Air conditioner filter screen blockage determination method and related equipment

Abstract

The invention relates to an air conditioner filter screen blockage determining method and related equipment. The method comprises the steps that the working current in the air conditioner operation process and the actual rotating speed of a draught fan are obtained; extracting a first feature vector based on the working current and the actual rotating speed; based on a pre-trained air conditioner filter screen blockage level prediction model, obtaining an air conditioner filter screen blockage level prediction result according to the first feature vector; and according to the air conditioner filter screen blockage level prediction result, the operation state of electric equipment associated with the air conditioner in the environment where the air conditioner is located is controlled. The method can improve the accuracy of the diagnosis result of the blockage degree of the air conditioner filter screen.

Description

Technical Field

[0001] This application relates to the field of air conditioning technology, and in particular to a method and related equipment for determining air conditioning filter blockage. Background Technology

[0002] Air conditioners, as core devices for regulating indoor temperature and humidity, have become deeply integrated into homes, offices, and various public places, serving as necessities for improving living and working comfort. However, over long-term operation, clogged filters can gradually affect the device's performance and safety.

[0003] When an air conditioner is working, it drives indoor air to form a circulating airflow. As this circulating air passes through the filter inside the air conditioner, particulate impurities such as dust, hair, and lint suspended in the air are intercepted by the filter and gradually adhere to its surface. Over time, these impurities accumulate and eventually clog the filter pores.

[0004] A clogged filter directly increases airflow resistance, significantly reducing the airflow from the air conditioner and thus weakening its heating or cooling efficiency. This not only prolongs the time it takes to reach the set temperature but also increases energy consumption. Furthermore, clogged filters are highly susceptible to harboring bacteria, mold, and other microorganisms. These harmful microorganisms can spread into the indoor air with the air conditioner's airflow, and if inhaled, may cause respiratory discomfort, allergies, and other health problems, posing a greater threat to vulnerable groups such as the elderly and children.

[0005] Since the air conditioning filter is located inside the air conditioner, it is extremely difficult to detect whether it is dirty or clogged. Currently, there are methods to judge the filter's clogging status based on the collected airflow from the air conditioner's outlet. However, since the airflow of different types and models of air conditioners varies, and there are other factors in the air conditioner's operating environment that affect the airflow, the accuracy of judging whether the air conditioning filter is dirty or clogged by relying solely on the airflow from the air conditioner's outlet is low. Summary of the Invention

[0006] This application provides a method and related equipment for determining air conditioner filter blockage, in order to solve the problem that the accuracy of methods for determining air conditioner filter blockage in related technologies is low.

[0007] In a first aspect, this application provides a method for determining air conditioner filter blockage, comprising: acquiring the operating current and the actual speed of the fan during the operation of the air conditioner; extracting a first feature vector based on the operating current and the actual speed; obtaining a predicted air conditioner filter blockage level based on the first feature vector using a pre-trained air conditioner filter blockage level prediction model; and controlling the operating state of electrical equipment associated with the air conditioner in the environment where the air conditioner is located based on the predicted air conditioner filter blockage level.

[0008] In a second aspect, this application provides a computer device including one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, and the one or more programs include instructions for performing the method described in the first aspect.

[0009] Thirdly, this application provides a non-volatile computer-readable storage medium containing a computer program that, when executed by one or more processors, causes the one or more processors to perform the method described in the first aspect.

[0010] The technical solutions provided in this application have the following advantages compared with the prior art: The air conditioner filter clogging determination method of this application embodiment predicts the clogging level of the air conditioner filter based on a pre-trained air conditioner filter clogging level prediction model according to the working current and actual fan speed during air conditioner operation. This improves the accuracy of the prediction results for the degree of air conditioner filter clogging. In addition, by controlling the operation of electrical equipment associated with the air conditioner according to the determined air conditioner filter clogging level, it can actively coordinate with other electrical equipment to optimize the environment when the air conditioner filter is clogged, thus avoiding the situation where the environment causes the air conditioner filter clogging to become more serious. Attached Figure Description

[0011] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0012] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0014] Figure 1 A schematic diagram of an exemplary system provided in an embodiment of this application is shown; Figure 2 An exemplary flow diagram of a method for determining air conditioner filter blockage provided in this application embodiment; Figure 3 An exemplary flow diagram of a method for determining air conditioner filter blockage provided in this application embodiment; Figure 4 An exemplary flow diagram of a method for determining air conditioner filter blockage provided in this application embodiment; Figure 5 An exemplary flow diagram of a method for determining air conditioner filter blockage provided in this application embodiment; Figure 6 This is a schematic diagram of the hardware structure of an exemplary computer device provided in an embodiment of this application. Detailed Implementation

[0015] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0016] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0017] For ease of description, spatial relative terms may be used in the text to describe the relative position or movement of one element or feature relative to another element or feature, as shown in the figure. These relative terms include, for example, "inside," "outside," "middle," "outer," "below," "below," "above," "front," "back," etc. Such spatial relative terms are intended to include different orientations of the device in use or operation, other than those depicted in the figure. For example, if the device in the figure undergoes a positional flip, orientation change, or change of motion, these directional indications will change accordingly. For instance, an element described as "below other elements or features" or "below other elements or features" will subsequently be oriented "above other elements or features" or "above other elements or features." Therefore, the example term "below" can include both upper and lower orientations. The device may be otherwise oriented (rotated 90 degrees or in other directions), and the spatial relative descriptors used in the text will be interpreted accordingly.

