Smoke dust concentration detection method and device, storage medium and program product

Abstract

The invention discloses a smoke concentration detection method and device, a storage medium and a program product, and relates to the field of concentration detection.The method comprises the steps that the target environment temperature and the target environment humidity of a target sampling point and the target morphological parameters, the target initial mass and the target sampling volume of target smoke particles are obtained; according to the target morphological parameters and a preset morphological parameter-mass increment corresponding table, determining different mass increments corresponding to the target smoke particles at different environment temperatures and different environment humidity; determining a target mass increment of the target smoke particles based on the target environment temperature and the target environment humidity; according to the target initial mass and the target mass increment, target final mass of the target smoke particles is determined; and dividing the target final mass by the target sampling volume to obtain the target smoke dust concentration of the target smoke dust particles. By implementing the method, the accuracy of smoke concentration detection is improved.

Description

Technical Field

[0001] The present application relates to the field of concentration detection, and in particular to a smoke concentration detection method, a detection device, a storage medium and a program product. Background Art

[0002] With the rapid development of industrial production, the impact of smoke emissions on environmental sanitation and human health has become increasingly significant. Accurate detection of smoke concentration is an important link in industrial production and is of great significance for controlling pollutant emissions, protecting environmental sanitation and human health. Especially in some special industrial production environments, due to large fluctuations in temperature and humidity, the physical properties of smoke particles will change, which puts higher requirements on the accurate detection of smoke concentration.

[0003] In the related art, the smoke concentration detection method mainly adopts the photoelectric detection method, that is, a transmitter and a receiver are set in the flue, and the detection device calculates the smoke concentration by utilizing the attenuation of light intensity caused by the scattering and absorption of smoke particles when the light beam passes through the smoke.

[0004] Since the photoelectric detection method mainly relies on the attenuation of light intensity to reflect the smoke concentration, when the water vapor content in the smoke is high or the temperature fluctuates drastically, the water vapor and temperature changes will interfere with the transmission of the light beam, causing fluctuations in the detection signal. Summary of the invention

[0005] The present application provides a smoke concentration detection method, a detection device, a storage medium and a program product for improving the accuracy of smoke concentration detection.

[0006] In a first aspect, the present application provides a smoke concentration detection method, which is applied to a detection system, and the method includes: obtaining a target ambient temperature, a target ambient humidity, and a target morphological parameter, a target initial mass, and a target sampling volume of a target sampling point; determining different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities according to the target morphological parameter and a preset morphological parameter-mass increment correspondence table, wherein the preset morphological parameter-mass increment correspondence table is used to represent the mass change of smoke particles with different morphological parameters at different ambient temperatures and different ambient humidities; determining a target mass increment of the target smoke particles based on the target ambient temperature and the target ambient humidity; determining a target final mass of the target smoke particles according to the target initial mass and the target mass increment; and dividing the target final mass by the target sampling volume to obtain a target smoke concentration of the target smoke particles.

[0007] By adopting the above technical scheme, the detection device obtains the ambient temperature, ambient humidity and morphological parameters of the smoke particles at the sampling point, and quantifies the influence of environmental factors on the mass of the smoke particles through a preset morphological parameter-mass increment correspondence table, and accurately determines its mass increment and smoke concentration, so as to alleviate the defect that the relevant smoke concentration detection method does not consider the influence of environmental factors on the mass of the smoke particles. It not only effectively reduces the detection error caused by environmental changes and makes the detection results more in line with the actual working conditions, but also enhances the adaptability of the detection method to diverse environments, and is applicable to all sampling scenarios regardless of temperature and humidity conditions.

[0008] In combination with some embodiments of the first aspect, in some embodiments, before the step of determining different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities according to the target morphological parameters and the preset morphological parameter-mass increment correspondence table, the method also includes: obtaining the morphological parameters and initial masses of a plurality of standard smoke particles, the sampling volume of the standard smoke particles being unit volume; measuring the mass changes of the plurality of standard smoke particles within a preset ambient temperature range and a preset ambient humidity range, respectively; associating the morphological parameters of the plurality of standard smoke particles with the corresponding mass changes to generate the preset morphological parameter-mass increment correspondence table.

