Filtering efficiency detecting instrument and method suitable for air filtering equipment of farm

Abstract

The present application relates to the technical field of particle counting equipment, in particular to a filter efficiency detection instrument and method suitable for air filtration equipment in a farm. The instrument is based on light scattering principle, combined with sheath gas technology to improve the detection accuracy of small particle size particles, and through the built-in particle diluter to expand the detection capability in high concentration environment. The device is provided with double measuring ports before and after filtration, realizing automatic switching of sampling, real-time calculation and intuitive display of filtration efficiency. Through the integration of 5G communication and cloud platform, automatic uploading, storage and analysis of detection data are realized, and intelligent association of data with the farm and equipment is supported. The present application is simple to operate, portable, high in precision and strong in adaptability, which can effectively improve the filtration efficiency detection level of air filtration equipment, reduce labor cost and error, and enhance the biological safety prevention and control capability of the farm.

Description

Technical Field

[0001] This invention relates to the field of particle counting equipment technology, and in particular to an instrument and method for testing the filtration efficiency of air filtration equipment in livestock farms. Background Technology

[0002] In large-scale farming, airborne dust particles and pathogens are easily spread through airflow, posing a significant threat to livestock health and production safety. To reduce the risk of transmission, farms commonly install air filtration equipment at air inlets. These filtration devices are mainly installed at the main air inlets of farms (pig sheds) to purify the air entering the sheds, intercepting dust and pathogens, and reducing the risk of disease in livestock. However, to evaluate the actual operating effect of filtration equipment, existing technologies mostly rely on particle counting methods, which involve detecting the number of particles of different sizes in the air before and after filtration and manually calculating the filtration efficiency. This type of method suffers from insufficient detection accuracy, difficulty adapting to the high concentration of particles and complex environmental conditions in farms, and the need for manual calculation of filtration efficiency, which is cumbersome. Furthermore, most equipment lacks automatic data transmission and centralized management capabilities, making real-time monitoring and data analysis difficult. Therefore, there is an urgent need for a detection instrument and method suitable for farm environments, capable of high-precision detection, automatic calculation of filtration efficiency, and support for intelligent data management. Summary of the Invention

[0003] This invention overcomes the shortcomings of the prior art and provides an instrument and method for testing the filtration efficiency of air filtration equipment in livestock farms.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: This invention provides a filtration efficiency testing instrument suitable for air filtration equipment in livestock farms, the filtration efficiency testing instrument comprising: Laser detection module: The laser detection module includes a laser device and a laser detection area. The laser device is used to emit detection laser beams with different incident waves. The laser detection area is the passage space for air particles, which is used to provide a detection site for the laser beam to irradiate and detect the concentration and distribution of air particles. Particle counter: The particle counter is used to calculate the positive correlation between the particle size and scale of air particles to obtain the number of particles, and the particle counter is set with an effective detection range; Particle diluter: The particle diluter is used to generate and output clean air with a known dilution concentration flow rate, so that the clean air is mixed with a high concentration of dust-laden airflow in a suitable proportion to reduce the concentration of particulate matter in the dust-laden airflow to be tested. IoT transmission module: The IoT transmission module is responsible for the automatic uploading of real-time data, enabling the detection data to be automatically associated with the corresponding air filter box of the corresponding pig farm and to obtain the current meteorological environment data of the target farm; Data analysis module: The data analysis module is used to analyze the electrical signal related to the intensity characteristics of scattered light converted by the photoelectric sensor. The signal intensity is positively correlated with the particle size and quantity. Efficiency Calculation Module: The efficiency calculation module is used to calculate the filtration efficiency of the air filtration equipment in the farm by combining the number of particles before filtration detected upstream and the number of particles after filtration detected downstream.

[0005] Furthermore, in a preferred embodiment of the present invention, the particle diluter, in conjunction with sheath gas technology, can output a clean airflow to envelop the dust-laden airflow being tested, thereby preventing the particles from continuously diffusing deep within the laser detection zone.

[0006] Furthermore, in a preferred embodiment of the present invention, the IoT transmission module uses 5G IoT technology and cellular mobile communication technology to support real-time data transmission and enable the particle counter to upload data in seconds.

[0007] Furthermore, in a preferred embodiment of the present invention, the IoT transmission module uses the locally supported Modbus TCP data transmission protocol and adopts a publish-subscribe model.

[0008] Furthermore, in a preferred embodiment of the present invention, device information and detection data in the cloud can be obtained through the Modbus TCP data transmission protocol, and the scattered filtering efficiency monitoring data can be uniformly uploaded to the cloud platform.

[0009] Furthermore, in a preferred embodiment of the present invention, the filtration efficiency testing instrument is equipped with a third-party data matching system, which is used to deeply bind the filtration efficiency testing instrument to a specific farm organization, and to automatically match data through the test number and the air filter box number.

[0010] The second aspect of this invention provides a method for detecting the filtration efficiency of air filtration equipment in livestock farms, applicable to any of the filtration efficiency testing instruments described in the invention, specifically comprising the following steps: S01: Activate the laser detection zone and use the laser beam emitted by the laser equipment to detect airborne particulate matter entering the laser detection zone from the target farm; S02: Obtain real-time scattering data of air particles detected by the laser beam in the laser detection area. Based on the empirical scattering data and deflection information of air particles of different sizes irradiated by the laser device, perform wave analysis and simulation of the light scattering propagation of the real-time scattering data to establish a real-time scattering light simulation field in the laser detection area. S03: The photoelectric sensor senses the changes in light intensity reflected in the laser detection area and the real-time scattered light simulation area, so as to convert the scattered light intensity characteristics into electrical signals and upload them to the data analysis module. S04: By relying on the prior momentum transfer matrix after the air particles are scattered by the laser beam under the current meteorological environment, the signal intensity of the electrical signal is analyzed and statistically analyzed from the coarse scale of particle concentration to the fine scale of particle distribution through the data analysis module, and the concentration data and distribution data of particles of different sizes in a unit volume of air are determined. S05: Based on the concentration data and distribution data transmitted to the particle counter, the particle size and scale of the air particles are cumulatively calculated in a positive correlation to obtain the number of particles contained in the current air particles. S06: Obtain the number of particles before filtration from the upstream concentration measurement and the number of particles after filtration from the downstream concentration measurement, import the number of particles before filtration and the number of particles after filtration into the efficiency calculation module for calculation, obtain the filtration efficiency result, and upload it to the detection display terminal.

