Air conditioning equipment, control anomaly detection method and device thereof and storage medium

Abstract

The invention discloses air conditioning equipment, a control anomaly detection method and device of the air conditioning equipment and a storage medium. The method comprises the steps that operation parameters of the air conditioning equipment in the operation process are obtained; time domain data corresponding to the operation parameters are converted into frequency domain data, and target detection data used for determining whether the control process of the air conditioner equipment is abnormal or not is obtained based on the frequency domain data; and the target detection data are input into a pre-training anomaly detection model, and whether the control process of the air conditioner equipment is abnormal or not is determined. According to the embodiment of the invention, the accuracy and efficiency of air conditioning equipment control anomaly detection can be improved.

Description

Technical Field

[0001] This application relates to the technical field of electrical appliance control, and particularly relates to an air conditioning device, a control anomaly detection method, a device and a storage medium thereof. Background Art

[0002] The control stability of a central air conditioning system has an important impact on the system's performance, operating efficiency, and user comfort. Timely detecting abnormal situations in the stability of the central air conditioning system during operation and taking corresponding measures to address such abnormal situations can ensure the stable operation of the central air conditioning system and meet people's needs for a comfortable environment. In related technologies, it is usually manual to determine whether there is an abnormality in the control stability of the central air conditioning system, and the accuracy and efficiency of the above detection method are relatively low. Summary of the Invention

[0003] In view of the above problems, this application provides an air conditioning device, a control anomaly detection method, a device and a storage medium thereof, so as to at least solve the technical problems of relatively low accuracy and efficiency in detecting control anomalies of air conditioning devices in related technologies.

[0004] According to the first aspect of the embodiments of this application, a control anomaly detection method for an air conditioning device is provided, including: obtaining operating parameters of the air conditioning device during operation; converting the time-domain data corresponding to the operating parameters into frequency-domain data, and obtaining target detection data for determining whether there is an anomaly in the control process of the air conditioning device based on the frequency-domain data; inputting the target detection data into a pre-trained anomaly detection model to determine whether there is an anomaly in the control process of the air conditioning device.

[0005] According to the second aspect of the embodiments of this application, a control anomaly detection device for an air conditioning device is provided, including: a first obtaining unit for obtaining operating parameters of the air conditioning device during operation; a conversion unit for converting the time-domain data corresponding to the operating parameters into frequency-domain data, and obtaining target detection data for determining whether there is an anomaly in the control process of the air conditioning device based on the frequency-domain data; a determination unit for inputting the target detection data into a pre-trained anomaly detection model to determine whether there is an anomaly in the control process of the air conditioning device.

[0006] According to the third aspect of the embodiments of this application, an electronic device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the control anomaly detection method for an air conditioning device in the first aspect through the computer program.

[0007] According to a fourth aspect of the embodiments of the present application, there is also provided a computer-readable storage medium storing a computer program, wherein the computer program is configured to execute the air conditioner control anomaly detection method in the first aspect when running.

[0008] In the embodiments of the present application, by obtaining the operating parameters of the air conditioner during operation; converting the time-domain data corresponding to the operating parameters into frequency-domain data, and obtaining target detection data for determining whether an anomaly occurs in the control process of the air conditioner based on the frequency-domain data; inputting the target detection data into a pre-trained anomaly detection model to determine whether an anomaly occurs in the control process of the air conditioner, the present application can not only improve the accuracy and efficiency of air conditioner control anomaly detection, but also give an early warning of anomalies in a timely manner, greatly saving labor costs; solving the technical problem of low accuracy and efficiency in air conditioner control anomaly detection in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present application. And in all the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0010] Figure 1 is a schematic diagram of an application environment of an optional air conditioner control anomaly detection method according to an embodiment of the present application;

[0011] Figure 2 is a schematic flowchart of an optional air conditioner control anomaly detection method according to an embodiment of the present application;

[0012] Figure 3 is a time-domain diagram corresponding to the operating parameters of an optional air conditioner according to an embodiment of the present application;

