Comprehensive detection system and method for plate-type air filter under multi-working-condition simulation

Abstract

The invention discloses a comprehensive detection system and method for a plate-type air filter under multi-working-condition simulation. The system comprises a load unit, an imaging unit, a sensing unit and a data processing unit. The load unit comprises a temperature control cabin, a pollution generation module and an airflow generation module, and can construct a gradient temperature field, spray an optical marking particle mixture and provide airflows with different intensities; the imaging unit obtains light imaging data of the surface and section of the filter; the sensing unit acquires temperature, pressure, thermal imaging and particle distribution data through temperature, pressure, infrared and optical counter sensor groups; and the data processing unit receives the data, establishes a filtering attenuation model, and judges whether the filter meets a preset performance index or not. According to the system, by simulating complex working conditions, multi-dimensional data acquisition and comprehensive performance evaluation are realized, and the accuracy and reliability of detection of the plate-type air filter are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of filter detection, and in particular to a comprehensive detection system and method for a plate-type air filter under multi-working condition simulation. Background Art

[0002] In the field of air purification, plate-type air filters are widely used in industrial production, medical treatment, building ventilation and other scenarios due to their simple structure and low cost. However, existing filter detection technology has obvious limitations: First, traditional detection equipment is difficult to simulate actual complex working conditions, such as high temperature, high humidity, air flow impact of different intensities and mixed pollutant environments, resulting in a large deviation between the test results and actual performance; second, the detection method is single, mostly focusing on a single indicator (such as filtration efficiency), and lacks a comprehensive evaluation of the filter performance under the coupling of multiple factors such as temperature, pressure, and particle adsorption; third, the existing detection system cannot achieve refined detection of the internal structural performance of the filter, and it is difficult to obtain performance parameters of different sections and different positions. It is impossible to accurately locate weak links in performance and it is difficult to meet the needs of modern high-efficiency filter research and development and quality control. Summary of the Invention

[0003] In response to the above problems, the present invention provides a comprehensive detection system and method for plate-type air filters under multi-working condition simulation.

[0004] A first aspect of the present invention provides a comprehensive detection system for plate-type air filters under multi-operating condition simulation, comprising:

[0005] A load unit comprising a temperature control chamber, a pollution generation module and an airflow generation module;

[0006] The temperature control chamber is loaded with a filter to be tested at a preset load position and is configured to define a gradient temperature field around the load position;

[0007] The contamination generating module is configured to spray an optical marking particle mixture toward the loading location;

[0008] The airflow generating module is configured to provide airflows of different intensities from one side of the load position to the other opposite side through airflow channels of different sizes and positions;

[0009] an imaging unit configured to obtain optical imaging data of the surface and each cross-sectional position of the filter to be tested;

[0010] a sensing unit comprising a temperature sensor group, a pressure sensor group, an infrared sensor group and an optical counter;

[0011] The temperature sensor group is configured to obtain temperature data of the gradient temperature field and each preset position of the filter to be tested;

[0012] The pressure sensor group is configured to obtain pressure data at various preset positions on the surface of the filter to be tested and inside the filter;

[0013] The infrared sensor group is configured to obtain thermal imaging data of each detected surface of the filter to be tested;

[0014] The optical counter is configured to obtain the distribution of the optical marker particle mixture on each detected surface and each cross section of the filter to be tested;

[0015] A data processing unit is configured to receive the optical imaging data, the temperature data, the pressure data, the thermal imaging data and the optical marker particle mixture distribution data, establish a filter attenuation model of the filter to be tested, and determine whether the filter to be tested meets the preset performance indicators based on the filter attenuation model.

