Air conditioner control method and device, and storage medium

Abstract

The application provides an air conditioner control method and device and a storage medium. The method comprises the following steps: firstly, obtaining environment data, historical behavior data corresponding to a target user, and peak-valley electricity price data; then, determining a user behavior type of the target user based on the historical behavior data, and determining predicted behavior data of the target user according to the user behavior type; then, determining an air conditioner operation strategy based on the environment data, the predicted behavior data and the peak-valley electricity price data; finally, controlling a working state of the air conditioner based on the air conditioner operation strategy, so as to automatically adjust the working state of the air conditioner according to the user demand and the peak-valley electricity price data, improve convenience, and reduce the expenditure of the user on electricity bills.

Description

Technical Field

[0001] This application relates to the field of air conditioning control technology, specifically to an air conditioning control method, device, and storage medium. Background Technology

[0002] Existing air conditioning systems typically employ fixed operating strategies. Generally, users control the air conditioner to turn it on and off in real time via mobile phone or remote control, while some air conditioners also allow for scheduled on / off.

[0003] However, if a user turns on the air conditioner and then leaves home or the air conditioner is unattended for an extended period of time, the air conditioner will often continue to run without the user's control. The lack of intelligent recognition and linkage control will also result in a waste of electricity. Summary of the Invention

[0004] This application provides an air conditioning control method, device, and storage medium that can automatically adjust the operation of the air conditioner according to user needs and peak and off-peak electricity price data, improving convenience and reducing users' electricity expenses.

[0005] In a first aspect, embodiments of this application provide an air conditioning control method, the air conditioning control method comprising: acquiring environmental data, historical behavior data corresponding to a target user, and peak-valley electricity price data; determining the user behavior type of the target user based on the historical behavior data, and determining the predicted behavior data of the target user based on the user behavior type; determining an air conditioning operation strategy based on the environmental data, the predicted behavior data, and the peak-valley electricity price data; and controlling the working state of the air conditioning based on the air conditioning operation strategy.

[0006] In some embodiments, determining the user behavior type of the target user based on the historical behavior data, and determining the predicted behavior data of the target user based on the user behavior type, includes: clustering the historical behavior data to obtain behavior clustering results; determining the user behavior type based on the behavior clustering results; and when the user behavior type is a regular user, inputting the historical behavior data into the behavior prediction model, and outputting the predicted behavior data corresponding to the target user through the behavior prediction model.

[0007] In some embodiments, determining the air conditioning operation strategy based on the environmental data, the predicted behavior data, and the peak-valley electricity price data includes: determining an initial operation strategy based on the environmental data and the predicted behavior data; wherein the predicted behavior data includes predicted arrival time, predicted departure time, predicted temperature data, predicted mode preference, and predicted wind speed data, and the initial operation strategy includes initial operating time, initial operating temperature, initial operating wind speed, and initial operating mode; and matching the air conditioning operation strategy based on the initial operation strategy and the peak-valley electricity price data.

[0008] In some embodiments, the peak-valley electricity price data includes peak-valley time periods, which include off-peak periods, off-peak periods, and peak periods. The electricity price during the off-peak period is lower than the electricity price during the off-peak period, and the electricity price during the off-peak period is lower than the electricity price during the peak period. The air conditioning operation strategy includes air conditioning operating time, air conditioning operating temperature, air conditioning operating fan speed, and air conditioning operating mode. Matching the air conditioning operation strategy based on the initial operation strategy and the peak-valley electricity price data includes: If the initial operating time overlaps with the off-peak period and the low-peak period, the air conditioner operating temperature is determined based on the initial operating temperature during the overlapping period, the air conditioner operating wind speed is determined based on the initial operating wind speed, and the air conditioner operating mode is determined based on the initial operating mode. If the start time of the initial operating time is during the peak period, the air conditioning operating time is determined to be the initial operating time plus the advance operating time, and the operating temperature during the advance operating time is determined based on the initial operating temperature. If the air conditioner's operating time overlaps with the peak period, the air conditioner's operating temperature and fan speed are adjusted based on the air conditioner's operating mode during the overlapping period.

[0009] In some embodiments, after determining the user behavior type based on the behavior clustering results, the air conditioning control method further includes: when the user behavior type is a random user, detecting whether there is human activity within a preset time period; when no human activity is detected within the preset time period, controlling the air conditioner to enter an energy-saving mode.

[0010] In some embodiments, after controlling the working state of the air conditioner based on the air conditioner operation strategy, the air conditioner control method further includes: when the air conditioner malfunctions, acquiring the equipment operation data of the air conditioner; determining the fault diagnosis result of the air conditioner based on the equipment operation data; and adjusting the working state of the air conditioner based on the fault diagnosis result.

[0011] In some embodiments, the device operating data includes at least one of filter data, refrigerant data, compressor data, and fan data; determining the fault diagnosis result of the air conditioner based on the device operating data includes: determining the fault diagnosis result as a level one fault when the filter data, refrigerant data, compressor data, and fan data meet a first fault condition; determining the fault diagnosis result as a level two fault when the filter data, refrigerant data, compressor data, and fan data meet a second fault condition; and determining the fault diagnosis result as a level three fault when the filter data, refrigerant data, compressor data, and fan data meet a third fault condition. The fault diagnosis result includes at least one of a Level 1 fault, a Level 2 fault, and a Level 3 fault. Adjusting the air conditioner's operating state based on the fault diagnosis result includes: if the fault diagnosis result is a Level 1 fault and the fault type is filter blockage, controlling the air conditioner's fan to rotate in reverse; if the fault diagnosis result is a Level 1 fault and the fault type is abnormal fan speed, adjusting the fan speed; if the fault diagnosis result is a Level 2 fault, reducing the operating frequency of the air conditioner's compressor; and if the fault diagnosis result is a Level 3 fault, controlling the air conditioner to switch to ventilation mode and shutting down the compressor.