[0013] Figure 4 is a frequency-domain diagram corresponding to the operating parameters of an optional air conditioner according to an embodiment of the present application;

[0014] Figure 5 is a prediction schematic diagram of a decision tree model of an optional air conditioner control anomaly detection method according to an embodiment of the present application;

[0015] Figure 6 is an anomaly display schematic diagram of another optional air conditioner control anomaly detection method according to an embodiment of the present application;

[0016] Figure 7It is a schematic flowchart of another optional air conditioner control abnormality detection method according to an embodiment of the present application;

[0017] Figure 8 It is a schematic structural diagram of an air conditioner control abnormality detection device provided by an embodiment of the present application;

[0018] Figure 9 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0019] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0020] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0021] Optionally, according to one aspect of the embodiments of the present application, an air conditioner control abnormality detection method is provided. As an optional implementation manner, the above air conditioner control abnormality detection method can be but is not limited to being applied to an application environment such as Figure 1 shown, such as Figure 1As shown, human-computer interaction can be carried out between user 102 and electronic device 104. The electronic device 104 includes a memory 106 and a processor 108. In this embodiment, the electronic device 104 can, but is not limited to, perform the following operations: obtaining the operating parameters of the air-conditioning device during operation; converting the time-domain data corresponding to the operating parameters into frequency-domain data, and obtaining target detection data for determining whether an abnormality occurs in the control process of the air-conditioning device based on the frequency-domain data; inputting the target detection data into a pre-trained anomaly detection model to determine whether an abnormality occurs in the control process of the air-conditioning device. The above is only an example, and this embodiment does not make any limitations in this regard.

[0022] In the related art, it is usually manual to judge whether the control stability of the central air-conditioning system is abnormal, and the accuracy and efficiency of the above detection method are relatively low.

[0023] To solve the above technical problems, as an optional implementation manner, as Figure 2 shown, the embodiment of the present application provides a method for detecting abnormal control of an air-conditioning device, including the following steps:

[0024] S202, obtaining the operating parameters of the air-conditioning device during operation.

[0025] Specifically, in the embodiment of the present application, the above operating parameters include but are not limited to the host load rate of the air-conditioning device, the host outlet water temperature, the water pump frequency, the water pump control temperature difference, the water pump control pressure difference, the cooling tower frequency, the cooling tower approach degree, etc., and this embodiment does not make any limitations in this regard.

[0026] S204, converting the time-domain data corresponding to the operating parameters into frequency-domain data, and obtaining target detection data for determining whether an abnormality occurs in the control process of the air-conditioning device based on the frequency-domain data.

[0027] S206, inputting the target detection data into a pre-trained anomaly detection model to determine whether an abnormality occurs in the control process of the air-conditioning device.

[0028] Specifically, for example, to determine whether the control process of the host load rate of the current air-conditioning device is abnormal during operation, by obtaining the time-domain data corresponding to the load rate of the air-conditioning device host in the past preset duration, and then converting the time-domain data into frequency-domain data; assuming that the preset duration is 24 hours, the time-domain data of the host load rate within these 24 hours can be obtained as Figure 3 shown, after converting the host load rate within these 24 hours into frequency data, it can be as Figure 4The frequency-domain data of the host load rate within the 24 hours as shown. Based on the effective values of each frequency-domain interval in the frequency-domain data, the effective value is determined as the target detection data for judging whether there is an abnormality in the control process of the air-conditioning equipment. In this application, the above time-domain data can be converted into frequency-domain data through the Fast Fourier Transform (FFT).

[0029] Input the effective values of each frequency-domain interval in the above multiple frequency-domain intervals into the pre-trained anomaly detection model to determine whether the frequency-domain values of each frequency-domain interval are abnormal values. If there are abnormal values, it is determined that there is an abnormality in the control process corresponding to the host load rate of the air-conditioning equipment; if there are no abnormal values, it is determined that there is no abnormality in the control process corresponding to the host load rate of the air-conditioning equipment.

