Method, device, electronic device, and storage medium for detecting device

Abstract

The disclosed embodiment discloses a method, apparatus, electronic device, and storage medium for detecting equipment, the method comprising: collecting audio data when the equipment is running; inputting the audio data into a pre-trained sound detection model, and obtaining status information output by the sound detection model, wherein the status information includes information indicating that the equipment is in a good state, information indicating that the equipment has assembly problems, and information indicating that the equipment has quality problems with predetermined parts. The technical solution of the embodiment of the present invention changes the traditional way of air conditioning quality detection relying on manual quality inspection, and can timely and accurately classify equipment quality problems, improve the factory qualification rate and reliability of equipment, and meet the user's high standard quality requirements.

Description

Technical Field

[0001] The embodiments of the present disclosure relate to the field of computer application technology, and in particular to a method, an apparatus, an electronic device, and a storage medium for detecting a device. Background Art

[0002] For a long time, equipment testing has generally been done manually. Manual testing relies on the experience and proficiency of the testers, and the evaluation standards are unstable and inconsistent, which often leads to false detection and missed detection. On the other hand, equipment testing is a heavy and boring physical labor for the testers, which is not conducive to improving production efficiency and saving labor costs. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure provide a method, an apparatus, an electronic device, and a storage medium for detecting a device, so as to improve the factory qualification rate and reliability of the device.

[0004] Other features and advantages of the embodiments of the present disclosure will become apparent from the following detailed description or may be learned in part by the practice of the embodiments of the present disclosure.

[0005] In a first aspect of the present disclosure, an embodiment of the present disclosure provides a method for detecting a device, including:

[0006] Collect audio data when the device is running;

[0007] The audio data is input into a pre-trained sound detection model to obtain status information output by the sound detection model, wherein the status information includes information indicating that the device is in good condition, information indicating that the device has assembly problems, and information indicating that predetermined parts of the device have quality problems.

[0008] In one embodiment, collecting audio data of a device during operation includes: after the device is assembled, placing the device in a predetermined enclosed space, and collecting audio data of the device through a sound sensor disposed in the enclosed space.

[0009] In one embodiment, after collecting audio data during device operation, before inputting the audio data into a pre-trained sound detection model, it also includes: determining whether there is an abnormal band in the audio data, and if so, extracting the abnormal band to input the abnormal band into the pre-trained sound detection model, otherwise determining that the device is in a good state.

[0010] In one embodiment, the assembly problems include loose assembly, assembly buckle problems, and excessive assembly distance problems.

[0011] In one embodiment, the equipment includes an air conditioner; the quality problems of the predetermined parts include motor abnormality, motor voltage abnormality, motor current abnormality, power instability, condenser cracks, and pipeline cracks.

[0012] In one embodiment, the sound detection model is trained by the following steps:

[0013] Acquire a training sample set, wherein the training sample includes audio data collected from a device and annotation information for representing status information of the device;

[0014] Determining an initialized sound detection model, wherein the initialized sound detection model includes a target layer for outputting status information of a device corresponding to the audio data;

[0015] By using a machine learning method, the audio data in the training samples in the training sample set is used as the input of the initialized sound detection model, and the annotation information corresponding to the input audio data is used as the expected output of the initialized sound detection model, so as to train the sound detection model.

[0016] In one embodiment, obtaining a training sample set includes:

[0017] Collecting audio data of the equipment to be repaired, and recording the time period of abnormal waveform in the audio data;

[0018] Extracting audio data corresponding to a time period in which the waveform is abnormal from the audio data to obtain abnormal audio data;

[0019] Repairing the equipment to be repaired to determine the status information of the equipment to be repaired before repair;

[0020] The abnormal audio data is used as a sample, and the state information is used as labeling information of the sample.

[0021] In a second aspect of the present disclosure, an embodiment of the present disclosure further provides a device for detecting a device, including:

[0022] An audio data acquisition unit, used to acquire audio data when the device is running;

[0023] A status detection unit is used to input the audio data into a pre-trained sound detection model to obtain status information output by the sound detection model, wherein the status information includes information indicating that the device is in good condition, information indicating that the device has assembly problems, and information indicating that predetermined parts of the device have quality problems.

[0024] In one embodiment, the audio data collection unit is used to: after the device is assembled, place the device in a predetermined enclosed space, and collect audio data of the device through a sound sensor configured in the enclosed space.

