Method and system for detecting purification and adsorption efficiency of range hood

Abstract

The invention relates to the technical field of range hood monitoring, in particular to a method and a system for detecting purification and adsorption efficiency of a range hood. The method comprises the following steps: acquiring each piece of temperature difference data between an airflow inlet side and an airflow outlet side; on the basis of each piece of temperature difference data and preset lampblack accumulation characteristics, transient convective disturbance data, lampblack accumulation adsorption data and adhesion layer heat storage and release data are recognized; based on the transient convective disturbance data and the oil fume accumulated adsorption data, separating the transient convective disturbance data from the oil fume accumulated adsorption data to obtain the oil fume accumulated adsorption data within a period of time after separation; determining an adsorption heat effect growth rate based on the lampblack accumulated adsorption data; and obtaining a detection result of the purification adsorption efficiency of the range hood based on the adsorption heat effect growth rate, the adhesion layer heat storage and release data and a preset growth threshold value. The invention aims to solve the problem that the detection accuracy of the purification and adsorption efficiency of the range hood is low due to the fact that the heat effect generated by the oil smoke adsorption behavior is difficult to accurately separate in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of range hood monitoring, and in particular to a method and system for detecting the purification and adsorption efficiency of a range hood. Background Art

[0002] The adsorption efficiency of the filter in the oil fume purification equipment is a key indicator to measure the performance of the equipment. The existing technology indirectly judges the thermal effect generated by the adsorption of oil fume particles on the filter surface by monitoring the temperature difference of the airflow before and after the filter, and then evaluates its adsorption performance. With the long-term use of the filter, oil stains will gradually accumulate on its surface, and the oil stains will significantly change the physical properties of the filter. The oil film formed on the filter will increase the heat conduction resistance, resulting in a delay and attenuation when the adsorbed heat is transferred to the temperature sensing element, making the measured temperature difference signal lower than the actual adsorption thermal effect intensity. In addition, the continuous accumulation of oil stains will clog some of the meshes of the filter, destroying the original uniform airflow distribution and causing uneven convective heat exchange effects, thereby generating a non-adsorptive temperature difference signal before and after the filter.

[0003] Furthermore, under high temperature, the oil stains attached to the filter may harden or even carbonize, forming a carbonized layer with heat storage capacity. The carbonized layer will slowly absorb and release heat during operation, further introducing additional temperature difference signals. Therefore, the temperature difference signal measured by the existing method contains a composite result of multiple physical processes, such as the adsorption heat conduction attenuation caused by the thermal resistance of the oil film, the non-adsorption convective heat transfer temperature difference caused by the 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 thus making it impossible to accurately detect the purification and adsorption efficiency of the range hood, thereby leading to misjudgment of the working status of the filter. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for detecting the purification adsorption efficiency of a range hood, so as to solve the problem that the existing technology is difficult to accurately separate the thermal effect generated by the oil fume adsorption behavior, resulting in low detection accuracy of the purification adsorption efficiency of the range hood.

[0005] To achieve the above object, the present invention adopts the following technical solution: a method for detecting the purification adsorption efficiency of a range hood, comprising: Obtaining each temperature difference data between the air flow inlet side and the air flow outlet side of the filter in the range hood over a period of time; Based on each of the temperature difference data and the preset oil smoke accumulation characteristics, identifying transient convection disturbance data, oil smoke accumulation adsorption data, and attachment layer heat storage and release data; Based on the transient convective disturbance data and the oil fume cumulative adsorption data, separating the transient convective disturbance data from the oil fume cumulative adsorption data to obtain the oil fume cumulative adsorption data within a period of time after separation; Based on the cumulative adsorption data of oil smoke over a period of time after separation, the adsorption heat effect growth rate is determined; Based on the adsorption heat effect growth rate, the heat storage and release data of the attachment layer and the preset growth threshold, the detection result of the range hood purification adsorption efficiency is obtained.

[0006] Furthermore, the present application also proposes that the steps of identifying transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the attached layer based on each of the temperature difference data and the preset oil fume accumulation characteristics include: Based on each of the temperature difference data, after confirming the temporal morphological characteristics of each of the temperature difference data, identifying the transient convective disturbance data and the attached layer heat storage and release data; determining a temperature trend characteristic based on each of the temperature difference data; Compare the temperature trend characteristics with the preset oil smoke accumulation characteristics to determine the oil smoke change trend parameters; Based on the oil fume variation trend parameter and each of the temperature difference data, the data during the period when the fan speed is increased is confirmed as the oil fume cumulative adsorption data.

[0007] Furthermore, the present application also proposes that the steps of obtaining each temperature difference data between the air flow inlet side and the air flow outlet side of the filter in the range hood over a period of time include: Obtaining initial data of each temperature difference between the air inlet side and the air outlet side of the filter in the range hood over a period of time; Each temperature difference initial data is smoothed to obtain each temperature difference data.

[0008] Furthermore, the present application also proposes that after the step of obtaining the detection result of the range hood purification adsorption efficiency based on the adsorption heat effect growth rate, the attachment layer storage and release heat data and the preset growth threshold, the following steps are also included: Sending the test results to a central management system; In response to the maintenance plan output by the central management system based on the detection results, determining the maintenance priority and maintenance resource information of each filter; A global maintenance plan for each filter is generated based on the maintenance priority and the maintenance resource information.

[0009] Furthermore, the present application also proposes that based on the transient convective disturbance data and the oil fume cumulative adsorption data, the steps of separating the transient convective disturbance data from the oil fume cumulative adsorption data to obtain the oil fume cumulative adsorption data for a period of time after separation include: Based on the transient convective disturbance data and the oil fume cumulative adsorption data, separating the transient convective disturbance data from the oil fume cumulative adsorption data to obtain the oil fume cumulative initial adsorption data; Determining adsorption heat conduction attenuation parameters based on the oil smoke cumulative adsorption data; The adsorption heat conduction attenuation parameter is used to correct the initial adsorption data of the accumulated fume, and the accumulated adsorption data of the fume within a period of time after separation is obtained.

[0010] Furthermore, the present application also proposes that the steps of confirming the adsorption heat conduction attenuation parameter based on the oil fume cumulative adsorption data include: Based on the oil smoke accumulation adsorption data, extracting the rate characteristics and attenuation base of the oil pollution accumulation heat release; Determine the carbonization degree of the accumulated oil stain based on the heat release rate characteristics; The attenuation base is corrected using the carbonization degree to confirm the adsorption heat conduction attenuation parameter.

[0011] Furthermore, the present application also proposes that the steps of confirming the carbonization degree of the accumulated oil pollution according to the heat release rate characteristics include: identifying, based on the heat release rate characteristic, a local region indicating a highest degree of carbonization in the heat release rate characteristic; Performing time series analysis on the local area with the highest carbonization degree to obtain a heat decay curve; The carbonization degree of the accumulated oil stains is determined based on the characteristic parameters of the heat decay curve.

[0012] Furthermore, the present application also proposes that the step of determining the adsorption heat effect growth rate based on the accumulated oil fume adsorption data within a period of time after separation includes: Quantitatively analyze the accumulated oil smoke adsorption data over a period of time after separation to obtain quantitative data, change trend indicators and oil pollution accumulation coefficients; Determine the comprehensive adsorption efficiency coefficient of the filter based on the change trend index and the oil accumulation coefficient; Based on the comprehensive adsorption efficiency coefficient and quantitative data of the filter, the adsorption heat effect growth rate is determined.