[0030] In the embodiment of this application, by obtaining the operating parameters of the air-conditioning equipment during operation; converting the time-domain data corresponding to the operating parameters into frequency-domain data, and obtaining the target detection data for determining whether there is an abnormality in the control process of the air-conditioning equipment based on the frequency-domain data; inputting the target detection data into the pre-trained anomaly detection model to determine whether there is an abnormality in the control process of the air-conditioning equipment, this application can not only improve the accuracy and efficiency of the air-conditioning equipment control anomaly detection, but also give early warnings of abnormal situations in a timely manner, greatly saving labor costs; solving the technical problem of low accuracy and efficiency in the air-conditioning equipment control anomaly detection in the related art.

[0031] In one or more embodiments, the inputting the target detection data into the pre-trained anomaly detection model to determine whether there is an abnormality in the control process of the air-conditioning equipment includes:[[]]

[0032] Input the target detection data corresponding to at least one type of the operating parameters into the pre-trained anomaly detection model, and output the control anomaly detection results corresponding to each type of operating parameters;

[0033] Based on the anomaly detection results of each type of the operating parameters, determine whether there is an abnormality in the control process of each type of the operating parameters.

[0034] Specifically, in the embodiment of this application, for example, when the target detection data corresponding to three types of operating parameters, namely the host load rate, the host outlet water temperature, and the pump frequency, of the current air-conditioning equipment are input into the pre-trained anomaly detection model, and the output control anomaly detection result corresponding to the host load rate is in a normal state, the output control anomaly detection result corresponding to the host outlet water temperature is in a normal state, and the output control anomaly detection result corresponding to the pump frequency is in an abnormal state, based on each detection result, it is determined that there is an abnormality in the control process of the pump frequency, and there is no abnormality in the control processes of the host load rate and the host outlet water temperature.

[0035] In addition, when an abnormality occurs in the control anomaly detection result corresponding to the operating parameters of any one of the above three types, it can be determined whether an abnormality occurs in the control process of the air conditioning device.

[0036] In one or more embodiments, obtaining target detection data for determining whether an abnormality occurs in the control process of the air conditioning device based on the frequency domain data includes:

[0037] Determine the frequency intervals in which the frequencies corresponding to the respective frequency domain data are located;

[0038] According to the amplitudes corresponding to the frequency domain data in each frequency interval, calculate the root mean square value corresponding to each frequency interval, and determine the root mean square value as the target detection data.

[0039] Specifically, in the embodiments of the present application, it is assumed that the frequency intervals in which the frequencies corresponding to the respective frequency domain data are located include [Hz1, Hz2], [Hz2, Hz3], and [Hz3, Hz4]. According to the amplitudes in each of the above 3 frequency intervals, calculate the root mean square (RMS) value corresponding to each amplitude. The calculation formula for the root mean square value here is: where N is the number of amplitudes in the current frequency interval, and X i is the i-th amplitude. Each of the above three frequency intervals corresponds to an RMS value.

[0040] In one or more embodiments, determining the frequency intervals in which the frequencies corresponding to the respective frequency domain data are located includes:

[0041] Determine a plurality of frequency intervals corresponding to a preset signal fluctuation period, and determine the frequency domain data within the plurality of frequency intervals based on the frequencies corresponding to the respective frequency domain data.

[0042] Specifically, in the embodiments of the present application, it is assumed that the duration corresponding to the above frequency domain data is 3 hours, and the preset signal fluctuation period is divided into 5 major intervals: 0 to 5 minutes, 5 to 30 minutes, 30 to 60 minutes, 60 to 120 minutes, and more than 120 minutes. Converting the above period into the corresponding frequency band, the frequency corresponding to the frequency domain data of 0 to 5 minutes can be obtained as above 0.0033 Hz, the frequency corresponding to the frequency domain data of 5 to 30 minutes is 0.00055 to 0.0033 Hz, the frequency corresponding to the frequency domain data of 30 to 60 minutes is 0.00027 to 0.00055 Hz, the frequency corresponding to the frequency domain data of 60 to 120 minutes is 0.00013 to 0.00027 Hz, and the frequency corresponding to the frequency domain data of more than 120 minutes is 0 to 0.00013 Hz.

