CN113670434B - Method and device for identifying sound abnormality of substation equipment and computer equipment - Google Patents

Method and device for identifying sound abnormality of substation equipment and computer equipment Download PDF

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CN113670434B
CN113670434B CN202110686609.9A CN202110686609A CN113670434B CN 113670434 B CN113670434 B CN 113670434B CN 202110686609 A CN202110686609 A CN 202110686609A CN 113670434 B CN113670434 B CN 113670434B
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sound data
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CN113670434A (en
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梁兆杰
田杰
方亮凯
李军暖
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Shenzhen Power Supply Bureau Co Ltd
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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01HMEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H17/00Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination

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Abstract

The application relates to a method and a device for identifying voice anomalies of substation equipment and computer equipment. The method comprises the following steps: acquiring running sound data of monitored equipment in a transformer substation in real time; extracting characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment; performing fault discrimination on the sound model to be discriminated and a normal sound model for representing normal operation of the monitored equipment through the fault discrimination model, and outputting the offset of the sound model to be discriminated; and determining the running state of the monitored equipment according to the offset. By adopting the method, the efficiency and the effectiveness of fault monitoring of the substation equipment can be improved.

Description

变电站设备声音异常识别方法、装置和计算机设备Method, device and computer equipment for identifying abnormal sound of substation equipment

技术领域technical field

本申请涉及变电站设备技术领域,特别是涉及一种变电站设备异常识别方法、装置和计算机设备。The present application relates to the technical field of substation equipment, in particular to a method, device and computer equipment for identifying abnormality of substation equipment.

背景技术Background technique

随着社会经济地快速发展,城市用电的需求也越来越大,电力设备的负荷也越来越大,电力设备一旦出现故障停机,就会给周边居民以及企业带来极大的不便甚至是损失。在实际应用过程中发现,变电站现有的设备故障监测,包括基本的工作参数监测以及基于振动的故障监测,都会出现漏检或者不能及时报警的情况。With the rapid development of social economy, the demand for electricity in cities is also increasing, and the load on power equipment is also increasing. Once power equipment fails and shuts down, it will bring great inconvenience to surrounding residents and enterprises. is a loss. In the actual application process, it is found that the existing equipment fault monitoring of substations, including basic working parameter monitoring and vibration-based fault monitoring, will have missed detection or cannot report to the police in time.

然而,在实际变电站设备检测过程中,采用人工听检方式来发现设备运转异常的细微表现,但是,人工检测存在人为的主观因素的影响,且存在设备故障监测的效率和有效性低的问题。However, in the actual detection process of substation equipment, the manual listening method is used to find subtle manifestations of abnormal equipment operation. However, manual detection is affected by human subjective factors, and there are problems of low efficiency and effectiveness of equipment fault monitoring.

发明内容Contents of the invention

基于此,有必要针对上述技术问题,提供一种能够提高变电站设备故障监测的效率和有效性的变电站设备声音异常识别方法、装置、计算机设备和存储介质。Based on this, it is necessary to address the above technical problems and provide a method, device, computer equipment and storage medium for identifying abnormal sound of substation equipment that can improve the efficiency and effectiveness of substation equipment fault monitoring.

一种变电站设备声音异常识别方法,所述方法包括:A method for identifying abnormal sound of substation equipment, the method comprising:

实时获取变电站中被监测设备的运行声音数据;Obtain real-time operating sound data of the monitored equipment in the substation;

对所述运行声音数据进行特征提取,得到用于表征所述被监测设备运行状态的待判别声音模型;performing feature extraction on the operating sound data to obtain a sound model to be discriminated for characterizing the operating state of the monitored equipment;

通过故障判别模型对所述待判别声音模型和用于表征所述被监测设备正常运行的正常声音模型进行故障判别,输出所述待判别声音模型的偏移量;Perform fault discrimination on the sound model to be judged and the normal sound model used to characterize the normal operation of the monitored equipment through the fault discrimination model, and output the offset of the sound model to be judged;

根据所述偏移量确定所述被监测设备的运行状态。The running state of the monitored equipment is determined according to the offset.

在其中一个实施例中,所述实时获取变电站中被监测设备的运行声音数据,包括:In one of the embodiments, the real-time acquisition of the operating sound data of the monitored equipment in the substation includes:

获取由采集设备实时采集的变电站中被监测设备的运行声音数据;所述采集设备包括在变电站环境中所使用的传声器和声音数据采集卡。The operation sound data of the monitored equipment in the substation collected by the collection device in real time is obtained; the collection device includes a microphone and a sound data collection card used in the substation environment.

在其中一个实施例中,所述对所述运行声音数据进行特征提取,得到用于表征所述被监测设备运行状态的待判别声音模型,包括:In one of the embodiments, the feature extraction of the operating sound data to obtain a sound model to be discriminated for characterizing the operating state of the monitored equipment includes:

对所述运行声音数据进行特征提取,得到所述运行声音数据的实时特征参数;performing feature extraction on the operating sound data to obtain real-time characteristic parameters of the operating sound data;

根据所述实时特征参数构建矩阵集合,得到用于表征所述被监测设备运行状态的待判别声音模型。A matrix set is constructed according to the real-time characteristic parameters to obtain a sound model to be discriminated for characterizing the operating state of the monitored equipment.

在其中一个实施例中,所述根据所述偏移量确定所述被监测设备的运行状态,包括:In one of the embodiments, the determining the operating state of the monitored device according to the offset includes:

当所述偏移量在预设值区间,确定所述被监测设备处于正常运行状态。When the offset is within the preset value range, it is determined that the monitored equipment is in a normal operation state.

在其中一个实施例中,所述方法还包括:In one embodiment, the method also includes:

获取所述被监测设备的运行参数;Obtaining the operating parameters of the monitored equipment;

当所述运行参数为预设运行参数时,将所述正常声音模型更新为所述待判别声音模型以及更新所述预设值区间,执行所述获取变电站中被监测设备的运行声音数据步骤。When the operation parameter is a preset operation parameter, the normal sound model is updated to the sound model to be judged and the preset value interval is updated, and the step of acquiring the operation sound data of the monitored equipment in the substation is performed.

在其中一个实施例中,所述根据所述偏移量确定所述被监测设备的运行状态,包括:In one of the embodiments, the determining the operating state of the monitored device according to the offset includes:

当所述偏移量不在预设值区间时,确定所述被监测设备处于异常运行状态,并生成报警指令;所述报警指令用于触发所述被监测设备上的报警设备进行响应,生成报警信息。When the offset is not in the preset value range, it is determined that the monitored device is in an abnormal operation state, and an alarm instruction is generated; the alarm instruction is used to trigger an alarm device on the monitored device to respond and generate an alarm information.

在其中一个实施例中,所述方法还包括:In one embodiment, the method also includes:

将所述运行状态发送至显示终端以不同的形式进行可视化显示。The running status is sent to the display terminal for visual display in different forms.

