WO2024001253A1 - 空调故障检测方法、装置、空调及电子设备 - Google Patents

空调故障检测方法、装置、空调及电子设备 Download PDF

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Publication number
WO2024001253A1
WO2024001253A1 PCT/CN2023/078432 CN2023078432W WO2024001253A1 WO 2024001253 A1 WO2024001253 A1 WO 2024001253A1 CN 2023078432 W CN2023078432 W CN 2023078432W WO 2024001253 A1 WO2024001253 A1 WO 2024001253A1
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WIPO (PCT)
Prior art keywords
spectrum data
shutdown
air conditioner
air
data groups
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PCT/CN2023/078432
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English (en)
French (fr)
Inventor
李鹏辉
孙艳斌
李海军
王彩平
李敬胜
杨文钧
Original Assignee
青岛海尔空调器有限总公司
青岛海尔空调电子有限公司
海尔智家股份有限公司
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Publication of WO2024001253A1 publication Critical patent/WO2024001253A1/zh

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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/32—Responding to malfunctions or emergencies
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/54—Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
    • F24F11/86—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling compressors within refrigeration or heat pump circuits
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/88—Electrical aspects, e.g. circuits
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/89—Arrangement or mounting of control or safety devices

Definitions

  • the present application relates to the technical field of smart air conditioners, and in particular to an air conditioner fault detection method, device, air conditioner and electronic equipment.
  • air conditioners provide people with convenient services such as cooling, heating, and dehumidification.
  • the current detection of air conditioner faults is not smart enough, and the action of the air conditioner after detection is not perfect enough.
  • This application provides an air conditioner fault detection method, device, air conditioner and electronic equipment to solve the shortcoming of the existing technology that the detection of air conditioner faults is not intelligent enough, improve the fault detection efficiency of the air conditioner, and provide timely reminders when the air conditioner fails. user effects.
  • This application provides an air conditioning fault detection method, including:
  • the real-time acquisition of multiple shutdown spectrum data groups of the air conditioner outdoor unit in the shutdown state based on environmental conditions, and multiple operating spectrum data groups in the operating state include:
  • a plurality of second shutdown spectrum data sets of the air conditioner outdoor unit in a shutdown state at a second time and a plurality of second operation spectrum data sets in an operating state at the second time are obtained.
  • multiple second shutdown spectrum data groups of the air conditioner outdoor unit in the shutdown state at the second time, and the second shutdown spectrum data group in the operating state at the second time are obtained.
  • multiple second operational spectrum data sets it also includes:
  • the pairing of the multiple shutdown spectrum data groups and the multiple operating spectrum data groups includes:
  • the plurality of second shutdown spectrum data sets and the plurality of second operating spectrum data sets are paired.
  • the target comparison spectrum data group is matched and analyzed through the air-conditioning cloud, and the analysis results are output, including:
  • the second outdoor temperature, the second indoor temperature and the second user setting data are in one-to-one correspondence;
  • Difference data between the sound pressure level corresponding to the operating frequency of the target comparison spectrum data group and the sound pressure level corresponding to the operating frequency of the reference spectrum data group are obtained, and the difference data is output as an analysis result.
  • determining whether there is an abnormality in the air conditioning operating system based on the analysis results and prompting the user includes:
  • the difference data is greater than the first target preset threshold, or the difference data is less than the second target preset threshold, it is confirmed that the air conditioning operating system is abnormal and the user is prompted.
  • the operating frequency of the target comparison spectrum data group is matched with a preset abnormality determination data group, and after abnormality determination is made on the air-conditioning operating system based on the difference data, Also includes:
  • confirming an abnormality in the air conditioner operating system and prompting the user includes:
  • This application also provides an air conditioning fault detection device, including:
  • An acquisition unit configured to acquire multiple first shutdown spectrum data groups of the air conditioner outdoor unit in the shutdown state at the first time and multiple first operation spectrum data groups in the operating state based on environmental conditions, and at the second time A plurality of second shutdown spectrum data groups in a shutdown state and a plurality of second operating spectrum data groups in a running state;
  • a replacement unit configured to confirm that the second time is later than the first time, and to replace each of the first shutdown spectrum data groups and the plurality of second shutdown spectrum data groups with the plurality of second shutdown spectrum data groups and the plurality of second operating spectrum data groups;
  • a comparison unit configured to pair the plurality of second shutdown spectrum data groups with the plurality of second operating spectrum data groups, obtain a plurality of comparison spectrum data groups, and obtain the plurality of comparison spectrum data groups from the plurality of second shutdown spectrum data groups.
  • a comparison spectrum data group selects a target comparison spectrum data group;
  • a matching unit configured to send the target comparison spectrum data group to the air-conditioning cloud, perform matching analysis on the target comparison spectrum data group through the air-conditioning cloud, and output the analysis results;
  • a determination unit is configured to determine whether there is an abnormality in the air conditioning operating system based on the analysis results and prompt the user.
  • the application also provides an air conditioner, including an indoor unit, an outdoor unit, a processor and a memory provided in the indoor unit or outdoor unit; and a program stored on the memory and executable on the processor. or an instruction, when the program or instruction is executed by the processor, any one of the air conditioning fault detection methods described above is executed.
  • the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.
  • the processor executes the program, it implements any one of the above mentioned air conditioning fault detections. method.
  • the present application also provides a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program When executed by a processor, it implements any one of the above air-conditioning fault detection methods.
  • the present application also provides a computer program product, which includes a computer program.
  • the computer program When the computer program is executed by a processor, it implements any one of the above air-conditioning fault detection methods.
  • the air conditioner fault detection method, device, air conditioner and electronic equipment provided by this application obtain multiple shutdown spectrum data groups of the air conditioner outdoor unit in the shutdown state and multiple operating spectrum data groups in the operating state based on environmental conditions, and then Compare multiple shutdown spectrum data groups with multiple operating spectrum data to obtain the target comparison spectrum data group. Further, match and analyze each target comparison spectrum data group through the air conditioner cloud, and output the analysis results. Finally, determine the air conditioner based on the analysis results. Check whether there are any abnormalities in the running system and prompt the user. This application obtains the target comparison spectrum data group in real time and updates the collected spectrum data group in real time, so that the statistical data is continuously replaced, thereby ensuring timeliness and accuracy.
  • the target comparison spectrum data set can be used to eliminate other factors that affect the operating frequency and sound pressure level of the air conditioner, and then analyze and process it through the cloud. It can detect the fault information of the air conditioner more accurately and prompt the user in a timely manner, thereby improving the air conditioner failure rate. Detection efficiency.
  • FIG 1 is one of the flow diagrams of the air conditioning fault detection method provided by this application.
  • FIG. 2 is the second schematic flow chart of the air conditioning fault detection method provided by this application.
  • FIG. 3 is a schematic structural diagram of the air conditioning fault detection device provided by this application.
  • Figure 4 is a schematic structural diagram of an electronic device provided by this application.