[0043] In one or more embodiments, before inputting the target detection data into the pre-trained anomaly detection model to determine whether there is an anomaly in the control process of the air conditioning device, the following steps are further included:

[0044] Obtain historical operation parameters for a preset duration, and select sample parameters based on various types of normal operation parameters and abnormal operation parameters in the historical operation parameters;

[0045] Use the sample parameters to train a preset classification model to obtain the pre-trained anomaly detection model.

[0046] In the examples of the present application, the above preset classification model includes, but is not limited to, classification models such as decision trees; for example, selecting various types of normal operation parameters and abnormal operation parameters from the historical operation parameters as sample parameters to train a preset decision tree model can obtain the pre-trained anomaly detection model.

[0047] In one example, assume that the above frequency domain data is divided into 5 frequency intervals. The RMS value of the first frequency interval is X[0], the RMS value of the second frequency interval is X[1], the RMS value of the third frequency interval is X[2], the RMS value of the fourth frequency interval is X[3], and the RMS value of the 5th frequency interval is X[4]; as Figure 5 shown, select that the RMS value of the 5th frequency interval is less than or equal to 137.053, the energy value entropy is 0.498, the number of samples samples is 30, and value = [16, 14] indicates that among the 30 samples, 16 are normal values and 14 are abnormal values; then the 30 samples can be divided into 2 groups of data, one group of data with a sample size of 19 and one group of data with a sample size of 11; in the group of data with a sample size of 19, the RMS value of each data is less than or equal to 18.845, the energy value entropy is 0.388, and among the 19 samples, 16 are normal values and 14 are abnormal values; in the group of data with a sample size of 11, the energy value entropy is 0, and among the 11 samples, 11 are normal values and 0 are abnormal values.

[0048] In one or more embodiments, the selecting sample parameters based on various types of normal operation parameters and abnormal operation parameters in the historical operation parameters includes:

[0049] Select a first quantity of root mean square values from the root mean square values corresponding to the target frequency interval to which the frequency domain data of the normal operation parameters belongs; the target frequency interval is used to indicate the interval with the highest probability of an anomaly occurring in the control process of the air conditioning device;

[0050] Select a second quantity of root mean square values from the root mean square values corresponding to each frequency interval to which the frequency domain data of the abnormal operation parameters belongs;

[0051] Determine the root mean square value of the first quantity and the root mean square value of the second quantity as the sample parameters.

[0052] Specifically, in the embodiments of the present application, assuming that the duration corresponding to the above frequency domain data is 3 hours, the preset signal fluctuation period is divided into: 0-5 minutes, 5-30 minutes, 30-60 minutes, 60-120 minutes, and more than 120 minutes, a total of 5 large intervals. Converting the above periods into corresponding frequency bands, 5 frequency intervals can be obtained. Among them, the anomalies within 0-5 minutes are caused by external disturbance effects, controlling the abnormal fluctuations to fall within the 5-30 minute period, and the anomalies occurring within 60-120 minutes and more than 120 minutes are caused by load changes. In order to improve the accuracy of abnormal detection of air conditioning equipment control, for example, among the historical parameters including 100 groups of 3-hour main engine load rate parameters, the first quantity is 20 and the second quantity is 30. Select the RMS values corresponding to 20 abnormal main engine load rates within 5-30 minutes from these 100 groups of data, and then randomly select 10 RMS values corresponding to normal main engine load rates from these 100 groups of data. Then, use the RMS values corresponding to 20 abnormal main engine load rates and the RMS values corresponding to 10 normal main engine load rates as the sample parameters.

[0053] In one or more embodiments, the air conditioning equipment control anomaly detection method further includes:

[0054] Based on the anomaly occurring in the control process of the air conditioning equipment, output the operating parameters of the type to which the anomaly belongs and the time period in which the anomaly occurs.

