A method and system for detecting the purification and adsorption efficiency of a smoke machine

Abstract

The present application relates to the technical field of smoke machine monitoring, and particularly relates to a smoke machine purification adsorption efficiency detection method and system. The method comprises: obtaining each temperature difference data between the air inlet side and the air outlet side; identifying transient convection disturbance data, oil fume accumulation adsorption data and attached layer heat storage data based on each temperature difference data and a preset oil fume accumulation characteristic; separating the transient convection disturbance data from the oil fume accumulation adsorption data based on the transient convection disturbance data and the oil fume accumulation adsorption data, and obtaining the oil fume accumulation adsorption data in a period after separation; determining an adsorption heat effect growth rate based on the oil fume accumulation adsorption data; and obtaining a detection result of the smoke machine purification adsorption efficiency based on the adsorption heat effect growth rate, the attached layer heat storage data and a preset growth threshold. The present application aims to solve the problem that the prior art cannot accurately separate the heat effect generated by oil fume adsorption behavior, resulting in low detection accuracy of the smoke machine purification adsorption efficiency.

Description

Technical Field

[0001] This invention relates to the field of flue gas monitoring technology, specifically to a method and system for detecting the purification and adsorption efficiency of flue gas machines. Background Technology

[0002] The adsorption efficiency of filters in fume purification equipment is a key indicator for evaluating equipment performance. Current technology indirectly assesses the thermal effect of fume particles adsorbing onto the filter surface by monitoring the temperature difference between the airflow before and after the filter, thus evaluating its adsorption performance. However, with prolonged use, oil gradually accumulates on the filter surface, significantly altering its physical properties. The oil film formed on the filter increases thermal conductivity resistance, causing delays and attenuation in the transfer of adsorbed heat to the temperature sensing element, resulting in a measured temperature difference signal lower than the actual adsorption heat effect. Furthermore, continuous oil accumulation can clog some of the filter's pores, disrupting the original uniform airflow distribution and triggering uneven convective heat transfer, thereby generating a non-adsorption-related temperature difference signal before and after the filter.

[0003] Furthermore, under high temperatures, the oil adhering to the filter screen may harden or even carbonize, forming a carbonized layer with heat storage capacity. This carbonized layer slowly absorbs and releases heat during operation, further introducing additional temperature difference signals. Therefore, the temperature difference signal measured by existing methods includes a composite result of multiple physical processes, such as the attenuation of adsorption heat conduction caused by oil film thermal resistance, the non-adsorption convective heat transfer temperature difference generated by airflow disturbance, and the heat storage and release effect of the carbonized layer. This makes it difficult for existing technologies to accurately separate and identify the thermal effects generated by the current oil fume adsorption behavior, resulting in low detection accuracy and consequently, an inability to accurately detect the purification and adsorption efficiency of the range hood, leading to misjudgments of the filter screen's working status. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for detecting the adsorption efficiency of a range hood, which solves the problem that existing technologies are unable to accurately separate the thermal effect generated by the adsorption behavior of oil fumes, resulting in low accuracy in detecting the adsorption efficiency of range hoods.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for detecting the purification and adsorption efficiency of a range hood, comprising:

[0006] Acquire data on the temperature difference between the airflow inlet and outlet sides at the filter in the range hood over a period of time;

[0007] Based on each of the temperature difference data and the preset oil fume accumulation characteristics, transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the attachment layer are identified.

[0008] Based on transient convection disturbance data and oil fume cumulative adsorption data, transient convection disturbance data is separated from the oil fume cumulative adsorption data to obtain oil fume cumulative adsorption data over a period of time after separation.

[0009] Based on the cumulative adsorption data of oil fumes over a period of time after separation, the growth rate of the adsorption heat effect is determined.

[0010] Based on the adsorption heat effect growth rate, the heat storage and release data of the attachment layer, and the preset growth threshold, the test results of the smoke machine's purification adsorption efficiency are obtained.

[0011] Furthermore, this application also proposes the following steps for identifying transient convection disturbance data, oil fume accumulation and adsorption data, and heat storage and release data of the adhering layer based on each of the aforementioned temperature difference data and preset oil fume accumulation characteristics:

[0012] Based on each of the temperature difference data, after confirming the temporal morphological characteristics of each of the temperature difference data, transient convection disturbance data and deposited layer heat storage and release data are identified.

[0013] Based on each of the temperature difference data, temperature trend characteristics are determined;

[0014] The temperature trend characteristics are compared with the preset oil fume accumulation characteristics to determine the oil fume change trend parameters;

[0015] Based on the oil fume change trend parameters and each of the temperature difference data, the data during the period of increased fan speed is identified as the cumulative oil fume adsorption data.

[0016] Furthermore, this application also proposes a step for obtaining temperature difference data between the airflow inlet side and the airflow outlet side at the filter in the range hood over a period of time, including:

[0017] Acquire initial data for each temperature difference between the airflow inlet and outlet sides at the filter in the range hood over a period of time;

[0018] The initial data for each temperature difference is smoothed to obtain the data for each temperature difference.

[0019] Furthermore, this application also proposes that after obtaining the detection result of the smoke hood's purification adsorption efficiency based on the adsorption heat effect growth rate, the heat storage and release data of the adhesion layer, and a preset growth threshold, the following steps are also included:

[0020] The test results are sent to the central management system;

[0021] In response to the maintenance plan output by the central management system based on the test results, the maintenance priority and maintenance resource information for each filter screen are determined;

[0022] Based on the maintenance priority and the maintenance resource information, a global maintenance plan is generated for each filter.

[0023] Furthermore, this application also proposes a step for separating transient convection disturbance data from oil fume cumulative adsorption data based on transient convection disturbance data and oil fume cumulative adsorption data, to obtain oil fume cumulative adsorption data over a period of time after separation, including:

[0024] Based on transient convection disturbance data and oil fume cumulative adsorption data, transient convection disturbance data is separated from the oil fume cumulative adsorption data to obtain initial oil fume cumulative adsorption data;

[0025] Based on the cumulative adsorption data of the oil fumes, the adsorption heat conduction attenuation parameters were confirmed;

[0026] The initial adsorption data of oil fume accumulation is corrected using the adsorption heat conduction attenuation parameter to obtain the cumulative adsorption data of oil fume over a period of time after separation.

[0027] Furthermore, this application also proposes a step for confirming the adsorption heat conduction attenuation parameter based on the accumulated oil fume adsorption data, including:

[0028] Based on the accumulated adsorption data of oil fumes, the rate characteristics and attenuation base of heat release from accumulated oil fumes are extracted;

[0029] Based on the characteristics of the heat release rate, the degree of carbonization of the accumulated oil is determined;

[0030] The attenuation baseline was corrected using the degree of carbonization to confirm the adsorption heat conduction attenuation parameters.

[0031] Furthermore, this application also proposes a step for determining the degree of carbonization of oil accumulation based on the rate characteristics of heat release, including:

[0032] Based on the heat release rate characteristics, identify the local regions in the heat release rate characteristics that indicate the highest degree of carbonization;

[0033] Time series analysis was performed on the local region with the highest degree of carbonization to obtain the heat decay curve;

[0034] The degree of carbonization of the accumulated oil is confirmed based on the characteristic parameters of the heat decay curve.

[0035] Furthermore, this application also proposes that the step of determining the growth rate of the adsorption heat effect based on the cumulative adsorption data of oil fumes over a period of time after separation includes:

[0036] Quantitative analysis was performed on the cumulative adsorption data of oil fumes over a period of time after separation to obtain quantitative data, trend indicators, and oil accumulation coefficient.

[0037] Based on the aforementioned trend indicators and oil accumulation coefficient, the overall adsorption efficiency coefficient of the filter screen is determined.

[0038] Based on the overall adsorption efficiency coefficient and quantitative data of the filter, the growth rate of the adsorption heat effect is determined.

[0039] Furthermore, this application also proposes the comprehensive adsorption efficiency coefficient and quantitative data based on the filter, and the steps for determining the growth rate of the adsorption heat effect include:

[0040] After confirming the rate of change of the quantified data, the cooking state intensity is obtained;

[0041] Based on the overall adsorption efficiency coefficient of the filter, the degradation index of the filter is identified.

