Plate air filter comprehensive detection system and method under multi-working condition simulation

Abstract

The application discloses a kind of multi-working condition simulation under plate air filter comprehensive detection system and method.System includes load unit, imaging unit, sensing unit and data processing unit.Load unit contains temperature control cabin, pollution generation module and airflow generation module, can construct gradient temperature field, spray optical marker particle mixture, and provide different intensity airflow;Imaging unit obtains the optical imaging data of filter surface and cross section;Sensing unit acquires temperature, pressure, infrared and optical counter sensor group, collects temperature, pressure, thermal imaging and particle distribution data;Data processing unit receives the above data, establishes filter attenuation model, judges whether filter meets preset performance index.The system realizes multidimensional data acquisition and comprehensive performance evaluation by simulating complex working conditions, effectively improves the accuracy and reliability of plate air filter detection.

Description

TECHNICAL FIELD

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

[0002] In the field of air purification, plate air filters are widely used in industrial production, medical treatment, building ventilation and other scenarios due to their simple structure and low cost. However, the existing filter detection technology has obvious limitations: firstly, traditional detection equipment cannot simulate actual complex working conditions such as high temperature, high humidity, different intensity airflow impact and mixed pollutant environment, resulting in a large deviation between the detection results and the actual use performance; secondly, the detection means is single, mainly focusing on a single index (such as filtration efficiency), and lacking comprehensive evaluation of the performance of the filter under the coupling action of multiple factors such as temperature, pressure and particle adsorption; thirdly, the existing detection system cannot realize fine detection of the internal structure performance of the filter, and cannot obtain performance parameters at different sections and different positions, cannot accurately locate the weak links of performance, and cannot meet the needs of modern efficient filter research and quality control. SUMMARY

[0003] In view of the above problems, the present application provides a plate air filter comprehensive detection system and method under multi-working condition simulation.

[0004] In a first aspect of the present application, a plate air filter comprehensive detection system under multi-working condition simulation is provided, comprising:

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

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

[0007] The pollution generation module is configured to spray a mixture of optical marker particles to the load position;

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

[0009] An imaging unit configured to acquire optical imaging data of the surface and each section 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 acquire 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 acquire pressure data of each preset position on the surface and inside of the filter to be tested;

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

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

[0015] The 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 a preset performance index according to the filter attenuation model.

[0016] As a preferred mode, when the system is loaded with the filter to be tested, a bearing area and a test area are arranged along different cross sections of the filter to be tested;

[0017] In the same cross section, the bearing area is arranged in continuous intervals or multiple intervals after being divided by the test area, and the arrangement intervals of the bearing areas are the same or different;

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

[0019] At least one set of two adjacent cross sections, and the same position of one cross section of at least one bearing area in one cross section is arranged as a test area;

[0020] Each of the bearing areas is provided with multiple sensor mounting positions arranged in uniform intervals or non-intervals, and each of the mounting positions is arranged at a pore path of the filter to be tested, and each of the test areas is a preset area region in the cross section.

[0021] As a preferred mode, 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 mounting position of the bearing area.

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

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

[0026] The second pressure sensor is configured to detect the airflow pressure of each mounting position of the bearing area.

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

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

[0029] According to the ratio of the sensor area of the bearing area to the area of the section where it is located, the equivalent thermal conductivity of the test area is calculated, and the fitting temperature value of the test area at each temperature layer is calculated by back calculation;

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

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

[0032] Compare the difference between the first temperature difference and the theoretical fitting temperature difference to obtain a temperature fitting correction value;

[0033] According to the temperature fitting correction value, calculate the conduction area correction value of each test area at its position, and calculate the temperature correction coefficient according to the conduction area correction value, and take the temperature correction coefficient as the error interval boundary of the fitting temperature value;

[0034] Obtain the light imaging data of each section, write the fitting temperature value of the test area and the actual temperature of the bearing area in each section into the light imaging data after dyeing, and obtain the thermal imaging data of each section.

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

[0036] Taking the airflow pressure collected by the first pressure sensor as the reference airflow pressure;

[0037] Calculate the structural damage pore area of the bearing area, obtain the actual pore area, and back calculate the actual pore airflow pressure of the test area.

