Processing method and system of audio system

Abstract

The embodiment of the invention provides an audio system processing method and system. The audio system processing method comprises the steps of obtaining operation data of target audio equipment; based on the operation data, judging whether abnormal equipment exists in the target audio equipment or not; in response to existence of the abnormal equipment, obtaining operation configuration of normal equipment in the target audio equipment; and correcting the operation configuration of the abnormal equipment based on the operation configuration of the normal equipment.

Description

Technical Field

[0001] This specification relates to the technical field of audio processing, and particularly relates to a method and a system for processing an audio system. Background Art

[0002] With the wide application of audio devices, it has become increasingly important to improve the operating stability and performance optimization of audio devices. However, during the operation of audio devices, abnormal situations such as impaired sound quality, playback stuttering, and noise interference may occur. Traditional methods for monitoring and maintaining audio devices often rely on manual inspections, usually setting abnormal reference values or ranges for the operating data of audio devices. When the operating data of an audio device approaches this reference value or range, it can be determined that the device may have an abnormality or a fault. This traditional fault determination method may not be able to identify potential problems of the device.

[0003] Therefore, it is desired to provide a method and a system for processing an audio system that can automatically detect and correct abnormal audio devices, effectively improving the operating efficiency and reliability of audio devices. Summary of the Invention

[0004] One or more embodiments of this specification provide a method for processing an audio system. The method includes: obtaining the operating data of a target audio device; based on the operating data, determining whether there is an abnormal device in the target audio device; in response to the existence of the abnormal device, obtaining the operating configuration of the normal devices in the target audio device; and based on the operating configuration of the normal devices, correcting the operating configuration of the abnormal device.

[0005] One or more embodiments of this specification provide a processing system for an audio system. The system includes: a first obtaining module, a determining module, a second obtaining module, and a correcting module; the first obtaining module is used to obtain the operating data of a target audio device; the determining module is used to determine whether there is an abnormal device in the target audio device based on the operating data; the second obtaining module is used to obtain the operating configuration of the normal devices in the target audio device in response to the existence of the abnormal device. Brief Description of the Drawings

[0006] This specification will be further described by way of exemplary embodiments, which will be described in detail through the drawings. These embodiments are not restrictive. In these embodiments, the same numbers represent the same structures, where:

[0007] Figure 1 is a schematic diagram of the application scenario of the processing system for an audio system shown in some embodiments of this specification;

[0008] Figure 2is an exemplary block diagram of a processing system of an audio system according to some embodiments of the present specification;

[0009] Figure 3 is an exemplary flowchart of a processing method of an audio system according to some embodiments of the present specification;

[0010] Figure 4 is an exemplary flowchart of determining whether there is an abnormal device according to some embodiments of the present specification;

[0011] Figure 5 is an exemplary flowchart of determining a reference operation range according to some embodiments of the present specification;

[0012] Figure 6 is a schematic diagram of clustering of operation data according to some embodiments of the present specification;

[0013] Figure 7 is a schematic diagram of correcting the operation configuration of an abnormal device according to some embodiments of the present specification. Detailed Embodiments

[0014] Figure 1 is a schematic diagram of an application scenario of a processing system of an audio system according to some embodiments of the present specification.

[0015] In some embodiments, the application scenario of the processing system of the audio system may include a target audio device 110, a processor 120, a network device 130, and a storage device 140.

[0016] The target audio device 110 refers to a hardware device for playing audio. For example, the target audio device 110 may include speakers, headphones, recorders, etc.

[0017] In some embodiments, the target audio device 110 may be composed of multiple audio devices. For example, the audio devices 110-1, 110-2... 110-n. In some embodiments, multiple audio devices may play the same audio at the same time.

[0018] The processor 120 may be used to process information and / or data related to the application scenario 100 of the processing system of the audio system. For example, the running time, running state, etc. of the audio device. In some embodiments, the processor 120 may process data, information, and / or processing results obtained from other devices or system components, and execute program instructions based on these data, information, and / or processing results to perform one or more functions described in the present specification.

[0019] Network 130 may include any network capable of facilitating information and / or data exchange. In some embodiments, one or more components of the application scenario 100 of the processing system of the audio system (e.g., the target audio device 110, the processor 120, the storage device 140, etc.) may exchange information and / or data via the network 130.

[0020] The storage device 140 may store data, instructions, and / or any other information. The storage device 140 may include one or more storage components, and each storage component may be an independent device or a part of other devices. In some embodiments, the storage device 140 may include a random access memory (RAM), a read-only memory (ROM), a removable memory, etc., or any combination thereof. In some embodiments, the storage device 140 may be connected to the network 130 to enable communication with one or more components in the application scenario 100 of the processing system of the audio system.

[0021] In some embodiments, the processor 120 and / or the storage device 140 may be integrated on the target audio device 110.

[0022] Figure 2 is an exemplary module diagram of the processing system of the audio system shown in some embodiments of this specification.

[0023] In some embodiments, as Figure 2 shown, the processing system 200 of the audio system may include a first acquisition module 210, a judgment module 220, a second acquisition module 230, and a correction module 240.

[0024] In some embodiments, the first acquisition module 210 may be configured to acquire the operation data of the target audio device.

[0025] In some embodiments, the judgment module 220 may be configured to judge whether there is an abnormal device in the target audio device based on the operation data.

[0026] In some embodiments, the judgment module 220 may further be configured to determine the statistical data of the sub-operation data based on the data type corresponding to the sub-operation data in the operation data; determine the reference operation interval of the sub-operation data based on the statistical data; and judge whether there is an abnormal device in the target audio device based on the reference operation interval. For more information on judging whether there is an abnormal device in the target audio device, refer to Figure 4 and its content.

[0027] In some embodiments, the determination module 220 may be further configured to obtain the sound source information of the target audio device; determine the scale of the reference operation interval based on the sound source information; and determine the reference operation interval of the sub-operation data based on the scale of the reference operation interval and the statistical data. For more information about the reference operation interval of the sub-operation data, see Figure 5 and its content.

[0028] In some embodiments, the determination module 220 may be further configured to cluster the operation data to obtain at least two clusters; in response to the boundary distances of the at least two clusters satisfying the abnormal condition, use the audio device corresponding to the operation data in the cluster that meets the preset condition as the abnormal device; where the preset condition is related to the distance between the operation data of the target audio device and the central operation data. For more information about the abnormal device, see Figure 6 and its content.

[0029] In some embodiments, the second acquisition module 230 may be configured to obtain the operation configuration of the normal device in the target audio device in response to the existence of an abnormal device.

[0030] In some embodiments, the calibration module 240 may be configured to calibrate the operation configuration of the abnormal device based on the operation configuration of the normal device.

[0031] In some embodiments, the processing system 200 of the audio system may further include an inspection module 250.

