CN112101301A - A kind of good sound stability early warning method, device and storage medium of screw water-cooling unit - Google Patents
A kind of good sound stability early warning method, device and storage medium of screw water-cooling unit Download PDFInfo
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Abstract
本发明提供一种螺杆水冷机组的好音稳定预警方法、装置及存储介质,方法包括:导入多个原始音频数据,并对多个所述原始音频数据进行数据清洗,经数据清洗后剩余的原始音频数据作为目标音频数据,得到多个目标音频数据,所述原始音频数据是通过螺杆水冷机组设备得到的;分别对各个所述目标音频数据进行特征提取,得到对应的特征点,并集合提取到的所有的特征点得到特征点数据集。本发明能够进一步提高了音频识别准确率,克服了传统好音稳定预警的工作量大、效率低下和准确率不够的缺陷,能自动对大量的音频数据进行智能检测和识别,实时检测出的音频数据,为好音稳定预警做及时地干预,具有了效率高,稳定性强以及准确率高的特点。
The invention provides a good sound stability early warning method, device and storage medium for a screw water-cooled unit. The method includes: importing a plurality of original audio data, and performing data cleaning on the plurality of original audio data, and the remaining original audio data after data cleaning The audio data is used as the target audio data to obtain a plurality of target audio data, and the original audio data is obtained by the screw water-cooling unit equipment; the feature extraction is performed on each of the target audio data respectively to obtain the corresponding feature points, and the collection is extracted. All the feature points get the feature point dataset. The present invention can further improve the accuracy of audio recognition, overcome the defects of large workload, low efficiency and insufficient accuracy of traditional good-sound stability early warning, and can automatically perform intelligent detection and recognition on a large amount of audio data, and real-time detected audio It has the characteristics of high efficiency, strong stability and high accuracy.
Description
技术领域technical field
本发明主要涉及音频识别技术领域,具体涉及一种螺杆水冷机组的好音稳定预警方法、装置及存储介质。The invention mainly relates to the technical field of audio recognition, and in particular relates to a good sound stability early warning method, device and storage medium for a screw water cooling unit.
背景技术Background technique
好音稳定预警本质上属于一种模式识别技术,在实际生活应用中,人们直观的对于音频的感官识别是基于音频层次最高层的语意层的,而且音频容易受到环境和传输转化设备的影响。Good sound stability warning is essentially a pattern recognition technology. In real life applications, people's intuitive sensory recognition of audio is based on the semantic layer of the highest level of audio, and audio is easily affected by the environment and transmission conversion equipment.
由于音频数据存在非人性化的风险、远程控制、低准确度和复杂性,它并不十分可靠。同时还有许多其它因素影响其准确性,比如说,声音样本的质量、情绪、背景噪音以及随着时间推移声音的变化等。目前还难以依靠仪器自动识别和识别,无法保证较高的识别准确率,同时,还难以实时判断和及时地预警。Due to the risk of dehumanization, remote control, low accuracy and complexity of audio data, it is not very reliable. There are many other factors that affect its accuracy, such as the quality of the sound sample, mood, background noise, and changes in sound over time. At present, it is still difficult to rely on the automatic identification and identification of instruments, and it is impossible to guarantee a high identification accuracy.
发明内容SUMMARY OF THE INVENTION
本发明所要解决的技术问题是针对现有技术的不足,提供一种螺杆水冷机组的好音稳定预警方法、装置及存储介质。The technical problem to be solved by the present invention is to aim at the deficiencies of the prior art, and to provide a good sound stability early warning method, device and storage medium for a screw water cooling unit.
本发明解决上述技术问题的技术方案如下:一种螺杆水冷机组的好音稳定预警方法,包括如下步骤:The technical solution of the present invention to solve the above-mentioned technical problems is as follows: a good sound stability early warning method for a screw water-cooled unit, comprising the following steps:
导入多个原始音频数据,并对多个所述原始音频数据进行数据清洗,经数据清洗后剩余的原始音频数据作为目标音频数据,得到多个目标音频数据,所述原始音频数据是通过螺杆水冷机组设备得到的;Import a plurality of original audio data, and perform data cleaning on a plurality of the original audio data. After the data cleaning, the remaining original audio data is used as the target audio data to obtain a plurality of target audio data. The original audio data is cooled by screw water. obtained by the unit equipment;
分别对各个所述目标音频数据进行特征提取,得到对应的特征点,并集合提取到的所有的特征点得到特征点数据集;Perform feature extraction on each of the target audio data respectively to obtain corresponding feature points, and collect all the extracted feature points to obtain a feature point data set;
构建训练模型,并根据所述特征点数据集对所述训练模型进行训练,得到音频识别模型;constructing a training model, and training the training model according to the feature point data set to obtain an audio recognition model;
对所述音频识别模型进行优化处理,得到音频识别优化模型;Optimizing the audio recognition model to obtain an audio recognition optimization model;
根据所述音频识别优化模型对待识别音频数据进行识别处理,得到音频数据的识别结果。The audio data to be recognized is recognized and processed according to the audio recognition optimization model to obtain a recognition result of the audio data.