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

[0039] The second aspect of the present application provides a multi-working-condition plate air filter comprehensive detection method, which comprises the following steps:

[0040] Comprising the following steps:

[0041] S1, a bearing area and a test area are arranged at different sections of the filter to be tested, and a pressure sensor and a temperature sensor are arranged in the bearing area;

[0042] S2, the filter to be tested is heated to obtain a plurality of temperature layers at different temperature gradients;

[0043] S3, the ambient temperature of the filter to be tested and the actual temperature of the bearing area are collected, and the thermal imaging data of each section is generated according to the tomographic light imaging of the filter to be tested and the thermal imaging of both sides;

[0044] S4, a gas flow is sprayed to the filter to be tested;

[0045] S5, the reference gas flow pressure of the surface of the filter to be tested, the pore gas flow pressure of the bearing area and the excitation conduction pressure are collected, and the actual pore gas flow pressure of the test area is calculated;

[0046] S6, a gas flow including an optical marker particle mixture is sprayed to the filter to be tested at a predetermined period, a tomographic light imaging is obtained, and the distribution of the optical marker particle mixture of each section is calculated;

[0047] S7, S6 is executed after S2, and a temperature decay model is established;

[0048] S8, S6 is executed after S4, and a gas pressure decay model is established;

[0049] S9, whether the filter to be tested meets a preset performance index is judged according to the temperature decay model and the gas pressure decay model.

[0050] As a preferred mode, the temperature decay model is established and includes the following steps:

[0051] The temperature data and the optical marker particle distribution data of the bearing area are obtained, the additional thermal resistance of the optical marker particle mixture is calculated, and the corrected temperature conduction coefficient is calculated according to the additional thermal resistance;

[0052] A correlation model of the temperature conduction coefficient and the particle capture efficiency is established, the influence decay rate of the change of the additional thermal resistance on the actual capture capacity of the filter to be tested is calculated according to the temperature conduction coefficient of the bearing area and the particle retention rate counted by the optical counter;

[0053] Based on the additional thermal resistance of the bearing area, the equivalent thermal resistance of the test area is inversely calculated according to the area weighting, and the influence decay rate is calculated;

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

[0055] As a preferred mode, the optical marker particle distribution data of the bearing area is obtained, and the reduction rate of the effective flow area of the pores is calculated.

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

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

[0058] Multi-condition precise simulation: through the temperature control cabin, pollution generation module and air flow generation module in the load unit, a gradient temperature field can be constructed, an optical marker particle mixture can be sprayed, and air flows with different intensities and flow directions can be provided, so that the working state of the filter in a complex environment is truly restored, and the detection result is more in line with the actual application scene.

[0059] Comprehensive performance deep detection: combined with the imaging unit, the sensing unit and the data processing unit, multi-dimensional data such as light imaging, temperature, pressure, thermal imaging and particle distribution on the surface and section of the filter can be synchronously obtained, a comprehensive filtration attenuation model including temperature attenuation and air pressure attenuation is established, and comprehensive and in-depth analysis of the performance of the filter is realized.

[0060] Fine detection and positioning: the system can accurately collect performance parameters at each position by setting bearing areas and test areas at different sections of the filter and scientifically arranging sensors, not only can the performance differences of different regions be quantified, but also the performance attenuation position can be quickly located, thereby providing accurate data support for filter structure optimization and process improvement.

[0061] Data processing intelligentization: the data processing unit performs complex calculation and model construction based on multi-source data, improves the data accuracy and reliability through temperature fitting correction, pressure back calculation and other algorithms, realizes intelligent evaluation and prediction of the performance of the filter, and effectively improves the detection efficiency and quality control level. BRIEF DESCRIPTION OF DRAWINGS

[0062] The present application is further illustrated by the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the following drawings.

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

[0064] Clearly, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present application.

[0065] In a first aspect of the present application, a comprehensive detection system for panel air filters under multiple working conditions is provided, as shown in the accompanying drawings, comprising: Figure 1

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

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

[0068] The pollution generation module is configured to spray a mixture of optical marker particles to the load position;

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

[0070] An imaging unit configured to acquire optical imaging data of the surface and cross-sectional positions 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 acquire 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 acquire pressure data of each preset position on the surface and inside the filter to be tested;

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

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

[0076] A data processing unit configured to receive the optical imaging data, the temperature data, the pressure data, the thermal imaging data and the distribution data of the mixture of optical marker particles, establish a filter attenuation model of the filter to be tested, and determine whether the filter to be tested meets a preset performance index according to the filter attenuation model. ​

[0077] In the embodiments of the present disclosure, the temperature control cabin, the pollution generation module and the air flow generation module are not described in detail, and providing temperature control, air flow injection and marker injection for the tested object are mature technical means in the art.

[0078] As a preferred mode, the system is loaded with the filter to be tested, and the bearing area and the test area are arranged along different sections of the filter to be tested.