一种变电站设备声音异常识别装置,所述装置包括:A device for identifying abnormal sound of substation equipment, said device comprising:

获取模块,用于实时获取变电站中被监测设备的运行声音数据;The acquisition module is used to acquire the operation sound data of the monitored equipment in the substation in real time;

特征提取模块,用于对所述运行声音数据进行特征提取,得到用于表征所述被监测设备运行状态的待判别声音模型;A feature extraction module, configured to perform feature extraction on the operating sound data to obtain a sound model to be discriminated for representing the operating state of the monitored equipment;

故障判别模块,用于通过故障判别模型对所述待判别声音模型和用于表征所述被监测设备正常运行的正常声音模型进行故障判别,输出所述待判别声音模型的偏移量;A fault discrimination module, configured to perform fault discrimination on the sound model to be judged and the normal sound model used to characterize the normal operation of the monitored equipment through a fault discrimination model, and output the offset of the sound model to be judged;

确定模块,用于根据所述偏移量确定所述被监测设备的运行状态。A determining module, configured to determine the running state of the monitored equipment according to the offset.

一种计算机设备,包括存储器和处理器,所述存储器存储有计算机程序,所述处理器执行所述计算机程序时实现以下步骤:A computer device, comprising a memory and a processor, the memory stores a computer program, and the processor implements the following steps when executing the computer program:

实时获取变电站中被监测设备的运行声音数据;Obtain real-time operating sound data of the monitored equipment in the substation;

对所述运行声音数据进行特征提取,得到用于表征所述被监测设备运行状态的待判别声音模型;performing feature extraction on the operating sound data to obtain a sound model to be discriminated for characterizing the operating state of the monitored equipment;

通过故障判别模型对所述待判别声音模型和用于表征所述被监测设备正常运行的正常声音模型进行故障判别,输出所述待判别声音模型的偏移量;Perform fault discrimination on the sound model to be judged and the normal sound model used to characterize the normal operation of the monitored equipment through the fault discrimination model, and output the offset of the sound model to be judged;

根据所述偏移量确定所述被监测设备的运行状态。The running state of the monitored equipment is determined according to the offset.

一种计算机可读存储介质,其上存储有计算机程序,所述计算机程序被处理器执行时实现以下步骤:A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

实时获取变电站中被监测设备的运行声音数据;Obtain real-time operating sound data of the monitored equipment in the substation;

对所述运行声音数据进行特征提取,得到用于表征所述被监测设备运行状态的待判别声音模型;performing feature extraction on the operating sound data to obtain a sound model to be discriminated for characterizing the operating state of the monitored equipment;

通过故障判别模型对所述待判别声音模型和用于表征所述被监测设备正常运行的正常声音模型进行故障判别,输出所述待判别声音模型的偏移量;Perform fault discrimination on the sound model to be judged and the normal sound model used to characterize the normal operation of the monitored equipment through the fault discrimination model, and output the offset of the sound model to be judged;

根据所述偏移量确定所述被监测设备的运行状态。The running state of the monitored equipment is determined according to the offset.

上述变电站设备声音异常识别方法、装置、计算机设备和存储介质,通过将实时获取变电站中被监测设备的运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型;通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量;根据偏移量确定被监测设备的运行状态;即通过实时采集被监测设备的运行声音数据,确定对应的待判别声音模型,通过比较待判别声音模型与正常声音模型之间的偏移量,来被监测设备的运行状态,排除人为的主观因素的影响,提高了变电站设备故障监测的效率和有效性。The above method, device, computer equipment, and storage medium for identifying abnormal sound of substation equipment obtain a sound model to be discriminated for characterizing the operating state of the monitored equipment by obtaining real-time operating sound data of the monitored equipment in the substation for feature extraction; The discriminant model conducts fault discrimination between the sound model to be discriminated and the normal sound model used to represent the normal operation of the monitored equipment, and outputs the offset of the sound model to be discriminated; the operating status of the monitored equipment is determined according to the offset; that is, through real-time acquisition Monitor the operating sound data of the equipment, determine the corresponding sound model to be judged, and compare the offset between the sound model to be judged and the normal sound model to determine the operating status of the monitored equipment, eliminate the influence of human subjective factors, and improve the Efficiency and effectiveness of substation equipment fault monitoring.

附图说明Description of drawings

图1为一个实施例中变电站设备声音异常识别方法的应用环境图;Fig. 1 is the application environment diagram of substation equipment sound abnormal recognition method in an embodiment;

图2为一个实施例中变电站设备声音异常识别方法的流程示意图;Fig. 2 is a schematic flow chart of a method for identifying abnormal sound of substation equipment in one embodiment;

图3为一个实施例中变电站设备声音异常识别步骤的流程示意图;Fig. 3 is a schematic flow chart of the abnormal sound recognition step of substation equipment in one embodiment;

图4为另一个实施例中变电站设备声音异常识别方法的流程示意图;Fig. 4 is a schematic flow chart of a method for identifying abnormal sound of substation equipment in another embodiment;

图5为一个实施例中变电站设备声音异常识别的应用场景示意图;Fig. 5 is a schematic diagram of an application scenario of substation equipment sound abnormality recognition in an embodiment;

图6为一个实施例中变电站设备声音异常识别装置的结构框图;Fig. 6 is a structural block diagram of a substation equipment sound abnormality recognition device in an embodiment;

图7为一个实施例中计算机设备的内部结构图。Figure 7 is an internal block diagram of a computer device in one embodiment.

具体实施方式Detailed ways

为了使本申请的目的、技术方案及优点更加清楚明白,以下结合附图及实施例,对本申请进行进一步详细说明。应当理解,此处描述的具体实施例仅仅用以解释本申请,并不用于限定本申请。In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

本申请提供的变电站设备声音异常识别方法,可以应用于如图1所示的应用环境中。其中,终端102通过网络与服务器104进行通信。服务器104获取终端102实时采集的变电站中被监测设备的运行声音数据;对运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型;通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量;根据偏移量确定被监测设备的运行状态。其中,终端102可以但不限于是用于采集变电站设备运行数据的采集终端,采集终端可以但不仅限于是集成有变电站专用采集设备(例如,专用传声器)的个人计算机、笔记本电脑、智能手机、平板电脑和便携式可穿戴设备,服务器104可以用独立的服务器或者是多个服务器组成的服务器集群来实现。The method for identifying abnormal sound of substation equipment provided in this application can be applied to the application environment shown in FIG. 1 . Wherein, the terminal 102 communicates with the server 104 through the network. The server 104 acquires the operating sound data of the monitored equipment in the substation collected by the terminal 102 in real time; the feature extraction is performed on the operating sound data to obtain a sound model to be discriminated for representing the operating state of the monitored equipment; The normal sound model used to characterize the normal operation of the monitored equipment is used for fault discrimination, and the offset of the sound model to be identified is output; the operating status of the monitored equipment is determined according to the offset. Among them, the terminal 102 can be, but not limited to, a collection terminal for collecting operation data of substation equipment. For computers and portable wearable devices, the server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

在一个实施例中,如图2所示,提供了一种变电站设备声音异常识别方法,以该方法应用于图1中的服务器为例进行说明,包括以下步骤:In one embodiment, as shown in FIG. 2, a method for identifying abnormal sound of substation equipment is provided. The method is applied to the server in FIG. 1 as an example for illustration, including the following steps:

步骤202,实时获取变电站中被监测设备的运行声音数据。Step 202, acquiring the operating sound data of the monitored equipment in the substation in real time.

