Ambient air particulate matter detection method and device and electronic equipment

Abstract

The invention discloses an ambient air particulate matter detection method and device and electronic equipment, and relates to the field of environment monitoring. The method comprises the following steps: acquiring environment data, and adjusting a voltage threshold value of a photoelectric sensor array based on the environment data to obtain a target voltage threshold value; under a target voltage threshold value, acquiring scattered light intensity distribution of the particulate matter to be detected under different wavelengths through a photoelectric sensor array; based on the scattered light intensity distribution, calculating a particle size distribution parameter of the to-be-detected particulate matter; on the basis of the particle size distribution parameters, in combination with the Mie scattering theory, establishing a function relationship between the particulate matter concentration and the scattered light intensity; and determining the target scattered light intensity from the scattered light intensity distribution, and inputting the target scattered light intensity into the function relationship to obtain the target concentration corresponding to the particulate matter to be detected. By implementing the technical scheme provided by the invention, the accuracy of particulate matter detection is improved.

Description

Technical Field

[0001] The present application relates to the field of environmental monitoring, and specifically to a method, device and electronic equipment for detecting ambient air particulate matter. Background Art

[0002] With the rapid development of industrialization and urbanization, air pollution is becoming increasingly serious, with particulate matter, in particular, becoming a significant factor affecting environmental quality and human health. The concentration and size distribution of particulate matter are directly related to its harmful effects on the environment and human health. Scientifically and accurately measuring the concentration and size distribution of particulate matter in ambient air has become a crucial task in environmental monitoring. Optical detection technology, due to its non-contact and high sensitivity, is widely used in the detection of ambient air particulate matter.

[0003] Currently, relevant particulate matter detection technologies mainly rely on single-wavelength light scattering methods. Single-wavelength light scattering methods are difficult to fully reflect the particle size distribution characteristics of particulate matter and cannot accurately analyze the physical properties of particulate matter, resulting in low accuracy of particulate matter detection.

[0004] Therefore, there is an urgent need for a method, device and electronic equipment for detecting ambient air particulate matter. Summary of the Invention

[0005] The present application provides a method, device and electronic equipment for detecting ambient air particulate matter, which improve the accuracy of particulate matter detection.

[0006] In a first aspect of the present application, a method for detecting ambient air particulate matter is provided, the method comprising: obtaining environmental data, and adjusting a voltage threshold of a photosensor array based on the environmental data to obtain a target voltage threshold; obtaining, under the target voltage threshold, a scattered light intensity distribution of the particulate matter to be measured at different wavelengths through the photosensor array; calculating, based on the scattered light intensity distribution, particle size distribution parameters of the particulate matter to be measured; establishing a functional relationship between particle concentration and scattered light intensity based on the particle size distribution parameters and in combination with Mie scattering theory; determining a target scattered light intensity from the scattered light intensity distribution, and inputting the target scattered light intensity into the functional relationship to obtain a target concentration corresponding to the particulate matter to be measured.

[0007] By adopting the above technical solution, by acquiring environmental data and adjusting the voltage threshold of the photosensor array based on this data, the photosensor array can maintain optimal operating conditions under various environmental conditions, thereby improving the measurement accuracy of scattered light intensity. By obtaining the scattered light intensity distribution of particles at different wavelengths at a target voltage threshold, the light scattering characteristics of the particles can be comprehensively and meticulously characterized. Then, using the scattered light intensity distribution data and combining it with Mie scattering theory, the particle size distribution parameters are calculated, and a quantitative functional relationship between particle concentration and scattered light intensity is established. Finally, by extracting the most representative target scattered light intensity value from the scattered light intensity distribution and substituting it into the established functional relationship, the concentration level of the measured particles can be quickly and accurately estimated. This method enables online, real-time, and continuous monitoring of particle concentration, improving detection efficiency and applicability. Furthermore, this method fully utilizes the multi-wavelength information and angular distribution characteristics of scattered light, providing a more comprehensive and detailed characterization of particles, thereby improving the reliability and accuracy of concentration estimation. This method solves the shortcomings of related technologies in environmental adaptability, detection accuracy, theoretical support, and anti-interference ability through technical solutions such as dynamic adjustment of sensor parameters, utilization of multi-wavelength information, theoretical expression support, and screening of target scattered light intensity, and significantly improves the accuracy, precision, reliability, and applicability of ambient air particulate matter detection.

[0008] Optionally, the particle size distribution parameters of the particulate matter to be measured are calculated based on the scattered light intensity distribution, specifically including: substituting the scattered light intensity formula of a single particle in Mie scattering theory into the particle size distribution function to obtain a theoretical expression for the scattered light intensity distribution based on the lognormal distribution; constructing an objective function with the particle size distribution parameter as the optimization variable according to the theoretical expression, the objective function being the sum of the squares of the differences between the measured scattered light intensity distribution and the theoretical scattered light intensity distribution calculated by the theoretical expression; solving the minimum value of the objective function to obtain the optimal estimate of the particle size distribution parameter, the optimal estimate including the mean and standard deviation of the logarithmic particle size; and calculating the mean, median and modulus of the particle size of the particulate matter to be measured according to the mean and standard deviation of the logarithmic particle size as the particle size distribution parameters of the particulate matter to be measured.