[0012] In some embodiments, the peak-valley electricity price data includes peak-valley time periods, which include off-peak periods, off-peak periods, and peak periods. The electricity price during the off-peak period is lower than the electricity price during the off-peak period, and the electricity price during the off-peak period is lower than the electricity price during the peak period. The air conditioning control method further includes: acquiring compressor current data of the air conditioner when the air conditioner is running; if the compressor current data continuously exceeds a current threshold for a preset time and is during the peak period, determining that the air conditioner load is abnormal, and adjusting the operating state of the air conditioner according to load adjustment measures; wherein, the load adjustment measures refer to measures to reduce the air conditioner load.

[0013] Secondly, embodiments of this application provide an air conditioning control device, comprising: a first data acquisition module, configured to acquire environmental data, historical behavior data corresponding to a target user, and peak-valley electricity price data; a data determination module, configured to determine the user behavior type of the target user based on the historical behavior data, and determine the predicted behavior data of the target user based on the user behavior type; a strategy determination module, configured to determine an air conditioning operation strategy based on the environmental data, the predicted behavior data, and the peak-valley electricity price data; and a state control module, configured to control the working state of the air conditioning based on the air conditioning operation strategy.

[0014] Thirdly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, the computer program being loaded by a processor to perform the steps in the air conditioning control method described above.

[0015] This application provides an air conditioning control method, device, and storage medium. First, it collects environmental data, historical behavioral data of target users, and peak-valley electricity price data to form a multi-dimensional input basis. Then, it mines user habits based on historical behavioral data to generate predictive behavioral data. Finally, it integrates environmental data, predictive behavioral data, and peak-valley electricity price data to determine the air conditioning operation strategy, thereby automatically adjusting the air conditioning operation to match user needs and peak-valley electricity price data, improving convenience while reducing user electricity costs. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of an air conditioning control method provided in an embodiment of the present invention; Figure 2 This is a supplementary flowchart of an air conditioning control method provided in another embodiment of the present invention; Figure 3 This is a supplementary flowchart of an air conditioning control method provided in another embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an air conditioning control device provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. Detailed Implementation

[0018] 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, and 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.

[0019] In the description of this application, the terms "first," "second," "third," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," "third," etc., may explicitly or implicitly include one or more features.

[0020] In this application, the term "exemplary" is used to mean "used as an example, illustration, or description." Any embodiment described as "exemplary" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0021] It should be noted that since the method in this application embodiment is executed in a computer device, the processing objects of each computer device exist in the form of data or information, such as time, which is essentially time information. It is understood that if size, quantity, position, etc. are mentioned in subsequent embodiments, they are all corresponding data that exist so that the computer device can process them. Specific details will not be elaborated here.

[0022] Please see Figure 1 , Figure 1 This is a schematic flowchart of an air conditioning control method provided in an embodiment of the present invention.

[0023] like Figure 1 As shown, this application embodiment provides an air conditioning control method, including the following steps S1 to S4: Step S1: Obtain environmental data, historical behavior data of the target user, and peak-valley electricity price data.

[0024] In this embodiment, environmental data refers to data related to the operating environment of the air conditioner. Environmental data may include ambient temperature, ambient humidity, light intensity, carbon dioxide concentration, etc. This environmental data can be collected in real time by sensors installed inside the air conditioner or its associated sensors. For example, a temperature sensor can collect ambient temperature, a humidity sensor can collect ambient humidity, a light sensor can collect light intensity, and a carbon dioxide sensor can collect carbon dioxide concentration, etc. These sensors generate data in real time according to a preset sampling frequency (e.g., 1Hz) and transmit it to the air conditioner's control system for processing.

[0025] Historical behavioral data refers to the operational data of target users when using the air conditioner in the past. This data can include arrival time (power-on time), departure time (power-off time), temperature data, mode preferences (such as cooling mode, heating mode, fan mode, etc.), fan speed data, and other behavioral data. Historical behavioral data can be obtained through records of user interactions with the air conditioner, or it can be directly retrieved from pre-stored historical behavioral data from local servers or cloud servers, or it can be uploaded by the user themselves.

[0026] Peak-valley electricity price data refers to data related to electricity costs. This data can include real-time electricity prices, peak-valley prices, and load warning signals. Specifically, peak-valley electricity price data can be obtained through a network connection established between the air conditioning control system or its associated smart gateway and the power system's power API (Application Programming Interface). The air conditioning system periodically (e.g., hourly) or in real-time calls this API interface to retrieve real-time electricity prices, peak-valley prices, load warning signals, and other electricity cost-related data from the power system database, ensuring the timeliness of electricity price information.

[0027] Step S2: Determine the target user's user behavior type based on historical behavior data, and determine the target user's predicted behavior data based on the user behavior type.

[0028] In some embodiments, the method of step S2 above, which involves determining the user behavior type of the target user based on historical behavior data and determining the predicted behavior data of the target user based on the user behavior type, may specifically include: clustering historical behavior data to obtain behavior clustering results; determining the user behavior type based on the behavior clustering results; and when the user behavior type is a regular user, inputting historical behavior data into a behavior prediction model and outputting the predicted behavior data corresponding to the target user through the behavior prediction model.

[0029] The behavior prediction model can be a prediction model built using an LSTM (Long Short-Term Memory) model, or other models set according to actual needs. The behavior prediction model is used to output predicted behavior data for the target user based on the target user's historical behavior data.

[0030] In this embodiment, the predicted behavioral data is inferred from historical behavioral data regarding the user's potential future air conditioning usage behavior. The predicted behavioral data may include predicted arrival time (time the air conditioner will be turned on), predicted temperature preference, predicted fan speed preference, predicted mode preference, and predicted usage duration.

[0031] Among them, the behavior clustering result is the clustering result output after clustering the historical behavior data. The behavior clustering result can be the label assigned to each user's historical behavior data. For example, all users can be divided into two clusters, and each user corresponds to the label of "Cluster 1" or "Cluster 2".

[0032] User behavior type is a classification of user behavior obtained by label mapping the behavior clustering results. User behavior types include regular users and random users. Regular users refer to users whose departure and return times are relatively regular, while random users refer to users whose departure and return times are relatively random.

[0033] Among them, predictive behavioral data refers to the quantitative results reflecting the probability of a user's future behavior when the user's historical behavioral data is input into the behavioral prediction model, which is a type of user with regular behavior. Predictive behavioral data includes behavioral data such as predicted arrival time, predicted departure time, predicted temperature data, predicted mode preferences (such as cooling mode, heating mode, and ventilation mode), and predicted wind speed data.

