Flying dust online detection method and system based on laser

Abstract

The invention provides a flying dust online detection method and system based on laser, and the method comprises the following steps: obtaining laser scattering signal intensity, particle concentration, wind speed, wind direction, humidity and other environmental meteorological data before and after the abnormality when the flying dust concentration of a flying dust monitoring region is monitored to be abnormal; calculating laser scattering energy and a particulate matter concentration change rate to obtain a flying dust concentration change coefficient; and judging an abnormal type according to the change coefficient and an abnormal identification criterion, and if the flying dust concentration in the region exceeds the standard, starting an early warning action to realize management of the flying dust monitoring region. According to the invention, the abnormal type of dust raising can be accurately identified, early warning is started in time, and the environment management and control capability of a dust raising monitoring area is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and more specifically, to a laser-based online dust detection method and system. Background Art

[0002] Existing dust monitoring technologies mainly rely on traditional particle sampling and analysis methods, such as filter sampling and weighing methods. Although these methods can provide relatively accurate particle concentration data, they have disadvantages such as long sampling time, poor real-time performance, and inability to continuously monitor. In addition, traditional methods usually require manual operation, which increases the monitoring cost and workload. In recent years, with the development of laser technology, laser scattering method has gradually been applied to the monitoring of particle concentration. The laser scattering method measures the intensity of the scattered signal of the laser by the particles to estimate the particle concentration. It has the advantages of fast response speed, wide measurement range, and continuous monitoring. However, the existing laser scattering method still has some shortcomings in dust monitoring, such as being sensitive to interference from environmental factors (such as wind speed, wind direction, humidity, etc.), which affects the accuracy of the measurement results; at the same time, there is a lack of effective abnormal identification and early warning mechanism, making it difficult to detect and deal with abnormal dust concentration events in a timely manner.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: it is impossible to accurately determine the type of abnormal dust concentration, it is impossible to initiate early warning actions in time, and it is difficult to achieve effective management and control of the dust monitoring area. Summary of the invention

[0004] The invention provides a laser-based online dust detection method and system.

[0005] In a first aspect of the present invention, a laser-based online dust detection method is provided, comprising: When abnormal dust concentration is detected in the dust monitoring area, obtain the laser scattering signal intensity, particle concentration, wind speed, wind direction, humidity and other environmental meteorological data in the monitoring area before and after the abnormality occurs; Based on the information obtained, the laser scattering energy and the particle concentration change rate in the monitoring area are calculated, and then the dust concentration change coefficient in the monitoring area is obtained; The calculation formula of laser scattering energy is: In the formula, To monitor the intensity of laser scattering signals in the area over time, is the monitoring period; The type of dust concentration anomaly is determined based on the dust concentration variation coefficient and dust concentration anomaly identification criteria in the monitoring area. If the anomaly type is that the dust concentration in the area exceeds the standard, an early warning action is initiated to achieve management of the dust monitoring area.

[0006] Furthermore, the dust concentration variation coefficient in the monitoring area is expressed as: In the formula, Indicates the coefficient of variation of dust concentration; , Respectively represent the laser scattering signal intensity before and after the abnormality occurs; , Represent the particle concentrations before and after the abnormality occurs, respectively.

[0007] Furthermore, the abnormal dust concentration identification criteria include: If the dust concentration variation coefficient in the monitoring area is greater than the first set threshold, and the particle concentration variation rate is greater than the second set threshold, it is judged as an event of excessive dust concentration in the area; If the dust concentration variation coefficient in the monitoring area is less than or equal to the first set threshold, but the particle concentration variation rate is greater than the second set threshold, it is judged as a dust diffusion event; If the dust concentration change coefficient in the monitoring area is greater than the first set threshold, but the particle concentration change rate is less than or equal to the second set threshold, it is judged as a dust source release event; If the dust concentration variation coefficient in the monitoring area is less than or equal to the first set threshold, and the particle concentration change rate is less than or equal to the second set threshold, it is judged as a normal environmental fluctuation.

[0008] Furthermore, the first set threshold and the second set threshold in the monitoring area are determined in the following manner: The first threshold = ; The second threshold setting = ; In the formula, It indicates the coefficient of variation of the average dust concentration in the monitoring area under normal environmental conditions. Indicates the maximum dust concentration variation coefficient under extreme environmental conditions in the monitoring area; It indicates the average particle concentration change rate in the monitoring area under normal environmental conditions. Indicates the maximum particle concentration change rate in the monitoring area under extreme environmental conditions; Said , , , The method to obtain is: Through statistical analysis of historical monitoring data, the average and maximum dust concentration change coefficients and particulate matter concentration change rates in the monitoring area under normal and extreme environmental conditions are calculated respectively.

[0009] Furthermore, the maximum dust concentration variation coefficient of the monitoring area under extreme environmental conditions is , expressed as: In the formula, , They represent the maximum and minimum laser scattering signal intensities in the monitoring area under extreme environmental conditions; , They respectively represent the maximum and minimum particle concentrations in the monitoring area under extreme environmental conditions.