[0016] As a preferred embodiment, when the system is loaded with the filter to be tested, a load-bearing area and a test area are set along different sections of the filter to be tested;

[0017] In the same section, the load-bearing areas are arranged in a continuous and spaced manner or are arranged in a spaced manner after being divided into the test areas, and the arrangement intervals of the load-bearing areas are the same or different;

[0018] In different sections, the arrangement intervals and arrangement orders of the bearing areas are the same or different;

[0019] There is at least one set of two adjacent sections, wherein one section has at least one load-bearing area and the same position of the adjacent sections is set as the test area;

[0020] Each of the bearing areas is provided with a plurality of sensor installation positions that are evenly or non-intervally distributed, and each of the installation positions is arranged at the pore path of the filter to be tested, and each of the test areas is a preset area in the cross section.

[0021] As a preferred embodiment, the temperature sensor group includes a first temperature sensor and a second temperature sensor.

[0022] The first temperature sensor is configured to detect first temperature data of each preset position of the gradient temperature field;

[0023] The second temperature sensor is configured to detect second temperature data of each of the mounting positions of the carrying area.

[0024] As a preferred embodiment, the pressure sensor group includes a first pressure sensor and a second pressure sensor;

[0025] The first pressure sensor is configured to detect the airflow pressure on the surface of each tested surface of the filter to be tested;

[0026] The second pressure sensor is configured to detect the airflow pressure at each of the installation positions of the carrying area.

[0027] As a preferred embodiment, the data processing unit is configured to perform the following steps:

[0028] Taking the first ambient temperature as the ambient reference temperature, obtaining the actual temperature collected by the second temperature sensor in the load-bearing area, and calculating the temperature conductivity coefficient of the load-bearing area for different temperature layers;

[0029] The equivalent thermal conductivity of the test area is calculated based on the ratio of the area of ​​the sensor in the load-bearing area to the area of ​​the cross section where it is located, and the fitting temperature value of the test area in each temperature layer is calculated in reverse order;

[0030] Acquire two sets of thermal imaging data on two opposite sides of the filter to be tested, and calculate a first temperature difference between the two sets of thermal imaging data;

[0031] Calculate the average thermal conductivity of all load-bearing areas and test areas between the two sets of thermal imaging data and calculate the theoretical fitting temperature difference;

[0032] Comparing the first temperature difference with the theoretical fitting temperature difference to obtain a temperature fitting correction value;

[0033] Calculating the conduction area correction value of each test area at its location based on the temperature fitting correction value, calculating the temperature correction coefficient based on the conduction area correction value, and using the temperature correction coefficient as the error interval boundary of the fitting temperature value;

[0034] The optical imaging data of each section is obtained, and the fitting temperature value of the test area and the actual temperature of the bearing area in each section are written into the optical imaging data and dyed to obtain the thermal imaging data of each section.

[0035] As a preferred embodiment, the data processing unit is configured to perform the following steps:

[0036] The airflow pressure collected by the first pressure sensor is used as a reference airflow pressure;

[0037] The structural failure pore area of ​​the bearing zone is calculated to obtain the actual pore area, and the actual pore airflow pressure of the test area is calculated in reverse.

[0038] As a preferred embodiment, the filtration attenuation model includes a temperature attenuation model and an air pressure attenuation model.

[0039] A second aspect of the present invention provides a comprehensive detection method for a plate-type air filter under multiple working conditions, comprising the following steps:

[0040] The process includes the following steps:

[0041] S1. Setting a load-bearing area and a test area at different sections of the filter to be tested, wherein a pressure sensor and a temperature sensor are configured in the load-bearing area;

[0042] S2, heating the filter to be tested at different temperature gradients to obtain multiple temperature layers;

[0043] S3. Collect the ambient temperature of the filter to be tested and the actual temperature of the bearing area, and generate thermal imaging data of each section based on the tomographic optical imaging image and the thermal imaging images on both sides of the filter to be tested;

[0044] S4, spraying air flow toward the filter to be tested;

[0045] S5. Collect the reference airflow pressure on the surface of the filter to be tested, the pore airflow pressure in the bearing area, and the excitation conduction pressure, and calculate the actual pore airflow pressure in the test area;

[0046] S6. Spraying an airflow including the optical marker particle mixture onto the filter to be tested at a preset period, obtaining a tomographic optical imaging image, and calculating the distribution of the optical marker particle mixture in each cross section;

[0047] S7, execute S2 and then execute S6 to establish a temperature decay model;

[0048] S8, executing S4 and then executing S6 to establish a pressure decay model;

[0049] S9. Determine whether the filter to be tested meets preset performance indicators based on the temperature decay model and the pressure decay model.