[0013] Furthermore, the present application also proposes the comprehensive adsorption efficiency coefficient and quantitative data based on the filter screen, and the steps for determining the adsorption heat effect growth rate include: After confirming the rate of change of the quantitative data, obtaining the cooking state intensity; Based on the comprehensive adsorption efficiency coefficient of the filter, confirm the deterioration index of the filter; Determining a contribution parameter of a comprehensive adsorption efficiency score based on the intensity of the cooking state and a deterioration index of the filter; The adsorption heat effect growth rate is determined based on the comprehensive adsorption efficiency coefficient of the filter screen, the quantitative data, and the contribution parameter.

[0014] The present application also provides a system for detecting the purification and adsorption efficiency of a range hood, the system comprising: An acquisition module, used to acquire each temperature difference data between the air flow inlet side and the air flow outlet side of the filter in the range hood over a period of time; an identification module for identifying transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the attachment layer based on each of the temperature difference data and preset oil fume accumulation characteristics; a separation module for separating the transient convective disturbance data from the oil fume cumulative adsorption data based on the transient convective disturbance data and the oil fume cumulative adsorption data, and obtaining the oil fume cumulative adsorption data within a period of time after separation; A determination module, for determining an adsorption heat effect growth rate based on the accumulated oil smoke adsorption data over a period of time after separation; The monitoring module is used to obtain the detection result of the range hood purification adsorption efficiency based on the adsorption heat effect growth rate, the heat storage and release data of the attachment layer and the preset growth threshold.

[0015] Compared with the prior art, the method and system for detecting the purification and adsorption efficiency of a range hood of the present invention have the following advantages: The present invention continuously acquires temperature difference data between the airflow inlet and outlet of the range hood filter over a period of time. Based on each acquired temperature difference data and combined with preset oil fume accumulation characteristics, it can identify transient convective disturbance data, oil fume accumulation adsorption data, and attached layer heat storage and release data. Based on the transient convective disturbance data and the oil fume accumulation adsorption data, the transient convective disturbance data is separated from the oil fume accumulation adsorption data. The resulting oil fume accumulation adsorption data for the period after separation reflects the thermal effect generated during the oil fume adsorption process, eliminating the interference of airflow disturbances. Based on the oil fume accumulation adsorption data for the period after separation, the adsorption heat effect growth rate is determined, measuring the dynamic changes in the filter's adsorption capacity. By combining the adsorption heat effect growth rate, attached layer heat storage and release data, and a preset growth threshold, the adsorption heat effect growth rate provides information on the filter's current adsorption activity, while the attached layer heat storage and release data supplements information on the impact of oil accumulation on the filter's overall thermophysical properties. This allows for an objective assessment of the filter's purification and adsorption efficiency, improving the accuracy of test results and accurately evaluating the range hood's purification and adsorption efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present invention, the following briefly introduces the drawings required for use in the specific embodiments. In all the drawings, each element or part is not necessarily drawn according to the actual scale.

[0017] Figure 1 The present invention is a flow chart of a method for detecting the purification and adsorption efficiency of a range hood.

[0018] Figure 2 This is a structural block diagram of a range hood purification and adsorption efficiency detection system of the present invention.

[0019] In the figure: 210, acquisition module; 220, identification module; 230, separation module; 240, determination module; 250, monitoring module.

[0020] The implementation and advantages of the functions of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] The following diagrams illustrate various embodiments of the present invention. For clarity, many practical details are included in the following description. However, it should be understood that these practical details are not intended to limit the present invention. In other words, in some embodiments of the present invention, these practical details are not essential. Furthermore, to simplify the drawings, some commonly used structures and components are depicted in simplified schematic form.

[0022] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the various components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0023] In addition, in the present invention, descriptions such as "first" and "second" are only used for descriptive purposes and do not specifically refer to the order or sequence, nor are they used to limit the present invention. They are only used to distinguish components or operations described with the same technical terms, and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between the various embodiments can be combined with each other, but they must be based on the ability of ordinary technicians in this field to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0024] Traditional range hood purification equipment indirectly determines the thermal effect generated by the adsorption of oil fume particles by monitoring the temperature difference between the airflow before and after the filter, and then evaluates its adsorption performance. However, after long-term use, oil stains will gradually accumulate on the filter surface. The attachments will significantly change the physical properties of the filter, including increasing heat conduction resistance, causing uneven airflow distribution, and forming a carbonized layer with heat storage capacity. This makes it impossible for simple temperature difference measurements to accurately reflect the true adsorption thermal effect, leading to misjudgment of the filter's working status and the failure of maintenance strategies. How to accurately separate and identify the real-time thermal effect generated by the current oil fume adsorption behavior in this complex and dynamically changing composite temperature difference signal to ensure accurate evaluation of the filter's adsorption efficiency and timely issuance of maintenance instructions is a technical challenge that needs to be solved urgently.

[0025] In order to further understand the content, features and effects of the present invention, the following embodiments are given as examples and described in detail with reference to the accompanying drawings: See also Figure 1 The present invention provides a method for detecting the purification adsorption efficiency of a range hood, comprising the following steps: S100. Obtain each temperature difference data between the airflow inlet and airflow outlet 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 airflow outlet of the range hood filter. This temperature difference data can be obtained by using a temperature measuring device such as a high-precision thermistor, thermocouple, or infrared temperature sensor to collect real-time temperatures before and after the filter and calculate the difference. This data is used to characterize the thermal effect generated by the filter during the oil fume adsorption process and other non-adsorption thermophysical phenomena.

[0026] S200, based on each of the temperature difference data and the preset oil smoke accumulation characteristics, identify transient convection disturbance data, oil smoke accumulation adsorption data, and attached layer heat storage and release data. The preset oil smoke accumulation characteristics refer to pre-established temperature change patterns or signal feature libraries associated with different oil smoke accumulation levels, which can be constructed based on historical operation 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, which are used to assist the system in distinguishing components in the composite temperature difference signal caused by different physical processes such as oil smoke adsorption, convection disturbances, and attached layer heat storage and release. Transient convection disturbance data is a temperature fluctuation signal caused by rapid changes in airflow velocity, direction, or pressure that is not caused by oil smoke adsorption. It can appear as a short-term, high-frequency temperature difference spike or drop. For example, it can be caused by uneven airflow caused by sudden changes in fan speed, external airflow disturbances, or partial blockage of the filter, and is used to identify and remove interference components in the composite temperature difference signal that are not related to oil smoke adsorption. The accumulated oil fume adsorption data is a temperature change signal generated primarily by the heat released by oil fume particles during adsorption on the filter surface. It can be expressed as a temperature difference trend that gradually accumulates with the increase in oil fume volume. For example, it can be a continuous temperature rise signal generated when the filter captures oil fume under stable airflow conditions, which is used to reflect the filter's true oil fume adsorption capacity and efficiency. The attached layer heat storage and release data is a temperature signal generated by the oily attached layer (especially the carbonized layer) accumulated on the filter surface absorbing or releasing heat when the temperature changes. It can be expressed as a temperature difference change with a certain thermal hysteresis effect. For example, after cooking stops, the temperature difference decays due to the slow heat dissipation of the attached layer. This is used to quantify the impact of oil accumulation on the thermophysical properties of the filter and distinguish it from the true adsorption heat effect.

[0027] S300, based on the transient convective disturbance data and the accumulated oil fume adsorption data, separating the transient convective disturbance data from the accumulated oil fume adsorption data to obtain the accumulated oil fume adsorption data within a period of time after separation. Separating the transient convective disturbance data is a process of removing the transient convective disturbance data from the accumulated oil fume adsorption data through signal processing technology, which can be achieved by using a filtering algorithm, a pattern recognition algorithm, or an adaptive signal processing method; for example, wavelet transform, Kalman filtering, or a machine learning-based classifier can be used to identify and filter out transient convective disturbances, the purpose of which is to obtain a purer and more accurate oil fume adsorption thermal effect signal and avoid interference of convective disturbances on adsorption efficiency evaluation.