[0010] Furthermore, the maximum laser scattering signal intensity in the monitoring area , expressed as: In the formula, To monitor the intensity of laser scattering signals in the area over time, is the Dirac function, is the time point when the intensity of the laser scattering signal reaches the maximum value under extreme environmental conditions. For the monitoring cycle.

[0011] Furthermore, the maximum particle concentration in the monitoring area , expressed as: In the formula, To monitor the concentration of particulate matter in the area over time, is the Dirac function, is the time point when the particle concentration reaches the maximum value under extreme environmental conditions. For the monitoring cycle.

[0012] Furthermore, the particle concentration change rate in the monitoring area , expressed as: In the formula, To monitor the concentration of particulate matter in the area at the current moment, To monitor the concentration of particulate matter in the area at the next moment, For the time interval.

[0013] In a second aspect of the present invention, there is provided a laser-based online dust detection system, comprising: The data acquisition module is used to obtain the laser scattering signal intensity, particle concentration, wind speed, wind direction, humidity and other environmental meteorological data in the monitoring area before and after the abnormality occurs when the dust concentration in the dust monitoring area is abnormal; The laser scattering energy calculation module is used to calculate the laser scattering energy according to the acquired laser scattering signal intensity. The calculation formula is: In the formula, To monitor the intensity of laser scattering signals in the area over time, is the monitoring period; A dust concentration variation coefficient calculation module is used to calculate the laser scattering energy and the particle concentration variation rate in the monitoring area based on the acquired information, and then obtain the dust concentration variation coefficient in the monitoring area; The dust concentration abnormality identification and warning action module is used to determine the type of dust concentration abnormality based on the dust concentration variation coefficient and dust concentration abnormality identification criterion in the monitoring area. If the abnormal type is that the dust concentration in the area exceeds the standard, the warning action is initiated to achieve management of the dust monitoring area.

[0014] Furthermore, the dust concentration variation coefficient in the monitoring area in the dust concentration abnormality identification and early warning action module is expressed as: In the formula, Indicates the coefficient of variation of dust concentration; , Respectively represent the laser scattering signal intensity before and after the abnormality occurs; , Represent the particle concentrations before and after the abnormality occurs, respectively.

[0015] The above-mentioned embodiments of the present invention have at least the following beneficial effects: the present invention can accurately calculate the dust concentration variation coefficient in the monitoring area by real-time monitoring of the laser scattering signal intensity and particulate matter concentration, combined with environmental meteorological data such as wind speed, wind direction, and humidity. This method can effectively identify abnormal changes in dust concentration, such as excessive dust concentration, dust diffusion events, and dust source release events, thereby improving the accuracy and reliability of monitoring. In addition, the method can also promptly initiate early warning actions based on abnormal identification criteria, realize dynamic management and control of the dust monitoring area, and help to take timely measures to deal with dust pollution events and reduce the impact on the environment and human health.

[0016] At the same time, the invention uses laser scattering technology, which has the advantages of fast response speed, wide measurement range, and continuous monitoring, and can achieve real-time monitoring and rapid response to dust concentration. Compared with traditional particle sampling and analysis methods, this method greatly improves monitoring efficiency, reduces monitoring costs and workload, is suitable for large-scale dust monitoring applications, and has broad prospects for promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which: Figure 1 A schematic diagram of a process flow of a laser-based online dust detection method provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a laser-based online dust detection system provided in one embodiment of the present invention; Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0019] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0020] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0021] Reference below Figure 1 , Figure 1 The following is a flow chart of a laser-based online dust detection method provided by an embodiment of the present invention. Figure 1 As shown, a laser-based online dust detection method 100 includes: Step 101, when the dust concentration in the dust monitoring area is monitored to be abnormal, the laser scattering signal intensity, particle concentration, and environmental meteorological data such as wind speed, wind direction, and humidity in the monitoring area before and after the abnormality occurs are obtained; Step 102, based on the acquired information, calculate the laser scattering energy and the particle concentration change rate in the monitoring area, and then obtain the dust concentration change coefficient in the monitoring area; The calculation formula of laser scattering energy is: In the formula, To monitor the intensity of laser scattering signals in the area over time, is the monitoring period; Step 103, judging the type of dust concentration anomaly according to the dust concentration variation coefficient and dust concentration anomaly identification criteria in the monitoring area, if the anomaly type is that the dust concentration in the area exceeds the standard, starting the early warning action to achieve management of the dust monitoring area.

[0022] It should be noted that the online dust detection method of the present invention determines whether the dust concentration is abnormal by monitoring the changes in the laser scattering signal intensity and the particle concentration. The laser scattering signal intensity refers to the intensity of the light signal scattered after the laser interacts with the particles in the monitoring area, and the particle concentration refers to the mass or quantity of particles in a unit volume of air. When the dust concentration is abnormal, the system will obtain relevant data before and after the abnormality occurs, including the laser scattering signal intensity, the particle concentration, and environmental meteorological data such as wind speed, wind direction, and humidity. The acquisition of these data is to more accurately analyze the changes in the dust concentration, so as to determine the type of abnormality and take corresponding early warning measures.

