Filter screen assembly filth blockage detection method and device, air conditioning equipment and medium

Abstract

The invention discloses a filter screen assembly filth blockage detection method and device, air conditioning equipment and a medium, the air conditioning equipment is provided with a draught fan and an air duct, a filter screen assembly is arranged at an air inlet of the air conditioning equipment, and the method comprises the steps that the first rotating speed value of the draught fan is obtained; the first rotating speed value is processed through a pre-established curved surface model, the current static pressure value in the air duct is obtained, and the curved surface model is a three-dimensional curved surface representing the relation among the target air volume, the fan rotating speed and the static pressure in the air duct; and determining the current filth blockage degree of the filter screen assembly according to the current static pressure value. The technical problems that hardware needs to be additionally arranged for detection of the filth blockage condition of the filter screen assembly, and accuracy is low are solved.

Description

Technical Field

[0001] This invention belongs to the field of home appliance technology, and particularly relates to a method, device, air conditioning equipment and medium for detecting dirt and clogging of filter components. Background Technology

[0002] Since ducted air conditioners with long ducts are installed in the ceiling, it is difficult to frequently check whether the air inlet filter is dirty or clogged. Therefore, the ducted air conditioner needs to be able to provide feedback to the user on the degree of filter clogging.

[0003] In some technologies that use sensors to detect filter clogging, wind pressure sensors are placed before and after the filter to detect the wind pressure before and after the filter. The clogging status of the filter is indirectly determined by the wind pressure difference before and after the filter. However, this solution adds related hardware, which increases hardware costs, and the wind pressure sensors have a limited lifespan and are difficult to place.

[0004] In some sensorless technologies for detecting filter clogging, the estimation of filter clogging based on fan operating data relies on a simple linear relationship, resulting in low accuracy in detecting filter clogging. Summary of the Invention

[0005] This invention provides a method, apparatus, air conditioning equipment, and medium for detecting filter clogging, to solve the technical problems of requiring additional hardware and having low accuracy in detecting filter clogging.

[0006] In a first aspect of the invention, a method for detecting filter clogging is provided, applied to an air conditioning device having a fan and an air duct, wherein the filter assembly is disposed at the air inlet of the air conditioning device, the method comprising: controlling the fan to operate in a constant air volume control mode and obtaining a first rotational speed value of the fan; processing the first rotational speed value through a pre-established surface model to obtain a current static pressure value in the air duct, wherein the surface model is a three-dimensional surface characterizing the relationship between the target air volume, the fan rotational speed and the static pressure in the air duct; and determining the current degree of clogging of the filter assembly based on the current static pressure value.

[0007] In conjunction with the first aspect, in some embodiments, determining the current degree of clogging of the filter assembly based on the current static pressure value includes: determining the current degree of clogging of the filter assembly based on the current static pressure value and a pre-established first relationship model, wherein the first relationship model characterizes the relationship between the static pressure in the duct and the degree of clogging of the filter assembly.

[0008] In conjunction with the first aspect, in some embodiments, determining the current degree of clogging of the filter assembly based on the current static pressure value and a pre-established first relational model includes: determining the static pressure difference between the current static pressure value and a reference static pressure value, wherein the reference static pressure value is obtained when the air conditioning equipment is installed in the operating environment and the filter assembly is not clogged; inputting the static pressure difference into the first relational model, and determining the current degree of clogging of the filter assembly through the first relational model.

[0009] In conjunction with the first aspect, in some embodiments, the above-mentioned filter assembly clogging detection method further includes: when the air conditioning equipment is installed in the usage environment and the filter assembly is not clogged, controlling the fan to operate in a constant air volume control mode and obtaining a second speed value of the fan; processing the second speed value through the surface model to obtain the reference static pressure value.

[0010] In conjunction with the first aspect, in some embodiments, the step of processing the first rotational speed value through a pre-established surface model to obtain the current static pressure value in the duct includes: inputting the first target airflow value currently set by the air conditioning device into the surface model to obtain a second relationship model corresponding to the first target airflow value, wherein the second relationship model characterizes the relationship between the fan rotational speed and the static pressure in the duct under the first target airflow value; inputting the first rotational speed value into the second relationship model, and determining the current static pressure value in the duct through the second relationship model.