[0009] By adopting the above technical solution, Mie scattering theory is fully utilized when calculating the particle size distribution parameters of the measured particles. The formula for the scattered light intensity of a single particle is substituted into the particle size distribution function, resulting in a theoretical expression for the scattered light intensity of particles across the entire particle size range. Then, by constructing the sum of the squares of the differences between the measured and theoretical scattered light intensities as the objective function, the particle size distribution parameter estimation problem is transformed into a parameter optimization problem. This approach not only considers the inherent connection between scattered light intensity and particle size distribution but also fully utilizes the statistical properties of the measured data, ensuring that the estimated results are most consistent with the actual situation. By solving the optimization problem, optimal estimates of the particle size distribution parameters, including the mean and standard deviation of the logarithmic particle size, can be obtained. Finally, through the mathematical properties of the lognormal distribution, various statistical characteristic parameters of the particle size, such as the mean, median, and mode, can be conveniently calculated, providing richer information for the characterization of the particle properties.

[0010] Optionally, the theoretical expression is specifically: Wherein, I(θ, λ) is the theoretical scattered light intensity distribution, I0 is the incident light intensity, θ is the scattering angle, λ is the incident light wavelength, s is the diameter of the particle to be measured, R is the distance between the particle to be measured and the photosensor array, μ is the mean of the logarithmic particle size, σ is the standard deviation of the logarithmic particle size, and F(θ, λ, s) is the Mie scattering factor, which is expressed as F(θ, λ, s) = S1(θ, λ, s). 2 +S2(θ,λ,s) 2 , S1 and S2 are preset Mie scattering amplitude functions, which are related to the diameter s of the particle to be measured, the scattering angle θ and the wavelength λ of the incident light.

[0011] By adopting the above technical solution and using this theoretical expression, we can quantitatively analyze the influence of particle size distribution parameters and measurement conditions on scattered light intensity, which is the theoretical basis for subsequent particle size distribution inversion and concentration estimation.

[0012] Optionally, the objective function is min∑[I_exp(θ,λ)-I(θ,λ)] 2 Wherein, I_exp(θ, λ) is the measured scattered light intensity distribution, and I(θ, λ) is the theoretical scattered light intensity distribution calculated by the theoretical expression.

[0013] Using this technical solution, the objective function is defined as the sum of the squares of the differences between the measured and theoretical scattered light intensities. This objective function aims to quantify how well the theoretical model fits the actual scattering data. By minimizing this sum of squares, a set of particle size distribution parameters can be found that ensures that the theoretically calculated scattered light intensity distribution matches the measured values ​​as closely as possible.

[0014] Optionally, determining the target scattered light intensity from the scattered light intensity distribution specifically includes: for each scattered light intensity value included in the scattered light intensity distribution, taking each scattered light intensity value as the center of a circle, and determining a circular neighborhood corresponding to each scattered light intensity value with a preset radius; calculating the number of scattered light intensity values ​​included in each circular neighborhood; if it is determined that the target number is greater than or equal to a preset density threshold, determining the target scattered light intensity value corresponding to the target number as a core value; determining a core neighborhood corresponding to each core value, and based on each core neighborhood; calculating the mean of all scattered light intensity values ​​in each core neighborhood to obtain a central scattered light intensity value of each core neighborhood; determining the maximum value among each central scattered light intensity value, and taking the maximum value as the target scattered light intensity.

[0015] By employing the above technical solution, based on the concept of density clustering, we can adaptively identify peak regions in the scattered light intensity distribution and extract the most representative scattered light intensity values. By treating each scattered light intensity value as a point on a two-dimensional plane and calculating the density of points within its neighborhood, we can reveal the local structural characteristics of the scattered light intensity distribution. If the neighborhood density of a point exceeds a preset density threshold, it indicates that the point is located in a dense area of ​​scattering intensity and can be used as a core point. By clustering these core points, high-density regions of the scattered light intensity distribution are identified, reflecting the dominant pattern of particle scattering. Within each high-density region, by calculating the average of all scattered light intensity values, we can obtain a measure of central tendency and reduce the influence of random noise. By selecting the maximum value among the central scattered light intensities of all high-density regions, we obtain the peak position of the entire scattered light intensity distribution, representing the dominant direction and intensity of particle scattering. Compared with simply selecting the maximum or average value, this method can more comprehensively and reliably characterize the scattered light distribution characteristics, extract the most valuable scattering information, and provide more accurate input for subsequent concentration estimation. Furthermore, the density clustering method can effectively address outliers and noise interference in the scattering signal.

[0016] Optionally, the environmental data includes current temperature, current humidity, and current air pressure, and adjusting the voltage threshold of the photosensor array based on the environmental data to obtain the target voltage threshold specifically includes: Among them, V is the target voltage threshold, T is the current temperature, T0 is the standard temperature, β is the temperature characteristic coefficient, H is the current humidity, H0 is the standard humidity, a is the humidity characteristic coefficient, P is the current air pressure, P0 is the standard air pressure, b is the air pressure characteristic coefficient, and V0 is the standard voltage under the standard temperature, standard humidity and standard air pressure.

[0017] By adopting the above technical solution and establishing a quantitative relationship between the voltage threshold and environmental parameters such as temperature, humidity, and air pressure, the optimal operating voltage of the photoelectric sensor array under the current environmental conditions can be calculated in real time.

[0018] Optionally, the establishing of a functional relationship between particle concentration and scattered light intensity based on the particle size distribution parameters and in combination with Mie scattering theory specifically includes: Wherein, C is the particle concentration, ρ is the density of the particle to be measured, μ is the mean of the logarithmic particle size, and σ is the standard deviation of the logarithmic particle size.

[0019] By adopting the above technical solution, the mass concentration of the particulate matter to be measured can be estimated directly from the particle size distribution parameters. This not only improves the timeliness and continuity of concentration measurement, but also reduces the consumption of manpower and material resources, and is suitable for large-scale, long-term environmental monitoring applications.