[0023] Specifically, the calculation formula for laser scattering energy is: ,in To monitor the intensity of laser scattering signals in the area over time, is the monitoring period. The monitoring period can be set according to the actual monitoring needs, for example, it can be set to a few minutes to a few hours to meet the requirements of different monitoring scenarios. The calculation formula for the particle concentration change rate is in, is the particle concentration at the current moment, is the particle concentration at the next moment, The time interval can be set according to the sampling frequency of the monitoring equipment, usually from a few seconds to a few minutes.

[0024] More specifically, the calculation formula for the dust concentration variation coefficient is: in, and Respectively represent the laser scattering signal intensity before and after the abnormality occurs, and They represent the particle concentration before and after the abnormality occurs. The settings of these parameters can be adjusted according to the specific monitoring environment and equipment performance to achieve the best monitoring effect.

[0025] Preferably, in order to improve the accuracy of monitoring, multiple monitoring points can be set in the monitoring area to obtain the laser scattering signal intensity and particle concentration data at different locations. By comparing the data at different monitoring points, the change in dust concentration and the type of abnormality can be more accurately determined.

[0026] Furthermore, other environmental monitoring data, such as temperature and air pressure, can be combined to further analyze the possible causes of changes in dust concentration. For example, when a sudden increase in particulate matter concentration is detected, the wind speed and wind direction data can be analyzed to determine whether it is a dust diffusion event or a dust source release event. If the wind speed is high and the wind direction points to the monitoring area, it may be a dust diffusion event; if the wind speed is low and there is an obvious dust source in the monitoring area, it may be a dust source release event. Through these detailed operating steps and alternatives, the practicality and reliability of the online dust detection method can be further improved.

[0027] In some embodiments, the dust concentration variation coefficient in the monitoring area is expressed as: In the formula, Indicates the coefficient of variation of dust concentration; , Respectively represent the laser scattering signal intensity before and after the abnormality occurs; , Represent the particle concentrations before and after the abnormality occurs, respectively.

[0028] It should be noted that the dust concentration variation coefficient is an important indicator to measure the degree of dust concentration variation in the monitoring area. It is calculated by comparing the changes in the laser scattering signal intensity and the particle concentration before and after the abnormality occurs. The laser scattering signal intensity reflects the scattering ability of particles to laser, while the particle concentration directly indicates the particle content per unit volume. By calculating the change rate of these two parameters, the change of dust concentration can be more accurately evaluated, thus providing reliable data support for dust monitoring and management. Specifically, the calculation formula for the dust concentration variation coefficient is: in, and Respectively represent the laser scattering signal intensity before and after the abnormality occurs, and They represent the particle concentration before and after the abnormality occurs. In practical applications, the laser scattering signal intensity can be collected in real time by a laser sensor, and the particle concentration can be indirectly measured by a particle sensor or laser scattering method. For example, when the laser scattering signal intensity is monitored to increase from 1000 units to 1200 units and the particle concentration increases from 50 μg / m3 to 60 μg / m3, the dust concentration change coefficient can be calculated as This value can be used to determine the degree of change in dust concentration and possible abnormal types.

[0029] Preferably, in order to improve the calculation accuracy of the dust concentration variation coefficient, a multi-point averaging method can be used, that is, multiple monitoring points are set in the monitoring area, the laser scattering signal intensity and particulate matter concentration data of each point are collected respectively, and then the dust concentration variation coefficient of each point is calculated, and finally the average value is taken as the dust concentration variation coefficient of the entire monitoring area.

[0030] Furthermore, we can also consider introducing the concept of time weighting to weight the coefficient of variation according to the time interval before and after the anomaly occurs. For example, if the time interval before and after the anomaly occurs is short, a larger weight can be given to highlight the rapid change of dust concentration in a short period of time. Through these detailed operation steps and alternatives, the calculation accuracy and reliability of the coefficient of variation of dust concentration can be further improved, providing more reliable data support for dust monitoring and management.

[0031] In some embodiments, the abnormal dust concentration identification criteria include: If the dust concentration variation coefficient in the monitoring area is greater than the first set threshold, and the particle concentration variation rate is greater than the second set threshold, it is judged as an event of excessive dust concentration in the area; If the dust concentration variation coefficient in the monitoring area is less than or equal to the first set threshold, but the particle concentration variation rate is greater than the second set threshold, it is judged as a dust diffusion event; If the dust concentration change coefficient in the monitoring area is greater than the first set threshold, but the particle concentration change rate is less than or equal to the second set threshold, it is judged as a dust source release event; If the dust concentration variation coefficient in the monitoring area is less than or equal to the first set threshold, and the particle concentration change rate is less than or equal to the second set threshold, it is judged as a normal environmental fluctuation.