[0018] Figure 1 A schematic diagram of an exemplary system provided in an embodiment of this application is shown.

[0019] like Figure 1 As shown, the system may include user equipment 102, terminal equipment 104, server 106, and database server 108. In an embodiment of this application, user equipment 102 is an air conditioner. A medium (e.g., a network) may be included between terminal equipment 104, user equipment 102, server 106, and database server 108 to provide a communication link. This network may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0020] Various software or applications (APPs) may be installed on the terminal device 104 and the user device 102, such as image processing software or applications, video conferencing software or applications, reading software or applications, video software or applications, social networking software or applications, payment software or applications, web browsers, and instant messaging tools. In some embodiments, these software or applications can be used to perform the method for determining air conditioner filter blockage.

[0021] The terminal device 104 and user device 102 here can be hardware or software. When the terminal device 104 and user device 102 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, e-book readers, MP3 players, laptops, and desktop computers (PCs). When the terminal device 104 and user device 102 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software programs or software modules (e.g., to provide distributed services) or as a single software program or software module. No specific limitations are made here.

[0022] Server 106 can be a server providing various services, such as a backend server supporting various applications displayed on terminal device 104 and user device 104. Database server 108 can also be a database server providing various services. It is understood that if server 106 can implement the relevant functions of database server 108, database server 108 may not need to be configured in the system.

[0023] The server 106 and database server 108 here can be either hardware or software. When they are hardware, they can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When they are software, they can be implemented as multiple software programs or software modules (for example, to provide distributed services), or as a single software program or software module. No specific limitations are made here.

[0024] It should be noted that the air conditioner filter clogging determination method provided in this application embodiment can be executed by server 106, or by the interaction of various devices in the system. It should be understood that... Figure 1 The number of terminal devices, users, servers, and database servers shown is merely illustrative. Depending on implementation needs, there can be any number of terminal devices, users, servers, and database servers.

[0025] As an exemplary scenario of this application embodiment, the server 106 can obtain the operating current and actual fan speed of the air conditioner during operation reported by the terminal device 104 or the air conditioner 102. Based on the operating current and actual fan speed, the server 106 extracts a first feature vector. Based on the air conditioner filter clogging level prediction model pre-trained in the server 106, the server predicts the air conditioner filter clogging level according to the first feature vector. Then, based on the predicted air conditioner filter clogging level, the server sends a command to the associated device 110 of the air conditioner 102 in the environment to control the operating status of the associated device 110.

[0026] This application provides a method for determining air conditioner filter blockage. Figure 2 This is an exemplary flowchart of the air conditioner filter clogging determination method provided in the embodiments of this application, such as... Figure 2 As shown, the method includes the following processing: In step 202, the operating current and the actual speed of the fan during the operation of the air conditioner are obtained.

[0027] The operating current during air conditioner operation refers to the overall operating current of the air conditioner unit or the compressor current. Optionally, the operating current of the air conditioner unit or compressor during operation can be detected by a current detection device. The fan speed can be acquired by a Hall sensor or pulse signal from the air conditioner motor.

[0028] In step 204, the first feature vector is extracted based on the operating current and the actual speed of the fan during the operation of the air conditioner; Furthermore, in step 202 above, other air conditioning operating parameters used to extract the first feature vector can also be obtained, such as the evaporator inlet and outlet air temperature difference, refrigerant pressure, and air conditioning set temperature / actual temperature. These operating parameters' first feature vectors can be extracted simultaneously when extracting the first feature vector based on the air conditioning operating current and the actual fan speed, resulting in a first feature vector characterizing the influence of air conditioning operating parameters on the air conditioning filter clogging level. Introducing these air conditioning operating parameters as indicators for predicting the air conditioning filter clogging level, based on the air conditioning operating current and the actual fan speed, can improve the accuracy of the air conditioning filter clogging level prediction model in complex environments.

[0029] In step 206, the first feature vector is provided to the pre-trained air conditioner filter clogging level prediction model to obtain the air conditioner filter clogging level prediction result; Optionally, the model can be based on a pre-trained air conditioner filter clogging level prediction model, using the first feature vector extracted based on the air conditioner's operating current and the fan's actual speed as the model input to obtain the air conditioner filter clogging level prediction result output by the model. Optionally, the air conditioner filter clogging level prediction result output by the model may include the air conditioner filter clogging level and confidence level.