[0055] Specifically, in the examples of the present application, as Figure 6 shown, when an anomaly occurs in the control process of the main engine load rate of the air conditioning equipment, output that the type to which the anomaly belongs is the main engine load rate, that is, Figure 6 the ordinate of can be switched to the main engine load rate, Figure 6 and the time period corresponding to the main engine load rate interval 602 in is the time period where the anomaly is located.

[0056] Based on the above embodiments, in an application embodiment, the embodiments of the present application provide an air conditioning equipment control anomaly detection method, including:

[0057] Obtain the historical operation data corresponding to the detected target parameter (such as the main engine load rate), segment it in groups of 3 hours, and then perform a fast Fourier transform (FFT) on the segmented time domain data to convert it into frequency domain data as Figure 4 shown.

[0058] The signal fluctuation period is divided into five major intervals: 0 - 5 minutes, 5 - 30 minutes, 30 - 60 minutes, 60 - 120 minutes, and over 120 minutes. By converting the above periods into corresponding frequency bands, the frequency corresponding to the frequency domain data of 0 - 5 minutes is above 0.0033 Hz, the frequency corresponding to the frequency domain data of 5 - 30 minutes is 0.00055 - 0.0033 Hz, the frequency corresponding to the frequency domain data of 30 - 60 minutes is 0.00027 - 0.00055 Hz, the frequency corresponding to the frequency domain data of 60 - 120 minutes is 0.00013 - 0.00027 Hz, and the frequency corresponding to the frequency domain data of over 120 minutes is 0 - 0.00013 Hz. The above frequency domain data is divided according to the above five frequency domains, and the RMS value corresponding to each amplitude is calculated according to the amplitudes in each of the five frequency intervals. where N is the number of amplitudes in the current frequency interval, and X i is the i-th amplitude.

[0059] Select a small batch of data as sample data (such as 30 RMS values) for manual marking of normal and abnormal identifications. The method for selecting the small batch of data is as follows: Among the historical parameters including 100 sets of 3-hour host load rate parameters, the first quantity is 20, and the second quantity is 30. Select 20 RMS values corresponding to abnormal host load rates from the 5 - 30 minutes selected from these 100 sets of data, and then randomly select 10 RMS values corresponding to normal host load rates from these 100 sets of data. Then, use the 20 RMS values corresponding to abnormal host load rates and the 10 RMS values corresponding to normal host load rates as the sample parameters.

[0060] Use the above sample parameters to train a decision tree model; verify the accuracy of the decision tree. When the accuracy of the decision tree reaches above a preset threshold, such as above 99%, it can be delivered for use. Otherwise, increase the training data volume or adjust the decision tree model to retrain the decision tree.

[0061] In the actual application stage, collect the operating parameters of the air conditioning equipment during operation, detect once every 3 hours. Detection can start when the time length of the signal data (operating parameters) to be collected reaches the preset duration; otherwise, continue to wait.

[0062] During detection, perform a fast Fourier transform on the signal data to obtain frequency domain data, segment the frequency domain data according to frequency intervals, and calculate the RMS value corresponding to each segment; use the trained decision tree model to detect whether the RMS value corresponding to each segment is an abnormal value. If an abnormal value appears, issue an alarm for abnormal control stability of the air conditioning equipment. If no abnormal value appears, continue to obtain the operating parameters of the subsequent time period.

[0063] According to another aspect of the embodiments of the present application, there is also provided an air conditioner control anomaly detection device for implementing the above-mentioned air conditioner control anomaly detection method, as Figure 8 shown, the device includes:

[0064] A first acquisition unit 802, configured to acquire the operating parameters of the air conditioner during operation;

[0065] A conversion unit 804, configured to convert the time-domain data corresponding to the operating parameters into frequency-domain data, and acquire target detection data for determining whether an anomaly occurs in the control process of the air conditioner based on the frequency-domain data;

[0066] A determination unit 806, configured to input the target detection data into a pre-trained anomaly detection model to determine whether an anomaly occurs in the control process of the air conditioner.