[0020] In a second aspect of the present application, an ambient air particulate matter detection device is provided, which includes an environmental data acquisition module, a light intensity distribution determination module, a particle size distribution calculation module, a functional relationship establishment module, and a target concentration calculation module, wherein: the environmental data acquisition module is used to acquire environmental data and adjust the voltage threshold of the photosensor array based on the environmental data to obtain a target voltage threshold; the light intensity distribution determination module is used to obtain the scattered light intensity distribution of the particulate matter to be measured at different wavelengths through the photosensor array under the target voltage threshold; the particle size distribution calculation module is used to calculate the particle size distribution parameters of the particulate matter to be measured based on the scattered light intensity distribution; the functional relationship establishment module is used to establish a functional relationship between the particle concentration and the scattered light intensity based on the particle size distribution parameters and combined with the Mie scattering theory; the target concentration calculation module is used to determine the target scattered light intensity from the scattered light intensity distribution, and input the target scattered light intensity into the functional relationship to obtain the target concentration corresponding to the particulate matter to be measured.

[0021] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0022] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed.

[0023] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. This method solves the shortcomings of related technologies in environmental adaptability, detection accuracy, theoretical support, and anti-interference ability through technical solutions such as dynamic adjustment of sensor parameters, utilization of multi-wavelength information, support of theoretical expressions, and screening of target scattered light intensity, and significantly improves the accuracy, precision, reliability, and applicability of ambient air particulate matter detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a flow chart of a method for detecting ambient air particulate matter disclosed in an embodiment of the present application; Figure 2 This is a module diagram of an ambient air particulate matter detection device disclosed in an embodiment of the present application; Figure 3 This is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: 201, environmental data acquisition module; 202, light intensity distribution determination module; 203, particle size distribution calculation module; 204, function relationship establishment module; 205, target concentration calculation module; 300, electronic device; 301, processor; 302, communication bus; 303, user interface; 304, network interface; 305, memory. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0028] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] This application provides a method for detecting ambient air particulate matter, referring to Figure 1 , Figure 1 This is a flow chart of a method for detecting ambient air particulate matter provided in an embodiment of the present application. This method is applied to a server, which is a server that executes an ambient air particulate matter detection program. The server can be a single server, a server cluster consisting of multiple servers, or a cloud computing service center. The method includes steps S101 to S105, which are as follows: Step S101: Acquire environmental data, and adjust the voltage threshold of the photosensor array based on the environmental data to obtain a target voltage threshold.

[0030] In step S101, the environmental data includes the current temperature, the current humidity, and the current air pressure. The voltage threshold of the photosensor array is adjusted based on the environmental data to obtain a target voltage threshold, specifically including: Where V is the target voltage threshold, T is the current temperature, T0 is the standard temperature, β is the temperature characteristic coefficient, H is the current humidity, H0 is the standard humidity, a is the humidity characteristic coefficient, P is the current air pressure, P0 is the standard air pressure, b is the air pressure characteristic coefficient, and V0 is the standard voltage under standard temperature, standard humidity, and standard air pressure.

[0031] Specifically, the server acquires environmental data, including current temperature, humidity, and air pressure. To achieve this, the server can communicate with various environmental sensors. For example, the server can connect to temperature, humidity, and air pressure sensors via a wired or wireless network. These sensors can be installed in the same location as the photoelectric sensor array to obtain the same environmental parameters as the environment in which the particulate matter to be measured resides. The server regularly receives data from these sensors and stores it in a database for subsequent processing.

[0032] After acquiring environmental data, the server begins adjusting the voltage threshold of the photosensor array. This process is based on the aforementioned formula, which accounts for the effects of temperature, humidity, and air pressure on the performance of the photosensor array. Parameters in the formula, such as the temperature characteristic coefficient β, the humidity characteristic coefficient a, and the air pressure characteristic coefficient b, can be determined through prior experimentation and calibration. These parameters reflect the photosensor array's sensitivity to environmental changes. The standard voltage V0 corresponds to the photosensor array's optimal operating voltage under standard environmental conditions.

[0033] The server substitutes the current environmental parameters into the formula to calculate the target voltage threshold V. The target voltage threshold V is a correction to the standard voltage to adapt to the current environmental conditions. For example, if the current temperature is higher than the standard temperature, the exponential term in the formula will be less than 1, causing the target voltage threshold to be lower than the standard voltage. This is because the sensitivity of the photosensor array generally increases at high temperatures, necessitating a reduction in operating voltage to avoid saturation. Similarly, changes in humidity and air pressure will also adjust the target voltage threshold through the linear term in the formula.

[0034] After calculating the target voltage threshold, the server sends it to the control circuit of the photosensor array. Based on the received target voltage threshold, the control circuit adjusts the operating voltage of each photosensor in the array. This ensures that the photosensor array operates with optimal sensitivity and dynamic range under current environmental conditions, providing reliable raw data for subsequent scattered light intensity measurements.

[0035] Step S102: Under the target voltage threshold, the scattered light intensity distribution of the particle to be measured at different wavelengths is obtained through the photoelectric sensor array.

[0036] In step S102, the server sends instructions to the light source controller, instructing it to sequentially activate light sources of different wavelengths. These light sources can be monochromatic LEDs or laser diodes, with wavelengths covering the visible and near-infrared bands. The server sets the activation time for each light source based on the response speed and sampling rate of the photosensor array to ensure stable and reliable scattered light intensity data.