[0030] For example, the air conditioning filter clogging level can be pre-classified into five levels according to the severity of the clogging, from light to severe: Level 1: Corresponds to the cleanliness of the air conditioner's filter; Level 2: The air conditioner filter is slightly clogged with dirt; Level 3: The air conditioner filter is moderately clogged; Level 4: The air conditioner's filter is severely clogged; Level 5: Corresponds to a severely clogged air conditioner filter.

[0031] Optionally, after obtaining the predicted air conditioning filter clogging level, the clogging level can be output to facilitate timely user awareness of the filter's clogging status. Furthermore, a description of the degree of clogging corresponding to each clogging level can be output, such as the descriptions for the five levels mentioned above. Additionally, corresponding prompts can be provided based on the clogging level; for example, if the current clogging level is four, the description could be "severely clogged air conditioning filter," recommending timely cleaning or replacement. Furthermore, by combining maintenance records of the same model of air conditioner, it can be determined that the current clogging is causing additional energy consumption, and this energy consumption information can also be displayed to the user.

[0032] In step 208, the operating status of electrical equipment associated with the air conditioner in the environment is controlled according to the air conditioner filter clogging level.

[0033] Optionally, a pre-established association between the air conditioner and other electrical appliances in the electrical environment can be established. Depending on the potential impact of air conditioner filter clogging, the electrical appliances associated with the air conditioner may include, for example, air handling systems and air purification systems, where the air handling system could be, for example, a fresh air system. The operating status of the associated electrical appliances can be controlled based on the air conditioner filter clogging level. This can be achieved by controlling the corresponding electrical appliances to turn on, or by controlling these electrical appliances to switch operating modes, based on a pre-set correspondence between the air conditioner filter clogging level and the associated electrical appliances. For example, when the air conditioner filter clogging level is greater than level three in the example above, that is, when the air conditioner filter is moderately clogged, the fresh air system in the associated electrical equipment is controlled to operate in indoor circulation mode to avoid severe clogging of the air conditioner filter; or, when the air conditioner filter clogging level is greater than level four in the example above, that is, when the air conditioner filter is severely clogged, the air purification system in the associated electrical equipment is controlled to turn on or switch to enhanced purification mode to improve indoor air quality. This not only prevents further clogging of the air conditioner filter but also prevents the dust accumulated on the filter from circulating into the room due to clogging, thus avoiding a deterioration in indoor air quality.

[0034] The air conditioner filter clogging determination method of this application embodiment predicts the clogging level of the air conditioner filter based on a pre-trained air conditioner filter clogging level prediction model, according to the operating current and actual fan speed during air conditioner operation. This improves the accuracy of the clogging prediction results. Furthermore, by controlling the operation of associated electrical equipment based on the determined air conditioner filter clogging level, the method can proactively coordinate with other electrical equipment to optimize the environment when the air conditioner filter is clogged, preventing the environment from exacerbating the clogging.

[0035] Figure 3 This is an exemplary flowchart of the air conditioner filter clogging determination method provided in the embodiments of this application, such as... Figure 3 As shown, the method is in Figure 2 The method shown can be further supplemented with the following processing: In step 302, after extracting the first feature vector based on the operating current and actual fan speed during the operation of the air conditioner, environmental data of the environment in which the air conditioner is located is obtained. Optionally, environmental data may include: indoor temperature and humidity, outdoor temperature and humidity, atmospheric pressure, and dust concentration (such as PM2.5 or PM10 concentration) of the environment where the air conditioner is located. This environmental data may be obtained from temperature and humidity sensors and dust sensors installed indoors and outdoors, or from meteorological data in weather applications or the Internet.

[0036] In step 304, a second feature vector is extracted based on the environmental data; Optionally, a second feature vector can be extracted based on environmental data to obtain a second feature vector that characterizes the degree of influence of environmental data on the air conditioner's filter clogging level. The second feature vector can be obtained by extracting the feature values ​​of each environmental data point based on their influence on the air conditioner filter clogging level.

[0037] In step 306, the first feature vector and the second feature vector are fused to obtain a fused feature vector; The first feature vector is fed into a pre-trained air conditioning filter clogging level prediction model to obtain the clogging level prediction result of the air conditioning filter, which may include: In step 308, the fused feature vector is provided to the pre-trained air conditioner filter clogging level prediction model to obtain the air conditioner filter clogging level prediction result.

[0038] The fused feature vector is used as the prediction vector of the pre-trained air conditioner filter clogging level prediction model. The air conditioner filter clogging level prediction model will output the air conditioner filter clogging level prediction result. Optionally, the prediction result may include the clogging level of the air conditioner filter and the confidence level of the clogging level.