[0032] In some embodiments, the inspection module 250 may be configured to obtain the target operation data; the target operation data is the operation data of the target audio device after the abnormal device is calibrated; determine whether the abnormal device is operating normally based on the target operation data; and in response to the abnormal device not operating normally, generate a maintenance instruction and send it to the maintenance personnel. For more descriptions about determining whether the abnormal device is operating normally, see Figure 3 Steps 350 to 370 and their content.

[0033] In some embodiments, one or more of the first acquisition module 210, the determination module 220, the second acquisition module 230, the calibration module 240, and the inspection module 250 may be integrated on the processor 120.

[0034] It should be noted that the above description of the processing system of the audio system and its modules is only for convenience of description and does not limit this specification to the scope of the examples given. It can be understood that for those skilled in the art, after understanding the principle of the system, they may, without departing from this principle, make any combination of the modules, or form a subsystem and connect it with other modules. In some embodiments, Figure 2The first acquisition module, the judgment module, the second acquisition module, and the calibration module disclosed in [the relevant part] can be different modules in a system, or a single module can implement the functions of two or more of the above-mentioned modules. For example, each module can share a storage module, or each module can have its own storage module separately. Such variations are all within the scope of protection of this specification.

[0035] In some embodiments, the processing method of the audio system includes: acquiring the operation data of the target audio device; based on the operation data, determining whether there is an abnormal device in the target audio device; in response to the existence of an abnormal device, acquiring the operation configuration of the normal devices in the target audio device; and based on the operation configuration of the normal devices, calibrating the operation configuration of the abnormal device.

[0036] Figure 3 is an exemplary flowchart of the processing method of the audio system according to some embodiments of this specification. As Figure 3 shown, process 300 includes the following steps. Process 300 can be executed by the processor 120 or the processing system 200 of the audio system.

[0037] Step 310, acquire the operation data of the target audio device.

[0038] The target audio device refers to one or more audio devices in a cluster system that are monitored, managed, and may need configuration calibration. A cluster system is a computing system that connects multiple independent processors through a network to work together, providing high availability, high performance, and high scalability. The cluster system includes the target audio device composed of multiple audio devices and a controller, etc. For the relevant description of the target audio device, see Figure 1 the corresponding content.

[0039] The operation data refers to the data related to the operation of the internal components of the audio device when playing the sound source. For example, the operation data can include, but is not limited to, the current, voltage, etc. flowing through the internal components of the audio device when playing the sound source.

[0040] In some embodiments, the processor can acquire the operation data of the target audio device based on a measuring instrument. Herein, the measuring instrument refers to an instrument used to collect and record the operation data of the audio device, such as an ammeter, a voltmeter, etc.

[0041] In some embodiments, the processor can acquire the operation data of the target audio device through other feasible means, such as acquiring user input, etc.

[0042] Step 320, based on the operation data, determine whether there is an abnormal device in the target audio device.

[0043] An abnormal device refers to an audio device that is not operating normally. For example, under the condition of playing the same sound source, the operation data of the abnormal device in the target audio device is significantly different from the operation data presented by the overall target audio device. For example, the difference between the current and / or voltage of the abnormal device and the average current and / or average voltage of multiple audio devices in the target audio device is greater than a preset difference value, etc.

[0044] The condition of playing the same sound source means that multiple audio devices of the target audio device play the same sound source synchronously at the same time and in the same space.

[0045] In some embodiments, the processor can determine the abnormal device through various methods. For example, the processor can, based on the operation data, determine whether there is an audio device whose operation data deviates from the corresponding operation data range in the first preset table by querying the first preset table. If it deviates, it indicates that there is an abnormal device. The first preset table is constructed based on the operation data of each audio device during its historical normal operation.

[0046] In some embodiments, the processor can also determine the abnormal device based on any other feasible method. In some embodiments, the processor can determine the statistical data of the sub-operation data based on the data type of the sub-operation data in the operation data; determine the reference operation range of the sub-operation data based on the statistical data; and determine whether there is an abnormal device in the target audio device based on the reference operation range. For more details, please refer to Figure 4 the corresponding content.

[0047] In some embodiments, the processor can cluster the operation data to obtain at least two clustering clusters; in response to the boundary distances of the at least two clustering clusters satisfying the abnormal condition, use the audio devices corresponding to the operation data in the clustering cluster that meets the preset condition as the abnormal device. For more details, see the relevant description in Figure 6 the relevant description.

[0048] Step 330, in response to the existence of an abnormal device, obtain the operation configuration of the normal devices in the target audio device.

[0049] A normal device refers to an audio device that can operate normally. For example, a normal device is an audio device in the target audio device other than the abnormal device. Under the condition of playing the same sound source, the difference between the operation data of the normal device and the operation data presented by the overall target audio device does not exceed the preset difference value.

[0050] The operation configuration of an audio device includes the hardware configuration, software configuration, system settings, etc. of the audio device. Considering cost control and ease of operation, in the embodiments of this specification, only the software configuration, system settings, etc. of the audio device are used as the adjustable operation configuration. Or, for example, the hardware configurations of the individual audio devices in the target audio device belong to the same configuration level, such as the hardware configurations are the same or the difference is within the preset configuration difference range.

[0051] For example, the running configuration may include but is not limited to equalizer (EQ) configuration, compressor (Compressor) configuration, reverb (Reverb) configuration, as well as volume control, sound effect settings and playback mode.

[0052] In some embodiments, the processor can obtain the running configuration of the normal device in the target audio device based on multiple methods. For example, the processor can obtain the running configuration of the normal device by calling the tools provided by the audio device, such as the device manager, system information, and command line interface. For another example, the processor can obtain the running configuration of the normal device based on professional audio software.

[0053] Step 340: Correct the operating configuration of the abnormal device based on the operating configuration of the normal device.

[0054] Correcting the operation configuration of the abnormal device refers to attempting to restore the abnormal device to normal operation by updating the operation configuration of the abnormal device. In some embodiments, the processor may replace the operation configuration of the abnormal device with the operation configuration of the normal device to correct the operation configuration of the abnormal device.

[0055] In some embodiments, the processor may also correct the operating configuration of the abnormal device in any other feasible manner.

[0056] In some embodiments, the processor may determine the standard audio device based on the operating data of the normal audio device; and correct the operating configuration of the abnormal device based on the operating configuration of the standard audio device. In some embodiments, the processor may obtain the usage record of the abnormal device; determine the component status of the abnormal device based on the usage record of the abnormal device; determine the adjustment parameters of the operating configuration of the abnormal device based on the component status; and correct the operating configuration of the abnormal device based on the adjustment parameters of the operating configuration. For more information on correcting the operating configuration of abnormal devices, please refer to Figure 7 And related instructions.