[0028] S400: Determine the adsorption heat effect growth rate based on the accumulated oil fume adsorption data over a period of time after separation. The adsorption heat effect growth rate is the rate at which the accumulated oil fume adsorption data after separation changes over time, and can be expressed as an increment of the adsorption heat effect per unit time. For example, it can be the result of performing a time derivative calculation or slope analysis on the adsorption heat effect signal, which is 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.

[0029] S500: Based on the adsorption heat effect growth rate, the heat storage and release data of the attached layer, and a preset growth threshold, a test result of the range hood purification adsorption efficiency is obtained. The preset growth threshold is a pre-set standard for determining whether the range hood purification adsorption efficiency has reached or fallen below a certain critical value. This threshold can be determined based on the design life of the filter, its performance degradation curve, or its maintenance strategy. For example, it can be a lower limit of the adsorption heat effect growth rate. When the actual growth rate falls below this threshold, it is considered that the filter efficiency has significantly decreased, providing an objective basis for judgment and guiding the maintenance and replacement of the range hood.

[0030] Specifically, to obtain temperature difference data between the airflow inlet and outlet at the range hood filter over a period of time, high-precision PT100 platinum resistance temperature sensors can be deployed on both the airflow inlet and outlet sides of the filter. An acquisition module, such as a multi-channel analog-to-digital converter, converts the continuous temperature signal into a digital data stream. A processor, such as an STM32 series processor, reads this digital temperature data in real time and calculates the instantaneous temperature difference between the inlet and outlet. This difference is stored as temperature difference data in internal memory and timestamped. Specifically, to identify transient convective disturbance data, oil fume accumulation adsorption data, and heat storage and release data from the adsorption layer, the microcontroller can execute a signal processing algorithm. This algorithm first performs a preliminary time series analysis on the temperature difference data, for example, using sliding average or exponential smoothing to identify its temporal morphological characteristics. Pre-set oil fume accumulation characteristics can be stored in a lookup table or a neural network model trained to learn typical patterns of temperature difference signals under different oil accumulation conditions. When real-time temperature difference data is input, the algorithm matches it with pre-set features to distinguish between transient spikes caused by sudden airflow changes, sustained temperature rises due to oil fume adsorption, and slow changes due to the heat capacity effect of the oil layer. For example, for transient convective disturbances, a high-pass filter can be used to capture their rapidly changing components; for accumulated oil fume adsorption, a low-pass filter can be used to extract its slowly changing trends; and for heat storage and release in the adsorption layer, curve fitting based on thermodynamic models can be used to identify its characteristics. Furthermore, to separate transient convective disturbance data from accumulated oil fume adsorption data, the microcontroller can employ adaptive filtering techniques such as the least mean square algorithm or the Kalman filter. Transient convective disturbance data can serve as a reference input, while accumulated oil fume adsorption data serves as the primary input. The filter learns the correlation between transient convective disturbances and accumulated oil fume adsorption data and dynamically subtracts the influence of convective disturbances from the accumulated oil fume adsorption data, resulting in a purer accumulated oil fume adsorption data over a period of time after separation. When determining the adsorption heat effect growth rate, the microcontroller performs linear regression analysis or differential calculation on the separated oil fume cumulative adsorption data to obtain its slope or rate of change over time, that is, the adsorption heat effect growth rate. For example, the average rate of change of the data can be calculated every certain time window. Finally, when the test results of the range hood purification adsorption efficiency are obtained, the microcontroller compares the calculated adsorption heat effect growth rate with the preset growth threshold. The preset growth threshold can be a fixed value stored in a non-volatile memory, or a parameter that is dynamically adjusted according to the service life of the filter. At the same time, combined with the heat storage and release data of the attachment layer, for example, by evaluating the changes in its heat capacity or thermal resistance, the judgment of the adsorption heat effect growth rate is corrected.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 attachment layer indicates that the accumulation of oil pollution has reached a critical state, it is judged that the range hood purification adsorption efficiency has significantly decreased, and the corresponding test results are output, for example, through an LED indicator light, a buzzer or a wireless communication module to the user interface or remote monitoring platform.

[0031] In this embodiment, each temperature difference data between the airflow inlet and airflow outlet of the filter in the range hood is obtained over a period of time. The original temperature difference data is a direct reflection of the working state of the filter, but it contains the superimposed influence of multiple physical processes. In view of this, based on each temperature difference data obtained and combined with the preset oil smoke accumulation characteristics, a preliminary signal decomposition is performed. Transient convection disturbance data, oil smoke accumulation adsorption data, and attached layer heat storage and release data can be identified. Transient convection disturbance data represents the instantaneous temperature fluctuation caused by airflow changes, while the attached layer heat storage and release data reflects the heat absorption and release generated by the oil layer on the filter surface due to heat capacity, and the oil smoke accumulation adsorption data preliminarily includes the heat generated by oil smoke adsorption. In order to further purify the oil smoke adsorption signal, based on the identified transient convection disturbance data and oil smoke accumulation adsorption data, an operation of separating the transient convection disturbance data from the oil smoke accumulation adsorption data is performed. This is because transient convection disturbances are often superimposed on the real adsorption signal, causing interference to it. Through precise separation, the cumulative oil fume adsorption data for a period of time after separation can be obtained. This data more purely reflects the thermal effect generated by the oil fume adsorption process, eliminating the interference of airflow disturbances. Subsequently, based on the cumulative oil fume adsorption data for a period of time after separation, the adsorption thermal effect growth rate is determined. This growth rate is an important indicator for measuring the dynamic changes in the filter's adsorption capacity, and its changing trend is directly related to the filter's performance degradation. Ultimately, in order to obtain comprehensive and accurate test results on the range hood's purification and adsorption efficiency, the adsorption thermal effect growth rate, the attached layer's heat storage and release data, and the preset growth threshold are comprehensively considered. The adsorption thermal effect growth rate provides information on the filter's current adsorption activity, while the attached layer's heat storage and release data supplements the information on the impact of oil accumulation on the filter's overall thermal physical properties. This allows for an objective judgment of the filter's purification and adsorption efficiency, providing a reliable basis for subsequent maintenance decisions.

[0032] In some of the above embodiments of the present application, the present application further proposes that the step of identifying transient convection disturbance data, oil fume accumulation adsorption data, and attached layer heat storage and release data based on each of the temperature difference data and the preset oil fume accumulation characteristics includes: Based on each temperature difference data set, after confirming the temporal morphological characteristics of each temperature difference data set, transient convective disturbance data and attached layer heat storage and release data are identified. Temporal morphological characteristics are the patterns of change in the temperature difference data along the time axis, specifically instantaneous fluctuations, periodic changes, duration, or rate of change, with the goal of distinguishing temperature signals caused by different physical processes.

[0033] Based on each of the temperature difference data, a temperature trend feature is determined. The temperature trend feature is the overall trend of the temperature difference data over time, which can be an increase, decrease, or stability. Its purpose is to reflect the changes in the macro thermal effect during the oil smoke accumulation and adsorption process.

[0034] Compare the temperature trend characteristics with the preset oil fume accumulation characteristics to determine the oil fume change trend parameters. Among them, the preset oil fume accumulation characteristics are established based on historical data or experimental results, and describe the typical pattern of temperature difference data changes during the oil fume accumulation adsorption process in the filter. Specifically, it can be a series of predefined temperature change curves, thresholds or mathematical models, which provide a reference benchmark for identifying real oil fume accumulation 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 that reflect the current oil fume accumulation adsorption state and change direction. Specifically, they can be numerical values, classification labels or state descriptions to guide the confirmation of subsequent oil fume accumulation adsorption data.