[0037] When a light source of a specific wavelength is turned on, its light will illuminate the particles to be measured and be scattered in all directions by the particles to be measured. Arrays of photosensors located at different angles will receive this scattered light and convert it into electrical signals. Because the voltage threshold of the photosensor array has been adjusted in step S101, the output signal intensity of each sensor is now within the optimal linear range and can accurately reflect the intensity of the incident scattered light. The server reads the electrical signals from the photosensor array via a data acquisition card and converts them into digitized scattered light intensity data. This data is temporarily stored in the server's memory or cache for rapid processing. For each wavelength, the server obtains a set of scattered light intensity data corresponding to different scattering angles. After completing the scattered light acquisition for one wavelength, the server sends a command to the light source controller to turn off the current light source and turn on the light source for the next wavelength. The above process is repeated multiple times until the scattered light intensity distribution data for all required wavelengths has been obtained.

[0038] After acquiring all the data, the server preprocesses the scattered light intensity data at each wavelength, such as removing background noise and normalizing it. The preprocessed data is organized into a structured dataset, with each row corresponding to a scattering angle and each column corresponding to a wavelength. Each element of the dataset represents the scattered light intensity measured at a specific scattering angle and wavelength. Finally, the server saves this scattered light intensity distribution dataset to a database or file system for subsequent particle size distribution calculations and concentration estimations. Step S103: Based on the scattered light intensity distribution, the particle size distribution parameters of the particles to be measured are calculated.

[0039] In step S103, the particle size distribution parameters of the particulate matter to be measured are calculated based on the scattered light intensity distribution, specifically including: substituting the scattered light intensity formula of a single particle in Mie scattering theory into the particle size distribution function to obtain a theoretical expression for the scattered light intensity distribution based on the lognormal distribution; constructing an objective function with the particle size distribution parameter as the optimization variable according to the theoretical expression, the objective function being the sum of the squares of the differences between the measured scattered light intensity distribution and the theoretical scattered light intensity distribution calculated according to the theoretical expression; solving the minimum value of the objective function to obtain the optimal estimate of the particle size distribution parameter, the optimal estimate including the mean and standard deviation of the logarithmic particle size; and calculating the mean, median and modulus of the particle size of the particulate matter to be measured according to the mean and standard deviation of the logarithmic particle size as the particle size distribution parameters of the particulate matter to be measured.

[0040] Specifically, the server reads a previously saved scattered light intensity distribution dataset from the database or file system. This dataset contains the scattered light intensity values ​​measured at different scattering angles and wavelengths. The server loads the dataset into memory for subsequent calculations. Next, the server constructs a theoretical model based on the Mie scattering theory. The Mie scattering theory gives a formula for the scattered light intensity of a single particle at a specific scattering angle and wavelength. The server substitutes this formula into the particle size distribution function and obtains a theoretical expression for the scattered light intensity of particles in the entire particle size range. Here, the server assumes that the particle size distribution of the particles obeys a lognormal distribution, which is a common distribution pattern of atmospheric particles. The lognormal distribution can be described by two parameters: the mean μ and standard deviation σ of the logarithmic particle size.

[0041] With a theoretical expression for scattered light intensity, the server can formulate an optimization problem. The goal is to find a set of particle size distribution parameters (μ and σ) such that the theoretically calculated scattered light intensity distribution matches the measured scattered light intensity distribution as closely as possible. To quantify this closeness, the server defines an objective function: the sum of the squares of the differences between the theoretical and measured values. The independent variables of this objective function are μ and σ. The server finds the μ and σ values ​​that minimize the objective function, which serves as the optimal estimate of the particle size distribution parameters.

[0042] To solve this optimization problem, the server can use a variety of numerical optimization algorithms, such as gradient descent. This algorithm iteratively adjusts the values ​​of μ and σ to gradually reduce the objective function until it reaches a minimum. In each iteration, the algorithm calculates the gradient of the objective function based on the current values ​​of μ and σ and updates μ and σ based on the direction and magnitude of the gradient. This process may require dozens to hundreds of iterations, depending on the choice of initial values ​​and the setting of convergence conditions.

[0043] When the optimization algorithm converges, the server obtains the optimal estimates of the particle size distribution parameters μ and σ. These two values ​​fully describe the particle size distribution characteristics of the measured particles. However, for a more intuitive presentation of the results, the server can also calculate some common particle size distribution metrics based on μ and σ, such as the arithmetic mean, median, and modulus. Finally, the server saves the calculated particle size distribution parameters (μ, σ) and related metrics (mean, median, and modulus) to a database or file system for subsequent query and analysis.

[0044] In a possible implementation, the theoretical expression is specifically: Where I(θ, λ) is the theoretical scattered light intensity distribution, I0 is the incident light intensity, θ is the scattering angle, λ is the incident light wavelength, s is the diameter of the particle to be measured, R is the distance between the particle to be measured and the photoelectric sensor array, μ is the mean of the logarithmic particle size, σ is the standard deviation of the logarithmic particle size, and F(θ, λ, s) is the Mie scattering factor, expressed as F(θ, λ, s) = S1(θ, λ, s). 2 +S2(θ,λ,s) 2 , S1 and S2 are preset Mie scattering amplitude functions, which are related to the diameter s of the particle to be measured, the scattering angle θ and the wavelength λ of the incident light.