[0050] As a preferred method, establishing the temperature attenuation model specifically includes the following steps:

[0051] Obtaining temperature data of the carrier area and optical marker particle distribution data, calculating the additional thermal resistance of the optical marker particle mixture, and calculating a corrected temperature conductivity coefficient based on the additional thermal resistance;

[0052] A correlation model between the temperature conductivity coefficient and the particle capture efficiency was established. The attenuation rate of the effect of the additional thermal resistance change on the actual capture capacity of the filter under test was calculated based on the temperature conductivity of the load-bearing area and the particle retention rate calculated by the optical counter.

[0053] Based on the additional thermal resistance of the load-bearing area, the equivalent thermal resistance of the test area is inferred according to the area weighting and its impact attenuation rate is calculated;

[0054] The correction interval of the decay rate is calculated by using the actual optical marker particle mixture obtained by the optical counter in the test area.

[0055] As a preferred method, the optical marker particle distribution data of the carrier area is obtained to calculate the pore effective flow area reduction rate;

[0056] The actual pore airflow pressure after particle attachment was obtained, and the linear relationship between the capture efficiency and the actual pore airflow pressure was calculated. This linear relationship was substituted into the optical marker particle distribution data obtained in the test area, and the linear relationship between the reduction rate of the effective pore flow area in the test area and the capture efficiency was deduced.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] Accurate simulation of multiple working conditions: Through the temperature control chamber, pollution generation module and airflow generation module in the load cell, it is possible to construct a gradient temperature field, spray a mixture of optically marked particles, and provide airflows of different intensities and directions, truly reproducing the working state of the filter in complex environments, and the test results are more in line with actual application scenarios.

[0059] Comprehensive performance in-depth detection: Combining the imaging unit, sensing unit and data processing unit, it can simultaneously obtain multi-dimensional data such as optical imaging, temperature, pressure, thermal imaging and particle distribution of the filter surface and cross-section, establish a comprehensive filtration attenuation model including temperature attenuation and air pressure attenuation, and realize comprehensive and in-depth analysis of filter performance.

[0060] Refined detection and positioning: By setting up load-bearing areas and test areas in different sections of the filter and scientifically arranging sensors, the system can accurately collect performance parameters at each location. It can not only quantify the performance differences in different areas, but also quickly locate the parts with performance attenuation, providing accurate data support for filter structure optimization and process improvement.

[0061] Intelligent data processing: The data processing unit performs complex calculations and model construction based on multi-source data. Through algorithms such as temperature fitting correction and pressure inverse calculation, it improves data accuracy and reliability, realizes intelligent evaluation and prediction of filter performance, and effectively improves detection efficiency and quality control level. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0063] Figure 1 It is a structural block diagram of the system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0065] The first aspect of the present invention provides a comprehensive detection system for plate-type air filters under multiple working condition simulations, such as Figure 1 Shown, including:

[0066] A load unit comprising a temperature control chamber, a pollution generation module and an airflow generation module;

[0067] The temperature control chamber is loaded with a filter to be tested at a preset load position and is configured to define a gradient temperature field around the load position;

[0068] The contamination generating module is configured to spray an optical marking particle mixture toward the loading location;

[0069] The airflow generating module is configured to provide airflows of different intensities from one side of the load position to the other opposite side through airflow channels of different sizes and positions;

[0070] an imaging unit configured to obtain optical imaging data of the surface and each cross-sectional position of the filter to be tested;

[0071] a sensing unit comprising a temperature sensor group, a pressure sensor group, an infrared sensor group and an optical counter;