[0025] In one embodiment, the apparatus further comprises an audio preliminary detection unit, which is used to collect audio data of the device during operation and before inputting the audio data into a pre-trained sound detection model:

[0026] Determine whether there is an abnormal band in the audio data, if so, extract the abnormal band to input the abnormal band into a pre-trained sound detection model, otherwise determine that the device is in a good state.

[0027] In one embodiment, the assembly problems include loose assembly, assembly buckle problems, and excessive assembly distance problems.

[0028] In one embodiment, the equipment includes an air conditioner; the quality problems of the predetermined parts include motor abnormality, motor voltage abnormality, motor current abnormality, power instability, condenser cracks, and pipeline cracks.

[0029] In one embodiment, the sound detection model in the state detection unit is obtained by training the following modules:

[0030] A sample acquisition module, used to acquire a training sample set, wherein the training sample includes audio data collected from a device and annotation information used to represent status information of the device;

[0031] A model determination module, used to determine an initialized sound detection model, wherein the initialized sound detection model includes a target layer for outputting status information of a device corresponding to the audio data;

[0032] The model training module is used to utilize a machine learning device to use the audio data in the training samples in the training sample set as the input of the initialized sound detection model, and to use the annotation information corresponding to the input audio data as the expected output of the initialized sound detection model to train the sound detection model.

[0033] In one embodiment, the sample acquisition module is used to: collect audio data of the equipment to be repaired, and record the time period of the abnormal waveform in the audio data;

[0034] Extracting audio data corresponding to a time period in which the waveform is abnormal from the audio data to obtain abnormal audio data;

[0035] Repairing the equipment to be repaired to determine the status information of the equipment to be repaired before repair;

[0036] The abnormal audio data is used as a sample, and the state information is used as labeling information of the sample.

[0037] In a third aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions, wherein when the executable instructions are executed by the processor, the electronic device executes the method in the first aspect.

[0038] In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method in the first aspect is implemented.

[0039] The beneficial technical effects of the technical solution proposed in the embodiments of the present disclosure are:

[0040] The disclosed embodiment changes the traditional way of air-conditioning quality inspection that relies on manual quality inspection. It collects audio data when the equipment is running; inputs the audio data into a pre-trained sound detection model to obtain status information output by the sound detection model, wherein the status information includes information indicating that the equipment is in a good state, information indicating that the equipment has assembly problems, and information indicating that predetermined parts of the equipment have quality problems. This can timely and accurately classify equipment quality problems, improve the factory qualification rate and reliability of equipment, and meet users' high-standard quality requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings required for use in the description of the embodiments of the present disclosure will be briefly introduced below. Obviously, the drawings described below are only part of the embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on the contents of the embodiments of the present disclosure and these drawings without paying any creative work.

[0042] Figure 1 is a flow chart of a method for detecting a device provided according to an embodiment of the present disclosure;

[0043] Figure 2 is a flow chart of another method for detecting a device provided according to an embodiment of the present disclosure;

[0044] Figure 3 is a flow chart of a method for training a sound detection model provided by an embodiment of the present disclosure;

[0045] Figure 4 is a schematic structural diagram of a detection device provided according to an embodiment of the present disclosure;

[0046] Figure 5is a structural schematic diagram of a training device for a sound detection model provided according to an embodiment of the present disclosure;

[0047] Figure 6 A schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0048] In order to make the technical problems solved by the embodiments of the present disclosure, the technical solutions adopted, and the technical effects achieved more clearly, the technical solutions of the embodiments of the present disclosure will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. Based on the embodiments of the embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the embodiments of the present disclosure.

[0049] It should be noted that the terms "system" and "network" in the embodiments of the present disclosure are often used interchangeably herein. The "and / or" mentioned in the embodiments of the present disclosure refers to any and all combinations including one or more of the related listed items. The terms "first", "second", etc. in the specification, claims and drawings of the present disclosure are used to distinguish different objects, rather than to limit a specific order.

[0050] It should also be noted that the following embodiments in the embodiments of the present disclosure may be executed separately, or in combination with each other, and the embodiments of the present disclosure do not impose specific limitations on this.

[0051] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0052] The technical solution of the embodiment of the present disclosure is further explained below with reference to the accompanying drawings and through specific implementation methods.

[0053] Figure 1 A flow chart of a method for detecting a device provided by an embodiment of the present disclosure is shown. This embodiment is applicable to the case where a device is detected based on the sound of the device running. The method can be executed by a detection device device configured in an electronic device, such as Figure 1 As shown, the method for detecting a device described in this embodiment includes:

[0054] In step S110, audio data of the device during operation is collected.