[0034] In some embodiments, the behavior prediction model may be a prediction model built using an LSTM (Long Short-Term Memory) model, or other models set according to actual needs.

[0035] Specifically, methods for building behavior prediction models can include: First, the historical behavioral data of regular users is preprocessed, including cleaning missing values ​​and removing outliers. Then, time features such as arrival and departure times are converted into timestamps or derived into hourly and weekday time features, while data is aligned by time granularity (e.g., hourly, daily) to ensure time series continuity. Next, features are categorized: numerical features such as temperature and wind speed are standardized or normalized to a uniform scale to avoid magnitude differences affecting model convergence; categorical features such as pattern preferences are encoded, and finally, the data is organized into a structured time series.

[0036] Next, a network structure based on the LSTM model is constructed. The input layer of the network structure corresponds to the number of preprocessed features. Hidden layers can be set in the network structure to capture time dependencies. Dropout layers can also be added to the network structure to prevent overfitting. The output layer of the network structure is set as a regression unit (such as predicting the probability of a user going home or leaving home) or a classification unit (such as predicting whether a user uses cooling mode or heating mode) according to the prediction target.

[0037] Subsequently, the prepared data is divided into training, validation, and test sets according to time sequence and a preset ratio (e.g., 7:2:1). During model training, multiple rounds of training are performed using the training set, and the loss value is observed using the validation set during training. Finally, the test set is used for metric evaluation. Then, the hyperparameters of the behavior prediction model are adjusted for iterative optimization until the behavior prediction model reaches the preset accuracy.

[0038] Finally, supplementary data can be added, including holiday features, data from the same period last week, and regularly collected actual data, to correct errors in the behavior prediction model.

[0039] In some embodiments, a method for clustering historical behavior data to obtain behavior clustering results may include: clustering historical behavior data based on a preset clustering method to obtain behavior clustering results.

[0040] The preset clustering method can be K-means clustering. K-means clustering is one of the commonly used clustering algorithms in the field of unsupervised machine learning. Its core goal is to divide unlabeled data into a pre-specified number (e.g., two) of non-overlapping clusters according to similarity, so that samples within the same cluster are highly similar and samples between different clusters are significantly different.

[0041] Specifically, clustering can be based on the standard deviation of a user's daily departure time, the standard deviation of a user's daily return time, the variance of a user's daily departure time, and the variance of a user's daily return time, etc. For example, users whose daily departure time standard deviation is ≤ a first preset time (e.g., 20 minutes) and whose daily return time standard deviation is ≤ a second preset time (e.g., 20 minutes) can be assigned to the first cluster; users outside the first cluster can be assigned to the second cluster.

[0042] In some embodiments, the method for determining user behavior type based on behavior clustering results may include: if the behavior clustering result is a first cluster, then the user behavior type is determined to be a regular user; if the behavior clustering result is a second cluster, then the user behavior type is determined to be a random user.

[0043] In some embodiments, after determining the user behavior type based on the behavior clustering results, the air conditioning control method further includes: when the user behavior type is a random user, detecting whether there is human activity within a preset time period; and when no human activity is detected within the preset time period, controlling the air conditioner to enter an energy-saving mode.

[0044] The preset duration can be 1 hour, 3 hours, etc., and can be set according to actual needs. No specific limit is set here.

[0045] The energy-saving mode refers to a mode that saves more energy compared to the normal operating mode. For example, if the normal operating mode in summer corresponds to an air conditioner operating temperature of 25℃ and a compressor frequency of 50Hz, then the energy-saving mode in summer could be an air conditioner operating temperature of 27℃ and a compressor frequency of 30Hz. Additionally, in energy-saving mode, some unnecessary functions can be turned off, such as turning off lights, auxiliary heating, dehumidification, and high-energy-consuming functions like fan oscillation.

[0046] In some embodiments, at least one of the following methods can be used: WiFi device connection to sensing, infrared sensor, millimeter-wave radar, sound sensor, and image sensor (camera) to detect human activity in real time.

[0047] Step S3: Determine the air conditioning operation strategy based on environmental data, predicted behavior data, and peak-valley electricity price data.

[0048] In some embodiments, step S3 above, the method of determining the air conditioning operation strategy based on environmental data, predicted behavior data, and peak-valley electricity price data, may specifically include: determining an initial operation strategy based on environmental data and predicted behavior data; and matching the air conditioning operation strategy based on the initial operation strategy and peak-valley electricity price data.

[0049] The initial operation strategy includes initial operation time, initial operation temperature, initial operation fan speed, and initial operation mode. The air conditioning operation strategy includes air conditioning operation time, air conditioning operation temperature, air conditioning operation fan speed, and air conditioning operation mode.

[0050] The predicted behavioral data includes predicted arrival time, predicted departure time, predicted temperature data, predicted mode preferences (such as cooling mode, heating mode, ventilation mode, sleep mode, etc.), and predicted wind speed data.

[0051] In some embodiments, the method for determining the initial operating strategy based on environmental data and predicted behavioral data may specifically include: determining the initial operating time based on predicted arrival time and predicted departure time; determining the initial operating temperature based on predicted temperature data; determining the initial operating wind speed based on predicted wind speed data; and determining the initial operating mode based on predicted mode preferences.

[0052] Specifically, the user's home time can be determined based on the predicted arrival and departure times; this home time is then used as the initial operating time. Predicted temperature data is used as the initial operating temperature, predicted wind speed data as the initial operating wind speed, and the predicted mode preference as the initial operating mode.

[0053] In some embodiments, peak-valley electricity price data includes peak-valley time periods and the corresponding electricity prices for each time period within those periods. Peak-valley time periods include off-peak periods, off-peak periods, and peak periods. Typically, the electricity price during off-peak periods is lower than the price during off-peak periods, and the electricity price during off-peak periods is lower than the price during peak periods.