[0042] Based on the cooking state intensity and filter degradation index, the contribution parameters of the comprehensive adsorption efficiency score are determined;

[0043] The growth rate of the adsorption heat effect is determined based on the overall adsorption efficiency coefficient of the filter, the quantitative data, and the contribution parameters.

[0044] This application also provides a system for detecting the adsorption efficiency of a range hood, the system comprising:

[0045] The acquisition module is used to acquire data on the temperature difference between the air inlet and outlet sides of the filter in the range hood over a period of time.

[0046] The identification module is used to identify transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the adhering layer based on each of the temperature difference data and preset oil fume accumulation characteristics.

[0047] The separation module is used to separate transient convection disturbance data from the cumulative oil fume adsorption data based on transient convection disturbance data and cumulative oil fume adsorption data, so as to obtain the cumulative oil fume adsorption data for a period of time after separation.

[0048] The determination module is used to determine the growth rate of the adsorption heat effect based on the cumulative adsorption data of oil fumes over a period of time after separation.

[0049] The monitoring module is used to obtain the detection results of the smoke machine's purification adsorption efficiency based on the growth rate of the adsorption heat effect, the heat storage and release data of the attachment layer, and the preset growth threshold.

[0050] Compared with the prior art, the method and system for detecting the adsorption efficiency of a smoke hood in this invention have the following advantages:

[0051] This invention continuously acquires temperature difference data between the airflow inlet and outlet sides of the filter in a range hood over a period of time. Based on each acquired temperature difference data, and combined with preset oil fume accumulation characteristics, transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the adhering layer can be identified. Transient convection disturbance data is separated from the oil fume accumulation adsorption data based on the transient convection disturbance data and the oil fume accumulation adsorption data. The oil fume accumulation adsorption data obtained over a period of time after separation reflects the thermal effect generated during the oil fume adsorption process, eliminating the interference of airflow disturbances. The growth rate of the adsorption heat effect is determined based on the oil fume accumulation adsorption data over a period of time after separation, measuring the dynamic change in the filter's adsorption capacity. This is achieved by comprehensively considering the adsorption heat effect growth rate, the heat storage and release data of the adhering layer, and a preset growth threshold. The adsorption heat effect growth rate provides information on the current adsorption activity of the filter, while the heat storage and release data of the adhering layer supplements information on the impact of oil accumulation on the overall thermophysical properties of the filter. This allows for an objective assessment of the filter's purification and adsorption efficiency, improving the accuracy of the detection results and enabling accurate evaluation of the range hood's purification and adsorption efficiency. Attached Figure Description

[0052] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0053] Figure 1 This is a flowchart of a method for detecting the purification and adsorption efficiency of a range hood according to the present invention.

[0054] Figure 2 This is a structural block diagram of a smoke machine purification and adsorption efficiency detection system according to the present invention.

[0055] In the diagram: 210, Acquisition module; 220, Identification module; 230, Separation module; 240, Determination module; 250, Monitoring module.

[0056] The implementation and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0057] The following drawings disclose several embodiments of the present invention. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential. Furthermore, for the sake of simplicity, some conventional structures and components will be shown in the drawings in a simple schematic manner.

[0058] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0059] Furthermore, in this invention, the use of terms such as "first" and "second" is for descriptive purposes only and does not specifically refer to any order or sequence, nor is it intended to limit the invention. They are merely used to distinguish components or operations described using the same technical terms, and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but only if they are feasible for those skilled in the art. If a combination of technical solutions is contradictory or impossible to implement, such a combination should be considered nonexistent and not within the scope of protection claimed by this invention.

[0060] Traditional range hood purification equipment indirectly assesses the thermal effect of oil fume particle adsorption by monitoring the temperature difference between the airflow before and after the filter, thus evaluating its adsorption performance. However, over long-term use, oil and grease gradually accumulate on the filter surface, significantly altering its physical properties, including increasing thermal conductivity resistance, causing uneven airflow distribution, and forming a carbonized layer with heat storage capacity. This makes simple temperature difference measurements inaccurate in reflecting the true adsorption thermal effect, leading to misjudgments of the filter's operating status and ineffective maintenance strategies. Accurately separating and identifying the real-time thermal effect generated by the current oil fume adsorption behavior within this complex and dynamically changing composite temperature difference signal is a pressing technical challenge that needs to be addressed to ensure accurate assessment of filter adsorption efficiency and timely issuance of maintenance instructions.

[0061] To further understand the content, features, and effects of this invention, the following embodiments are provided, and detailed descriptions are given below in conjunction with the accompanying drawings:

[0062] Please see Figure 1 This invention provides a method for detecting the adsorption efficiency of a range hood, comprising the following steps:

[0063] S100. Acquire temperature difference data between the airflow inlet and outlet sides of the filter in the range hood over a period of time. The temperature difference data is the temperature difference between the airflow inlet and outlet sides of the filter in the range hood. It can be obtained by using high-precision thermistors, thermocouples, or infrared temperature sensors to collect temperatures in real time before and after the filter and calculate the difference. This data is used to characterize the thermal effects generated by the filter during the adsorption of oil fumes and other non-adsorption thermophysical phenomena.

[0064] S200. Based on each of the temperature difference data and the preset oil fume accumulation characteristics, identify transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the adhering layer. The preset oil fume accumulation characteristics refer to a pre-established temperature change pattern or signal feature library related to different degrees of oil fume accumulation. This library can be constructed based on historical operating data, experimental calibration data, or physical model simulation results. For example, it can include typical waveforms, frequency responses, or statistical distributions of temperature difference signals under different oil stain thicknesses and carbonization degrees, used to assist the system in distinguishing components in the composite temperature difference signal caused by different physical processes such as oil fume adsorption, convection disturbance, and heat storage and release in the adhering layer. Transient convection disturbance data are temperature fluctuation signals caused by rapid changes in airflow velocity, direction, or pressure that are not generated by oil fume adsorption. These can manifest as short-term, high-frequency temperature difference peaks or sudden drops. For example, they can be caused by sudden changes in fan speed, external airflow disturbance, or uneven airflow due to partial filter blockage, used to identify and remove interference components unrelated to oil fume adsorption from the composite temperature difference signal. Cumulative oil fume adsorption data is primarily a temperature change signal generated by the heat released during the adsorption of oil fume particles on the filter surface. It can be represented as a gradually accumulating temperature difference trend with increasing oil fume volume; for example, it can be a continuous temperature rise signal generated when the filter captures oil fumes under stable airflow conditions, reflecting the filter's true oil fume adsorption capacity and efficiency. Heat storage and release data of the oil deposit layer is a temperature signal generated by the absorption or release of heat by the oil deposit layer (especially the carbonized layer) accumulated on the filter surface when the temperature changes. It can be represented by a temperature difference change with a certain thermal hysteresis effect; for example, the temperature difference decay caused by the slow heat dissipation of the deposit layer after cooking stops. This data is used to quantify the impact of oil accumulation on the filter's thermophysical properties and distinguish it from the actual adsorption heat effect.

[0065] S300. Based on transient convection disturbance data and cumulative oil fume adsorption data, the transient convection disturbance data is separated from the cumulative oil fume adsorption data to obtain the cumulative oil fume adsorption data over a period of time after separation. Separating the transient convection disturbance data is a process of removing it from the cumulative oil fume adsorption data using signal processing techniques. This can be achieved using filtering algorithms, pattern recognition algorithms, or adaptive signal processing methods. For example, wavelet transform, Kalman filtering, or machine learning-based classifiers can be used to identify and filter out transient convection disturbances. The purpose is to obtain a purer and more accurate signal of the oil fume adsorption heat effect, avoiding interference from convection disturbances on the adsorption efficiency assessment.

[0066] S400. Based on the cumulative adsorption data of oil fumes over a period of time after separation, determine the growth rate of the adsorption heat effect. The growth rate of the adsorption heat effect is the rate at which the cumulative adsorption data of oil fumes changes over time after separation. It can be expressed as the increment of the adsorption heat effect per unit time; for example, it can be the result of calculating the time derivative or slope analysis of the adsorption heat effect signal, used to dynamically evaluate the changing trend of the filter's adsorption performance and determine whether the filter is approaching saturation or its efficiency is decreasing.