[0009] By adopting the above technical solution, the detection device obtains the mass change of standard smoke particles with different morphological parameters under different ambient temperatures and different ambient humidity, and generates a preset morphological parameter-mass increment correspondence table, so as to accurately predict the mass change law of different types of smoke particles under various environmental conditions. This preset morphological parameter-mass increment correspondence table established based on experimental data significantly improves the scientificity and reliability of the detection method, provides strong support for accurately evaluating environmental impacts, and ensures the accuracy and repeatability of the detection results.

[0010] In combination with some embodiments of the first aspect, in some embodiments, the different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities are determined based on the target morphological parameters and the preset morphological parameter-mass increment correspondence table, specifically including: calculating the matching degree between the target morphological parameters and the morphological parameters in the preset morphological parameter-mass increment correspondence table; determining the mass change data corresponding to the N morphological parameters with the highest matching degree; and based on the mass change data corresponding to the N morphological parameters, using a weighted average algorithm to calculate the different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities.

[0011] By adopting the above technical solution, the detection device calculates the matching degree between the target morphological parameters and the morphological parameters in the preset morphological parameter-mass increment correspondence table, and selects the closest N morphological parameters for analysis, which effectively improves the detection accuracy. It not only considers the influence of a single morphological parameter, but also comprehensively considers the influence of multiple similar morphological parameters through a weighted average algorithm, making the calculation of the mass increment more accurate and reasonable.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining the target mass increment of the target smoke particles based on the target ambient temperature and the target ambient humidity, the method also includes: obtaining the historical ambient temperature and the historical ambient humidity of the target sampling point; based on the historical ambient temperature and the historical ambient humidity, predicting the average ambient temperature and the average ambient humidity within a preset time period in the future; based on the average ambient temperature and the average ambient humidity, predicting the mass change trend of the target smoke particles to determine the smoke concentration change of the target smoke particles.

[0013] By adopting the above technical solution, the detection device analyzes the historical ambient temperature and humidity of the target sampling point, predicts the trend of environmental parameters within a preset time period in the future, and then calculates the trend of smoke particle mass and concentration. This predictive analysis enables environmental monitoring to shift from passive response to active warning so that preventive measures can be taken in advance.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of dividing the target final mass by the target sampling volume to obtain the target smoke concentration of the target smoke particles, the method also includes: obtaining the smoke concentration of each sampling point; establishing a smoke concentration distribution map based on the smoke concentration; and determining an abnormal area based on the smoke concentration distribution map, wherein the smoke concentration of the abnormal area exceeds a preset smoke concentration threshold.

[0015] By adopting the above technical solution, the detection device collects smoke concentrations at multiple sampling points and constructs a complete smoke concentration distribution map to intuitively display abnormal areas where smoke concentrations exceed the preset smoke concentration threshold. This visual monitoring method makes the smoke concentration distribution clearer and more intuitive, making it easier for managers to quickly judge the pollution situation and formulate targeted governance measures.

[0016] In combination with some embodiments of the first aspect, in some embodiments, after determining an abnormal area based on the smoke concentration distribution map and the smoke concentration in the abnormal area exceeds a preset smoke concentration threshold, the method further includes: obtaining historical smoke concentration data and environmental parameter data of the abnormal area; establishing a smoke diffusion prediction model based on the historical smoke concentration data and the environmental parameter data; predicting the smoke diffusion range of the abnormal area based on the smoke diffusion prediction model; and issuing an early warning when the smoke diffusion range exceeds a preset safety range.

[0017] By adopting the above technical solution, the detection device analyzes the historical smoke concentration data and environmental parameter data of the abnormal area, establishes a smoke diffusion prediction model, and realizes dynamic monitoring and early warning of smoke diffusion. When the predicted smoke diffusion range exceeds the preset safety range, the detection device will issue an early warning in time to provide sufficient preparation time for emergency response. This prediction and early warning mechanism based on historical data has greatly improved the initiative and foresight of environmental monitoring, and provided strong technical support for preventing major environmental pollution incidents.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of dividing the target final mass by the target sampling volume to obtain the target smoke concentration of the target smoke particles, the method also includes: obtaining composition information of the target smoke particles; determining the component source and component impact based on the composition information; and formulating pollution control measures according to the component source and the component impact.