本发明解决上述技术问题的另一技术方案如下:一种螺杆水冷机组的好音稳定预警装置,包括:Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a good sound stability early warning device for a screw water-cooled unit, comprising:
数据清洗模块,用于导入多个原始音频数据,并对多个所述原始音频数据进行数据清洗,经数据清洗后剩余的原始音频数据作为目标音频数据,得到多个目标音频数据,所述原始音频数据是通过螺杆水冷机组设备得到的;The data cleaning module is used to import a plurality of original audio data, and perform data cleaning on the plurality of said original audio data, and the remaining original audio data after data cleaning is used as target audio data to obtain a plurality of target audio data. Audio data is obtained through screw water cooling unit equipment;
特征提取模块,用于分别对各个所述目标音频数据进行特征提取,得到对应的特征点,并集合提取到的所有的特征点得到特征点数据集;a feature extraction module, which is used to perform feature extraction on each of the target audio data respectively to obtain corresponding feature points, and collect all the extracted feature points to obtain a feature point data set;
模型训练模块,用于构建训练模型,并根据所述特征点数据集对所述训练模型进行训练,得到音频识别模型;a model training module for constructing a training model, and training the training model according to the feature point data set to obtain an audio recognition model;
优化处理模块,用于对所述音频识别模型进行优化处理,得到音频识别优化模型;an optimization processing module for performing optimization processing on the audio recognition model to obtain an audio recognition optimization model;
好音稳定预警模块,用于根据所述音频识别优化模型对待识别音频数据进行识别处理,得到音频数据的识别结果。A good sound stability early warning module is used for identifying and processing the audio data to be recognized according to the audio recognition optimization model to obtain a recognition result of the audio data.
本发明解决上述技术问题的另一技术方案如下:一种螺杆水冷机组的好音稳定预警装置,包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序,当所述处理器执行所述计算机程序时,实现如上所述的螺杆水冷机组的好音稳定预警方法。Another technical solution of the present invention to solve the above-mentioned technical problems is as follows: a good sound stability early warning device for a screw water cooling unit, comprising a memory, a processor and a computer program stored in the memory and running on the processor, When the processor executes the computer program, the above-mentioned method for early warning of good sound stability of the screw water cooling unit is realized.
本发明解决上述技术问题的另一技术方案如下:一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,当所述计算机程序被处理器执行时,实现如上所述的螺杆水冷机组的好音稳定预警方法。Another technical solution of the present invention to solve the above technical problem is as follows: a computer-readable storage medium, the computer-readable storage medium stores a computer program, when the computer program is executed by a processor, the above-mentioned screw is realized Good sound stability early warning method for water-cooled units.
本发明的有益效果是:通过分别对多个原始音频数据的数据清洗得到多个目标音频数据,可以筛除含缺失值的数据,还可以分析出更多有用信息的数据以及对识别和识别影响更大的信息,便于根据得到的目标音频数据制作数据集,从而便于获得识别和识别准确率更高的识别模型,分别对多个目标音频数据的特征提取,得到特征点数据集,构建训练模型并根据特征点数据集对训练模型的训练得到音频识别模型,能有效提高对好音稳定预警的可靠性和稳定性,根据预设调整参数对音频识别模型的优化处理得到音频识别优化模型,根据音频识别优化模型对待识别音频数据的识别处理得到音频数据的识别结果,能够进一步提高了音频识别准确率,克服了传统好音稳定预警的工作量大、效率低下和准确率不够的缺陷,能自动对大量的音频数据进行智能检测和识别,实时检测出的音频数据,为好音稳定预警做及时地干预,具有了效率高,稳定性强以及准确率高的特点。The beneficial effects of the present invention are: by cleaning the data of a plurality of original audio data to obtain a plurality of target audio data, the data with missing values can be filtered out, and the data with more useful information can be analyzed and the impact on identification and identification can be analyzed. With larger information, it is convenient to make a data set according to the obtained target audio data, so as to obtain a recognition model with higher recognition and recognition accuracy, and extract the features of multiple target audio data respectively to obtain a feature point data set and build a training model. And the audio recognition model is obtained by training the training model according to the feature point data set, which can effectively improve the reliability and stability of the good sound stability early warning. The audio recognition optimization model is obtained by optimizing the audio recognition model according to the preset adjustment parameters. The audio recognition optimization model is to process the recognition audio data to obtain the recognition result of the audio data, which can further improve the audio recognition accuracy, and overcome the defects of large workload, low efficiency and insufficient accuracy of the traditional good sound stability early warning. Intelligent detection and identification of a large amount of audio data, real-time detection of audio data, timely intervention for good sound stability early warning, with high efficiency, strong stability and high accuracy.