[0011] In conjunction with the first aspect, in some embodiments, the surface model is established based on pre-determined full three-dimensional mapping data; wherein the full three-dimensional mapping data includes M sets of three-dimensional mapping data corresponding to M candidate air volume values, each set of the three-dimensional mapping data including a pair of multiple rotational speed values ​​and multiple static pressure values ​​obtained under that candidate air volume value, and M is an integer greater than 1.

[0012] In conjunction with the first aspect, in some embodiments, the surface model is established in advance through the following steps: for the M candidate airflow values, based on the pairs of multiple rotational speed values ​​and multiple static pressure values ​​obtained under the candidate airflow values, a mapping relationship curve corresponding to the candidate airflow values ​​is established, the mapping relationship curve being a curve characterizing the relationship between the fan rotational speed and the static pressure in the duct under the candidate airflow values; based on the M mapping relationship curves corresponding to the M candidate airflow values, the full three-dimensional mapping data is established.

[0013] In conjunction with the first aspect, in some implementations, the surface model is formed by splicing together N sub-surface models, each of which corresponds one-to-one with N airflow intervals, and each airflow interval includes multiple candidate airflow values, where N is an integer greater than 1.

[0014] In conjunction with the first aspect, in some embodiments, the first relational model is established in advance through the following steps: simulating various degrees of clogging of the filter assembly; for each simulated degree of clogging, obtaining the static pressure value in the air duct under that simulated degree of clogging through the surface model; and establishing the first relational model based on the various simulated degrees of clogging and the static pressure value in the air duct obtained under each simulated degree of clogging.

[0015] In a second aspect of the invention, a filter assembly clogging detection device is provided, applied to an air conditioning device having a fan and an air duct, wherein the filter assembly is disposed at the air inlet of the air conditioning device, and the filter assembly clogging detection device includes: a speed acquisition unit for controlling the fan to operate in a constant airflow control mode and acquiring a first speed value of the fan; a static pressure determination unit for processing the first speed value through a pre-established surface model to obtain a current static pressure value in the air duct, wherein the surface model is a three-dimensional surface characterizing the relationship between the target airflow, the fan speed, and the static pressure in the air duct; and a degree determination unit for determining the current degree of clogging of the filter assembly based on the current static pressure value.

[0016] In a third aspect of the invention, an air conditioning device is provided, having a fan and an air duct, and further comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the filter assembly clogging detection method described in any of the above embodiments.

[0017] In a fourth aspect of the invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the filter assembly clogging detection method described in any of the above embodiments.

[0018] The one or more technical solutions provided in the embodiments of the present invention achieve at least the following technical effects or advantages:

[0019] By controlling the fan to operate in a constant airflow control mode and obtaining the fan's first rotational speed value, the current static pressure value within the duct is obtained by processing the first rotational speed value through a pre-established surface model. The surface model is a three-dimensional surface representing the relationship between the target airflow, fan speed, and static pressure within the duct. The current degree of clogging of the filter assembly is determined based on the current static pressure value. This technical solution estimates the static pressure within the duct using a surface model, enabling a more accurate estimation of the static pressure based on the fan's rotational speed when the airflow is constant. Therefore, it improves the accuracy of determining the degree of clogging of the filter assembly based on the static pressure within the duct, and eliminates the need for additional hardware on the equipment, achieving more precise detection of filter assembly clogging in sensorless scenarios. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 The flowcharts of filter assembly clogging detection methods provided in some embodiments of the present invention are shown.