[0019] By adopting the above technical solution, the detection device obtains the composition information of smoke particles, so as to accurately determine the source of the components and evaluate the impact of different components on the environment. Based on these analysis results, the detection device can provide a scientific basis for formulating targeted pollution control measures.

[0020] In a second aspect, an embodiment of the present application provides a detection system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the detection system to perform the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions, and when the above-mentioned computer program product runs on a detection system, the above-mentioned detection system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, which, when executed on a detection system, enable the detection system to execute the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understandable that the detection system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiment of the present application. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method, which will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By adopting the above technical solution, the detection device obtains the ambient temperature, ambient humidity and morphological parameters of the smoke particles at the sampling point, and quantifies the impact of environmental factors on the mass of the smoke particles through a preset morphological parameter-mass increment correspondence table, and accurately determines its mass increment and smoke concentration, so as to alleviate the defect that the relevant smoke concentration detection method does not consider the impact of environmental factors on the mass of the smoke particles. It not only effectively reduces the detection error caused by environmental changes and makes the detection results more in line with the actual working conditions, but also enhances the adaptability of the detection method to diverse environments, and is applicable to all sampling scenarios regardless of temperature and humidity conditions.

[0025] 2. By adopting the above technical solution, the detection device obtains the mass change of standard smoke particles with different morphological parameters under different ambient temperatures and different ambient humidity, and generates a preset morphological parameter-mass increment correspondence table, so as to accurately predict the mass change law of different types of smoke particles under various environmental conditions. This preset morphological parameter-mass increment correspondence table established based on experimental data significantly improves the scientificity and reliability of the detection method, provides strong support for the accurate assessment of environmental impact, and ensures the accuracy and repeatability of the detection results.

[0026] 3. By adopting the above technical solution, the detection device analyzes the historical ambient temperature and humidity of the target sampling point, predicts the trend of environmental parameters within the preset time period in the future, and then calculates the trend of smoke particle mass and concentration. This predictive analysis enables environmental monitoring to shift from passive response to active warning so that preventive measures can be taken in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of a smoke concentration detection method in an embodiment of the present application; Figure 2 is another flow chart of the smoke concentration detection method in the embodiment of the present application; Figure 3It is a schematic diagram of the structure of a physical device of the detection system in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0030] The following is a description of the process of the method provided by this implementation. Figure 1 , is a flow chart of a smoke concentration detection method in an embodiment of the present application.

[0031] S101, obtaining a target ambient temperature, a target ambient humidity, and a target morphological parameter, a target initial mass, and a target sampling volume of a target smoke particle at a target sampling point; Among them, the target sampling point refers to the specific spatial position used to collect target smoke particles; the target ambient temperature refers to the atmospheric temperature value at the target sampling point, usually in degrees Celsius; the target ambient humidity refers to the relative humidity value of the air at the target sampling point, usually expressed as a percentage; the target smoke particles refer to the smoke particles to be measured collected at the target sampling point; the target morphological parameters refer to the geometric characteristics of the target smoke particles, including particle size, shape factor, specific surface area, etc.; the target initial mass refers to the original weight of the sampled target smoke particles; the target sampling volume refers to the volume of the collected air sample.

[0032] Specifically, the detection device collects real-time temperature data of the target sampling point through a temperature sensor to obtain the target ambient temperature, and collects real-time humidity data through a humidity sensor to obtain the target ambient humidity. At the same time, the detection device uses a microscopic image analysis system to obtain the target morphological parameters of the target smoke particles, measures the target initial mass of the target smoke particles through a high-precision electronic balance, and uses an air sampler to record the target sampling volume. The above data collection needs to be carried out under relatively stable environmental conditions to ensure the accuracy of the data.