[0057] In some embodiments, the processing method of the audio system also includes: obtaining target operating data; the target operating data is the operating data of the target audio device after the abnormal device is calibrated; based on the target operating data, determining whether the abnormal device is operating normally; in response to the abnormal device not operating normally, generating a maintenance instruction and sending it to maintenance personnel.

[0058] In some embodiments, process 300 further includes steps 350 to 370 .

[0059] Step 350, obtaining target operation data.

[0060] The target operating data is the operating data of the target audio device after the abnormal device has been corrected. For example, the target operating data may include, but is not limited to, the operating data of the abnormal device after correction.

[0061] For more descriptions on obtaining the operating data, etc., refer to the corresponding content of step 310.

[0062] Step 360: Based on the target operating data, determine whether the abnormal device is operating normally.

[0063] The method of determining whether the abnormal device is operating normally based on the target operating data is similar to the method of determining whether there is an abnormal device in the target audio device based on the operating data. The difference is that this judgment is only for the operating data of the device previously identified as abnormal. For specific descriptions, refer to the corresponding content of step 320.

[0064] Step 370: In response to the abnormal device not operating normally, generate a maintenance instruction and send it to the maintenance personnel.

[0065] The abnormal device not operating normally means that the abnormal device has not returned to the normal operating state after correction. For example, based on the target operating data, it is still identified as an abnormal device. In some embodiments, when the processor responds to the abnormal device not operating normally, it can generate a maintenance instruction accordingly. The maintenance instruction can be an instruction indicating the maintenance personnel to repair the target audio device or the abnormal device. When the correction based on the operating configuration still cannot solve the abnormal problem of the abnormal device, the abnormal device may be abnormal due to a hardware failure. Therefore, it is necessary for the maintenance personnel to perform corresponding processing to make the abnormal device return to normal operation.

[0066] In some embodiments, the processor sends the maintenance instruction to the maintenance personnel through channels such as email, workflow system, mobile device application, or instant messaging tool, so that the maintenance personnel can repair the abnormal device in a timely manner.

[0067] In some embodiments of this specification, by continuously monitoring the operating data of the target audio device, the processor can timely detect and determine the abnormal devices existing in the audio system and perform automatic recovery operations such as correcting the operating configuration in a timely manner, which can effectively reduce the manual maintenance workload and improve the stability and reliability of the entire audio system.

[0068] In some embodiments of this specification, first correct the abnormal device based on the automatic recovery method, and only notify the maintenance personnel for maintenance when the automatic recovery fails. This can help the maintenance personnel screen out unnecessary maintenance work. At the same time, it can also help the maintenance personnel eliminate problems such as incorrect configuration so that the maintenance personnel can find the fault point faster and improve the overall maintenance efficiency.

[0069] Figure 4It is an exemplary flowchart for determining whether there is an abnormal device according to some embodiments of this specification. Process 400 may include the following steps. Process 400 may be executed by the processor 120 or the processing system 200 of the audio system.

[0070] Step 410, based on the data type corresponding to the sub - running data in the running data, obtain the statistical data of the sub - running data.

[0071] Sub - running data refers to a smaller unit or component that constitutes the running data. The data type refers to a data attribute of the running data. For example, the type to which the data belongs, the acquisition time of the data, the device model to which the data belongs, etc.

[0072] In some embodiments, the processor can divide the running data into multiple types of sub - running data based on various methods. For example, the processor can divide the running data into multiple types of sub - running data based on the types to which various types of data in the running data belong. Another example is that the processor can divide the running data into multiple types of sub - running data based on the acquisition time of the running data.

[0073] Taking the division based on the type to which the data belongs as an example, one type of sub - running data can correspond to one data type. When the running data includes both current and voltage flowing through the internal components of the audio device when playing a sound source, the running data can be divided into two types of sub - running data, including sub - running data corresponding to the current and sub - running data corresponding to the voltage.

[0074] In some embodiments, when dividing the sub - running data based on the type to which the data belongs, the types of sub - running data can be updated correspondingly based on the types included in the running data. Only as an example, when the running data also includes the temperature, humidity, audio quality indicators (such as signal - to - noise ratio, distortion) of the audio device, etc., the sub - running data can also include sub - running data corresponding to temperature, humidity, audio quality indicators, etc., respectively.

[0075] Statistical data is the result obtained after mathematical processing and analysis of the numerical values corresponding to a set of data, and can reflect information such as the central tendency and dispersion degree of the set of numerical values. For example, the statistical data of the sub - running data may include, but is not limited to, the extreme values, mean values, variances, etc. of the sub - running data. In some embodiments, the statistical data of the sub - running data may refer to the statistical data of the corresponding sub - running data of multiple components in the audio device at the same moment.

[0076] In some embodiments, after the processor divides the running data into multiple types of sub - running data based on the data type of the running data, the statistical data of each type of sub - running data can be obtained respectively.

[0077] For example, the processor may perform statistical analysis on the acquired sub - operation data, and obtain the respective extreme values, means, variances, etc. corresponding to the sub - operation data of each data type through statistical calculation methods.

[0078] Step 420: Based on the statistical data, determine the reference operation interval of the sub - operation data.

[0079] The reference operation interval can be used to evaluate whether the operation state of the audio device is normal. For example, the reference operation interval can be the value range of each sub - operation data in the operation data when the audio device is operating normally or abnormally.

[0080] In some embodiments, the processor may determine the reference operation interval of the sub - operation data through various methods based on the statistical data of the sub - operation data.

[0081] Taking the reference operation interval as the value range of each sub - operation data in the operation data when the audio device is operating normally as an example. The processor may, based on the statistical data of the sub - operation data of each normal device obtained, statistically calculate the extreme values of the statistical data of its sub - operation data (such as the maximum and minimum values in the already - calculated current means, etc.), and use the maximum value as the upper limit of the reference operation interval of the corresponding sub - operation data, and the minimum value as the lower limit of the reference operation interval of the corresponding sub - operation data, thereby obtaining the reference operation interval of each sub - operation data.

[0082] In some embodiments, the processor may also determine the reference operation interval based on the mean and variance in the statistical data of the sub - operation data. By way of example only, the lower limit of the reference operation interval of the corresponding sub - operation data is set as the difference between the mean and the variance of this sub - operation data of all normal devices, and the upper limit is set as the sum of the mean and the variance.

[0083] In some embodiments, the processor may also acquire the sound source information of the target audio device; based on the sound source information, determine the scale of the reference operation interval; based on the scale of the reference operation interval and the statistical data, determine the reference operation interval of the sub - operation data. For specific descriptions, see Figure 5 the corresponding content.

[0084] Step 430: Based on the reference operation interval, determine whether there are abnormal devices in the target audio device.