其中,运行声音数据是通过采集终端上的采集设备响应实时采集指令进行采集得到的,采集设备包括在变电站环境中所使用的传声器和声音数据采集卡,专用传声器用于有效屏蔽外界环境噪声的影响,即过滤外界环境的噪声,得到去噪后的被监测设备运行时的声音数据;声音数据采集卡是指对去噪后的被监测设备运行时的声音数据进行数据采集,并在声音的可听频率范围内保证更好的频率响应、动态范围和信噪比,声音数据采集卡适应不同的工业现场环境,支持有线网络、4g网络和wifi网络等不同网络制式的数据传输方式。Among them, the operating sound data is collected by the collection equipment on the collection terminal in response to real-time collection instructions. The collection equipment includes microphones and sound data collection cards used in the substation environment. The special microphone is used to effectively shield the impact of external environmental noise , that is to filter the noise of the external environment, and obtain the sound data of the monitored equipment after denoising; the sound data acquisition card is to collect the sound data of the denoised Better frequency response, dynamic range and signal-to-noise ratio are guaranteed within the listening frequency range. The sound data acquisition card adapts to different industrial site environments and supports data transmission methods of different network standards such as wired network, 4g network and wifi network.

具体地,通过采集终端上的采集设备响应实时采集指令进行采集,得到变电站中至少一种被监测设备的运行声音数据,将实时采集的运行声音数据上传至服务器的设备声音数据库进行存储,并将采集的运行声音数据与对应的被监测设备标识进行关联。Specifically, the acquisition equipment on the acquisition terminal responds to the real-time acquisition instruction to collect, obtain the operation sound data of at least one monitored equipment in the substation, upload the real-time collected operation sound data to the equipment sound database of the server for storage, and The collected running sound data is associated with the corresponding monitored equipment identification.

步骤204,对运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型。Step 204, feature extraction is performed on the operating sound data to obtain a sound model to be discriminated for representing the operating state of the monitored equipment.

其中,特征提取是指对采集的运行声音数据进行声纹特征提取,即梅尔倒谱系数特征提取(Mel-scaleFrequency Cepstral Coefficients,MFCC);MFCC特征提取的方式可以通过现有的任意一种方式实现,在此不做赘述。Among them, feature extraction refers to the voiceprint feature extraction of the collected running sound data, that is, the feature extraction of Mel-scale Frequency Cepstral Coefficients (MFCC); the method of MFCC feature extraction can be through any existing method implementation, and will not be repeated here.

具体地,对接收的运行声音数据进行MFCC特征提取,得到运行声音数据的实时特征参数;根据实时特征参数构建矩阵集合,得到用于表征被监测设备运行状态的待判别声音模型。其中,声音模型用于表征特定设备在某一时刻的运行状态,例如,正常运行或异常运行;不同设备的声音模型是不同的。Specifically, MFCC feature extraction is performed on the received operating sound data to obtain real-time characteristic parameters of the operating sound data; a matrix set is constructed according to the real-time characteristic parameters to obtain a sound model to be discriminated for representing the operating state of the monitored equipment. Wherein, the sound model is used to represent the operating state of a specific device at a certain moment, for example, normal operation or abnormal operation; the sound models of different devices are different.

步骤206,通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量。Step 206 , perform fault discrimination on the sound model to be judged and the normal sound model used to represent the normal operation of the monitored equipment through the fault discrimination model, and output the offset of the sound model to be judged.

其中,正常声音模型是预先根据设备正常运行时刻采集的运行声音数据,对正常运行时刻采集的运行声音数据进行预处理(例如,预加重、分帧、加窗等方式),对预处理后的运行声音数据进行MFCC特征提取,得到正常状态下的运行声音数据的特征参数;根据特征参数构建矩阵集合得到的。Among them, the normal sound model is to preprocess the running sound data collected at the normal running time according to the running sound data collected at the normal running time of the equipment in advance (for example, pre-emphasis, framing, windowing, etc.), and the preprocessed The operating sound data is subjected to MFCC feature extraction to obtain the characteristic parameters of the operating sound data under normal conditions; it is obtained by constructing a matrix set according to the characteristic parameters.

故障判别模型是预先训练好的,用于检测待判别声音模型与正常声音模型之间的偏移量,即将被监测设备不同时刻的待判别声音模型中的特征值跟设备正常运行时的正常声音模型的特征值进行比对,以此判断待判别声音模型是否跟已建立模型发生重大偏移,并根据偏离程度判断此时设备运行是否正常。The fault discrimination model is pre-trained and used to detect the offset between the sound model to be judged and the normal sound model, that is, the eigenvalues in the sound model to be judged at different times of the monitored equipment and the normal sound when the equipment is running normally Compare the eigenvalues of the models to judge whether the sound model to be judged has a significant deviation from the established model, and judge whether the equipment is operating normally according to the degree of deviation.

具体地,通过预先训练好的故障判别模型对不同时刻的待判别声音模型和被监测设备正常运行的正常声音模型进行故障判别,输出不同时刻的待判别声音模型的偏移量。Specifically, the pre-trained fault discrimination model is used to perform fault discrimination on the sound model to be judged at different times and the normal sound model of the normal operation of the monitored equipment, and the offset of the sound model to be judged at different times is output.

步骤208,根据偏移量确定被监测设备的运行状态。Step 208, determine the running state of the monitored equipment according to the offset.

其中,被监测设备的运行状态包括正常运行和异常运行。Wherein, the running state of the monitored equipment includes normal running and abnormal running.

具体地,将偏移量与预设值区间进行比较,当偏移量在预设值区间,确定被监测设备处于正常运行状态;当偏移量不在预设值区间时,确定被监测设备处于异常运行状态;其中,预设概率值是预先确定的。例如,偏移量是一个0-1的数值,预设值区间为0.8-1,当偏移量在这个预设值区间且数值越接近于1,说明待判别声音越接近已建立的模型(即,相似度越高);反之,越接近0,说明待判别声音模型越偏离已建立模型(即相似度很低)。Specifically, the offset is compared with the preset value interval, and when the offset is within the preset value interval, it is determined that the monitored equipment is in a normal operating state; when the offset is not within the preset value interval, it is determined that the monitored equipment is in An abnormal operating state; wherein, the preset probability value is predetermined. For example, the offset is a value of 0-1, and the preset value range is 0.8-1. When the offset is in the preset value range and the value is closer to 1, it means that the sound to be discriminated is closer to the established model ( That is, the higher the similarity); on the contrary, the closer it is to 0, the more the sound model to be discriminated deviates from the established model (ie the similarity is very low).