附图说明Description of drawings
图1为本发明实施例提供的螺杆水冷机组的好音稳定预警方法的流程示意图;Fig. 1 is the schematic flow chart of the good sound stability early warning method of the screw water-cooled unit provided by the embodiment of the present invention;
图2为本发明实施例提供的螺杆水冷机组的好音稳定预警装置的模块框图。FIG. 2 is a block diagram of a module of a good sound stability early warning device of a screw water cooling unit provided by an embodiment of the present invention.
具体实施方式Detailed ways
以下结合附图对本发明的原理和特征进行描述,所举实例只用于解释本发明,并非用于限定本发明的范围。The principles and features of the present invention will be described below with reference to the accompanying drawings. The examples are only used to explain the present invention, but not to limit the scope of the present invention.
图1为本发明实施例提供的螺杆水冷机组的好音稳定预警方法的流程示意图。FIG. 1 is a schematic flowchart of a good sound stability early warning method for a screw water-cooled unit provided by an embodiment of the present invention.
如图1所示,一种螺杆水冷机组的好音稳定预警方法,包括如下步骤:As shown in Figure 1, a good sound stability early warning method for a screw water-cooled unit includes the following steps:
导入多个原始音频数据,并对多个所述原始音频数据进行数据清洗,经数据清洗后剩余的原始音频数据作为目标音频数据,得到多个目标音频数据,所述原始音频数据是通过螺杆水冷机组设备得到的;Import a plurality of original audio data, and perform data cleaning on a plurality of the original audio data. After the data cleaning, the remaining original audio data is used as the target audio data to obtain a plurality of target audio data. The original audio data is cooled by screw water. obtained by the unit equipment;
分别对各个所述目标音频数据进行特征提取,得到对应的特征点,并集合提取到的所有的特征点得到特征点数据集;Perform feature extraction on each of the target audio data respectively to obtain corresponding feature points, and collect all the extracted feature points to obtain a feature point data set;
构建训练模型,并根据所述特征点数据集对所述训练模型进行训练,得到音频识别模型;constructing a training model, and training the training model according to the feature point data set to obtain an audio recognition model;
对所述音频识别模型进行优化处理,得到音频识别优化模型;Optimizing the audio recognition model to obtain an audio recognition optimization model;
根据所述音频识别优化模型对待识别音频数据进行识别处理,得到音频数据的识别结果。The audio data to be recognized is recognized and processed according to the audio recognition optimization model to obtain a recognition result of the audio data.
应理解地,数据清洗是指发现并纠正数据文件中可识别的错误的最后一道程序,包括检查数据一致性,处理无效值和缺失值等。It should be understood that data cleaning refers to the final process of finding and correcting identifiable errors in data files, including checking data consistency, handling invalid and missing values, etc.
应理解地,根据所述预设调整参数对所述音频识别模型进行优化处理,得到音频识别优化模型。It should be understood that the audio recognition model is optimized according to the preset adjustment parameters to obtain an audio recognition optimization model.
具体地,利用人工手动调参方法进行参数调优,能保证得到所述音频识别模型对应的最优参数,能进一步确保所述音频识别优化模型对音频的识别准确率,实时地检测出音频样放电的音频数据,为临床做及时地干预。Specifically, using the manual manual parameter tuning method to perform parameter tuning can ensure that the optimal parameters corresponding to the audio recognition model can be obtained, further ensure the audio recognition accuracy of the audio recognition optimization model, and detect audio samples in real time. The audio data of the discharge can be used for clinical intervention in a timely manner.
上述实施例中,通过分别对多个原始音频数据的数据清洗得到多个目标音频数据,可以筛除含缺失值的数据,还可以分析出更多有用信息的数据以及对识别和识别影响更大的信息,便于根据得到的目标音频数据制作数据集,从而便于获得识别和识别准确率更高的识别模型,分别对多个目标音频数据的特征提取,得到特征点数据集,构建训练模型并根据特征点数据集对训练模型的训练得到音频识别模型,能有效提高对好音稳定预警的可靠性和稳定性,根据预设调整参数对音频识别模型的优化处理得到音频识别优化模型,根据音频识别优化模型对待识别音频数据的识别处理得到音频数据的识别结果,能够进一步提高了音频识别准确率,克服了传统好音稳定预警的工作量大、效率低下和准确率不够的缺陷,能自动对大量的音频数据进行智能检测和识别,实时检测出的音频数据,为好音稳定预警做及时地干预,具有了效率高,稳定性强以及准确率高的特点。In the above-mentioned embodiment, by cleaning the data of a plurality of original audio data to obtain a plurality of target audio data, the data with missing values can be filtered out, and the data with more useful information can also be analyzed, which has a greater impact on the identification and identification. It is convenient to make a data set according to the obtained target audio data, so as to obtain a recognition model with higher recognition and recognition accuracy, extract the features of multiple target audio data respectively, obtain a feature point data set, build a training model and according to The feature point dataset is used to train the training model to obtain an audio recognition model, which can effectively improve the reliability and stability of the stable early warning of good sounds. The optimized model processes the audio data to be recognized to obtain the audio data recognition results, which can further improve the audio recognition accuracy, overcome the defects of large workload, low efficiency and insufficient accuracy of the traditional good sound stability early warning, and can automatically detect a large number of The audio data is intelligently detected and recognized, and the audio data detected in real time can be intervened in time for good sound stability early warning, which has the characteristics of high efficiency, strong stability and high accuracy.