[0055] There are many methods for collecting audio data of a device during operation. For example, after the device is assembled, the device is placed in a predetermined enclosed space, and the audio data of the device is collected by a sound sensor disposed in the enclosed space.

[0056] In step S120, the audio data is input into a pre-trained sound detection model to obtain status information output by the sound detection model.

[0057] The status information includes information indicating that the equipment is in good condition, information indicating that there are assembly problems with the equipment, and information indicating that there are quality problems with predetermined parts of the equipment.

[0058] The status information of the device includes various types. For example, the assembly problems may include loose assembly problems, assembly buckle problems, and excessive assembly distance problems. The component quality problems include but are not limited to motor problems.

[0059] Taking air conditioners as an example, we can automatically capture and store the audio data of air conditioners on the production line to form an industry database, and perform big data analysis on the air conditioner data to derive the types of common problems. For example, it can include the above-mentioned assembly problems such as loose assembly, assembly buckle problems, and excessive assembly distance problems, as well as parts quality problems. For air conditioners, quality problems of predetermined parts mostly include abnormal motors, abnormal motor voltage, abnormal motor current, power instability, cracks in condenser tubes, and cracks in pipes.

[0060] This embodiment requires that the sound detection model can obtain output result information for representing the status information of the device after the audio data of the input device is input. The training method and characteristics of the specific model are not limited in this embodiment. There is a separate exemplary embodiment after this embodiment, which provides a training method for a sound detection model. This method or a similar method can be used to train the sound detection model required by this step, and the following training method can also be deformed or adaptively modified as long as it can meet the requirements of this step.

[0061] This embodiment changes the traditional way of air conditioning quality inspection that relies on manual quality inspection. It collects audio data when the equipment is running; inputs the audio data into a pre-trained sound detection model to obtain status information output by the sound detection model, wherein the status information includes information indicating that the equipment is in good condition, information indicating that the equipment has assembly problems, and information indicating that predetermined parts of the equipment have quality problems. This can timely and accurately classify equipment quality problems, improve the factory qualification rate and reliability of equipment, and meet users' high-standard quality requirements.

[0062] Figure 2 FIG. 2 is a flow chart of another method for detecting a device provided by an embodiment of the present disclosure. This embodiment is based on the above-mentioned embodiment and has been improved and optimized. Figure 2As shown, the method for detecting a device described in this embodiment includes:

[0063] In step S210, after the device is assembled, the device is placed in a predetermined enclosed space, and audio data of the device is collected by a sound sensor configured in the enclosed space.

[0064] For example, at the end of a device assembly production line, the assembled device is pushed into a predetermined enclosed space to isolate it from external noise, and audio data of the device is collected through a sound sensor disposed in the enclosed space.

[0065] In one embodiment, after collecting the audio data,

[0066] In step S220, determine whether there is an abnormal band in the audio data, if so, execute step S230, otherwise execute step S250.

[0067] For example, the waveforms of audio data of multiple such devices in normal state can be compared to determine an abnormal sound detection threshold for distinguishing between devices in normal state and devices in abnormal state. The waveform of the audio is subjected to abnormal sound detection according to the abnormal sound detection threshold to determine whether there is an abnormal band.

[0068] In step S230, the abnormal band is extracted and step S240 is executed.

[0069] In step S240, the abnormal band is input into a pre-trained sound detection model, and the state information output by the sound detection model is obtained, and the process ends.

[0070] In step S250, it is determined that the device is in good condition, and the process ends.

[0071] Figure 3 is a flow chart of a method for training a sound detection model provided by an embodiment of the present disclosure, such as Figure 3 As shown, the training method of the sound detection model described in the embodiment of the present disclosure includes:

[0072] In step S310, a training sample set is obtained, wherein the training sample includes audio data collected from a device and annotation information for representing status information of the device.

[0073] In step S320, an initialized sound detection model is determined, wherein the initialized sound detection model includes a target layer for outputting status information of a device corresponding to the audio data.

[0074] In step S330, using a machine learning method, the audio data in the training samples in the training sample set is used as the input of the initialized sound detection model, and the annotation information corresponding to the input audio data is used as the expected output of the initialized sound detection model to train the sound detection model.