[0022] Figure 2 A schematic diagram of a surface model in some embodiments of the present invention is shown;

[0023] Figure 3 A schematic diagram of the mapping relationship curves corresponding to various candidate air volume values ​​in some embodiments of the present invention is shown;

[0024] Figure 4 The diagram shows a schematic of the filter assembly clogging detection device provided in some embodiments of the present invention;

[0025] Figure 5 A schematic diagram of the structure of an air conditioning device provided in some embodiments of the present invention is shown. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0027] In addition, in the present invention, descriptions such as "first" and "second" are for descriptive purposes only and should not be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of such features. In addition, the technical solutions between the various embodiments can be combined with each other, but this must be based on the fact that they can be implemented by ordinary technicians in this field. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0028] This invention provides a method for detecting filter clogging, applicable to air conditioning equipment with fans and ducts, such as air conditioning units (including ducted air conditioners, multi-split systems, window air conditioners, etc.), air purifiers, heaters, or fresh air systems. The filter assembly is located at the air inlet of the air conditioning equipment. In some embodiments, the air conditioning equipment is a split type with an indoor unit and an outdoor unit, where the fan and duct are both part of the indoor unit, and the filter assembly is located at the air inlet of the indoor unit.

[0029] like Figure 1 As shown, the method for detecting dirt and clogging of the filter assembly of an air conditioning device provided by the present invention includes the following steps S101 to S103.

[0030] S101: Control the fan to operate in constant air volume control mode and obtain the first speed value of the fan.

[0031] In some embodiments, S101 may include: controlling the fan to operate in a constant airflow control mode, and after the fan operation has made the actual airflow at the air outlet of the air conditioning device constant at a first target airflow value, obtaining a first fan speed value. It should be noted that controlling the fan of the air conditioning device to operate in a constant airflow control mode so that the actual airflow at the air outlet of the air conditioning device is constant at the first target airflow value means that the actual airflow at the air outlet is equal to or approximately equal to the first target airflow value. Approximately equal means that the difference between the actual airflow at the air outlet and the first target airflow value is less than a preset difference threshold. The difference can be the absolute value of the difference or other forms.

[0032] In some implementations, in response to an airflow setting command, a target airflow for the constant airflow control mode is set as a first target airflow value, which is any airflow value within the adjustable range of the target airflow. For example, the adjustable range of the target airflow is 100–2400 m³ / h. 3 / h, assuming the first target airflow value is 1400m³ / h 3 / h, then, the fan speed is controlled in constant air volume control mode to keep the actual air volume at the air outlet of the air conditioning equipment constant at 1400m³ / h. 3 / h.

[0033] In some implementations, the airflow setting command can originate from a host computer. The airflow setting command is generated based on the user's airflow setting operation on the host computer and sent to the MCU (Microcontroller Unit) of the air conditioning equipment. The host computer can be a remote control, wired controller, mobile terminal, or control panel located on the air conditioning equipment, etc.

[0034] In some implementations, controlling the fan of the air conditioning equipment to operate in a constant airflow control mode may include: collecting the actual characteristic value of a characteristic parameter and calculating the target characteristic value of the characteristic parameter every time interval T1; and adjusting the fan speed according to the relationship between the currently collected actual characteristic value and the currently calculated target characteristic value, where T1 is the control cycle of the constant airflow control mode. It is understood that the actual adjustment rule for adjusting the fan speed according to the relationship between the target characteristic value and the actual characteristic value will vary depending on the fan structure. If the fan is a centrifugal fan, adjusting the fan speed according to the relationship between the target characteristic value and the actual characteristic value includes: if the currently collected actual characteristic value is greater than the previously calculated target characteristic value, decreasing the fan speed; and if the currently collected actual characteristic value is less than the currently calculated target characteristic value, increasing the fan speed. If the fan is an axial flow fan, the fan speed is adjusted according to the relationship between the target feature value and the actual feature value, including: if the current actual feature value is greater than the current calculated target feature value, the fan speed is increased; if the current actual feature value is less than the current calculated target feature value, the fan speed is decreased.

[0035] In some implementations, a first target airflow value and the current fan speed value are input into a pre-established surface model X. This surface model X is a three-dimensional surface that characterizes the quantitative relationship between the target airflow, fan speed, and characteristic parameters. The target characteristic value of the characteristic parameter is calculated using this surface model X. In other implementations, linear interpolation can be performed based on the first target airflow value, the current fan speed value, and multiple pre-established relationship curves corresponding to multiple preset airflow values ​​(each relationship curve characterizes the relationship between fan speed and characteristic parameters under that preset airflow value) to obtain the target characteristic value of the characteristic parameter.