[0045] Specifically, the server reads the preset Mie scattering amplitude functions S1 and S2 from the database or file system. These two functions are pre-calculated based on the Mie scattering theory, and they describe the scattering amplitude of a single particle under given diameter, scattering angle, and incident light wavelength. The server stores S1 and S2 in memory for quick call when calculating the scattering factor F(θ, λ, s). Next, the server begins to construct an expression for the theoretical scattered light intensity distribution. According to the given formula, the scattered light intensity I(θ, λ) is obtained by integrating the particle size s. The integral function consists of three parts: the first part is It represents the scattered light intensity of a single particle with a diameter of s at a scattering angle of θ and a wavelength of λ. Where I0 is the incident light intensity, R is the distance between the particle and the photosensor array, and F(θ, λ, s) is the Mie scattering factor, which can be calculated from S1 and S2.

[0046] The second part is the probability density function of the lognormal distribution, which represents the probability of a particle with diameter s appearing. This function contains two parameters: the mean μ and standard deviation σ of the logarithmic particle size. The third part, ds, represents the integration of small increments of particle size.

[0047] To calculate the integral, the server selects a suitable numerical integration algorithm, such as the trapezoidal method. This algorithm converts the integral into a summation problem by dividing the integral interval into several subintervals and approximating the integrand with a simple function (such as a polynomial) in each subinterval. The accuracy of the integral depends on the number of subintervals and the order of the approximate function. The server can select the appropriate algorithm and parameters based on the required accuracy and computational efficiency. When calculating the integral, the server needs to calculate different scattering angles θ and wavelengths λ separately to obtain a two-dimensional theoretical scattered light intensity distribution I(θ, λ). This distribution is a function of μ and σ, which represents the scattered light intensity of the particles at different scattering angles and wavelengths under given particle size distribution parameters.

[0048] After obtaining the theoretical scattered light intensity distribution, the server can compare it with the measured scattered light intensity distribution, construct the objective function, and use the optimization algorithm to solve for the optimal μ and σ values. The specific optimization process is similar to the previous answer and will not be repeated here.

[0049] It's important to note that the computational complexity of the theoretical scattered light intensity distribution is high, especially when the integration interval is wide or when there are many sampling points for scattering angles and wavelengths. To accelerate the calculation, the server can employ parallel computing, distributing the integration calculations across multiple processor cores or computers. Furthermore, the server can pre-calculate and store frequently used integration results using lookup tables or interpolation methods, enabling quick recall during the optimization process and reducing the overhead of repeated calculations.

[0050] In one possible implementation, the objective function is min∑[I_exp(θ,λ)-I(θ,λ)] 2 Wherein, I_exp(θ, λ) is the measured scattered light intensity distribution, and I(θ, λ) is the theoretical scattered light intensity distribution calculated by the theoretical expression.

[0051] Specifically, the server reads the measured scattered light intensity distribution data I_exp(θ, λ) from the database or file system. This data is collected by a photoelectric sensor array at different scattering angles θ and wavelengths λ, reflecting the scattering characteristics of the particles to be measured in the actual environment. The server loads this two-dimensional data array into memory for comparison with the results of theoretical calculations. The server then calculates the corresponding theoretical scattered light intensity distribution I(θ, λ) based on the current particle size distribution parameters μ and σ. The specific calculation process involves the integration of the particle size s, which can be implemented using a numerical integration method. The server stores the calculated theoretical scattered light intensity distribution in another two-dimensional array, the dimension of which is the same as the measured data.

[0052] Next, the server calculates the difference between the theoretical and measured scattered light intensity distributions, as defined by the objective function. Specifically, the server iterates over all sampling points for scattering angle θ and wavelength λ, calculating the difference between the theoretical value I(θ, λ) and the measured value I_exp(θ, λ) at each point. The squares of these differences are then accumulated into a total error variable. This process can be implemented using two nested for loops: the outer loop iterates over the scattering angles, and the inner loop iterates over the wavelengths.

[0053] After traversing all sampling points, the server calculates the total error between the theoretical and measured scattered light intensity distributions for the current particle size distribution parameters. This total error is the value of the objective function. The smaller the total error, the closer the theoretical results are to the actual results, and the more accurate the estimated particle size distribution parameters.

[0054] To find the optimal particle size distribution parameters, the server searches the parameter space, continuously adjusting the values ​​of μ and σ to minimize the objective function. This can be achieved through numerical optimization algorithms, which iteratively calculate the gradient (or approximate gradient) of the objective function at each step and update the parameters based on the direction and magnitude of the gradient, gradually reducing the objective function value until it reaches a minimum.

[0055] Step S104: Based on the particle size distribution parameters and in combination with Mie scattering theory, a functional relationship between the particle concentration and the scattered light intensity is established.

[0056] In step S104, based on the particle size distribution parameters and in combination with Mie scattering theory, a functional relationship between the particle concentration and the scattered light intensity is established, specifically including: Where C is the particle concentration, ρ is the density of the particles to be measured, μ is the mean of the logarithmic particle size, and σ is the standard deviation of the logarithmic particle size.

[0057] Specifically, the server first derives a theoretical expression for the relationship between the scattered light intensity of a single particle under given illumination conditions and its particle size, based on the fundamental principles of Mie scattering theory. Mie scattering theory describes the scattering characteristics of spherical particles on plane light waves. It provides a quantitative relationship between scattered light intensity, scattering angle, incident light wavelength, and particle size. The server then substitutes the formula for scattered light intensity for a single particle into the particle size distribution function of the entire particle system and integrates it, thereby deriving a theoretical expression for the scattered light intensity distribution of a polydisperse particle system. This requires the particle size distribution parameters calculated in the previous step, such as the logarithmic mean μ and the logarithmic standard deviation σ. The particle size distribution function can typically be described using empirical formulas such as the lognormal distribution. Next, the server derives a relationship between scattered light intensity and particle volume concentration, taking into account the particle density ρ. By integrating the particle volume over the entire size range and multiplying it by the particle density, the total particle mass concentration is obtained.

