different areas of construction sites, and production lines in metallurgical workshops), various environmental sensors are arranged together with dust sensors to collect parameters such as dust concentration, temperature, humidity and wind speed in the air, forming a comprehensive environmental monitoring network.

[0110] Data collection and processing: The data processing module receives data on dust concentration C, temperature T, humidity H and wind speed V in real time. The system calculates the comprehensive impact value E of environmental factors based on the following formula to analyze the impact of the environment on dust concentration:

[0111] E=w1·T+w2·H+w3·V

[0112] Among them, w1, w2 and w3 are the weight coefficients of temperature, humidity and wind speed, respectively, reflecting the relative impact of each environmental factor on dust concentration. The specific weight value is determined according to the characteristics of different environments and historical monitoring data (see the example value description below for details).

[0113] The system combines dust concentration C and environmental impact value E for analysis to generate a more accurate trend prediction model f(t,E). The trend prediction algorithm not only considers the change of dust concentration, but also the dynamic effects of temperature, humidity and wind speed.

[0114] For example, the trend function f(t,E) fitted by the least squares method can help the system predict the rising or falling trend of dust concentration in advance, thereby assisting managers to take corresponding protective measures. Alarm and dynamic threshold adjustment: The system dynamically adjusts the alarm threshold C based on a comprehensive analysis of environmental factors and dust concentration. warn and C critical , making it more in line with real-time environmental conditions. The following is an example of dynamic threshold adjustment strategy for different environments:

[0115] Construction site: Under high wind speed conditions (such as wind speed V exceeding 5m / s), dust is more likely to spread. The system automatically warn and C critical Each is reduced by 10% to improve the alarm sensitivity under high wind speed conditions. Mine environment: If the humidity H is lower than 70%, dust is not easy to settle in the air. The system will C warn and C criticalAdd 15% each to ensure alarm accuracy in dry conditions.

[0116] Manufacturing workshop (chemical workshop): If the temperature T exceeds 35℃, the air flow increases and the risk of dust diffusion increases. The system automatically changes C warn and C critical Each is reduced by 10% to adapt to the impact of rising temperature.

[0117] The visual interface of the system displays real-time dust concentration C, temperature T, humidity H and wind speed V data, and intuitively displays the trend chart of the impact of environmental factors on dust concentration. Users can clearly view the dynamic changes of dust concentration and its relationship with environmental parameters through the interface, which is convenient for formulating more targeted dust reduction measures and protection strategies.

[0118] Construction site environment: Temperature (T): usually fluctuates between 5°C and 35°C. Higher temperatures increase air mobility, making dust more easily dispersed. Set weight w1 = 0.2.

[0119] Humidity (H): Humidity is between 20% and 80%. Dust settles easily when humidity is high. Set weight w2 = 0.5.

[0120] Wind speed (V): Wind speed is usually between 0mls and 8m / s. When the wind speed is high, the dust spreads quickly. Set the weight w3 = 0.3. Comprehensive impact value:

[0121] E=0.2·T+0.5·H+0.3·V

[0122] Mine environment: Temperature (T): usually between 10°C and 25°C. Temperature has little effect on dust diffusion, and the weight w1 is set to 0.1.

[0123] Humidity (H): Humidity is as high as 60% to 95%. Increased humidity helps dust settling. Set the weight w2 = 0.7.

[0124] Wind speed (V): The wind speed is low, generally between 0m / s and 1.5m / s, and the weight w3 is set to 0.2.

[0125] Comprehensive impact value:

[0126] E=0.1·T+0.7·H+0.2·V

[0127] Chemical workshop environment: Temperature (T): usually between 20°C and 40°C. High temperature increases the risk of dust diffusion. The weight w1 is set to 0.6.

[0128] Humidity (H): When humidity is between 30% and 60%, the impact is relatively small, and the weight w2 is set to 0.2.

[0129] Wind speed (V): The wind speed is generally between 0m / s and 3m / s, and the weight w3 is set to 0.2.

[0130] Comprehensive impact value:

[0131] E=0.6·T+0.2·H+0.2·V

[0132] This embodiment improves the accuracy and intelligence of the dust monitoring system by introducing environmental parameters such as temperature, humidity and wind speed. The system can not only monitor dust concentration in real time, but also conduct intelligent analysis and early warning based on changes in environmental conditions, ensuring that the diffusion and sedimentation characteristics of dust can be effectively monitored in different environments. At the same time, the system's dynamic threshold adjustment function makes the alarm more sensitive and accurate, especially under special environmental conditions, the system's adaptability has been significantly improved.