[0036] In some implementations, the characteristic parameters can be any of the following parameters of the fan: total power, total negative bus current, torque, magnitude of the vector sum of dq-axis currents, actual q-axis current (when id=0 control is used), square of the magnitude of the vector sum of dq-axis currents, square of the magnitude of the vector sum of dq-axis voltages, and magnitude of the vector sum of dq-axis voltages.

[0037] In some implementations, after the operation of the fan keeps the actual air volume at the air outlet of the air conditioning equipment constant at the first target air volume value, the fan speed of the air conditioning equipment is sampled multiple times, and the first speed value is calculated by averaging the multiple sampled values.

[0038] S102: The first rotational speed value is processed by the surface model to obtain the current static pressure value in the duct. The surface model represents the relationship between the target air volume, the fan speed and the static pressure in the duct.

[0039] In some implementations, processing the first rotational speed value using a surface model to obtain the current static pressure value within the duct can include: inputting a first target airflow value into the surface model to obtain a second relationship model corresponding to the first target airflow value, wherein the second relationship model characterizes the relationship between the fan rotational speed and the static pressure within the duct under the first target airflow value; inputting the first rotational speed value into the second relationship model, and outputting the current static pressure value within the duct through the second relationship model. It can be understood that the second relationship model is a continuous curve characterizing the relationship between the fan rotational speed and the static pressure within the duct under the first target airflow value.

[0040] In some implementations, in response to an airflow setting command, a target airflow can be set as a first target airflow value, and the set first target airflow value can be input into a surface model to obtain a second relational model corresponding to the first target airflow value. Then, a first rotational speed value is periodically acquired according to a preset detection cycle. After each acquisition of the first rotational speed value, the currently acquired first rotational speed value is input into the second relational model, and the current static pressure value in the air duct is output through the second relational model.

[0041] It is understandable that the surface model used in step S102 can be referenced. Figure 2 As shown, a three-dimensional surface characterizes the quantitative relationship between the target air volume, fan speed, and static pressure within the duct. The model function corresponding to this surface model is P = F(n, Q), where P is the static pressure within the duct, n is the fan speed, and Q is the target air volume. It should be noted that... Figure 2 This is only for displaying the model style of the curved surface; the coordinate values ​​of the XYZ axes should be based on actual measurements. Figure 2 The coordinate values ​​of the XYZ axes shown are not considered as limitations. The first target air volume value is input into the model function P = F(n, Q) of the surface model to perform dimensionality reduction, so as to obtain the model function of the second relationship model corresponding to the first target air volume value, which is Pi = Fi(ni), where Pi = Fi(ni) represents the linear relationship between the fan speed and the static pressure in the duct under the first target air volume value.

[0042] In some implementations, the surface model used in step S102 is established based on pre-measured full-volume three-dimensional mapping data. Specifically, the surface model is obtained by performing three-dimensional modeling using the full-volume three-dimensional mapping data. The full-volume three-dimensional mapping data includes M sets of three-dimensional mapping data corresponding to M candidate airflow values. Each set of three-dimensional mapping data includes pairs of multiple rotational speed values ​​and multiple static pressure values ​​obtained under that candidate airflow value. M is an integer greater than 1, meaning that one rotational speed value corresponds to one static pressure value.

[0043] In some implementations, the full three-dimensional mapping data of the M candidate air volume values ​​(Q1, Q2, Q3, Q4...Qm) are shown in Table 1 below:

[0044] Table 1. Full 3D Mapping Data

[0045]

[0046] In some implementations, for each of the M candidate airflow values, a mapping curve is established based on the paired static pressure values ​​and multiple rotational speed values ​​obtained under that candidate airflow value. This mapping curve characterizes the relationship between the fan rotational speed and the static pressure in the duct under that candidate airflow value. Based on the M mapping curves corresponding to the M candidate airflow values, full-scale three-dimensional mapping data is established. In some implementations, there can be multiple candidate airflow values, such as 800 m³ / h, 1000 m³ / h, 1200 m³ / h, 1400 m³ / h, 1600 m³ / h, 1800 m³ / h, 2000 m³ / h, 2200 m³ / h, and 2400 m³ / h. The mapping curves corresponding to each of these candidate airflow values ​​can be referenced. Figure 3 As shown.