[0085] In some embodiments, the processor may determine whether there are abnormal devices in the target audio device through various methods based on the reference operation interval.

[0086] In some embodiments, the processor determines whether an audio device is an abnormal device based on each sub - operating data of the audio device. For example, taking the reference operating range as the range of values of each sub - operating data in the operating data when the audio device is operating normally, if there is at least one sub - operating data of a certain audio device that is not within the reference operating range corresponding to the sub - operating data, then the audio device is an abnormal device.

[0087] In some embodiments of this specification, the processor determines the reference operating range based on the statistical data of the sub - operating data, which reflects the dynamic adaptability of the method. The statistical data of the sub - operating data can more comprehensively reflect the characteristics of the audio device when it is operating normally, enabling the determination of abnormal devices to reduce manual intervention, improving the objectivity and accuracy of judgment, and at the same time enhancing the fault monitoring efficiency.

[0088] It should be noted that the above description of the process for determining whether there is an abnormal device is only for illustration and example, and does not limit the scope of application of this specification. For those skilled in the art, under the guidance of this specification, various modifications and changes can be made to the process for determining whether there is an abnormal device. However, these modifications and changes are still within the scope of this specification.

[0089] Figure 5 is an exemplary flowchart for determining the reference operating range shown in some embodiments of this specification. As Figure 5 shown, process 500 includes the following steps. Process 500 can be executed by the processor 120 or the processing system 200 of the audio system.

[0090] Step 510, obtain the sound source information of the target audio device.

[0091] The sound source information refers to the spectral information of the sound source played by the target audio device. For example, the sound source information can include, but is not limited to, the frequency, amplitude, etc. of the sound source. In some embodiments, the sound source information can be characterized based on statistical data such as the mean, variance, and extreme values of the frequency and / or amplitude of the sound source.

[0092] In some embodiments, the processor can obtain the sound source information of the target audio device in various ways. For example, the processor can use digital signal processing techniques and related software or programming languages (such as Python) to obtain the sound source information of the target audio device. Another example is that the processor can obtain the sound source information of the target audio device by calling the system - stored data. Among them, if multiple audio devices in the target audio device are playing different sound sources, it is necessary to obtain the sound source information of all the sound sources being played correspondingly, and the target audio device can be divided into multiple groups based on the difference in the sound sources, with each group corresponding to the same sound source.

[0093] In this embodiment, it is assumed that the sound sources played by the target audio devices are the same. When the played sound sources are different, the difference is that the processing unit can be a group. For example, the audio devices in a group correspond to the target audio devices with the same played sound source.

[0094] Step 520: Based on the sound source information, determine the scale of the reference operation interval.

[0095] The scale of the reference operation interval can correspond to the data range covered by the upper and lower limits of the reference operation interval. For example, the larger the scale of the reference operation interval, the larger the data range of the reference operation interval. The processor can first determine the scale of the reference operation interval to further determine the reference operation interval of the corresponding scale.

[0096] In some embodiments, the processor can determine the scale of the reference operation interval based on the sound source information in various ways. For example, the processor can determine the scale of the reference operation interval based on the relationship between the mean and variance of the frequency and / or amplitude of the sound source in the sound source information. Only as an example, the larger the variance or mean of the sub-operation data, the larger the scale of the reference operation interval of the sub-operation data.

[0097] In some embodiments, the processor can determine the scale of the reference operation interval by querying a second preset table based on the sound source information. The second preset table can record the scales of the operation intervals corresponding to different audio devices when they operate normally under various sound source information. The second preset table can be constructed based on historical normal audio devices, their historical sound source information and historical operation interval scales during normal audio playback.

[0098] In some embodiments, the scale of the reference operation interval is negatively correlated with the similarity between the component states of the audio device.

[0099] The components of the audio device refer to the component parts such as speakers, power amplifiers, and sound cards in the audio device.

[0100] The component state refers to the usage situation of the components. For example, the component state can include but is not limited to the usage years, repair times, etc. of the components. In some embodiments, the component state can also include changes in resistance, changes in diaphragm performance, etc. caused by component aging.

[0101] In some embodiments, the processor can obtain the component state of the audio device based on the retrieved usage records, repair records, etc. of the audio device. For more content on how to determine the component state, reference can be made to Figure 7 and related descriptions.

[0102] The similarity between component states refers to the degree of similarity between component states. Taking only the component states including the service life and the number of repairs of components as an example, the processor can determine the similarity between component states based on the difference values of parameters such as the service life and the number of repairs between different components. For example, the greater the difference value, the lower the similarity between component states.

[0103] In some embodiments, the lower the similarity between component states, the larger the scale of the reference operating range. The lower the similarity between component states indicates that the difference in the operating conditions of the audio device itself is greater. At this time, the reference operating range needs to be enlarged to avoid being determined as an abnormal device due to differences in the component states of the audio device itself.

[0104] In some embodiments of this specification, the scale of the reference operating range is associated with the similarity between the component states of the audio device, so that the processing system of the audio system can automatically adapt to changes in the component states of different audio devices. In the case of differences in the component states of the audio device, the reference operating range can be dynamically adjusted by real-time analyzing the similarity of component states, so as to maintain an efficient fault detection and diagnosis ability.

[0105] Step 530, determine the reference operating range of the sub-operating data based on the scale of the reference operating range and the statistical data.

[0106] In some embodiments, the processor can determine the reference operating range of the sub-operating data in various ways based on the scale of the reference operating range and the statistical data of the sub-operating data.

[0107] In some embodiments, the upper and lower limits of the reference operating range of the sub-operating data can be two values symmetric about its mean. In some embodiments, the upper and lower limit values of the reference operating range of the sub-operating data can be represented based on the mean or variance in its statistical data. The scale of the reference operating range can be represented by a variance correlation value, and the variance correlation value can characterize the relationship between the difference between the upper and lower limits of the reference operating range of the sub-operating data and the mean or variance of the sub-operating data. The larger the scale of the reference operating range, the greater the difference between the upper and lower limits of the reference operating range of the sub-operating data, and the greater the variance correlation value.

[0108] In some embodiments, the variance correlation value can be represented based on numerical values, such as positive integers 1, 2,... n, etc. Among them, when the variance correlation value takes the value of n, it means that the difference between the upper and lower limits of the reference operating range of the sub-operating data is n times the variance of the sub-operating data.

[0109] In some embodiments, the processor may quantify the scale of the reference running interval through the variance correlation value. For example, the processor may query a quantization table based on the scale of the reference running interval to determine the corresponding variance correlation value. The quantization table is formulated based on the relationship between the scale of the reference running intervals of historically normally operating audio devices and specific reference running intervals. For example, the larger the scale of the reference running interval, the larger the value of the variance correlation value.