可选地,作为本发明的一个实施例,所述分别对各个所述目标音频数据进行特征提取,得到对应的特征点的过程包括:Optionally, as an embodiment of the present invention, the process of performing feature extraction on each of the target audio data to obtain corresponding feature points includes:
分别对多个所述目标音频数据进行降维处理,得到多个降维后的音频数据;Respectively perform dimension reduction processing on a plurality of the target audio data to obtain a plurality of dimension-reduced audio data;
利用预设的语谱图分别对多个所述降维后的音频数据进行特征提取,得到多个特征点。Feature extraction is performed on a plurality of the dimensionality-reduced audio data by using a preset spectrogram to obtain a plurality of feature points.
应理解地,在机器学习和统计学领域,降维是指在某些限定条件下,降低随机变量个数,得到一组“不相关”主变量的过程。对数据进行降维一方面可以节省计算机的储存空间,另一方面可以剔除数据中的噪声并提高机器学习算法的性能;数据降维的根本:降低数据维度、降维后的数据能尽可能的代表原始数据。It should be understood that in the field of machine learning and statistics, dimensionality reduction refers to the process of reducing the number of random variables to obtain a set of "uncorrelated" main variables under certain limited conditions. On the one hand, dimensionality reduction of data can save the storage space of the computer, on the other hand, it can eliminate the noise in the data and improve the performance of the machine learning algorithm; represents the original data.
应理解地,利用LDA线性判别分析算法分别对多个所述目标音频数据进行降维处理,得到多个降维后的音频数据。It should be understood that the LDA linear discriminant analysis algorithm is used to perform dimensionality reduction processing on a plurality of the target audio data respectively to obtain a plurality of dimensionally reduced audio data.
具体地,分别将多个所述目标音频数据在低维度上进行投影,从而让音频文本从数千维降到k维,使投影后的所述目标音频数据类内方差最小,类间方差最大;将降维到新的特征空间上的所述目标音频数据进行压缩,并尽可能保留信息,得到多个所述降维后的音频数据。Specifically, a plurality of the target audio data are projected on low dimensions, so that the audio text is reduced from thousands of dimensions to k dimensions, so that the intra-class variance of the projected target audio data is minimized and the inter-class variance is maximized ; compress the target audio data reduced in dimension to a new feature space, and retain information as much as possible to obtain a plurality of audio data after the dimension reduction.
应理解地,语谱图(Spectrogram)是时序相关的傅里叶分析的显示图像,可以反映音乐信号频谱随时间改变而变换,语谱图的横坐标是时间,纵坐标是频率,坐标点值为语音数据能量。由于是采用二维平面表达三维信息,所以能量值的大小是通过颜色来表示的,颜色深,表示该点的语音能量越强。It should be understood that the spectrogram is a display image of time-series-related Fourier analysis, which can reflect the transformation of the spectrum of the music signal with time. The abscissa of the spectrogram is time, the ordinate is frequency, and the coordinate point value. is the voice data energy. Since the two-dimensional plane is used to express three-dimensional information, the energy value is represented by color. The darker the color, the stronger the speech energy of the point.
语谱图中显示了大量与音乐信号特性相关的信息,如共振峰、能量等频域参数随时间的变化情况,它同时具有时域波形与频谱图的特点。也就是说,语谱图本身包含了音乐信号的所有的频谱信息,没有经过任何加工,所以语谱图关于音乐的信息是无损的。The spectrogram shows a lot of information related to the characteristics of the music signal, such as the variation of frequency domain parameters such as formants and energy over time. It has the characteristics of time domain waveform and spectrogram at the same time. That is to say, the spectrogram itself contains all the spectral information of the music signal without any processing, so the information about the music of the spectrogram is lossless.