[0072] The temperature sensor group is configured to obtain temperature data of the gradient temperature field and each preset position of the filter to be tested;

[0073] The pressure sensor group is configured to obtain pressure data at various preset positions on the surface of the filter to be tested and inside the filter;

[0074] The infrared sensor group is configured to obtain thermal imaging data of each detected surface of the filter to be tested;

[0075] The optical counter is configured to obtain the distribution of the optical marker particle mixture on each detected surface and each cross section of the filter to be tested;

[0076] A data processing unit is configured to receive the optical imaging data, the temperature data, the pressure data, the thermal imaging data and the optical marker particle mixture distribution data, establish a filter attenuation model of the filter to be tested, and determine whether the filter to be tested meets the preset performance indicators based on the filter attenuation model.

[0077] In the embodiments disclosed herein, the temperature control chamber, the pollution generating module and the airflow generating module are not described in detail. In this field, providing temperature control, airflow injection and marker injection for the tested object are all relatively mature technical means.

[0078] As a preferred embodiment, when the system is loaded with the filter to be tested, a load-bearing area and a test area are set along different sections of the filter to be tested;

[0079] In the same section, the load-bearing areas are arranged in a continuous and spaced manner or are arranged in a spaced manner after being divided into the test areas, and the arrangement intervals of the load-bearing areas are the same or different;

[0080] In different sections, the arrangement intervals and arrangement orders of the bearing areas are the same or different;

[0081] There is at least one set of two adjacent sections, wherein one section has at least one load-bearing area and the same position of the adjacent sections is set as the test area;

[0082] Each of the bearing areas is provided with a plurality of sensor installation positions that are evenly or non-intervally distributed, and each of the installation positions is arranged at the pore path of the filter to be tested, and each of the test areas is a preset area in the cross section.

[0083] As a preferred embodiment, the temperature sensor group includes a first temperature sensor and a second temperature sensor.

[0084] The first temperature sensor is configured to detect first temperature data of each preset position of the gradient temperature field;

[0085] The second temperature sensor is configured to detect second temperature data of each of the mounting positions of the carrying area.

[0086] As a preferred embodiment, the pressure sensor group includes a first pressure sensor and a second pressure sensor;

[0087] The first pressure sensor is configured to detect the airflow pressure on the surface of each tested surface of the filter to be tested;

[0088] The second pressure sensor is configured to detect the airflow pressure at each of the installation positions of the carrying area.

[0089] As a preferred embodiment, the data processing unit is configured to perform the following steps:

[0090] Taking the first ambient temperature as the ambient reference temperature, obtain the actual temperature collected by the second temperature sensor in the load-bearing area, and calculate the temperature conductivity coefficient of the load-bearing area for different temperature layers:

[0091] Temperature conductivity coefficient = (measured temperature of the load-bearing area - ambient reference temperature) ÷ vertical distance between layers

[0092] The equivalent thermal conductivity of the test area is calculated based on the ratio of the area of ​​the sensor in the load-bearing area to the area of ​​the cross section where it is located:

[0093] Equivalent thermal conductivity = average thermal conductivity × (1-sensor projected area / total cross-sectional area)

[0094] Reverse calculation of the fitting temperature values ​​of the test area in each temperature layer:

[0095] Fitting temperature value = ambient reference temperature + (temperature gradient × equivalent thermal conductivity)

[0096] Obtain two sets of thermal imaging data on two opposite sides of the filter to be tested, and calculate the first temperature difference between the two sets of thermal imaging data:

[0097] First temperature difference = |left thermal imaging temperature - right thermal imaging temperature|

[0098] Calculate the average thermal conductivity of all load-bearing areas and test areas between the two sets of thermal imaging data, and calculate the theoretical fitting temperature difference:

[0099] Theoretical fitting temperature difference = (heat flux density × filter thickness) ÷ average thermal conductivity of the entire area