[0011] Furthermore, in a preferred embodiment of the present invention, step S02 specifically includes the following steps: Obtain the design drawings of the filtration efficiency testing instrument and the model parameter information of the laser equipment. Based on the design drawings, use PROE modeling software to model the three-dimensional spatial scalar domain of the laser detection area. By retrieving model parameter information and three-dimensional spatial scalar domain through big data, we can obtain the empirical scattering angle and the bias characteristics expressed by the scattering behavior when the laser device is located in the laser detection area and irradiates air particles of different predetermined particle sizes. By analyzing the bias characteristics using probability density, the HG bias phase function of the scattering angle in the three-dimensional scalar domain is established to obtain the detection incident direction and preset laser parameters of the laser device. Based on preset laser parameters, the empirical scattering angle is randomly sampled along the inverse function of the detection incident direction using the HG deflection phase function to generate a scalar fluctuation matrix of the scattering azimuth angle. Based on the scalar fluctuation matrix, a scalar fluctuation control equation for the laser beam irradiating air particles in the laser detection area is constructed. The real-time scattering angle amplitude and corresponding scattering flux of air particles detected by the laser beam in the laser detection area are obtained through the feedback port of the laser equipment. Define the real-time scattering angle amplitude as the angular flux and the scattering flux as a finite scattering term. Introduce the Rytov transform algorithm to perform an amplitude-phase Legendre expansion on the angular flux and the finite scattering term to generate the real-time scattering wave field expansion and its included perturbation spherical harmonic function. The real-time scattered wave field expansion is substituted into the scalar wave control equation for solution. During the solution process, an orthogonal projection operation is performed on each perturbation spherical harmonic function to form a set of spatial scattering variable equations for real-time detection of air particles by laser beam. The propagation state of scattered light in the real-time wave field is tracked and updated according to the spatial scattering variable equations and restored to the three-dimensional spatial scalar domain to generate a real-time simulated field of scattered light in the laser detection area.

[0012] Furthermore, in a preferred embodiment of the present invention, step S04 specifically includes the following steps: By using optical knowledge graphs to identify and obtain the scattering laws of particle concentration and distribution in air particles of different sizes, we can also construct the scattering propagation domain of the laser detection area. A short-time Fourier algorithm is introduced to analyze the analog photoelectric signal in order to obtain the signal intensity reflecting the intensity characteristics of the scattered light in the real-time scattered light simulation site. Based on the scattering law, the scattering propagation situation domain is divided into a coarse-resolution situation domain at the particle concentration scale and a fine-resolution situation domain at the particle distribution scale. Historical scattered light wave data of air particles with particle size range corresponding to the current meteorological environment under the condition of laser beam irradiation based on empirical test cases were obtained. Collision control simulation of the traceable momentum transfer of scattered light was carried out based on historical scattered light wave data to obtain the prior momentum transfer matrix. The light intensity sensing timing strategy of the photoelectric sensor is obtained. The transition time window between the coarse and fine state subdomains is preset according to the light intensity sensing timing strategy. The scattering state observation model is constructed based on the wave equation of the real-time scattered light simulation field. Using the scattering state observation model, forward modeling is performed in the coarse-resolution situation domain to calculate the advancing trajectory of the signal intensity fitted along the transition time window, generating the coarse-resolution forward integral and simulated momentum transfer matrix of the global particle situation structure. The misalignment deviation between the simulated momentum transfer matrix and the prior momentum transfer matrix is ​​calculated using a hash operator to obtain the momentum transfer hash degree. The resolution cost function value is then constructed based on the momentum transfer hash degree constraint. Based on the prior momentum transfer matrix, a threshold for the resolution cost function is preset, and a predetermined propagation tangent vector of the fine-resolution situation domain is obtained. Then, the coarse-resolution forward integral interpolation is mapped to the fine-resolution situation domain along the predetermined propagation tangent vector for inversion propagation, so that the particle concentration scale is progressively advanced to the particle distribution scale, and the fine-resolution inverse integral is output. Repeat the steps of forward modeling and inverse propagation, combine coarse-resolution forward integration and fine-resolution backward integration to analyze and statistically analyze the scattering vector until the resolution cost function value is lower than the resolution cost function threshold. Based on the statistical results, determine the concentration and distribution of particles of different sizes in the unit volume of air in the laser detection area.

[0013] Furthermore, in a preferred embodiment of the present invention, the step of obtaining historical scattered light wave data when a laser beam irradiates air particles corresponding to a particle size range under current meteorological environmental data conditions through empirical detection cases, and performing collision control analysis on the traceable momentum transfer of the scattered light based on the historical scattered light wave data to obtain the prior momentum transfer matrix, specifically includes the following steps: The current meteorological environment data of the target farm is obtained through the Internet of Things, and the experience of detecting air particulate matter by laser equipment emitting laser beams from the detection incident direction according to preset laser parameters is obtained through big data. By retrieving and identifying current meteorological and environmental data using a pollutant knowledge graph, the potential particle size range of airborne particulate matter enriched within the target farm can be obtained. Based on empirical detection cases, historical incident wave vector variation data and corresponding incident wavelengths were retrieved when the laser beam hit different particle sizes within the potential particle size range in a continuous time sequence, as well as historical scattering wavelength variation data and corresponding scattering wavelengths after the collision. The jump deviation of the incident wavelength accompanying each historical incident wave vector change data with respect to the accompanying scattered wavelength of the corresponding historical scattered wavelength change data is calculated to obtain a series of transient wavelength differences in continuous time. The disorder degree transfer entropy function is determined based on the regular gradient between each transient wavelength difference. Extract the incident divergence velocity of the laser beam and the density of air particles when the historical incident wavelength change data and historical scattering wavelength change data were generated; A collision particle-light scattering logic model is established by introducing particle collision theory. Based on the law of conservation of momentum as the control premise, the incident divergence velocity and density are calculated to obtain the equilibrium distribution function. Based on the disorder degree transfer entropy function, the momentum transfer entropy constraint boundary of the collision particle-light scattering logic model is defined. The equilibrium distribution function is simultaneously imported to search for solutions in the non-equilibrium direction within the momentum transfer entropy constraint boundary. The collision entropy parameters are output, and the collision operator is constructed based on the collision entropy parameters. The collision operator is used to import the historical incident wave vector variation data and the corresponding historical scattering wavelength variation data into the collision particle-light scattering logic model to perform collision control, establish the momentum distribution trajectory, and construct the prior momentum transfer matrix of the laser beam after scattering from air particles of different sizes.