[0047] In other implementations, for scenarios with a wide range of target airflow and air pressure, the surface model in step S102 is constructed by stitching together N sub-surface models. Each of the N sub-surface models corresponds one-to-one with N airflow intervals, and each airflow interval includes multiple candidate airflow values, where N is an integer greater than 1. It can be understood that for each candidate airflow value within the airflow interval, multiple pairs of rotational speed values ​​and multiple static pressure values ​​are obtained under that candidate airflow value, and a mapping relationship curve is established accordingly. Based on the mapping relationship curves corresponding to each candidate airflow value within the airflow interval, the three-dimensional mapping data of that airflow interval is obtained, and a sub-surface model is established based on the three-dimensional mapping data of that airflow interval.

[0048] In some implementations, the controlled unit is used as the test object, and multiple pairs of static pressure values ​​and multiple rotational speed values ​​are obtained for each candidate airflow value. This can be achieved by adjusting the static pressure p in the air duct. i and the fan speed n of the controlled machine i This process ensures that the actual airflow at the outlet of the controlled unit remains constant within a given candidate airflow value. Multiple static pressure and rotational speed values ​​are then obtained for this candidate airflow value, with a one-to-one correspondence between the static pressure and rotational speed values. In practice, the static pressure within the controlled unit's duct is gradually increased from 0 Pa in fixed increments, such as 0 Pa, 20 Pa, 40 Pa, 60 Pa, 80 Pa, and so on. After each adjustment of the duct static pressure, the fan speed of the controlled unit is adjusted at that static pressure value until the actual airflow at the outlet of the controlled unit is constant within the given candidate airflow value. The fan speed value is then obtained, resulting in a pair of static pressure and rotational speed values. It is understood that the controlled unit can be a prototype installed in an experimental environment.

[0049] S103: Determine the current degree of clogging of the filter assembly based on the current static pressure value.

[0050] In some implementations, step S103 may include: determining the current degree of clogging of the filter assembly based on the current static pressure value and a preset first relationship model, wherein the first relationship model characterizes the relationship between the static pressure in the duct and the degree of clogging of the filter assembly.

[0051] In some other embodiments, step S103 may include: pre-setting multiple static pressure thresholds that correspond one-to-one with various degrees of clogging, comparing the current static pressure value with the multiple preset static pressure thresholds, and determining the current degree of clogging of the filter assembly.

[0052] In some embodiments, the filter assembly clogging detection method provided by the present invention establishes a first relationship model in advance through the following steps: simulating various degrees of clogging in the filter assembly; for each simulated degree of clogging, obtaining the static pressure value in the duct under that simulated degree of clogging using a pre-established surface model; and establishing a first relationship model based on the simulated various degrees of clogging and the obtained static pressure values ​​in the duct under each simulated degree of clogging. It can be understood that the first relationship model is a continuous curve characterizing the relationship between the static pressure in the duct and the degree of clogging.

[0053] In some implementations, a prototype installed in an experimental environment is used as the controlled object, simulating various degrees of clogging on the prototype. For each simulated degree of clogging, the prototype's fan is controlled to operate in a constant airflow control mode, ensuring that the airflow at the prototype's outlet remains constant after reaching a second target airflow value. The current fan speed is then acquired and input into a pre-established surface model to obtain the static pressure value within the duct under that simulated degree of clogging. It should be noted that the second target airflow value can be any airflow value within the adjustable range of the target airflow.

[0054] To improve the accuracy of detecting the degree of dirt and clogging of filter components, it is necessary to maintain the same state of various loads of the air conditioning equipment during the two processes of establishing the first relationship model and measuring the reference static pressure, such as the same swing angle of the swing mechanism.

[0055] In other implementations, different degrees of clogging can be simulated for filter components with different materials and pore sizes, and the static pressure value of each filter component under each degree of clogging can be obtained. A first relationship model is established based on the simulated clogging degree of various filter components and the obtained static pressure value, thereby expanding the application scope of the first relationship model.

