All-air system and filter screen monitoring method thereof

Abstract

The invention relates to an all-air system and a filter screen monitoring method thereof. The all-air system comprises a fan, an air pipe, a heat exchange module, a filter screen module, a sensor module, a control module and a reminding module, the filter screen module is replaceable, a computer program / instruction is stored in the control module, and when the computer program / instruction is executed by the control module, the reminding module is started to remind the user of the air pipe. The filter screen monitoring method comprises the steps that the stage use duration of a filter screen module, the air volume of a draught fan, pollutant concentration data and air pressure data are obtained, and the pollutant concentration data and the air pressure data are generated by monitoring an inlet and an outlet of the filter screen module through a sensor module; calculating a pollutant concentration difference value and a wind pressure difference value between an inlet and an outlet of the filter screen module; inputting the stage use duration, the air volume, the pollutant concentration difference value and the air pressure difference value into a blocking rate calculation model based on a random forest algorithm to obtain a filter screen blocking rate; when the filter screen blockage rate reaches a preset threshold value, a filter screen blockage warning is sent out through a reminding module.

Description

Technical Field

[0001] The present application relates to an all-air system, and in particular to a method for monitoring the filter screen of an all-air system. Background Art

[0002] An all-air system is a centralized air handling system. Outdoor fresh air and indoor return air are centralized to the main unit of the all-air system for temperature adjustment, humidity adjustment, filtration and purification, etc. The processed air is transported from the main unit to different indoor spaces (such as different bedrooms, studies) through air ducts.

[0003] The filter screen module of the main unit is used to filter and purify the air. After long-term use, the filter screen module will have problems such as blockage and decreased purification efficiency, and professional personnel are required to clean and maintain it. The air conditions in different regions and different seasons are different, resulting in different loss rates of the filter screen modules of the all-air systems in different regions and different seasons. To avoid cleaning the filter screen module too early or too late, it is necessary to accurately monitor the state of the filter screen module. Summary of the Invention

[0004] The technical problem to be solved by the present application is: how to accurately monitor the state of the filter screen module of an all-air system.

[0005] To solve the above technical problem, the present application provides a method for monitoring the filter screen of an all-air system. The all-air system includes a fan, a filter screen module, a sensor module, a control module, and a reminder module. The filter screen module is replaceable. The method for monitoring the filter screen includes:

[0006] Step S1: Obtain the stage usage duration of the filter screen module, the air volume of the fan, the pollutant concentration data, and the wind pressure data. The pollutant concentration data and the wind pressure data are generated by the sensor module monitoring the inlet and outlet of the filter screen module;

[0007] Step S2: Calculate the pollutant concentration difference and the wind pressure difference between the inlet and outlet of the filter screen module according to the pollutant concentration data and the wind pressure data at the inlet and outlet of the filter screen module;

[0008] Step S3: Input the stage usage duration, the air volume, the pollutant concentration difference, and the wind pressure difference into a blockage rate calculation model based on the random forest algorithm to obtain the filter screen blockage rate. The blockage rate calculation model is pre-trained based on sample data;

[0009] Step S4: When the filter screen blockage rate rises to a predetermined threshold, send a filter screen blockage warning through the reminder module.

[0010] Step S4: When the filter screen blockage rate rises to a predetermined threshold, send a filter screen blockage warning through the reminder module.

[0011] This application calculates the filter clogging rate based on multiple factors such as the stage usage duration, air volume, pollutant concentration difference, and air pressure difference through a clogging rate calculation model based on the random forest algorithm, so as to realize relatively accurate monitoring of the status of the filter module. Since the filter module is replaceable, when the filter clogging rate reaches a predetermined threshold, the filter module can be removed, cleaned and maintained, and then reinstalled or replaced with a new filter module.