[0058] Step S105: determining a target scattered light intensity from the scattered light intensity distribution, and inputting the target scattered light intensity into the functional relationship to obtain a target concentration corresponding to the particulate matter to be measured.

[0059] In step S105, the target scattered light intensity is determined from the scattered light intensity distribution, specifically including: for each scattered light intensity value included in the scattered light intensity distribution, taking each scattered light intensity value as the center of a circle, and determining a circular neighborhood corresponding to each scattered light intensity value with a preset radius; calculating the number of scattered light intensity values ​​included in each circular neighborhood; if it is determined that the target number is greater than or equal to a preset density threshold, determining the target scattered light intensity value corresponding to the target number as a core value; determining a core neighborhood corresponding to each core value, and based on each core neighborhood; calculating the mean of all scattered light intensity values ​​in each core neighborhood to obtain the central scattered light intensity value of each core neighborhood; determining the maximum value among each central scattered light intensity value, and taking the maximum value as the target scattered light intensity.

[0060] Specifically, the server reads the previously acquired scattered light intensity distribution data from the database or file system. This data is a two-dimensional array, in which each element represents the scattered light intensity value measured at a specific scattering angle and wavelength. The server loads this array into memory for subsequent analysis and calculations. Next, the server performs a cluster analysis on the scattered light intensity distribution to determine the target scattered light intensity. Specifically, the server regards the scattered light intensity values ​​as points on a two-dimensional plane, aggregates the points into clusters by calculating the distance between the points and the neighborhood density, and identifies noise points.

[0061] The server first sets two parameters: a preset radius and a preset density threshold. For each point in the scattered light intensity distribution, the server defines a circular neighborhood with that point as the center and the preset radius as the radius. The server then calculates the number of points within each circular neighborhood, i.e., the density of that neighborhood. If the neighborhood density of a point is greater than or equal to the preset density threshold, the point is marked as a core point.

[0062] The server then clusters all core points. Specifically, starting from any core point, the server finds all core points that are density-reachable to it and aggregates them into a cluster. Density-reachable between two core points means that there is a path of core points between them, and the distance between each pair of adjacent core points on the path is less than or equal to a preset density threshold. This process is repeated until all core points are assigned to a cluster.

[0063] After clustering is completed, the server calculates the central scattered light intensity value of each cluster. Specifically, for each cluster, the server finds the scattered light intensity values ​​corresponding to all points in the cluster, calculates their arithmetic mean, and uses it as the central scattered light intensity value of the cluster. This value represents the average level of scattered light intensity within the cluster. Finally, the server selects the maximum value among the central scattered light intensity values ​​of all clusters as the target scattered light intensity. This value corresponds to the peak area in the scattered light intensity distribution and represents the strongest scattering effect of the measured particles on the incident light.

[0064] After determining the target scattered light intensity, the server inputs it into the previously established functional relationship to obtain the target concentration value for the particles being measured. This value represents the average concentration level of particles expected under the current particle size distribution and measurement conditions. Finally, the server saves the target concentration value to a database or file system and generates a report displaying the estimated particle concentration.

[0065] Reference Figure 2 The present application also provides an ambient air particulate matter detection device, which is a server. The server includes an environmental data acquisition module 201, a light intensity distribution determination module 202, a particle size distribution calculation module 203, a function relationship establishment module 204, and a target concentration calculation module 205, wherein: the environmental data acquisition module 201 is used to acquire environmental data and adjust the voltage threshold of the photosensor array based on the environmental data to obtain a target voltage threshold; the light intensity distribution determination module 202 is used to obtain the scattered light intensity distribution of the particles to be measured at different wavelengths through the photosensor array under the target voltage threshold; the particle size distribution calculation module 203 is used to calculate the particle size distribution parameters of the particles to be measured based on the scattered light intensity distribution; the function relationship establishment module 204 is used to establish a function relationship between the particle concentration and the scattered light intensity based on the particle size distribution parameters and combined with the Mie scattering theory; the target concentration calculation module 205 is used to determine the target scattered light intensity from the scattered light intensity distribution, and input the target scattered light intensity into the function relationship to obtain the target concentration corresponding to the particles to be measured.

[0066] In one possible embodiment, the particle size distribution calculation module 203 calculates the particle size distribution parameters of the particulate matter to be measured based on the scattered light intensity distribution, specifically including: the particle size distribution calculation module 203 substitutes the scattered light intensity formula of a single particle in the Mie scattering theory into the particle size distribution function to obtain a theoretical expression for the scattered light intensity distribution based on the lognormal distribution; the particle size distribution calculation module 203 constructs an objective function with the particle size distribution parameter as the optimization variable according to the theoretical expression, and the objective function is the sum of the squares of the differences between the measured scattered light intensity distribution and the theoretical scattered light intensity distribution calculated by the theoretical expression; the particle size distribution calculation module 203 solves the minimum value of the objective function to obtain the optimal estimate of the particle size distribution parameter, and the optimal estimate includes the mean and standard deviation of the logarithmic particle size; the particle size distribution calculation module 203 calculates the mean, median and modulus of the particle size of the particulate matter to be measured according to the mean and standard deviation of the logarithmic particle size as the particle size distribution parameters of the particulate matter to be measured.