[0100] The temperature fitting correction value is obtained by comparing the first temperature difference with the theoretical fitting temperature difference:

[0101] Temperature fitting correction value = |first temperature difference - theoretical fitting temperature difference|

[0102] According to the temperature fitting correction value, calculate the conduction area correction value of each test area at its location:

[0103] Conduction area correction value = temperature fitting correction value ÷ (material density × specific heat capacity)

[0104] Calculate the temperature correction factor based on the conduction area correction value:

[0105] Temperature correction coefficient = conduction area correction value ÷ theoretical area of ​​test area

[0106] Using the temperature correction coefficient as the error interval boundary of the fitting temperature value;

[0107] The optical imaging data of each section is obtained, and the fitting temperature value of the test area and the actual temperature of the bearing area in each section are written into the optical imaging data and dyed to obtain the thermal imaging data of each section.

[0108] As a preferred embodiment, the data processing unit is configured to perform the following steps:

[0109] The airflow pressure collected by the first pressure sensor is used as the reference airflow pressure;

[0110] Calculate the structural failure pore area of ​​the load-bearing zone:

[0111] Structural damage area = (1-actual pressure of the load-bearing area / reference airflow pressure) × total cross-sectional area

[0112] The actual pore area is obtained and the actual pore airflow pressure in the test area is calculated in reverse:

[0113] Actual pressure in the test area = reference airflow pressure × (theoretical area of ​​the test area) / (theoretical area of ​​the test area - structural damage area).

[0114] As a preferred embodiment, the filtration attenuation model includes a temperature attenuation model and an air pressure attenuation model.

[0115] A second aspect of the present invention provides a comprehensive detection method for a plate-type air filter under multiple working conditions, comprising the following steps:

[0116] S1. Setting a load-bearing area and a test area at different sections of the filter to be tested, wherein a pressure sensor and a temperature sensor are configured in the load-bearing area;

[0117] S2, heating the filter to be tested at different temperature gradients to obtain multiple temperature layers;

[0118] S3. Collect the ambient temperature of the filter to be tested and the actual temperature of the bearing area, and generate thermal imaging data of each section based on the tomographic optical imaging image and the thermal imaging images on both sides of the filter to be tested;

[0119] S4, spraying air flow toward the filter to be tested;

[0120] S5. Collect the reference airflow pressure on the surface of the filter to be tested, the pore airflow pressure in the bearing area, and the excitation conduction pressure, and calculate the actual pore airflow pressure in the test area;

[0121] S6. Spraying an airflow including the optical marker particle mixture onto the filter to be tested at a preset period, obtaining a tomographic optical imaging image, and calculating the distribution of the optical marker particle mixture in each cross section;

[0122] S7, execute S2 and then execute S6 to establish a temperature decay model;

[0123] S8, executing S4 and then executing S6 to establish a pressure decay model;

[0124] S9. Determine whether the filter to be tested meets preset performance indicators based on the temperature decay model and the pressure decay model.

[0125] As a preferred method, establishing a temperature attenuation model specifically includes the following steps:

[0126] Obtain the temperature data of the carrier area and the distribution data of the optical marking particles, and calculate the additional thermal resistance of the optical marking particle mixture:

[0127] Additional thermal resistance = (temperature rise rate in the particle-free area - temperature rise rate in the particle area) ÷ thermal conductivity of the material

[0128] Calculate the corrected temperature conductivity based on the additional thermal resistance:

[0129] Corrected conductivity = initial conductivity ÷ (1 + (additional thermal resistance × particle coverage))

[0130] A correlation model between the temperature conductivity coefficient and the particle capture efficiency is established. The attenuation rate of the effect of the additional thermal resistance change on the actual capture capacity of the filter to be tested is calculated based on the temperature conductivity coefficient of the load-bearing area and the particle retention rate statistically calculated by the optical counter:

[0131] Impact attenuation rate = |baseline capture efficiency - current capture efficiency| ÷ additional thermal resistance