上述变电站设备声音异常识别方法中,通过将实时获取变电站中被监测设备的运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型;通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量;根据偏移量确定被监测设备的运行状态;即通过实时采集被监测设备的运行声音数据,确定对应的待判别声音模型,通过比较待判别声音模型与正常声音模型之间的偏移量,来被监测设备的运行状态,排除人为的主观因素的影响,提高了变电站设备故障监测的效率和有效性。In the above substation equipment sound anomaly identification method, by extracting the features of the operating sound data of the monitored equipment in the substation acquired in real time, a sound model to be discriminated for representing the operating state of the monitored equipment is obtained; through the fault discrimination model, the sound model to be discriminated and The normal sound model used to characterize the normal operation of the monitored equipment is used for fault discrimination, and the offset of the sound model to be identified is output; the operating status of the monitored equipment is determined according to the offset; that is, by collecting the operating sound data of the monitored equipment in real time, Determine the corresponding sound model to be judged, and compare the offset between the sound model to be judged and the normal sound model to check the operating status of the monitored equipment, eliminate the influence of human subjective factors, and improve the efficiency and efficiency of substation equipment fault monitoring. effectiveness.

在一个实施例中,如图3所示,提供了一种变电站设备声音异常识别步骤,以该方法应用于图1中的服务器为例进行说明,包括以下步骤:In one embodiment, as shown in FIG. 3 , a step for identifying abnormal sound of substation equipment is provided. The method is applied to the server in FIG. 1 as an example for illustration, including the following steps:

步骤302,获取变电站中被监测设备的运行声音数据。Step 302, acquiring the running sound data of the monitored equipment in the substation.

步骤304,对运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型。Step 304, feature extraction is performed on the running sound data to obtain a sound model to be discriminated for representing the running state of the monitored equipment.

步骤306,对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量。Step 306 , perform fault discrimination on the sound model to be judged and the normal sound model used to represent the normal operation of the monitored equipment, and output the offset of the sound model to be judged.

步骤308,判断偏移量是否在预设值区间,若是,执行步骤310;否则执行步骤316。Step 308, determine whether the offset is within the preset value range, if yes, execute step 310; otherwise, execute step 316.

步骤310,确定被监测设备处于正常运行状态。Step 310, determining that the monitored equipment is in a normal operating state.

步骤312,获取被监测设备的运行参数。Step 312, acquiring the operating parameters of the monitored equipment.

其中,运行参数包括变电站设备的运行时长。Wherein, the operating parameters include the operating hours of the substation equipment.

步骤314,当运行参数为预设运行参数时,将正常声音模型更新为待判别声音模型以及更新预设值区间。Step 314, when the operating parameter is a preset operating parameter, update the normal sound model to the sound model to be discriminated and update the preset value range.

其中,变电站设备随着运行时长的增加,设备会产生损耗,导致正常运行的声音数据也会发生改变,即对正常运行声音数据进行特征提取,得到的特征参数也会发生改变;也就是说,随着变电站设备的运行,在不同时段下正常声音模型的特征参数也是不同的。Among them, as the operation time of the substation equipment increases, the equipment will produce loss, resulting in the change of the sound data of normal operation, that is, the feature extraction of the sound data of normal operation, and the obtained characteristic parameters will also change; that is, With the operation of the substation equipment, the characteristic parameters of the normal sound model are also different in different time periods.

具体地,当变电站设备的运行时长达到预设时长时,获取当前时刻正常运行状态下的运行声音数据,对运行声音数据进行特征提取,得到运行声音数据的当前特征参数,根据当前特征参数构建矩阵集合,将得到的当前声音模型作为正常声音模型,并更新,执行获取变电站中被监测设备的运行声音数据,对变电站被监测设备的运行状态进行监测。Specifically, when the running time of the substation equipment reaches the preset time, the running sound data under the normal running state at the current moment is obtained, the feature extraction is performed on the running sound data, the current characteristic parameters of the running sound data are obtained, and the matrix is constructed according to the current characteristic parameters Assemble, use the obtained current sound model as a normal sound model, and update it, execute to obtain the operating sound data of the monitored equipment in the substation, and monitor the operating status of the monitored equipment in the substation.

步骤316,确定被监测设备处于异常运行状态。Step 316, determining that the monitored equipment is in an abnormal operation state.

可选地,在一个实施例中,将被监测设备的运行状态发送至显示终端进行可视化显示,可以获取变电站各设备的运行状态;当被监测设备处于异常运行状态,并生成报警指令;报警指令用于触发被监测设备上的报警设备进行响应,生成报警信息;并将报警信息发送至维修人员所在终端,及时通知维修人员,对设备故障进行及时检修。Optionally, in one embodiment, the operating status of the monitored equipment is sent to the display terminal for visual display, and the operating status of each equipment in the substation can be obtained; when the monitored equipment is in an abnormal operating state, an alarm command is generated; the alarm command It is used to trigger the alarm equipment on the monitored equipment to respond and generate alarm information; and send the alarm information to the terminal where the maintenance personnel are located, and notify the maintenance personnel in time to repair equipment failures in time.

上述变电站设备声音异常识别步骤中,通过将实时获取变电站中被监测设备的运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型;通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量;通过比较偏移量与预设偏移量区间的关系,确定被监测设备的运行状态,通过比较待判别声音模型与正常声音模型之间的偏移量,来被监测设备的运行状态,排除人为的主观因素的影响,提高了变电站设备故障监测的效率和有效性;除此之外,根据被监测设备的运行参数对被监测设备的正常声音模型进行更新,进一步,提高了故障监测的准确性。In the above substation equipment sound abnormality identification step, by extracting the features of the operating sound data of the monitored equipment in the substation obtained in real time, a sound model to be discriminated for representing the operating state of the monitored equipment is obtained; the sound model to be discriminated through the fault discrimination model and The normal sound model used to characterize the normal operation of the monitored equipment is used for fault discrimination, and the offset of the sound model to be identified is output; by comparing the relationship between the offset and the preset offset interval, the operating status of the monitored equipment is determined. Comparing the offset between the to-be-discriminated sound model and the normal sound model, the operating status of the monitored equipment is eliminated, and the influence of human subjective factors is eliminated, which improves the efficiency and effectiveness of substation equipment fault monitoring; in addition, according to The operating parameters of the monitored equipment update the normal sound model of the monitored equipment, further improving the accuracy of fault monitoring.

在另一个实施例中,如图4所示,提供了一种变电站设备声音异常识别方法,以该方法应用于图1中的服务器为例进行说明,包括以下步骤:In another embodiment, as shown in FIG. 4, a method for identifying abnormal sound of substation equipment is provided. The method is applied to the server in FIG. 1 as an example for illustration, including the following steps:

步骤402,实时获取变电站中被监测设备的运行声音数据。Step 402, acquiring the operating sound data of the monitored equipment in the substation in real time.

步骤404,对运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型。Step 404, performing feature extraction on the running sound data to obtain a sound model to be identified for representing the running state of the monitored equipment.

步骤406,通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量。Step 406 , perform fault discrimination on the sound model to be judged and the normal sound model used to represent the normal operation of the monitored equipment through the fault discrimination model, and output the offset of the sound model to be judged.

步骤408,根据偏移量确定被监测设备的运行状态。Step 408, determine the running state of the monitored equipment according to the offset.