[0035] Based on the oil fume variation trend parameter and each of the temperature difference data, the data during the period when the fan speed is increased is confirmed as the oil fume cumulative adsorption data.

[0036] Specifically, to identify the temporal morphological characteristics of each temperature difference data point, methods such as wavelet transform or Fourier transform can be used to analyze the frequency components and instantaneous energy distribution of the temperature difference signal. This can identify high-frequency, short-duration pulses of transient convective disturbances and low-frequency, slowly varying heat storage and release data from the adsorption layer. For example, transient convective disturbances may manifest as high-frequency spikes, while heat storage and release from the adsorption layer may manifest as slow baseline drift. Furthermore, to determine the temperature trend characteristics, a sliding average or linear regression analysis can be performed on the temperature difference data to smooth out transient fluctuations and reveal an overall upward, downward, or stable temperature trend over time. This identified temperature trend characteristic is then compared with a pre-stored, preset oil smoke accumulation characteristic, which can be a database containing multiple typical oil smoke accumulation patterns. For example, by calculating the similarity (e.g., Euclidean distance or correlation coefficient) between the current temperature trend characteristic and each pattern in the database, the oil smoke variation trend parameter, such as rapid accumulation, slow accumulation, or stable state, can be determined. Finally, by combining the oil fume variation trend parameter with the range hood fan speed control signal or actual speed data, the temperature difference data corresponding to the fan speed increase period is identified as the oil fume cumulative adsorption data. For example, when the fan switches from low to high speed and is accompanied by a cumulative trend indicated by the oil fume variation trend parameter, the temperature difference data segment at that time is marked as oil fume cumulative adsorption data. This allows for more accurate extraction of thermal effect data directly related to oil fume adsorption from complex temperature signals.

[0037] In this embodiment, by performing a refined analysis of the temperature difference data between the airflow inlet side and the airflow outlet side of the range hood filter, the transient convection disturbance data, the oil fume accumulation adsorption data, and the attached layer heat storage and release data can be effectively distinguished and identified. Specifically, by confirming the temporal morphological characteristics of the temperature difference data, the effects of transient convection disturbance and attached layer heat storage and release can be effectively separated; by determining the temperature trend characteristics and comparing them with the preset oil fume accumulation characteristics, the oil fume change trend can be accurately judged; and by combining the data during the period of increasing the fan speed, the oil fume accumulation adsorption data can be accurately confirmed. This allows pure oil fume accumulation adsorption thermal effect data to be extracted from the complex composite temperature signal, thereby providing a reliable data basis for the subsequent accurate detection of the range hood purification adsorption efficiency, significantly improving the accuracy of the test results.

[0038] In some of the above embodiments of the present application, the present application further proposes that the steps of obtaining each temperature difference data between the air flow inlet side and the air flow outlet side of the filter in the range hood over a period of time include: Acquire initial data of each temperature difference between the air flow inlet side and the air flow outlet side of the filter in the range hood over a period of time.

[0039] Each of the initial temperature difference data is smoothed to obtain temperature difference data. Smoothing is a filtering or averaging operation performed on the original data sequence to eliminate random noise and transient fluctuations in the data, thereby revealing underlying trends or patterns in the data. This can be achieved using algorithms such as moving average, exponential smoothing, or Kalman filtering to improve the signal-to-noise ratio of the data and ensure the accuracy and stability of subsequent data analysis.

[0040] In this embodiment, the temperature difference data between the air inlet side and the air outlet side of the filter in the range hood are pre-processed to improve the accuracy of subsequent analysis. Specifically, each initial 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 is first obtained. The initial data comes directly from the sensor measurement and may be affected by factors such as environmental interference, sensor noise itself, or instantaneous airflow fluctuations, resulting in irregular noise or spikes in the data. In order to eliminate these interferences and ensure the stability and reliability of the data, each initial temperature difference data is smoothed. Smoothing can effectively filter out high-frequency noise components in the data, making the data curve smoother and better reflecting the actual temperature change trend. Thus, each temperature difference data is obtained, and the smoothed data has higher accuracy and stability. Since the 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 smoke accumulation adsorption data, and heat storage and release data of the attached layer. Through this solution's smoothing process, noise in the data is effectively suppressed, enabling the subsequent recognition algorithm to more accurately capture the true temperature variation characteristics caused by physical processes such as fume adsorption, convection disturbances, and heat storage and release in the attached layer. This significantly improves the overall accuracy and reliability of range hood purification adsorption efficiency testing. This enables the entire detection method to operate more robustly in complex and changing real-world operating environments, avoiding misjudgments caused by data quality issues and further enhancing the accuracy of filter operating status assessments.

[0041] In some of the above embodiments of the present application, the present application further proposes that after the step of obtaining the detection result of the range hood purification adsorption efficiency based on the adsorption heat effect growth rate, the attachment layer storage and release heat data and the preset growth threshold, the following steps are further included: The test results are sent to a central management system, which is a centralized information processing platform, such as a server cluster, cloud computing platform, or dedicated management software system, that receives, stores, and analyzes test data from multiple range hoods or filters, and performs unified decision-making and management.

[0042] In response to the maintenance plan output by the central management system based on the test results, the maintenance priority and maintenance resource information of each filter are determined. Among them, 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 the preset maintenance strategy, historical data and operating status. It may include specific operations such as cleaning, replacement, and inspection, and its purpose is to guide the subsequent maintenance work of the filter. The maintenance priority is an indicator for sorting the maintenance needs of different filters based on the test results of the filter and other factors. It can be expressed in numerical values, levels or classification labels to ensure that resources are allocated first to the filters that need it 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 spare parts required, tools and equipment, and maintenance time windows, etc., to provide specific resource basis for maintenance plans.

[0043] Based on the maintenance priorities and maintenance resource information, a global maintenance plan is generated for each filter. This plan comprehensively considers the maintenance priorities and available maintenance resources of all filters, creating a coordinated maintenance schedule and task allocation plan for each filter 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.

[0044] Specifically, after obtaining the test results of the range hood's purification and adsorption efficiency, the test result data packet is transmitted to a central management system deployed in the cloud, for example, via a wireless communication module. The central management system can be a back-end application based on a microservices architecture, running on the cloud platform and comprising a data reception service, a data analysis service, and a decision engine service. The data analysis service analyzes the received test results. For example, if the adsorption heat effect growth rate of a filter falls below a preset threshold, this indicates a decrease in adsorption efficiency. The decision engine service then outputs a maintenance plan based on the analysis results, combined with the filter's historical maintenance history, current operating time, and its criticality within the kitchen system. For example, for filters with a severe decrease in adsorption efficiency and long operating time, the maintenance plan may recommend immediate replacement; for filters with a slight decrease in adsorption efficiency, regular cleaning may be recommended. In response to the maintenance plan output by the central management system, the maintenance priority and maintenance resource information for each filter can be determined based on preset rules or machine learning models. For example, for filters recommended for immediate replacement, their maintenance priority may be set to high, and the maintenance resource information may include the need for one professional maintenance personnel and an estimated maintenance time of two hours. For filters that are recommended for regular cleaning, their priority can be set to medium, and the maintenance resource information can include that 1 general maintenance person is required and the estimated maintenance time is 0.5 hours. Based on these determined maintenance priorities and maintenance resource information, a scheduling algorithm can be used to generate a global maintenance plan for each filter. The plan can be a visual Gantt chart that shows the maintenance start time, end time, responsible person and required resources for each filter, and takes into account the availability of maintenance personnel, spare parts inventory and the off-peak operating hours of the kitchen. For example, the system can automatically schedule high-priority filters to be replaced when the kitchen is shut down at night, while medium-priority filters are scheduled for cleaning during the idle period before lunch the next day.