[0039] For example, the air conditioner filter clogging determination method of this application embodiment is executed by a server that has deployed a trained air conditioner filter clogging level prediction model. Predicting the air conditioner filter clogging level based on the air conditioner filter clogging level prediction model may include the following processing: The air conditioning unit reports data such as current, speed, and operating status to the server in real time via smart sockets / Hall sensors; After receiving the data, the server adjusts the current value by combining it with real-time weather parameters, and then compares it with historical data through model feature matching to output the final prediction result of the air conditioner filter blockage level, such as the first to fifth level blockage levels in the example above. The system triggers alerts based on the level: Levels 1 and 2 do not send alert messages; Level 3 actively pushes a "cleaning suggestion" alert message; Levels 4 and 5 push a "cleaning immediately suggestion" alert message.

[0040] Figure 4 This is an exemplary flowchart of the air conditioner filter clogging determination method provided in the embodiments of this application, such as... Figure 4 As shown, the method is in Figure 2 The method shown can be further supplemented with the following processing: Before obtaining the operating current and actual operating speed of the fan during air conditioner operation, the air conditioner filter clogging level prediction model is trained. Training the air conditioner filter clogging level prediction model may specifically include: In step 402, historical operating data of the air conditioner and environmental data of the environment in which the air conditioner is located are obtained; The historical operating data of the air conditioner may include: the operating current of the air conditioner during operation, the actual speed of the fan, and the reference current of the air conditioner at each speed. Furthermore, the historical operating data of the air conditioner can also include the air conditioner's running time and cumulative number of times it has been turned on. This historical operating data can be obtained from the air conditioner controller and current transformer. Since this historical operating data is also related to the degree of clogging of the air conditioner filter, introducing this data as an indicator to predict the degree of clogging of the air conditioner filter can further improve the accuracy of the prediction results.

[0041] In step 404, the historical operating data and the environmental data are labeled based on the actual clogging level of the air conditioner filter in the historical maintenance record of the air conditioner to obtain the first training dataset; Optionally, the air conditioner's historical maintenance record can be the air conditioner's manual maintenance record. This record may include information such as the air conditioner's maintenance time, maintenance method, and the actual clogging level of the air conditioner filter. The actual clogging level of the air conditioner filter can be used as the label of the training data in the training dataset to label the training data and obtain training data-label data pairs.

[0042] In step 406, a model for predicting the clogging level of the air conditioner filter is trained based on the first training dataset.

[0043] Since ambient temperature affects the internal resistance of air conditioner motors, leading to deviations in current at the same speed / airflow, incorporating environmental data on the actual fan current during the prediction of air conditioner filter clogging level can further improve the accuracy of model predictions. After the air conditioner filter clogging level prediction model is trained, it can be deployed on a server. Based on this, when the air conditioning unit reports the air conditioner's operating current in real time, the model deployed on the server can output the air conditioner filter clogging level prediction result by comparing feature matching with historical data.

[0044] Optionally, the air conditioner filter clogging level prediction model can be continuously optimized through comparative learning. For example, training the air conditioner filter clogging level prediction model based on the first training dataset may include the following processing: Construct positive and negative sample pairs: positive samples are data of "same environment - same operating mode - normal filter", and negative samples are data of "same environment - same operating mode - clogged filter"; calculate the feature distance between samples, minimize the distance of samples of the same type and maximize the distance of samples of different types by contrast loss function, and output the distance between the real-time sample and the positive sample cluster in the embedding space. Convert the distance into the air conditioner filter clogging level.

[0045] Online incremental learning: Every 1000 new data points collected, the model parameters are automatically adjusted to correct deviations caused by the environment or user habits.

[0046] In one or more embodiments of this application, the method for determining air conditioner filter blockage may further include: When the air conditioner is powered on for the first time, the reference current corresponding to each fan speed of the air conditioner is collected. Optionally, the air conditioner can be sequentially locked at preset fixed speeds and run at each preset fixed speed for a specified duration. During the specified duration, the operating current of the entire air conditioner is periodically collected, and the average value is taken after removing the maximum and minimum current values ​​to obtain the reference current corresponding to the current fixed speed.

[0047] It should be noted that after replacing the air conditioner filter, the process of collecting the reference current corresponding to each fan speed can be retried to update the reference current for each fan speed. Additionally, after cleaning the air conditioner filter, the "initial power-on reference current" also needs to be collected again to overwrite historical reference data and avoid reference deviations caused by equipment aging.

[0048] Set the airflow attenuation range for the reference current corresponding to each rotation speed; The limiting current corresponding to each rotation speed is determined based on the lower limit of the airflow attenuation range; For example, assuming the airflow attenuation range is set to "80% of the rated airflow at this speed", when the actual airflow is less than 80% of the rated airflow, the corresponding operating current of the air conditioner is the limit current.

[0049] Calculate the corrected limiting current value corresponding to the limiting current value under different ambient temperatures; Optionally, since temperature affects the internal resistance of the air conditioner motor, resulting in current deviation at the same speed / airflow, the limit current value can be corrected according to the actual ambient temperature of the environment where the air conditioner is located.