[0110] Merely by way of example, when the scale of the reference running interval is large, the value of the variance correlation value is 3; when the scale of the reference running interval is medium, the value of the variance correlation value is 2; when the scale of the reference running interval is small, the value of the variance correlation value is 1.

[0111] Since the upper and lower limits of the reference running interval of the sub-running data can be two values symmetric about its mean, and the value of the variance correlation value can represent the multiple relationship between the difference between the upper and lower limits of the reference running interval of the sub-running data and the variance of the sub-running data, therefore, after determining the variance correlation value and the statistical data of the sub-running data, the processor can determine the reference running interval of the sub-running data.

[0112] Merely by way of example, when the variance correlation value is 2, the reference running interval can be expressed as [mean - variance, mean + variance]. For example, if the variance correlation value determined based on the scale of the reference running interval is 3, the mean of the sub-running data determined based on the statistical data is M and the variance is N, then the reference running interval is [M - 1.5N, M + 1.5N].

[0113] In some embodiments of the present specification, according to different sound source information, the processor dynamically adjusts the scale of the reference running interval of the sub-running data, which helps to flexibly cope with different playback scenarios and enhances the versatility and practicality of the system.

[0114] In some embodiments, determining whether there is an abnormal device in the target audio device based on the running data includes: clustering the running data to obtain at least two clustering clusters; in response to the boundary distances of at least two clustering clusters satisfying the abnormal condition, taking the audio devices corresponding to the running data in the clustering clusters that meet the preset conditions as abnormal devices; wherein the preset conditions are related to the distance between the running data of the target audio device and the central running data.

[0115] In some embodiments, the processor may cluster multiple audio devices according to a clustering algorithm based on the distances between the running data of the audio devices. The clustering clusters obtained based on the clustering can characterize the aggregation of the running data of the audio devices. For example, the running data of the audio devices belonging to one clustering cluster are relatively more similar to each other.

[0116] In some embodiments, the processor can perform clustering based on various methods. For example, clustering based on the overall operating data of the audio device as the clustering elements, such as the clustering elements including the current and voltage of the audio device. Another example is clustering based on each sub-operating data in the operating data as the clustering elements, such as clustering based on the current of the audio device as the clustering element and clustering based on the voltage of the audio device as the clustering element, etc. Additionally, the processor can perform clustering through different clustering methods based on the determined clustering elements. The clustering methods can include the K-means clustering algorithm, density-based clustering method (DBSCAN), etc.

[0117] Taking the clustering elements including the current and voltage of the audio device and using the K-means clustering algorithm as an example, the clustering process is described as follows:

[0118] S31, randomly select k audio devices from the target audio devices, and use the operating data (i.e., current and voltage) of these k audio devices as the clustering elements included in the initial clustering center points. Here, k can be equal to 2 or greater than 2. In this embodiment, k is taken as 2 for example.

[0119] S32, for each of the remaining audio devices (hereinafter referred to as the pending devices), calculate the distance between the operating data of the pending device and the operating data corresponding to the k clustering center points, and assign the pending device to the clustering cluster where the clustering center point with the closest distance is located.

[0120] For example, the processor can form a feature vector from the current and voltage of the pending device, and calculate the vector distance between this feature vector and the feature vectors corresponding to each clustering center point, and use this vector distance as the distance between the operating data. Among them, the feature vector corresponding to the clustering center point is the vector constructed based on the clustering elements of the clustering center point, and the vector distance can be the cosine distance, Euclidean distance, etc.

[0121] S33, calculate the average value of the operating data of the clustering cluster where each clustering center point is located, and use the calculated average value as the new clustering center point. In response to the change of the clustering center point, the processor, based on the new clustering center point, takes the audio devices other than the audio device corresponding to the new clustering center point as the pending devices, and re-clusters them in the manner of the above step S32, and recalculate the average value of the operating data of the clustering cluster where each clustering center point is located.

[0122] S34, in response to reaching the preset stop condition, stop clustering and obtain the final clustering result. Among them, the preset stop condition includes that the number of clustering times reaches the pre-set maximum number or the clustering center point no longer changes, etc. Among them, when steps S32 and S33 are executed once, the number of clustering times is incremented by 1 time.

[0123] S35. Based on the final clustering result, multiple audio devices are divided into k different clustering clusters.

[0124] After obtaining the final clustering result, the processor can further calculate the boundary distance between the clustering clusters and further determine whether there are abnormal devices based on whether the boundary distance meets the abnormal condition.

[0125] The boundary distance between the clustering clusters refers to the shortest distance between the clustering clusters determined based on the boundary points on the edges of the clustering clusters.

[0126] As Figure 6 shown is the final clustering result obtained after clustering the target audio devices in some embodiments of this specification. As Figure 6 shown, the clustering result is that the target audio devices are divided into 2 clustering clusters, namely clustering cluster A and clustering cluster B. Among them, the audio device a1 in clustering cluster A is the boundary point closest to clustering cluster B, and the audio device b2 in clustering cluster B is the boundary point closest to clustering cluster A. Then, the boundary distance between clustering clusters A and B is the distance between the running data of audio device a1 and audio device b2.

[0127] In some embodiments, the processor can determine the boundary distance through various methods. For example, the processor can be based on two sets of corresponding boundary points on the edges of the two clustering clusters, such as set A of boundary points and set B of boundary points. Among them, set A of boundary points and set B of boundary points are all the boundary points of clustering cluster A and all the boundary points of clustering cluster B respectively. The processor can sequentially take a boundary point from set A of boundary points and calculate the distance between the running data corresponding to this boundary point and the running data corresponding to each boundary point in set B of boundary points, and select the shortest distance as the boundary distance between clustering cluster A and clustering cluster B.

[0128] In some embodiments, when clustering is performed separately based on each sub-running data to determine the clustering result, the processor needs to calculate the boundary distance of the clustering result corresponding to each sub-running data respectively.

[0129] In some embodiments, the processor can determine the boundary distance in any other way. For example, the processor can also obtain it by means such as software automatically reading measurements.

[0130] The abnormal condition refers to the condition that the boundary distance of the clustering cluster meets when it is determined that there are abnormal devices.

[0131] In some embodiments, the abnormal condition includes that the boundary distance between clusters exceeds a boundary distance threshold; the boundary distance threshold is determined based on the distance between the operation data of the target audio device. Since the number of abnormal devices in the target audio device is generally smaller than that of normal devices, in some embodiments of this specification, whether there are abnormal devices is screened based on the setting of the abnormal condition, and only when it is determined that there are abnormal devices, the abnormal devices are further determined. This can simplify the judgment process to a certain extent and reduce the amount of data processing.

[0132] For example, in response to the boundary distance between two clusters not satisfying the abnormal condition, it is considered that there are no abnormal devices, and there is no need to specifically judge the abnormal devices.