[0014] The beneficial technical effects of this invention are as follows: This invention integrates a laser detection module, a particle counter, a particle diluter, an IoT transmission module, a data analysis module, and an efficiency calculation module. It can accurately detect the filtration efficiency of air filtration equipment, enabling high-quality operation and maintenance management of the equipment. This effectively reduces the risk of pathogen transmission through dust particles, resulting in a 30% reduction in disease incidence and loss rate in the group's breeding pig farm, significantly reducing economic losses in livestock farming. Secondly, by introducing light scattering detection and sheath gas protection technology, the detection accuracy of small-diameter particles is effectively improved. Combined with the built-in particle diluter, the applicability of the equipment in high-concentration dust environments is significantly broadened, avoiding the counting distortion problems that occur in traditional detection methods. This instrument relies on IoT communication and cloud platform data management to achieve automatic uploading, centralized analysis, and visual management of detection data, improving the real-time performance, accuracy, and traceability of the data. Overall, this invention significantly improves the efficiency and accuracy of air filtration equipment operation status assessment, reducing the tedious and error-prone manual summarization, data entry, and reporting processes. This helps reduce the risk of pathogen transmission in livestock farms, improving the level of biological livestock safety management and economic benefits. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the three-dimensional structure of the instrument; Figure 2 This is a schematic diagram of the internal structure of this instrument; Figure 3 This is a flowchart illustrating a method for testing the filtration efficiency of air filtration equipment suitable for livestock farms.

[0017] The annotations in the attached figures are explained as follows: 101. Laser detection module; 1011. Laser equipment; 1012. Laser detection area; 102. Particle counter; 103. Particle diluter; 104. Internet of Things transmission module; 105. Data analysis module; 106. Efficiency calculation module. Detailed Implementation

[0018] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner. Therefore, they only show the components related to the present invention. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0019] In the description of this application, it should be understood that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships based on the orientation or positional relationships shown in the accompanying drawings, are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the scope of protection of this application. Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0020] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art will understand the specific meaning of the above terms in this application based on the specific circumstances.

[0021] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0022] like Figures 1-2 As shown, the first aspect of the present invention provides a filtration efficiency testing instrument suitable for air filtration equipment in livestock farms, the filtration efficiency testing instrument comprising: Laser detection module 101: The laser detection module includes a laser device 1011 and a laser detection area 1012. The laser device 1011 is used to emit detection laser beams with different incident waves. The laser detection area 1012 is an entry space for air particles and is used to provide a detection site for the concentration and distribution of air particles to be irradiated by the laser beam. Particle counter 102: The particle counter 102 is used to calculate the positive correlation between the particle size and scale of air particles to obtain the number of particles, and the particle counter 102 is provided with an effective detection range. Particle diluter 103: The particle diluter 103 is used to generate and output clean air with a known dilution concentration flow rate, so that the clean air is mixed with the high-concentration dust-laden airflow in a suitable proportion to reduce the concentration of particulate matter in the dust-laden airflow to be detected. IoT transmission module 104: The IoT transmission module 104 is responsible for the automatic uploading of real-time data, so that the detection data is automatically associated with the corresponding air filter box of the corresponding pig farm and the current meteorological environment data of the target farm is obtained. Data analysis module 105: The data analysis module 105 is used to analyze the electrical signal about the intensity characteristics of scattered light converted by the photoelectric sensor. The signal intensity is positively correlated with the particle size and quantity of the particles. Efficiency calculation module 106: The efficiency calculation module 106 is used to calculate the filtration efficiency of the air filtration equipment in the farm by combining the number of particles before filtration detected upstream and the number of particles after filtration detected downstream.

[0023] It should be noted that this instrument uses light scattering for particle detection. Specifically, when dust-laden airflow passes through the laser detection zone 1012, air particles cause the laser beam to scatter. The photoelectric sensor detects the propagation of the scattered light and encodes it into an electrical signal. The detection instrument analyzes this signal through a data analysis module to accurately determine the concentration and distribution of particles of different sizes per unit volume of air. The signal strength is positively correlated with the particle size and quantity. Therefore, based on the statistical concentration and distribution information, the particle count is calculated, outputting the particle counts upstream (before filtration) and downstream (after filtration) of the air filtration equipment. Finally, the particle count is calculated by the efficiency calculation module to obtain the filtration efficiency data of the air filtration equipment in the target farm. This invention solves a series of problems in traditional air filtration equipment management, such as reliance on manual data collection and statistics, manual data entry and reporting, and cumbersome and error-prone operations. It overcomes the limitations of insufficient detection range in existing instruments and equipment. Furthermore, the equipment can adapt to various weather conditions, improving the detection quality of air filtration data in farms.

[0024] Furthermore, in a preferred embodiment of the present invention, the particle diluter 103 is equipped with sheath gas technology, which can output a clean airflow to envelop the dusty airflow being tested, thereby preventing the particles from continuously diffusing deep within the laser detection zone 1012.

[0025] It should be noted that the particle diluter 103 can output a clean airflow with a specific dilution concentration at a precise ratio and mix it with the high-concentration dust-laden airflow inside the laser detection zone 1012, thereby reducing the particulate matter concentration of the dust-laden airflow and bringing it within the effective detection range of the particle counter 102. This avoids phenomena such as optical path blockage, overlapping counting, or detection failure of the particle counter 102 due to excessively high air particulate matter concentration, significantly improving the applicable detection concentration range. This invention integrates the particle diluter 103 and the particle counter 102 into a single unit, making detection operation more convenient and simple, and effectively optimizing the detection errors of traditional equipment.

[0026] Furthermore, in a preferred embodiment of the present invention, the IoT transmission module 104 uses 5G IoT technology and cellular mobile communication technology to support real-time data transmission and enable the particle counter 102 to upload data in seconds.

[0027] Furthermore, in a preferred embodiment of the present invention, the IoT transmission module 104 uses the locally supported Modbus TCP data transmission protocol and adopts a publish-subscribe model.

[0028] Furthermore, in a preferred embodiment of the present invention, the device information and detection data in the cloud can be obtained through the Modbus TCP data transmission protocol, and the scattered filtering efficiency monitoring data can be uniformly uploaded to the cloud platform.

[0029] It should be noted that this instrument is equipped with two measurement ports, one for pre-filtration and one for post-filtration, which can realize the autonomous switching between pre-filtration and post-filtration detection during the detection process. It can automatically calculate the filtration efficiency data based on the number of particles before and after filtration, and supports direct query and traceability.

[0030] Furthermore, in a preferred embodiment of the present invention, the filtration efficiency testing instrument is equipped with a third-party data matching system, which is used to deeply bind the filtration efficiency testing instrument to a specific farm organization, and to automatically match data through the test number and the air filter box number.

[0031] It should be noted that this invention first registers device information in the cloud and then uses 4G / 5G IoT technology to automatically transmit on-site detection data to Huawei Cloud. A third-party data matching system (Kingdee Cloud Sky System) obtains the device information and detection data from Huawei Cloud through the data transmission protocol.