[0067] S500. Based on the adsorption heat effect growth rate, the heat storage and release data of the adsorption layer, and the preset growth threshold, the detection result of the range hood's purification adsorption efficiency is obtained. The preset growth threshold is a pre-set standard used to determine whether the range hood's purification adsorption efficiency has reached or fallen below a certain critical value. It can be determined based on the filter's design life, performance degradation curve, or maintenance strategy; for example, it can be a lower limit of the adsorption heat effect growth rate. When the actual growth rate is lower than this threshold, the filter efficiency is considered to have significantly decreased, providing an objective basis for judgment to guide the maintenance and replacement of the range hood.

[0068] Specifically, to acquire temperature difference data between the airflow inlet and outlet sides of the filter in the range hood over a period of time, high-precision PT100 platinum resistance temperature sensors can be deployed at both the inlet and outlet sides of the filter. A data acquisition module, such as a multi-channel analog-to-digital converter, converts the continuous temperature signal into a digital data stream. For example, an STM32 series processor is responsible for reading this digital temperature data in real time, calculating the instantaneous temperature difference between the inlet and outlet, storing the difference as temperature difference data in internal memory, and timestamping it. Specifically, when identifying transient convection disturbance data, oil fume accumulation and adsorption data, and heat storage and release data of the adhering layer, the microcontroller can run a signal processing algorithm. This algorithm first performs preliminary time-series analysis on the temperature difference data, such as by using moving averages or exponential smoothing to identify its temporal morphological characteristics. Preset oil fume accumulation characteristics can be stored in a lookup table or neural network model, which learns typical patterns of temperature difference signals under different oil accumulation states through training. When real-time temperature difference data is input, the algorithm matches it with preset features to distinguish between instantaneous spikes caused by sudden airflow changes, sustained temperature rises caused by oil fume adsorption, and slow changes caused by the heat capacity effect of the oil layer. For example, a high-pass filter can be used to capture rapidly changing components of transient convection disturbances; a low-pass filter can be used to extract the slow changing trend of oil fume accumulation adsorption; and curve fitting based on a thermodynamic model can be used to identify the characteristics of heat storage and release in the adsorption layer. Furthermore, to separate transient convection disturbance data from the oil fume accumulation adsorption data, the microcontroller can employ adaptive filtering techniques, such as the least mean square algorithm or a Kalman filter. The transient convection disturbance data can be used as a reference input, while the oil fume accumulation adsorption data is used as the main input. The filter learns the correlation between transient convection disturbances and oil fume accumulation adsorption data, dynamically subtracting the influence of convection disturbances from the oil fume accumulation adsorption data to obtain purer oil fume accumulation adsorption data over a period of time after separation. To determine the growth rate of the adsorption heat effect, the microcontroller performs linear regression analysis or difference calculations on the cumulative adsorption data of the separated oil fumes to obtain the slope or rate of change over time, i.e., the growth rate of the adsorption heat effect. For example, the average rate of change of this data can be calculated at regular time windows. Finally, when obtaining the detection results of the smoke hood's purification adsorption efficiency, the microcontroller compares the calculated growth rate of the adsorption heat effect with a preset growth threshold. The preset growth threshold can be a fixed value stored in non-volatile memory or a parameter dynamically adjusted according to the filter's lifespan. Simultaneously, the judgment of the adsorption heat effect growth rate is corrected by incorporating data on the heat storage and release of the adsorption layer, such as by assessing changes in its heat capacity or thermal resistance.If the growth rate of the adsorption heat effect is lower than the preset growth threshold, and the heat storage and release data of the attached layer indicates that the oil accumulation has reached a critical state, it is determined that the purification adsorption efficiency of the range hood has decreased significantly, and the corresponding detection results are output, for example, by sending them to the user interface or remote monitoring platform through LED indicator lights, buzzers or wireless communication modules.

[0069] In this embodiment, temperature difference data between the airflow inlet and outlet sides of the filter in the range hood is acquired over a period of time. The raw temperature difference data directly reflects the filter's operating state, but it includes the combined effects of multiple physical processes. Therefore, based on each acquired temperature difference data point and a preset characteristic of oil fume accumulation, preliminary signal decomposition is performed. This identifies transient convection disturbance data, oil fume accumulation adsorption data, and heat storage / release data of the adhering layer. Transient convection disturbance data represents instantaneous temperature fluctuations caused by airflow changes, while the heat storage / release data of the adhering layer reflects the heat absorption and release generated by the heat capacity of the oil layer on the filter surface, and the oil fume accumulation adsorption data initially includes the heat generated by oil fume adsorption. To further purify the oil fume adsorption signal, based on the identified transient convection disturbance data and oil fume accumulation adsorption data, the transient convection disturbance data is separated from the oil fume accumulation adsorption data. This is because transient convection disturbances often superimpose on the actual adsorption signal, causing interference. Precise separation yields cumulative oil fume adsorption data over a period after separation. This data more purely reflects the thermal effect generated during the oil fume adsorption process, eliminating interference from airflow disturbances. Subsequently, based on this cumulative oil fume adsorption data, the adsorption heat effect growth rate is determined. This growth rate is a crucial indicator of the dynamic change in the filter's adsorption capacity, and its trend directly correlates with the filter's performance degradation. Finally, to obtain comprehensive and accurate test results for the range hood's purification and adsorption efficiency, the adsorption heat effect growth rate, the heat storage and release data of the adsorbent layer, and a preset growth threshold are comprehensively considered. The adsorption heat effect growth rate provides information on the filter's current adsorption activity, while the heat storage and release data of the adsorbent layer supplements information on the impact of oil accumulation on the overall thermophysical properties of the filter. This allows for an objective assessment of the filter's purification and adsorption efficiency, providing a reliable basis for subsequent maintenance decisions.

[0070] In some embodiments described above, this application further proposes the step of identifying transient convection disturbance data, oil fume accumulation and adsorption data, and heat storage and release data of the adhering layer based on each of the temperature difference data and preset oil fume accumulation characteristics, including:

[0071] Based on each temperature difference data point, after confirming the temporal morphological characteristics of each temperature difference data point, transient convective disturbance data and deposited layer heat storage and release data are identified. The temporal morphological characteristics refer to the change pattern of the temperature difference data along the time axis, specifically including instantaneous fluctuations, periodic changes, duration, or rate of change, etc., with the aim of distinguishing temperature signals caused by different physical processes.

[0072] Based on each of the temperature difference data, a temperature trend characteristic is determined. This temperature trend characteristic is the overall trend of the temperature difference data over time, specifically it can be an increase, a decrease, or a stable state. Its purpose is to reflect the changes in the macroscopic thermal effect during the accumulation and adsorption of cooking fumes.

[0073] The temperature trend characteristics are compared with preset oil fume accumulation characteristics to determine the oil fume change trend parameters. The preset oil fume accumulation characteristics are established based on historical data or experimental results, describing typical patterns of temperature difference data changes during the oil fume accumulation and adsorption process on the filter. Specifically, they can be a series of predefined temperature change curves, thresholds, or mathematical models, providing a reference benchmark for identifying actual oil fume accumulation and adsorption data. The oil fume change trend parameters are obtained by comparing the actual temperature trend characteristics with the preset oil fume accumulation characteristics. They are quantitative indicators reflecting the current oil fume accumulation and adsorption state and direction of change. Specifically, they can be numerical values, classification labels, or state descriptions, guiding the subsequent confirmation of oil fume accumulation and adsorption data.

[0074] Based on the oil fume change trend parameters and each of the temperature difference data, the data during the period of increased fan speed is identified as the cumulative oil fume adsorption data.