[0067] In a possible implementation, the theoretical expression is specifically: Where I(θ, λ) is the theoretical scattered light intensity distribution, I0 is the incident light intensity, θ is the scattering angle, λ is the incident light wavelength, s is the diameter of the particle to be measured, R is the distance between the particle to be measured and the photoelectric sensor array, μ is the mean of the logarithmic particle size, σ is the standard deviation of the logarithmic particle size, and F(θ, λ, s) is the Mie scattering factor, expressed as F(θ, λ, s) = S1(θ, λ, s). 2 +S2(θ,λ,s) 2 , S1 and S2 are preset Mie scattering amplitude functions, which are related to the diameter s of the particle to be measured, the scattering angle θ and the wavelength λ of the incident light.

[0068] In one possible implementation, the objective function is min∑[I_exp(θ,λ)-I(θ,λ)] 2 Wherein, I_exp(θ, λ) is the measured scattered light intensity distribution, and I(θ, λ) is the theoretical scattered light intensity distribution calculated by the theoretical expression.

[0069] In one possible embodiment, the target concentration calculation module 205 determines the target scattered light intensity from the scattered light intensity distribution, specifically including: the target concentration calculation module 205 uses each scattered light intensity value included in the scattered light intensity distribution as the center of a circle, and determines a circular neighborhood corresponding to each scattered light intensity value with a preset radius; the target concentration calculation module 205 calculates the number of scattered light intensity values ​​included in each circular neighborhood; if the target concentration calculation module 205 determines that the target number is greater than or equal to a preset density threshold, the target scattered light intensity value corresponding to the target number is determined as a core value; the target concentration calculation module 205 determines the core neighborhood corresponding to each core value, and based on each core neighborhood; the target concentration calculation module 205 calculates the mean of all scattered light intensity values ​​in each core neighborhood to obtain the central scattered light intensity value of each core neighborhood; determines the maximum value among each central scattered light intensity value, and uses the maximum value as the target scattered light intensity.

[0070] In one possible implementation, the environmental data includes current temperature, current humidity, and current air pressure. The environmental data acquisition module adjusts the voltage threshold of the photosensor array based on the environmental data to obtain a target voltage threshold, specifically including: Where V is the target voltage threshold, T is the current temperature, T0 is the standard temperature, β is the temperature characteristic coefficient, H is the current humidity, H0 is the standard humidity, a is the humidity characteristic coefficient, P is the current air pressure, P0 is the standard air pressure, b is the air pressure characteristic coefficient, and V0 is the standard voltage under standard temperature, standard humidity, and standard air pressure.

[0071] In one possible implementation, the functional relationship establishing module establishes a functional relationship between particle concentration and scattered light intensity based on the particle size distribution parameters and in combination with Mie scattering theory, specifically including: Where C is the particle concentration, ρ is the density of the particles to be measured, μ is the mean of the logarithmic particle size, and σ is the standard deviation of the logarithmic particle size.

[0072] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0073] This application also provides an electronic device. Figure 3 , Figure 33. This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. The electronic device 300 may include: at least one processor 301, at least one network interface 304, a user interface 303, a memory 305, and at least one communication bus 302.

[0074] The communication bus 302 is used to implement the connection and communication between these components.

[0075] The user interface 303 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 303 may also include a standard wired interface and a wireless interface.

[0076] The network interface 304 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface).

[0077] The processor 301 may include one or more processing cores. The processor 301 utilizes various interfaces and lines to connect various parts of the entire server. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 305, and calling data stored in the memory 305, the processor 301 performs various server functions and processes data. Optionally, the processor 301 may be implemented in at least one hardware form of a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 301 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display; and the modem is used to handle wireless communications. It is understood that the modem may not be integrated into the processor 301 and may be implemented separately on a single chip.

[0078] Among them, the memory 305 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 305 includes a non-transitory computer-readable storage medium. The memory 305 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 305 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 305 may also be optionally at least one storage device located away from the aforementioned processor 301. Refer to Figure 3 , the memory 305 as a computer storage medium may include an operating system, a network communication module, a user interface module and an application program of an ambient air particulate matter detection method.

[0079] exist Figure 3 In the electronic device 300 shown, the user interface 303 is mainly used to provide an input interface for the user and obtain the data input by the user; and the processor 301 can be used to call an application program for a method for detecting ambient air particulate matter stored in the memory 305. When executed by one or more processors 301, the electronic device 300 executes one or more of the methods described in the above embodiments. It should be noted that for the aforementioned method embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should know that this application is not limited to the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0080] The present application further provides a computer-readable storage medium storing instructions, which, when executed by one or more processors 301 , enable the electronic device 300 to perform one or more of the methods described in the above embodiments.

[0081] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0082] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0083] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0084] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0085] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0086] The foregoing is merely an exemplary embodiment of the present disclosure and is not intended to limit the scope of the present disclosure. In other words, any equivalent variations and modifications made in accordance with the teachings of the present disclosure are still within the scope of the present disclosure. Those skilled in the art will readily conceive of other embodiments of the present disclosure after considering the disclosure and the practical implications thereof.

[0087] This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not described herein. The description and examples are to be considered as exemplary only, and the scope and spirit of the present disclosure are to be defined by the claims.