可选地,在一个实施例中,将运行状态发送至显示终端以不同的形式进行可视化显示。Optionally, in one embodiment, the running status is sent to the display terminal for visual display in different forms.

具体地,将被监测设备的运行状态发送至显示终端,将运行状态以web网页的形式进行可视化显示或者以超文本语言将运行状态显示在界面上,可以直观获取被监测设备的实时运行状态。Specifically, the running status of the monitored equipment is sent to the display terminal, and the running status is visually displayed in the form of a web page or displayed on the interface in hypertext language, so that the real-time running status of the monitored equipment can be intuitively obtained.

步骤410,当被监测设备处于正常运行状态时,获取被监测设备的运行参数。Step 410, when the monitored equipment is in a normal operating state, obtain the operating parameters of the monitored equipment.

步骤412,当运行参数为预设运行参数时,将正常声音模型更新为待判别声音模型以及更新预设值区间。Step 412, when the operating parameter is a preset operating parameter, update the normal sound model to the sound model to be discriminated and update the preset value interval.

具体地,当运行参数为预设运行参数时,将正常声音模型更新为待判别声音模型以及更新预设值区间,执行获取变电站中被监测设备的运行声音数据步骤,获取被监测设备的实时运行状态。Specifically, when the operating parameters are preset operating parameters, the normal sound model is updated to the sound model to be discriminated and the preset value interval is updated, and the step of obtaining the operating sound data of the monitored equipment in the substation is performed to obtain the real-time operation of the monitored equipment state.

步骤414,将运行状态发送至显示终端以不同的形式进行可视化显示。Step 414, sending the running status to the display terminal for visual display in different forms.

可选地,在一个实施例中,当偏移量不在预设值区间时,确定被监测设备处于异常运行状态,并生成报警指令;报警指令用于触发被监测设备上的报警设备进行响应,生成报警信息;并将报警信息发送至维修人员所在终端,及时通知维修人员,对设备故障进行及时检修。Optionally, in one embodiment, when the offset is not in the preset value interval, it is determined that the monitored equipment is in an abnormal operation state, and an alarm instruction is generated; the alarm instruction is used to trigger an alarm device on the monitored equipment to respond, Generate alarm information; send the alarm information to the terminal where the maintenance personnel are located, notify the maintenance personnel in time, and carry out timely maintenance of equipment failures.

以下为变电站设备声音异常识别的应用场景,如图5所示,通过采集终端上传声器获取变电站中被监测设备的运行声音数据,通过与传声器相连的采集卡(例如,双通道采集卡)将采集的运行声音数据通过有线网络、4g网络和wifi网络等不同网络制式的数据传输方式传输至服务器(可以是云端,也可以是本地服务器)中,在服务器中对运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型,通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量,根据偏移量确定被监测设备的运行状态,将得到的运行状态进行发送至监控平台(例如,客户监控终端)进行可视化显示;即通过实时采集被监测设备的运行声音数据,确定对应的待判别声音模型,通过比较待判别声音模型与正常声音模型之间的偏移量,来确定被监测设备的运行状态,排除人为的主观因素的影响,提高了变电站设备故障监测的效率和有效性。The following is the application scenario for abnormal sound recognition of substation equipment. As shown in Figure 5, the operating sound data of the monitored equipment in the substation is obtained through the microphone on the acquisition terminal, and the acquisition card (for example, a dual-channel acquisition card) connected to the microphone is collected. The operating sound data is transmitted to the server (which can be the cloud or a local server) through data transmission methods of different network standards such as wired network, 4G network, and wifi network. The feature extraction of the operating sound data is carried out in the server, and the obtained The to-be-discriminated sound model is used to represent the operating state of the monitored equipment. The fault is judged by the to-be-discriminated sound model and the normal sound model used to represent the normal operation of the monitored equipment through the fault discrimination model, and the offset of the to-be-discriminated sound model is output. The shift determines the running status of the monitored equipment, and sends the obtained running status to the monitoring platform (for example, customer monitoring terminal) for visual display; that is, by collecting the running sound data of the monitored equipment in real time, the corresponding sound model to be judged is determined , by comparing the offset between the to-be-discriminated sound model and the normal sound model, the operating status of the monitored equipment is determined, and the influence of human subjective factors is eliminated, which improves the efficiency and effectiveness of substation equipment fault monitoring.

上述变电站设备声音异常识别步骤中,通过将实时获取变电站中被监测设备的运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型;通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量;通过比较偏移量与预设偏移量区间的关系,确定被监测设备的运行状态,当确定被监测设备处于正常运行状态下时,根据被监测设备的运行参数对被监测设备的正常声音模型进行更新,在提高了变电站设备故障监测的效率和有效性的基础上进一步提高故障监测的准确性。In the above substation equipment sound abnormality identification step, by extracting the features of the operating sound data of the monitored equipment in the substation obtained in real time, a sound model to be discriminated for representing the operating state of the monitored equipment is obtained; the sound model to be discriminated through the fault discrimination model and The normal sound model used to represent the normal operation of the monitored equipment is used for fault discrimination, and the offset of the sound model to be identified is output; by comparing the relationship between the offset and the preset offset interval, the operating status of the monitored equipment is determined. When it is determined that the monitored equipment is in normal operation, the normal sound model of the monitored equipment is updated according to the operating parameters of the monitored equipment, and the accuracy of fault monitoring is further improved on the basis of improving the efficiency and effectiveness of substation equipment fault monitoring sex.

应该理解的是,虽然图2-4的流程图中的各个步骤按照箭头的指示依次显示,但是这些步骤并不是必然按照箭头指示的顺序依次执行。除非本文中有明确的说明,这些步骤的执行并没有严格的顺序限制,这些步骤可以以其它的顺序执行。而且,图2-4中的至少一部分步骤可以包括多个步骤或者多个阶段,这些步骤或者阶段并不必然是在同一时刻执行完成,而是可以在不同的时刻执行,这些步骤或者阶段的执行顺序也不必然是依次进行,而是可以与其它步骤或者其它步骤中的步骤或者阶段的至少一部分轮流或者交替地执行。It should be understood that although the various steps in the flow charts in FIGS. 2-4 are displayed sequentially as indicated by the arrows, these steps are not necessarily executed sequentially in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least some of the steps in Figures 2-4 may include multiple steps or stages, these steps or stages are not necessarily executed at the same moment, but may be executed at different moments, the execution of these steps or stages The sequence is not necessarily performed sequentially, but may be performed alternately or alternately with other steps or at least a part of steps or stages in other steps.

在一个实施例中,如图6所示,提供了一种变电站设备声音异常识别装置,包括:获取模块602、特征提取模块604、故障判别模块606和确定模块608,其中:In one embodiment, as shown in FIG. 6 , a device for identifying abnormal sounds of substation equipment is provided, including: an acquisition module 602, a feature extraction module 604, a fault discrimination module 606, and a determination module 608, wherein:

获取模块602,用于实时获取变电站中被监测设备的运行声音数据。The obtaining module 602 is used to obtain the running sound data of the monitored equipment in the substation in real time.

特征提取模块604,用于对运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型。The feature extraction module 604 is configured to perform feature extraction on the running sound data to obtain a sound model to be discriminated for representing the running state of the monitored equipment.