[0032] It should be noted that the dust concentration abnormality identification criterion is a standard used to determine whether the dust concentration change in the monitoring area is an abnormal situation. By setting the threshold, normal environmental fluctuations and abnormal events can be distinguished, thereby achieving effective monitoring and early warning of dust pollution. The first set threshold and the second set threshold are used to determine whether the dust concentration change coefficient and the particle concentration change rate meet the abnormal standards. For example, when the dust concentration change coefficient is greater than the first set threshold, it means that the dust concentration has changed to a large extent, which may be an abnormal event; when the particle concentration change rate is greater than the second set threshold, it means that the particle concentration is changing rapidly, which may also be an abnormal event. Specifically, the determination of the first set threshold and the second set threshold needs to be analyzed based on the historical data and environmental conditions of the monitoring area. The first set threshold can be set to a certain multiple of the average dust concentration change coefficient under normal environmental conditions in the monitoring area, such as 1.5 times or 2 times. The second set threshold can be set to a certain multiple of the average particulate matter concentration change rate under normal environmental conditions in the monitoring area, such as 2 times or 3 times.

[0033] More specifically, the threshold can be adjusted and optimized according to the specific conditions of the monitoring area, combined with expert experience and on-site survey results. For example, in areas with severe dust pollution such as industrial areas or construction sites, the threshold can be appropriately increased to avoid frequent false alarms; in areas with higher air quality requirements such as residential areas or nature reserves, the threshold can be appropriately lowered to improve the sensitivity of monitoring.

[0034] Preferably, in order to improve the accuracy of abnormality identification, a method of dynamically adjusting the threshold can be adopted. That is, the first set threshold and the second set threshold are dynamically adjusted according to the real-time environmental conditions and historical monitoring data of the monitoring area. For example, when the wind speed is high or the humidity is high, the threshold can be appropriately increased to reduce the interference of environmental factors on the monitoring results; when the air quality is poor or there is an obvious dust source, the threshold can be appropriately lowered to timely discover and handle abnormal events.

[0035] Furthermore, machine learning algorithms can be introduced to automatically identify abnormal events through training models, further improving the intelligent level of monitoring. Through these refined operation steps and alternatives, it is possible to better adapt to the monitoring needs under different environmental conditions and improve the accuracy and reliability of dust concentration abnormality identification. In some embodiments, the first set threshold and the second set threshold in the monitoring area are determined in the following manner: The first threshold = ; The second threshold setting = ; In the formula, It indicates the coefficient of variation of the average dust concentration in the monitoring area under normal environmental conditions. Indicates the maximum dust concentration variation coefficient under extreme environmental conditions in the monitoring area; It indicates the average particle concentration change rate in the monitoring area under normal environmental conditions. Indicates the maximum particle concentration change rate in the monitoring area under extreme environmental conditions; Said , , , The method to obtain is: Through statistical analysis of historical monitoring data, the average and maximum dust concentration change coefficients and particulate matter concentration change rates in the monitoring area under normal and extreme environmental conditions are calculated respectively.

[0036] It should be noted that the first set threshold and the second set threshold are key parameters for determining whether the coefficient of change of dust concentration and the rate of change of particulate matter concentration are abnormal. The first set threshold is determined based on the average and maximum coefficients of change of dust concentration in the monitoring area under normal and extreme environmental conditions, while the second set threshold is determined based on the average and maximum rates of change of particulate matter concentration. These thresholds are set to ensure that the monitoring system can accurately identify abnormal changes in dust concentration under different environmental conditions, so as to take corresponding early warning measures in a timely manner.

[0037] Specifically, the first threshold is set The average dust concentration variation coefficient under normal environmental conditions in the monitoring area can be calculated and the maximum dust concentration variation coefficient under extreme environmental conditions To determine, the formula is Similarly, the second threshold is set The average particle concentration change rate under normal environmental conditions can be calculated and the maximum particle concentration change rate under extreme environmental conditions To determine, the formula is These parameters can be obtained through statistical analysis of historical monitoring data, for example, by collecting monitoring data over a period of one year or longer and calculating the average and maximum values ​​under different environmental conditions.

[0038] Preferably, in order to improve the accuracy and adaptability of the threshold setting, a dynamic adjustment mechanism can be adopted. For example, the first set threshold and the second set threshold are dynamically adjusted according to seasonal changes or weather forecast information. In the sandstorm season or extreme weather conditions, the threshold can be appropriately increased to reduce false alarms; in the season or weather conditions with good air quality, the threshold can be appropriately lowered to improve the sensitivity of monitoring.

[0039] Furthermore, adaptive algorithms can be introduced to automatically adjust thresholds based on real-time monitoring data and changes in environmental conditions, so that the monitoring system can better adapt to different monitoring environments. Through these detailed operation steps and alternatives, the accuracy and reliability of the monitoring system can be further improved, ensuring timely and effective identification and early warning of abnormal dust concentration events.