[0054] In some embodiments, the method for matching air conditioning operation strategies based on initial operation strategies and peak-valley electricity price data may specifically include: if there is a period in the initial operation time that overlaps with off-peak and off-peak periods, the air conditioning operation temperature is determined based on the initial operation temperature during the overlapping period, the air conditioning operation fan speed is determined based on the initial operation fan speed, and the air conditioning operation mode is determined based on the initial operation mode; if the start time of the initial operation time is during a peak period, the air conditioning operation time is determined to be the initial operation time plus the advance operation time, and the operation temperature during the advance operation time is determined based on the initial operation temperature; if the air conditioning operation time overlaps with a peak period, the air conditioning operation temperature and air conditioning operation fan speed are adjusted based on the air conditioning operation mode during the overlapping period.

[0055] Specifically, the advance running time is a preset time period before the initial running time. The duration of the advance running time can be dynamically adjusted according to the building's thermal insulation performance. For example, for buildings with a thermal insulation coefficient ≤1.5W / (㎡·K), the advance running time is set to 1 hour; for buildings with a thermal insulation coefficient ≥3.0W / (㎡·K), the advance running time is set to 2 hours.

[0056] In some embodiments, the method for determining the operating temperature within the advance operating time based on the initial operating temperature may include: if in cooling mode, the operating temperature within the advance operating time is lower than the initial operating temperature by a first preset temperature (e.g., 2 degrees Celsius); if in heating mode, the operating temperature within the advance operating time is higher than the initial operating temperature by a second preset temperature (e.g., 3 degrees Celsius). For example, if a user returns home at 7:00 AM the next day, and the user is home from 7:00 AM to 3:00 PM, then the initial operating time is from 7:00 AM to 3:00 PM. If 10:00 PM to 6:00 AM is a low-temperature period, and 6:00 AM to 10:00 PM is a peak period, then pre-cooling or pre-heating can be started one hour in advance, i.e., the advance operating time is from 6:00 AM to 7:00 AM. In cooling mode, the operating temperature within the advance operating time is 1 or 2 degrees Celsius lower than the initial operating temperature. In heating mode, the operating temperature within the advance operating time is 1 or 2 degrees Celsius higher than the initial operating temperature. Thus, the building's thermal inertia is utilized to maintain the temperature, reducing the compressor's full-load operation during peak hours.

[0057] In some embodiments, if the air conditioner's operating time overlaps with peak hours, the method for adjusting the air conditioner's operating temperature and fan speed based on the air conditioner's operating mode during the overlapping period may specifically include: during the overlapping period, if in cooling mode, the operating temperature during the overlapping period is a third preset temperature (e.g., 1 degree Celsius) lower than the initial operating temperature, and the fan speed during the overlapping period is a preset level lower than the initial operating fan speed (e.g., one or two levels lower); during the overlapping period, if in heating mode, the operating temperature during the overlapping period is a fourth preset temperature (e.g., 1 degree Celsius) higher than the initial operating temperature, and the fan speed during the overlapping period is a preset level lower than the initial operating fan speed (e.g., one or two levels lower). For example, when the air conditioner's operating mode is cooling mode, if the air conditioner's operating time overlaps with peak hours, such as when a peak grid signal (e.g., an orange warning signal pushed by the power company) is detected during air conditioner operation, the air conditioner's operating temperature is automatically increased by 1 to 2 degrees Celsius, and the fan speed is reduced (e.g., switching from "high" to "medium" or from "medium" to "low").

[0058] The first, second, third, and fourth preset temperatures can be the same or different. All four preset temperatures are within the range of 1 to 3 degrees Celsius, and can be set according to actual needs.

[0059] The operating wind speed may include multiple wind speed settings arranged in order of magnitude, such as three speed settings, five speed settings, etc. For example, if the operating wind speed includes a first speed setting, a second speed setting, and a third speed setting, then the first speed setting is less than the second speed setting, and the second speed setting is less than the third speed setting.

[0060] Step S4: Control the working status of the air conditioner based on the air conditioner operation strategy.

[0061] The air conditioning operation strategy includes air conditioning operating time, air conditioning fan speed, air conditioning operating temperature, and air conditioning operating mode. The air conditioning working status includes air conditioning operating mode, operating temperature, and operating fan speed.

[0062] This application provides an air conditioning control method. Compared with existing technologies, traditional air conditioners only control start and stop based on the difference between the current temperature and the set temperature, failing to anticipate changes in user demand. This solution achieves dynamic adjustment of the air conditioner by integrating multi-dimensional data, starting cooling in advance before the user's expected return, utilizing off-peak electricity prices to pre-cool the indoor environment. Compared to fixed reservation modes, this solution can adapt to temporary travel changes, avoiding ineffective operation during unoccupied periods, while reducing equipment load during peak electricity price periods. Through the above technical solution, this application achieves matching of air conditioning operation strategy with user behavior patterns, reducing energy waste during unoccupied periods. While ensuring user comfort, it optimizes equipment operation times through peak and off-peak electricity price data, reducing overall electricity costs. It can also automatically adapt to fluctuations in environmental parameters, dynamically adjusting cooling output to reduce energy loss caused by frequent start and stop. In summary, this application can automatically adjust the air conditioner's operation according to user needs and peak and off-peak electricity price data, improving convenience while reducing user electricity expenses.

[0063] In some embodiments, such as Figure 2 As shown, after executing step S4 and controlling the air conditioner's operating state based on the air conditioner operation strategy, the air conditioner control method further includes the following steps S5 to S7: Step S5: When the air conditioner malfunctions, obtain the equipment operation data of the air conditioner.

[0064] The equipment operation data includes various data collected during air conditioner operation. These include filter data, refrigerant data, compressor data, and fan data. Filter data indicates the condition of the filter. Refrigerant data indicates the condition of the refrigerant in the compressor. Compressor data indicates the wear of the compressor bearings. Fan data indicates the fan speed.

[0065] Filter data includes filter air pressure difference. This can be obtained by collecting data from wind pressure sensors and PM2.5 sensors, followed by Kalman filtering for noise reduction and pressure difference trend analysis. Specifically, first, basic wind pressure data related to the filter is collected using a wind pressure sensor, and air quality data is collected using a PM2.5 sensor. Then, the collected wind pressure and air quality data are processed. First, Kalman filtering is used to remove interference noise to ensure data accuracy. Then, pressure difference trend analysis is performed based on the denoised, valid data, and finally, the filter air pressure difference is determined through the analysis results.