[0012] This application also provides an all-air system, which includes a fan, an air duct, a heat exchange module, a filter module, a sensor module, a control module, and a reminder module. The filter module is replaceable. The control module stores computer programs / instructions, and when the computer programs / instructions are executed by the control module, the steps of the filter monitoring method provided by this application are realized. Description of the Drawings

[0013] Figure 1 It is a flowchart of the filter monitoring method in the second embodiment of this application;

[0014] Figure 2 It is a training flowchart of the initial model in the second embodiment of this application. Detailed Embodiments

[0015] Embodiment 1:

[0016] This embodiment provides an all-air system, which includes a main unit and an air duct. The main unit includes a chassis, which has an air inlet and an air outlet. Inside the chassis, a filter module, a heat exchange module, a humidification module, a control box, and a fan are sequentially arranged from the air inlet to the air outlet. The heat exchange module is used to adjust the temperature of the air, and the humidification module is used to increase the humidity of the air. The side of the filter module close to the air inlet is the inlet of the filter module, and the side of the filter module close to the air outlet is the outlet of the filter module. The main unit also includes a reminder module and a sensor module arranged inside the chassis. The sensor module includes PM2.5 sensors, VOC (Volatile Organic Compounds) sensors, and wind speed sensors installed at the inlet and outlet of the filter module. The control box is provided with a control module and a communication module, and the reminder module includes a communication module and an audible and visual warning module arranged on the chassis.

[0017] The filter module is replaceable and can be removed from the lower side of the chassis. After the filter module is removed, it can be cleaned and maintained and then reinstalled, or it can be directly discarded and replaced with a new filter module. The filter module includes a primary filter, an IFD electrostatic precipitator filter, an H10 + formaldehyde removal filter, and a manganese dioxide formaldehyde removal filter arranged in sequence from the inlet to the outlet. The primary filter is used to filter out large particles, the IFD electrostatic precipitator filter is used to filter out particles and microorganisms, and the H10 +The formaldehyde removal filter (such as the H11 formaldehyde removal filter) is used to decompose formaldehyde and filter out particulate matter, and the manganese dioxide formaldehyde removal filter is used to remove the gas generated by decomposing formaldehyde.

[0018] The communication module is used to send the operation information of the all-air system (such as the remaining life of the filter module) to the bound user communication terminal (such as a mobile phone, a tablet computer). The control module stores computer programs / instructions, and when the computer programs / instructions are executed by the control module, the steps of the filter monitoring method in Embodiment 2 are implemented.

[0019] Embodiment 2:

[0020] This embodiment provides a filter monitoring method for an all-air system. Through this filter monitoring method, the filter module in Embodiment 1 can be monitored. When the filter blockage rate reaches a predetermined threshold, a filter blockage warning is issued, so that the filter module can be cleaned, maintained or replaced at an appropriate time.

[0021] See Figure 1 , the filter monitoring method includes:

[0022] Step S1: Obtain the stage usage duration of the filter module, the air volume of the fan, the pollutant concentration data and the wind pressure data. The pollutant concentration data and the wind pressure data are generated by the sensor module monitoring the inlet and outlet of the filter module; the pollutant concentration data includes PM2.5 concentration data and VOC concentration data. The PM2.5 concentration data, the VOC concentration data and the wind pressure data are respectively the means of the data generated by the PM2.5 sensor, the VOC sensor and the wind speed sensor monitoring for a predetermined duration. By sampling multiple times within a predetermined duration and taking the mean of multiple sampling results, the problem of inaccurate single-sampling data is solved. The stage usage duration of the filter module is the usage duration since the filter module was last cleaned, maintained or replaced, that is, the usage duration of the all-air system since the filter module was last cleaned, maintained or replaced. This stage usage duration can be realized by the timing unit in the control module. After each cleaning, maintenance or replacement of the filter module, the timing unit is reset and starts timing from 0 again.

[0023] Step S2: Calculate the pollutant concentration difference and the wind pressure difference between the inlet and outlet of the filter module based on the pollutant concentration data and the wind pressure data at the inlet and outlet of the filter module; subtract the pollutant concentration data at the outlet of the filter module from the pollutant concentration data at the inlet of the filter module to obtain the pollutant concentration difference; subtract the wind pressure data at the outlet of the filter module from the wind pressure data at the inlet of the filter module to obtain the wind pressure difference; the pollutant concentration difference includes the PM2.5 concentration difference (the difference between the PM2.5 concentrations at the inlet and outlet of the filter module) and the VOC concentration difference (the difference between the VOC concentrations at the inlet and outlet of the filter module). The pollutant concentration difference and the wind pressure difference are multiple parameters. Based on these multiple parameters, comprehensively judging the clogging condition of the filter module can achieve a more accurate judgment compared to only based on one parameter (such as only based on the wind pressure difference). Those skilled in the art can add, reduce, or replace the sensors for monitoring pollutants as needed, so that the specific pollutants involved in the pollutant concentration difference are different from those in this embodiment.