[0079] In the same section, the bearing area is arranged in continuous intervals or multiple intervals after being divided by the test area, and the arrangement intervals of the bearing area are the same or different.

[0080] In different sections, the arrangement intervals and the arrangement order of the bearing area are the same or different.

[0081] At least two adjacent sections have at least one bearing area, and the same position of the adjacent section of the bearing area is arranged as a test area.

[0082] Each of the bearing areas is provided with multiple sensor mounting positions arranged in uniform intervals or non-intervals, and each of the mounting positions is arranged at the pore path of the filter to be tested, and each of the test areas is a pre-set area in the section.

[0083] As a preferred mode, 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 pre-set position of the gradient temperature field.

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

[0086] As a preferred mode, the pressure sensor group includes a first pressure sensor and a second pressure sensor.

[0087] The first pressure sensor is configured to detect the air flow pressure of each detected surface of the filter to be tested.

[0088] The second pressure sensor is configured to detect the air flow pressure of each mounting position of the bearing area.

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

[0090] Taking the first environmental temperature as the environmental reference temperature, the actual temperature collected by the second temperature sensor in the bearing area is obtained, and the temperature conduction coefficient of the bearing area is calculated for different temperature layers.

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

[0092] According to the proportion of the area of the bearing area sensor to the area of the section where it is located, the equivalent thermal conductivity coefficient of the test area is calculated:

[0093] Equivalent thermal conductivity coefficient = average thermal conductivity coefficient × (1 - projected area of sensor / total area of section)

[0094] The fitting temperature value of the test area at each temperature layer is calculated by back calculation:

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

[0096] Two sets of thermal imaging data of the two opposite sides of the filter to be tested are obtained, and a first temperature difference between the two sets of thermal imaging data is calculated:

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

[0098] The average thermal conductivity coefficient of all bearing areas and test areas between the two sets of thermal imaging data is calculated, and a theoretical fitting temperature difference is calculated:

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

[0100] The difference between the first temperature difference and the theoretical fitting temperature difference is compared to obtain a temperature fitting correction value:

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

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

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

[0104] A temperature correction coefficient is calculated according to the conduction area correction value:

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

[0106] The temperature correction coefficient is taken as the error interval boundary of the fitting temperature value;

[0107] Obtain the light imaging data of each section, write the fitting temperature value of the test area and the actual temperature of the bearing area in each section into the light imaging data after dyeing to obtain the thermal imaging data of each section.

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

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

[0110] Calculate the structural damage pore area of the bearing area:

[0111] Structural damage area = (1 - measured pressure of bearing area / reference airflow pressure) x total area of cross section

[0112] Get the actual pore area, and calculate the actual pore airflow pressure of the test area by back calculation:

[0113] Test area actual pressure = reference airflow pressure x (test area theoretical area) / (test area theoretical area-structural damage area).

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

[0115] In a second aspect of the present application, a multi-condition plate air filter comprehensive detection method is provided, comprising the following steps:

[0116] S1, setting a bearing area and a test area at different cross sections of the filter to be tested, and arranging pressure sensors and temperature sensors in the bearing area;

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

[0118] S3, collecting the ambient temperature of the filter to be tested and the actual temperature of the bearing area, and generating thermal imaging data of each cross section according to the tomographic light imaging diagram and the thermal imaging diagram of both sides of the filter to be tested;

[0119] S4, spraying airflow to the filter to be tested;

[0120] S5, collecting the reference airflow pressure of the surface of the filter to be tested, the pore airflow pressure of the bearing area, and the excitation conduction pressure, and calculating the actual pore airflow pressure of the test area;

[0121] S6, spraying airflow including optical marker particle mixture to the filter to be tested at a preset period, obtaining a tomographic light imaging diagram, and calculating the distribution of the optical marker particle mixture of each cross section;

[0122] S7, performing S6 after performing S2, and establishing a temperature attenuation model;

[0123] S8, performing S6 after performing S4, and establishing an air pressure attenuation model;

[0124] S9, judging whether the filter to be tested meets the preset performance index according to the temperature attenuation model and the air pressure attenuation model.

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

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

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

[0128] Calculate the corrected temperature conduction coefficient according to the additional thermal resistance:

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

[0130] Establish a correlation model between the temperature conduction coefficient and the particle capture efficiency, and calculate the influence attenuation rate of the additional thermal resistance change on the actual capture capacity of the filter to be tested according to the temperature conduction coefficient of the bearing area and the particle retention rate counted by the optical counter:

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

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

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

[0134] Calculate its influence attenuation rate;

[0135] The actual thermal resistance is obtained by the actual optical marker particle mixture obtained by the optical counter through 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 influence attenuation rate of the bearing area. Compare the correction interval with the error interval boundary of the fitted temperature value. If the difference is within a preset proportion range, it is considered that the capture capacity attenuation rate of the sensor does not affect the influence attenuation rate of the actual capture capacity of the filter to be tested by the change of thermal resistance.