语谱图中的花纹有横线,乱纹和竖直条等,横线是与时间轴平行的黑色带纹,它们是共振峰,从横线对应的频率和带宽可以确定相应的共振峰频率和带宽,在一段音频的语谱图中有没有横线出现是判断它是否是浊音的重要标志;竖直条是与时间轴垂直的窄黑条,每个竖直条相当于一个基音,条纹的起点相当于声纹脉冲的起点,条纹之间的距离表示基音,条纹越密表示基音频率越高。The patterns in the spectrogram include horizontal lines, random patterns and vertical bars. The horizontal lines are black stripes parallel to the time axis. They are formants. The corresponding formant frequency can be determined from the frequency and bandwidth corresponding to the horizontal lines. and bandwidth, whether there is a horizontal line in the spectrogram of a piece of audio is an important sign to judge whether it is a voiced sound; the vertical bar is a narrow black bar perpendicular to the time axis, each vertical bar is equivalent to a pitch, and the stripe The starting point is equivalent to the starting point of the voiceprint pulse, the distance between the stripes represents the pitch, and the denser the stripes, the higher the pitch frequency.
上述实施例中,分别对多个目标音频数据的降维处理得到多个降维后的音频数据,利用预设的语谱图分别对多个降维后的音频数据的特征提取得到多个特征点,便于得到对螺杆水冷机组的好音稳定预警影响更大的主要特征,减小了后续步骤的运算量,后续支持向量机只需要较少的训练数据即可得到较高的准确率。In the above embodiment, the dimensionality reduction processing of multiple target audio data is performed to obtain multiple dimensionally reduced audio data, and the preset spectrogram is used to extract the features of the multiple dimensionally reduced audio data to obtain multiple features. It is convenient to obtain the main features that have a greater impact on the sound stability and early warning of the screw water-cooled unit, reducing the amount of computation in the subsequent steps, and the subsequent support vector machine only needs less training data to obtain higher accuracy.
可选地,作为本发明的一个实施例,所述根据所述特征点数据集对所述训练模型进行训练,得到音频识别模型的过程包括:Optionally, as an embodiment of the present invention, the process of training the training model according to the feature point data set to obtain an audio recognition model includes:
S1:将所述特征点数据集随机划分成特征点训练集和特征点测试集;S1: randomly divide the feature point data set into a feature point training set and a feature point test set;
S2:基于支持向量机算法构建模型,得到支持向量机结构;S2: build a model based on the support vector machine algorithm, and obtain the support vector machine structure;
S3:将所述特征点训练集和所述特征点测试集一并输入所述支持向量机结构进行最优识别超平面的寻找,得到最优识别超平面,并根据所述最优识别超平面得到支持向量集和VC可信度;S3: Input the feature point training set and the feature point test set together into the support vector machine structure to search for the optimal recognition hyperplane, obtain the optimal recognition hyperplane, and according to the optimal recognition hyperplane Get support vector set and VC credibility;
S4:根据所述VC可信度对所述支持向量集进行判别处理,得到判别函数,并根据所述判别函数生成训练模型;S4: carry out discriminant processing on the support vector set according to the VC credibility, obtain a discriminant function, and generate a training model according to the discriminant function;
S5:根据所述特征点训练集和所述特征点测试集对所述训练模型进行模型筛选处理,得到音频识别模型。S5: Perform model screening processing on the training model according to the feature point training set and the feature point test set to obtain an audio recognition model.
应理解地,由于所述特征点数据集每次随机分成特征点训练集和特征点测试集均是随机划分的,因此每次的随机比例均不相同,可以调用train_test_split函数进行随机划分。It should be understood that since the feature point data set is randomly divided into the feature point training set and the feature point test set each time, the random proportions are different each time, and the train_test_split function can be called to perform random division.
具体地,支持向量机(Support Vector Machine,,简称SVM)是一种监督学习算法,SVM理论提供了一种避开高维空间的复杂性,直接用此空间的内积函数(既是核函数),再利用在线性可分的情况下的求解方法直接求解对应的高维空间的决策问题.当核函数已知,可以简化高维空间问题的求解难度.同时SVM是基于小样本统计理论的基础上的,这符合机器学习的目的.而且支持向量机比神经网络具有较好的泛化推广能力,对于噪声数据和存在缺失值的数据具有很好的鲁棒性,并且具有较快的学习深度,综合了多种机器识别算法的优势,性能高、稳定性强。Specifically, Support Vector Machine (SVM for short) is a supervised learning algorithm. SVM theory provides a way to avoid the complexity of high-dimensional space and directly use the inner product function of this space (that is, a kernel function) , and then use the solution method in the case of linear separability to directly solve the decision problem in the corresponding high-dimensional space. When the kernel function is known, the difficulty of solving the high-dimensional space problem can be simplified. At the same time, SVM is based on the basis of small sample statistical theory Above, this is in line with the purpose of machine learning. And support vector machine has better generalization ability than neural network, good robustness to noisy data and data with missing values, and has a faster learning depth , which combines the advantages of a variety of machine recognition algorithms, with high performance and strong stability.