  • the air conditioner in the embodiment of the present application may be a wall-mounted air conditioner, a cabinet air conditioner, a ceiling air conditioner, etc.
  • the type of air conditioner is not limited here.
  • the execution subject of the air-conditioning fault detection method in the embodiment of the present application may be a controller.
  • the execution subject of the air-conditioning fault detection method in the embodiment of the present application may also be a server.
  • the execution subject is not limited here.
  • the air conditioner fault detection method in the embodiment of the present application will be described below by taking the execution subject as the controller as an example.
  • the air conditioning fault detection method includes the following steps:
  • Step 110 Obtain multiple first shutdown spectrum data groups of the air conditioner outdoor unit in the shutdown state at the first time and multiple first operation spectrum data groups in the operating state based on environmental conditions, and obtain multiple first operation spectrum data groups in the second time situation. a plurality of second shutdown spectrum data groups in the shutdown state and a plurality of second operating spectrum data groups in the running state.
  • this embodiment is based on the air conditioner in the shutdown state and the running state respectively. Spectrum data sets under different environmental conditions. They are recorded as shutdown spectrum data group and running spectrum data group respectively.
  • the environmental conditions in this embodiment mainly refer to time conditions and temperature conditions, such as data collection time, indoor temperature, outdoor temperature, etc.
  • the purpose of this embodiment is to obtain the spectrum data set in real time to eliminate the influence of time factors, because under different time conditions, the operating frequency of the air conditioner may change due to environmental influences. Therefore, by analyzing the shutdown state and operating state Real-time collection of spectrum data can improve the accuracy and effectiveness of spectrum data, thereby improving the accuracy of abnormal detection of air conditioner operation.
  • the operating spectrum data group represents the frequency and intensity of the air conditioner operation (ie, the sound pressure level corresponding to the frequency).
  • the sound pressure level is based on the characteristics of the human ear's response to changes in sound intensity, and a pair of quantities is derived to represent the size of the sound as the sound intensity level. According to research, the square of the sound pressure is proportional to the sound intensity, so Sound intensity levels can be converted into sound pressure levels.
  • the sound pressure level in this embodiment is the characteristic of the sound intensity of the air conditioner that can be heard by the human ear.
  • the sound pressure level at different operating frequencies represents the operating status of the compressor module and the fan module of the air conditioner.
  • Step 120 Confirm that the second time is later than the first time, and replace each first shutdown spectrum data group and each first shutdown spectrum data group with the plurality of second shutdown spectrum data groups and the plurality of second operating spectrum data groups. First run spectrum data set.
  • the detailed process of confirming that the second time is later than the first time and performing spectrum data collection at the second time is: first, perform the first spectrum data acquisition based on environmental conditions (i.e., outdoor temperature, indoor temperature, etc.) , and record the first time, that is, the first time.
  • environmental conditions i.e., outdoor temperature, indoor temperature, etc.
  • the time for collecting spectrum data for the second time is determined, that is, the second time.
  • the statistical time interval in this embodiment can be set to 1 hour, that is, spectrum data is collected once every hour.
  • this embodiment needs to collect the spectrum data of the air conditioner in the shutdown state and the operating state at different times. For example, set the shutdown spectrum data groups A1, A2, and A3 in the shutdown state.
  • the outdoor temperatures are Tx1, Tx2, and Tx3 respectively; the indoor temperatures are tx1, tx2, and tx3; the times are Nx1, Nx2, and Nx3; and the user setting data are respectively is Vx1, Vx2, Vx3.
  • the outdoor temperatures are Ty1, Ty2, and Ty3 respectively; the indoor temperatures are ty1, ty2, and ty3; the times are Ny1, Ny2, and Ny3; and the user setting data are Vy1 and Vy2.
  • Vy3 its operating frequencies are H1, H2, H3 respectively and the corresponding sound pressure levels are Hy1, Hy2 and Hy3 respectively.
  • This embodiment collects and obtains spectrum data of different outdoor temperatures, indoor temperatures, and user setting data in operating states and shutdown states at different times to obtain multiple sets of common data at different times, thereby ensuring that the data is stored in the cloud.
  • the data that matches the comparison spectrum data group can be found based on the real-time collected data, and then the difference with the cloud data can be obtained, ensuring the accuracy of air conditioning fault detection and improving the efficiency of data matching.
  • the spectrum data group needs to be continuously updated according to the time process. Specifically reflected in: as time continues to advance, more and more data will be generated, which may cause data confusion and the time and acquired data cannot be matched. Therefore, each time a new spectrum data set is acquired, both the shutdown spectrum data set and the operating spectrum data set acquired last time need to be replaced.
  • this embodiment uses spectrum data acquisition every hour, that is to say, data replacement and update are performed every hour, the updated spectrum data group is retained, and the spectrum data is obtained according to the updated spectrum data group.
  • This embodiment further improves the accuracy of the spectrum data by updating the spectrum data in real time, and improves the efficiency of obtaining the comparative spectrum data group, thereby improving the efficiency of cloud computing and air conditioning anomaly analysis.
  • Step 130 Pair the plurality of second shutdown spectrum data groups with the plurality of second operating spectrum data groups, obtain multiple comparison spectrum data groups, and select a target comparison spectrum data group from the plurality of comparison spectrum data groups.
  • this embodiment is to match the data in the shutdown state with the data in the running state.
  • the specific embodiment is as follows: according to the temperature condition of any operating spectrum data group, find the spectrum and sound pressure level corresponding to the shutdown spectrum data group under this temperature condition. That is, by controlling irrelevant variables, factors other than air conditioner operation are excluded, and only the spectrum data sets in the shutdown state and the operating state are considered.
  • the difference between the running state and the shutdown state is used as a comparison spectrum data set to eliminate other factors that affect the operating frequency and sound pressure level of the air conditioner, thus improving the accuracy of the data and enabling more accurate detection of air conditioner fault information.
  • Step 140 Send the target comparison spectrum data set to the air-conditioning cloud, perform matching analysis on the target comparison spectrum data set through the air-conditioning cloud, and output the analysis results.
  • the air-conditioning cloud in this embodiment can be a remote Internet used for cloud computing.
  • the comparison spectrum data group is analyzed through the cloud data, and the indoor temperature, outdoor temperature and air-conditioning user set temperature of the comparison spectrum data group are respectively Correspond to the cloud data one by one, and the difference between the sound pressure level under actual operating conditions corresponding to the comparative spectrum data group and the standard data in the cloud is obtained as the analysis result and output.
  • Step 150 Determine whether there is an abnormality in the air conditioning operating system based on the analysis results and prompt the user.
  • this embodiment determines whether there is an abnormality in the operating system of the air conditioner based on the difference between the sound pressure level in the actual operating state and the standard data in the cloud at different frequencies of the air conditioner operation, thereby allowing the user to further determine whether the fan module and the compressor Whether the module is abnormal, if abnormal, prompt the user in time, if normal, keep the running status and continue running.