故障判别模块606,用于通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量。The fault discrimination module 606 is configured to perform fault discrimination on the sound model to be judged and the normal sound model used to represent the normal operation of the monitored equipment through the fault judgment model, and output the offset of the sound model to be judged.

确定模块608,用于根据偏移量确定被监测设备的运行状态。A determining module 608, configured to determine the running state of the monitored equipment according to the offset.

上述变电站设备声音异常识别装置,通过将实时获取变电站中被监测设备的运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型;通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量;根据偏移量确定被监测设备的运行状态;即通过实时采集被监测设备的运行声音数据,确定对应的待判别声音模型,通过比较待判别声音模型与正常声音模型之间的偏移量,来被监测设备的运行状态,排除人为的主观因素的影响,提高了变电站设备故障监测的效率和有效性。The above-mentioned substation equipment sound abnormality recognition device extracts the characteristics of the operating sound data of the monitored equipment in the substation obtained in real time, and obtains the sound model to be discriminated for representing the operating state of the monitored equipment; The normal sound model that represents the normal operation of the monitored equipment is used for fault discrimination, and the offset of the sound model to be identified is output; the operating status of the monitored equipment is determined according to the offset; that is, the operating sound data of the monitored equipment is collected in real time to determine The corresponding sound model to be judged, by comparing the offset between the sound model to be judged and the normal sound model, the operating status of the monitored equipment is eliminated, and the influence of human subjective factors is eliminated, which improves the efficiency and effectiveness of substation equipment fault monitoring. sex.

在另一个实施例中,提供了一种变电站设备声音异常识别装置,除包括获取模块602、特征提取模块604、故障判别模块606和确定模块608之外,还包括:构建模块、更新模块和可视化模块,其中:In another embodiment, a device for identifying abnormal sound of substation equipment is provided. In addition to including an acquisition module 602, a feature extraction module 604, a fault judgment module 606, and a determination module 608, it also includes: a construction module, an update module, and a visualization module, where:

在一个实施例中,获取模块602还用于获取由采集设备实时采集的变电站中被监测设备的运行声音数据;采集设备包括在变电站环境中所使用的传声器和声音数据采集卡。In one embodiment, the acquisition module 602 is also used to acquire the operating sound data of the monitored equipment in the substation collected by the acquisition device in real time; the acquisition device includes a microphone and a sound data acquisition card used in the substation environment.

在一个实施例中,特征提取模块604还用于对运行声音数据进行特征提取,得到运行声音数据的实时特征参数。In one embodiment, the feature extraction module 604 is further configured to perform feature extraction on the running sound data to obtain real-time feature parameters of the running sound data.

构建模块,根据实时特征参数构建矩阵集合,得到用于表征被监测设备运行状态的待判别声音模型。The construction module constructs a matrix set according to real-time characteristic parameters, and obtains a sound model to be discriminated for representing the operating state of the monitored equipment.

在一个实施例中,确定模块608还用于当偏移量在预设值区间,确定被监测设备处于正常运行状态。In one embodiment, the determining module 608 is further configured to determine that the monitored device is in a normal operating state when the offset is within a preset value range.

在一个实施例中,获取模块602还用于获取被监测设备的运行参数。In one embodiment, the obtaining module 602 is also used to obtain operating parameters of the monitored equipment.

更新模块,用于当运行参数为预设运行参数时,将正常声音模型更新为待判别声音模型以及更新预设值区间。The update module is used to update the normal sound model to the sound model to be discriminated and update the preset value interval when the operating parameter is a preset operating parameter.

在一个实施例中,确定模块608还用于当偏移量不在预设值区间时,确定被监测设备处于异常运行状态,并生成报警指令;报警指令用于触发被监测设备上的报警设备进行响应,生成报警信息。In one embodiment, the determination module 608 is also used to determine that the monitored equipment is in an abnormal operation state when the offset is not in the preset value interval, and generate an alarm instruction; the alarm instruction is used to trigger the alarm device on the monitored equipment to perform In response, an alarm message is generated.

可视化模块,用于将运行状态发送至显示终端以不同的形式进行可视化显示。The visualization module is used to send the running status to the display terminal for visual display in different forms.

在一个实施例中,通过将实时获取变电站中被监测设备的运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型;通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量;通过比较偏移量与预设偏移量区间的关系,确定被监测设备的运行状态,当确定被监测设备处于正常运行状态下时,根据被监测设备的运行参数对被监测设备的正常声音模型进行更新,从而避免因为设备运行时间的变化而导致的误报警;也就是说在提高了变电站设备故障监测的效率和有效性的基础上进一步提高故障监测的准确性;当偏移量不在预设值区间时,确定被监测设备处于异常运行状态,并生成报警指令;报警指令用于触发被监测设备上的报警设备进行响应,生成报警信息;并将报警信息发送至维修人员所在终端,及时通知维修人员,对设备故障进行及时检修,提高了变电站的安全性。In one embodiment, by performing feature extraction on the operating sound data of the monitored equipment in the substation obtained in real time, a sound model to be discriminated for characterizing the operating state of the monitored equipment is obtained; through the fault discrimination model, the sound model to be discriminated is used for characterization The normal sound model of the normal operation of the monitored equipment performs fault discrimination, and outputs the offset of the sound model to be identified; by comparing the relationship between the offset and the preset offset interval, the operating status of the monitored equipment is determined. When the monitored When the equipment is in normal operation, the normal sound model of the monitored equipment is updated according to the operating parameters of the monitored equipment, so as to avoid false alarms caused by changes in equipment running time; On the basis of the efficiency and effectiveness of the system, the accuracy of fault monitoring is further improved; when the offset is not in the preset value range, it is determined that the monitored equipment is in an abnormal operating state, and an alarm command is generated; the alarm command is used to trigger the alarm on the monitored equipment. The alarm equipment responds to generate alarm information; the alarm information is sent to the terminal where the maintenance personnel are located, the maintenance personnel are notified in time, and the equipment failure is timely repaired, which improves the safety of the substation.

关于变电站设备声音异常识别装置的具体限定可以参见上文中对于变电站设备声音异常识别方法的限定,在此不再赘述。上述变电站设备声音异常识别装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中,也可以以软件形式存储于计算机设备中的存储器中,以便于处理器调用执行以上各个模块对应的操作。For specific limitations on the device for identifying abnormal sounds of substation equipment, please refer to the above-mentioned limitations on the method for identifying abnormal sounds of substation equipment, and details will not be repeated here. Each module in the above-mentioned device for identifying abnormal sound of substation equipment can be fully or partially realized by software, hardware and combinations thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, and can also be stored in the memory of the computer device in the form of software, so that the processor can invoke and execute the corresponding operations of the above-mentioned modules.