[0133] Among them, the determination of the boundary distance threshold includes: calculating the distance between the operation data of any two audio devices in the cluster closest to the central operation data; comparing the distance between the operation data of any two audio devices, and taking the maximum distance as the boundary distance threshold.

[0134] The central operation data refers to the central value of the operation data of the target audio device. For example, the central operation data can be statistical data such as the mean value of the operation data of the target audio device.

[0135] In some embodiments, when clustering is performed separately based on each sub-operation data to determine the clustering result, the processor needs to determine a central operation data for each sub-operation data respectively. For example, the central operation data can include central current operation data, central voltage operation data, etc.

[0136] In some embodiments, the processor can determine the cluster closest to the central operation data (hereinafter referred to as the target cluster) based on the vector distance between the operation data corresponding to the cluster centers of each cluster and the central value of the operation data of the target audio device.

[0137] For the target cluster, the processor can calculate the distance between the operation data of two audio devices when any two audio devices are selected until the distances between the operation data of all audio devices in the target cluster and the operation data of other audio devices are calculated. The processor can take the maximum distance as the boundary distance threshold.

[0138] In some embodiments of this specification, by setting the boundary distance threshold and monitoring whether the boundary distance between clusters exceeds this threshold, the processor can timely discover and handle potential abnormal conditions. This helps to prevent system instability or performance degradation caused by excessive differences in the operation data between audio devices, thereby improving the stability and reliability of the entire system.

[0139] The preset condition is the condition for determining whether an audio device is an abnormal device. The preset condition is related to the distance between the operation data of the audio device and the central operation data. For example, the preset condition may include the operation data of the audio device corresponding to the cluster center of the cluster (hereinafter referred to as the central distance), and the distance from the central operation data is the maximum value among the central distances corresponding to all clusters.

[0140] In some embodiments, in response to the boundary distances of the at least two clusters satisfying the abnormal condition, the processor may calculate the central distances corresponding to the cluster centers of the two clusters respectively, and use the audio device corresponding to the operation data in the cluster with the largest distance as the abnormal device.

[0141] In some embodiments, when clustering is performed separately based on each type of sub-operation data to determine the clustering result, when the processor determines whether there is an abnormal device, it needs to independently evaluate the clusters corresponding to each type of sub-operation data. The specific evaluation process is similar to the foregoing process and will not be elaborated here.

[0142] In some embodiments of this specification, by performing clustering analysis on the operation data, the processor can automatically group similar operation data into a set to form a cluster. It can quickly identify abnormal operation data, thereby improving the efficiency of fault detection.

[0143] In some embodiments, after detecting an abnormal device, the processor may determine a standard audio device based on the operation data of normal audio devices; and correct the operation configuration of the abnormal device based on the operation configuration of the standard audio device.

[0144] Figure 7 It is a schematic diagram of correcting the operation configuration of an abnormal device according to some embodiments of this specification.

[0145] As Figure 7 shown, the processor 120 may determine a standard audio device 720 based on the operation data 710 of normal audio devices; and correct the operation configuration 730 of the abnormal device based on the operation configuration of the standard audio device 720.

[0146] The standard audio device refers to an audio device with relatively stable operation data and a relatively low possibility of abnormal conditions among normal audio devices. For example, the standard audio device may include the audio device with the most recent factory time, the most stable operation data, the fewest audio playback lags, and the smoothest playback among multiple normal audio devices.

[0147] In some embodiments, the standard audio device may be determined based on multiple methods. For example, the processor 120 may determine a standard audio device by selecting a device with relatively good playback sound quality and / or a relatively new device factory date among normal audio devices.

[0148] In some embodiments, the standard audio device can also be determined based on other feasible methods. For example, the standard audio device can be determined by system preset.

[0149] In some embodiments, the standard audio device is determined based on the audio device with the longest uptime among normal devices.

[0150] In some embodiments, the standard audio device is determined by the criterion of the longest uptime, so that the operating configuration of the determined standard audio device is more stable.

[0151] In some embodiments, the processor 120 can correct the operating configuration of the abnormal device by overwriting the operating configuration of the standard audio device on the abnormal device. For example, replace the operating configuration of the abnormal device with the operating configuration of the standard audio device. The specific method is similar to the correction of the operating configuration of the abnormal device and will not be elaborated here.

[0152] For more information about correcting the operating configuration of the abnormal device, refer to Figure 3 and its related content.

[0153] In some embodiments, the processor 120 corrects the operating configuration of the abnormal device by means of configuration replacement, which can quickly restore the normal operation of the abnormal device and reduce the risks of fault troubleshooting and misconfiguration.

[0154] In some embodiments, after the processor 120 corrects the operating configuration of the abnormal device based on the operating configuration of the standard audio device, if there is still an abnormal audio device, it indicates that there may be a fault or aging of the audio device, etc., which cannot be directly solved by the entire set of configuration updates. Further correction is required.

[0155] As Figure 7 shown, after the processor 120 corrects the operating configuration of the abnormal device, it can determine whether the abnormal device is operating normally 740 (that is, determine whether the corrected abnormal device is still an abnormal device); in response to the abnormal device not operating normally, the processor 120 can obtain the usage record of the abnormal device 750; based on the usage record of the abnormal device, determine the component status 760; based on the component status, determine the adjustment parameter 770; based on the adjustment parameter of the operating configuration, correct the operating configuration of the abnormal device 780. Among them, the method of determining whether the corrected abnormal device is still an abnormal device is the same as the method of determining whether there is an abnormal device in the target audio device. For specific reference, see Figure 3 the corresponding content.

[0156] The usage record refers to the relevant information and / or data of the current and historical usage of the abnormal device. For example, the usage record may include the usage duration of using the abnormal device each time, the spectral characteristics of the audio played during use, the volume setting, etc.

[0157] In some embodiments, the spectral characteristics of the audio may include characteristics such as the frequency and amplitude of the audio. In some embodiments, the spectral characteristics may be represented by statistical data. For example, the spectral characteristics may be represented by statistical data such as the mean, variance, extreme values, etc. of the frequency and amplitude of the audio played each time.

[0158] In some embodiments, the processor 120 may obtain the usage record from the storage device 140 through the network 130 or obtain the usage record of the abnormal device by obtaining user input, etc.

[0159] In some embodiments, the component state may characterize the aging condition of the component. For example, the component state may include changes in resistance caused by component aging, changes in the performance of the tympanic membrane, etc. The change in resistance means that after the component ages, its resistance will increase. When the same voltage is applied, due to the increase in resistance, the current will decrease, so the heat generation will increase, thus affecting the operating data and playback effect of the audio device. The change in the performance of the tympanic membrane is the change in the maximum sound energy that the tympanic membrane can withstand. When the maximum sound energy is exceeded, the sound emitted by the audio device will be abnormal, such as cracking sound, etc. The sound energy is the energy carried by the sound during propagation, and is positively correlated with the frequency and amplitude of the sound. For more descriptions, reference can be made to Figure 5 the relevant description.