  • an abnormality occurs in the compressor module and fan module of the air conditioner, that is, when the air conditioner fails, it can be directly displayed on the screen of the air conditioner indoor unit, and a fault light can be used to send out a warning signal to prompt the user, or it can also be generated.
  • the fault code is sent to the user end, and the user end can provide cloud feedback of the fault code to the maintenance personnel for fault inspection.
  • the compressor module fault and the fan module fault can be set to different fault codes.
  • the code for the compressor module fault is F1 and the code for the fan fault is F2.
  • the air conditioner fault detection method obtained by the embodiment of the present application obtains multiple shutdown spectrum data groups of the air conditioner outdoor unit in the shutdown state and multiple operating spectrum data groups in the operating state based on environmental conditions, and then combines the multiple shutdown spectrum data groups. The data group is compared with multiple operating spectrum data to obtain the target comparison spectrum data group. Each target comparison spectrum data group is further matched and analyzed through the air-conditioning cloud, and the analysis results are output. Finally, based on the analysis results, it is determined whether there is an abnormality in the air-conditioning operating system. and prompt the user.
  • This application obtains the target comparison spectrum data group in real time and updates the collected spectrum data group in real time, so that the statistical data is continuously replaced, thereby ensuring timeliness and accuracy.
  • the target comparison spectrum data set can be used to eliminate other factors that affect the operating frequency and sound pressure level of the air conditioner, and then analyze and process it through the cloud. It can detect the fault information of the air conditioner more accurately and prompt the user in a timely manner, thereby improving the air conditioner failure rate. Detection efficiency.
  • the target comparison spectrum data group is matched and analyzed through the air-conditioning cloud, and the analysis results are output, including:
  • Step 210 Match and analyze the target comparison spectrum data set and the preset standard spectrum set through the air-conditioning cloud.
  • Step 220 Obtain a reference spectrum data group corresponding to the target comparison spectrum data group in the standard spectrum group; wherein the reference outdoor temperature, reference indoor temperature and reference user setting data of the reference spectrum data group are compared with the target The second outdoor temperature, the second indoor temperature and the second user setting data of the spectrum data group are in one-to-one correspondence.
  • Step 230 Obtain the difference data between the sound pressure level corresponding to the operating frequency of the target comparison spectrum data group and the sound pressure level corresponding to the operating frequency of the reference spectrum data group, and output the difference data as an analysis result.
  • this embodiment provides a method for comparing the spectrum data set and the reference spectrum data set by the target.
  • the acquisition process of the target comparison spectrum data group is: compare each collected shutdown spectrum data group with the operating spectrum data group, and compare the data corresponding to the shutdown spectrum data group matched under the temperature conditions of the operating spectrum data group. value calculation to obtain multiple sets of comparative spectrum data sets.
  • the intensity of the operating spectrum of the air conditioner is not only determined by the operating frequency of the air conditioner, but is also affected by other factors, such as outdoor temperature, indoor temperature and other conditions. Therefore, it is necessary to obtain the difference between the sound pressure level when the air conditioner is running and when it is stopped under the same external conditions, and then calculate the difference between the difference and the standard value again through the cloud.
  • this embodiment provides a detailed process of matching spectrum data groups through the air-conditioning cloud.
  • the obtained comparison spectrum data group and the standard spectrum data group preset in the cloud are matched and analyzed, that is, K1, K2, and K3 find matching data groups C1, C2, and C3 in the cloud data.
  • K1, K2, and K3 find matching data groups C1, C2, and C3 in the cloud data.
  • the difference X in sound pressure level between the reference spectrum data group C1 and the target comparison spectrum data group K1 is calculated, and the difference X is output as an analysis result for fault analysis of the air conditioner.
  • the sound pressure level of the cloud data under different outdoor temperatures, indoor temperatures, and user setting data can be obtained while excluding time factors and external influencing factors.
  • the standard data is used to calculate the difference from the actual sound pressure level for fault analysis, ensuring the accuracy of fault analysis and improving the efficiency of fault detection.
  • determining whether there is an abnormality in the air conditioning operating system based on the analysis results and prompting the user includes:
  • the difference data is greater than the first target preset threshold, or the difference data is less than the second target preset threshold, it is confirmed that the air conditioning operating system is abnormal and the user is prompted.
  • the above embodiment provides a detailed process for determining faults of the compressor module and the fan module based on the difference X in sound pressure levels between the reference spectrum data set C1 and the target comparison spectrum data set K1 under different operating frequencies. .
  • the first preset range of the air conditioner operating frequency is set to 1Hz-50Hz, which is mainly used to determine whether the compressor module is abnormal; the second preset range is set to greater than or equal to 50Hz, which is mainly used to determine whether the fan module is abnormal.
  • the first target preset threshold is set to 10DB, and the second target preset threshold is set to -10DB.
  • the press module is determined to be abnormal
  • the fan module When the operating frequency is above 50Hz, if X>10DB, or X ⁇ -10DB, the fan module is determined to be abnormal;
  • This embodiment performs fault judgment on the sound pressure level difference at different operating frequencies, thereby more accurately detecting faults on the compressor module and fan module of the air conditioner, and obtains different results corresponding to different operating frequencies and different differences.
  • the fault detection results improve the efficiency and accuracy of fault detection.
  • the air-conditioning fault detection device provided by the present application will be described below.
  • the air-conditioning fault detection device described below and the air-conditioning fault detection method described above may correspond to each other.
  • the air conditioning fault detection device includes:
  • the acquisition unit 310 is used to acquire multiple shutdown spectrum data groups of the air conditioner outdoor unit in the shutdown state, and multiple operating spectrum data groups in the operating state;
  • the comparison unit 320 is used to pair the multiple shutdown spectrum data groups with the multiple operating spectrum data groups, obtain multiple comparison spectrum data groups, and select a target comparison spectrum data group from the multiple comparison spectrum data groups;
  • the matching unit 330 is used to send the target comparison spectrum data group to the air-conditioning cloud, perform matching analysis on the target comparison spectrum data group through the air-conditioning cloud, and output the analysis results;
  • the determination unit 340 is configured to determine whether there is an abnormality in the air conditioning operating system based on the analysis results and prompt the user.
  • the air conditioner fault detection device obtained by the embodiment of the present application obtains multiple shutdown spectrum data groups of the air conditioner outdoor unit in the shutdown state and multiple operating spectrum data groups in the operating state based on environmental conditions, and then combines the multiple shutdown spectrum data groups. The data group is compared with multiple operating spectrum data to obtain the target comparison spectrum data group. Each target comparison spectrum data group is further matched and analyzed through the air-conditioning cloud, and the analysis results are output. Finally, based on the analysis results, it is determined whether there is an abnormality in the air-conditioning operating system. and prompt the user. This application obtains the target comparison spectrum data group in real time and updates the collected spectrum data group in real time, so that the statistical data is continuously replaced, thereby ensuring timeliness and accuracy.