应理解地,VC可信度即为VC维置信度或置信风险,所谓VC维是对函数类的一种度量,可以简单的理解为问题的复杂程度,VC维越高,一个问题就越复杂。例如:很多分类函数能够在样本集上轻易达到100%的正确率,在真实分类时却一塌糊涂(即所谓的推广能力差,或泛化能力差)。此时的情况便是选择了一个足够复杂的分类函数(即它的VC维很高),能够精确的记住每一个样本,但对样本之外的数据一律分类错误。It should be understood that the VC credibility is the VC dimension confidence or confidence risk. The so-called VC dimension is a measure of the function class, which can be simply understood as the complexity of the problem. The higher the VC dimension, the more complex a problem. . For example, many classification functions can easily achieve 100% accuracy on the sample set, but they are messed up in the real classification (that is, the so-called poor generalization ability, or poor generalization ability). The situation at this time is that a sufficiently complex classification function is selected (that is, its VC dimension is high), which can accurately remember each sample, but the data outside the sample is always classified incorrectly.
具体地,将所述特征点训练集和所述特征点测试集均输入所述支持向量机结构中,利用支持向量机的样本特征空间找出各类别特征样本与其他特征样本的最优识别超平面,得到代表各类别样本的所述支持向量集及其相应的所述VC可信度,形成判别个特征的所述判别函数,得到所述训练模型。Specifically, both the feature point training set and the feature point test set are input into the support vector machine structure, and the sample feature space of the support vector machine is used to find out the optimal recognition performance of each category of feature samples and other feature samples. plane, obtain the support vector set representing each category of samples and the corresponding VC reliability, form the discriminant function for discriminating individual features, and obtain the training model.
上述实施例中,将所述特征点数据集随机划分成特征点训练集和特征点测试集,能保证数据的客观性,减少人为因素,有效提高后续识别模型的准确率,将特征点训练集和特征点测试集一并输入支持向量机结构的最优识别超平面寻找得到最优识别超平面,并根据最优识别超平面得到支持向量集和VC可信度,根据VC可信度对支持向量集的判别处理得到训练模型;根据特征点训练集和特征点测试集对训练模型的模型筛选处理得到音频识别模型,能使得待识别音频数据的准确率一直保持在较高水平,提高了音频识别的稳定性和可靠性。In the above embodiment, the feature point data set is randomly divided into the feature point training set and the feature point test set, which can ensure the objectivity of the data, reduce human factors, effectively improve the accuracy of the subsequent identification model, and divide the feature point training set. Enter the optimal recognition hyperplane of the support vector machine structure together with the feature point test set to find the optimal recognition hyperplane, and obtain the support vector set and VC reliability according to the optimal recognition hyperplane, and according to the VC reliability, support The training model is obtained by the discriminative processing of the vector set; the audio recognition model is obtained by the model screening of the training model according to the feature point training set and the feature point test set, which can keep the accuracy of the audio data to be recognized at a high level and improve the audio frequency. Stability and reliability of identification.
可选地,作为本发明的一个实施例,所述步骤S5的过程包括:Optionally, as an embodiment of the present invention, the process of step S5 includes:
S51:根据预设迭代训练次数将所述特征点训练集输入至所述训练模型中进行迭代训练,得到第一检测模型;S51: Input the feature point training set into the training model for iterative training according to a preset number of iterative training times to obtain a first detection model;
S52:将所述特征点测试集输入所述第一检测模型中进行检测,得到第一准确率,并判断所述第一准确率是否达到预设预期值,若是,则将所述第一检测模型作为音频识别模型,再对所述音频识别模型进行优化处理,若否,则执行步骤S53;S52: Input the feature point test set into the first detection model for detection to obtain a first accuracy rate, and determine whether the first accuracy rate reaches a preset expected value, and if so, apply the first detection rate The model is used as an audio recognition model, and then the audio recognition model is optimized, and if not, step S53 is performed;
S53:根据所述预设迭代训练次数将所述特征点测试集输入至所述训练模型进行迭代训练,得到第二检测模型;S53: Input the feature point test set into the training model for iterative training according to the preset iterative training times to obtain a second detection model;
S54:将所述特征点训练集输入所述第二检测模型中进行检测,得到第二准确率;S54: Input the feature point training set into the second detection model for detection to obtain a second accuracy rate;
S55:判断所述第二准确率是否达到所述预设预期值,若是,则将所述第二检测模型作为所述音频识别模型,再对所述音频识别模型进行优化处理,若否,则返回所述步骤S1。S55: Determine whether the second accuracy rate reaches the preset expected value, if so, use the second detection model as the audio recognition model, and then perform optimization processing on the audio recognition model, if not, then Return to the step S1.