[0075] Specifically, to confirm the temporal characteristics of each temperature difference data point, wavelet transform or Fourier transform methods can be used to analyze the frequency components and instantaneous energy distribution of the temperature difference signal. This allows for the identification of high-frequency, short-duration, pulse-like transient convective disturbance data, as well as low-frequency, slowly changing heat storage and release data in the deposited layer. For example, transient convective disturbances may manifest as high-frequency spikes, while heat storage and release in the deposited layer may exhibit slow baseline drift. Simultaneously, to determine the temperature trend characteristics, the temperature difference data can be processed using a moving average or linear regression analysis to smooth out instantaneous fluctuations and reveal the overall upward, downward, or stable trend of temperature change over time. Subsequently, this determined temperature trend characteristic is compared with pre-stored preset oil fume accumulation characteristics. These preset oil fume accumulation characteristics can be a database containing various typical oil fume accumulation patterns. For example, by calculating the similarity (such as Euclidean distance or correlation coefficient) between the current temperature trend characteristic and each pattern in the database, the oil fume change trend parameters can be determined, such as rapid accumulation, slow accumulation, or a stable state. Finally, by combining the parameters of oil fume variation trend and monitoring the speed control signal or actual speed data of the range hood fan, the temperature difference data corresponding to the increase in fan speed is identified as the cumulative oil fume adsorption data. For example, when the fan switches from low speed to high speed, if it is accompanied by a cumulative trend indicated by the oil fume variation trend parameters, the temperature difference data segment at this time is marked as the cumulative oil fume adsorption data. This allows for more accurate extraction of thermal effect data directly related to oil fume adsorption from complex temperature signals.

[0076] In this embodiment, by performing refined analysis of the temperature difference data between the airflow inlet and outlet sides at the range hood filter, transient convection disturbance data, cumulative oil fume adsorption data, and heat storage and release data of the adhering layer are effectively distinguished and identified. Specifically, by confirming the temporal morphological characteristics of the temperature difference data, the influence of transient convection disturbance and heat storage and release of the adhering layer can be effectively separated; by determining the temperature trend characteristics and comparing them with preset oil fume accumulation characteristics, the trend of oil fume change can be accurately determined; and by combining the data during the fan speed increase, the cumulative oil fume adsorption data can be accurately confirmed. This allows for the extraction of pure data on the cumulative oil fume adsorption heat effect from complex composite temperature signals, thus providing a reliable data foundation for the subsequent accurate detection of the range hood's purification and adsorption efficiency, significantly improving the accuracy of the detection results.

[0077] In some embodiments described above, this application also proposes a step for obtaining temperature difference data between the airflow inlet side and the airflow outlet side at the filter screen of the range hood over a period of time, including:

[0078] Acquire initial data for each temperature difference between the airflow inlet and outlet sides at the filter in the range hood over a period of time.

[0079] Each initial temperature difference data point is smoothed to obtain individual temperature difference data. Smoothing involves filtering or averaging the original data sequence to eliminate random noise and transient fluctuations, thereby revealing potential trends or patterns. Specifically, this can be achieved using algorithms such as moving averages, exponential smoothing, or Kalman filtering to improve the signal-to-noise ratio and ensure the accuracy and stability of subsequent data analysis.

[0080] In this embodiment, the accuracy of subsequent analysis is improved by preprocessing the temperature difference data between the airflow inlet and outlet sides of the filter in the range hood. Specifically, initial data for each temperature difference between the airflow inlet and outlet sides of the filter in the range hood is first acquired over a period of time. This initial data comes directly from sensor measurements and may be affected by environmental interference, sensor noise, or instantaneous airflow fluctuations, resulting in irregular noise or spikes in the data. To eliminate these interferences and ensure data stability and reliability, the initial data for each temperature difference is smoothed. Smoothing effectively filters out high-frequency noise components in the data, making the data curve smoother and better reflecting the true temperature change trend. Thus, the smoothed temperature difference data has higher accuracy and stability. Because smoothed temperature difference data is provided, this solution can provide more reliable and accurate input for the subsequent identification of transient convection disturbance data, oil fume cumulative adsorption data, and heat storage and release data of the adhering layer. Through the smoothing process of this solution, noise in the data is effectively suppressed, enabling subsequent recognition algorithms to more accurately capture the true temperature change characteristics caused by physical processes such as oil fume adsorption, convection disturbance, and heat storage and release in the adhering layer. This significantly improves the overall accuracy and reliability of the range hood's purification and adsorption efficiency detection. It also allows the entire detection method to operate more robustly in complex and variable real-world operating environments, avoiding misjudgments due to data quality issues and thus enhancing the accuracy of filter performance assessment.

[0081] In some embodiments described above in this application, this application further proposes that after obtaining the detection result of the smoke hood's purification adsorption efficiency based on the adsorption heat effect growth rate, the heat storage and release data of the adhesion layer, and a preset growth threshold, the following steps are also included:

[0082] The test results are sent to the central management system. The central management system is a centralized information processing platform, which can be a server cluster, a cloud computing platform, or a dedicated management software system. It is responsible for receiving, storing, and analyzing test data from multiple smoke machines or filters, and for unified decision-making and management.

[0083] In response to the maintenance plan output by the central management system based on the test results, the maintenance priority and maintenance resource information for each filter are determined. The maintenance plan is a series of maintenance suggestions or instructions automatically or semi-automatically generated by the central management system based on the received test results, combined with preset maintenance strategies, historical data, and operating status. It may include specific operations such as cleaning, replacement, and repair, and its purpose is to guide subsequent filter maintenance. Maintenance priority is an indicator that ranks the maintenance needs of different filters based on the filter test results and other factors. It can be represented by numerical values, levels, or category labels, ensuring that resources are allocated preferentially to the filters that need them most. Maintenance resource information is data on various resources required to perform maintenance operations. It may include the number of maintenance personnel, the type and quantity of required spare parts, tools and equipment, and maintenance time windows, providing a specific resource basis for the maintenance plan.

[0084] Based on the maintenance priorities and maintenance resource information, a global maintenance plan is generated for each filter. This global maintenance plan comprehensively considers the maintenance priorities and available maintenance resources of all filters, developing a coordinated maintenance schedule and task allocation plan for each filter to be executed within a specific time period. This plan can be a detailed task list, Gantt chart, or automated scheduling instructions to optimize overall maintenance efficiency and resource utilization.

[0085] Specifically, once the test results of the range hood's purification and adsorption efficiency are obtained, the test result data packet is sent to a central management system deployed in the cloud, for example, via a wireless communication module. The central management system can be a backend application based on a microservice architecture, running on a cloud platform, and includes data receiving services, data analysis services, and a decision engine service. The data analysis service parses the received test results; for example, if the growth rate of the adsorption heat effect of a certain filter is lower than a preset threshold, it indicates that its adsorption efficiency has decreased. The decision engine service outputs a maintenance plan based on the analysis results, combined with the filter's historical maintenance records, current operating time, and its criticality in the entire kitchen system. For example, for filters with severely decreased adsorption efficiency and long operating times, the maintenance plan may recommend immediate filter replacement; for filters with slightly decreased adsorption efficiency, it may recommend scheduling regular cleaning. Responding to the maintenance plan output by the central management system, the maintenance priority and maintenance resource information for each filter can be determined according to preset rules or machine learning models. For example, for filters recommended for immediate replacement, their maintenance priority can be set to high, and maintenance resource information may include the need for one professional maintenance personnel and an estimated maintenance time of 2 hours. For filters recommended for regular cleaning, their priority can be set to medium, and maintenance resource information can include the need for one general maintenance personnel and an estimated maintenance time of 0.5 hours. Based on these determined maintenance priorities and resource information, a global maintenance plan can be generated for each filter using a scheduling algorithm. The plan can be a visual Gantt chart showing the start and end times of maintenance for each filter, the responsible personnel, and the required resources, taking into account the availability of maintenance personnel, spare parts inventory, and off-peak kitchen operating hours. For example, the system can automatically schedule high-priority filters to be replaced during nighttime kitchen shutdowns, while medium-priority filters are scheduled for cleaning during the off-peak hours before lunch the following day.

[0086] In this embodiment, the test results of the smoke hood's purification and adsorption efficiency are sent to the central management system, achieving centralized management of the test data and providing data support for subsequent maintenance decisions. Responding to the maintenance plan output by the central management system based on the test results, the maintenance priority and resource information for each filter are determined. This allows for optimized allocation of maintenance resources based on the actual condition of the filters, avoiding waste. Based on the maintenance priority and resource information, a global maintenance plan is generated for each filter, standardizing and streamlining the maintenance process. This ensures the quality and efficiency of maintenance work and solves the problems of test results not being effectively translated into maintenance actions and unreasonable allocation of maintenance resources.