  • the target comparison spectrum data set can be used to eliminate other factors that affect the operating frequency and sound pressure level of the air conditioner, and then analyze and process it through the cloud. It can detect the fault information of the air conditioner more accurately and prompt the user in a timely manner, thus improving the air conditioner fault. fault detection efficiency.
  • the matching unit is specifically used for:
  • the second outdoor temperature, the second indoor temperature and the second user setting data are in one-to-one correspondence;
  • Difference data between the sound pressure level corresponding to the operating frequency of the target comparison spectrum data group and the sound pressure level corresponding to the operating frequency of the reference spectrum data group are obtained, and the difference data is output as an analysis result.
  • the determination unit is specifically used to:
  • the difference data is greater than the first target preset threshold, or the difference data is less than the second target preset threshold, it is confirmed that the air conditioning operating system is abnormal and the user is prompted.
  • the determination unit is specifically used to:
  • An embodiment of the present application also provides an air conditioner.
  • the air conditioner includes an indoor unit, an outdoor unit, a processor and a memory provided in the indoor unit or the outdoor unit; it also includes a program or instruction stored in the memory and executable on the processor. When the program or instruction is executed by the processor, the above air conditioner fault detection method is performed.
  • the method includes:
  • Figure 4 illustrates a schematic diagram of the physical structure of an electronic device.
  • the electronic device may include: a processor (processor) 410, a communications interface (Communications Interface) 420, a memory (memory) 430 and a communication bus 440.
  • the processor 410, the communication interface 420, and the memory 430 complete communication with each other through the communication bus 440.
  • the processor 410 can call logical instructions in the memory 430 to perform an air conditioning fault detection method, which method includes:
  • the above-mentioned logical instructions in the memory 430 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.
  • the technical solution of the present application essentially contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product.
  • the computer software product is stored in a storage medium and includes a number of instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program code. .
  • the present application also provides a computer program product.
  • the computer program product includes a computer program.
  • the computer program can be stored on a non-transitory computer-readable storage medium.
  • the computer program can Execute the air conditioning fault detection method provided by each of the above methods, which includes:
  • the present application also provides a non-transitory computer-readable storage medium on which a computer program is stored.
  • the computer program is implemented when executed by the processor to perform the air-conditioning fault detection method provided by each of the above methods.
  • the method includes :
  • the device embodiments described above are only illustrative.
  • 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 may be located in One location, or it can be distributed across multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Persons of ordinary skill in the art can understand and implement the method without any creative effort.