优选地,所述预设预期值为0.90。Preferably, the preset expected value is 0.90.
上述实施例中,通过得到的第一检测模型和第二检测模型,能保证较高的识别准确率,并得到符合预期的音频识别模型,当第一检测模型的第一准确率未达到预期值时,对特征点测试集的训练得到第二检测模型,并利用特征点训练集来检测得到第二准确率,相当于交换训练集和测试集,能进一步能保证得到符合预期的识别模型,能使得达到预期值对应的音频识别模型来检测待识别音频数据的准确率一直保持在较高水平,提高了音频识别的稳定性和可靠性。In the above-mentioned embodiment, the obtained first detection model and the second detection model can ensure a high recognition accuracy rate, and obtain an audio recognition model that meets the expectations, when the first accuracy rate of the first detection model does not reach the expected value. When , the second detection model is obtained by training the test set of feature points, and the second accuracy rate is obtained by using the training set of feature points, which is equivalent to exchanging the training set and the test set, which can further ensure that the expected recognition model can be obtained. The accuracy of detecting the audio data to be recognized by the audio recognition model corresponding to the expected value is kept at a high level, and the stability and reliability of audio recognition are improved.
图2为本发明实施例提供的螺杆水冷机组的好音稳定预警装置的模块框图。FIG. 2 is a block diagram of a module of a good sound stability early warning device of a screw water cooling unit provided by an embodiment of the present invention.
可选地,作为本发明的另一个实施例,如图2所示,一种螺杆水冷机组的好音稳定预警装置,包括:Optionally, as another embodiment of the present invention, as shown in Figure 2, a good sound stability early warning device for a screw water-cooled unit includes:
数据清洗模块,用于导入多个原始音频数据,并对多个所述原始音频数据进行数据清洗,经数据清洗后剩余的原始音频数据作为目标音频数据,得到多个目标音频数据,所述原始音频数据是通过螺杆水冷机组设备得到的;The data cleaning module is used to import a plurality of original audio data, and perform data cleaning on the plurality of said original audio data, and the remaining original audio data after data cleaning is used as target audio data to obtain a plurality of target audio data. Audio data is obtained through screw water cooling unit equipment;
特征提取模块,用于分别对各个所述目标音频数据进行特征提取,得到对应的特征点,并集合提取到的所有的特征点得到特征点数据集;a feature extraction module, which is used to perform feature extraction on each of the target audio data respectively to obtain corresponding feature points, and collect all the extracted feature points to obtain a feature point data set;
模型训练模块,用于构建训练模型,并根据所述特征点数据集对所述训练模型进行训练,得到音频识别模型;a model training module for constructing a training model, and training the training model according to the feature point data set to obtain an audio recognition model;
优化处理模块,用于对所述音频识别模型进行优化处理,得到音频识别优化模型;an optimization processing module for performing optimization processing on the audio recognition model to obtain an audio recognition optimization model;
好音稳定预警模块,用于根据所述音频识别优化模型对待识别音频数据进行识别处理,得到音频数据的识别结果。A good sound stability early warning module is used for identifying and processing the audio data to be recognized according to the audio recognition optimization model to obtain a recognition result of the audio data.
可选地,作为本发明的一个实施例,所述特征提取模块具体用于:Optionally, as an embodiment of the present invention, the feature extraction module is specifically used for:
分别对多个所述目标音频数据进行降维处理,得到多个降维后的音频数据;Respectively perform dimension reduction processing on a plurality of the target audio data to obtain a plurality of dimension-reduced audio data;
利用预设的语谱图分别对多个所述降维后的音频数据进行特征提取,得到多个特征点。Feature extraction is performed on a plurality of the dimensionality-reduced audio data by using a preset spectrogram to obtain a plurality of feature points.
可选地,作为本发明的一个实施例,所述模型训练模块具体用于:Optionally, as an embodiment of the present invention, the model training module is specifically used for:
将所述特征点数据集随机划分成特征点训练集和特征点测试集;Randomly dividing the feature point data set into a feature point training set and a feature point test set;
基于支持向量机算法构建模型,得到支持向量机结构;Build a model based on the support vector machine algorithm, and get the support vector machine structure;
将所述特征点训练集和所述特征点测试集一并输入所述支持向量机结构进行最优识别超平面的寻找,得到最优识别超平面,并根据所述最优识别超平面得到支持向量集和VC可信度;Input the feature point training set and the feature point test set together into the SVM structure to search for the optimal recognition hyperplane, obtain the optimal recognition hyperplane, and obtain support according to the optimal recognition hyperplane vector set and VC credibility;
根据所述VC可信度对所述支持向量集进行判别处理,得到判别函数,并根据所述判别函数生成训练模型;According to the VC credibility, the support vector set is discriminated to obtain a discriminant function, and a training model is generated according to the discriminant function;
根据所述特征点训练集和所述特征点测试集对所述训练模型进行模型筛选处理,得到音频识别模型。Perform model screening processing on the training model according to the feature point training set and the feature point test set to obtain an audio recognition model.