[0050] Experiments can be repeated at different ambient temperatures to obtain a temperature correction coefficient. This temperature correction coefficient can then be used to correct the limiting current value at different ambient temperatures, resulting in the corrected limiting current value.

[0051] The reference current corresponding to each fan speed, the limit current corresponding to each fan speed, and the corrected limit current corresponding to each fan speed are added to the first training dataset to obtain the training dataset of the air conditioner filter blockage level prediction model.

[0052] Because the training dataset for the air conditioner filter clogging level prediction model includes the limiting current corresponding to each fan speed and the corrected limiting current corresponding to each fan speed, these data take into account the impact of airflow reduction and ambient temperature on the air conditioner's operating current. Therefore, using these data as indicators to predict the air conditioner filter clogging level, and training the prediction model based on this dataset, makes the model's prediction results more consistent with reality and improves the accuracy of the prediction results.

[0053] Figure 5 This is an exemplary flowchart of the air conditioner filter clogging determination method provided in the embodiments of this application, such as... Figure 5 As shown, in this method, controlling the operating status of electrical equipment in the environment where the air conditioner is located based on the air conditioner filter clogging level may include: In step 2082, if the air conditioner filter clogging level is higher than the first preset level, the air handling system in the control environment is switched from outdoor circulation mode to indoor circulation mode, or the air handling system is controlled to operate in indoor circulation mode. Optionally, after determining that the air conditioning filter clogging level is higher than the first preset level, if the air handling system in the environment is in operation and in outdoor circulation mode, control the air handling system to switch from outdoor circulation mode to indoor circulation mode; if the air handling system in the environment is in a closed state, control the air handling system to start and operate in indoor circulation mode.

[0054] Using the above example, the clogging level of the air conditioning filter is divided into the above five levels. The first preset level can be, for example, level two of the above levels, that is, the air conditioning filter is slightly clogged. If the clogging level of the air conditioning filter is higher than level two, it means that the clogging level of the air conditioning filter is at least level three, that is, the air conditioning filter is moderately clogged. In this case, the fresh air system (an example of the above air handling system) is controlled to operate in indoor circulation mode to reduce outdoor dust entering the room and prevent the filter from becoming more clogged.

[0055] In step 2084, the air purification system in the control environment is switched from a low-intensity purification mode to a high-intensity purification mode, or the air purification system is controlled to operate in a high-intensity purification mode.

[0056] Optionally, after determining that the air conditioning filter clogging level is higher than the first preset level, if the air purification system in the environment is running but not in high-intensity purification mode, then the air purification system will be switched to high-intensity purification mode. If the air purification system in the environment is off, then the air purification system will be turned on and run in high-intensity purification mode. In this case, controlling the air purification system to run in high-intensity purification mode can improve indoor air quality, prevent the air conditioning filter clogging from worsening, and also prevent dust accumulated on the air conditioning filter from being back-transmitted into the room, thus preventing a deterioration in indoor air quality.

[0057] Furthermore, while the air purification system operates in high-intensity purification mode, it can also monitor indoor air quality in real time, such as monitoring indoor PM2.5 concentration. If the concentration drops to the standard range, the air purification system can be turned off to avoid energy waste.

[0058] Optionally, to enable rapid adaptation to new linked devices, when a new device is connected to the platform, the device can automatically report its own functions. These functions may include the device's operating mode (e.g., a fresh air system supports "external circulation / internal circulation / mixed circulation," and an air purifier supports "auto / enhanced / sleep mode"). Based on this, according to the intervention requirements of the air conditioner filter clogging level, the system can match the functions of the new device and automatically generate a linkage strategy. For example, adding a "humidifier": when the clogging level is ≥4 and the indoor humidity is <30%, the humidifier will automatically turn on to 50% humidity to prevent dry air from exacerbating dust re-suspension.

[0059] In one or more embodiments of this application, the method for determining air conditioner filter blockage may further include: Obtain the predicted level of air conditioning filter blockage and the maintenance records of the air conditioner; Optionally, paired data of "model-predicted filter clogging level and air conditioner manual maintenance records" from the previous day can be extracted.

[0060] The predicted air conditioning filter clogging level is matched with the air conditioning filter clogging level in the maintenance record to obtain the matching result; The matching results include matching data pairs and deviation data pairs. In the matching data pairs, the predicted air conditioning filter clogging level matches the actual clogging level of the air conditioning filter in the maintenance record; however, in the deviation data pairs, the predicted air conditioning filter clogging level does not match the actual clogging level of the air conditioning filter in the maintenance record.