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

[0138] Influence attenuation rate = |baseline capture efficiency - current capture efficiency| ÷ additional thermal resistance + K, K is the additional influence interval of the sensor.

[0139] As a preferred mode, the air pressure decay model includes the following steps:

[0140] Obtain optical labeled particle distribution data of the bearing area, calculate the reduction rate of the effective flow area of the pore:

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

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

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

[0144] Substitute the linear relationship into the optical labeled particle distribution data obtained in the test area to calculate the linear relationship between the reduction rate of the effective flow area of the pore in the test area and the capture efficiency:

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

[0146] The embodiment of the present disclosure tests the differences between the bearing area and the test area in detail. Since the data of the test area cannot be directly obtained by the sensor, the sensor itself is introduced into the data calibration of the bearing area and the test area as an influencing variable through the temperature data and the gas flow data of the bearing area. The thermal conductivity coefficient obtained in the bearing area is used to calculate the thermal conductivity coefficient of the test area, and then the thermal imaging data of each cross-sectional position is generated. The thermal imaging data is the actual test obtained thermal conduction inside the filter to be tested. Similarly, the structural pore damage caused by the installation of the sensor is taken as an influencing factor, and the complex variable is replaced by a single variable of the sensor to obtain the performance test of the filter to be tested under a single variable.

[0147] Among them, the data processing unit of the embodiment of the present disclosure counts the optical labeled particle mixture obtained by the embodiment of the present disclosure after calculating the data of the test area during testing. Since the light imaging cross-sectional diagram of the test area is known, the difference between the actual value and the calculated value of the influence attenuation rate of the reduction rate of the effective flow area of the pore, i.e. the additional thermal resistance change, on the actual capture capacity of the filter to be tested can be further established, and then the correction and calibration are obtained.

[0148] After that, the embodiment of the present disclosure obtains two groups of attenuation models about temperature and gas 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 drawings are illustrative of embodiments of the present disclosure and are not intended to be limiting. Other embodiments can include structural, logical, electrical, process, and other changes. Embodiments are merely representative of possible variations. Individual components and functions are optional unless explicitly required, and the order of operations can be varied. Portions and features of some embodiments can be included in, or substituted for, those of other embodiments. Also, words used in this document and claims are words of description, not limitation. As used in the description and claims herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and / or" as used herein refers to any one or more of the associated listed items, optionally including zero of the associated listed items. Additionally, the term "comprising" and variations thereof as used herein are intended to be open-ended terms that specify the presence of the stated features, elements, actions, operations, components, and / or members, but do not preclude the presence or addition of one or more other features, elements, actions, operations, components, members, and / or groups thereof. The term "consisting of" as used herein is intended to be a closed term that specifies the presence of the stated features, elements, actions, operations, components, and / or members, and does not preclude the presence or addition of one or more other features, elements, actions, operations, components, members, and / or groups thereof. The term "include," and derivations thereof, as used in this document, means the inclusion of one or more features, elements, actions, operations, components, and / or members, but not the exclusion of any other features, elements, actions, operations, components, members, and / or groups thereof. The term "include" does not mean "consist only of" or "consist exclusively of," unless clearly indicated otherwise by the context. The term "exemplary" is used in the sense of serving as an example, instance, or illustration. Any implementation described herein as exemplary is not necessarily to be construed as preferred or advantageous over other implementations. Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that, together, the steps of any method disclosure as described herein can include computer program command steps and / or computer-implemented steps. The term "coupled" as used herein means the joining of two members together such that the members together can function as a single unit.

[0150] Those skilled in the art can understand that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on specific application and design constraints of the technical solutions. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the embodiments of the present disclosure. It is clear to those skilled in the art that, for the convenience and brevity of description, the specific working processes of the above-described devices, apparatuses, and units can refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.