[0132] Based on the additional thermal resistance of the load-bearing area, the equivalent thermal resistance of the test area is inferred according to the area weighting:

[0133] Equivalent thermal resistance = additional thermal resistance of the load-bearing area × (test area area / load-bearing area area) × temperature gradient factor

[0134] Calculate its impact attenuation rate;

[0135] The actual thermal resistance is obtained by the actual optical marker particle mixture obtained by the optical counter in the test area;

[0136] Calculate the correction interval of the attenuation rate, which is the difference between the actual optical marker particle mixture and the attenuation rate of the carrier area. Compare the correction interval with the error interval boundary of the fitting temperature value. If the difference between the two is within the preset ratio range, it is considered that the sensor's capture capacity attenuation rate does not affect the attenuation rate of the thermal resistance change on the actual capture capacity of the filter to be tested.

[0137] Otherwise, the data after multiple interval tests are counted, and the average difference between the correction interval after the additional thermal resistance changes and the error interval boundary of the fitting temperature value is calculated. The average value is used as the additional influence interval of the sensor to recalculate the influence attenuation rate.

[0138] Impact attenuation rate = |baseline capture efficiency - current capture efficiency| ÷ additional thermal resistance + K, where K is the additional impact range of the sensor.

[0139] As a preferred method, establishing the pressure decay model includes the following steps:

[0140] Obtain the optical marker particle distribution data of the load-bearing area and calculate the pore effective flow area reduction rate:

[0141] Area reduction rate = (original pore area - area after particle attachment) ÷ original pore area

[0142] Obtain the actual pore airflow pressure after particle attachment and calculate the linear relationship between capture efficiency and actual pore airflow pressure:

[0143] Linear slope = (original capture efficiency - current capture efficiency) ÷ (reference airflow pressure - actual pore pressure)

[0144] Substituting this linear relationship into the optical marker particle distribution data obtained in the test area, the linear relationship between the reduction rate of the effective flow area of ​​the pores in the test area and the capture efficiency was calculated:

[0145] Test area reduction rate = load-bearing area reduction rate × (test area particle density / load-bearing area particle density) × (test area average pore size / load-bearing area average pore size).

[0146] The embodiment of the present disclosure has tested the differences between the load-bearing area and the test area in detail. Since the data of the test area cannot be obtained directly through the sensor, the sensor itself is introduced as an influencing variable into the data calibration of the load-bearing area and the test area through the temperature data and airflow data of the load-bearing area. The thermal conductivity of the test area is calculated by the thermal conductivity obtained from the load-bearing area, and then the thermal imaging data of each cross-sectional position is generated. The thermal imaging data is the heat conduction condition inside the filter to be tested obtained from the actual test. Similarly, the embodiment of the present disclosure also replaces the complex variable with a single variable with the intervention of the sensor by taking the structural pore damage caused by the installation of the sensor as an influencing factor, so as to obtain the performance test condition of the filter to be tested under the single variable.

[0147] Among them, during the test, the data processing unit of the embodiment of the present disclosure calculates the data of the test area and then counts the optical marker particle mixture obtained by the embodiment of the present disclosure. Since the optical imaging cross-sectional diagram of the test area is known, it is possible to further establish the difference between the actual value and the estimated value of the attenuation rate of the effective flow area reduction rate of the pore, that is, the influence of the additional thermal resistance change on the actual capture capacity of the filter to be tested, and thus obtain correction and calibration.

[0148] Afterwards, the embodiment of the present disclosure obtains two sets of attenuation models regarding temperature and air pressure, and compares them with the preset performance indicators of the filter to be tested to determine whether the performance indicators are qualified.

[0149] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible variations. Unless explicitly required, individual components and functions are optional, and the order of operations may vary. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to also include plural forms. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of one or more associated listings. In addition, when used in this application, the term "comprise" and its variations "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups of these. In the absence of further restrictions, an element defined by the sentence "comprising a..." does not exclude the presence of other identical elements in the process, method or device that includes the element. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments can be referenced to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can be found in the description of the method part.