[0066] Refrigerant data includes the low-pressure side pressure value. Sensor data can be acquired using pressure sensors and infrared gas sensors. Based on this sensor data, wavelet transform is used to extract characteristic frequencies to obtain the low-pressure side pressure value. Specifically, firstly, pressure sensors and infrared gas sensors are used to acquire raw pressure data and gas characteristic data related to the low-pressure side of the refrigerant, respectively. Then, based on the acquired sensor data, wavelet transform is used to accurately extract characteristic frequency information from the data. By analyzing the correlation between these characteristic frequencies and the low-pressure side pressure, the final low-pressure side pressure value of the refrigerant is obtained.

[0067] Compressor data includes compressor vibration frequency and compressor noise. Sensor data can be acquired using vibration sensors (such as triaxial vibration sensors) and noise sensors. The sensor data is then processed based on Mel-Frequency Cepstral Coefficients (MFCC) and Support Vector Machine (SVM) classification to obtain the compressor vibration frequency and noise. Specifically, the raw vibration data of the compressor during operation is first acquired using a vibration sensor, and the raw noise data generated by the compressor is simultaneously acquired using a noise sensor. Then, key feature parameters are extracted from the raw vibration and noise data using Mel-Frequency Cepstral Coefficients to capture the frequency distribution and amplitude variation characteristics of the signals. Subsequently, the SVM classification algorithm is used to classify and identify the extracted feature parameters, ultimately obtaining the accurate compressor vibration frequency and compressor noise.

[0068] The wind turbine data includes wind turbine speed, wind turbine current, and speed fluctuation data. Wind turbine speed can be acquired using a speed sensor, and wind turbine current can be acquired using a current sensor. Speed ​​fluctuation data can be obtained by performing deviation analysis on the curves of wind turbine speed and wind turbine current.

[0069] Step S6: Based on the equipment operation data, determine the fault diagnosis results of the air conditioner.

[0070] In some embodiments, the equipment operating data includes at least one of filter data, refrigerant data, compressor data, and fan data. Step S6 above, determining the fault diagnosis result of the air conditioner based on the equipment operating data, may specifically include: when the filter data, refrigerant data, compressor data, and fan data meet a first fault condition, determining the fault diagnosis result as a level one fault; when the filter data, refrigerant data, compressor data, and fan data meet a second fault condition, determining the fault diagnosis result as a level two fault; and when the filter data, refrigerant data, compressor data, and fan data meet a third fault condition, determining the fault diagnosis result as a level three fault.

[0071] In some embodiments, when the filter data, refrigerant data, compressor data, and fan data meet the first fault conditions, the fault diagnosis result is determined to be a Level 1 fault, including: when the filter air pressure difference is greater than a first preset air pressure difference (e.g., 150 Pa) and the duration is greater than a time threshold (e.g., 5 minutes), and at the same time the refrigerant data, compressor data, and fan data are all within the normal range, the fault diagnosis result is determined to be a Level 1 fault, and the fault type is filter blockage; when the fan speed fluctuation data is greater than a first preset speed range (e.g., ±10%), and at the same time the filter data, refrigerant data, and compressor data are all within the normal range, the fault diagnosis result is determined to be a Level 1 fault, and the fault type is abnormal fan speed.

[0072] The first preset air pressure is the core air pressure threshold for determining filter blockage, and it is usually set as the upper limit of the normal air pressure difference when the filter is not blocked.

[0073] The time threshold is a time criterion for excluding instantaneous fluctuations. It is usually set to a relatively short fixed duration, such as 5 minutes.

[0074] The first preset speed range is the allowable fluctuation range of the fan speed during normal operation. The first preset speed range is usually set based on the rated speed of the fan, for example, ±10% of the rated speed of the fan.

[0075] In some embodiments, when the filter data, refrigerant data, compressor data, and fan data meet the second fault conditions, the fault diagnosis result is determined to be a level two fault, including: if the compressor vibration frequency is greater than a preset vibration frequency (e.g., 60Hz) and the compressor noise is greater than a preset noise threshold (e.g., 55dB), and the filter data, refrigerant data, and fan data are all within the normal range, then the fault diagnosis result is determined to be a level two fault, and the fault type is compressor abnormal noise.

[0076] The preset vibration frequency is the upper limit of the vibration frequency when the compressor is running normally (e.g., 60Hz). The preset vibration frequency can be set based on the compressor's design performance and operational stability requirements.

[0077] The preset noise threshold is the upper limit of noise in decibels when the compressor is running normally (e.g., 55dB). The preset noise threshold can be set in combination with user experience (e.g., indoor quiet requirements) and component safety standards.

[0078] In some embodiments, when the filter data, refrigerant data, compressor data, and fan data meet the third fault condition, the fault diagnosis result is determined to be a level three fault, including: if the low-pressure side pressure value in the refrigerant data is less than a preset pressure value (e.g., 0.5 MPa), and the filter data, compressor data, and fan data are all within the normal range, then the fault diagnosis result is determined to be a level three fault, and the fault type is refrigerant leakage.

[0079] The preset pressure value is the lower limit of the refrigerant pressure during normal operation on the low-pressure side (e.g., 0.5 MPa). The preset pressure value can be set based on the physical properties of the refrigerant (such as boiling point and condensing pressure) and the heat exchange requirements of the air conditioner.

[0080] Step S7: Adjust the working status of the air conditioner based on the fault diagnosis results.

[0081] In some embodiments, the fault diagnosis result includes at least one of a Level 1 fault, a Level 2 fault, and a Level 3 fault. Step S7, adjusting the air conditioner's operating state based on the fault diagnosis result, may specifically include: if the fault diagnosis result is a Level 1 fault and the fault type is filter blockage, controlling the air conditioner's fan to rotate in reverse; if the fault diagnosis result is a Level 1 fault and the fault type is abnormal fan speed, adjusting the fan speed; if the fault diagnosis result is a Level 2 fault, reducing the operating frequency of the air conditioner's compressor; and if the fault diagnosis result is a Level 3 fault, controlling the air conditioner to switch to ventilation mode and shutting off the compressor.