[0087] In some embodiments described above, this application also proposes a step of separating transient convection disturbance data from the cumulative oil fume adsorption data based on transient convection disturbance data and cumulative oil fume adsorption data, to obtain cumulative oil fume adsorption data over a period of time after separation, including:

[0088] Based on transient convection disturbance data and cumulative oil fume adsorption data, transient convection disturbance data is separated from the cumulative oil fume adsorption data to obtain initial cumulative oil fume adsorption data. The transient convection disturbance data refers to temperature difference signals caused by instantaneous changes in airflow velocity, direction, or pressure during the operation of the range hood, which are not generated by oil fume adsorption. This can be identified using time-domain analysis, frequency-domain analysis, or pattern recognition methods based on machine learning, to distinguish interference signals unrelated to oil fume adsorption. The cumulative oil fume adsorption data is the temperature difference signal corresponding to the thermal effect generated when oil fume particles adsorb onto the filter surface. This can be confirmed by comparing temperature trend characteristics with preset oil fume accumulation characteristics, reflecting the true oil fume adsorption state of the filter. The initial cumulative oil fume adsorption data is obtained after initially removing transient convection disturbance data from the cumulative oil fume adsorption data. This can be initially separated using methods such as signal differential analysis, regression analysis, or adaptive filtering, to provide basic data for subsequent accurate correction.

[0089] Based on the accumulated oil fume adsorption data, the adsorption heat conduction attenuation parameter was confirmed. This parameter is a quantitative indicator reflecting the degree of decrease in heat transfer efficiency due to the formation of an oil layer during the accumulation of oil fumes on the filter surface. It can be confirmed using methods such as analysis of the heat release rate characteristics based on oil accumulation, attenuation model fitting, or empirical calibration curves, to quantify the attenuation effect of oil on the adsorption heat effect signal.

[0090] The initial adsorption data of oil fume accumulation is corrected using the adsorption heat conduction attenuation parameter to obtain the cumulative adsorption data of oil fume over a period of time after separation.

[0091] In this embodiment, a correction mechanism for the accumulated oil fume adsorption data is introduced to solve the problem of inaccurate data that may result from direct separation. Specifically, based on transient convection disturbance data and accumulated oil fume adsorption data, transient convection disturbance data is initially separated from the accumulated oil fume adsorption data to obtain initial accumulated oil fume adsorption data. This initially eliminates the interference of transient airflow changes on the temperature signal, allowing subsequent processing to focus on signals related to oil fume adsorption. Since the accumulation of oil on the filter screen changes its thermal conductivity, simple separation cannot completely eliminate the errors caused by this physical change. Based on the accumulated oil fume adsorption data, adsorption heat conduction attenuation parameters are identified to quantify the hindering and attenuating effect of the oil layer on the transfer of adsorbed heat. By analyzing the accumulated oil fume adsorption data itself, heat conduction attenuation information related to the degree and properties of oil accumulation can be extracted, such as the influence of oil thickness, density, or carbonization degree on heat conduction. Subsequently, the identified adsorption heat conduction attenuation parameters are used to correct the initial accumulated oil fume adsorption data. The calibration process compensates for the heat transfer attenuation caused by the oil layer, allowing the initial adsorption data of accumulated oil fumes to more accurately reflect the actual heat effect of oil fume adsorption. Calibration effectively eliminates or reduces the interference of transient convection disturbances and the thermal resistance of the oil layer on the adsorption heat effect signal, thus obtaining more accurate data on the accumulated oil fume adsorption over a period of time after separation. This more accurately reflects the true adsorption state of the filter, providing a more reliable and refined data foundation for determining the subsequent growth rate of the heat effect of adsorption and for testing the adsorption efficiency of the range hood. It effectively addresses the complex physical changes caused by oil accumulation during long-term use of the filter, ensuring the accuracy of the test results.

[0092] In some of the embodiments described above in this application, this application also proposes a step for confirming the adsorption heat conduction attenuation parameter based on the accumulated oil fume adsorption data, including:

[0093] Based on the accumulated oil fume adsorption data, the rate characteristics and attenuation baseline of heat release from oil accumulation are extracted. The rate characteristics of heat release from oil accumulation refer to the speed at which the heat generated inside or on the surface of the oil during accumulation on the filter screen changes over time due to chemical reactions, phase changes, or physical adsorption processes. This can be obtained by monitoring the temperature change rate of the filter screen surface, infrared thermal imaging analysis, or microcalorimeter measurement, reflecting the activity level, structural state, and potential carbonization trend of oil accumulation. The attenuation baseline is the inherent attenuation amount or proportion caused by thermal conduction resistance when the oil fume adsorption heat effect passes through the oil layer under ideal or standard conditions. This can be obtained through experimental calibration, theoretical model calculation, or regression analysis based on historical data, providing a basic reference value for thermal conduction attenuation for subsequent correction.

[0094] Based on the heat release rate characteristics, the degree of carbonization of the accumulated oil is determined. The degree of carbonization refers to the extent to which oil, under high temperature or prolonged exposure, forms carbonaceous or carbon-like substances. This can be quantified by analyzing indicators such as the color, hardness, chemical composition changes, or heat release rate of the oil, thus characterizing the impact of the oil layer on thermal conductivity, as carbonization significantly alters the thermal conductivity of the oil layer.

[0095] The attenuation baseline was corrected using the degree of carbonization to confirm the adsorption heat conduction attenuation parameters.

[0096] In this embodiment, the rate characteristics and attenuation base of heat release from accumulated oil fumes are extracted based on the accumulated oil fume adsorption data. The rate characteristics of heat release reflect the dynamic thermal behavior of oil fumes accumulating on the filter surface, while the attenuation base provides a basic quantitative indicator of heat conduction attenuation. Based on the extracted rate characteristics of heat release, the degree of carbonization of accumulated oil fumes is further confirmed. Because the degree of carbonization of oil fumes directly affects its heat release rate and pattern, analyzing these rate characteristics can effectively infer the carbonization state of the oil fumes. The confirmed degree of carbonization is used to refine the extracted attenuation base, thereby obtaining more accurate adsorption heat conduction attenuation parameters. Since the degree of carbonization of accumulated oil fumes is incorporated into the confirmation process of adsorption heat conduction attenuation parameters, these parameters can more realistically reflect the heat conduction characteristics of the filter in actual operation, especially after long-term oil accumulation and carbonization. By combining the adsorption heat conduction attenuation parameters with the transient convection disturbance data separated from the accumulated oil fume adsorption data in the previous steps, the initial adsorption data of accumulated oil fumes can be more accurately corrected. The improved calibration accuracy directly optimizes the quality of the accumulated oil fume adsorption data over a period of time after separation, enabling the data to more accurately represent the true adsorption heat effect of the filter. Ultimately, the high-precision data provides a solid foundation for subsequent determination of the adsorption heat effect growth rate and obtaining the test results of the range hood's purification adsorption efficiency. This effectively solves the problem in existing technologies where inaccurate adsorption heat conduction attenuation parameters caused by oil carbonization affect the overall test results.

[0097] In some embodiments described above, this application also proposes a step for determining the degree of carbonization of oil accumulation based on the rate characteristics of heat release, including:

[0098] Based on the heat release rate characteristics, local regions indicating the highest degree of carbonization are identified within these characteristics. These local regions are specific time periods or sets of data points exhibiting the fastest heat release or the highest heat release peak within the heat release rate characteristics. Specifically, they can be identified through peak detection, gradient analysis, or local maximum search of the heat release rate data. The aim is to focus on the areas with the most severe oil carbonization, thereby reducing interference from other non-carbonization factors on the overall heat release rate and improving the accuracy of carbonization degree assessment.