[0061] Based on the matching results, the matched data pairs are determined as positive sample data, and the biased data pairs are determined as negative sample data; For example, for one pair of data, if the model predicts the air conditioner filter clogging level as level three, and the actual clogging level of the air conditioner filter in the maintenance record is also level three, then the model's predicted clogging level equals the actual clogging level of the air conditioner filter. This indicates that the current data pair is a matching data pair, and the current data pair is used as positive sample data to strengthen the weight of the corresponding features in the model. For another pair of data, if the model predicts the air conditioner filter clogging level as level three, and the actual clogging level of the air conditioner filter is level four, then the model's predicted clogging level does not equal the actual clogging level of the air conditioner filter. This indicates that the current data pair is a biased data pair, and the current data pair is used as negative sample data to inversely optimize the model's temperature correction coefficient and current deviation threshold, among other parameters.

[0062] The positive sample data and the negative sample data are added to the training dataset of the air conditioner filter blockage level prediction model to obtain the updated training dataset; The air conditioner filter clogging level prediction model is further updated and trained based on the updated training dataset to obtain the updated air conditioner filter clogging level prediction model.

[0063] Optionally, to simplify model training, the model can be optimized only when the proportion of negative sample data to total sample data is higher than a certain proportion. Since the training dataset is expanded according to the actual maintenance records of air conditioners during the optimization training process, the prediction accuracy of the optimized model is higher.

[0064] In one or more embodiments of this application, the method for determining air conditioner filter blockage may further include: After controlling the operating status of the electrical equipment associated with the air conditioner in the environment where the air conditioner is located based on the predicted result of the air conditioner filter blockage level, the identification of the air conditioner and the operating status of the electrical equipment are obtained. Obtain the energy consumption data of the air conditioner; Optionally, the air conditioner's energy consumption data can be obtained through the energy consumption statistics module in the air conditioner's corresponding application installed on the user's device. Alternatively, if the air conditioner is powered by a separate circuit, its energy consumption data can be obtained from the electricity meter in that circuit.

[0065] The system displays the air conditioner's identification, the predicted level of air conditioner filter blockage, the operating current, the actual rotation speed, the energy consumption data, and the operating status of the electrical equipment.

[0066] For example, a data platform, such as an application for air conditioning, can be built to centrally manage the data and display the air conditioner's identification, the current level of blockage of the air conditioner filter, the air conditioner's operating current or fan speed, energy consumption data, and the status of linked equipment in real time, allowing users to intuitively view the overall status.

[0067] In one or more embodiments of this application, the method for determining air conditioner filter blockage may further include: If the predicted level of air conditioner filter blockage is not higher than the second preset level, the first energy consumption data of the air conditioner is acquired and stored. After determining that the air conditioner filter clogging level is higher than the second preset level, the second energy consumption data of the air conditioner is obtained; The first energy consumption data is compared with the second energy consumption data to generate an energy consumption comparison result; Output the air conditioner filter clogging level and the energy consumption comparison results.

[0068] Taking the five-level classification of air conditioner filter clogging levels as an example, the second preset level could be, for example, level two, indicating slight clogging. When the clogging level is no higher than level two, the air conditioner filter is considered relatively clean. At this point, the first energy consumption data of the air conditioner is acquired and stored. Once the clogging level is detected to be higher than the second preset level, the second energy consumption data is acquired again. The first and second energy consumption data could, for example, be the power consumption per unit time of the air conditioner under the same operating mode. By comparing the first and second energy consumption data, the extra energy consumption caused by filter clogging can be determined. This extra energy consumption is presented to the user, allowing them to intuitively understand the energy waste caused by filter clogging. For example, the energy consumption comparison result presented to the user could be: "The air conditioner filter is clogged to level four, consuming xx kWh more per hour; it is recommended to clean the filter within 48 hours."

[0069] This application also provides a computer device, including one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, and the one or more programs include instructions for performing the air conditioner filter clogging determination method described in this application.

[0070] This application also provides a non-volatile computer-readable storage medium containing a computer program, which, when executed by one or more processors, causes the one or more processors to perform the air conditioner filter blockage determination described in this application.

[0071] It should be noted that the method in this embodiment can be executed by a single device, such as a computer or server. The method can also be applied in a distributed scenario, where multiple devices cooperate to complete the task. In such a distributed scenario, one of these devices may execute only one or more steps of the method in this embodiment, and the multiple devices will interact with each other to complete the method described.

[0072] It should be noted that the above description describes some embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in a different order than that shown in the above embodiments and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0073] For ease of description, the above computer devices are described in terms of function, divided into various modules. Of course, in implementing this application, the functions of each module can be implemented in one or more software and / or hardware.

[0074] The computer device described in the above embodiments is used to implement the corresponding air conditioning filter clogging determination method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0075] This application also provides a computer device for implementing the above-described method for determining air conditioner filter blockage. Figure 6 This illustration shows a schematic diagram of the hardware structure of an exemplary computer device 600 provided in an embodiment of this application. The computer device 600 can be used to implement... Figure 1 Server 106 can also be used to implement Figure 1 Terminal device 102 and user device 104. In some scenarios, this computer device 600 can also be used to implement... Figure 1 Database server 108.