[0045] In this embodiment, the test results of the range hood's purification and adsorption efficiency are sent to the central management system, realizing centralized management of the test data and providing data support for subsequent maintenance decisions. In response to the maintenance plan output by the central management system based on the test results, the maintenance priority and maintenance resource information of each filter are determined, and the maintenance resources can be optimized according to the actual status of the filter, avoiding the waste of maintenance resources. Based on the maintenance priority and maintenance resource information, a global maintenance plan is generated for each filter, so that the maintenance work can be standardized and streamlined, thereby ensuring the quality and efficiency of the maintenance work and solving the problems of the failure to effectively convert the test results into maintenance actions and the unreasonable allocation of maintenance resources.

[0046] In some of the above embodiments of the present application, the present application further proposes separating the transient convective disturbance data from the oil fume cumulative adsorption data based on the transient convective disturbance data and the oil fume cumulative adsorption data, and obtaining the oil fume cumulative adsorption data within a period of time after separation, including the following steps: Based on the transient convection disturbance data and the oil fume accumulation adsorption data, the transient convection disturbance data is separated from the oil fume accumulation adsorption data to obtain the oil fume accumulation initial adsorption data. Among them, the transient convection disturbance data is the temperature difference signal caused by non-oil fume adsorption caused by transient changes in air flow velocity, direction or pressure during the operation of the range hood. It can be identified by time domain analysis, frequency domain analysis or machine learning-based pattern recognition methods to distinguish interference signals unrelated to oil fume adsorption. The oil fume accumulation adsorption data is the temperature difference signal corresponding to the thermal effect generated when oil fume particles are adsorbed on the filter surface. It can be confirmed by comparing the temperature trend characteristics with the preset oil fume accumulation characteristics to reflect the actual oil fume adsorption state of the filter. The oil fume accumulation initial adsorption data is the data obtained after preliminarily removing the transient convection disturbance data from the oil fume accumulation adsorption data. It can be preliminarily separated by signal difference, regression analysis or adaptive filtering to provide basic data for subsequent precise correction.

[0047] Based on the accumulated oil smoke adsorption data, an adsorption heat conduction attenuation parameter is determined. The adsorption heat conduction attenuation parameter is a quantitative indicator reflecting the degree to which heat transfer efficiency decreases due to the formation of an oil layer during the accumulation of oil smoke on the filter surface. This parameter can be determined by analyzing the characteristic heat release rate of accumulated oil, fitting an attenuation model, or using an empirical calibration curve to quantify the attenuation effect of oil on the adsorption heat effect signal.

[0048] The adsorption heat conduction attenuation parameter is used to correct the initial adsorption data of the accumulated fume, and the accumulated adsorption data of the fume within a period of time after separation is obtained.

[0049] In this embodiment, by introducing a correction mechanism for the accumulated oil fume adsorption data, the problem that direct separation may lead to inaccurate data is solved. Specifically, based on the transient convection disturbance data and the accumulated oil fume adsorption data, the transient convection disturbance data is preliminarily separated from the accumulated oil fume adsorption data, thereby obtaining the initial accumulated oil fume adsorption data. By preliminarily eliminating the interference of instantaneous airflow changes on the temperature signal, subsequent processing can focus on the signal related to oil fume adsorption. Since the accumulation of oil on the filter will change its thermal conductivity characteristics, simple separation cannot completely eliminate the errors caused by this physical change. Based on the accumulated oil fume adsorption data, the adsorption heat conduction attenuation parameter is confirmed to quantify the obstruction and attenuation effect of the oil layer on the adsorption heat transfer. By analyzing the accumulated oil fume adsorption data itself, thermal conduction attenuation information related to the degree and nature of oil accumulation can be extracted, such as the effect of the thickness, density or carbonization degree of the oil on heat conduction. Subsequently, the confirmed adsorption heat conduction attenuation parameter is used to correct the accumulated oil fume initial adsorption data. The calibration process compensates for the heat transfer attenuation caused by the oil layer, allowing the initial oil fume adsorption data to more accurately reflect the actual oil fume adsorption thermal effect. Calibration effectively eliminates or reduces the interference of transient convection disturbances and the thermal resistance of the oil layer on the adsorption thermal effect signal, thereby obtaining more accurate oil fume adsorption data for a period of time after separation. This more accurately reflects the filter's true adsorption state, providing a more reliable and detailed data foundation for subsequent determination of the adsorption thermal effect growth rate and testing of the range hood's purification adsorption efficiency. It can effectively address the complex physical changes caused by oil accumulation during long-term filter use, ensuring the accuracy of test results.

[0050] In some of the above embodiments of the present application, the present application further proposes that the step of confirming the adsorption heat conduction attenuation parameter based on the oil smoke cumulative adsorption data includes: Based on the oil smoke accumulation adsorption data, the rate characteristics and attenuation base of the heat release of oil pollution accumulation are extracted. Among them, the rate characteristics of the heat release of oil pollution accumulation are the speed of change of the heat generated by chemical reactions, phase changes or physical adsorption inside or on the surface of the filter during the accumulation of oil pollution on the filter surface. It can be obtained by monitoring the temperature change rate of the filter surface, infrared thermal imaging analysis or microcalorimeter measurement, etc., and reflects the activity level, structural state and potential carbonization trend of oil pollution accumulation. The attenuation base is the inherent attenuation or attenuation ratio caused by the heat conduction resistance when the oil smoke adsorption heat effect passes through the oil pollution layer under ideal or standard conditions. It can be obtained by experimental calibration, theoretical model calculation or regression analysis based on historical data, etc., and provides a basic heat conduction attenuation reference value for subsequent correction.

[0051] The degree of carbonization of the accumulated oil is determined based on the heat release rate characteristics. Carbonization refers to the degree to which carbonaceous or carbonaceous-like substances are formed in the oil under high temperature or prolonged conditions. This degree can be quantified by analyzing indicators such as the oil's color, hardness, chemical composition changes, or heat release rate. This indicates the extent to which the oil layer affects thermal conductivity, as carbonization significantly alters the oil layer's thermal conductivity.

[0052] The attenuation base is corrected using the carbonization degree to confirm the adsorption heat conduction attenuation parameter.

[0053] In this embodiment, the heat release rate characteristics and attenuation base of accumulated oil are extracted based on the accumulated oil adsorption data. The heat release rate characteristics reflect the dynamic thermal behavior of oil accumulation on the filter surface, while the attenuation base provides a basic quantitative indicator of heat conduction attenuation. The extracted heat release rate characteristics are further used to determine the degree of carbonization of the accumulated oil. Because the degree of carbonization of the oil directly affects the rate and pattern of its heat release, analyzing these rate characteristics can effectively infer the carbonization state of the oil. The determined carbonization degree is used to refine the extracted attenuation base, thereby obtaining a more accurate adsorption heat conduction attenuation parameter. Because the degree of carbonization of the accumulated oil is incorporated into the determination of the adsorption heat conduction attenuation parameter, this parameter more accurately reflects the actual filter operation, particularly the heat conduction characteristics after long-term oil accumulation and carbonization. Combining the adsorption heat conduction attenuation parameter with the transient convective disturbance data separated from the accumulated oil adsorption data in the previous step enables a more precise correction of the initial accumulated oil adsorption data. This improved calibration accuracy directly optimizes the quality of the accumulated oil fume adsorption data over a period of time after separation, enabling it to more accurately represent the filter's true adsorption heat effect. Ultimately, this high-precision data provides a solid foundation for determining the adsorption heat effect growth rate and obtaining test results on the range hood's purification adsorption efficiency. This effectively addresses the existing issue of inaccurate adsorption heat conduction attenuation parameters due to oil carbonization, which in turn affects overall test results.