[0033] S102, determining different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities according to the target morphological parameters and a preset morphological parameter-mass increment correspondence table, wherein the preset morphological parameter-mass increment correspondence table is used to represent mass changes of smoke particles with different morphological parameters at different ambient temperatures and different ambient humidities; Among them, the preset morphological parameter-mass increment correspondence table refers to a data comparison table established through a large number of experiments; the mass increment represents the change value of the mass of the smoke particles under specific environmental conditions; and the mass change is used to represent the proportional relationship of the mass change under different environmental conditions.

[0034] Specifically, first, the detection device calculates the matching degree between the acquired target morphological parameters and the morphological parameters in the preset morphological parameter-mass increment correspondence table, and selects the closest groups of morphological parameters. Then, based on these morphological parameters, the detection device searches for mass change data under different ambient temperatures and different ambient humidities in the preset morphological parameter-mass increment correspondence table. Finally, the detection device uses a weighted average algorithm to comprehensively calculate the possible mass changes of the target smoke particles under various environmental conditions. This process needs to consider the similarity weights of the morphological parameters to ensure the accuracy of the calculation results.

[0035] Optionally, under normal circumstances, according to the target morphological parameters and the preset morphological parameter-mass increment correspondence table, determining the different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities can be achieved in the following ways, which are not limited here: calculating the matching degree between the target morphological parameters and the morphological parameters in the preset morphological parameter-mass increment correspondence table; determining the mass change data corresponding to the N morphological parameters with the highest matching degree; based on the mass change data corresponding to the N morphological parameters, using a weighted average algorithm to calculate the different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities.

[0036] S103, determining a target mass increment of the target smoke particles based on the target ambient temperature and the target ambient humidity; The target mass increment refers to the mass change value of the target smoke particles under the target ambient temperature and target ambient humidity.

[0037] Specifically, first, the detection device compares the target ambient temperature and target ambient humidity with the different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities. If the target ambient temperature and the target ambient humidity just match, the detection device can directly obtain the corresponding target mass increment. If the target ambient temperature and the target ambient humidity fall between the preset ambient temperature and the preset ambient humidity, the detection device can use an interpolation algorithm to calculate the corresponding target mass increment. The interpolation algorithm refers to a method for numerical estimation between known data points. This calculation process needs to take into account the joint influence of ambient temperature and ambient humidity, and may require the use of a two-dimensional interpolation method. At the same time, it is also necessary to consider the impact of the fluctuation range of environmental parameters on the calculation results.

[0038] S104, determining a target final mass of the target smoke particle according to the target initial mass and the target mass increment; The target final mass refers to the actual mass of the target smoke particles after taking into account the influence of the target ambient temperature and the target ambient humidity.

[0039] Specifically, first, the detection device calibrates the measurement error of the target initial mass and applies the mass correction coefficient to eliminate the error. The mass correction coefficient refers to the calibration parameter used to compensate for the measurement error. It can be pre-set or determined according to actual conditions, and is not limited here. Then, the detection device performs algebraic operations on the calibrated target initial mass and the calculated target mass increment to obtain the theoretical target final mass. Finally, the detection device also needs to consider the uncertainty caused by fluctuations in environmental parameters and determine the confidence interval of the target final mass through statistical analysis. This calculation process needs to strictly follow metrological specifications to ensure the reliability and traceability of the data.

[0040] S105 , dividing the target final mass by the target sampling volume to obtain a target smoke concentration of the target smoke particles.

[0041] The target smoke concentration refers to the mass content of the target smoke particles in the target sampling volume.

[0042] Specifically, first, the detection device performs a standard state correction on the target sampling volume, considers the influence of temperature and pressure on the target sampling volume during the sampling process, and applies a volume correction coefficient. The volume correction coefficient refers to a calibration parameter used to compensate for the influence of temperature and pressure. It can be pre-set or determined according to actual conditions, and is not limited here. Then, the detection device divides the calibrated target final mass by the corrected target sampling volume to obtain a preliminary target smoke concentration. Next, the detection device needs to perform unit conversion to ensure that the results meet the unit requirements specified in national standards. Finally, by calculating the uncertainty of each link in the measurement process, a final concentration measurement result with a reliability description is given. This calculation process needs to consider various influencing factors to ensure the scientificity and accuracy of the measurement results.