在一个实施例中,提供了一种计算机设备,该计算机设备可以是终端,其内部结构图可以如图7所示。该计算机设备包括通过系统总线连接的处理器、存储器、通信接口、显示屏和输入装置。其中,该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统和计算机程序。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该计算机设备的通信接口用于与外部的终端进行有线或无线方式的通信,无线方式可通过WIFI、运营商网络、NFC(近场通信)或其他技术实现。该计算机程序被处理器执行时以实现一种变电站设备声音异常识别方法。该计算机设备的显示屏可以是液晶显示屏或者电子墨水显示屏,该计算机设备的输入装置可以是显示屏上覆盖的触摸层,也可以是计算机设备外壳上设置的按键、轨迹球或触控板,还可以是外接的键盘、触控板或鼠标等。In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure may be as shown in FIG. 7 . The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Wherein, the processor of the computer device is used to provide calculation and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, an operator network, NFC (Near Field Communication) or other technologies. When the computer program is executed by a processor, a method for identifying abnormal sound of substation equipment is realized. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a button, a trackball or a touch pad provided on the casing of the computer device , and can also be an external keyboard, touchpad, or mouse.

本领域技术人员可以理解,图7中示出的结构,仅仅是与本申请方案相关的部分结构的框图,并不构成对本申请方案所应用于其上的计算机设备的限定,具体的计算机设备可以包括比图中所示更多或更少的部件,或者组合某些部件,或者具有不同的部件布置。Those skilled in the art can understand that the structure shown in FIG. 7 is only a block diagram of a part of the structure related to the solution of this application, and does not constitute a limitation on the computer equipment to which the solution of this application is applied. The specific computer equipment can be More or fewer components than shown in the figures may be included, or some components may be combined, or have a different arrangement of components.

在一个实施例中,提供了一种计算机设备,包括存储器和处理器,存储器中存储有计算机程序,该处理器执行计算机程序时实现以下步骤:In one embodiment, a computer device is provided, including a memory and a processor, a computer program is stored in the memory, and the processor implements the following steps when executing the computer program:

实时获取变电站中被监测设备的运行声音数据;Obtain real-time operating sound data of the monitored equipment in the substation;

对运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型;Feature extraction is performed on the operating sound data to obtain a sound model to be discriminated for representing the operating state of the monitored equipment;

通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量;Fault discrimination is performed on the sound model to be judged and the normal sound model used to represent the normal operation of the monitored equipment through the fault discrimination model, and the offset of the sound model to be judged is output;

根据偏移量确定被监测设备的运行状态。Determine the running status of the monitored equipment according to the offset.

在一个实施例中,处理器执行计算机程序时还实现以下步骤:In one embodiment, the following steps are also implemented when the processor executes the computer program:

获取由采集设备实时采集的变电站中被监测设备的运行声音数据;采集设备包括在变电站环境中所使用的传声器和声音数据采集卡。Obtain the operating sound data of the monitored equipment in the substation collected by the acquisition device in real time; the acquisition device includes a microphone and a sound data acquisition card used in the substation environment.

在一个实施例中,处理器执行计算机程序时还实现以下步骤:In one embodiment, the following steps are also implemented when the processor executes the computer program:

对运行声音数据进行特征提取,得到运行声音数据的实时特征参数;Feature extraction is performed on the operating sound data to obtain real-time characteristic parameters of the operating sound data;

根据实时特征参数构建矩阵集合,得到用于表征被监测设备运行状态的待判别声音模型。The matrix set is constructed according to the real-time characteristic parameters, and the sound model to be discriminated is obtained to characterize the operating state of the monitored equipment.

在一个实施例中,处理器执行计算机程序时还实现以下步骤:In one embodiment, the following steps are also implemented when the processor executes the computer program:

当偏移量在预设值区间,确定被监测设备处于正常运行状态预设值区间。在一个实施例中,处理器执行计算机程序时还实现以下步骤:When the offset is in the preset value interval, it is determined that the monitored equipment is in the preset value interval in a normal operation state. In one embodiment, the following steps are also implemented when the processor executes the computer program:

获取被监测设备的运行参数;Obtain the operating parameters of the monitored equipment;

当运行参数为预设运行参数时,将正常声音模型更新为待判别声音模型以及更新预设值区间,执行获取变电站中被监测设备的运行声音数据步骤。When the operating parameter is the preset operating parameter, the normal sound model is updated to the sound model to be judged and the preset value interval is updated, and the step of acquiring the operating sound data of the monitored equipment in the substation is performed.

在一个实施例中,处理器执行计算机程序时还实现以下步骤:In one embodiment, the following steps are also implemented when the processor executes the computer program:

当偏移量不在预设值区间时,确定被监测设备处于异常运行状态,并生成报警指令;报警指令用于触发被监测设备上的报警设备进行响应,生成报警信息。When the offset is not in the preset value interval, it is determined that the monitored equipment is in an abnormal operation state, and an alarm command is generated; the alarm command is used to trigger an alarm device on the monitored equipment to respond and generate alarm information.

在一个实施例中,处理器执行计算机程序时还实现以下步骤:In one embodiment, the following steps are also implemented when the processor executes the computer program:

将运行状态发送至显示终端以不同的形式进行可视化显示。Send the running status to the display terminal for visual display in different forms.

在一个实施例中,提供了一种计算机可读存储介质,其上存储有计算机程序,计算机程序被处理器执行时实现以下步骤:In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

实时获取变电站中被监测设备的运行声音数据;Obtain real-time operating sound data of the monitored equipment in the substation;

对运行声音数据进行特征提取,得到用于表征被监测设备运行状态的待判别声音模型;Feature extraction is performed on the operating sound data to obtain a sound model to be discriminated for representing the operating state of the monitored equipment;

通过故障判别模型对待判别声音模型和用于表征被监测设备正常运行的正常声音模型进行故障判别,输出待判别声音模型的偏移量;Fault discrimination is performed on the sound model to be judged and the normal sound model used to represent the normal operation of the monitored equipment through the fault discrimination model, and the offset of the sound model to be judged is output;

根据偏移量确定被监测设备的运行状态。Determine the running status of the monitored equipment according to the offset.

在一个实施例中,计算机程序被处理器执行时还实现以下步骤:In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

获取由采集设备实时采集的变电站中被监测设备的运行声音数据;采集设备包括在变电站环境中所使用的传声器和声音数据采集卡。Obtain the operating sound data of the monitored equipment in the substation collected by the acquisition device in real time; the acquisition device includes a microphone and a sound data acquisition card used in the substation environment.

在一个实施例中,计算机程序被处理器执行时还实现以下步骤:In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

对运行声音数据进行特征提取,得到运行声音数据的实时特征参数;Feature extraction is performed on the operating sound data to obtain real-time characteristic parameters of the operating sound data;

根据实时特征参数构建矩阵集合,得到用于表征被监测设备运行状态的待判别声音模型。The matrix set is constructed according to the real-time characteristic parameters, and the sound model to be discriminated is obtained to characterize the operating state of the monitored equipment.