[0054] In some of the above embodiments of the present application, the present application further proposes that the step of determining the carbonization degree of the accumulated oil stain according to the heat release rate characteristics includes: Based on the heat release rate characteristic, a local region in the heat release rate characteristic indicating the highest degree of carbonization is identified. The local region indicating the highest degree of carbonization is a specific time period or set of data points in the heat release rate characteristic that exhibits the fastest heat release or the highest heat release peak. Specifically, this can be identified by performing peak detection, gradient analysis, or local maximum search on the heat release rate data. This is done by focusing on the region with the most severe carbonization of the oil stain, thereby reducing interference from other non-carbonization factors on the overall heat release rate and improving the accuracy of carbonization degree determination.

[0055] A time series analysis is performed on the local area with the highest degree of carbonization to obtain a heat decay curve. Time series analysis is a method of analyzing data points arranged in chronological order. Specifically, it can be through techniques such as curve fitting, Fourier transform, wavelet analysis, or statistical models (such as ARIMA models) to reveal the patterns and trends of data changes over time. The purpose is to gain a deeper understanding of the dynamic process of heat release decaying over time, thereby providing richer information for quantifying the degree of carbonization. The heat decay curve is a trend graph or data sequence showing the change in heat release rate over time within the local area with the highest degree of carbonization. Specifically, it can be obtained by smoothing and normalizing the heat release data of the identified local area. It aims to intuitively display the heat release characteristics of the carbonized area and provide a basis for the subsequent extraction of characteristic parameters.

[0056] The degree of carbonization of the accumulated oil is determined based on the characteristic parameters of the thermal decay curve. These parameters are numerical indicators that quantify the characteristics of the thermal decay curve. Specifically, they may include decay rate (such as half-life or initial decay slope), decay amplitude (such as the difference between peak and steady-state values), decay time (such as the time required to reach a certain decay ratio), or area under the curve. These parameters provide quantitative information on the degree of carbonization of the oil, making the determination of the degree of carbonization more objective and accurate.

[0057] In this embodiment, the heat release rate characteristics are used to identify local regions with the highest degree of carbonization. Because oil carbonization does not occur uniformly, localized regions may exhibit higher degrees of carbonization. Heat release characteristics more directly reflect the carbonization state. By focusing on these localized regions, the influence 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 then performed on the identified local regions with the highest degree of carbonization to generate heat decay curves. Because time series analysis can reveal the dynamic patterns of heat release over time, rather than just the instantaneous rate, it provides a deeper understanding of the carbonization process. For example, regions with a high degree of carbonization may exhibit more rapid heat decay or a specific decay pattern. Finally, the degree of carbonization accumulated in the oil is determined based on the characteristic parameters of the heat decay curve. These parameters, such as the decay rate and decay amplitude, provide quantitative information about the degree of carbonization, thus overcoming the limitations of relying solely on a single rate characteristic and making the determination of the oil's carbonization degree more accurate and reliable. The step of determining the carbonization degree of accumulated oil based on the heat release rate characteristics provides a more accurate input for subsequent use of the carbonization degree to correct the attenuation base, thereby determining the adsorption heat conduction attenuation parameter. This improves the accuracy of the correction of the initial adsorption data of accumulated oil smoke, ultimately making the test results of the range hood purification adsorption efficiency more precise. This effectively solves the problem of carbonization degree judgment deviation caused by interference from various factors in the existing technology, and improves the accuracy of range hood purification adsorption efficiency testing.

[0058] In some of the above embodiments of the present application, the present application further proposes that the step of determining the adsorption heat effect growth rate based on the accumulated oil fume adsorption data within a period of time after separation includes: Quantitative analysis of the accumulated oil fume adsorption data over a period of time after separation is performed to obtain quantitative data, trend indicators, and oil accumulation coefficients. Quantitative data is a direct numerical representation of the degree of accumulated oil fume adsorption, which can be obtained in the form of adsorption amount, adsorption rate, or total adsorption heat, providing an objective numerical basis for the accumulated oil fume adsorption status.

[0059] The filter's comprehensive adsorption efficiency coefficient is determined based on the trend index and the oil accumulation coefficient. The trend index is a parameter that reflects the temporal evolution of the adsorption process. It can be characterized by growth rate, decay rate, slope, or fluctuation frequency, capturing the dynamic evolution of adsorption performance. The oil accumulation coefficient is a numerical value that characterizes the degree of oil accumulation on the filter surface. It can be determined based on oil thickness, oil coverage area, or chemical composition analysis, quantifying the actual impact of oil on the filter's adsorption performance.

[0060] The adsorption heat effect growth rate is determined based on the filter's comprehensive adsorption efficiency coefficient and quantitative data. The filter's comprehensive adsorption efficiency coefficient is used to assess the filter's actual adsorption capacity. This can be determined using a weighted average, product model, or neural network model that combines trend indicators and the oil accumulation coefficient, providing a comprehensive basis for filter performance evaluation. The adsorption heat effect growth rate is the rate at which the adsorption heat effect changes over time. This can be determined using 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. It reflects the dynamic changes in the filter's adsorption performance.

[0061] In this embodiment, an in-depth quantitative analysis is performed on the cumulative adsorption data of oil smoke within a period of time after separation to determine the growth rate of the adsorption heat effect. Specifically, a multi-dimensional analysis is performed on the cumulative adsorption data of oil smoke after separation to obtain quantitative data, change trend indicators and oil accumulation coefficients. The quantitative data directly reflects the degree of oil fume adsorption, the change trend indicators reveal the dynamic characteristics of the adsorption process, and the oil accumulation coefficient quantifies the actual impact of oil on the surface of the filter on the adsorption performance. Multi-dimensional information together constructs a comprehensive understanding of the adsorption state of the filter. Based on the obtained change trend indicators and oil accumulation coefficients, the comprehensive adsorption efficiency coefficient of the filter is confirmed. Specifically, the calculation formula of the comprehensive adsorption efficiency coefficient C of the filter is as follows: ; Among them, C refers to the comprehensive adsorption efficiency coefficient of the filter. and are the weights of the change trend index and the oil pollution accumulation coefficient, respectively, and ;T refers to the change trend index. The smaller the T value, the more obvious the downward trend of adsorption efficiency, and the greater its negative impact on the comprehensive adsorption efficiency; Z refers to the oil accumulation coefficient. The larger the Z value, the more oil accumulation, and the greater its negative impact on the comprehensive adsorption efficiency.

[0062] At the same time, the change pattern of adsorption efficiency over time and the influence of oil accumulation are integrated to form a comprehensive indicator that can evaluate the actual adsorption capacity of the filter. This avoids the evaluation bias that may be caused by 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 adsorption heat effect growth rate 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 calculation formula for the adsorption heat effect growth rate is as follows: ; in, Refers to the growth rate of adsorption heat effect in the time period p, Refers to the rate of change of quantitative data within a period of time p.

[0063] This solution provides reliable input for subsequent range hood purification adsorption efficiency test results based on the adsorption heat effect growth rate, heat storage and release data of the attached layer, and a preset growth threshold. This allows the entire range hood purification adsorption efficiency test method to accurately assess the actual working status of the range hood filter, effectively avoiding misjudgments and omissions. This in turn provides timely and effective guidance for filter maintenance, extending equipment life and ensuring purification effectiveness.