[0032] The second aspect of this invention provides a method for detecting the filtration efficiency of air filtration equipment in livestock farms, applicable to any of the filtration efficiency testing instruments described in the invention, such as... Figure 3 As shown, the specific steps include: S01: Activate the laser detection zone and use the laser beam emitted by the laser equipment to detect airborne particulate matter entering the laser detection zone from the target farm; S02: Obtain real-time scattering data of air particles detected by the laser beam in the laser detection area. Based on the empirical scattering data and deflection information of air particles of different sizes irradiated by the laser device, perform wave analysis and simulation of the light scattering propagation of the real-time scattering data to establish a real-time scattering light simulation field in the laser detection area. S03: The photoelectric sensor senses the changes in light intensity reflected in the laser detection area and the real-time scattered light simulation area, so as to convert the scattered light intensity characteristics into electrical signals and upload them to the data analysis module. S04: By relying on the prior momentum transfer matrix after the air particles are scattered by the laser beam under the current meteorological environment, the signal intensity of the electrical signal is analyzed and statistically analyzed from the coarse scale of particle concentration to the fine scale of particle distribution through the data analysis module, and the concentration data and distribution data of particles of different sizes in a unit volume of air are determined. S05: Based on the concentration data and distribution data transmitted to the particle counter, the particle size and scale of the air particles are cumulatively calculated in a positive correlation to obtain the number of particles contained in the current air particles. S06: Obtain the number of particles before filtration from the upstream concentration measurement and the number of particles after filtration from the downstream concentration measurement, import the number of particles before filtration and the number of particles after filtration into the efficiency calculation module for calculation, obtain the filtration efficiency result, and upload it to the detection display terminal.

[0033] Furthermore, in a preferred embodiment of the present invention, step S02 specifically includes the following steps: Obtain the design drawings of the filtration efficiency testing instrument and the model parameter information of the laser equipment. Based on the design drawings, use PROE modeling software to model the three-dimensional spatial scalar domain of the laser detection area. By retrieving model parameter information and three-dimensional spatial scalar domain through big data, we can obtain the empirical scattering angle and the bias characteristics expressed by the scattering behavior when the laser device is located in the laser detection area and irradiates air particles of different predetermined particle sizes. By analyzing the bias characteristics using probability density, the HG bias phase function of the scattering angle in the three-dimensional scalar domain is established to obtain the detection incident direction and preset laser parameters of the laser device. Based on preset laser parameters, the empirical scattering angle is randomly sampled along the inverse function of the detection incident direction using the HG deflection phase function to generate a scalar fluctuation matrix of the scattering azimuth angle. Based on the scalar fluctuation matrix, a scalar fluctuation control equation for the laser beam irradiating air particles in the laser detection area is constructed. The real-time scattering angle amplitude and corresponding scattering flux of air particles detected by the laser beam in the laser detection area are obtained through the feedback port of the laser equipment. Define the real-time scattering angle amplitude as the angular flux and the scattering flux as a finite scattering term. Introduce the Rytov transform algorithm to perform an amplitude-phase Legendre expansion on the angular flux and the finite scattering term to generate the real-time scattering wave field expansion and its included perturbation spherical harmonic function. The real-time scattered wave field expansion is substituted into the scalar wave control equation for solution. During the solution process, an orthogonal projection operation is performed on each perturbation spherical harmonic function to form a set of spatial scattering variable equations for real-time detection of air particles by laser beam. The propagation state of scattered light in the real-time wave field is tracked and updated according to the spatial scattering variable equations and restored to the three-dimensional spatial scalar domain to generate a real-time simulated field of scattered light in the laser detection area.

[0034] It should be noted that the device of this invention uses light scattering to detect airborne particulate matter. Specifically, a laser beam irradiates the incoming airborne particulate matter, causing scattering. The intensity of the scattered light is then captured by a photoelectric sensor and converted into an electrical signal output. However, due to the uneven particle size distribution of high-concentration dust-laden airflows, and the potential presence of different types of non-spherical particles, such as dust, fibers, or biological particles, the scattering behavior of the laser beam within the detection area is exceptionally complex. This can lead to highly coherent and strongly coupled scattering phenomena, resulting in optical path obstruction and blind zone noise in the information transmission of laser scattering within the closed detection area. This undoubtedly increases the difficulty and accuracy of the photoelectric sensor in sensing effective global laser scattering information, severely affecting the reliability of subsequent filtration efficiency analysis. To address this, this method first constructs a continuous scalar field containing spatial coordinates and medium properties—a three-dimensional spatial scalar domain—in the high-dimensional space of the laser detection area. This domain is used to define the spatial boundary conditions and medium distribution for subsequent light propagation and scattering calculations, clarifying the spatial tracking carrier of the laser propagation trajectory and the scattering local area. Simultaneously, big data is used to obtain empirical scattering angles and bias characteristics defining forward and backward scattering behaviors of laser-irradiated particles of different sizes. Both are historical data provided by real measurement experience. The empirical scattering angle defines the anisotropic intensity of laser scattering characteristics, and in this method, it is used as a scalar parameter to compress complex scattering behavior. The bias characteristics describe and control the scattering direction preference, such as the spontaneous backscattering and completely random scattering of laser light on particles of certain sizes. Empirical anchoring directly affects the scattering behavior of the light propagation path. Next, probability density analysis of these empirical bias characteristics is used to establish a probability density function for the scattering angle, describing the prior probability distribution of the laser beam scattered in a certain direction, i.e., the HG bias phase function, which illustrates the scattering direction distribution rules under different particle size conditions. Subsequently, constrained by the distribution rules of the scattering direction, the preset parameters of the laser device are used to randomly sample empirical scattering angles along its detection incident direction. Specifically, the inverse function method is employed to randomly generate scattering angles that conform to physical laws and the output of the laser device. This transforms the abstract probability distribution of the scattering rate into spatial scalar samples of the specific scattering path of the scattering source. Based on these samples, an angular orientation random field (scalar fluctuation matrix) is constructed, and the scattering fluctuation behavior is statistically analyzed, i.e., the scalar fluctuation control equation. This equation describes the wandering propagation of the laser beam in a non-uniform particle medium, ensuring the authenticity and reconstruction of the captured scattering light propagation during actual detection.