[0040] In some embodiments, the maximum dust concentration variation coefficient of the monitoring area under extreme environmental conditions is , expressed as: In the formula, , They represent the maximum and minimum laser scattering signal intensities in the monitoring area under extreme environmental conditions; , They respectively represent the maximum and minimum particle concentrations in the monitoring area under extreme environmental conditions.

[0041] It should be noted that the maximum dust concentration variation coefficient under extreme environmental conditions is an important parameter used to evaluate the degree of change in dust concentration in the most unfavorable environmental conditions in the monitoring area. Extreme environmental conditions usually refer to conditions such as extremely high wind speed, extremely low humidity or extremely high particle concentration, which may lead to drastic changes in dust concentration. The maximum dust concentration variation coefficient is calculated by comparing the maximum and minimum laser scattering signal intensities and particle concentrations under extreme environmental conditions. It can effectively reflect the maximum range of dust concentration changes under extreme conditions and provide a basis for setting reasonable monitoring thresholds. Specifically, the calculation formula for the maximum dust concentration variation coefficient under extreme environmental conditions is: in, and They represent the maximum and minimum laser scattering signal intensities in the monitoring area under extreme environmental conditions, and They respectively represent the maximum and minimum particle concentrations in the monitoring area under extreme environmental conditions.

[0042] More specifically, in practical applications, these parameters can be obtained through data recording of monitoring equipment under extreme environmental conditions. For example, in the sandstorm season, the maximum laser scattering signal intensity recorded by the monitoring equipment is 2000 units and the minimum is 500 units. The maximum particle concentration is 100 micrograms / cubic meter and the minimum is 20 micrograms / cubic meter. The maximum dust concentration variation coefficient is This value can be used to evaluate the changes in dust concentration in the monitoring area under extreme conditions.

[0043] Preferably, in order to improve the calculation accuracy and reliability of the maximum dust concentration variation coefficient, a multi-point monitoring and data fusion method can be used. Multiple monitoring points are set up in the monitoring area to collect laser scattering signal intensity and particulate matter concentration data under extreme environmental conditions, and then the data of each point are fused to calculate the maximum dust concentration variation coefficient of the entire monitoring area.

[0044] Furthermore, it is possible to combine meteorological data and geographic information to make more accurate predictions and judgments on extreme environmental conditions, thereby improving the accuracy and reliability of monitoring results. For example, the wind speed and humidity data provided by the meteorological station can more accurately determine the occurrence time and duration of extreme environmental conditions, thereby improving the calculation accuracy of the maximum dust concentration variation coefficient.

[0045] In some embodiments, the maximum laser scattering signal intensity within the monitoring area , expressed as: In the formula, To monitor the intensity of laser scattering signals in the area over time, is the Dirac function, is the time point when the intensity of the laser scattering signal reaches the maximum value under extreme environmental conditions. For the monitoring cycle.

[0046] It should be noted that the maximum laser scattering signal intensity refers to the maximum value of the laser scattering signal intensity in the monitoring area under extreme environmental conditions. It is one of the important parameters for evaluating the change of particle concentration in the monitoring area under extreme conditions. By calculating the maximum laser scattering signal intensity, we can more accurately understand the scattering ability of particles to laser under extreme environmental conditions, thereby providing reliable data support for monitoring and early warning. Extreme environmental conditions usually include high wind speed, low humidity or high particle concentration, which may cause drastic changes in particle concentration, thereby affecting the intensity of the laser scattering signal. Specifically, the calculation formula for the maximum laser scattering signal intensity is: in, To monitor the intensity of laser scattering signals in the area over time, is the Dirac function, is the time point when the intensity of the laser scattering signal reaches the maximum value under extreme environmental conditions. For the monitoring cycle.

[0047] More specifically, in practical applications, the intensity of the laser scattering signal can be collected in real time by a laser sensor, and the monitoring period can be set according to specific monitoring requirements, for example, it can be set to a few minutes to a few hours. For example, when the monitoring period is 1 hour, the intensity of the laser scattering signal recorded by the laser sensor reaches a maximum value of 2000 units at a certain moment, then the maximum laser scattering signal intensity at that moment is 2000 units.

[0048] Preferably, in order to improve the calculation accuracy and reliability of the maximum laser scattering signal intensity, the collected laser scattering signal intensity data can be preprocessed by using data smoothing and filtering methods. For example, a moving average filter or a high-pass filter can be used to remove noise and outliers, thereby obtaining more stable and accurate maximum laser scattering signal intensity data.

[0049] Furthermore, other environmental monitoring data, such as wind speed and humidity, can be combined to more accurately judge and predict extreme environmental conditions, thereby improving the accuracy and reliability of the calculation results. For example, if the wind speed suddenly increases and the humidity decreases, it can be judged that extreme environmental conditions have occurred, and the maximum laser scattering signal intensity can be calculated more accurately.