[0064] In some of the above embodiments of the present application, the present application further proposes the comprehensive adsorption efficiency coefficient and quantitative data based on the filter screen, and the steps of determining the adsorption heat effect growth rate include: After confirming the rate of change of the quantitative data, the cooking state intensity is obtained. The rate of change of the quantitative data is the speed at which the quantitative value extracted from the accumulated oil fume adsorption data changes over time within a specific time period. It can be calculated by differentiating, derivatizing or sliding average the quantitative data to reflect the dynamic process of oil 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 cooking mode set by the user in the kitchen, and external environmental factors are included in the evaluation of the adsorption heat effect growth rate. Specifically, the steps for constructing the mapping relationship between the rate of change of quantitative data and the cooking state intensity are as follows: 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., stir-frying). At each cooking intensity, the filter's quantitative data and its rate of change were continuously monitored and recorded. Statistical analysis of the experimental data revealed typical ranges and patterns in the rate of change of the quantitative data at different cooking intensities. For example, with light cooking, the rate of change of the quantitative data might be low and stable; with medium cooking, the rate of change might increase significantly; and with heavy cooking, the rate of change might peak and fluctuate rapidly. Based on the data analysis results, a series of thresholds were defined to classify the rate of change of the quantitative data into different levels of cooking intensity. For example, a low rate threshold, L1, was set; a rate of change below L1 was considered light cooking; a medium rate threshold, L2, was set; a rate of change between L1 and L2 was considered medium cooking; and a rate of change above L2 was considered heavy cooking. These thresholds established a direct mapping between the rate of change of the quantitative data and the level of cooking intensity. When the rate of change of the quantitative data is obtained in real time, it will be compared with the preset threshold to quickly and accurately determine the current cooking state intensity. Based on the comprehensive adsorption efficiency coefficient of the filter, the filter degradation index is confirmed. Among them, the filter degradation index is a quantitative representation of the decline in the adsorption performance of the filter. Specifically, it can be confirmed by analyzing the historical usage time of the filter, cleaning cycle, oil accumulation, airflow resistance change, or detecting physical and chemical changes on the filter surface through specific sensors to evaluate the impact of the filter's own state on the adsorption heat effect.

[0065] The filter degradation index takes into account the filter's historical usage. The longer a filter is used, the more physical or chemical changes may occur in its internal structure and surface adsorbent material, leading to a natural decrease in adsorption performance. By recording the filter's cumulative operating time and comparing it with a pre-set lifespan curve, a preliminary assessment of its degree of degradation can be made. Furthermore, the filter degradation index also considers the cumulative amount of oil smoke handled. As the filter handles oil smoke, oil accumulates, clogging the mesh, increasing airflow resistance, and potentially forming a carbonized layer. The greater the cumulative amount of oil smoke handled, the higher the degree of filter degradation. The cumulative amount of oil smoke handled can be estimated by monitoring parameters such as the range hood's operating power, air volume, and cooking intensity. Furthermore, the filter's cleaning cycle and maintenance records also influence the determination of degradation indicators. Regular cleaning and maintenance can slow filter degradation, while irregular or incomplete maintenance can accelerate it. By analyzing the filter's maintenance history, degradation indicators can be adjusted.

[0066] Filter degradation indicators can also directly monitor changes in the filter's physical properties. For example, by integrating a micro pressure sensor or airflow sensor on the filter surface, the pressure differential or airflow velocity distribution across the filter can be monitored in real time. A significant increase in the pressure differential or severely uneven airflow distribution directly indicates the degree of filter clogging and performance degradation, which can be quantified as a degradation indicator.

[0067] According to the intensity of the cooking state and the degradation index of the filter, the contribution parameter of the comprehensive adsorption efficiency score is determined. Among them, the contribution parameter of the comprehensive adsorption efficiency score is the weight or correction factor of the influence of the intensity of the cooking state and the filter degradation index on the comprehensive adsorption efficiency evaluation result of the filter. It can be determined by methods such as expert experience, machine learning model training or historical data regression analysis to quantify the relative importance of different factors to the adsorption efficiency evaluation. According to the comprehensive adsorption efficiency coefficient of the filter, the quantitative data and the contribution parameter, the adsorption heat effect growth rate is determined. The calculation formula of the contribution parameter D is as follows: ; in, It is a preset weight coefficient of the intensity of the cooking state, such as 2 to 5 thousandths; is a preset weight coefficient for the filter degradation index, such as 0.5 to 0.8 per thousand; the preset weight coefficient reflects the relative importance of the impact on adsorption efficiency; X refers to the intensity of the cooking state, which is quantified as a value from 0 to 100, where 0 represents no cooking and 100 represents high-intensity cooking; Y refers to the filter degradation index, which is also quantified as a value from 0 to 100, where 0 represents no degradation and 100 represents complete degradation.

[0068] According to the comprehensive adsorption efficiency coefficient of the filter screen, the quantitative data and the contribution parameters, the specific formula for determining the adsorption heat effect growth rate is as follows:

[0069] This embodiment introduces cooking state intensity and filter degradation indicators to more accurately determine the adsorption heat effect growth rate. Specifically, after quantitatively analyzing the accumulated oil fume adsorption data over a period of time after separation, obtaining the quantitative data, a trend indicator, and an oil accumulation coefficient, and determining the filter's comprehensive adsorption efficiency coefficient based on the trend indicator and the oil accumulation coefficient, this solution further determines the rate of change of the quantitative data and, based on this, derives the cooking state intensity. This is because the rate of change of the quantitative data directly reflects the dynamic process of oil fume generation and, thus, indirectly indicates the current intensity of cooking activity. Furthermore, based on the filter's comprehensive adsorption efficiency coefficient, this solution determines the filter's degradation index. This direct quantification of the filter's performance degradation compensates for the deficiency of the comprehensive adsorption efficiency coefficient alone in fully reflecting the filter's actual condition. Subsequently, based on the acquired cooking state intensity and the confirmed filter degradation index, this solution determines the contribution parameters to the comprehensive adsorption efficiency score. This allows the system to dynamically adjust the weights of various factors in the adsorption efficiency assessment based on actual operating conditions and the filter's own condition, thereby avoiding the evaluation bias that may be introduced by fixed models. Ultimately, the system determines the adsorption heat effect growth rate based on the filter's comprehensive adsorption efficiency coefficient, quantitative data, and dynamically adjusted contribution parameters. This multi-dimensional, dynamically adjusted evaluation method ensures that the calculated adsorption heat effect growth rate more accurately reflects the filter's true adsorption performance under actual usage conditions, effectively overcoming the inaccurate evaluation issues in existing technologies caused by external environmental and filter degradation. In this way, this solution can provide a more reliable basis for testing range hood purification adsorption efficiency, thereby supporting more accurate maintenance decisions.

[0070] See also Figure 2 The present application also provides a range hood 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.

[0071] The acquisition module 210 is used to acquire each temperature difference data between the air flow inlet side and the air flow outlet side of the filter in the range hood within a period of time.

[0072] The identification module 220 is used to identify transient convection disturbance data, oil fume accumulation adsorption data and adhesion layer heat storage and release data based on each of the temperature difference data and preset oil fume accumulation characteristics.

[0073] The separation module 230 is used to separate the transient convective disturbance data from the oil fume cumulative adsorption data based on the transient convective disturbance data and the oil fume cumulative adsorption data, and obtain the oil fume cumulative adsorption data within a period of time after separation; The determination module 240 is used to determine the adsorption heat effect growth rate based on the accumulated oil smoke adsorption data within a period of time after separation.