[0024] Step S3: Input the stage usage duration, the air volume, the pollutant concentration difference, and the wind pressure difference into the clogging rate calculation model based on the random forest algorithm to obtain the filter clogging rate. The clogging rate calculation model is pre-trained based on sample data. The stage usage duration, the air volume, the pollutant concentration difference, and the wind pressure difference are multiple factors. Based on these multiple factors, comprehensively judging the clogging condition of the filter module can achieve a more accurate judgment compared to only based on one factor.

[0025] The fan of the all-air system operates at different speeds and outputs different air volume sizes. The control module can obtain the air volume data according to the fan speed. The wind pressure difference is the wind speed pressure difference between the inlet and outlet of the filter module. As the filter clogs, the wind pressure difference will gradually increase. The principle of using the pollutant concentration difference to evaluate the filter clogging rate is that if the pollutant concentration difference between the inlet and outlet of the filter module is greater than a lower limit, it indicates that the filter module effectively filters pollutants. The principle of using the stage usage duration to evaluate the filter clogging rate is that the longer the filter module is used, the greater the possibility of clogging.

[0026] Step S4: When the filter clogging rate rises to a predetermined threshold (that is, when the filter clogging rate is greater than or equal to this threshold), issue a filter clogging warning through the reminder module. This threshold is determined by those skilled in the art according to needs, for example, set to 80%.

[0027] In step S4, the issuing of the filter clogging warning through the reminder module includes:

[0028] The communication module of the reminder module sends an APP reminder or a text message reminder to the bound user communication terminal, and / or gives an audible and visual warning through the audible and visual warning module of the reminder module. By establishing the binding relationship between the all-air system and the user communication terminal before using the all-air system, data exchange between the all-air system and the user communication terminal can be realized. For example, a control instruction is sent from the user communication terminal to the all-air system, and for example, the all-air system sends operation information to the user communication terminal. The audible and visual warning module can be an LED warning light and a speaker.

[0029] After the step S3 is executed n times, the filter monitoring method further includes:

[0030] Step N1: According to the life prediction formula Calculate the remaining life L of the filter module r , in days; η max is a predetermined limit value of the filter clogging rate, that is, 100%; L t is the total life of the filter module, which is determined during the research and development of the filter module, is positively correlated with the dust capacity of the filter module, and is in days; n is the total number of calculations of the filter clogging rate, that is, the total number of times of executing step S3, and is an integer greater than 0; η i is the filter clogging rate calculated for the i-th time.

[0031] Step N2: When L r is shortened to a predetermined warning value (that is, when L r is less than or equal to the warning value), a filter replacement reminder is sent through the reminder module. This warning value is determined by those skilled in the art according to needs, for example, set to 7.

[0032] The sending of the filter replacement reminder through the reminder module includes:

[0033] Sending an APP reminder or a text message reminder to the bound user communication terminal through the reminder module.

[0034] In this embodiment, in addition to giving a reminder when the filter module is severely clogged through step S4, a reminder is also given when the remaining life is short through steps N1 and N2, with both methods working simultaneously. The filter clogging rate and the remaining life can be synchronized to the user communication terminal in real time, enabling the user to always know the status of the filter module.

[0035] In this embodiment, the sample data is input into the initial model based on the random forest algorithm, and the initial model is trained to obtain a clogging rate calculation model; the sample data includes the stage usage duration, air volume, pollutant concentration difference, wind pressure difference, and filter clogging rate.

[0036] Step S3 includes: the clogging rate calculation model makes a prediction based on the stage usage duration, air volume, pollutant concentration difference, and wind pressure difference, and outputs the filter clogging rate; the filter clogging rate is the average of the prediction results of different decision trees of the clogging rate calculation model.