  • each embodiment can be implemented by software plus a necessary general hardware platform, and of course, it can also be implemented by hardware.
  • the computer software product can be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., including a number of instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or certain parts of the embodiments.

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Abstract

本申请提供一种空调故障检测方法、装置、空调及电子设备，方法包括：获取多个第二停机频谱数据组以及第二运行频谱数据组；将多个第二停机频谱数据组和多个第二运行频谱数据组进行配对，获取多个对比频谱数据组，并从多个对比频谱数据组选取目标对比频谱数据组；将目标对比频谱数据组发送至空调云端，通过空调云端对目标对比频谱数据组进行匹配分析，并输出分析结果；基于分析结果判定空调运行系统是否存在异常并对用户进行提示。本申请能够实时对采集到的频谱数据组进行更新，从而保证时效性和精确度。并且通过云端计算能够更加精确地检测空调的故障信息，及时地对用户进行提示，从而提高了空调故障检测的效率。

Description

空调故障检测方法、装置、空调及电子设备
相关申请的交叉引用
本申请要求于2022年06月30日提交的申请号为202210769712.4，发明名称为“空调故障检测方法、装置、空调及电子设备”的中国专利申请的优先权，其通过引用方式全部并入本文。
技术领域
本申请涉及智能空调技术领域，尤其涉及一种空调故障检测方法、装置、空调及电子设备。
背景技术
在人们的日常工作生活中，空调为人们提供制冷、制热、除湿等便捷服务，然而现在对空调故障的检测还不够智能，检测到后空调动作不够完善。
因此，如何提高空调的故障检测效率，在空调出现故障时及时提醒用户，是当前亟需解决的技术问题。
发明内容
本申请提供一种空调故障检测方法、装置、空调及电子设备，用以解决现有技术中对空调故障的检测还不够智能的缺陷，实现提高空调的故障检测效率，在空调出现故障时及时提醒用户的效果。
本申请提供一种空调故障检测方法，包括：
基于环境条件获取空调外机在第一时间情况下的停机状态下的多个第一停机频谱数据组和运行状态下的多个第一运行频谱数据组，以及在第二时间情况下的停机状态下的多个第二停机频谱数据组和运行状态下的多个第二运行频谱数据组；
确认所述第二时间晚于所述第一时间，并利用所述多个第二停机频谱数据组和多个第二运行频谱数据组替换每个第一停机频谱数据组和每个第一运行频谱数据组；将所述多个第二停机频谱数据组和多个第二运行频谱 数据组进行配对，获取多个对比频谱数据组，并从所述多个对比频谱数据组选取目标对比频谱数据组；
将所述目标对比频谱数据组发送至空调云端，通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果；
基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示。
根据本申请提供的一种空调故障检测方法，所述基于环境条件实时获取空调外机在停机状态下的多个停机频谱数据组，以及在运行状态下的多个运行频谱数据组，包括：
基于环境条件获取空调外机在第一时间停机状态下的多个第一停机频谱数据组，以及在第一时间运行状态下的多个第一运行频谱数据组；
基于第一时间和预设的统计时间间隔，确认第二时间；
基于环境条件和第二时间，获取空调外机在第二时间停机状态下的多个第二停机频谱数据组，以及在第二时间运行状态下的多个第二运行频谱数据组。
根据本申请提供的一种空调故障检测方法，基于环境条件和第二时间，获取空调外机在第二时间停机状态下的多个第二停机频谱数据组，以及在第二时间运行状态下的多个第二运行频谱数据组之后，还包括：
利用所述多个第二停机频谱数据组和多个第二运行频谱数据组替换每个第一停机频谱数据组和每个第一运行频谱数据组；
所述将所述多个停机频谱数据组和多个运行频谱数据组进行配对，包括：
将所述多个第二停机频谱数据组和多个第二运行频谱数据组进行配对。
根据本申请提供的一种空调故障检测方法，所述通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果，包括：
通过空调云端将所述目标对比频谱数据组与预设的标准频谱组进行匹配分析；
在所述标准频谱组中获取与目标对比频谱数据组对应的参考频谱数据组；其中，所述参考频谱数据组的参考室外温度、参考室内温度以及参考用户设置数据与所述目标对比频谱数据组的第二室外温度、第二室内温度以及第二用户设置数据一一对应；
获取所述目标对比频谱数据组对应运行频率的声压级和参考频谱数据组对应运行频率的声压级之间的差值数据，并将所述差值数据作为分析结果进行输出。
根据本申请提供的一种空调故障检测方法，所述基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示，包括：
将所述目标对比频谱数据组的运行频率与预设的异常判定数据组进行匹配，并基于所述差值数据对空调运行系统进行异常判定；
在所述差值数据大于第一目标预设阈值，或所述差值数据小于第二目标预设阈值的情况下，确认空调运行系统异常并对用户进行提示。
根据本申请提供的一种空调故障检测方法，将所述目标对比频谱数据组的运行频率与预设的异常判定数据组进行匹配，并基于所述差值数据对空调运行系统进行异常判定之后，还包括：
在所述差值数据小于或等于第一目标预设阈值且所述差值数据大于或等于第二目标预设阈值的情况下，确认空调运行系统正常。
根据本申请提供的一种空调故障检测方法，所述确认空调运行系统异常并对用户进行提示，包括：
在空调运行系统异常，所述运行频率小于预设临界值的情况下，提示用户所述压机模块出现异常；
在空调运行系统异常，所述运行频率大于或等于预设临界值的情况下，提示用户所述风机模块出现异常。
本申请还提供一种空调故障检测装置，包括：
获取单元，用于基于环境条件获取空调外机在第一时间情况下的停机状态下的多个第一停机频谱数据组和运行状态下的多个第一运行频谱数据组，以及在第二时间情况下的停机状态下的多个第二停机频谱数据组和运行状态下的多个第二运行频谱数据组；
替换单元，用于确认所述第二时间晚于所述第一时间，并利用所述多个第二停机频谱数据组和多个第二运行频谱数据组替换每个第一停机频谱数据组和每个第一运行频谱数据组；对比单元，用于将所述多个第二停机频谱数据组和多个第二运行频谱数据组进行配对，获取多个对比频谱数据组，并从所述多个对比频谱数据组选取目标对比频谱数据组；
匹配单元，用于将所述目标对比频谱数据组发送至空调云端，通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果；
判定单元，用于基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示。
本申请还提供一种空调，包括室内机、室外机和设置在所述室内机或室外机中的处理器和存储器；还包括存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时执行上述任一项所述空调故障检测方法。
本申请还提供一种电子设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述程序时实现如上述任一种所述空调故障检测方法。
本申请还提供一种非暂态计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现如上述任一种所述空调故障检测方法。
本申请还提供一种计算机程序产品，包括计算机程序，所述计算机程序被处理器执行时实现如上述任一种所述空调故障检测方法。
本申请提供的空调故障检测方法、装置、空调及电子设备，通过基于环境条件获取空调外机在停机状态下的多个停机频谱数据组，以及在运行状态下的多个运行频谱数据组，然后将多个停机频谱数据组和多个运行频谱数据进行对比，获取目标对比频谱数据组，进一步通过空调云端对每个目标对比频谱数据组进行匹配分析，并输出分析结果，最后基于分析结果判定空调运行系统是否存在异常并对用户进行提示。本申请通过实时获取目标对比频谱数据组，实时对采集到的频谱数据组进行更新，使得统计数据不断进行替换，从而保证时效性和精确度。并且能够利用目标对比频谱数据组排除其他影响空调运行频率和声压级的因素，然后通过云端进行分析处理，能够更加精确地检测空调的故障信息，及时地对用户进行提示，从而提高了空调故障检测的效率。