在一个实施例中,计算机程序被处理器执行时还实现以下步骤:In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

当偏移量在预设值区间,确定被监测设备处于正常运行状态。在一个实施例中,计算机程序被处理器执行时还实现以下步骤:When the offset is within the preset value range, it is determined that the monitored equipment is in a normal operating state. In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

获取被监测设备的运行参数;Obtain the operating parameters of the monitored equipment;

当运行参数为预设运行参数时,将正常声音模型更新为待判别声音模型以及更新预设值区间,执行获取变电站中被监测设备的运行声音数据步骤。When the operating parameter is the preset operating parameter, the normal sound model is updated to the sound model to be judged and the preset value interval is updated, and the step of acquiring the operating sound data of the monitored equipment in the substation is performed.

在一个实施例中,计算机程序被处理器执行时还实现以下步骤:In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

当偏移量不在预设值区间时,确定被监测设备处于异常运行状态,并生成报警指令;报警指令用于触发被监测设备上的报警设备进行响应,生成报警信息。When the offset is not in the preset value interval, it is determined that the monitored equipment is in an abnormal operation state, and an alarm command is generated; the alarm command is used to trigger an alarm device on the monitored equipment to respond and generate alarm information.

在一个实施例中,计算机程序被处理器执行时还实现以下步骤:In one embodiment, when the computer program is executed by the processor, the following steps are also implemented:

将运行状态发送至显示终端以不同的形式进行可视化显示。Send the running status to the display terminal for visual display in different forms.

本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程,是可以通过计算机程序来指令相关的硬件来完成,的计算机程序可存储于一非易失性计算机可读取存储介质中,该计算机程序在执行时,可包括如上述各方法的实施例的流程。其中,本申请所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用,均可包括非易失性和易失性存储器中的至少一种。非易失性存储器可包括只读存储器(Read-OnlyMemory,ROM)、磁带、软盘、闪存或光存储器等。易失性存储器可包括随机存取存储器(Random Access Memory,RAM)或外部高速缓冲存储器。作为说明而非局限,RAM可以是多种形式,比如静态随机存取存储器(Static Random Access Memory,SRAM)或动态随机存取存储器(Dynamic Random Access Memory,DRAM)等。Those of ordinary skill in the art can understand that realizing all or part of the processes in the methods of the above embodiments can be completed by instructing related hardware through computer programs, and the computer programs can be stored in a non-volatile computer-readable storage medium , when the computer program is executed, it may include the procedures of the embodiments of the above-mentioned methods. Wherein, any references to memory, storage, database or other media used in the various embodiments provided in the present application may include at least one of non-volatile memory and volatile memory. The non-volatile memory may include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory or optical memory, and the like. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

以上实施例的各技术特征可以进行任意的组合,为使描述简洁,未对上述实施例中的各个技术特征所有可能的组合都进行描述,然而,只要这些技术特征的组合不存在矛盾,都应当认为是本说明书记载的范围。The technical features of the above embodiments can be combined arbitrarily. To make the description concise, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features, they should be It is considered to be within the range described in this specification.

以上所述实施例仅表达了本申请的几种实施方式,其描述较为具体和详细,但并不能因此而理解为对发明专利范围的限制。应当指出的是,对于本领域的普通技术人员来说,在不脱离本申请构思的前提下,还可以做出若干变形和改进,这些都属于本申请的保护范围。因此,本申请专利的保护范围应以所附权利要求为准。The above-mentioned embodiments only represent several implementation modes of the present application, and the description thereof is relatively specific and detailed, but it should not be construed as limiting the scope of the patent for the invention. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the scope of protection of the patent application should be based on the appended claims.

Claims (10)

1. A method for identifying a sound abnormality of a substation device, the method comprising:
acquiring operation sound data of monitored equipment in a transformer substation, which is acquired by acquisition equipment in real time; the acquisition equipment comprises a microphone and a sound data acquisition card which are used in a transformer substation environment;
acquiring the operation parameters of the monitored equipment; the operation parameters comprise the operation time length of the monitored equipment;
extracting characteristics of the operation sound data to obtain a sound model to be distinguished for representing the operation state of the monitored equipment;
performing fault discrimination on the sound model to be discriminated and a normal sound model used for representing normal operation of the monitored equipment through a fault discrimination model, and outputting the offset of the sound model to be discriminated;
determining the running state of the monitored equipment according to the offset;
the determining the running state of the monitored equipment according to the offset comprises the following steps:
when the offset is in a preset value interval, determining that the monitored equipment is in a normal running state preset value interval;
and when the operation parameters are preset operation parameters, updating the normal sound model into the sound model to be judged and updating the preset value interval, and executing the step of acquiring the operation sound data of the monitored equipment in the transformer substation, which is acquired by the acquisition equipment in real time.
2. The method according to claim 1, wherein the feature extracting the operation sound data to obtain a sound model to be discriminated for characterizing the operation state of the monitored device includes:
extracting the characteristics of the operation sound data to obtain real-time characteristic parameters of the operation sound data;
and constructing a matrix set according to the real-time characteristic parameters to obtain a sound model to be distinguished, wherein the sound model is used for representing the running state of the monitored equipment.
3. The method of claim 2, wherein the feature extracting the operation sound data comprises:
and extracting the mel-frequency spectrum coefficient characteristics of the operation sound data.
4. The method of claim 1, wherein said determining the operational status of the monitored device based on the offset comprises:
when the offset is not in the preset value interval, determining that the monitored equipment is in an abnormal operation state, and generating an alarm instruction; the alarm instruction is used for triggering alarm equipment on the monitored equipment to respond and generating alarm information.
5. The method according to claim 1, wherein the method further comprises:
and sending the running state to a display terminal to perform visual display in different forms.
6. The method of claim 1, wherein the manner in which the normal acoustic model is derived comprises:
and collecting the operation sound data of the equipment in the normal operation time, preprocessing the operation sound data in the normal operation time and extracting the characteristic of the mel-frequency spectrum coefficient to obtain the characteristic parameters of the operation sound data in the normal operation state, and constructing a matrix for the characteristic parameters of the operation sound data in the normal operation state to obtain the normal sound model.
7. The method of claim 1, wherein the operational status of the monitored device includes normal operation and abnormal operation.
8. A substation equipment sound abnormality identification device, characterized in that the device comprises:
the acquisition module is used for acquiring the operation sound data of the monitored equipment in the transformer substation, which is acquired by the acquisition equipment in real time; the acquisition equipment comprises a microphone and a sound data acquisition card which are used in a transformer substation environment;
the acquisition module is also used for acquiring the operation parameters of the monitored equipment; the operation parameters comprise the operation time length of the monitored equipment;
the feature extraction module is used for extracting features of the operation sound data to obtain a sound model to be distinguished, which is used for representing the operation state of the monitored equipment;
the fault judging module is used for carrying out fault judgment on the sound model to be judged and the normal sound model used for representing the normal operation of the monitored equipment through the fault judging model and outputting the offset of the sound model to be judged;
the determining module is used for determining the running state of the monitored equipment according to the offset;
the determining module is further used for determining that the monitored equipment is in a preset value interval in a normal running state when the offset is in the preset value interval;
and the updating module is used for updating the normal sound model into the sound model to be judged and updating the preset value interval when the operation parameter is a preset operation parameter.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
CN202110686609.9A 2021-06-21 2021-06-21 Method and device for identifying sound abnormality of substation equipment and computer equipment Active CN113670434B (en)

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