[0133] In summary, through comprehensive monitoring and analysis of environmental factors, this system can provide more comprehensive dust concentration monitoring and more accurate early warning support for complex workplaces, significantly improving workplace safety and reducing the threat of dust to workers' health.

[0134] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. Intelligent workplace dust monitoring and early warning system, including dust sensor module, data processing and storage module, wireless communication module, alarm module and visual interface, characterized by: Dust sensor module: used to collect dust particle concentration in the air in real time and transmit the data to the data processing and storage module; the calculation formula of dust concentration C is: Where k is the proportionality coefficient, I i is the particle light scattering signal intensity, n is the number of particles detected; Data processing and storage module: including microprocessor and data storage unit, receiving dust concentration data and calculating the instantaneous value C(t) and average value C of dust concentration avg ,in: Where T is the number of measurements within a specific time period, C(t) is the instantaneous measurement value of dust concentration, and the data storage unit saves real-time and historical concentration data for subsequent analysis; Wireless communication module: supports Wi-Fi, Bluetooth or LoRa communication modes, transmits data to remote terminal devices, and facilitates users to view monitoring results in real time and receive warning threshold parameters set by users; Alarm module: When the dust concentration C exceeds the preset threshold C warn When C≥C, the multi-level alarm function is activated, including sound and light alarm or sending early warning signals to the terminal. The early warning condition formula is: if C≥C warn , then start the alarm module; Visual interface: Displays real-time dust concentration C(t), average concentration C avg And historical concentration trend charts provide intuitive dust concentration changes.

2. The intelligent workplace dust monitoring and early warning system according to claim 1 is characterized by: The dust sensor module adopts an optical sensor to detect the dust concentration in the air through an optical scattering method, and can detect the concentration of particles of different particle sizes, including PM1.0, PM2.5, and PM10.

3. The intelligent workplace dust monitoring and early warning system according to claim 1 is characterized by: The data processing and storage module calculates the peak value of the dust concentration C max And the growth rate ΔC, where the growth rate calculation formula is: Where Δt is the time interval, and C(t) and C(t-1) are the concentration values ​​at adjacent time points.

4. The intelligent workplace dust monitoring and early warning system according to claim 1 is characterized by: The data processing module predicts the future dust concentration based on the historical data trend and uses the least squares method to establish the trend function f(t) to minimize the error e: Where f(t) is the prediction function and C(t) is the actual measured concentration.

5. The intelligent workplace dust monitoring and early warning system according to claim 1 is characterized by: The wireless communication module supports receiving remote commands, allowing users to adjust system parameters or warning thresholds through mobile terminals or computers to achieve remote management.

6. The intelligent workplace dust monitoring and early warning system according to claim 1 is characterized by: The alarm module has a multi-level alarm function. When the dust concentration C reaches different thresholds, it issues alarms of different intensities, including yellow alarms, red alarms, etc., to adapt to different dust concentration conditions: if C ≥ C critical , a red alarm is issued ifC warn ≤C <C critical , a yellow alarm is issued.

7. The intelligent workplace dust monitoring and early warning system according to claim 1 is characterized by: The dust concentration historical data recorded in the data processing and storage module is stored in chronological order and managed by day, month and year. Users can query the dust concentration records of any time period through the terminal.

8. The intelligent workplace dust monitoring and early warning system according to claim 1 is characterized by: The visualization interface has a user interaction function, which supports the user to manually set the alarm threshold C of the dust concentration. warn and C critical , and display real-time concentration, trend forecasts and historical changes through charts.

9. The intelligent workplace dust monitoring and early warning system according to claim 8 is characterized in that The system includes an automatic learning module, which automatically optimizes the alarm threshold C based on historical dust concentration data. warn and C critical ,The optimization calculation formula is as follows: Where 0≤α,β≤1 is the learning rate.

10. The intelligent workplace dust monitoring and early warning system according to any one of claims 1 to 9, characterized in that: The system supports extended environmental monitoring functions and can be integrated with temperature, humidity and wind speed sensors to comprehensively analyze the environmental influencing factor E of dust concentration. The calculation formula is: E=w1·T+w2·H+w3·V Where T is temperature, H is humidity, V is wind speed, and w1, w2, and w3 are corresponding weight coefficients used to calculate the comprehensive impact of environmental factors on dust concentration.

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