[0099] Time series analysis is performed on the local region with the highest degree of carbonization to obtain the heat decay curve. Time series analysis is a method of analyzing data points arranged in chronological order. Specifically, it can use techniques such as curve fitting, Fourier transform, wavelet analysis, or statistical models (such as the ARIMA model) to reveal the patterns and trends of data changes over time. Its purpose is to gain a deeper understanding of the dynamic process of heat release decaying over time, thereby providing richer information for the quantification of carbonization degree. The heat decay curve is a trend graph or data sequence of heat release rate changes over time within the local region with the highest degree of carbonization. Specifically, it can be obtained by smoothing and normalizing the heat release data of the identified local region, visually demonstrating the heat release characteristics of the carbonized region and providing a basis for subsequent feature parameter extraction.

[0100] The degree of carbonization of oil accumulation is determined based on the characteristic parameters of the heat decay curve. These characteristic parameters are numerical indicators that quantify the characteristics of the heat decay curve. Specifically, they may include decay rate (e.g., half-life or initial decay slope), decay magnitude (e.g., the difference between peak and steady-state values), decay time (e.g., the time required to reach a certain decay ratio), or area under the curve, providing quantitative information about the degree of oil carbonization and making the assessment of carbonization more objective and accurate.

[0101] In this embodiment, by identifying local areas indicating the highest degree of carbonization based on the heat release rate characteristics, since oil carbonization is not uniform but may occur in localized areas with higher carbonization levels, the heat release characteristics more directly reflect the carbonization state. By focusing on these localized areas, the interference of non-carbonization factors such as oil type, cooking temperature, and filter material on the overall heat release rate can be effectively reduced, thereby improving the accuracy of the initial judgment. Time series analysis is performed on the identified localized areas with the highest degree of carbonization to obtain heat decay curves. Because time series analysis can reveal the dynamic law of heat release changes over time, rather than just the instantaneous rate, it allows for a deeper understanding of the carbonization process; for example, areas with a high degree of carbonization may experience more rapid heat decay or exhibit specific decay patterns. Finally, the degree of carbonization of oil accumulation is confirmed based on the characteristic parameters of the heat decay curve. Because the characteristic parameters of the heat decay curve, such as decay rate and decay amplitude, can provide quantitative information about the degree of carbonization, it overcomes the limitations of judging solely based on a rate characteristic, making the confirmation of the degree of oil carbonization more accurate and reliable. The step of determining the degree of carbonization of oil accumulation based on the rate characteristics of heat release provides a more accurate input for subsequent correction of the attenuation baseline using the degree of carbonization, thereby confirming the adsorption heat conduction attenuation parameters. This improves the accuracy of correcting the initial adsorption data of oil fume accumulation, ultimately making the detection results of the range hood's purification adsorption efficiency more accurate. It effectively solves the problem of carbonization degree judgment deviation caused by various factors in the existing technology, and improves the accuracy of range hood purification adsorption efficiency detection.

[0102] In some embodiments described above, this application also proposes a step for determining the growth rate of the adsorption heat effect based on the cumulative adsorption data of oil fumes over a period of time after separation, including:

[0103] Quantitative analysis was performed on the cumulative adsorption data of oil fumes over a period of time after separation to obtain quantitative data, trend indicators, and oil accumulation coefficients. The quantitative data directly represents the degree of cumulative oil fume adsorption, and can be obtained in the form of adsorption amount, adsorption rate, or total adsorption heat, providing objective numerical evidence of the cumulative adsorption state of oil fumes.

[0104] Based on the aforementioned trend indicators and oil accumulation coefficient, the overall adsorption efficiency coefficient of the filter screen is determined. The trend indicator reflects the change in the adsorption process over time and can be characterized by growth rate, decay rate, slope of change, or fluctuation frequency, capturing the dynamic evolution of adsorption performance. The oil accumulation coefficient is a numerical value characterizing the degree of oil accumulation on the filter screen surface. It can be determined based on oil thickness, oil coverage area, or oil chemical composition analysis, quantifying the actual impact of oil on the filter screen's adsorption performance.

[0105] Based on the comprehensive adsorption efficiency coefficient and quantitative data of the filter, the growth rate of the adsorption heat effect is determined. The comprehensive adsorption efficiency coefficient of the filter is used to evaluate its actual adsorption capacity. It can be confirmed using methods such as a weighted average combining trend indicators and oil accumulation coefficients, a product model, or a neural network model, providing a comprehensive basis for filter performance evaluation. The growth rate of the adsorption heat effect is the rate at which the adsorption heat effect changes over time. It can be determined using methods such as the increment of the adsorption heat effect per unit time, the instantaneous slope of the adsorption heat effect curve, or a growth factor calculated based on a specific model, reflecting the dynamic changes in the filter's adsorption performance.

[0106] In this embodiment, the growth rate of the adsorption heat effect is determined by conducting in-depth quantitative analysis of the cumulative adsorption data of oil fumes over a period of time after separation. Specifically, multi-dimensional analysis is performed on the cumulative adsorption data of oil fumes after separation to obtain quantitative data, trend indicators, and oil accumulation coefficient. Quantitative data directly reflects the degree of oil fume adsorption, trend indicators reveal the dynamic characteristics of the adsorption process, and the oil accumulation coefficient quantifies the actual impact of oil on the filter surface on adsorption performance. This multi-dimensional information collectively constructs a comprehensive understanding of the filter's adsorption state. Based on the obtained trend indicators and oil accumulation coefficient, the overall adsorption efficiency coefficient of the filter is confirmed. Specifically, the formula for calculating the overall adsorption efficiency coefficient C of the filter is as follows:

[0107]

[0108] Where C refers to the overall adsorption efficiency coefficient of the filter. and The weights are respectively the trend indicator and the oil accumulation coefficient, and T refers to the trend indicator. The smaller the T value, the more obvious the downward trend in adsorption efficiency, and the greater its negative impact on the overall adsorption efficiency. Z refers to the oil accumulation coefficient. The larger the Z value, the more oil accumulates, and the greater its negative impact on the overall adsorption efficiency.

[0109] Simultaneously, the variation of adsorption efficiency over time is integrated with the impact of oil accumulation to form a comprehensive index that can assess the actual adsorption capacity of the filter. This avoids evaluation biases that may arise from a single data dimension, making the judgment of filter performance more reliable. Based on the confirmed comprehensive adsorption efficiency coefficient and quantitative data of the filter, the growth rate of the adsorption heat effect is determined. The comprehensive adsorption efficiency coefficient of the filter provides a correction factor for the current adsorption capacity of the filter, while the quantitative data provides information on the actual adsorption amount. The formula for calculating the growth rate of the adsorption heat effect is as follows:

[0110]

[0111] in, The growth rate of the adsorption heat effect within the time period p. This refers to the rate of change of data within a time period p.

[0112] This solution provides reliable input for subsequent testing of the smoke hood's purification and adsorption efficiency based on the growth rate of the adsorption heat effect, the heat storage and release data of the adsorption layer, and a preset growth threshold. This enables the entire smoke hood purification and adsorption efficiency testing method to accurately assess the actual working state of the smoke hood filter, effectively avoiding misjudgments and missed reports. Consequently, it can provide timely and effective guidance for filter maintenance, extend equipment lifespan, and ensure purification effectiveness.