[0043] By adopting the above technical scheme, the detection device obtains the ambient temperature, ambient humidity and morphological parameters of the smoke particles at the sampling point, and quantifies the influence of environmental factors on the mass of the smoke particles through a preset morphological parameter-mass increment correspondence table, and accurately determines its mass increment and smoke concentration, so as to alleviate the defect that the relevant smoke concentration detection method does not consider the influence of environmental factors on the mass of the smoke particles. It not only effectively reduces the detection error caused by environmental changes and makes the detection results more in line with the actual working conditions, but also enhances the adaptability of the detection method to diverse environments, and is applicable to all sampling scenarios regardless of temperature and humidity conditions.

[0044] The following is a more detailed description of the process of the method provided by this implementation. Figure 2 , is another flow chart of the smoke concentration detection method in the embodiment of the present application.

[0045] S201, obtaining a target ambient temperature, a target ambient humidity, and a target morphological parameter, a target initial mass, and a target sampling volume of a target smoke particle at a target sampling point; For details, please refer to step S101, which will not be described in detail here.

[0046] S202, obtaining morphological parameters and initial masses of a plurality of standard smoke particles, wherein the sampling volume of the standard smoke particles is a unit volume; measuring mass changes of the plurality of standard smoke particles within a preset ambient temperature range and a preset ambient humidity range respectively; associating the morphological parameters of the plurality of standard smoke particles with corresponding mass changes, and generating a preset morphological parameter-mass increment correspondence table; Among them, standard smoke particles refer to typical and representative smoke particle samples; morphological parameters are a set of parameters used to characterize the physical properties of smoke particles, including particle size distribution, shape coefficient, specific surface area, etc.; initial mass refers to the mass of smoke particles measured under standard environmental conditions (20°C, 65% relative humidity); unit volume refers to the volume of 1 cubic meter of air under standard conditions (0°C, 101.325kPa); the preset ambient temperature range refers to the temperature range of -10°C to 40°C; the preset ambient humidity range refers to the relative humidity range of 20% to 90%; the mass change indicates the increase or decrease in the mass of smoke particles under different environmental conditions; the preset morphological parameter-mass increment correspondence table refers to a database that records the mass change rules of unit volume of smoke particles with different morphological parameters under various environmental conditions.

[0047] Specifically, before establishing the preset morphological parameter-mass increment correspondence table, the detection device needs to conduct a large number of standard sample tests. First, the detection device collects typical smoke particle samples from different industrial sources, uses a high-resolution electron microscope to analyze the morphological characteristics of the smoke particle samples, and obtains morphological parameters including particle size distribution, shape coefficient, etc. At the same time, the detection device uses a microbalance to measure the initial mass of the smoke particle samples under standard environmental conditions. Then, the detection device places the smoke particle samples in environmental test boxes with different temperatures and different humidity combinations, with each temperature point separated by 5°C and the humidity point separated by 10%. It continuously monitors for 24 hours under each environmental condition and records the mass change. Finally, the detection device uses data analysis software to process the test data, establishes a multidimensional correlation model between morphological parameters and mass changes, and generates a preset morphological parameter-mass increment correspondence table containing complete environmental impact laws. The preset morphological parameter-mass increment correspondence table will serve as an important reference for subsequent smoke concentration detection.

[0048] S203, determining different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities according to the target morphological parameters and a preset morphological parameter-mass increment correspondence table, wherein the preset morphological parameter-mass increment correspondence table is used to represent mass changes of smoke particles with different morphological parameters at different ambient temperatures and different ambient humidities; For details, please refer to step S102, which will not be described in detail here.

[0049] S204, determining a target mass increment of the target smoke particles based on the target ambient temperature and the target ambient humidity; For details, please refer to step S103, which will not be described in detail here.

[0050] S205, determining a target final mass of the target smoke particle according to the target initial mass and the target mass increment; For details, please refer to step S104, which will not be described in detail here.

[0051] S206: Divide the target final mass by the target sampling volume to obtain a target smoke concentration of the target smoke particles.