[0160] In some embodiments, the processor may determine the component state based on multiple methods.

[0161] In some embodiments, the processor 120 may construct a usage feature vector of the abnormal device based on the usage record of the abnormal device. For example, the processor 120 may construct a usage feature vector p based on the usage record (x, y, m), where the usage record (x, y, m) may represent that the usage duration of the abnormal device is x, the spectral characteristics are y, and the volume setting is m.

[0162] In some embodiments, the processor 120 may determine the component state of the abnormal device based on the similarity between the usage feature vector and multiple reference vectors in the feature vector database. For example, the processor 120 may use the reference vector whose similarity with the usage feature vector satisfies the preset similarity condition as the target vector, and use the component state corresponding to the target vector as the component state of the current abnormal device. In some embodiments, the preset similarity condition may be the maximum similarity or the similarity being greater than the similarity threshold.

[0163] In some embodiments, the processor 120 may construct a feature vector database based on the maintenance and / or servicing records of a large number of audio devices of the same model. For example, the processor 120 may construct a reference vector based on the usage records of the maintained and / or serviced audio devices, and use the measured component status of the audio device as the component status corresponding to the reference vector.

[0164] In some embodiments, the processor 120 may also determine the component status in other ways. For example, the processor 120 may determine the component status based on usage records through machine learning models such as neural network models.

[0165] Adjustment parameters refer to the adjustment information corresponding to the specific settings and / or metrics that indicate the adjustment of the operating configuration of the audio device. For example, the adjustment parameters may include the adjustment direction and adjustment amplitude of the configuration parameters of different software and hardware (such as equalizers, compressors, reverbs, etc.). By way of example only, the adjustment parameters may include the adjustment direction, adjustment amplitude, or adjusted mode of the setting parameters of equalizers, compressors, reverbs, as well as volume, sound effects, and playback modes.

[0166] In some embodiments, the processor 120 may determine the change in the eardrum performance and the changed resistance, etc. through the component status of the abnormal device, and then determine the maximum sound energy that the eardrum performance can withstand based on the change in the eardrum performance; based on the maximum sound energy and the audio characteristics of the current playback sound source (for example, statistical data such as the mean, variance, and extreme values of frequency and amplitude), determine the maximum playback power of the abnormal device for the current playback sound source; based on the maximum playback power and the changed resistance, determine the adjustment parameters through a preset rule.

[0167] In some embodiments, the processor 120 may also determine the change in the eardrum performance in other ways. For example, the processor 120 may determine the change in the eardrum performance through methods such as sound analysis, sensor monitoring, and temperature monitoring.

[0168] The maximum sound energy refers to the highest sound power level that the eardrum can handle without physical damage or performance distortion. In some embodiments, the processor 120 may determine the maximum sound energy through experimental data, physical measurements, modeling analysis, etc.

[0169] In some embodiments, after determining the maximum sound energy of the abnormal device for the current playback sound source, the processor 120 may determine the maximum amplitude of the abnormal device for the current playback sound source based on the following formula (1):

[0170] Among them, A is the amplitude, E is the sound energy, P is the medium density, w is the frequency of the sound (i.e., the frequency of the currently playing sound source, which can be represented based on frequency statistical data or based on the frequency corresponding to the current moment), and u is the wave speed. The sound energy, medium density, frequency of the sound, and wave speed can be obtained by physical measurement methods such as calling system data, sensors, etc.

[0171] In some embodiments, the processor 120 can determine the maximum playback power of the abnormal device for the currently playing sound source based on the previously determined amplitude and the design parameters of the audio device. The design parameters of the audio device can be obtained by means such as retrieving the factory record or consulting the manual.

[0172] In some embodiments, the preset rules may include the mapping relationship between the maximum playback power, the component state of the audio device, and the target operating configuration of the audio device.

[0173] In some embodiments, the processor 120 can determine the preset rules based on the fitting of historical data or the calculation of historical experimental data.

[0174] In some embodiments, the processor 120 can adjust the audio device to different component states (such as the performance of the eardrum, the states corresponding to different component resistances) or use an audio device with different component states as the test device, and configure the test device to different operating configurations, and play different sound sources at different maximum playback powers respectively in each operating configuration, and obtain the operating configuration with the best playback effect when the test device plays each sound source at different maximum playback powers in each component state, so as to obtain the mapping relationship in the preset rules. The playback effect can be obtained by professional audio test software, etc.

[0175] After the processor obtains the target operating configuration of the abnormal device based on the preset rules, it can determine the adjustment parameters based on the difference between the target operating configuration and the current operating configuration of the abnormal device.

[0176] In some embodiments, the processor 120 can adjust and update the operating configuration of the abnormal device based on the adjustment parameters to correct the operating configuration of the abnormal device. For example, the processor 120 can read the adjustment parameters and check the current configuration, confirm the parameters to be adjusted, and perform corresponding parameter adjustments according to the adjustment direction and adjustment amplitude in the adjustment parameters.

[0177] In some embodiments, the processor 120 corrects the operating configuration of the abnormal device through the usage record of the abnormal device, thereby improving the operating efficiency, reliability, and adaptability of the audio device, while realizing the early prediction of audio device abnormalities, and ultimately reducing the failure rate to exert the best performance of the audio device.

[0178] In some embodiments, after the processor 120 corrects the operating configuration of the abnormal device through the usage record of the abnormal device, when monitoring the abnormal device and other unadjusted audio devices, since there may be significant differences in the operating configurations of the abnormal device and other unadjusted audio devices, that is, it is not possible to directly adopt, based on the operating data of the abnormal device and other unadjusted audio devices, a simple unified judgment method such as Figure 3 , Figure 4 , Figure 6 to determine whether there are still abnormal devices. To facilitate the processor to still adopt the method in Figure 3 , Figure 4 to judge abnormal devices, the processor 120 can preprocess the operating data of the second audio device, and based on the preprocessed operating data of the second audio device, judge whether the second audio device is operating normally.

[0179] In some embodiments, the second audio device refers to the abnormal device corrected based on the usage record.

[0180] In some embodiments, the first audio device refers to the audio device that has not been corrected based on the usage record (i.e., other unadjusted audio devices).

[0181] Preprocessing refers to the processing of the operating data of the second audio device. Preprocessing can be understood as converting the operating data of the second audio device under the adjusted (i.e., corrected based on the usage record) operating configuration into the operating data equivalent to that under the operating configuration of the first audio device, converting the two into corresponding identical or similar operating configuration standards, so as to directly judge and monitor abnormal devices based on the operating data of the two.