[0150] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these effects are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described effects, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0151] The flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to the embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for implementing the specified logical functions. In some alternative implementations, the functions marked in the boxes can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. In the descriptions corresponding to the flowcharts and block diagrams in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in an order different from that disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed in parallel, or they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by dedicated hardware-based devices that perform the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. The comprehensive detection system of plate-type air filter under multi-working condition simulation is characterized by: include: A load unit comprising a temperature control chamber, a pollution generation module and an airflow generation module; The temperature control chamber is loaded with a filter to be tested at a preset load position and is configured to define a gradient temperature field around the load position; The contamination generating module is configured to spray an optical marking particle mixture toward the loading location; The airflow generating module is configured to provide airflows of different intensities from one side of the load position to the other opposite side through airflow channels of different sizes and positions; an imaging unit configured to obtain optical imaging data of the surface and each cross-sectional position of the filter to be tested; a sensing unit comprising a temperature sensor group, a pressure sensor group, an infrared sensor group and an optical counter; The temperature sensor group is configured to obtain temperature data of the gradient temperature field and each preset position of the filter to be tested; The pressure sensor group is configured to obtain pressure data at various preset positions on the surface of the filter to be tested and inside the filter; The infrared sensor group is configured to obtain thermal imaging data of each detected surface of the filter to be tested; The optical counter is configured to obtain the distribution of the optical marker particle mixture on each detected surface and each cross section of the filter to be tested; A data processing unit is configured to receive the optical imaging data, the temperature data, the pressure data, the thermal imaging data and the optical marker particle mixture distribution data, establish a filter attenuation model of the filter to be tested, and determine whether the filter to be tested meets the preset performance indicators based on the filter attenuation model.

2. The plate-type air filter comprehensive detection system under multi-working condition simulation according to claim 1 is characterized in that: When the system is loaded with a filter to be tested, a load-bearing area and a test area are set along different sections of the filter to be tested; In the same section, the load-bearing areas are arranged in a continuous and spaced manner or are arranged in a spaced manner after being divided into the test areas, and the arrangement intervals of the load-bearing areas are the same or different; In different sections, the arrangement intervals and arrangement orders of the bearing areas are the same or different; There is at least one set of two adjacent sections, wherein one section has at least one load-bearing area and the same position of the adjacent sections is set as the test area; Each of the carrying areas is provided with a plurality of sensor installation positions that are evenly or non-intervally distributed, and each of the installation positions is arranged at the pore path of the filter to be tested, and each of the test areas is a preset area in the cross section.

3. The plate-type air filter comprehensive detection system under multi-working condition simulation according to claim 2 is characterized in that: The temperature sensor group includes a first temperature sensor and a second temperature sensor, The first temperature sensor is configured to detect first temperature data of each preset position of the gradient temperature field; The second temperature sensor is configured to detect second temperature data of each of the mounting positions of the carrying area.

4. The plate-type air filter comprehensive detection system under multi-operating condition simulation according to claim 2 is characterized in that: The pressure sensor group includes a first pressure sensor and a second pressure sensor; The first pressure sensor is configured to detect the airflow pressure on the surface of each tested surface of the filter to be tested; The second pressure sensor is configured to detect the airflow pressure at each of the installation positions of the carrying area.