[0037] The training method of the initial model based on the random forest algorithm can refer to existing technical materials or Figure 2 . Each decision tree independently generates a prediction result (y1, y2,..., yN). In this embodiment, the output result of the model is the average (y_avg) of the prediction results of all decision trees. The sample data used during training is as follows in the table:

[0038]

[0039]

[0040] In the above table, delta_p, flow_rate, pm25, voc, time, and clogging_ratio are the wind pressure difference, air volume, PM2.5 concentration difference, VOC concentration difference, stage usage duration, and filter clogging rate respectively.

[0041] The code involved in training and using the model is for example:

[0042]

[0043]

[0044]

Claims

1. A filter monitoring method for a full air system, the full air system comprising a fan, a filter module, a sensor module, a control module and a reminder module, the filter module is replaceable, characterized in that: The filter monitoring method includes: Step S1: obtaining the stage use time of the filter module, the air volume of the fan, the pollutant concentration data and the wind pressure data, where the pollutant concentration data and the wind pressure data are generated by the sensor module monitoring the inlet and outlet of the filter module; Step S2: Calculating the pollutant concentration difference and wind pressure difference between the inlet and outlet of the filter module according to the pollutant concentration data and wind pressure data at the inlet and outlet of the filter module; Step S3: inputting the usage time, air volume, pollutant concentration difference and wind pressure difference of the stage into a blockage rate calculation model based on a random forest algorithm to obtain the filter blockage rate, where the blockage rate calculation model is pre-trained based on sample data; Step S4: When the filter blockage rate rises to a predetermined threshold, a filter blockage warning is issued through the reminder module.

2. The filter monitoring method according to claim 1, characterized in that: The sensor module includes a PM2.5 sensor, a VOC sensor and a wind speed sensor installed at the inlet and outlet of the filter module. The pollutant concentration data includes PM2.5 concentration data and VOC concentration data. The PM2.5 concentration data, VOC concentration data and wind pressure data are respectively the average of the data generated by the PM2.5 sensor, VOC sensor and wind speed sensor monitoring for a predetermined period of time.

3. The filter monitoring method according to claim 1, characterized in that: The training method of the congestion rate calculation model includes: The sample data is input into the initial model based on the random forest algorithm, and the initial model is trained to obtain the blockage rate calculation model; the sample data includes the stage usage time, air volume, pollutant concentration difference, wind pressure difference and filter blockage rate; Step S3 includes: the blockage rate calculation model predicts according to the stage usage time, air volume, pollutant concentration difference and wind pressure difference, and outputs the filter blockage rate; the filter blockage rate is the average of the prediction results of different decision trees of the blockage rate calculation model.

4. The filter monitoring method according to claim 1, characterized in that: The issuing of a filter blockage warning by the reminder module includes: The reminder module sends an APP reminder or a text message reminder to the bound user communication terminal, and / or an audible and visual warning is performed through the reminder module.

5. The filter monitoring method according to claim 1, characterized in that: The filter monitoring method also includes: According to the life prediction formula Calculate the remaining life L of the filter module r ; L t is the total life of the filter module, η max is a predetermined filter blockage rate limit value, n is the total number of calculations of the filter blockage rate, η i is the filter blockage rate calculated for the i-th time; L r When it is shortened to a preset warning value, a filter replacement reminder is sent through the reminder module.

6. The filter monitoring method according to claim 5, characterized in that: The sending of the filter replacement reminder by the reminder module includes: The reminder module sends APP reminders or SMS reminders to the bound user communication terminal.

7. The filter monitoring method according to claim 1, characterized in that: The filter module includes a primary filter, an IFD electrostatic dust filter, and an H10 + Formaldehyde removal filter and manganese dioxide formaldehyde removal filter.

8. A full air system, comprising a fan, an air duct, a heat exchange module, a filter module, a sensor module, a control module and a reminder module, wherein the filter module is replaceable, characterized in that: The control module stores a computer program / instruction, and when the computer program / instruction is executed by the control module, the steps of the filter monitoring method as claimed in any one of claims 1 to 7 are implemented.

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