[0035] It should be noted that this method subsequently switches the perspective of detecting actual airborne particles using a laser beam, performing a spherical harmonic expansion of the real-time scattering angle amplitude as the angular flux, and similarly expanding the scattering flux as a finite scattering term. Here, the real-time scattering angle amplitude represents the intensity or amplitude of the real-time scattered light at different scattering angles; while the scattering flux describes the energy power of the scattered wave passing through a unit area per unit time, and is closely related to the scattering intensity. Specifically, for the expansion of the real-time scattering angle amplitude, orthogonal basis functions are used to replace the continuous angle variable, discretizing it into a finite number of coefficients, thereby encoding the real-time scattering angle information into moments of different orders. For the expansion of the scattering flux, the complex angle-coupled scattering relationship is decomposed into a polynomial series, and the scattering anisotropy is decomposed into contribution components of different orders. Substituting the expansion into the scalar wave control equation improves the resolvability of the scattered light propagation in real-time particle detection; and the orthogonal projection of each perturbation spherical harmonic function during the process sharply reduces the coupling of orthogonal variables in the scattered light path of highly coherent and strongly coupled scattering phenomena, improving the reliability and stability of the scattered light intensity expression. This method enables the construction of a light model field for display of the scattered light propagation when a laser device detects particulate matter in the incoming dusty air in real time within the detection area. This reduces the omissions and tracking errors in the global perception of complex scattered light behavior by photoelectric sensors in high-concentration gas detection scenarios, ensuring the accuracy and reliability of the filtering efficiency detection information source.

[0036] Furthermore, in a preferred embodiment of the present invention, step S04 specifically includes the following steps: By using optical knowledge graphs to identify and obtain the scattering laws of particle concentration and distribution in air particles of different sizes, we can also construct the scattering propagation domain of the laser detection area. A short-time Fourier algorithm is introduced to analyze the analog photoelectric signal in order to obtain the signal intensity reflecting the intensity characteristics of the scattered light in the real-time scattered light simulation site. Based on the scattering law, the scattering propagation situation domain is divided into a coarse-resolution situation domain at the particle concentration scale and a fine-resolution situation domain at the particle distribution scale. Historical scattered light wave data of air particles with particle size range corresponding to the current meteorological environment under the condition of laser beam irradiation based on empirical test cases were obtained. Collision control analysis of the traceable momentum transfer of scattered light was performed based on historical scattered light wave data to obtain the a priori momentum transfer matrix. The light intensity sensing timing strategy of the photoelectric sensor is obtained. The transition time window between the coarse and fine state subdomains is preset according to the light intensity sensing timing strategy. The scattering state observation model is constructed based on the wave equation of the real-time scattered light simulation field. Using the scattering state observation model, forward modeling is performed in the coarse-resolution situation domain to calculate the advancing trajectory of the signal intensity fitted along the transition time window, generating the coarse-resolution forward integral and simulated momentum transfer matrix of the global particle situation structure. The misalignment deviation between the simulated momentum transfer matrix and the prior momentum transfer matrix is ​​calculated using a hash operator to obtain the momentum transfer hash degree. The resolution cost function value is then constructed based on the momentum transfer hash degree constraint. Based on the prior momentum transfer matrix, a threshold for the resolution cost function is preset, and a predetermined propagation tangent vector of the fine-resolution situation domain is obtained. Then, the coarse-resolution forward integral interpolation is mapped to the fine-resolution situation domain along the predetermined propagation tangent vector for inversion propagation, so that the particle concentration scale is progressively advanced to the particle distribution scale, and the fine-resolution inverse integral is output. Repeat the steps of forward modeling and inverse propagation, combine coarse-resolution forward integration and fine-resolution backward integration to analyze and statistically analyze the scattering vector until the resolution cost function value is lower than the resolution cost function threshold. Based on the statistical results, determine the concentration and distribution of particles of different sizes in the unit volume of air in the laser detection area.

[0037] It should be noted that photoelectric sensors convert the intensity characteristics of scattered light into electrical signals, and the signal intensity is positively correlated with the particle size and quantity. However, the data analysis models used in existing filtration efficiency detection equipment usually rely on human experience and the inference of particle motion rules. This makes it difficult to quantify the global concentration gradient of particles from a bird's-eye view, and the local distribution pattern cannot be traced in detail. There are inherent limitations in particle dynamics calculations, which can easily lead to serious analytical biases in the final statistical results of dust-laden gas particles per unit volume. To address this, this method constructs a scattering propagation pattern domain in the laser detection area. This scattering propagation pattern domain is used to describe the global situation of light propagation and particle interaction, forming a distribution correlation between particle size, concentration, and laser scattering behavior. It is a semantic refinement framework of the multi-resolution scattering propagation hierarchy (coarse resolution concentration grid → fine resolution distribution grid) in physical space. Among them, the scattering laws include, but are not limited to, the particle size-wavelength relationship and the scattering mechanism law of enhanced forward scattering. This method, based on these scattering laws, divides the constructed scattering propagation domain into two levels of resolution: coarse and fine. The coarse resolution level dominates the analysis and acquisition of the global concentration structure, while the fine resolution level dominates the local detail recovery of the dynamic particle distribution trend. The two resolution levels have clear division of labor and are coupled with each other, providing a multi-scale analytical basis for data inference of the concentration and distribution of particles of different sizes. Since the scattering phenomenon of laser beams hitting air particles is essentially a momentum transfer between the incident wave and the scattered wave with different directions and wave vector magnitudes, this is the underlying scattering logic that must be followed when exploring particle size. Therefore, this method performs traceable a priori collision simulation of the traceable momentum transfer caused by laser scattering from particles of different sizes based on historical scattered light wave data, thereby constraining the reference benchmark (a priori momentum transfer matrix) of random scattering behavior and effectively improving the controllability of subsequent particle concentration and distribution statistics.

[0038] It should be noted that the transition time window fixes the sensing timing of the photoelectric sensor, restricts the solution space, and avoids divergence in the transition from concentration structure to distribution details when advancing signal intensity. First, forward modeling is performed at coarse resolution, essentially capturing the main physical characteristics of the electrical signal at the low-frequency level, thereby deriving an approximate response at the particle concentration scale. The coarse-resolution forward integral reveals the macroscopic expression of particle concentration per unit volume of gas under the signal intensity characteristics of scattered light; the simulated momentum transfer matrix is ​​its momentum transfer prediction data. The momentum transfer hash quantifies the deviation of the simulated background field based on momentum transfer at signal intensity compared to the prior background. This momentum transfer hash anchors the propagation target and statistical scale of the forward and inverse transition from coarse resolution of particle concentration to fine resolution of particle distribution, ensuring the statistical rationality of particulate matter concentration and distribution. Next, the resolution is further improved by mapping the coarse-resolution forward integral interpolation to a higher resolution level (fine-resolution situation domain) for inverse propagation, thus achieving a key leap from the coarse concentration scale to the fine distribution scale, realizing a continued search effect from the global optimal trend to local details. By inverting the subdivided dynamic domain, the distribution boundaries, details, and small-scale structures of particulate matter are gradually restored, improving the spatial resolution within a unit volume of gas. This method enables the analysis of the global structure of particle concentration at a "coarse resolution" level based on the intensity of the scattered signal, and then gradually transitions to a "fine resolution" level, thereby restoring the particle distribution pattern. This allows for accurate analysis and statistical analysis of the concentration and distribution of particulate matter of different sizes in a unit volume of air, ensuring the reliability of subsequent particle count calculations.