可选地,作为本发明的一个实施例,所述模型训练模块具体用于:Optionally, as an embodiment of the present invention, the model training module is specifically used for:
根据预设迭代训练次数将所述特征点训练集输入至所述训练模型中进行迭代训练,得到第一检测模型;Inputting the feature point training set into the training model for iterative training according to a preset number of iterative training times to obtain a first detection model;
将所述特征点测试集输入所述第一检测模型中进行检测,得到第一准确率,并判断所述第一准确率是否达到预设预期值,若是,则将所述第一检测模型作为音频识别模型,再对所述音频识别模型进行优化处理,若否,则根据所述预设迭代训练次数将所述特征点测试集输入至所述训练模型进行迭代训练,得到第二检测模型;Input the feature point test set into the first detection model for detection, obtain a first accuracy rate, and determine whether the first accuracy rate reaches a preset expected value, and if so, use the first detection model as audio recognition model, and then perform optimization processing on the audio recognition model, if not, input the feature point test set into the training model for iterative training according to the preset iterative training times to obtain a second detection model;
将所述特征点训练集输入所述第二检测模型中进行检测,得到第二准确率;Inputting the feature point training set into the second detection model for detection to obtain a second accuracy rate;
判断所述第二准确率是否达到所述预设预期值,若是,则将所述第二检测模型作为所述音频识别模型,再对所述音频识别模型进行优化处理,若否,则再次将所述特征点数据集随机划分成特征点训练集和特征点测试集。Determine whether the second accuracy rate reaches the preset expected value, if so, use the second detection model as the audio recognition model, and then perform optimization processing on the audio recognition model, if not, use the audio recognition model again. The feature point data set is randomly divided into a feature point training set and a feature point test set.
可选地,本发明的另一个实施例提供一种螺杆水冷机组的好音稳定预警装置,包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序,当所述处理器执行所述计算机程序时,实现如上所述的螺杆水冷机组的好音稳定预警方法。该装置可为计算机等装置。Optionally, another embodiment of the present invention provides a good sound stability early warning device for a screw water cooling unit, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, when When the processor executes the computer program, the above-mentioned good-sound stability early warning method of the screw water-cooling unit is realized. The device can be a computer or the like.
可选地,本发明的另一个实施例提供一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,当所述计算机程序被处理器执行时,实现如上所述的螺杆水冷机组的好音稳定预警方法。Optionally, another embodiment of the present invention provides a computer-readable storage medium, the computer-readable storage medium stores a computer program, when the computer program is executed by the processor, the above-mentioned screw water cooling is realized Good sound stability early warning method for the crew.
所属领域的技术人员可以清楚地了解到,为了描述的方便和简洁,上述描述的装置和单元的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described devices and units may refer to the corresponding processes in the foregoing method embodiments, which will not be repeated here.
在本申请所提供的几个实施例中,应该理解到,所揭露的装置和方法,可以通过其它的方式实现。例如,以上所描述的装置实施例仅仅是示意性的,例如,单元的划分,仅仅为一种逻辑功能划分,实际实现时可以有另外的划分方式,例如多个单元或组件可以结合或者可以集成到另一个系统,或一些特征可以忽略,或不执行。In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, for example, multiple units or components may be combined or integrated. to another system, or some features can be ignored, or not implemented.
作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本发明实施例方案的目的。Units described as separate components may or may not be physically separated, and components shown as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solutions in the embodiments of the present invention.
另外,在本发明各个实施例中的各功能单元可以集成在一个处理单元中,也可以是各个单元单独物理存在,也可以是两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现,也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.
集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时,可以存储在一个计算机可读取存储介质中。用于这样的理解,本发明的技术方案本质上或者说对现有技术做出贡献的部分,或者该技术方案的全部或部分可以以软件产品的形式体现出来,该计算机软件产品存储在一个存储介质中,包括若干指令用以使得一台计算机设备(可以是个人计算机,服务器,或者网络设备等)执行本发明各个实施例方法的全部或部分步骤。而前述的存储介质包括:U盘、移动硬盘、只读存储器(ROM,Read-Only Memory)、随机存取存储器(RAM,Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。The integrated unit, if implemented as a software functional unit and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. For such understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage device. The medium includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .
以上,仅为本发明的具体实施方式,但本发明的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本发明揭露的技术范围内,可轻易想到各种等效的修改或替换,这些修改或替换都应涵盖在本发明的保护范围之内。因此,本发明的保护范围应以权利要求的保护范围为准。The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or modifications within the technical scope disclosed by the present invention. Replacement, these modifications or replacements should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
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