[0050] In some embodiments, the maximum particle concentration in the monitoring area , expressed as: In the formula, To monitor the concentration of particulate matter in the area over time, is the Dirac function, is the time point when the particle concentration reaches the maximum value under extreme environmental conditions. For the monitoring cycle.

[0051] It should be noted that the maximum particle concentration refers to the maximum value of the particle concentration in the monitoring area under extreme environmental conditions. It is one of the important indicators for evaluating the degree of particle pollution in the monitoring area under extreme conditions. By calculating the maximum particle concentration, we can more accurately understand the distribution and changes of particles under extreme environmental conditions, thereby providing reliable data support for monitoring and early warning. Extreme environmental conditions usually include high wind speed, low humidity or high particle emissions, which may cause drastic changes in particle concentration and thus affect air quality.

[0052] Specifically, the calculation formula for the maximum particle concentration is: in, To monitor the concentration of particulate matter in the area over time, is the Dirac function, is the time point when the particle concentration reaches the maximum value under extreme environmental conditions. For the monitoring cycle.

[0053] More specifically, in actual applications, the particle concentration can be collected in real time by the particle sensor, and the monitoring period can be set according to specific monitoring needs, for example, it can be set to a few minutes to a few hours. For example, when the monitoring period is 1 hour, the particle concentration recorded by the particle sensor reaches a maximum value of 100 micrograms / cubic meter at a certain moment, then the maximum particle concentration at that moment is 100 micrograms / cubic meter.

[0054] Preferably, in order to improve the calculation accuracy and reliability of the maximum particle concentration, the collected particle concentration data can be preprocessed by using data smoothing and filtering methods. For example, a moving average filter or a high-pass filter can be used to remove noise and outliers, thereby obtaining more stable and accurate maximum particle concentration data.

[0055] Furthermore, it is also possible to combine other environmental monitoring data, such as wind speed, humidity, etc., to make more accurate judgments and predictions on extreme environmental conditions, thereby improving the accuracy and reliability of the calculation results. For example, if the wind speed suddenly increases and the humidity decreases, it can be judged that extreme environmental conditions have occurred, and the maximum particle concentration can be calculated more accurately.

[0056] In some embodiments, the particle concentration change rate in the monitoring area , expressed as: In the formula, To monitor the concentration of particulate matter in the area at the current moment, To monitor the concentration of particulate matter in the area at the next moment, For the time interval.

[0057] It should be noted that the particle concentration change rate is an important indicator to measure the speed at which the particle concentration in the monitoring area changes over time. It is obtained by calculating the ratio of the difference between the particle concentration at the current moment and the next moment to the time interval. The calculation of the particle concentration change rate can help the monitoring system to detect the rapid change of particle concentration in time, so as to determine whether there are abnormal events such as dust diffusion or dust source release, and provide a basis for early warning and management.

[0058] Specifically, the calculation formula for the particle concentration change rate is: in, is the particle concentration at the current moment, is the particle concentration at the next moment, is the time interval.

[0059] More specifically, in practical applications, the particle concentration can be collected in real time by a particle sensor, and the time interval can be set according to the sampling frequency of the monitoring equipment, usually a few seconds to a few minutes. For example, if the particle concentration is 50 micrograms / cubic meter at a certain moment and 60 micrograms / cubic meter at the next moment (1 minute interval), the particle concentration change rate is μg / m3 / min.

[0060] Preferably, in order to improve the calculation accuracy and reliability of the particle concentration change rate, the collected particle concentration data can be preprocessed by using data smoothing and filtering methods. For example, a moving average filter or a high-pass filter can be used to remove noise and outliers, thereby obtaining more stable and accurate particle concentration change rate data.

[0061] Furthermore, other environmental monitoring data, such as wind speed and humidity, can be combined to conduct a more comprehensive analysis of changes in particle concentration, thereby improving the accuracy and reliability of monitoring results. For example, if a sudden increase in wind speed and a rapid rise in particle concentration are detected, it can be judged as a dust diffusion event, and the particle concentration change rate can be calculated more accurately.

[0062] The above-mentioned embodiments of the present invention have the following beneficial effects: The method and system described in the present invention can realize accurate monitoring and analysis of dust concentration changes by comprehensively analyzing the laser scattering signal intensity, particulate matter concentration and environmental meteorological data. The method uses laser scattering energy and particulate matter concentration change rate to calculate the dust concentration change coefficient, and can effectively identify different types of abnormal dust events, such as excessive dust concentration, dust diffusion and dust source release. This method not only improves the accuracy and reliability of monitoring, but also can timely initiate early warning actions according to abnormal identification criteria, realize dynamic management and control of dust monitoring areas, and help to take timely measures to deal with dust pollution events and reduce the impact on the environment and human health.