[0056] Understandably, the model function of the first relational model can be expressed as T = F(p), where P is the static pressure inside the air duct and T is the degree of clogging of the filter assembly.

[0057] In some implementations, the first relationship model is established with a static pressure of 0 in the duct when the filter assembly is not clogged. However, after the air conditioning equipment is installed in the usage environment, it is affected by factors such as the usage environment and duct length. Even if the filter assembly is not clogged, there will still be a certain static pressure in the duct. Therefore, in some implementations, determining the current degree of clogging of the filter assembly based on the current static pressure value and the pre-established first relationship model can include: determining the static pressure difference between the current static pressure value and the reference static pressure value; inputting the static pressure difference into the first relationship model; and determining the current degree of clogging of the filter assembly through the first relationship model. This improves the accuracy of filter assembly clogging detection. By introducing a reference static pressure, the difference between the air conditioning equipment's installation environment and the initial first relationship model is eliminated.

[0058] Understandably, the model function of the first relational model can be expressed as T = F(p - p0), where P is the current static pressure value, p0 is the reference static pressure value, and T is the degree of clogging of the filter assembly. The reference static pressure value is obtained after the air conditioning equipment is installed in the usage environment and no clogging has occurred.

[0059] In some implementations, when the air conditioning equipment is installed in the usage environment and the filter assembly is not clogged, the fan of the air conditioning equipment is controlled to operate in a constant air volume control mode. After the operation of the air conditioning equipment fan makes the actual air volume at the air outlet of the air conditioning equipment constant to a set second target air volume value, the second speed value of the fan is obtained. The second target air volume value is any air volume value within the adjustable range of the target air volume. The second speed value is processed by a surface model to obtain a reference static pressure value.

[0060] The process of processing the second rotational speed value using a surface model is the same as or similar to the process of processing the first rotational speed value using a surface model. The second target air volume value is input into the surface model to obtain the second relationship model corresponding to the second target air volume value. The first rotational speed value is input into the second relationship model corresponding to the second target air volume value, and the reference static pressure value is output through the second relationship model corresponding to the second target air volume value.

[0061] In some implementations, the air conditioning unit's fan can be controlled in a constant air volume control mode during the first startup after installation in the usage environment to obtain a reference static pressure value.

[0062] In other implementations, in order to improve the accuracy of obtaining the reference static pressure value, a static pressure value can be obtained for each of the multiple target air volume values, and the average value of the static pressure values ​​obtained for the multiple target air volume values ​​can be calculated to obtain the reference static pressure value.

[0063] Based on the same inventive concept, this invention also provides a filter assembly clogging detection device, applied to air conditioning equipment with a fan and air duct, wherein the filter assembly is disposed at the air inlet of the air conditioning equipment, such as... Figure 4 As shown, the filter assembly clogging detection device includes: a speed acquisition unit 401, used to control the fan to operate in a constant air volume control mode and acquire the first speed value of the fan; a static pressure determination unit 402, used to process the first speed value through a pre-established surface model to obtain the current static pressure value in the duct, the surface model being a three-dimensional surface characterizing the relationship between the target air volume, the fan speed, and the static pressure in the duct; and a degree determination unit 403, used to determine the current degree of clogging of the filter assembly based on the current static pressure value.

[0064] In some implementations, the degree determination unit 403 may include: a first determination subunit, used to determine the current degree of clogging of the filter assembly based on the current static pressure value and a pre-established first relationship model, wherein the first relationship model characterizes the relationship between the static pressure in the duct and the degree of clogging of the filter assembly.

[0065] In some implementations, the first determining subunit may be used to: determine the static pressure difference between the current static pressure value and the reference static pressure value, the reference static pressure value being obtained when the air conditioning equipment is installed in the operating environment and the filter assembly is not clogged; input the static pressure difference into a first relational model, and determine the current degree of clogging of the filter assembly through the first relational model.