[0075] In some embodiments, a training sample set may be obtained in the following manner: collecting audio data of the equipment to be repaired, and recording the time period in the audio data where the waveform is abnormal; extracting the audio data corresponding to the time period in the audio data where the waveform is abnormal to obtain abnormal audio data; repairing the equipment to be repaired to determine the status information of the equipment to be repaired before repair; using the abnormal audio data as a sample, and using the status information as annotation information for the sample.

[0076] The technical solution of this embodiment discloses a method for training a sound detection model, by obtaining a training sample set including audio data collected from a device and annotation information used to represent status information of the device, and utilizing a machine learning method, using the audio data in the training samples in the training sample set as the input of an initialized sound detection model, and using the annotation information corresponding to the input audio data as the expected output of the initialized sound detection model, the sound detection model is trained to determine the status information of the device based on the sound of the device, so as to timely and accurately classify equipment quality problems, improve the factory qualification rate and reliability of the equipment, and meet the high quality standards of users.

[0077] Figure 4 A schematic diagram of the structure of another detection device provided by an embodiment of the present disclosure is shown. Figure 4 As shown, the detection device described in this embodiment includes an audio data acquisition unit 410 and a state detection unit 420.

[0078] The audio data collection unit 410 is configured to collect audio data when the device is running.

[0079] The status detection unit 420 is configured to input the audio data into a pre-trained sound detection model to obtain status information output by the sound detection model, wherein the status information includes information indicating that the device is in good condition, information indicating that the device has assembly problems, and information indicating that predetermined parts of the device have quality problems.

[0080] In some embodiments, the audio data collection unit 410 is configured to place the device in a predetermined enclosed space after the device is assembled, and collect audio data of the device through a sound sensor configured in the enclosed space.

[0081] In some embodiments, the device further comprises an audio initial detection unit ( Figure 4 (not shown in the figure), the audio initial inspection unit is configured to, after collecting the audio data of the device during operation and before inputting the audio data into a pre-trained sound detection model: determine whether there is an abnormal band in the audio data, if so, extract the abnormal band to input the abnormal band into the pre-trained sound detection model, otherwise determine that the device is in a good state.

[0082] In some embodiments, the assembly problems include loose assembly, assembly buckle problems, and excessive assembly distance problems.

[0083] In some embodiments, the equipment includes an air conditioner; the quality problems of the predetermined parts include motor abnormalities, motor voltage abnormalities, motor current abnormalities, power instability problems, condenser cracks, and pipe cracks.

[0084] In some embodiments, the sound detection model in the state detection unit 420 is obtained by training various modules of a sound detection model training device.

[0085] The device of the detection device provided in this embodiment can execute the method of the detection device provided in the method embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0086] Figure 5 is a structural diagram of a training device for a sound detection model provided by an embodiment of the present disclosure, such as Figure 5 As shown, the training device for the sound detection model described in this embodiment includes a sample acquisition module 510, a model determination module 520 and a model training module 530.

[0087] The sample acquisition module 510 is configured to acquire a training sample set, wherein the training sample includes audio data collected from a device and annotation information for representing status information of the device.

[0088] The model determination module 520 is configured to determine an initialized sound detection model, wherein the initialized sound detection model includes a target layer for outputting status information of a device corresponding to audio data.

[0089] The model training module 530 is configured to utilize a machine learning device to use the audio data in the training samples in the training sample set as the input of the initialized sound detection model, and to use the annotation information corresponding to the input audio data as the expected output of the initialized sound detection model to train the sound detection model.

[0090] In some embodiments, the sample acquisition module 510 is configured to:

[0091] Collecting audio data of the equipment to be repaired, and recording the time period of abnormal waveform in the audio data;

[0092] Extracting audio data corresponding to a time period in which the waveform is abnormal from the audio data to obtain abnormal audio data;

[0093] Repairing the equipment to be repaired to determine the status information of the equipment to be repaired before repair;

[0094] The abnormal audio data is used as a sample, and the state information is used as labeling information of the sample.

[0095] The sound detection model training device provided in this embodiment can execute the sound detection model training method provided in the method embodiment of the present disclosure, and has the corresponding functional modules and beneficial effects of the execution method.

[0096] Reference below Figure 6 , which shows a schematic diagram of the structure of an electronic device 600 suitable for implementing the embodiment of the present disclosure. The terminal device in the embodiment of the present disclosure may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0097] like Figure 6 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 are also stored. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0098] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0099] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the present disclosure are executed.