[0082] Specifically, if the fault diagnosis result is a level one fault and the fault type is filter blockage, the air conditioner fan is controlled to rotate in reverse to use airflow to blow away dust and help resolve the fault. If the filter pressure difference exceeds the second preset pressure difference (e.g., 120Pa) after the fan rotates in reverse multiple times (e.g., 3 times), a prompt to the user equipment indicating that the filter needs to be replaced can be sent.

[0083] The second preset air pressure difference is a secondary critical value used by the air conditioner to determine whether the filter clogging problem has been alleviated by backflushing after the air conditioner uses the fan to reverse the rotation to blow dust off the filter. Its value is less than the first preset air pressure difference. The second preset air pressure difference is less than the first preset air pressure difference. For example, the first preset air pressure difference can be 150 Pa, and the second preset air pressure difference can be 120 Pa.

[0084] Specifically, if the fault diagnosis result is a Level 1 fault and the fault type is abnormal fan speed, the fan speed is adjusted. This can be achieved by dynamically adjusting the fan motor voltage using a PID algorithm to correct the speed deviation to within a second preset speed range (±5%). The second preset speed range is smaller than the first preset speed range; for example, the first preset speed range can be set within ±10%, and the second preset speed range can be set within ±5%.

[0085] Specifically, if the fault diagnosis result is a level two fault, the fault type is abnormal compressor noise. The solution is to reduce the operating frequency of the air conditioner's compressor. Specifically, the compressor's operating frequency can be lowered by a preset value (e.g., 10Hz) to extend its service life, and a compressor malfunction alert can be sent to the user's device.

[0086] Specifically, if the fault diagnosis result is a level three fault, the fault type is refrigerant leakage. In this case, the air conditioner should be switched to ventilation mode and the compressor should be shut down. Additionally, fault codes and air conditioner sensor data logs can be sent to the manufacturer's after-sales system or the user's equipment to facilitate compressor repair by maintenance personnel or the user.

[0087] In some embodiments, peak-valley electricity price data includes peak-valley time periods. These periods include off-peak hours, off-peak hours, and peak hours. Typically, the electricity price during off-peak hours is lower than the price during off-peak hours, and the price during off-peak hours is lower than the price during peak hours. For example... Figure 3 As shown, the air conditioning control method further includes the following steps S8 to S9: Step S8: When the air conditioner is running, obtain the compressor current data of the air conditioner.

[0088] Step S9: If the compressor current data continues to exceed the current threshold for a preset time and is during peak hours, determine that the air conditioning load is abnormal, and adjust the working status of the air conditioner according to the load adjustment measures.

[0089] Specifically, if the compressor current data continuously exceeds the current threshold (e.g., 110% of the compressor's rated current value) for a preset time (e.g., 10 seconds) and this occurs during peak hours, then the air conditioner is determined to be under abnormal load, and the air conditioner's operating status is adjusted according to load regulation measures.

[0090] The preset time refers to the time standard for judging whether the compressor current continues to exceed the standard. It is used to eliminate the interference of instantaneous current fluctuations. For example, the preset time can be set to 10 seconds.

[0091] The current threshold is a critical value that defines whether the compressor current is too high. It is usually related to the compressor's rated current value. For example, the current threshold can be set to 110% of the compressor's rated current value.

[0092] Load regulation measures refer to measures to reduce the air conditioning load, which may include, but are not limited to, reducing the compressor operating frequency, turning off the auxiliary heating function, or switching to ventilation mode, in order to reduce energy consumption and reduce the possibility of equipment damage.

[0093] Specifically, the air conditioner's operating status is adjusted according to load regulation measures, including: if the air conditioner is currently operating in cooling mode, the current operating temperature is increased (e.g., increased by 1 degree Celsius); if the air conditioner is currently operating in heating mode, the current operating temperature is decreased (e.g., decreased by 1 degree Celsius) to reduce heat exchange requirements; after this, if the air conditioner is still under abnormal load, the air conditioner compressor frequency is reduced by a preset frequency value (e.g., reduced by 10Hz) to reduce power consumption. The preset frequency value is a fixed amount by which the compressor frequency is reduced. The preset frequency value is used to further reduce the air conditioner compressor's operating frequency after temperature regulation proves ineffective.

[0094] This application provides an air conditioning control method. First, it collects environmental data, historical user behavior data, and peak / valley electricity price data to form a multi-dimensional input foundation. Then, based on historical behavior data, it mines user habits (such as turning on the air conditioner after commuting, preferring lower temperatures at night), generating predictive behavior data (such as predicting the user will return home at 18:30 and therefore needing to start the air conditioner at 18:00). Finally, it comprehensively considers environmental data, predictive behavior data, and peak / valley electricity price data to determine the air conditioning operation strategy, thereby automatically adjusting the air conditioner's operation to match user needs and peak / valley electricity price data, improving convenience and reducing user electricity costs. Furthermore, the air conditioning control method provided in this application can also determine the air conditioner's fault diagnosis results based on equipment operation data and adjust the air conditioner's operating status accordingly, enabling automatic fault detection and repair, thus improving the air conditioner's safety and stability.

[0095] To better implement the air conditioning control method in the embodiments of this application, an air conditioning control device is also provided in the embodiments of this application, such as... Figure 4 As shown, the air conditioning control device 200 includes: The first data acquisition module 201 is used to acquire environmental data, historical behavior data of target users, and peak-valley electricity price data. The data determination module 202 is used to determine the user behavior type of the target user based on historical behavior data, and to determine the predicted behavior data of the target user based on the user behavior type; The strategy determination module 203 is used to determine the air conditioning operation strategy based on environmental data, predictive behavior data, and peak-valley electricity price data. The status control module 204 is used to control the working status of the air conditioner based on the air conditioner operation strategy.

[0096] In some embodiments, the data determination module 202 is specifically used to: cluster historical behavior data to obtain behavior clustering results; determine user behavior types based on behavior clustering results; and when the user behavior type is a regular user, input historical behavior data into a behavior prediction model and output predicted behavior data corresponding to the target user through the behavior prediction model.

[0097] In some embodiments, the strategy determination module 203 is specifically used to: determine an initial operating strategy based on environmental data and predicted behavior data; and match an air conditioning operating strategy based on the initial operating strategy and peak-valley electricity price data.