[0182] In some embodiments, the processor 120 can determine the equivalent coefficient of the operating data of the second audio device based on the adjustment parameter; perform equivalent processing on the operating data of the second audio device based on the equivalent coefficient to obtain the preprocessed operating data of the second audio device.

[0183] In some embodiments, equivalent processing refers to processing the operating data of the second audio device into the operating data (i.e., the operating data after equivalent processing) under the same or similar operating configuration standard as the first audio device. In some embodiments, equivalent processing can be performed based on the equivalent coefficient. For example, the operating data after equivalent processing can be expressed as: the operating data of the second audio device after equivalent processing = the actual operating data of the second audio device × the target equivalent coefficient. The target equivalent coefficient refers to the equivalent coefficient used when processing the operating data of the second audio device into the operating data under the same or similar operating configuration standard as the first audio device.

[0184] In some embodiments, the processor may query a preset table to determine a target equivalent coefficient based on the operating configuration of the first audio device, the operating configuration of the second audio device, and the audio characteristics of the currently playing sound source.

[0185] Among them, the preset table may be constructed based on historical data or experimental data. For example, the processor may use audio devices without any faults and abnormalities to play sound sources with different audio characteristics according to different operating configurations, and collect the audio characteristics and actual operating data during the playback. Then, the processor may obtain the equivalent coefficients corresponding to the operating data corresponding to different operating configurations based on the ratio between the operating data corresponding to different operating configurations, so as to construct the preset table.

[0186] Merely by way of example, the preset table includes the operating configuration 1 of the audio device, the operating configuration 2, and the audio characteristics of the playing sound source X, and the equivalent coefficient for converting the operating data corresponding to the operating configuration 1 into the operating data corresponding to the operating configuration 2. Among them, the equivalent coefficient for converting the operating data corresponding to the operating configuration 1 into the operating data corresponding to the operating configuration 2 = the operating data corresponding to the operating configuration 2 ÷ the operating data corresponding to the operating configuration 1.

[0187] The operating data of the audio device when playing different audio characteristics is different. Therefore, the audio data obtained at each moment corresponds to the operating data of the audio played at that moment; then the operating data at each moment can obtain the corresponding equivalent coefficient according to the above preset table. Correspondingly, when the processor performs equivalent processing on the operating data of the second audio device, it can find the target equivalent coefficient corresponding to the corresponding situation at each moment based on the preset table for the operating data at each moment, and perform equivalent processing on the operating data at the corresponding moment.

[0188] In some embodiments, after the processor 120 preprocesses the operating data of the second audio device, it can determine whether the second audio device is operating normally based on the preprocessed operating data.

[0189] In some embodiments, the way for the processor 120 to determine whether the second audio device is operating normally based on the preprocessed operating data may be adopted as Figure 3 or Figure 4 or Figure 6 the way of judging abnormal devices in Figure 3 or Figure 4 or Figure 6 The corresponding content.

[0190] In some embodiments, based on the operation data of the second audio device after preprocessing, determining whether the second audio device is operating normally can unify the data standards, quickly identify device problems, and make adjustments or repairs in a timely manner, while further ensuring the normal operation of the audio device.

[0191] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification can be regarded as consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A processing method for an audio system, characterized in that, The method is executed by a processor, and the method includes: Obtain the operation data of the target audio device; Based on the operation data, determine whether there is an abnormal device in the target audio device; In response to the existence of the abnormal device, obtain the operation configuration of the normal device in the target audio device; Based on the operation configuration of the normal device, correct the operation configuration of the abnormal device.

2. The method according to claim 1, characterized in that, The operation data includes sub-operation data of one or more data types, and the determining whether there is an abnormal device in the target audio device based on the operation data includes: Based on the data type corresponding to the sub-operation data in the operation data, determine the statistical data of the sub-operation data; Based on the statistical data, determine the reference operation range of the sub-operation data; Based on the reference operation range, determine whether there is the abnormal device in the target audio device.

3. The method according to claim 2, wherein The determining the reference operation range of the sub-operation data based on the statistical data includes: Obtain the sound source information of the target audio device; Based on the sound source information, determine the scale of the reference operation range; Based on the scale of the reference operation range and the statistical data, determine the reference operation range of the sub-operation data.

4. The method according to claim 1, wherein The determining whether there is an abnormal device in the target audio device based on the operation data includes: Cluster the operation data to obtain at least two clustering clusters; In response to the boundary distance of the at least two clustering clusters satisfying the abnormal condition, use the audio device corresponding to the operation data in the clustering cluster that meets the preset condition as the abnormal device; wherein, the preset condition is related to the distance between the operation data of the target audio device and the central operation data.

5. The method according to claim 1, wherein The method further includes: Obtain target operation data; the target operation data is the operation data of the target audio device after the abnormal device is corrected; Based on the target operation data, determine whether the abnormal device is operating normally; In response to the abnormal device not operating normally, generate a maintenance instruction and send it to the maintenance personnel.

6. A processing system for an audio system, characterized in that, It includes a first acquisition module, a judgment module, a second acquisition module, and a correction module; The first acquisition module is configured to obtain the operation data of the target audio device; The judgment module is configured to determine whether there is an abnormal device in the target audio device based on the operation data; The second acquisition module is configured to obtain the operation configuration of the normal device in the target audio device in response to the existence of the abnormal device; The correction module is configured to correct the operation configuration of the abnormal device based on the operation configuration of the normal device.

7. The system according to claim 6, wherein The operation data includes sub-operation data of one or more data types, and the judgment module is further configured to: Based on the data type corresponding to the sub-operation data in the operation data, determine the statistical data of the sub-operation data; Based on the statistical data, determine the reference operation range of the sub-operation data; Based on the reference operation range, determine whether there is the abnormal device in the target audio device.

8. The system according to claim 7, characterized in that, The judgment module is further configured to: Obtain the sound source information of the target audio device; Determine the scale of the reference operating interval based on the sound source information; Determine the reference operating interval of the sub-operating data based on the scale of the reference operating interval and the statistical data.

9. The system according to claim 6, characterized in that, The judgment module is further configured to: Cluster the operating data to obtain at least two clusters; In response to the boundary distances of the at least two clusters satisfying the abnormal condition, use the audio device corresponding to the operating data in the cluster that meets the preset condition as the abnormal device; wherein, the preset condition is related to the distance between the operating data of the target audio device and the central operating data.

10. The system according to claim 6, wherein The system further includes an inspection module, and the inspection module is configured to: Obtain target operating data; the target operating data is the operating data of the target audio device after the abnormal device is corrected. Based on the target operating data, determine whether the abnormal device is operating normally; In response to the abnormal device not operating normally, generate a maintenance instruction and send it to the maintenance personnel.

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