[0100] It should be noted that the computer-readable medium in the embodiment of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the embodiment of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device. In the embodiment of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0101] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0102] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: collects audio data when the device is running; inputs the audio data into a pre-trained sound detection model, and obtains status information output by the sound detection model, wherein the status information includes information indicating that the device is in good condition, information indicating that the device has assembly problems, and information indicating that predetermined parts of the device have quality problems.

[0103] Computer program code for performing the operations of the disclosed embodiments may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0105] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0106] The above description is only a preferred embodiment of the embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the embodiment of the present disclosure is not limited to the technical solution formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned public concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiment of the present disclosure (but not limited to) to form a technical solution.

Claims

1. A method for detecting a device, characterized in that: include: Collect audio data when the device is running; Input the audio data into a pre-trained sound detection model to obtain status information output by the sound detection model, wherein the status information includes information indicating that the device is in a good state, information indicating that the device has an assembly problem, and information indicating that a predetermined component of the device has a quality problem; Wherein, after collecting the audio data of the device during operation and before inputting the audio data into the pre-trained sound detection model, the method further includes: Determine whether there is an abnormal band in the audio data, if so, extract the abnormal band to input the abnormal band into the pre-trained sound detection model, otherwise determine that the device is in a good state; Wherein, determining whether there is an abnormal band in the audio data includes: Comparing the waveforms of the audio data of a plurality of such devices in a normal state, and determining an abnormal sound detection threshold for distinguishing a device in a normal state from a device in an abnormal state; Comparing the waveform of the audio data with the abnormal sound detection threshold to determine whether the abnormal band exists in the audio data; The audio data collected during operation of the acquisition device includes: After the device is assembled, the device is placed in a predetermined enclosed space, and audio data of the device is collected by a sound sensor arranged in the enclosed space.

2. The method according to claim 1, characterized in that The assembly problems include loose assembly, assembly buckle problems, and excessive assembly distance problems.

3. The method according to claim 1, characterized in that The equipment includes air conditioning; The quality problems of the predetermined parts include motor abnormalities, motor voltage abnormalities, motor current abnormalities, power instability problems, condenser crack problems, and pipeline crack problems.

4. The method according to any one of claims 1 to 3, characterized in that: The sound detection model is trained by the following steps: Acquire a training sample set, wherein the training sample includes audio data collected from a device and annotation information for representing status information of the device; Determining an initialized sound detection model, wherein the initialized sound detection model includes a target layer for outputting status information of a device corresponding to the audio data; By using a machine learning method, the audio data in the training samples in the training sample set is used as the input of the initialized sound detection model, and the annotation information corresponding to the input audio data is used as the expected output of the initialized sound detection model to train the sound detection model.

5. The method according to claim 4, characterized in that Obtaining a training sample set includes: Collecting audio data of the equipment to be repaired, and recording the time period of abnormal waveform in the audio data; Extracting audio data corresponding to a time period in which the waveform is abnormal from the audio data to obtain abnormal audio data; Repairing the equipment to be repaired to determine the status information of the equipment to be repaired before repair; The abnormal audio data is used as a sample, and the state information is used as labeling information of the sample.

6. A device for detecting equipment, characterized in that: include: An audio data acquisition unit, used to acquire audio data when the device is running; a state detection unit, used to input the audio data into a pre-trained sound detection model, and obtain state information output by the sound detection model, wherein the state information includes information indicating that the device is in a good state, information indicating that the device has an assembly problem, and information indicating that a predetermined component of the device has a quality problem; Wherein, the device further comprises an audio preliminary inspection unit; The audio initial inspection unit is used to determine whether there is an abnormal band in the audio data after the audio data of the device is collected during operation and before the audio data is input into the pre-trained sound detection model, and if so, extract the abnormal band to input the abnormal band into the pre-trained sound detection model, otherwise, determine that the device is in a good state; Wherein, determining whether there is an abnormal band in the audio data includes: Comparing the waveforms of the audio data of a plurality of such devices in normal states, determining an abnormal sound detection threshold for distinguishing between devices in normal states and devices in abnormal states; comparing the waveform of the audio data with the abnormal sound detection threshold, determining whether the abnormal band exists in the audio data; The audio data acquisition unit is specifically used to place the device in a predetermined enclosed space after the device is assembled, and to collect audio data of the device through a sound sensor configured in the enclosed space.

7. An electronic device, characterized in that: include: processor; as well as A memory for storing executable instructions, wherein when the executable instructions are executed by the processor, the electronic device executes the method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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