[0098] In some embodiments, peak-valley electricity price data includes peak-valley time periods, which include off-peak periods, off-peak periods, and peak periods. The electricity price during off-peak periods is lower than the electricity price during off-peak periods, and the electricity price during off-peak periods is lower than the electricity price during peak periods. The air conditioning operation strategy includes air conditioning operating time, air conditioning operating temperature, air conditioning operating fan speed, and air conditioning operating mode. The strategy determination module 203 is specifically used for: if there is a time overlap with off-peak and off-peak periods in the initial operating time, determining the air conditioning operating temperature based on the initial operating temperature, determining the air conditioning operating fan speed based on the initial operating fan speed, and determining the air conditioning operating mode based on the initial operating mode during the overlapped time; if the start time of the initial operating time is in the peak period, determining the air conditioning operating time as the initial operating time plus the advance operating time, and determining the operating temperature during the advance operating time based on the initial operating temperature; if the air conditioning operating time overlaps with the peak period, adjusting the air conditioning operating temperature and air conditioning operating fan speed based on the air conditioning operating mode during the overlapped period.

[0099] In some embodiments, the data determination module 202 is further configured to: detect whether there is human activity within a preset time period when the user behavior type is random user; and control the air conditioner to enter energy-saving mode when no human activity is detected within the preset time period.

[0100] In some embodiments, after controlling the operating state of the air conditioner based on the air conditioner operation strategy, the air conditioner control device 200 further includes: The second data acquisition module is used to acquire the equipment operation data of the air conditioner when the air conditioner malfunctions. The diagnostic determination module is used to determine the fault diagnosis results of the air conditioner based on the equipment operation data; The first state adjustment module is used to adjust the working state of the air conditioner based on the fault diagnosis results.

[0101] In some embodiments, the equipment operating data includes at least one of filter data, refrigerant data, compressor data, and fan data; the diagnostic determination module is specifically used to: determine the fault diagnosis result as a level one fault when the filter data, refrigerant data, compressor data, and fan data meet a first fault condition; determine the fault diagnosis result as a level two fault when the filter data, refrigerant data, compressor data, and fan data meet a second fault condition; and determine the fault diagnosis result as a level three fault when the filter data, refrigerant data, compressor data, and fan data meet a third fault condition.

[0102] The fault diagnosis results include at least one of Level 1, Level 2, and Level 3 faults. The first state adjustment module is specifically used to: control the air conditioner fan to rotate in reverse if the fault diagnosis result is Level 1 fault and the fault type is filter blockage; adjust the fan speed if the fault diagnosis result is Level 1 fault and the fault type is abnormal fan speed; reduce the operating frequency of the air conditioner compressor if the fault diagnosis result is Level 2 fault; and control the air conditioner to switch to ventilation mode and shut down the compressor if the fault diagnosis result is Level 3 fault.

[0103] In some embodiments, the peak-valley electricity price data includes peak-valley time periods, which include off-peak periods, off-peak periods, and peak periods. The electricity price during off-peak periods is lower than the electricity price during off-peak periods, and the electricity price during off-peak periods is lower than the electricity price during peak periods. The air conditioning control device 200 also includes: The third data acquisition module is used to acquire the compressor current data of the air conditioner when it is running; The second state adjustment module is used to determine the abnormal air conditioning load if the compressor current data continuously exceeds the current threshold for a preset time and is during peak hours, and then adjust the working state of the air conditioner according to the load adjustment measures; wherein, the load adjustment measures refer to measures to reduce the air conditioning load.

[0104] This application embodiment also provides a terminal device that integrates any of the air conditioning control devices provided in this application embodiment. The computer device includes: One or more processors; Memory; and One or more applications, wherein the applications are stored in memory and configured to be executed by a processor in the steps of the air conditioning control method in any of the embodiments described above.

[0105] This application also provides a computer device that integrates any of the air conditioning control methods and apparatus provided in this application. For example... Figure 5 As shown, it illustrates a structural schematic diagram of the computer device involved in the embodiments of this application, specifically: The computer device may include components such as a processor 801 with one or more processing cores, a memory 802 with one or more computer-readable storage media, a power supply 803, and an input unit 804. Those skilled in the art will understand that... Figure 5 The computer device structure shown does not constitute a limitation on the computer device and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 801 is the control center of the computer device. It connects various parts of the computer device via various interfaces and lines. By running or executing software programs and / or modules stored in the memory 802, and by calling data stored in the memory 802, it performs various functions of the computer device and processes data, thereby providing overall monitoring of the computer device. Optionally, the processor 801 may include one or more processing cores; preferably, the processor 801 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into the processor 801.

[0106] The memory 802 can be used to store software programs and modules. The processor 801 executes various functional applications and data processing by running the software programs and modules stored in the memory 802. The memory 802 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 802 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 802 may also include a memory controller to provide the processor 801 with access to the memory 802.

[0107] The computer device also includes a power supply 803 that supplies power to the various components. Preferably, the power supply 803 can be logically connected to the processor 801 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 803 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0108] The computer device may also include an input unit 804, which can be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0109] Although not shown, the computer device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 801 in the computer device loads the executable files corresponding to the processes of one or more application programs into the memory 802 according to the following instructions, and the processor 801 runs the application programs stored in the memory 802 to realize various functions, as follows: Acquire environmental data, historical behavioral data of target users, and peak-valley electricity price data; Based on historical behavioral data, determine the user behavior type of the target user, and based on the user behavior type, determine the predicted behavioral data of the target user; Based on environmental data, predictive behavior data, and peak-valley electricity price data, determine the air conditioning operation strategy; Based on the air conditioning operation strategy, control the working status of the air conditioner.

[0110] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be performed by instructions, or by instructions controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0111] Therefore, embodiments of this application provide a computer-readable storage medium, which may include: read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk, etc. A computer program is stored thereon, and the computer program is loaded by a processor to execute the steps in any of the air conditioning control methods provided in embodiments of this application. For example, the computer program loaded by the processor can execute the following steps: Acquire environmental data, historical behavioral data of target users, and peak-valley electricity price data; Based on historical behavioral data, determine the user behavior type of the target user, and based on the user behavior type, determine the predicted behavioral data of the target user; Based on environmental data, predictive behavior data, and peak-valley electricity price data, determine the air conditioning operation strategy; Based on the air conditioning operation strategy, control the working status of the air conditioner.