[0052] For details, please refer to step S105, which will not be described in detail here.

[0053] S207, obtaining the smoke concentration of each sampling point; establishing a smoke concentration distribution map based on the smoke concentration; determining an abnormal area according to the smoke concentration distribution map, where the smoke concentration of the abnormal area exceeds a preset smoke concentration threshold; Among them, the sampling point refers to the fixed smoke concentration detection position set up in the factory area; the smoke concentration refers to the mass content of smoke particles in unit volume of air, usually in mg / m³; the smoke concentration distribution map refers to a visual map that displays the degree of smoke pollution in the factory area in two or three dimensions; the preset smoke concentration threshold refers to the upper limit of smoke concentration specified by the national environmental standards, usually 10 mg / m³; the abnormal area refers to the area where the smoke concentration exceeds the preset smoke concentration threshold.

[0054] Specifically, after the detection device completes the measurement of the smoke concentration at each sampling point, it needs to conduct an overall pollution distribution analysis. First, the detection device obtains the smoke concentration of all sampling points. The layout of the sampling points follows the principle of equal spacing, generally 50-100 meters apart. Then, the detection device uses the Geographic Information System (GIS) software and the Kriging interpolation method to perform spatial interpolation processing on the smoke concentration of discrete sampling points to generate a continuous smoke concentration distribution map, using different colors to distinguish different smoke concentrations, such as green for 0-5mg / m³, yellow for 5-10mg / m³, and red for more than 10mg / m³. Finally, the detection device uses an image analysis algorithm to identify contiguous areas where the smoke concentration continues to exceed the preset concentration threshold, mark them as abnormal areas, and calculate the area and boundary coordinates of the abnormal areas.

[0055] S208, obtaining historical smoke concentration data and environmental parameter data of the abnormal area; establishing a smoke diffusion prediction model based on the historical smoke concentration data and the environmental parameter data; predicting the smoke diffusion range of the abnormal area according to the smoke diffusion prediction model; and issuing an early warning when the smoke diffusion range exceeds a preset safety range; Among them, historical smoke concentration data refers to the smoke concentration monitoring records of abnormal areas within a preset period of time in the past (such as the past 30 days); environmental parameter data include ambient temperature and ambient humidity; the smoke diffusion prediction model refers to the mathematical model of pollutant migration and diffusion based on the principles of fluid mechanics; the smoke diffusion range refers to the migration coverage area of ​​pollutants in the entire factory area; the preset safety range refers to the maximum allowable radius of pollutant diffusion, usually 1,000 meters outside the factory boundary; the early warning is used to prompt users.

[0056] Specifically, after determining the abnormal area, the detection device immediately starts the diffusion prediction and early warning program. First, the detection device retrieves the historical smoke concentration data of the abnormal area for the past 30 days from the database, including the smoke concentration records every hour in the past, and obtains environmental parameter data such as wind direction, wind speed, air pressure, temperature, and humidity. Then, the detection device uses the Gaussian plume model as the basic framework, combined with the BP neural network algorithm, inputs the historical smoke concentration data into the model for training, and establishes a localized diffusion prediction model. Next, the detection device inputs the real-time environmental parameter data into the model to predict the diffusion direction, rate, and range of smoke in the next 24 hours. When the prediction results show that the smoke diffusion range may exceed 1,000 meters outside the factory boundary, the detection device automatically issues an early warning and notifies emergency response personnel through various means such as text messages and emails.

[0057] S209, obtaining the historical ambient temperature and the historical ambient humidity of the target sampling point; based on the historical ambient temperature and the historical ambient humidity, predicting the average ambient temperature and the average ambient humidity within a preset time period in the future; based on the average ambient temperature and the average ambient humidity, predicting the mass change trend of the target smoke particles to determine the smoke concentration change of the target smoke particles; Among them, historical ambient temperature refers to the temperature data set recorded hourly at the target sampling point within the past preset time period (such as the past 30 days); historical ambient humidity refers to the relative humidity data set recorded hourly at the target sampling point within the past preset time period (such as the past 30 days); the future preset time period refers to the predicted time span, usually 24 hours; the average ambient temperature refers to the weighted average temperature value within the future preset time period; the average ambient humidity refers to the weighted average relative humidity value within the future preset time period; the mass change trend refers to the direction and rate of mass change of smoke particles under the predicted environmental conditions; the smoke concentration change refers to the law of change of the mass of smoke particles per unit volume over time.