[0039] Furthermore, in a preferred embodiment of the present invention, the step of obtaining historical scattered light wave data when a laser beam irradiates air particles corresponding to a particle size range under current meteorological environmental data conditions through empirical detection cases, and performing collision control analysis on the traceable momentum transfer of the scattered light based on the historical scattered light wave data to obtain the prior momentum transfer matrix, specifically includes the following steps: The current meteorological environment data of the target farm is obtained through the Internet of Things, and the experience of detecting air particulate matter by laser equipment emitting laser beams from the detection incident direction according to preset laser parameters is obtained through big data. By retrieving and identifying current meteorological and environmental data using a pollutant knowledge graph, the potential particle size range of airborne particulate matter enriched within the target farm can be obtained. Based on empirical detection cases, historical incident wave vector variation data and corresponding incident wavelengths were retrieved when the laser beam hit different particle sizes within the potential particle size range in a continuous time sequence, as well as historical scattering wavelength variation data and corresponding scattering wavelengths after the collision. The jump deviation of the incident wavelength accompanying each historical incident wave vector change data with respect to the accompanying scattered wavelength of the corresponding historical scattered wavelength change data is calculated to obtain a series of transient wavelength differences in continuous time. The disorder degree transfer entropy function is determined based on the regular gradient between each transient wavelength difference. Extract the incident divergence velocity of the laser beam and the density of air particles when the historical incident wavelength change data and historical scattering wavelength change data were generated; A collision particle-light scattering logic model is established by introducing particle collision theory. Based on the law of conservation of momentum as the control premise, the incident divergence velocity and density are calculated to obtain the equilibrium distribution function. Based on the disorder degree transfer entropy function, the momentum transfer entropy constraint boundary of the collision particle-light scattering logic model is defined. The equilibrium distribution function is simultaneously imported to search for solutions in the non-equilibrium direction within the momentum transfer entropy constraint boundary. The collision entropy parameters are output, and the collision operator is constructed based on the collision entropy parameters. The collision operator is used to import the historical incident wave vector variation data and the corresponding historical scattering wavelength variation data into the collision particle-light scattering logic model to perform collision control, establish the momentum distribution trajectory, and construct the prior momentum transfer matrix of the laser beam after scattering from air particles of different sizes.

[0040] It should be noted that, for the simulation of prior momentum transfer, this method acquires historical incident wave vector variation data and corresponding historical scattering wavelength variation data for the potential air particulate size range under current meteorological conditions. These historical data reflect the vector changes between the incident and scattered waves when the laser beam detects the current air particulate size. Furthermore, since scattering is further quantified at the synergistic level of beam wavelength change, the wavelength data accompanying the laser beam during the generation of these variations are also acquired. The direction and magnitude of momentum transfer between the incident and scattered waves define the scattering vector, which is reflected by the instantaneous vector difference between the incident wavelength and the scattered wave field. Therefore, by calculating a series of transient wavelength differences between the accompanying incident wavelength and the accompanying scattering wavelength, these transient wavelength differences describe the state information transfer changes during the laser beam collision with particulate size scattering process, measuring the degree of non-equilibrium and disorder of the scattering state under historical wave vector data. Thus, based on the disorder transfer entropy function preset by these transient wavelength differences, a physical feasibility constraint that fits the actual transfer state is established. Then, based on the law of conservation of momentum as the control premise, the incident divergence velocity and density are calculated, which can recover the macroscopic scattering variables from the dynamics of the laser beam and the microscopic distribution of air particles. The equilibrium distribution function of its calculation output describes the distribution characteristics of the laser beam from the current air particle detection incident to the scattering tending to the equilibrium state, constructs the non-equilibrium solution support for the momentum equilibrium state discussion, and provides the target physical scene state and collision frequency for subsequent collision simulation modeling of laser scattering.

[0041] It should be noted that by importing the equilibrium distribution function into the momentum transfer entropy constraint boundary to search for solutions in the non-equilibrium direction, the equilibrium deviation of momentum transfer between the incident and scattered waves is extracted, and a momentum collision control basis, i.e., a collision operator, is established based on the incident divergence velocity and density. This method enables traceable a priori collision simulation of traceable momentum transfer caused by particle size-dependent laser scattering based on historical scattered light wave data. It statistically constrains the random scattering behavior of air particle concentration and distribution within a unit volume of gas, ensuring that particle statistics follow the momentum transfer law of laser scattering, thus improving the accuracy and reliability of the results.

[0042] In addition, the method for testing the filtration efficiency of air filtration equipment in livestock farms also includes the following steps: The current dilution concentration flow rate of the particle diluent and the concentration of the particulate matter to be detected in the dust-laden airflow are obtained, and the preset flow control strategy of the particle diluent is also obtained. The dilution attenuation index of clean air for dust-laden airflow with the concentration of particulate matter to be detected under different dilution concentration flow conditions is extracted through empirical particle dilution cases, and the disturbance step size of dilution flow control is set based on the dilution attenuation index. Construct the dilution concentration parameter space of the particle diluent, and use the perturbation criterion of changing only one dilution concentration variable each time. Based on the preset flow control strategy, construct the single-factor perturbation path of the particle diluent for different dilution concentration flow outputs. The detection failure value that causes the particle counter to deviate due to the concentration of the particulate matter to be detected is obtained. If the detection failure value exceeds the effective detection range of the particle counter, a counting jitter term that reduces the impact of the concentration of the particulate matter to be detected is constructed based on the failure deviation value between the detection failure value and the upper limit of the effective detection range. Using the perturbation step size, the count jitter term is injected into each of the current dilution concentration flow rates according to the single-factor perturbation path to perform univariate perturbation operation and discrete partial derivative calculation, so as to obtain the standard deviation of the influence of the current dilution concentration flow rate on the elimination of the failure deviation value. The impact effect index of reducing the current dilution concentration flow rate to reduce the high-concentration dust-laden airflow is determined based on the standard deviation of the impact. The impact effect index is then uploaded to the particle diluter to adjust the current dilution flow rate output of the clean air, so as to control the mixing ratio of the clean air and the high-concentration dust-laden airflow.