[0076] like Figure 6 As shown, the computer device 600 may include: a processor 602, a memory 604, a network interface 606, a peripheral interface 608, and a bus 610. The processor 602, memory 604, network interface 606, and peripheral interface 608 are interconnected within the computer device 600 via the bus 610.

[0077] Processor 602 may be a central processing unit (CPU), image processor, neural network processor (NPU), microcontroller (MCU), programmable logic device, digital signal processor (DSP), application-specific integrated circuit (ASIC), or one or more integrated circuits. Processor 602 can be used to perform functions related to the technology described in this application. In some embodiments, processor 602 may also include multiple processors integrated into a single logic component. For example, such as... Figure 6 As shown, processor 602 may include multiple processors 602a, 602b and 602c.

[0078] Memory 604 can be configured to store data (e.g., instructions, computer code, etc.). Figure 6 As shown, the data stored in memory 604 may include program instructions (e.g., one or more programs for implementing the display content scheduling method of this application embodiment) and data to be processed (e.g., the memory may store configuration files of other modules, etc.). Processor 602 may also access the program instructions and data stored in memory 604 and execute the program instructions to operate on the data to be processed. Memory 604 may include volatile storage devices or non-volatile storage devices. In some embodiments, memory 604 may include random access memory (RAM), read-only memory (ROM), optical disk, magnetic disk, hard disk, solid-state drive (SSD), flash memory, memory stick, etc.

[0079] Network interface 606 can be configured to provide communication with other external devices to computer device 600 via a network. This network can be any wired or wireless network capable of transmitting and receiving data. For example, the network can be a wired network, a local wireless network (e.g., Bluetooth, WiFi, Near Field Communication (NFC), etc.), a cellular network, the Internet, or a combination thereof. It is understood that the type of network is not limited to the specific examples described above.

[0080] The peripheral interface 608 can be configured to connect the computer device 600 to one or more peripheral devices to enable information input and output. For example, peripheral devices may include input devices such as keyboards, mice, touchpads, touch screens, microphones, and various sensors, as well as output devices such as displays, speakers, vibrators, and indicator lights.

[0081] Bus 610 can be configured to transfer information between various components of computer device 600 (such as processor 602, memory 604, network interface 606, and peripheral interface 608), such as internal buses (e.g., processor-memory bus), external buses (USB port, PCI-E bus), etc.

[0082] It should be noted that although the architecture of the computer device 600 described above only shows the processor 602, memory 604, network interface 606, peripheral interface 608, and bus 610, in specific implementations, the architecture of the computer device 600 may also include other components necessary for normal operation. Furthermore, those skilled in the art will understand that the architecture of the computer device 600 described above may only include the components necessary for implementing the embodiments of this application, and does not necessarily include all the components shown in the figures.

[0083] Based on the same inventive concept, corresponding to any of the above embodiments, this application also provides a non-volatile computer-readable storage medium containing a computer program, which, when executed by one or more processors, causes the one or more processors to perform the air conditioner filter blockage determination method.

[0084] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0085] The computer program stored in the storage medium of the above embodiments is used to cause the one or more processors to execute the air conditioner filter clogging determination method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0086] Based on the same inventive concept, corresponding to the air conditioner filter clogging determination method in any of the above embodiments, this application also provides a computer program product, which includes one or more computer programs. In some embodiments, the one or more computer programs are executable by one or more processors to cause the one or more processors to perform the air conditioner filter clogging determination method. Corresponding to the execution entity for each step in each embodiment of the air conditioner filter clogging determination method, the processor executing the corresponding step may belong to the corresponding execution entity.

[0087] The computer program product of the above embodiments is used to cause the processor to execute the air conditioner filter clogging determination method as described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0088] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of this application (including the claims) is limited to these examples; within the framework of this application, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of this application as described above, which are not provided in the details for the sake of brevity.

[0089] Additionally, to simplify the description and discussion, and to avoid obscuring the embodiments of this application, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. Furthermore, the apparatus may be shown in block diagram form to avoid obscuring the embodiments of this application, and this also takes into account the fact that the details of the implementation of these block diagram apparatuses are highly dependent on the platform on which the embodiments of this application will be implemented (i.e., these details should be fully understood by those skilled in the art). While specific details (e.g., circuits) have been set forth to describe exemplary embodiments of this application, it will be apparent to those skilled in the art that the embodiments of this application can be implemented without these specific details or with variations thereof. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0090] Although this application has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed.

[0091] The embodiments of this application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this application should be included within the protection scope of this application.

[0092] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0093] Although terms such as first, second, third, etc., may be used in this document to describe multiple elements, components, regions, layers, and / or segments, these elements, components, regions, layers, and / or segments should not be limited by these terms. These terms may be used only to distinguish one element, component, region, layer, or segment from another. Unless the context clearly indicates otherwise, terms such as "first," "second," and other numerical terms used herein do not imply order or sequence. Therefore, the first element, component, region, layer, or segment discussed below may be referred to as the second element, component, region, layer, or segment without departing from the teachings of the exemplary embodiments.