[0058] Specifically, when predicting the change of smoke concentration, the detection device first needs to extract the historical ambient temperature and historical ambient humidity of the target sampling point within the past preset time, and the data collection frequency is once an hour. Then, the detection device uses time series analysis methods, such as the ARIMA model, to perform trend analysis and periodicity analysis on the historical ambient temperature and historical ambient humidity, and at the same time combines meteorological forecast data and uses a multivariate regression algorithm to predict the ambient temperature and ambient humidity within the future preset time, thereby obtaining the average ambient temperature and average ambient humidity within the future preset time. On this basis, the detection device substitutes the average ambient temperature and average ambient humidity into the previously established preset morphological parameter-mass increment correspondence table, calculates the mass change value of the target smoke particles in different time periods, and combines the change of the target sampling volume to draw the smoke concentration change curve of the target smoke particles within the future preset time.

[0059] S210, obtaining component information of the target smoke particles; determining component sources and component impacts based on the component information; and formulating pollution control measures according to the component sources and component impacts.

[0060] Among them, composition information refers to the content of various chemical components in the target smoke particles and their physical and chemical properties; the source of the composition refers to the industrial process or emission source that produces the target smoke particles; the composition impact refers to the degree of harm of different chemical components in the target smoke particles to the environment and health; pollution control measures refer to the technical management plans formulated to reduce the target smoke particles.

[0061] Specifically, first, the detection device uses an X-ray fluorescence spectrometer (XRF) and an ion chromatograph to analyze the composition of the collected target smoke particles to obtain detailed composition information including heavy metals, inorganic salts, organic matter, etc. Then, the detection device compares the composition information with the industrial pollution source characteristic database, and determines the specific sources of various components through chemical fingerprint recognition technology, such as coal burning, metallurgy, chemical industry and other industrial processes. At the same time, the detection device evaluates the impact of various components on the environment and human health based on relevant toxicology databases. Finally, based on the composition analysis results, the detection device automatically generates a graded pollution control recommendation plan from the two dimensions of technical feasibility and economic rationality, including specific measures such as process improvement, dust removal equipment upgrade, and raw material substitution, and quantitatively evaluates the expected effects of various pollution control measures.

[0062] The following describes the detection system in the embodiment of the present invention from the perspective of hardware processing. Figure 3 , is a schematic diagram of a physical device structure of a detection system in an embodiment of the present application.

[0063] It should be noted that Figure 3The structure of the detection system 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.

[0064] like Figure 3 As shown, the detection system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage part 308 to the random access memory (RAM) 303, such as executing the method described in the above embodiment. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through the bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0065] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a button switch, etc.; an output section 307 including a liquid crystal display (LCD) and an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 310 as needed so that a computer program read therefrom is installed into the storage section 308 as needed.

[0066] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication part 309, and / or installed from a removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are performed.

[0067] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0068] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present invention. Each box in the flowchart or block diagram may represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of a code contains one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions marked in the box may also occur in an order different from that marked in the accompanying drawings.

[0069] Specifically, the detection system of this embodiment includes a processor and a memory. The memory stores a computer program. When the computer program is executed by the processor, the smoke concentration detection method provided in the above embodiment is implemented.

[0070] As another aspect, the present invention further provides a computer-readable storage medium, which may be included in the detection system described in the above embodiment; or may exist independently without being assembled into the detection system. The above storage medium carries one or more computer programs, and when the above one or more computer programs are executed by a processor of the detection system, the detection system implements the smoke concentration detection method provided in the above embodiment.

[0071] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0072] As used in the above embodiments, the term "when..." may be interpreted to mean "if..." or "after..." or "in response to determining..." or "in response to detecting...", depending on the context. Similarly, the phrases "upon determining..." or "if (the stated condition or event) is detected" may be interpreted to mean "if determining..." or "in response to determining..." or "upon detecting (the stated condition or event)" or "in response to detecting (the stated condition or event)", depending on the context.