[0151] The diagrams of the flow and block diagrams show the architecture, functionality, and operation of possible implementations of apparatuses, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flow and block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logic functions. In some alternative implementations, the actions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession can in fact be executed substantially concurrently or the actions of a block can be performed in the reverse order, depending upon the functionality involved. These diagrams of the flow and block diagrams are also intended to include any connected data storage and data processing artifacts and structures that can affect the operation of the subject matter described. If warranted, specific data storage artifacts can be shown in a block diagram and / or a flow diagram and referred to in the accompanying text. Conversely, no indication of such data storage artifacts should not be construed to imply that such data storage is not a possible implementation. In some alternative implementations, the actions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession can in fact be executed substantially concurrently or the actions of a block can be performed in the reverse order, depending upon the functionality involved. The description of a flow or block diagram of a process, method, or computer program product should not be construed to mean that all of the actions or steps are required to be performed in the order presented, nor that they are performed at all.

Claims

1. A comprehensive detection system for panel air filter under multi-condition simulation, characterized in that, The system comprises: a load unit comprising a temperature control cabin, a pollution generation module and an air flow generation module; the temperature control cabin is configured to load a filter to be tested at a preset load position and to define a gradient temperature field around the load position; the pollution generation module is configured to spray a mixture of optical marker particles to the load position; the air flow generation module is configured to provide air flows with different intensities to the load position from one side to the opposite side through air flow channels with different sizes and positions; an imaging unit configured to acquire optical imaging data of the surface and cross-sectional positions 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 acquire temperature data of the gradient temperature field and each preset position of the filter to be tested; the pressure sensor group is configured to acquire pressure data of each preset position on the surface and inside the filter to be tested; the infrared sensor group is configured to acquire thermal imaging data of each detected surface of the filter to be tested; the optical counter is configured to acquire the distribution of the mixture of optical marker particles on each detected surface and cross section of the filter to be tested; a data processing unit configured to receive the optical imaging data, the temperature data, the pressure data, the thermal imaging data and the distribution data of the mixture of optical marker particles, establish a filter attenuation model of the filter to be tested, and determine whether the filter to be tested meets a preset performance index according to the filter attenuation model; when the system loads the filter to be tested, a bearing area and a test area are arranged along different cross sections of the filter to be tested; in the same cross section, the bearing area is arranged in continuous intervals or multiple intervals after being divided by the test area, and the arrangement intervals of the bearing areas are the same or different; in different cross sections, the arrangement intervals and arrangement sequences of the bearing areas are the same or different; at least one set of two adjacent cross sections, and the same position of one cross section of at least one bearing area in one cross section is arranged as a test area in the adjacent cross section; each bearing area is provided with multiple sensor mounting positions arranged at uniform intervals or non-intervals, and each mounting position is arranged at a pore path of the filter to be tested, and each test area is a preset area region in the cross section; the temperature sensor group comprises 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 sensor position of the bearing area; the data processing unit is configured to perform the following steps: taking the first temperature data as an environmental reference temperature, acquiring actual temperatures collected by the second temperature sensor in the bearing area, calculating temperature conduction coefficients of the bearing area for different temperature layers; calculating an equivalent thermal conductivity coefficient of the test area according to the area ratio of the sensor area of the bearing area to the area of the cross section, and inversely calculating fitting temperature values of the test area in each temperature layer; acquiring two sets of thermal imaging data of two opposite sides of the filter to be tested, calculating a first temperature difference of the two sets of thermal imaging data; Calculate the average thermal conductivity of the whole bearing area and the test area between the two groups of thermal imaging data, and calculate the theoretical fitting temperature difference; Compare the difference between the first temperature difference and the theoretical fitting temperature difference to obtain the temperature fitting correction value; According to the temperature fitting correction value, calculate the conduction area correction value of each test area at its position, and calculate the temperature correction coefficient according to the conduction area correction value, and take the temperature correction coefficient as the error interval boundary of the fitting temperature value; Obtain the optical imaging data of each section, write the fitting temperature value of each test area in each section and the actual temperature of the bearing area into the optical imaging data after dyeing, and obtain the thermal imaging data of each section.

2. The multi-condition simulation based panel air filter comprehensive detection system according to claim 1, wherein, 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 of each detected surface of the filter to be tested; The second pressure sensor is configured to detect the airflow pressure of each sensor position of the bearing area.

3. The multi-condition simulated panel air filter comprehensive detection system according to claim 2, characterized in that, The data processing unit is configured to perform the following steps: Taking the airflow pressure collected by the first pressure sensor as the reference airflow pressure; Calculate the structure damage pore area of the bearing area to obtain the actual pore area, and inversely calculate the actual pore airflow pressure of the test area.

4. The multi-condition simulated panel air filter comprehensive testing system of claim 1, wherein, The filter attenuation model includes a temperature attenuation model and an air pressure attenuation model.

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