[0066] In some embodiments, the filter assembly clogging detection device further includes a reference determination unit, used to: control the fan to operate in a constant air volume control mode when the air conditioning equipment is installed in the operating environment and the filter assembly is not clogged, and obtain a second speed value of the fan; process the second speed value through a surface model to obtain a reference static pressure value.

[0067] In some embodiments, the static pressure determination unit 402 includes: a model processing subunit, used to input the currently set first target air volume value of the air conditioning equipment into the surface model to obtain a second relationship model corresponding to the first target air volume value, the second relationship model representing the relationship between the fan speed and the static pressure in the duct under the first target air volume value; and a static pressure calculation subunit, used to input the first speed value into the second relationship model and determine the current static pressure value in the duct through the second relationship model.

[0068] In some implementations, the surface model is established based on pre-determined full three-dimensional mapping data; wherein the full three-dimensional mapping data includes M sets of three-dimensional mapping data corresponding to M candidate air volume values, each set of three-dimensional mapping data including a pair of multiple rotational speed values ​​and multiple static pressure values ​​obtained under that candidate air volume value, and M is an integer greater than 1.

[0069] In some embodiments, the filter assembly clogging detection device further includes a first modeling unit for performing the step of pre-establishing a surface model: for M candidate airflow values, based on multiple pairs of rotational speed values ​​and multiple static pressure values ​​obtained under the candidate airflow value, establishing a mapping relationship curve corresponding to the candidate airflow value, the mapping relationship curve being a curve characterizing the relationship between the fan speed and the static pressure in the duct under the candidate airflow value; and establishing full three-dimensional mapping data based on the M mapping relationship curves corresponding to the M candidate airflow values.

[0070] In some implementations, the surface model is formed by splicing together N sub-surface models, with each of the N sub-surface models corresponding to one of the N airflow intervals. Each airflow interval includes multiple candidate airflow values, where N is an integer greater than 1.

[0071] In some embodiments, the filter assembly clogging detection device further includes a second modeling unit for performing the step of pre-establishing a first relational model: simulating various degrees of clogging in the filter assembly; for each simulated degree of clogging, obtaining the static pressure value in the duct under that simulated degree of clogging through a surface model; and establishing the first relational model based on the various simulated degrees of clogging and the static pressure value in the duct obtained under each simulated degree of clogging.

[0072] The specific functions of each functional unit in the above-mentioned device have been described in detail in the filter assembly clogging detection method provided in some embodiments of the present invention, and will not be elaborated here.

[0073] Based on the same inventive concept, embodiments of the present invention also provide an air conditioning device, comprising a fan and an air duct, such as... Figure 5 As shown, the air conditioning device further includes: a processor 502; and a memory 504 for storing executable instructions of the processor 502, wherein the processor 502 is configured to execute the instructions to implement the filter assembly clogging detection method described in any of the above embodiments.

[0074] Among them, Figure 5In this document, a bus architecture (represented by bus 500) is used. Bus 500 may include any number of interconnected buses and bridges, linking various circuits including one or more processors represented by processor 502 and memory represented by memory 504. Bus 500 may also link various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and therefore will not be described further herein. Bus interface 505 provides an interface between bus 500 and receiver 501 and transmitter 503. Receiver 501 and transmitter 503 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices over a transmission medium. Processor 502 is responsible for managing bus 500 and general processing, while memory 504 can be used to store data used by processor 502 during operation.

[0075] Based on the same inventive concept, the present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the program is executed by a processor, it implements the filter assembly clogging detection method described in any embodiment of the first aspect.

[0076] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 Devices that specify the functions in one or more boxes.

[0077] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction device, which is implemented in a process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0079] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0080] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

[0081] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A method for detecting dirt clogging in a filter assembly, characterized in that, An air conditioning device having a fan and ductwork, wherein the filter assembly is disposed at the air inlet of the air conditioning device, the method comprising: The fan is controlled to operate in a constant air volume control mode, and the first speed value of the fan is obtained; The first rotational speed value is processed by a pre-established surface model to obtain the current static pressure value in the duct. The surface model is a three-dimensional surface that characterizes the relationship between the target air volume, the fan speed and the static pressure in the duct. The current degree of clogging of the filter assembly is determined based on the current static pressure value.