附图说明
为了更清楚地说明本申请或现有技术中的技术方案，下面将对实施例 或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请提供的空调故障检测方法的流程示意图之一；
图2是本申请提供的空调故障检测方法的流程示意图之二；
图3是本申请提供的空调故障检测装置的结构示意图；
图4是本申请提供的电子设备的结构示意图。
具体实施方式
为使本申请的目的、技术方案和优点更加清楚，下面将结合本申请中的附图，对本申请中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
下面结合图1-图4描述本申请的空调故障检测方法、装置、空调及电子设备。
需要说明的是，本申请实施例的空调可以是壁挂式空调、立柜式空调以及吊顶式空调等，此处对空调的类型不做限制。
本申请实施例的空调故障检测方法的执行主体可以是控制器，当然，在一些实施例中，本申请实施例的空调故障检测方法的执行主体还可以是服务器，此处对执行主体不作限制。下面以执行主体为控制器为例来对本申请实施例的空调故障检测方法进行说明。
参照图1，本申请提供的空调故障检测方法，包括以下步骤：
步骤110，基于环境条件获取空调外机在第一时间情况下的停机状态下的多个第一停机频谱数据组和运行状态下的多个第一运行频谱数据组，以及在第二时间情况下的停机状态下的多个第二停机频谱数据组和运行状态下的多个第二运行频谱数据组。
具体地，本实施例是在空调停机状态下以及运行的状态下，分别基于 不同的环境条件下的频谱数据组。分别记为停机频谱数据组和运行频谱数据组。
需指出，本实施例中的环境条件主要指的是时间条件和温度条件，比如数据采集时间、室内温度、室外温度等。本实施例通过实时获取频谱数据组，其目的便是为了排除时间因素造成的影响，因为在不同的时间条件下，空调的运行频率可能会因环境影响而改变，因此通过对停机状态以及运行状态下的频谱数据进行实时采集，能够提升频谱数据的精确性和有效性，从而提升空调运行异常检测的精确性。
还需指出，运行频谱数据组表示空调运行的频率以及强度(即频率对应的声压级)。需要说明的是，声压级为根据人耳对声音强弱变化响应的特性，引出一个对数量来表示声音的大小作为声强级，而根据研究，声压的平方与声强成正比，所以声强级可以转换成声压级。
本实施例中的声压级即为人耳可听到的空调运行的声音强度的特性，通过在不同运行频率下的声压级来表示空调的压机模块和风机模块的运行状态。
步骤120，确认所述第二时间晚于所述第一时间，并利用所述多个第二停机频谱数据组和多个第二运行频谱数据组替换每个第一停机频谱数据组和每个第一运行频谱数据组。
需要说明的是，确认第二时间晚于第一时间，并在第二时间进行频谱数据采集的详细流程为：首先，基于环境条件(即室外温度、室内温度等)进行第一次频谱数据获取，并记录下第一次的时间，即第一时间。
然后，根据预设的统计时间间隔，确定第二次进行频谱数据采集的时间，即第二时间。需要说明的是，本实施例中的统计时间间隔可设置为1小时，也就是每个一个小时进行一次频谱数据的采集。
最后，根据第二时间，以及第二时间对应的环境条件，获取空调外机在第二时间下的停机频谱数据组和运行频谱数据组。
本实施例为了保证对空调故障检测的准确性，需要对不同时间的停机状态和运行状态下的空调的频谱数据进行采集。例如，设置停机状态的停机频谱数据组A1、A2、A3，其分别为在室外温度为Tx1、Tx2、Tx3；室内温度为tx1、tx2、tx3；时间为Nx1、Nx2、Nx3；用户设置数据分别为Vx1、 Vx2、Vx3。另外，设置频谱数据组B1、B2、B3，其分别为在室外温度为Ty1、Ty2、Ty3；室内温度为ty1、ty2、ty3；时间为Ny1、Ny2、Ny3；用户设置数据分别为Vy1、Vy2、Vy3；其运行的频率分别为H1、H2、H3以及对应的声压级分别为Hy1、Hy2、Hy3。
参照下表1-表3：
表1：
表2：
表3：
本实施例通过对不同时间的运行状态和停机状态下的不同室外温度、室内温度以及用户设置数据的情况的频谱数据进行采集获取，获得不同时间下的多组普遍的数据，从而能够保证在云端数据中能够根据实时采集的数据找到与对比频谱数据组匹配的数据，进而获取与云端数据的差值，保证了对空调故障检测的准确性以及提高了数据匹配的效率。
进一步地，在本实施例中需要根据时间进程，对频谱数据组进行不断更新。具体体现于：根据时间的不断推进，会产生越来越多的数据，可能会造成数据混乱，时间和获取的数据无法进行匹配的现象。因此，在每次获取新的频谱数据组时，需要对上一次获取的停机频谱数据组以及运行频谱数据组均进行替换。
参见上述描述，本实施例采用每隔一小时进行频谱数据的获取，也就是说每隔一小时便进行一次数据替换和更新，将更新后的频谱数据组保留，并根据更新后的频谱数据组获取对比频谱数据组，从而进行云端计算和空调异常分析等处理。本实施例通过实时进行频谱数据更新，进一步地提升了频谱数据的精准程度，并且提升了对比频谱数据组的获取效率，进而提高了云端计算和空调异常分析的效率。
步骤130，将所述多个第二停机频谱数据组和多个第二运行频谱数据组进行配对，获取多个对比频谱数据组，并从所述多个对比频谱数据组选取目标对比频谱数据组。
具体地，本实施例即为：将停机状态下的数据和运行状态下的数据进行匹配。具体体现为：根据任一运行频谱数据组的温度条件，找到该温度条件下停机频谱数据组对应的频谱和声压级。也就是通过控制无关变量，将空调运行以外的其他因素排除，仅考虑在停机状态以及运行状态下的频谱数据组。
利用运行状态和停机状态下的差值作为对比频谱数据组，排除了其他影响空调运行频率和声压级的因素，从而提高了数据的精确度，能够更加精确地检测空调的故障信息。
步骤140，将所述目标对比频谱数据组发送至空调云端，通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果。
具体地，本实施例中的空调云端可以为用于进行的云计算的远程互联网，通过云端数据对对比频谱数据组进行分析，将对比频谱数据组的室内温度、室外温度以及空调用户设置温度分别一一和云端数据进行对应，得出对比频谱数据组对应的实际运行状态下的声压级和云端的标准数据之间的差别作为分析结果并进行输出。
步骤150，基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示。
具体地，本实施例根据空调运行的不同频率下，实际运行状态下的声压级和云端的标准数据之间的差别判定空调的运行系统是否存在异常，从而使用户进一步判断风机模块和压机模块是否出现异常，若出现异常则及时向用户进行提示，若正常则保持该运行状态继续运行。
需要说明的是，本实施例中在空调的压机模块和风机模块出现异常即空调故障时，可直接显示在空调室内机的屏幕上，以故障灯发出警示信号对用户进行提示，也可生成故障代码发送给用户端，用户端可将该故障代码进行云反馈给维修人员进行故障检查。
需要进一步说明的是，为了使维修人员能够清楚地了解到故障所在，可将压机模块故障和风机模块故障设置为不同的故障代码，如压机模块故障的代码为F1，风机故障的代码为F2。
本申请实施例提供的空调故障检测方法，通过基于环境条件获取空调外机在停机状态下的多个停机频谱数据组，以及在运行状态下的多个运行频谱数据组，然后将多个停机频谱数据组和多个运行频谱数据进行对比，获取目标对比频谱数据组，进一步通过空调云端对每个目标对比频谱数据组进行匹配分析，并输出分析结果，最后基于分析结果判定空调运行系统是否存在异常并对用户进行提示。本申请通过实时获取目标对比频谱数据组，实时对采集到的频谱数据组进行更新，使得统计数据不断进行替换，从而保证时效性和精确度。并且能够利用目标对比频谱数据组排除其他影响空调运行频率和声压级的因素，然后通过云端进行分析处理，能够更加精确地检测空调的故障信息，及时地对用户进行提示，从而提高了空调故障检测的效率。
参照图2，基于以上实施例，所述通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果，包括：
步骤210，通过空调云端将所述目标对比频谱数据组与预设的标准频谱组进行匹配分析。
步骤220，在所述标准频谱组中获取与目标对比频谱数据组对应的参考频谱数据组；其中，所述参考频谱数据组的参考室外温度、参考室内温度以及参考用户设置数据与所述目标对比频谱数据组的第二室外温度、第二室内温度以及第二用户设置数据一一对应。
步骤230，获取所述目标对比频谱数据组对应运行频率的声压级和参考频谱数据组对应运行频率的声压级之间的差值数据，并将所述差值数据作为分析结果进行输出。