[0113] In some embodiments described above, this application also proposes the step of determining the growth rate of the adsorption heat effect based on the comprehensive adsorption efficiency coefficient and quantitative data of the filter screen, including:

[0114] After confirming the rate of change of the quantified data, the cooking state intensity is obtained. The rate of change of the quantified data refers to how quickly a quantified value extracted from the accumulated adsorption data of cooking fumes changes over a specific time period. It can be calculated using methods such as differencing, differentiation, or moving averages of the quantified data, reflecting the dynamic process of fume generation or adsorption. The cooking state intensity is the degree to which cooking activities affect the operating environment of the range hood. Specifically, it can be obtained by monitoring parameters such as smoke concentration, temperature, humidity, fan speed, or user-set cooking modes in the kitchen, incorporating external environmental factors into the assessment of the adsorption heat effect growth rate. The steps for constructing the mapping relationship between the rate of change of the quantified data and the cooking state intensity are as follows:

[0115] In a controlled experimental environment, cooking activities of varying intensities were simulated, such as light cooking (e.g., porridge), medium cooking (e.g., stir-frying), and heavy cooking (e.g., high-heat stir-frying). At each cooking intensity, the quantified data and its rate of change of the filter were continuously monitored and recorded. Statistical analysis of the experimental data revealed typical ranges and patterns of the rate of change of the quantified data at different cooking intensities. For example, during light cooking, the rate of change of the quantified data might be low and stable; during medium cooking, the rate of change would increase significantly; and during heavy cooking, the rate of change might reach a peak and fluctuate rapidly. Based on the data analysis results, a series of thresholds were defined to classify the rate of change of the quantified data into different cooking intensity levels. For example, a low rate threshold L1 was set; when the rate of change was below L1, it was considered light cooking; a medium rate threshold L2 was set; when the rate of change was between L1 and L2, it was considered medium cooking; and when the rate of change was above L2, it was considered heavy cooking. These thresholds established a direct mapping relationship between the rate of change of the quantified data and the cooking intensity level. When the rate of change of quantitative data is acquired in real time, it is compared with a preset threshold to quickly and accurately determine the intensity of the current cooking state. Based on the comprehensive adsorption efficiency coefficient of the filter, the degradation index of the filter is identified. The degradation index is a quantitative characterization of the decline in the filter's adsorption performance. Specifically, it can be confirmed by analyzing the filter's historical usage time, cleaning cycle, amount of oil accumulation, changes in airflow resistance, or by detecting physicochemical changes on the filter surface using specific sensors, in order to assess the impact of the filter's own condition on the adsorption heat effect.

[0116] The degradation index of a filter screen considers its historical usage time. The longer the filter screen is used, the more physical or chemical changes may occur in its internal structure and surface adsorption materials, leading to a natural decline in adsorption performance. By recording the cumulative operating time of the filter screen and comparing it with a preset lifespan curve, its degree of degradation can be preliminarily judged. Secondly, the degradation index of the filter screen also needs to consider the cumulative amount of oil fumes processed. During the process of processing oil fumes, oil and dirt gradually accumulate on the filter screen, clogging the mesh, increasing airflow resistance, and potentially forming a carbonized layer. The greater the cumulative amount of oil fumes processed, the higher the degree of filter screen degradation is generally. The cumulative amount of oil fumes processed can be estimated by monitoring parameters such as the operating power, airflow, and cooking intensity of the range hood. Furthermore, the filter screen's cleaning cycle and maintenance records also affect the confirmation of degradation indexes. Regular cleaning and maintenance can slow down the degradation process, while irregular or missing maintenance may accelerate it. By analyzing the filter screen's maintenance history, degradation indexes can be corrected.

[0117] Meanwhile, filter degradation indicators can also directly monitor changes in the filter's physical properties. For example, by integrating miniature pressure sensors or airflow sensors onto the filter surface, the pressure difference or airflow velocity distribution before and after the filter can be monitored in real time. A significant increase in pressure difference or severe unevenness in airflow distribution directly indicates the degree of filter clogging and performance degradation, which can thus be quantified as degradation indicators.

[0118] Based on the cooking intensity and filter degradation index, the contribution parameter of the comprehensive adsorption efficiency score is determined. This contribution parameter represents the weight or correction factor of the influence of cooking intensity and filter degradation index on the overall adsorption efficiency assessment result. It can be determined using methods such as expert experience, machine learning model training, or historical data regression analysis to quantify the relative importance of different factors in the adsorption efficiency assessment. Based on the filter's comprehensive adsorption efficiency coefficient, the quantified data, and the contribution parameter, the adsorption heat effect growth rate is determined. The formula for calculating the contribution parameter D is as follows:

[0119]

[0120] in, It is a preset weighting coefficient for the intensity of the cooking state, such as 2 to 5 per thousand; The preset weighting coefficient for the filter deterioration index is 0.5% to 0.8%. The preset weighting coefficient reflects the relative importance of the impact on adsorption efficiency. X refers to the cooking state intensity, which is quantified into a value from 0 to 100, where 0 represents no cooking and 100 represents high-intensity cooking. Y refers to the filter deterioration index, which is also quantified into a value from 0 to 100, where 0 represents no deterioration and 100 represents complete deterioration.

[0121] Based on the overall adsorption efficiency coefficient of the filter, the quantitative data, and the contribution parameters, the specific formula for determining the growth rate of the adsorption heat effect is as follows:

[0122]

[0123] In this embodiment, by introducing cooking state intensity and filter degradation index, the determination of the adsorption heat effect growth rate is made more accurate. Specifically, after quantitatively analyzing the cumulative adsorption data of oil fumes over a period of time after separation, and obtaining quantitative data, trend indicators, and oil accumulation coefficients, and confirming the comprehensive adsorption efficiency coefficient of the filter based on the trend indicators and oil accumulation coefficients, this scheme further confirms the rate of change of the quantitative data and obtains the cooking state intensity accordingly. Because the rate of change of the quantitative data can directly reflect the dynamic process of oil fume generation, it indirectly indicates the current cooking activity intensity. Simultaneously, based on the comprehensive adsorption efficiency coefficient of the filter, this scheme confirms the filter degradation index, directly quantifying the performance degradation of the filter itself, thus compensating for the insufficiency of relying solely on the comprehensive adsorption efficiency coefficient to fully reflect the actual state of the filter. Subsequently, based on the obtained cooking state intensity and confirmed filter degradation index, this scheme determines the contribution parameters of the comprehensive adsorption efficiency score. This allows the system to dynamically adjust the weights of various factors in the adsorption efficiency evaluation according to the actual working conditions and the filter's own condition, thereby avoiding evaluation biases that may arise from a fixed model. Finally, based on the filter's comprehensive adsorption efficiency coefficient, quantitative data, and dynamically adjusted contribution parameters, the system determines the adsorption heat effect growth rate. This multi-dimensional, dynamically adjusted evaluation method allows the calculated adsorption heat effect growth rate to more accurately reflect the filter's true adsorption performance under actual usage conditions, effectively overcoming the problem of inaccurate evaluation caused by external environment and filter degradation in existing technologies. In this way, this solution provides a more reliable basis for testing the adsorption efficiency of range hoods, thereby supporting more precise maintenance decisions.

[0124] Please see Figure 2 This application also provides a smoke machine purification adsorption efficiency detection system, which includes an acquisition module 210, an identification module 220, a separation module 230, a determination module 240, and a monitoring module 250.

[0125] The acquisition module 210 is used to acquire temperature difference data between the air inlet side and the air outlet side of the filter in the range hood over a period of time.

[0126] The identification module 220 is used to identify transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the adhering layer based on each of the temperature difference data and preset oil fume accumulation characteristics.

[0127] The separation module 230 is used to separate the transient convection disturbance data from the cumulative oil fume adsorption data based on the transient convection disturbance data and the cumulative oil fume adsorption data, so as to obtain the cumulative oil fume adsorption data for a period of time after separation.

[0128] The determination module 240 is used to determine the growth rate of the adsorption heat effect based on the cumulative adsorption data of oil fumes over a period of time after separation.

[0129] The monitoring module 250 is used to obtain the detection results of the smoke machine's purification adsorption efficiency based on the growth rate of the adsorption heat effect, the heat storage and release data of the attachment layer, and the preset growth threshold.

[0130] In this embodiment, by integrating the data processing and analysis steps of the method for detecting the thermal effect of the smoke hood adsorption process into a modular detection system, automated and non-invasive detection of the thermal effect of the smoke hood adsorption process is achieved. Specifically, the acquisition module 210 first continuously collects temperature difference data of the airflow before and after the smoke hood filter, providing a data stream for the detection process. Subsequently, the identification module 220 receives this temperature difference data and, combined with preset oil fume accumulation characteristics, uses signal processing and pattern recognition technology to decompose the data stream into three components: transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the adsorption layer. The decomposition process is the core of solving the problem of interference from composite temperature difference signals, enabling subsequent analysis to focus on the adsorption heat effect. Next, the separation module 230, based on the identified transient convection disturbance data, removes the non-adsorption temperature difference caused by airflow disturbance from the oil fume accumulation adsorption data, thereby obtaining the oil fume accumulation adsorption data. The separation step ensures that the evaluation of the adsorption heat effect is not interfered with by external transient factors. Based on this, module 240 uses the accumulated adsorption data of the separated oil fumes to calculate the growth rate of the adsorption heat effect, which directly reflects the changing trend of the filter's adsorption performance over time. Finally, monitoring module 250 integrates the growth rate of the adsorption heat effect, the heat storage and release data of the attached layer, and the preset growth threshold to generate the detection result of the range hood's purification adsorption efficiency. The entire system, through the collaboration and data flow of its various modules, effectively solves the problems of low efficiency and insufficient accuracy of traditional methods in multi-range hood scenarios, realizing real-time evaluation of the adsorption status of the range hood filter without requiring physical modification to the range hood itself, thus improving the accuracy, convenience, and practicality of the detection.