[0063] In addition, this method uses laser scattering technology, which has the advantages of fast response speed, wide measurement range, and continuous monitoring, and can achieve real-time monitoring and rapid response to dust concentration. Compared with traditional particle sampling and analysis methods, this method greatly improves monitoring efficiency, reduces monitoring costs and workload, and is suitable for large-scale dust monitoring applications. The threshold determined by statistical analysis of historical monitoring data further enhances the adaptability and stability of the system, enabling it to operate stably under different environmental conditions, and has broad prospects for promotion and application.

[0064] like Figure 2 As shown, in some embodiments, a laser-based online dust detection system 200 includes: The data acquisition module 201 is used to acquire the laser scattering signal intensity, particle concentration, wind speed, wind direction, humidity and other environmental meteorological data in the monitoring area before and after the abnormality occurs when the dust concentration in the dust monitoring area is abnormal; The laser scattering energy calculation module 202 is used to calculate the laser scattering energy according to the acquired laser scattering signal intensity, and the calculation formula is: In the formula, To monitor the intensity of laser scattering signals in the area over time, is the monitoring period; The dust concentration variation coefficient calculation module 203 is used to calculate the laser scattering energy and the particle concentration variation rate in the monitoring area based on the acquired information, and then obtain the dust concentration variation coefficient in the monitoring area; The dust concentration abnormality identification and warning action module 204 is used to determine the type of dust concentration abnormality based on the dust concentration variation coefficient and dust concentration abnormality identification criteria in the monitoring area. If the abnormality type is that the dust concentration in the area exceeds the standard, the warning action is initiated to achieve management of the dust monitoring area.

[0065] It is understandable that the modules described in the laser-based dust online detection system 200 are similar to those described in the reference Figure 1 The steps in the described laser-based online dust detection method correspond to each other. Therefore, the operations, features and beneficial effects described above for the laser-based online dust detection method are also applicable to the laser-based online dust detection system 200 and the modules contained therein, and will not be repeated here.

[0066] In some embodiments, the dust concentration variation coefficient in the monitoring area in the dust concentration abnormality identification and early warning action module is expressed as: In the formula, Indicates the coefficient of variation of dust concentration; , Respectively represent the laser scattering signal intensity before and after the abnormality occurs; , Represent the particle concentrations before and after the abnormality occurs, respectively.

[0067] It should be noted that the dust concentration variation coefficient is an important indicator for evaluating the degree of change in particle concentration in the monitoring area. It is calculated by comparing the laser scattering signal intensity and particle concentration before and after the abnormality occurs. The laser scattering signal intensity reflects the scattering ability of particles to laser, while the particle concentration directly indicates the particle content per unit volume. By calculating the change rate of these two parameters, the change in dust concentration can be more accurately evaluated, thereby providing reliable data support for dust monitoring and management.

[0068] Specifically, the calculation formula for the dust concentration variation coefficient is: in, and Respectively represent the laser scattering signal intensity before and after the abnormality occurs, and Respectively represent the particle concentration before and after the abnormality occurs.

[0069] More specifically, in practical applications, the intensity of the laser scattering signal can be collected in real time by a laser sensor, and the particle concentration can be indirectly measured by a particle sensor or laser scattering method. For example, when the intensity of the laser scattering signal is monitored to increase from 1000 units to 1200 units, and the particle concentration increases from 50 μg / m3 to 60 μg / m3, the dust concentration change coefficient can be calculated as This value can be used to determine the degree of change in dust concentration and possible abnormal types.

[0070] Preferably, in order to improve the calculation accuracy of the dust concentration variation coefficient, a multi-point averaging method can be used, that is, multiple monitoring points are set in the monitoring area, the laser scattering signal intensity and particulate matter concentration data of each point are collected respectively, and then the dust concentration variation coefficient of each point is calculated, and finally the average value is taken as the dust concentration variation coefficient of the entire monitoring area.

[0071] Furthermore, we can also consider introducing the concept of time weighting to weight the coefficient of variation according to the time interval before and after the anomaly occurs. For example, if the time interval before and after the anomaly occurs is short, a larger weight can be given to highlight the rapid change of dust concentration in a short period of time. Through these detailed operation steps and alternatives, the calculation accuracy and reliability of the coefficient of variation of dust concentration can be further improved, providing more reliable data support for dust monitoring and management.

[0072] Reference below Figure 3 , which shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include but are not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0073] like Figure 3 As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 to a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0074] Typically, the following devices may be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0075] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0076] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A laser-based online dust detection method, characterized in that: The following steps are involved: When abnormal dust concentration is detected in the dust monitoring area, obtain the laser scattering signal intensity, particle concentration, wind speed, wind direction, humidity and other environmental meteorological data in the monitoring area before and after the abnormality occurs; Based on the information obtained, the laser scattering energy and the particle concentration change rate in the monitoring area are calculated, and then the dust concentration change coefficient in the monitoring area is obtained; The calculation formula of laser scattering energy is: In the formula, To monitor the intensity of laser scattering signals in the area over time, is the monitoring period; The type of dust concentration anomaly is determined based on the dust concentration variation coefficient and dust concentration anomaly identification criteria in the monitoring area. If the anomaly type is that the dust concentration in the area exceeds the standard, an early warning action is initiated to achieve management of the dust monitoring area.