Claims

1. A method for detecting ambient air particulate matter, characterized in that: The method comprises: Acquiring environmental data, and adjusting a voltage threshold of a photosensor array based on the environmental data to obtain a target voltage threshold; obtaining, through the photosensor array, an intensity distribution of scattered light of the particulate matter to be measured at different wavelengths at the target voltage threshold; Calculating the particle size distribution parameters of the particulate matter to be measured based on the scattered light intensity distribution; Based on the particle size distribution parameters and combined with Mie scattering theory, a functional relationship between particle concentration and scattered light intensity is established; the target scattered light intensity is determined from the scattered light intensity distribution, and the target scattered light intensity is input into the functional relationship to obtain the target concentration corresponding to the particle to be measured.

2. The method according to claim 1, characterized in that Calculating the particle size distribution parameters of the particulate matter to be measured based on the scattered light intensity distribution specifically includes: Substituting the scattered light intensity formula of a single particle in Mie scattering theory into the particle size distribution function, we obtain the theoretical expression of the scattered light intensity distribution based on the log-normal distribution. According to the theoretical expression, constructing an objective function with the particle size distribution parameter as the optimization variable, wherein the objective function is the sum of squares of the differences between the measured scattered light intensity distribution and the theoretical scattered light intensity distribution calculated by the theoretical expression; Solving the minimum value of the objective function to obtain optimal estimates of particle size distribution parameters, wherein the optimal estimates include the mean and standard deviation of the logarithmic particle size; According to the mean and standard deviation of the logarithmic particle size, the mean, median and modulus of the particle size of the particle to be measured are calculated as the particle size distribution parameters of the particle to be measured.

3. The method according to claim 2, characterized in that The theoretical expression is specifically: Wherein, I(θ, λ) is the theoretical scattered light intensity distribution, I0 is the incident light intensity, θ is the scattering angle, λ is the incident light wavelength, s is the diameter of the particle to be measured, R is the distance between the particle to be measured and the photosensor array, μ is the mean of the logarithmic particle size, σ is the standard deviation of the logarithmic particle size, and F(θ, λ, s) is the Mie scattering factor, which is expressed as F(θ, λ, s) = S1(θ, λ, s). 2 +S2(θ,λ,s) 2 , S1 and S2 are preset Mie scattering amplitude functions, which are related to the diameter s of the particle to be measured, the scattering angle θ and the wavelength λ of the incident light.

4. The method according to claim 3, characterized in that The objective function is min∑[I_exp(θ,λ)-I(θ,λ)] 2 Wherein, I_exp(θ, λ) is the measured scattered light intensity distribution, and I(θ, λ) is the theoretical scattered light intensity distribution calculated by the theoretical expression.

5. The method according to claim 1, characterized in that Determining the target scattered light intensity from the scattered light intensity distribution specifically includes: For each scattered light intensity value included in the scattered light intensity distribution, taking each scattered light intensity value as a circle center, and determining a circular neighborhood corresponding to each scattered light intensity value with a preset radius; Calculating the number of scattered light intensity values ​​included in each of the circular neighborhoods; If it is determined that the number of targets is greater than or equal to the preset density threshold, the target scattered light intensity value corresponding to the number of targets is determined as the core value; Determine the core neighborhood corresponding to each of the core values, and based on each of the core neighborhoods; Calculating the mean of all scattered light intensity values ​​within each core neighborhood to obtain the central scattered light intensity value of each core neighborhood; The maximum value among the central scattered light intensity values ​​is determined, and the maximum value is used as the target scattered light intensity.

6. The method according to claim 1, characterized in that The environmental data includes current temperature, current humidity, and current air pressure. Adjusting the voltage threshold of the photosensor array based on the environmental data to obtain a target voltage threshold specifically includes: Among them, V is the target voltage threshold, T is the current temperature, T0 is the standard temperature, β is the temperature characteristic coefficient, H is the current humidity, H0 is the standard humidity, a is the humidity characteristic coefficient, P is the current air pressure, P0 is the standard air pressure, b is the air pressure characteristic coefficient, and V0 is the standard voltage under the standard temperature, standard humidity and standard air pressure.

7. The method according to claim 1, characterized in that The method of establishing a functional relationship between particle concentration and scattered light intensity based on the particle size distribution parameters and Mie scattering theory specifically includes: Wherein, C is the particle concentration, ρ is the density of the particle to be measured, μ is the mean of the logarithmic particle size, and σ is the standard deviation of the logarithmic particle size.

8. An ambient air particulate matter detection device, characterized in that: The device comprises an environmental data acquisition module (201), a light intensity distribution determination module (202), a particle size distribution calculation module (203), a function relationship establishment module (204), and a target concentration calculation module (205), wherein: The environmental data acquisition module (201) is used to acquire environmental data and adjust the voltage threshold of the photosensor array based on the environmental data to obtain a target voltage threshold; The light intensity distribution determination module (202) is used to obtain the scattered light intensity distribution of the particles to be measured at different wavelengths through the photosensor array under the target voltage threshold; The particle size distribution calculation module (203) is used to calculate the particle size distribution parameters of the particle to be measured based on the scattered light intensity distribution; The functional relationship establishing module (204) is used to establish a functional relationship between particle concentration and scattered light intensity based on the particle size distribution parameters and in combination with Mie scattering theory; The target concentration calculation module (205) is used to determine the target scattered light intensity from the scattered light intensity distribution, and input the target scattered light intensity into the functional relationship to obtain the target concentration corresponding to the particulate matter to be measured.

9. An electronic device, characterized in that: The electronic device (300) comprises a processor (301), a memory (305), a user interface (303) and a network interface (304), wherein the memory (305) is used to store instructions, the user interface (303) and the network interface (304) are used to communicate with other devices, and the processor (301) is used to execute the instructions stored in the memory (305) so that the electronic device (300) executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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