具体地，本实施例提供了通过目标对比频谱数据组和参考频谱数据组 进行数据分析的过程。
目标对比频谱数据组的获取过程为：将每个采集到的停机频谱数据组和运行频谱数据组进行对比，将在运行频谱数据组的温度条件下匹配到的停机频谱数据组对应的数据进行差值计算，得到多组对比频谱数据组。
需要说明的是，由于在空调实际运行情况下，空调的运行频谱的强度不只是由空调运行频率决定，也会收到其他因素的影响，如室外温度、室内温度等条件。因此，需要在同样的外界条件下，获得空调运行状态和停机状态下的声压级的差值，然后通过云端再次计算差值与标准值之间的差值。
在多组对比频谱数据组中选择在某一温度条件下的数据组作为目标对比频谱数据组K1，其中目标对比频谱数据组的值均为在此温度条件下的运行频谱数据组B1和停机频谱数据组A1之间的差值。也就是根据B1的温度值，在A1的数据中，找到Ty1＝Tx1，ty1＝Tx1，Vy1＝Vx1的A1值，进行运算K1＝B1-A1，从而生成数据组K。
如下表4-表5所示。
表4：
表5：
另一方面，本实施例提供了通过空调云端对频谱数据组进行匹配的详细过程。首先，将获取的对比频谱数据组和云端预设的标准频谱数据组进行匹配分析，即将K1、K2、K3在云端数据中找到与之匹配的数据组C1、C2、C3。参照下表6和表7：
表6：

表7：
然后，将K1作为目标对比频谱数据组，根据K1对应的Tk1、tk1、Vk1，在云端数据找到Ty1＝Tz1，ty1＝Tz1，Nk1＝Nz1，Vy1＝Vz1下对应的标准频谱数据组C1作为参考频谱数据组，从而获取C1的声压级。也就是说，找到在K1的室外温度、室内温度以及用户设置数据下的云端数据的声压级的标准数据。
然后计算参考频谱数据组C1和目标对比频谱数据组K1之间的声压级的差值X，将差值X作为分析结果进行输出以进行空调的故障分析。
参照下表8：
本实施例通过在空调云端的数据与对比频谱数据组进行匹配，能够在排除时间因素以及外界影响因素的前提下，获取不同的室外温度、室内温度以及用户设置数据下的云端数据的声压级的标准数据，从而计算出与实际声压级的差值进行故障分析，保证了故障分析的准确性以及提高了故障检测的效率。
基于以上实施例，所述基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示，包括：
将所述目标对比频谱数据组的运行频率与预设的异常判定数据组进行匹配，并基于所述差值数据对空调运行系统进行异常判定；
在所述差值数据大于第一目标预设阈值，或所述差值数据小于第二目标预设阈值的情况下，确认空调运行系统异常并对用户进行提示。
具体可以体现为：
1、在所述运行频率小于预设临界值的情况下，提示用户所述压机模块出现异常；
2、在所述运行频率大于或等于预设临界值的情况下，提示用户所述风机模块出现异常。
在所述差值数据小于或等于第一目标预设阈值且所述差值数据大于或等于第二目标预设阈值的情况下，确认空调运行系统正常。
具体地，以上实施例提供了在不同的运行频率下，根据参考频谱数据组C1和目标对比频谱数据组K1之间的声压级的差值X进行压机模块和风机模块故障判断的详细过程。
其中，空调运行频率的第一预设范围设置为1Hz-50Hz，主要用于判定压机模块是否出现异常；第二预设范围设置为大于等于50Hz，主要用于判定风机模块是否出现异常。第一目标预设阈值设置为10DB，第二目标预设阈值设置为-10DB。
根据以上的差值X、设置的预设范围以及预设阈值，可分为如下情况：
当运行频率为1-50Hz时，如果X＞10DB，或X＜-10DB，则判定压机模块异常；
当运行频率为1-50Hz时，如果10DB≤X≤10DB，则判定压机模块正常；
当运行频率为50Hz以上时，如果X＞10DB，或X＜-10DB，则判定风机模块异常；
当运行频率为50Hz以上时，如果10DB≤X≤10DB，则判定风机模块正常。
具体如下表9所示：

本实施例通过对在不同运行频率下的声压级差值进行故障判断，从而更加精确地对空调的压机模块以及风机模块进行故障检测，对应不同的运行频率和不同的差值得到不同的故障检测结果，提高了故障检测的效率和准确性。
下面对本申请提供的空调故障检测装置进行描述，下文描述的空调故障检测装置与上文描述的空调故障检测方法可相互对应参照。
参照图3，本申请提供的空调故障检测装置，包括：
获取单元310，用于获取空调外机在停机状态下的多个停机频谱数据组，以及在运行状态下的多个运行频谱数据组；
对比单元320，用于将所述多个停机频谱数据组和多个运行频谱数据组进行配对，获取多个对比频谱数据组，并从所述多个对比频谱数据组选取目标对比频谱数据组；
匹配单元330，用于将所述目标对比频谱数据组发送至空调云端，通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果；
判定单元340，用于基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示。
本申请实施例提供的空调故障检测装置，通过基于环境条件获取空调外机在停机状态下的多个停机频谱数据组，以及在运行状态下的多个运行频谱数据组，然后将多个停机频谱数据组和多个运行频谱数据进行对比，获取目标对比频谱数据组，进一步通过空调云端对每个目标对比频谱数据组进行匹配分析，并输出分析结果，最后基于分析结果判定空调运行系统是否存在异常并对用户进行提示。本申请通过实时获取目标对比频谱数据组，实时对采集到的频谱数据组进行更新，使得统计数据不断进行替换，从而保证时效性和精确度。并且能够利用目标对比频谱数据组排除其他影响空调运行频率和声压级的因素，然后通过云端进行分析处理，能够更加精确地检测空调的故障信息，及时地对用户进行提示，从而提高了空调故 障检测的效率。
基于以上实施例，匹配单元具体用于：
通过空调云端将所述目标对比频谱数据组与预设的标准频谱组进行匹配分析；
在所述标准频谱组中获取与目标对比频谱数据组对应的参考频谱数据组；其中，所述参考频谱数据组的参考室外温度、参考室内温度以及参考用户设置数据与所述目标对比频谱数据组的第二室外温度、第二室内温度以及第二用户设置数据一一对应；
获取所述目标对比频谱数据组对应运行频率的声压级和参考频谱数据组对应运行频率的声压级之间的差值数据，并将所述差值数据作为分析结果进行输出。
基于以上实施例，判定单元具体用于：
将所述目标对比频谱数据组的运行频率与预设的异常判定数据组进行匹配，并基于所述差值数据对空调运行系统进行异常判定；
在所述差值数据大于第一目标预设阈值，或所述差值数据小于第二目标预设阈值的情况下，确认空调运行系统异常并对用户进行提示。
在所述差值数据小于或等于第一目标预设阈值且所述差值数据大于或等于第二目标预设阈值的情况下，确认空调运行系统正常。
基于以上实施例，判定单元具体用于：
在空调运行系统异常，所述运行频率小于预设临界值的情况下，提示用户所述压机模块出现异常；
在空调运行系统异常，所述运行频率大于或等于预设临界值的情况下，提示用户所述风机模块出现异常。
本申请实施例还提供一种空调，空调包括室内机、室外机和设置在室内机或室外机中的处理器和存储器；还包括存储在存储器上并可在处理器上运行的程序或指令，程序或指令被处理器执行时执行如上述空调故障检测方法。该方法包括：
基于环境条件获取空调外机在第一时间情况下的停机状态下的多个第一停机频谱数据组和运行状态下的多个第一运行频谱数据组，以及在第二时间情况下的停机状态下的多个第二停机频谱数据组和运行状态下的多个 第二运行频谱数据组；
确认所述第二时间晚于所述第一时间，并利用所述多个第二停机频谱数据组和多个第二运行频谱数据组替换每个第一停机频谱数据组和每个第一运行频谱数据组；将所述多个第二停机频谱数据组和多个第二运行频谱数据组进行配对，获取多个对比频谱数据组，并从所述多个对比频谱数据组选取目标对比频谱数据组；
将所述目标对比频谱数据组发送至空调云端，通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果；
基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示。
图4示例了一种电子设备的实体结构示意图，如图4所示，该电子设备可以包括：处理器(processor)410、通信接口(Communications Interface)420、存储器(memory)430和通信总线440，其中，处理器410，通信接口420，存储器430通过通信总线440完成相互间的通信。处理器410可以调用存储器430中的逻辑指令，以执行空调故障检测方法，该方法包括：
基于环境条件获取空调外机在第一时间情况下的停机状态下的多个第一停机频谱数据组和运行状态下的多个第一运行频谱数据组，以及在第二时间情况下的停机状态下的多个第二停机频谱数据组和运行状态下的多个第二运行频谱数据组；