[0067] In the embodiments of the present application, by acquiring the operating parameters of the air conditioner during operation; converting the time-domain data corresponding to the operating parameters into frequency-domain data, and acquiring target detection data for determining whether an anomaly occurs in the control process of the air conditioner based on the frequency-domain data; inputting the target detection data into a pre-trained anomaly detection model to determine whether an anomaly occurs in the control process of the air conditioner, the present application can not only improve the accuracy and efficiency of air conditioner control anomaly detection, but also timely give early warnings of anomalies, greatly saving labor costs; and solves the technical problem of low accuracy and efficiency in air conditioner control anomaly detection in the related art.

[0068] In one or more embodiments, the above-mentioned determination unit 806 includes:

[0069] An input module, configured to input the target detection data corresponding to at least one type of the operating parameters into a pre-trained anomaly detection model, and output the control anomaly detection results corresponding to each type of operating parameters;

[0070] A first determination module, configured to determine whether an anomaly occurs in the control process of each type of operating parameters based on the anomaly detection results of each type of operating parameters.

[0071] In one or more embodiments, the conversion unit 804 includes:

[0072] A second determination module, configured to determine the frequency range where the frequency corresponding to each frequency-domain data is located;

[0073] A calculation module, configured to calculate the root mean square value corresponding to each frequency range according to the amplitude value corresponding to the frequency-domain data in each frequency range, and determine the root mean square value as the target detection data.

[0074] In one or more embodiments, the second determination module includes:

[0075] A determination subunit, configured to determine a plurality of frequency intervals corresponding to a preset signal fluctuation period, and determine the frequency-domain data within the plurality of frequency intervals based on the frequencies corresponding to the respective frequency-domain data.

[0076] In one or more embodiments, the air-conditioning equipment control anomaly detection device further includes:

[0077] A second acquisition unit, configured to acquire historical operation parameters for a preset duration, and select sample parameters based on the normal operation parameters and abnormal operation parameters of various types in the historical operation parameters;

[0078] A training unit, configured to use the sample parameters to train a preset classification model to obtain the pre-trained anomaly detection model.

[0079] In one or more embodiments, the second acquisition unit includes:

[0080] A first selection module, configured to select a first number of root mean square values from the respective root mean square values corresponding to the target frequency interval to which the frequency-domain data of the normal operation parameters belongs; the target frequency interval is used to indicate the interval with the highest probability of anomaly in the control process of the air-conditioning equipment;

[0081] A second selection module, configured to select a second number of root mean square values from the respective root mean square values corresponding to the respective frequency intervals to which the frequency-domain data of the abnormal operation parameters belongs;

[0082] A third determination module, configured to determine the first number of root mean square values and the second number of root mean square values as the sample parameters.

[0083] In one or more embodiments, the air-conditioning equipment control anomaly detection device further includes:

[0084] An output unit, configured to output the operation parameters of the type to which the anomaly belongs and the time period in which the anomaly occurs based on an anomaly in the control process of the air-conditioning equipment.

[0085] According to another aspect of the embodiments of the present application, there is also provided an electronic device for implementing the above-mentioned air-conditioning equipment control anomaly detection method, and the electronic device may be as Figure 1The electronic device shown is configured in an air-conditioning device or a terminal device installed with a client for detecting abnormal control of an air-conditioning device. In this embodiment, the electronic device is taken as a clothing processing device as an example for illustration. Optionally, in this embodiment, the above-mentioned clothing processing device can access a computer network through a wired or wireless network, communicate with a terminal device, receive a control instruction from the terminal device, and execute a corresponding operation according to the control instruction.