2. The method as described in claim 1, characterized in that, Determining the current degree of clogging of the filter assembly based on the current static pressure value includes: Based on the current static pressure value and a pre-established first relationship model, the current degree of clogging of the filter assembly is determined. The first relationship model characterizes the relationship between the static pressure in the duct and the degree of clogging of the filter assembly.

3. The method as described in claim 2, characterized in that, Determining the current degree of clogging of the filter assembly based on the current static pressure value and a pre-established first relationship model includes: Determine the static pressure difference between the current static pressure value and the reference static pressure value, wherein the reference static pressure value is obtained when the air conditioning equipment is installed in the operating environment and the filter assembly is not clogged; The static pressure difference is input into the first relationship model, and the current degree of clogging of the filter assembly is determined through the first relationship model.

4. The method as described in claim 3, characterized in that, Also includes: When the air conditioning equipment is installed in the operating environment and the filter assembly is not clogged, the fan is controlled to operate in a constant air volume control mode, and the second speed value of the fan is obtained; The reference static pressure value is obtained by processing the second rotational speed value through the surface model.

5. The method as described in claim 4, characterized in that, The step of processing the first rotational speed value through a pre-established curved surface model to obtain the current static pressure value within the air duct includes: The first target air volume value currently set by the air conditioning equipment is input into the surface model to obtain a second relationship model corresponding to the first target air volume value. The second relationship model characterizes the relationship between the fan speed and the static pressure in the air duct under the first target air volume value. The first rotational speed value is input into the second relational model, and the current static pressure value in the air duct is determined through the second relational model.

6. The method as described in claim 1, characterized in that, The surface model is established based on pre-determined full-volume three-dimensional mapping data; The full three-dimensional mapping data includes M sets of three-dimensional mapping data corresponding to M candidate air volume values. Each set of three-dimensional mapping data includes multiple pairs of rotational speed values ​​and multiple static pressure values ​​obtained under that candidate air volume value, where M is an integer greater than 1.

7. The method as described in claim 6, characterized in that, The surface model is pre-established through the following steps: For the M candidate air volume values, a mapping relationship curve corresponding to the candidate air volume value is established based on the pairs of multiple speed values ​​and multiple static pressure values ​​obtained under the candidate air volume value. The mapping relationship curve is a curve characterizing the relationship between the fan speed and the static pressure in the air duct under the candidate air volume value. Based on the M mapping relationship curves corresponding to the M candidate air volume values, the full three-dimensional mapping data is established.

8. The method as described in claim 1, characterized in that, The surface model is formed by splicing together N sub-surface models. Each of the N sub-surface models corresponds one-to-one with N airflow intervals. Each airflow interval includes multiple candidate airflow values, where N is an integer greater than 1.

9. The method as described in claim 2, characterized in that, The first relational model is established in advance through the following steps: Simulate various degrees of clogging in the filter assembly; For each simulated degree of clogging, the static pressure value in the duct is obtained using the surface model under that simulated degree of clogging. The first relationship model is established based on the simulated levels of clogging and the static pressure values ​​obtained in the duct under each simulated level of clogging.

10. A filter assembly clogging detection device, characterized in that, An air conditioning device with a fan and ductwork, wherein the filter assembly is disposed at the air inlet of the air conditioning device, and the filter assembly clogging detection device includes: The speed acquisition unit is used to control the fan to operate in a constant air volume control mode and to acquire the first speed value of the fan; The static pressure determination unit is used to process the first rotational speed value through a pre-established surface model to obtain the current static pressure value in the duct. The surface model is a three-dimensional surface that characterizes the relationship between the target air volume, the fan speed and the static pressure in the duct. The degree determination unit is used to determine the current degree of clogging of the filter assembly based on the current static pressure value.

11. An air conditioning device, characterized in that, The device includes a fan and an air duct, and further includes: a processor; and a memory for storing executable instructions of the processor, wherein the processor is configured to execute the instructions to implement the filter assembly clogging detection method as described in any one of claims 1 to 9.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the filter assembly clogging detection method according to any one of claims 1-9.

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