[0043] It should be noted that the filtration efficiency testing instrument of this invention is equipped with a particle diluter and sheath gas technology, which can mix clean air of known flow rate with high-concentration dust-laden airflow in a certain proportion to reduce the particulate matter concentration in the dust-laden sample gas and improve detection accuracy. However, existing technologies typically control particle diluters using simple threshold judgment and concentration-flow adjustment based on inherent proportional formulas, resulting in inaccurate concentration proportions of the output clean gas. This leads to deviations in the mixing ratio with high-concentration dust-laden air, making it difficult for the dust-laden airflow concentration to fall within the effective detection range of the particle counter. To address this, this method first defines a perturbation step size for dilution flow rate control. This perturbation step size controls the amplitude or scale of each single perturbation to the dilution concentration flow rate (input variable). The dilution attenuation index reflects the dilution performance or concentration reduction effectiveness of clean air systems with different concentrations in the dust-laden airflow with the particulate matter concentration to be detected, thus fixing the motivation for perturbing the input variable. Next, a single-variable perturbation rule is constructed for the flow rate output of the particle diluent under different dilution concentrations, forming a single-factor perturbation path. This clarifies the independent influence of the dilution concentration variable and clearly identifies which dilution concentration variable caused the output change that led to the decrease in the concentration of the detected particulate matter. The preset flow control strategy defines the rationality logic of the flow control output. Furthermore, if the detection failure value exceeds the effective detection range of the particle counter, it indicates that the introduction of dust-laden airflow caused the particle counter to fail, exceeding its effective detection range, indicating that the current concentration of the dust-laden airflow is too high. Therefore, to ensure that the detected concentration of the dust-laden airflow always falls within the effective detection range of the particle counter, this method constructs a counting jitter term to reduce the impact of the detected particulate matter concentration through a failure deviation value. The failure deviation value imposes an effective detection range constraint on the dilution concentration flow rate control of the particle diluent, ensuring that its clean air concentration control ratio can minimize the high concentration characteristics of the dust-laden gas and keep it within the effective detection range of the counter to the greatest extent possible. Subsequently, the counting jitter term is injected into the current dilution concentration flow rate according to the single-factor perturbation path to perform a single-variable perturbation operation. Finally, the calculated impact effect index quantifies the effectiveness of the current dilution concentration flow rate in reducing the concentration of dust-laden airflow.

[0044] It should be noted that this method can apply a disturbance to the dilution concentration flow rate parameter input to the particle diluent while always falling within the effective detection range of the particle counter. This quantifies the impact of the disturbance on the optimization of the high-concentration dust-laden airflow concentration. Based on this, the clean air flow rate output of the particle diluent is adjusted, so that clean air and high-concentration dust-laden airflow are mixed in a precise ratio, reducing the particulate matter concentration in the sample gas. This avoids the detection failure of the particle counter due to excessively high particulate matter concentration, and achieves intelligent control of particle dilution.

[0045] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An instrument for testing the filtration efficiency of air filtration equipment in livestock farms, characterized in that, The filtration efficiency testing instrument includes: Laser detection module: The laser detection module includes a laser device and a laser detection area. The laser device is used to emit detection laser beams with different incident waves. The laser detection area is the passage space for air particles, which is used to provide a detection site for the laser beam to irradiate and detect the concentration and distribution of air particles. Particle counter: The particle counter is used to calculate the positive correlation between the particle size and scale of air particles to obtain the number of particles, and the particle counter is set with an effective detection range; Particle diluter: The particle diluter is used to generate and output clean air with a known dilution concentration flow rate, so that the clean air is mixed with a high concentration of dust-laden airflow in a suitable proportion to reduce the concentration of particulate matter in the dust-laden airflow to be tested. IoT transmission module: The IoT transmission module is responsible for the automatic uploading of real-time data, enabling the detection data to be automatically associated with the corresponding air filter box of the corresponding pig farm and to obtain the current meteorological environment data of the target farm; Data analysis module: The data analysis module is used to analyze the electrical signal related to the intensity characteristics of scattered light converted by the photoelectric sensor. The signal intensity is positively correlated with the particle size and quantity. Efficiency Calculation Module: The efficiency calculation module is used to calculate the filtration efficiency of the air filtration equipment in the farm by combining the number of particles before filtration detected upstream and the number of particles after filtration detected downstream.

2. The filtration efficiency testing instrument for air filtration equipment in livestock farms according to claim 1, characterized in that, The particle diluter, combined with sheath gas technology, can output a clean airflow to envelop the dust-laden airflow being tested, preventing the particles from continuously diffusing deep within the laser detection zone.

3. The filtration efficiency testing instrument for air filtration equipment in livestock farms according to claim 1, characterized in that, The IoT transmission module uses 5G IoT technology and cellular mobile communication technology to support real-time data transmission and enable the particle counter to upload data in seconds.

4. The filtration efficiency testing instrument for air filtration equipment in livestock farms according to claim 1, characterized in that, The IoT transmission module uses the locally supported Modbus TCP data transmission protocol and adopts a publish-subscribe model.

5. The filtration efficiency testing instrument for air filtration equipment in livestock farms according to claim 4, characterized in that, The Modbus TCP data transmission protocol can be used to obtain device information and detection data from the cloud, and to uniformly upload the scattered filter efficiency monitoring data to the cloud platform.

6. The filtration efficiency testing instrument for air filtration equipment in livestock farms according to claim 1, characterized in that, The filtration efficiency testing instrument is equipped with a third-party data matching system, which is used to deeply bind the filtration efficiency testing instrument with specific farm organizations and automatically match data through the test number and the air filter box number.

7. A method for testing the filtration efficiency of air filtration equipment in livestock farms, applied to the filtration efficiency testing instrument for air filtration equipment in livestock farms as described in any one of claims 1-6, characterized in that, Specifically, the following steps are included: S01: Activate the laser detection zone and use the laser beam emitted by the laser equipment to detect airborne particulate matter entering the laser detection zone from the target farm; S02: Obtain real-time scattering data of air particles detected by the laser beam in the laser detection area. Based on the empirical scattering data and deflection information of air particles of different sizes irradiated by the laser device, perform wave analysis and simulation of the light scattering propagation of the real-time scattering data to establish a real-time scattering light simulation field in the laser detection area. S03: The photoelectric sensor senses the changes in light intensity reflected in the laser detection area and the real-time scattered light simulation site, so as to convert the scattered light intensity characteristics into electrical signals and upload them to the data analysis module. S04: By relying on the prior momentum transfer matrix after the air particles are scattered by the laser beam under the current meteorological environment, the signal intensity of the electrical signal is analyzed and statistically analyzed from the coarse scale of particle concentration to the fine scale of particle distribution through the data analysis module, and the concentration data and distribution data of particles of different sizes in a unit volume of air are determined. S05: Based on the concentration data and distribution data transmitted to the particle counter, the particle size and scale of the air particles are cumulatively calculated in a positive correlation to obtain the number of particles contained in the current air particles. S06: Obtain the number of particles before filtration from the upstream concentration measurement and the number of particles after filtration from the downstream concentration measurement, import the number of particles before filtration and the number of particles after filtration into the efficiency calculation module for calculation, obtain the filtration efficiency result, and upload it to the detection display terminal.