5. The plate-type air filter comprehensive detection system under multi-operating condition simulation according to claim 3 is characterized in that: The data processing unit is configured to perform the following steps: Taking the first temperature data as the ambient reference temperature, obtaining the actual temperature collected by the second temperature sensor in the load-bearing area, and calculating the temperature conductivity coefficient of the load-bearing area for different temperature layers; The equivalent thermal conductivity of the test area is calculated based on the ratio of the area of ​​the sensor in the load-bearing area to the area of ​​the cross section where it is located, and the fitting temperature value of the test area in each temperature layer is calculated in reverse order; Acquire two sets of thermal imaging data on two opposite sides of the filter to be tested, and calculate a first temperature difference between the two sets of thermal imaging data; Calculate the average thermal conductivity of all load-bearing areas and test areas between the two sets of thermal imaging data and calculate the theoretical fitting temperature difference; Comparing the first temperature difference with the theoretical fitting temperature difference to obtain a temperature fitting correction value; Calculating the conduction area correction value of each test area at its location based on the temperature fitting correction value, calculating the temperature correction coefficient based on the conduction area correction value, and using the temperature correction coefficient as the error interval boundary of the fitting temperature value; The optical imaging data of each section is obtained, and the fitting temperature value of the test area and the actual temperature of the bearing area in each section are written into the optical imaging data and dyed to obtain the thermal imaging data of each section.

6. The plate-type air filter comprehensive detection system under multi-operating condition simulation according to claim 4 is characterized in that: The data processing unit is configured to perform the following steps: The airflow pressure collected by the first pressure sensor is used as a reference airflow pressure; The structural failure pore area of ​​the bearing zone is calculated to obtain the actual pore area, and the actual pore airflow pressure of the test area is calculated in reverse.

7. The plate-type air filter comprehensive detection system under multi-operating condition simulation according to claim 1 is characterized in that: The filtration attenuation model includes a temperature attenuation model and an air pressure attenuation model.

8. A comprehensive detection method for plate-type air filters under multiple working condition simulations is characterized by: The process includes the following steps: S1. Setting a load-bearing area and a test area at different sections of the filter to be tested, wherein a pressure sensor and a temperature sensor are configured in the load-bearing area; S2, heating the filter to be tested at different temperature gradients to obtain multiple temperature layers; S3. Collect the ambient temperature of the filter to be tested and the actual temperature of the bearing area, and generate thermal imaging data of each section based on the tomographic optical imaging image and the thermal imaging images on both sides of the filter to be tested; S4, spraying air flow toward the filter to be tested; S5. Collect the reference airflow pressure on the surface of the filter to be tested, the pore airflow pressure in the bearing area, and the excitation conduction pressure, and calculate the actual pore airflow pressure in the test area; S6. Spraying an airflow including the optical marker particle mixture onto the filter to be tested at a preset period, obtaining a tomographic optical imaging image, and calculating the distribution of the optical marker particle mixture in each cross section; S7, execute S2 and then execute S6 to establish a temperature decay model; S8, executing S4 and then executing S6 to establish a pressure decay model; S9. Determine whether the filter to be tested meets preset performance indicators based on the temperature decay model and the pressure decay model.

9. The method according to claim 8, characterized in that Establishing the temperature attenuation model specifically includes the following steps: Obtaining temperature data of the carrier area and optical marker particle distribution data, calculating the additional thermal resistance of the optical marker particle mixture, and calculating a corrected temperature conductivity coefficient based on the additional thermal resistance; A correlation model between the temperature conductivity coefficient and the particle capture efficiency was established. The attenuation rate of the effect of the additional thermal resistance change on the actual capture capacity of the filter under test was calculated based on the temperature conductivity of the load-bearing area and the particle retention rate calculated by the optical counter. Based on the additional thermal resistance of the load-bearing area, the equivalent thermal resistance of the test area is inferred according to the area weighting and its impact attenuation rate is calculated; The correction interval of the decay rate is calculated by using the actual optical marker particle mixture obtained by the optical counter in the test area.

10. The method according to claim 8, characterized in that Obtain the optical marker particle distribution data of the bearing area and calculate the pore effective flow area reduction rate; The actual pore airflow pressure after particle attachment was obtained, and the linear relationship between the capture efficiency and the actual pore airflow pressure was calculated. This linear relationship was substituted into the optical marker particle distribution data obtained in the test area, and the linear relationship between the reduction rate of the effective pore flow area in the test area and the capture efficiency was deduced.

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