[0074] The monitoring module 250 is used to obtain the detection result of the range hood 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.

[0075] In this embodiment, by integrating the data processing and analysis steps of the range hood adsorption process thermal effect detection method into a modular detection system, automated, non-invasive detection of the range hood adsorption process thermal effect is achieved. Specifically, the acquisition module 210 first continuously collects temperature difference data of the airflow before and after the range 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, applies signal processing and pattern recognition techniques to decompose the data stream into three components: transient convective disturbance data, oil fume accumulation adsorption data, and heat storage and release data from the adsorption layer. This decomposition process is key to resolving the problem of interference from composite temperature difference signals, allowing subsequent analysis to focus on the adsorption thermal effect. Next, the separation module 230, based on the identified transient convective disturbance data, removes the non-adsorption temperature difference caused by airflow disturbances from the oil fume accumulation adsorption data, thereby obtaining the oil fume accumulation adsorption data. This separation step ensures that the assessment of the adsorption thermal effect is not interfered with by external transient factors. On this basis, the determination module 240 uses the separated oil smoke cumulative adsorption data to calculate the adsorption heat effect growth rate, which directly reflects the changing trend of the filter adsorption performance over time. Finally, the monitoring module 250 combines the adsorption heat effect growth rate, the heat storage and release data of the attachment layer, and the preset growth threshold to generate the detection results of the range hood purification adsorption efficiency. Through the collaboration and data flow of various modules, the entire system effectively solves the problems of low efficiency and insufficient accuracy of traditional methods in multi-range hood scenarios, and realizes real-time evaluation of the adsorption status of the range hood filter without the need for physical modification of the range hood itself, thereby improving the accuracy, convenience and practicality of the detection.

[0076] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the present invention specification.

Claims

1. A method for detecting the purification adsorption efficiency of a range hood, characterized in that: include: Obtaining each temperature difference data between the air flow inlet side and the air flow outlet side of the filter in the range hood over a period of time; Based on each of the temperature difference data and the preset oil smoke accumulation characteristics, identifying transient convection disturbance data, oil smoke accumulation adsorption data, and attachment layer heat storage and release data; Based on the transient convective disturbance data and the oil fume cumulative adsorption data, separating the transient convective disturbance data from the oil fume cumulative adsorption data to obtain the oil fume cumulative adsorption data within a period of time after separation; Based on the cumulative adsorption data of oil smoke over a period of time after separation, the adsorption heat effect growth rate is determined; Based on the adsorption heat effect growth rate, the heat storage and release data of the attachment layer and the preset growth threshold, the detection result of the range hood purification adsorption efficiency is obtained.

2. The method for detecting the purification and 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 attached 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, identifying the transient convective disturbance data and the attached layer heat storage and release data; determining a temperature trend characteristic based on each of the temperature difference data; Compare the temperature trend characteristics with the preset oil smoke accumulation characteristics to determine the oil smoke change trend parameters; Based on the oil fume variation trend parameter and each of the temperature difference data, the data during the period when the fan speed is increased is confirmed as the oil fume cumulative adsorption data.

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

4. The method for detecting the purification and adsorption efficiency of a range hood according to claim 1, characterized in that: After obtaining the detection result of the range hood purification adsorption efficiency based on the adsorption heat effect growth rate, the heat storage and release data of the attachment layer and the preset growth threshold, the method further includes: Sending the test results to a central management system; In response to the maintenance plan output by the central management system based on the detection results, determining the maintenance priority and maintenance resource information of each filter; A global maintenance plan for each filter is generated based on the maintenance priority and the maintenance resource information.

5. The method for detecting the purification and adsorption efficiency of a range hood according to claim 1, characterized in that: Based on the transient convective disturbance data and the oil fume cumulative adsorption data, the step of separating the transient convective disturbance data from the oil fume cumulative adsorption data to obtain the oil fume cumulative adsorption data for a period of time after separation includes: Based on the transient convective disturbance data and the oil fume cumulative adsorption data, separating the transient convective disturbance data from the oil fume cumulative adsorption data to obtain the oil fume cumulative initial adsorption data; Determining adsorption heat conduction attenuation parameters based on the oil smoke cumulative adsorption data; The adsorption heat conduction attenuation parameter is used to correct the initial adsorption data of the accumulated fume, and the accumulated adsorption data of the fume within a period of time after separation is obtained.

6. A method for detecting the purification and adsorption efficiency of a range hood according to claim 5, characterized in that: The step of determining the adsorption heat conduction attenuation parameter based on the oil smoke accumulation adsorption data includes: Based on the oil smoke accumulation adsorption data, extracting the rate characteristics and attenuation base of the oil pollution accumulation heat release; Determine the carbonization degree of the accumulated oil stain based on the heat release rate characteristics; The attenuation base is corrected using the carbonization degree to confirm the adsorption heat conduction attenuation parameter.

7. A method for detecting the purification and adsorption efficiency of a range hood according to claim 6, characterized in that: According to the heat release rate characteristics, the steps of confirming the carbonization degree of the accumulated oil pollution include: identifying, based on the heat release rate characteristic, a local region indicating a highest degree of carbonization in the heat release rate characteristic; Performing time series analysis on the local area with the highest carbonization degree to obtain a heat decay curve; The carbonization degree of the accumulated oil stains is determined based on the characteristic parameters of the heat decay curve.

8. The method for detecting the purification and adsorption efficiency of a range hood according to claim 1, characterized in that: The step of determining the adsorption heat effect growth rate based on the accumulated oil smoke adsorption data within a period of time after separation includes: Quantitatively analyze the accumulated oil smoke adsorption data over a period of time after separation to obtain quantitative data, change trend indicators and oil pollution accumulation coefficients; Determine the comprehensive adsorption efficiency coefficient of the filter based on the change trend index and the oil accumulation coefficient; Based on the comprehensive adsorption efficiency coefficient and quantitative data of the filter, the adsorption heat effect growth rate is determined.

9. The method for detecting the purification and adsorption efficiency of a range hood according to claim 8, characterized in that: The step of determining the adsorption heat effect growth rate based on the comprehensive adsorption efficiency coefficient and quantitative data of the filter screen includes: After confirming the rate of change of the quantitative data, obtaining the cooking state intensity; Based on the comprehensive adsorption efficiency coefficient of the filter, confirm the deterioration index of the filter; Determining a contribution parameter of a comprehensive adsorption efficiency score based on the intensity of the cooking state and a deterioration index of the filter; The adsorption heat effect growth rate is determined based on the comprehensive adsorption efficiency coefficient of the filter screen, the quantitative data, and the contribution parameter.

10. A range hood purification adsorption efficiency detection system, characterized in that: The system includes: An acquisition module, used to acquire each temperature difference data between the air flow inlet side and the air flow outlet side of the filter in the range hood over a period of time; an identification module for identifying transient convection disturbance data, oil fume accumulation adsorption data, and heat storage and release data of the attachment layer based on each of the temperature difference data and preset oil fume accumulation characteristics; a separation module for separating the transient convective disturbance data from the oil fume cumulative adsorption data based on the transient convective disturbance data and the oil fume cumulative adsorption data, and obtaining the oil fume cumulative adsorption data within a period of time after separation; A determination module, for determining an adsorption heat effect growth rate based on the accumulated oil smoke adsorption data over a period of time after separation; The monitoring module is used to obtain the detection result of the range hood purification adsorption efficiency based on the adsorption heat effect growth rate, the heat storage and release data of the attachment layer and the preset growth threshold.

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