[0073] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

Claims

1. A method for detecting smoke concentration, characterized in that: Applied to a detection system, the method comprises: Obtaining a target ambient temperature, a target ambient humidity, and target morphological parameters, a target initial mass, and a target sampling volume of a target smoke particle at a target sampling point; Determine different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities according to the target morphological parameters and a preset morphological parameter-mass increment correspondence table, wherein the preset morphological parameter-mass increment correspondence table is used to represent the mass changes of smoke particles with different morphological parameters at different ambient temperatures and different ambient humidities; Determining a target mass increment of the target smoke particles based on the target ambient temperature and the target ambient humidity; Determining a target final mass of the target soot particle according to the target initial mass and the target mass increment; The target final mass is divided by the target sampling volume to obtain the target smoke concentration of the target smoke particles.

2. The method according to claim 1, characterized in that Before the step of determining different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities according to the target morphological parameters and the preset morphological parameter-mass increment correspondence table, the method further includes: Acquiring morphological parameters and initial masses of a plurality of standard smoke particles, wherein the sampling volume of the standard smoke particles is a unit volume; Within a preset ambient temperature range and a preset ambient humidity range, respectively measuring mass changes of the plurality of standard smoke particles; The morphological parameters of the plurality of standard smoke particles are associated with the corresponding mass changes to generate the preset morphological parameter-mass increment correspondence table.

3. The method according to claim 1, characterized in that Determining different mass increments corresponding to the target smoke particles at different ambient temperatures and different ambient humidities according to the target morphological parameters and the preset morphological parameter-mass increment correspondence table specifically includes: Calculating the matching degree between the target morphological parameters and the morphological parameters in the preset morphological parameter-mass increment correspondence table; Determine the mass change data corresponding to the N morphological parameters with the highest matching degree; Based on the mass change data corresponding to the N morphological parameters, a weighted average algorithm is used to calculate different mass increments corresponding to the target smoke particles under different ambient temperatures and different ambient humidities.

4. The method according to claim 1, characterized in that: After the step of determining the target mass increment of the target soot particles based on the target ambient temperature and the target ambient humidity, the method further includes: Obtaining the historical ambient temperature and historical ambient humidity of the target sampling point; Based on the historical ambient temperature and the historical ambient humidity, predict the average ambient temperature and the average ambient humidity within a preset time period in the future; Based on the average ambient temperature and the average ambient humidity, the mass change trend of the target smoke particles is predicted to determine the smoke concentration change of the target smoke particles.

5. The method according to claim 1, characterized in that After the step of dividing the target final mass by the target sampling volume to obtain the target smoke concentration of the target smoke particles, the method further includes: Obtain smoke concentration at each sampling point; Based on the smoke concentration, establishing a smoke concentration distribution map; An abnormal area is determined according to the smoke concentration distribution map, and the smoke concentration in the abnormal area exceeds a preset smoke concentration threshold.

6. The method according to claim 5, characterized in that After the step of determining an abnormal area according to the smoke concentration distribution map, wherein the smoke concentration in the abnormal area exceeds a preset smoke concentration threshold, the method further includes: Acquiring historical smoke concentration data and environmental parameter data of the abnormal area; Based on the historical smoke concentration data and the environmental parameter data, a smoke diffusion prediction model is established; Predicting the smoke diffusion range of the abnormal area according to the smoke diffusion prediction model; When the smoke diffusion range exceeds the preset safety range, an early warning is issued.

7. The method according to claim 1, characterized in that After the step of dividing the target final mass by the target sampling volume to obtain the target smoke concentration of the target smoke particles, the method further includes: Obtaining composition information of the target smoke particles; Based on the ingredient information, determine ingredient sources and ingredient impacts; Develop pollution control measures based on the sources of the ingredients and the impacts of the ingredients.

8. A detection system, characterized in that: The detection system comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions so that the detection system executes the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a detection system, the detection system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a detection system, the detection system is caused to perform the method according to any one of claims 1 to 7.

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