[0094] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for determining air conditioner filter blockage, characterized in that, include: Obtain the operating current and actual fan speed during the operation of the air conditioner; A first feature vector is extracted based on the operating current and the actual rotational speed; The air conditioner filter clogging level prediction model, based on the pre-trained model, obtains the air conditioner filter clogging level prediction result according to the first feature vector. The operating status of electrical equipment associated with the air conditioner in the environment is controlled based on the predicted clogging level of the air conditioner filter.

2. The method according to claim 1, characterized in that, The method further includes: Obtain environmental data of the environment in which the air conditioner is located; Extract a second feature vector based on the environmental data; The first feature vector and the second feature vector are fused to obtain a fused feature vector; The air conditioner filter clogging level prediction model, based on a pre-trained model, obtains the air conditioner filter clogging level prediction result according to the first feature vector, including: The fused feature vector is provided to a pre-trained air conditioner filter clogging level prediction model to obtain the air conditioner filter clogging level prediction result.

3. The method according to claim 1, characterized in that, The method further includes: Training the air conditioner filter clogging level prediction model specifically includes: Acquire historical operating data of the air conditioner and environmental data of the environment in which the air conditioner is located. The historical operating data includes: the operating current of the air conditioner during operation, the actual speed of the fan, and the reference current of the air conditioner at each speed. The historical operating data and the environmental data are labeled based on the actual clogging level of the air conditioning filter in the historical maintenance records of the air conditioner to obtain the first training dataset; The air conditioner filter clogging level prediction model is obtained by training based on the first training dataset.

4. The method according to claim 3, characterized in that, The method further includes: When the air conditioner is powered on for the first time, the reference current corresponding to each fan speed is collected. Set the airflow attenuation range for the reference current corresponding to each rotation speed; The limiting current corresponding to each rotation speed is determined based on the lower limit of the airflow attenuation range; Calculate the corrected limiting current corresponding to the limiting current under different ambient temperatures; The reference current corresponding to each fan speed, the limit current corresponding to each fan speed, and the corrected limit current corresponding to each fan speed are added to the first training dataset to obtain the training dataset of the air conditioner filter blockage level prediction model.

5. The method according to claim 4, characterized in that, The method further includes: Obtain the predicted clogging level of the air conditioner filter and the maintenance records of the air conditioner; The predicted air conditioning filter clogging level is matched with the actual clogging level of the air conditioning filter in the maintenance record to obtain a matching result. The matching result includes matching data pairs and deviation data pairs. In the matching data pairs, the predicted air conditioning filter clogging level is consistent with the actual clogging level of the air conditioning filter in the maintenance record. In the deviation data pairs, the predicted air conditioning filter clogging level is inconsistent with the actual clogging level of the air conditioning filter in the maintenance record. Based on the matching results, the matched data pairs are determined as positive sample data, and the biased data pairs are determined as negative sample data; The positive sample data and the negative sample data are added to the training dataset of the air conditioner filter blockage level prediction model to obtain the updated training dataset; The air conditioner filter clogging level prediction model is further updated and trained based on the updated training dataset to obtain the updated air conditioner filter clogging level prediction model.

6. The method according to claim 1, characterized in that, Controlling the operating status of electrical equipment associated with the air conditioner in its environment based on the predicted air conditioner filter clogging level includes: If the air conditioner filter clogging level is higher than the first preset level, control the air handling system in the environment to switch from outdoor circulation mode to indoor circulation mode, or control the air handling system to operate in indoor circulation mode; Control the air purification system in the environment to switch from a low-intensity purification mode to a high-intensity purification mode, or control the air purification system to operate in a high-intensity purification mode.

7. The method according to claim 1, characterized in that, The method further includes: After controlling the operating status of the electrical equipment associated with the air conditioner in the environment where the air conditioner is located based on the predicted result of the air conditioner filter blockage level, the identification of the air conditioner and the operating status of the electrical equipment are obtained. Obtain the energy consumption data of the air conditioner; The system displays the air conditioner's identification, the predicted level of air conditioner filter blockage, the operating current, the actual rotation speed, the energy consumption data, and the operating status of the electrical equipment.

8. The method according to claim 7, characterized in that, The method further includes: If the predicted level of air conditioner filter blockage is not higher than the second preset level, the first energy consumption data of the air conditioner is acquired and stored. After determining that the air conditioner filter clogging level is higher than the second preset level, the second energy consumption data of the air conditioner is obtained; The first energy consumption data is compared with the second energy consumption data to generate an energy consumption comparison result; Output the air conditioner filter clogging level and the energy consumption comparison results.

9. A computer device comprising one or more processors, a memory; and one or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, the one or more programs comprising instructions for performing the method of any one of claims 1 to 8.

10. A non-volatile computer-readable storage medium comprising a computer program, which, when executed by one or more processors, causes the one or more processors to perform the method of any one of claims 1 to 8.

Images