确认所述第二时间晚于所述第一时间，并利用所述多个第二停机频谱数据组和多个第二运行频谱数据组替换每个第一停机频谱数据组和每个第一运行频谱数据组；将所述多个第二停机频谱数据组和多个第二运行频谱数据组进行配对，获取多个对比频谱数据组，并从所述多个对比频谱数据组选取目标对比频谱数据组；
将所述目标对比频谱数据组发送至空调云端，通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果；
基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示。
此外，上述的存储器430中的逻辑指令可以通过软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该 计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
另一方面，本申请还提供一种计算机程序产品，所述计算机程序产品包括计算机程序，计算机程序可存储在非暂态计算机可读存储介质上，所述计算机程序被处理器执行时，计算机能够执行上述各方法所提供的空调故障检测方法，该方法包括：
基于环境条件获取空调外机在第一时间情况下的停机状态下的多个第一停机频谱数据组和运行状态下的多个第一运行频谱数据组，以及在第二时间情况下的停机状态下的多个第二停机频谱数据组和运行状态下的多个第二运行频谱数据组；
确认所述第二时间晚于所述第一时间，并利用所述多个第二停机频谱数据组和多个第二运行频谱数据组替换每个第一停机频谱数据组和每个第一运行频谱数据组；将所述多个第二停机频谱数据组和多个第二运行频谱数据组进行配对，获取多个对比频谱数据组，并从所述多个对比频谱数据组选取目标对比频谱数据组；
将所述目标对比频谱数据组发送至空调云端，通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果；
基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示。
又一方面，本申请还提供一种非暂态计算机可读存储介质，其上存储有计算机程序，该计算机程序被处理器执行时实现以执行上述各方法提供的空调故障检测方法，该方法包括：
基于环境条件获取空调外机在第一时间情况下的停机状态下的多个第一停机频谱数据组和运行状态下的多个第一运行频谱数据组，以及在第二时间情况下的停机状态下的多个第二停机频谱数据组和运行状态下的多个第二运行频谱数据组；
确认所述第二时间晚于所述第一时间，并利用所述多个第二停机频谱数据组和多个第二运行频谱数据组替换每个第一停机频谱数据组和每个第 一运行频谱数据组；将所述多个第二停机频谱数据组和多个第二运行频谱数据组进行配对，获取多个对比频谱数据组，并从所述多个对比频谱数据组选取目标对比频谱数据组；
将所述目标对比频谱数据组发送至空调云端，通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果；
基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下，即可以理解并实施。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件。基于这样的理解，上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)执行各个实施例或者实施例的某些部分所述的方法。
最后应说明的是：以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。

Claims (10)

  1. 一种空调故障检测方法，包括：
    基于环境条件获取空调外机在第一时间情况下的停机状态下的多个第一停机频谱数据组和运行状态下的多个第一运行频谱数据组，以及在第二时间情况下的停机状态下的多个第二停机频谱数据组和运行状态下的多个第二运行频谱数据组；
    确认所述第二时间晚于所述第一时间，并利用所述多个第二停机频谱数据组和多个第二运行频谱数据组替换每个第一停机频谱数据组和每个第一运行频谱数据组；将所述多个第二停机频谱数据组和多个第二运行频谱数据组进行配对，获取多个对比频谱数据组，并从所述多个对比频谱数据组选取目标对比频谱数据组；
    将所述目标对比频谱数据组发送至空调云端，通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果；
    基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示。
  2. 根据权利要求1所述的空调故障检测方法，其中，所述通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果，包括：
    通过空调云端将所述目标对比频谱数据组与预设的标准频谱组进行匹配分析；
    在所述标准频谱组中获取与目标对比频谱数据组对应的参考频谱数据组；其中，所述参考频谱数据组的参考室外温度、参考室内温度以及参考用户设置数据与所述目标对比频谱数据组的第二室外温度、第二室内温度以及第二用户设置数据一一对应；
    获取所述目标对比频谱数据组对应运行频率的声压级和参考频谱数据组对应运行频率的声压级之间的差值数据，并将所述差值数据作为分析结果进行输出。
  3. 根据权利要求2所述的空调故障检测方法，其中，所述基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示，包括：
    将所述目标对比频谱数据组的运行频率与预设的异常判定数据组进行匹配，并基于所述差值数据对空调运行系统进行异常判定；
    在所述差值数据大于第一目标预设阈值，或所述差值数据小于第二目标预设阈值的情况下，确认空调运行系统异常并对用户进行提示。
  4. 根据权利要求3所述的空调故障检测方法，其中，将所述目标对比频谱数据组的运行频率与预设的异常判定数据组进行匹配，并基于所述差值数据对空调运行系统进行异常判定之后，还包括：
    在所述差值数据小于或等于第一目标预设阈值且所述差值数据大于或等于第二目标预设阈值的情况下，确认空调运行系统正常。
  5. 根据权利要求3所述的空调故障检测方法，其中，所述确认空调运行系统异常并对用户进行提示，包括：
    在空调运行系统异常，所述运行频率小于预设临界值的情况下，提示用户所述压机模块出现异常；
    在空调运行系统异常，所述运行频率大于或等于预设临界值的情况下，提示用户所述风机模块出现异常。
  6. 一种空调故障检测装置，包括：
    获取单元，用于基于环境条件获取空调外机在第一时间情况下的停机状态下的多个第一停机频谱数据组和运行状态下的多个第一运行频谱数据组，以及在第二时间情况下的停机状态下的多个第二停机频谱数据组和运行状态下的多个第二运行频谱数据组；
    替换单元，用于确认所述第二时间晚于所述第一时间，并利用所述多个第二停机频谱数据组和多个第二运行频谱数据组替换每个第一停机频谱数据组和每个第一运行频谱数据组；对比单元，用于将所述多个第二停机频谱数据组和多个第二运行频谱数据组进行配对，获取多个对比频谱数据组，并从所述多个对比频谱数据组选取目标对比频谱数据组；
    匹配单元，用于将所述目标对比频谱数据组发送至空调云端，通过空调云端对所述目标对比频谱数据组进行匹配分析，并输出分析结果；
    判定单元，用于基于所述分析结果判定空调运行系统是否存在异常并对用户进行提示。
  7. 一种空调，包括室内机、室外机和设置在所述室内机或室外机中的处理器和存储器；还包括存储在所述存储器上并可在所述处理器上运行的程序或指令，所述程序或指令被所述处理器执行时执行如权利要求1至 5任一项所述空调故障检测方法。
  8. 一种电子设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，，所述处理器执行所述程序时实现如权利要求1至5任一项所述空调故障检测方法。
  9. 一种非暂态计算机可读存储介质，其上存储有计算机程序，，所述计算机程序被处理器执行时实现如权利要求1至5任一项所述空调故障检测方法。
  10. 一种计算机程序产品，包括计算机程序，，所述计算机程序被处理器执行时实现如权利要求1至5任一项所述空调故障检测方法。
PCT/CN2023/078432 2022-06-30 2023-02-27 空调故障检测方法、装置、空调及电子设备 WO2024001253A1 (zh)

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