[0086] Optionally, the above-mentioned terminal device includes, but is not limited to, mobile phones, laptop computers, tablet computers, personal digital assistants, MIDs (Mobile Internet Devices), desktop computers, smart TVs, etc. The client for detecting abnormal control of an air-conditioning device in the embodiments of the present application includes, but is not limited to, clients such as video clients, instant messaging clients, browser clients, and education clients that provide control services for clothing processing devices. The above-mentioned network may include, but is not limited to: wired networks, wireless networks, where the wired network includes: local area networks, metropolitan area networks, and wide area networks, and the wireless network includes: Bluetooth, WIFI, and other networks that implement wireless communication.

[0087] As Figure 9 shown, the clothing processing device includes a memory 902 and a processor 904. A computer program is stored in the memory 902, and the processor 904 is configured to execute the steps in any of the above method embodiments through the computer program.

[0088] Optionally, in this embodiment, the above-mentioned processor may be configured to execute the following steps through a computer program:

[0089] S1, obtaining the operating parameters of the air-conditioning device during operation;

[0090] S2, converting the time-domain data corresponding to the operating parameters into frequency-domain data, and obtaining target detection data for determining whether an abnormality occurs in the control process of the air-conditioning device based on the frequency-domain data;

[0091] S3, inputting the target detection data into a pre-trained anomaly detection model to determine whether an abnormality occurs in the control process of the air-conditioning device.

[0092] Optionally, those of ordinary skill in the art can understand that Figure 9 the structure shown is only schematic Figure 9 and does not limit the structure of the above-mentioned electronic device. For example, the electronic device may further include more or fewer components (such as a network interface, etc.) than those shown Figure 9 in, or have a different configuration from that shown Figure 9 in.

[0093] Among them, the memory 902 can be used to store software programs and modules, such as the program instructions / modules corresponding to the air-conditioning equipment control anomaly detection method and device in the embodiments of the present application. The processor 904 executes various functional applications and data processing by running the software programs and modules stored in the memory 902, that is, implements the above-mentioned air-conditioning equipment control anomaly detection method. The memory 902 may include a high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory 902 may further include a memory remotely disposed relative to the processor 904, and these remote memories can be connected to the terminal device through a network. Examples of the above-mentioned network include but are not limited to the Internet, enterprise intranets, local area networks, mobile communication networks, and combinations thereof. Among them, the memory 902 can specifically but not limitedly be used to store the operating parameters of the air-conditioning equipment during operation. As an example, as Figure 9 shown, the above-mentioned memory 902 may but is not limited to include the first acquisition unit 802, conversion unit 804, and determination unit 806 in the above-mentioned air-conditioning equipment control anomaly detection device. In addition, it may also include but is not limited to other module units in the above-mentioned air-conditioning equipment control anomaly detection device, which will not be elaborated in this example.

[0094] Optionally, the above-mentioned transmission device 906 is used to receive or send data via a network. Specific examples of the above-mentioned network may include wired networks and wireless networks. In one instance, the transmission device 906 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers through a network cable, so as to communicate with the Internet or local area network. In one instance, the transmission device 906 is a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet wirelessly.

[0095] In addition, the above-mentioned laundry treatment device further includes: a display 908 for displaying the abnormal conditions of the air-conditioning equipment; and a connection bus 910 for connecting each module component in the above-mentioned electronic device.

[0096] In one or more embodiments, the present application also provides a computer program product or computer program, which includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above-mentioned air-conditioning equipment control anomaly detection method. Among them, the computer program is configured to execute the steps in any one of the above method embodiments when running.

[0097] Optionally, in this embodiment, the above computer-readable storage medium may be configured to store a computer program for performing the following steps:

[0098] S1. Obtain the operating parameters of the air-conditioning device during operation;

[0099] S2. Convert the time-domain data corresponding to the operating parameters into frequency-domain data, and obtain target detection data for determining whether an abnormality occurs in the control process of the air-conditioning device based on the frequency-domain data;

[0100] S3. Input the target detection data into a pre-trained anomaly detection model to determine whether an abnormality occurs in the control process of the air-conditioning device.