2. The laser-based online dust detection method according to claim 1 is characterized in that: The coefficient of variation of dust concentration in the monitoring area is expressed as: In the formula, Indicates the coefficient of variation of dust concentration; , Respectively represent the laser scattering signal intensity before and after the abnormality occurs; , Represent the particle concentrations before and after the abnormality occurs, respectively.

3. The laser-based online dust detection method according to claim 1 is characterized in that: The abnormal dust concentration identification criteria include: If the dust concentration variation coefficient in the monitoring area is greater than the first set threshold, and the particle concentration variation rate is greater than the second set threshold, it is judged as an event of excessive dust concentration in the area; If the dust concentration variation coefficient in the monitoring area is less than or equal to the first set threshold, but the particle concentration variation rate is greater than the second set threshold, it is judged as a dust diffusion event; If the dust concentration change coefficient in the monitoring area is greater than the first set threshold, but the particle concentration change rate is less than or equal to the second set threshold, it is judged as a dust source release event; If the dust concentration variation coefficient in the monitoring area is less than or equal to the first set threshold, and the particle concentration change rate is less than or equal to the second set threshold, it is judged as a normal environmental fluctuation.

4. The laser-based online dust detection method according to claim 2 is characterized in that: The first set threshold and the second set threshold within the monitoring area are determined in the following manner: The first threshold = ; The second threshold setting = ; In the formula, It indicates the coefficient of variation of the average dust concentration in the monitoring area under normal environmental conditions. Indicates the maximum dust concentration variation coefficient under extreme environmental conditions in the monitoring area; It indicates the average particle concentration change rate in the monitoring area under normal environmental conditions. Indicates the maximum particle concentration change rate in the monitoring area under extreme environmental conditions; Said , , , The method to obtain is: Through statistical analysis of historical monitoring data, the average and maximum dust concentration change coefficients and particulate matter concentration change rates in the monitoring area under normal and extreme environmental conditions are calculated respectively.

5. The laser-based online dust detection method according to claim 4 is characterized in that: The maximum dust concentration variation coefficient of the monitoring area under extreme environmental conditions , expressed as: In the formula, , They represent the maximum and minimum laser scattering signal intensities in the monitoring area under extreme environmental conditions; , They respectively represent the maximum and minimum particle concentrations in the monitoring area under extreme environmental conditions.

6. The laser-based online dust detection method according to claim 5 is characterized in that: The maximum laser scattering signal intensity within the monitoring area , expressed as: In the formula, To monitor the intensity of laser scattering signals in the area over time, is the Dirac function, is the time point when the intensity of the laser scattering signal reaches the maximum value under extreme environmental conditions. For the monitoring cycle.

7. The laser-based online dust detection method according to claim 6 is characterized in that: The maximum particle concentration in the monitoring area , expressed as: In the formula, To monitor the concentration of particulate matter in the area over time, is the Dirac function, is the time point when the particle concentration reaches the maximum value under extreme environmental conditions. For the monitoring cycle.

8. The laser-based online dust detection method according to claim 7 is characterized in that: The change rate of particle concentration in the monitoring area , expressed as: In the formula, To monitor the concentration of particulate matter in the area at the current moment, To monitor the concentration of particulate matter in the area at the next moment, is the time interval.

9. A laser-based dust online detection system, characterized in that: include: The data acquisition module is used to obtain the laser scattering signal intensity, particle concentration, wind speed, wind direction, humidity and other environmental meteorological data in the monitoring area before and after the abnormality occurs when the dust concentration in the dust monitoring area is abnormal; The laser scattering energy calculation module is used to calculate the laser scattering energy according to the acquired laser scattering signal intensity. The calculation formula is: In the formula, To monitor the intensity of laser scattering signals in the area over time, is the monitoring period; A dust concentration variation coefficient calculation module is used to calculate the laser scattering energy and the particle concentration variation rate in the monitoring area based on the acquired information, and then obtain the dust concentration variation coefficient in the monitoring area; The dust concentration abnormality identification and warning action module is used to determine the type of dust concentration abnormality based on the dust concentration variation coefficient and dust concentration abnormality identification criterion in the monitoring area. If the abnormal type is that the dust concentration in the area exceeds the standard, the warning action is initiated to achieve management of the dust monitoring area.

10. The laser-based dust online detection system according to claim 9, characterized in that: The dust concentration variation coefficient in the monitoring area in the dust concentration abnormality identification and early warning action module is expressed as: In the formula, Indicates the coefficient of variation of dust concentration; , Respectively represent the laser scattering signal intensity before and after the abnormality occurs; , Represent the particle concentrations before and after the abnormality occurs, respectively.

Images