8. The method for detecting the filtration efficiency of air filtration equipment in livestock farms according to claim 7, characterized in that, S02 specifically includes the following steps: Obtain the design drawings of the filtration efficiency testing instrument and the model parameter information of the laser equipment. Based on the design drawings, use PROE modeling software to model the three-dimensional spatial scalar domain of the laser detection area. By retrieving model parameter information and three-dimensional spatial scalar domain through big data, we can obtain the empirical scattering angle and the bias characteristics expressed by the scattering behavior when the laser device is located in the laser detection area and irradiates air particles of different predetermined particle sizes. By analyzing the bias characteristics using probability density, the HG bias phase function of the scattering angle in the three-dimensional scalar domain is established to obtain the detection incident direction and preset laser parameters of the laser device. Based on preset laser parameters, the empirical scattering angle is randomly sampled along the inverse function of the detection incident direction using the HG deflection phase function to generate a scalar fluctuation matrix of the scattering azimuth angle. Based on the scalar fluctuation matrix, a scalar fluctuation control equation for the laser beam irradiating air particles in the laser detection area is constructed. The real-time scattering angle amplitude and corresponding scattering flux of air particles detected by the laser beam in the laser detection area are obtained through the feedback port of the laser equipment. Define the real-time scattering angle amplitude as the angular flux and the scattering flux as a finite scattering term. Introduce the Rytov transform algorithm to perform an amplitude-phase Legendre expansion on the angular flux and the finite scattering term to generate the real-time scattering wave field expansion and its included perturbation spherical harmonic function. The real-time scattered wave field expansion is substituted into the scalar wave control equation for solution. During the solution process, an orthogonal projection operation is performed on each perturbation spherical harmonic function to form a set of spatial scattering variable equations for real-time detection of air particles by laser beam. The propagation state of scattered light in the real-time wave field is tracked and updated according to the spatial scattering variable equations and restored to the three-dimensional spatial scalar domain to generate a real-time simulated field of scattered light in the laser detection area.

9. The method for testing the filtration efficiency of air filtration equipment in livestock farms according to claim 7, characterized in that, S04 specifically includes the following steps: By using optical knowledge graphs to identify and obtain the scattering laws of particle concentration and distribution in air particles of different sizes, we can also construct the scattering propagation domain of the laser detection area. A short-time Fourier algorithm is introduced to analyze the analog photoelectric signal in order to obtain the signal intensity reflecting the intensity characteristics of the scattered light in the real-time scattered light simulation site. Based on the scattering law, the scattering propagation situation domain is divided into a coarse-resolution situation domain at the particle concentration scale and a fine-resolution situation domain at the particle distribution scale. Historical scattered light wave data of air particles with particle size range corresponding to the current meteorological environment under the condition of laser beam irradiation based on empirical test cases were obtained. Collision control analysis of the traceable momentum transfer of scattered light was performed based on historical scattered light wave data to obtain the a priori momentum transfer matrix. The light intensity sensing timing strategy of the photoelectric sensor is obtained. The transition time window between the coarse and fine state subdomains is preset according to the light intensity sensing timing strategy. The scattering state observation model is constructed based on the wave equation of the real-time scattered light simulation field. Using the scattering state observation model, forward modeling is performed in the coarse-resolution situation domain to calculate the advancing trajectory of the signal intensity fitted along the transition time window, generating the coarse-resolution forward integral and simulated momentum transfer matrix of the global particle situation structure. The misalignment deviation between the simulated momentum transfer matrix and the prior momentum transfer matrix is ​​calculated using a hash operator to obtain the momentum transfer hash degree. The resolution cost function value is then constructed based on the momentum transfer hash degree constraint. Based on the prior momentum transfer matrix, a threshold for the resolution cost function is preset, and a predetermined propagation tangent vector of the fine-resolution situation domain is obtained. Then, the coarse-resolution forward integral interpolation is mapped to the fine-resolution situation domain along the predetermined propagation tangent vector for inversion propagation, so that the particle concentration scale is progressively advanced to the particle distribution scale, and the fine-resolution inverse integral is output. Repeat the steps of forward modeling and inverse propagation, combine coarse-resolution forward integration and fine-resolution backward integration to analyze and statistically analyze the scattering vector until the resolution cost function value is lower than the resolution cost function threshold. Based on the statistical results, determine the concentration and distribution of particles of different sizes in the unit volume of air in the laser detection area.

10. The method for detecting the filtration efficiency of air filtration equipment in livestock farms according to claim 9, characterized in that, The process involves obtaining historical scattered light wave data when a laser beam irradiates airborne particles within a certain size range under current meteorological conditions using empirical testing cases. Based on this historical scattered light wave data, collision control analysis is performed on the traceable momentum transfer of the scattered light to obtain the prior momentum transfer matrix. Specifically, this includes the following steps: The current meteorological environment data of the target farm is obtained through the Internet of Things, and the experience of detecting air particulate matter by laser equipment emitting laser beams from the detection incident direction according to preset laser parameters is obtained through big data. By retrieving and identifying current meteorological and environmental data using a pollutant knowledge graph, the potential particle size range of airborne particulate matter enriched within the target farm can be obtained. Based on empirical detection cases, historical incident wave vector variation data and corresponding incident wavelengths were retrieved when the laser beam hit different particle sizes within the potential particle size range in a continuous time sequence, as well as historical scattering wavelength variation data and corresponding scattering wavelengths after the collision. The jump deviation of the incident wavelength accompanying each historical incident wave vector change data with respect to the accompanying scattered wavelength of the corresponding historical scattered wavelength change data is calculated to obtain a series of transient wavelength differences in continuous time. The disorder degree transfer entropy function is determined based on the regular gradient between each transient wavelength difference. Extract the incident divergence velocity of the laser beam and the density of air particles when the historical incident wavelength change data and historical scattering wavelength change data were generated; A collision particle-light scattering logic model is established by introducing particle collision theory. Based on the law of conservation of momentum as the control premise, the incident divergence velocity and density are calculated to obtain the equilibrium distribution function. Based on the disorder degree transfer entropy function, the momentum transfer entropy constraint boundary of the collision particle-light scattering logic model is defined. The equilibrium distribution function is simultaneously imported to search for solutions in the non-equilibrium direction within the momentum transfer entropy constraint boundary. The collision entropy parameters are output, and the collision operator is constructed based on the collision entropy parameters. The collision operator is used to import the historical incident wave vector variation data and the corresponding historical scattering wavelength variation data into the collision particle-light scattering logic model to perform collision control, establish the momentum distribution trajectory, and construct the prior momentum transfer matrix of the laser beam after scattering from air particles of different sizes.

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