[0101] Optionally, in this embodiment, those of ordinary skill in the art can understand that all or part of the steps in the above various methods of the embodiment can be completed by a program instructing the relevant hardware of the terminal device. The program can be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0102] The serial numbers of the embodiments of the present application above are only for description and do not represent the advantages or disadvantages of the embodiments.

[0103] If the integrated unit in the above embodiment is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in the above computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing one or more computer devices (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention.

[0104] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0105] In several embodiments provided by the present application, it should be understood that the disclosed client can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0106] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0107] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0108] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for detecting abnormal control of an air conditioning device, characterized in that, The method includes: Obtaining the operating parameters of the air conditioning equipment during operation; Converting the time-domain data corresponding to the operating parameters into frequency-domain data, and obtaining target detection data for determining whether an abnormality occurs in the control process of the air conditioning equipment based on the frequency-domain data; Inputting the target detection data into a pre-trained anomaly detection model to determine whether an abnormality occurs in the control process of the air conditioning equipment.

2. The method according to claim 1, wherein The step of inputting the target detection data into a pre-trained anomaly detection model to determine whether an abnormality occurs in the control process of the air conditioning equipment includes: Inputting the target detection data corresponding to at least one type of the operating parameters into a pre-trained anomaly detection model, and outputting control anomaly detection results corresponding to each type of operating parameters; Based on the anomaly detection results of each type of the operating parameters, determining whether an abnormality occurs in the control process of each type of the operating parameters.

3. The method according to claim 1 or 2, characterized in that, The step of obtaining target detection data for determining whether an abnormality occurs in the control process of the air conditioning equipment based on the frequency-domain data includes: Determining the frequency range where the frequency corresponding to each of the frequency-domain data is located; Calculating the root mean square value corresponding to each frequency range according to the amplitude value of the frequency-domain data within each frequency range, and determining the root mean square value as the target detection data.

4. The method according to claim 3, wherein The step of determining the frequency range where the frequency corresponding to each of the frequency-domain data is located includes: Determining a plurality of frequency ranges corresponding to a preset signal fluctuation period, and determining the frequency-domain data within the plurality of frequency ranges based on the frequency corresponding to each of the frequency-domain data.

5. The method according to claim 3, characterized in that Before the step of inputting the target detection data into a pre-trained anomaly detection model to determine whether an abnormality occurs in the control process of the air conditioning equipment, it further includes: Obtaining historical operating parameters for a preset duration, and selecting sample parameters based on the normal operating parameters and abnormal operating parameters of each type in the historical operating parameters; Using the sample parameters to train a preset classification model to obtain the pre-trained anomaly detection model.

6. The method according to claim 5, wherein The step of selecting sample parameters based on the normal operating parameters and abnormal operating parameters of each type in the historical operating parameters includes: Selecting a first number of root mean square values from the root mean square values corresponding to the target frequency range of the frequency-domain data of the normal operating parameters; the target frequency range is used to indicate the range with the highest probability of an abnormality occurring in the control process of the air conditioning equipment; Selecting a second number of root mean square values from the root mean square values corresponding to each frequency range of the frequency-domain data of the abnormal operating parameters; Determining the first number of root mean square values and the second number of root mean square values as the sample parameters.

7. The method according to claim 1, characterized in that The method further includes: Based on an abnormality occurring in the control process of the air conditioning equipment, outputting the operating parameters of the type to which the abnormality belongs and the time period in which the abnormality occurs.

8. An abnormal detection device for air conditioner equipment, characterized in that, The device includes: A first acquisition unit for acquiring the operating parameters of the air conditioning equipment during operation; A conversion unit for converting the time-domain data corresponding to the operating parameters into frequency-domain data, and obtaining target detection data for determining whether an abnormality occurs in the control process of the air conditioning equipment based on the frequency-domain data; A determination unit is configured to input the target detection data into a pre-trained anomaly detection model to determine whether an anomaly occurs in the control process of the air conditioning device.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor runs the computer program to implement the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program, when running, executes the method according to any one of claims 1 to 7.

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