[0112] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the detailed descriptions of other embodiments above, which will not be repeated here.

[0113] In practice, each of the above units or structures can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units or structures, please refer to the previous method embodiments, which will not be repeated here.

[0114] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0115] The above provides a detailed description of an air conditioning control method, device, and storage medium provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. An air conditioning control method, characterized in that, The air conditioning control method includes: Acquire environmental data, historical behavioral data of target users, and peak-valley electricity price data; Based on the historical behavior data, the user behavior type of the target user is determined, and the predicted behavior data of the target user is determined based on the user behavior type; Based on the environmental data, the predicted behavior data, and the peak-valley electricity price data, an air conditioning operation strategy is determined. Based on the aforementioned air conditioning operation strategy, the working status of the air conditioner is controlled.

2. The air conditioning control method according to claim 1, characterized in that, The step of determining the user behavior type of the target user based on the historical behavior data, and determining the predicted behavior data of the target user based on the user behavior type, includes: Cluster the historical behavior data to obtain behavior clustering results; Based on the behavior clustering results, the user behavior type is determined; When the user's behavior type is a regular user, the historical behavior data is input into the behavior prediction model, and the behavior prediction model outputs the predicted behavior data corresponding to the target user.

3. The air conditioning control method according to claim 1, characterized in that, The step of determining the air conditioning operation strategy based on the environmental data, the predicted behavior data, and the peak-valley electricity price data includes: Based on the environmental data and the predicted behavior data, an initial operating strategy is determined; wherein, the predicted behavior data includes predicted return time, predicted departure time, predicted temperature data, predicted mode preference, and predicted wind speed data, and the initial operating strategy includes initial operating time, initial operating temperature, initial operating wind speed, and initial operating mode. Based on the initial operating strategy and the peak-valley electricity price data, the air conditioning operating strategy is matched.

4. The air conditioning control method according to claim 3, characterized in that, The peak-valley electricity price data includes peak-valley time periods, which include off-peak periods, off-peak periods, and peak periods. The electricity price during the off-peak period is lower than the electricity price during the off-peak period, and the electricity price during the off-peak period is lower than the electricity price during the peak period. The air conditioning operation strategy includes air conditioning operating time, air conditioning operating temperature, air conditioning operating fan speed, and air conditioning operating mode. Matching the air conditioning operation strategy based on the initial operation strategy and the peak-valley electricity price data includes: If the initial operating time overlaps with the off-peak period and the low-peak period, the air conditioner operating temperature is determined based on the initial operating temperature during the overlapping period, the air conditioner operating wind speed is determined based on the initial operating wind speed, and the air conditioner operating mode is determined based on the initial operating mode. If the start time of the initial operating time is during the peak period, the air conditioning operating time is determined to be the initial operating time plus the advance operating time, and the operating temperature during the advance operating time is determined based on the initial operating temperature. If the air conditioner's operating time overlaps with the peak period, the air conditioner's operating temperature and fan speed are adjusted based on the air conditioner's operating mode during the overlapping period.

5. The air conditioning control method according to claim 2, characterized in that, After determining the user behavior type based on the behavior clustering results, the air conditioning control method further includes: When the user behavior type is random, detect whether there is human activity within a preset time period; If no human activity is detected within the preset time period, the air conditioner is controlled to enter energy-saving mode.

6. The air conditioning control method according to claim 1, characterized in that, After controlling the air conditioner's operating state based on the air conditioner operation strategy, the air conditioner control method further includes: When the air conditioner malfunctions, acquire the equipment operation data of the air conditioner; Based on the equipment operation data, the fault diagnosis result of the air conditioner is determined; Adjust the operating status of the air conditioner based on the fault diagnosis results.

7. The air conditioning control method according to claim 6, characterized in that, The equipment operating data includes at least one of filter data, refrigerant data, compressor data, and fan data; determining the fault diagnosis result of the air conditioner based on the equipment operating data includes: When the filter data, the refrigerant data, the compressor data, and the fan data meet the first fault condition, the fault diagnosis result is determined to be a level one fault; When the filter data, the refrigerant data, the compressor data, and the fan data meet the second fault condition, the fault diagnosis result is determined to be a level two fault; When the filter data, the refrigerant data, the compressor data, and the fan data meet the third fault condition, the fault diagnosis result is determined to be a level three fault. The fault diagnosis result includes at least one of a level 1 fault, a level 2 fault, and a level 3 fault; adjusting the operating state of the air conditioner based on the fault diagnosis result includes: If the fault diagnosis result is a level one fault and the fault type is filter blockage, then control the air conditioner fan to rotate in reverse. If the fault diagnosis result is a level one fault and the fault type is abnormal fan speed, then adjust the fan speed. If the fault diagnosis result is a level 2 fault, then reduce the operating frequency of the air conditioner's compressor; If the fault diagnosis result is a level three fault, then control the air conditioner to switch to ventilation mode and shut down the compressor.

8. The air conditioning control method according to claim 4, characterized in that, The air conditioning control method further includes: When the air conditioner is running, the compressor current data of the air conditioner is acquired; If the compressor current data continues to exceed the current threshold for a preset time and is during the peak period, the air conditioning load is determined to be abnormal, and the working status of the air conditioner is adjusted according to the load adjustment measures; wherein, the load adjustment measures refer to measures to reduce the air conditioning load.

9. An air conditioning control device, characterized in that, The air conditioning control device includes: The first data acquisition module is used to acquire environmental data, historical behavior data of target users, and peak-valley electricity price data. The data determination module is used to determine the user behavior type of the target user based on the historical behavior data, and to determine the predicted behavior data of the target user based on the user behavior type; The strategy determination module is used to determine the air conditioning operation strategy based on the environmental data, the predicted behavior data, and the peak-valley electricity price data. The status control module is used to control the working status of the air conditioner based on the air conditioner operation strategy.

10. A computer-readable storage medium, characterized in that, It stores a computer program, which is loaded by a processor to execute the steps of the air conditioning control method according to any one of claims 1 to 8.

Images