[0131] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention specification.

Claims

1. A method for detecting the adsorption efficiency of a range hood, characterized in that, include: Acquire data on the temperature difference between the airflow inlet and outlet sides at the filter in the range hood over a period of time; Based on each of the temperature difference data and the preset oil fume accumulation characteristics, transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the attachment layer are identified. Based on transient convection disturbance data and oil fume cumulative adsorption data, transient convection disturbance data is separated from the oil fume cumulative adsorption data to obtain oil fume cumulative adsorption data over a period of time after separation. Based on the cumulative adsorption data of oil fumes over a period of time after separation, the growth rate of the adsorption heat effect is determined. Based on the adsorption heat effect growth rate, the heat storage and release data of the adhesion layer, and the preset growth threshold, the test results of the smoke machine's purification adsorption efficiency are obtained. The steps for separating transient convection disturbance data from the cumulative adsorption data of oil fumes, based on transient convection disturbance data and cumulative adsorption data of oil fumes, to obtain cumulative adsorption data of oil fumes over a period of time after separation, include: Based on transient convection disturbance data and oil fume cumulative adsorption data, transient convection disturbance data is separated from the oil fume cumulative adsorption data to obtain initial oil fume cumulative adsorption data; Based on the cumulative adsorption data of the oil fumes, the adsorption heat conduction attenuation parameters were confirmed; The initial adsorption data of oil fume accumulation is corrected using the adsorption heat conduction attenuation parameter to obtain the cumulative adsorption data of oil fume over a period of time after separation. Based on the accumulated oil fume adsorption data, the steps for confirming the adsorption heat conduction attenuation parameters include: Based on the accumulated adsorption data of oil fumes, the rate characteristics and attenuation base of heat release from accumulated oil fumes are extracted; Based on the characteristics of the heat release rate, the degree of carbonization of the accumulated oil is determined; The attenuation baseline was corrected using the degree of carbonization to confirm the adsorption heat conduction attenuation parameters; The steps for determining the degree of carbonization of oil accumulation based on the characteristics of the heat release rate include: Based on the heat release rate characteristics, identify the local regions in the heat release rate characteristics that indicate the highest degree of carbonization; Time series analysis was performed on the local region with the highest degree of carbonization to obtain the heat decay curve; The degree of carbonization of the accumulated oil is confirmed based on the characteristic parameters of the heat decay curve.

2. The method for detecting the adsorption efficiency of a range hood according to claim 1, characterized in that, The step of identifying transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the adhering layer based on each of the temperature difference data and the preset oil fume accumulation characteristics includes: Based on each of the temperature difference data, after confirming the temporal morphological characteristics of each of the temperature difference data, transient convection disturbance data and deposited layer heat storage and release data are identified. Based on each of the temperature difference data, temperature trend characteristics are determined; The temperature trend characteristics are compared with the preset oil fume accumulation characteristics to determine the oil fume change trend parameters; Based on the oil fume change trend parameters and each of the temperature difference data, the data during the period of increased fan speed is identified as the cumulative oil fume adsorption data.

3. The method for detecting the adsorption efficiency of a range hood according to claim 1, characterized in that, The steps for obtaining temperature difference data between the airflow inlet and outlet sides of the filter in the range hood over a period of time include: Acquire initial data for each temperature difference between the airflow inlet and outlet sides at the filter in the range hood over a period of time; The initial data for each temperature difference is smoothed to obtain the data for each temperature difference.

4. The method for detecting the adsorption efficiency of a range hood according to claim 1, characterized in that, The step of obtaining the test result of the smoke hood's purification adsorption efficiency based on the adsorption heat effect growth rate, the heat storage and release data of the adhesion layer, and the preset growth threshold further includes: The test results are sent to the central management system; In response to the maintenance plan output by the central management system based on the test results, the maintenance priority and maintenance resource information for each filter screen are determined; Based on the maintenance priority and the maintenance resource information, a global maintenance plan is generated for each filter.

5. The method for detecting the adsorption efficiency of a range hood according to claim 1, characterized in that, The step of determining the growth rate of the adsorption heat effect based on the cumulative adsorption data of oil fumes over a period of time after separation includes: Quantitative analysis was performed on the cumulative adsorption data of oil fumes over a period of time after separation to obtain quantitative data, trend indicators, and oil accumulation coefficient. Based on the aforementioned trend indicators and oil accumulation coefficient, the overall adsorption efficiency coefficient of the filter screen is determined. Based on the overall adsorption efficiency coefficient and quantitative data of the filter, the growth rate of the adsorption heat effect is determined.

6. The method for detecting the adsorption efficiency of a range hood according to claim 5, characterized in that, The step of determining the growth rate of the adsorption heat effect based on the comprehensive adsorption efficiency coefficient and quantitative data of the filter screen includes: After confirming the rate of change of the quantified data, the cooking state intensity is obtained; Based on the overall adsorption efficiency coefficient of the filter, the degradation index of the filter is identified. Based on the cooking state intensity and filter degradation index, the contribution parameters of the comprehensive adsorption efficiency score are determined; The growth rate of the adsorption heat effect is determined based on the overall adsorption efficiency coefficient of the filter, the quantitative data, and the contribution parameters.

7. A system for detecting the adsorption efficiency of a range hood, characterized in that, The system includes: The acquisition module is used to acquire data on the temperature difference between the air inlet and outlet sides of the filter in the range hood over a period of time. The identification module is used to identify transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the adhering layer based on each of the temperature difference data and preset oil fume accumulation characteristics. The separation module is used to separate transient convection disturbance data from the cumulative oil fume adsorption data based on transient convection disturbance data and cumulative oil fume adsorption data, so as to obtain the cumulative oil fume adsorption data for a period of time after separation. The determination module is used to determine the growth rate of the adsorption heat effect based on the cumulative adsorption data of oil fumes over a period of time after separation. The monitoring module is used to obtain the detection results of the smoke machine's purification adsorption efficiency based on the growth rate of the adsorption heat effect, the heat storage and release data of the attachment layer, and the preset growth threshold. The steps for separating transient convection disturbance data from the cumulative adsorption data of oil fumes, based on transient convection disturbance data and cumulative adsorption data of oil fumes, to obtain cumulative adsorption data of oil fumes over a period of time after separation, include: Based on transient convection disturbance data and oil fume cumulative adsorption data, transient convection disturbance data is separated from the oil fume cumulative adsorption data to obtain initial oil fume cumulative adsorption data; Based on the cumulative adsorption data of the oil fumes, the adsorption heat conduction attenuation parameters were confirmed; The initial adsorption data of oil fume accumulation is corrected using the adsorption heat conduction attenuation parameter to obtain the cumulative adsorption data of oil fume over a period of time after separation. Based on the accumulated oil fume adsorption data, the steps for confirming the adsorption heat conduction attenuation parameters include: Based on the accumulated adsorption data of oil fumes, the rate characteristics and attenuation base of heat release from accumulated oil fumes are extracted; Based on the characteristics of the heat release rate, the degree of carbonization of the accumulated oil is determined; The attenuation baseline was corrected using the degree of carbonization to confirm the adsorption heat conduction attenuation parameters; The steps for determining the degree of carbonization of oil accumulation based on the characteristics of the heat release rate include: Based on the heat release rate characteristics, identify the local regions in the heat release rate characteristics that indicate the highest degree of carbonization; Time series analysis was performed on the local region with the highest degree of carbonization to obtain the heat decay curve; The degree of carbonization of the accumulated oil is confirmed based on the characteristic parameters of the heat decay curve.

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