技术领域Technical Field
本发明涉及大数据和声学技术领域，具体为基于自主网的噪声监测装置及噪声溯源优化方法。The present invention relates to the field of big data and acoustic technology, and in particular to a noise monitoring device based on an autonomous network and a noise source tracing optimization method.
背景技术Background Art
在城市化进程发展不断加速的今天，城市噪声污染已成为一个严重的环境问题，对人们的健康和生活质量产生了严重负面影响，严格控制噪声的产生和传播变得越来越迫切，而在控制过程中对噪声的监控识别尤为重要。传统的城市噪声监测技术在面对这一挑战时显现出明显的缺陷，只能监测区域环境的噪声大小，不能具体识别产生噪声的来源和如何产生的，传统的噪声监测方法通常依赖于有限数量的分散式传感器，这些传感器覆盖范围有限，难以全面捕捉城市各个区域的噪声情况，这就导致了监测结果的局限性，容易出现漏报和误报；由于传感器的数量有限，无法实现对城市范围内的噪声事件进行实时有效的监测，这意味着噪声问题得不到及时发现和解决，居民的生活受到持续干扰；传统方法在噪声源的溯源方面由于缺乏详细的声学特征信息，传统技术往往难以追溯到具体的噪声来源和由谁产生的噪声；这使得城市管理部门难以采取有效的控制措施，因为无法明确哪些噪声源形成了噪声污染；且传统噪声监测方法在数据处理和传输过程中存在延迟，无法满足实时性要求；无法满足城市噪声事件需要即时处理和响应的要求，传统解决噪声的方法具有严重的延迟滞后性，进一步加剧了城市噪声污染问题。综上所述，传统的城市噪声监测技术在全面性、实时性和噪声源溯源等方面存在明显缺陷，无法满足当今城市化进程中对噪声管理的需求；为此发明人提出于自主网的噪声监测装置及改进Seq2Seq算法的噪声溯源优化方法，采用一种创新的技术来克服这些问题，提高噪声监测的准确性、实时性和可操作性，有效解决噪声污染问题。As the urbanization process continues to accelerate, urban noise pollution has become a serious environmental problem, which has had a serious negative impact on people's health and quality of life. It has become increasingly urgent to strictly control the generation and spread of noise, and monitoring and identifying noise during the control process is particularly important. Traditional urban noise monitoring technology has obvious defects in the face of this challenge. It can only monitor the noise level of the regional environment, but cannot specifically identify the source of the noise and how it is generated. Traditional noise monitoring methods usually rely on a limited number of distributed sensors. These sensors have limited coverage and it is difficult to fully capture the noise conditions in various areas of the city, which leads to limitations in monitoring results and is prone to missed reports and false alarms. Due to the limited number of sensors, it is impossible to achieve real-time and effective monitoring of noise events within the city, which means that noise problems cannot be discovered and solved in a timely manner, and residents' lives are continuously disturbed. In terms of tracing the source of noise, traditional methods often have difficulty tracing back to the specific source of noise and who generates the noise due to the lack of detailed acoustic feature information. This makes it difficult for urban management departments to take effective control measures because it is impossible to clearly identify which noise sources form noise pollution. In addition, traditional noise monitoring methods have delays in data processing and transmission, and cannot meet real-time requirements. They cannot meet the requirements of urban noise events that require immediate processing and response. Traditional methods for solving noise have serious delays and lags, which further aggravates the problem of urban noise pollution. In summary, traditional urban noise monitoring technology has obvious defects in comprehensiveness, real-time and noise source tracing, and cannot meet the needs of noise management in today's urbanization process. For this reason, the inventor proposed a noise monitoring device based on an autonomous network and a noise source tracing optimization method of an improved Seq2Seq algorithm, using an innovative technology to overcome these problems, improve the accuracy, real-time and operability of noise monitoring, and effectively solve the problem of noise pollution.
发明内容Summary of the invention
为解决上述技术问题，本发明提出了基于自主网的噪声监测装置及噪声溯源优化方法，通过设置自主网的噪声监测装置的设备盒和后盖，在设备盒上部安装收音麦克风和通讯天线，在设备盒内安装电路板和蓄电池，在电路板一侧设置电源接口；基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法的步骤为，步骤一、自主网的噪声监测装置部署；步骤二、噪声数据采集以及处理；步骤三、自主网声学声纹库构建；步骤四、改进Seq2Seq算法模型网络构建；基于自主网的噪声监测装置及噪声溯源优化方法自主网建立云端声纹库，采用多模型Seq2Seq噪声监测网络架构，使用余弦计算和中间声纹优先匹配原则进行噪声溯源，快速确定噪声来源，有效快速解决噪声污染。To solve the above technical problems, the present invention proposes a noise monitoring device based on an autonomous network and a noise source tracing optimization method. By setting a device box and a back cover of the noise monitoring device of the autonomous network, a radio microphone and a communication antenna are installed on the upper part of the device box, a circuit board and a battery are installed in the device box, and a power interface is set on one side of the circuit board; the steps of the noise source tracing optimization method of the improved Seq2Seq algorithm based on the noise monitoring device of the autonomous network are as follows: step one, deployment of the noise monitoring device of the autonomous network; step two, noise data collection and processing; step three, construction of an acoustic voiceprint library of the autonomous network; step four, construction of an improved Seq2Seq algorithm model network; the noise monitoring device and the noise source tracing optimization method based on the autonomous network establish a cloud voiceprint library in the autonomous network, adopt a multi-model Seq2Seq noise monitoring network architecture, use cosine calculation and intermediate voiceprint priority matching principle to trace the noise source, quickly determine the source of noise, and effectively and quickly solve noise pollution.
为实现上述目的，本发明采取的技术方案是：To achieve the above object, the technical solution adopted by the present invention is:
基于自主网的噪声监测装置，包括有收音麦克风、通讯天线、设备盒、电路板、蓄电池、电源接口和后盖，所述基于自主网的噪声监测装置设置有设备盒和后盖，所述设备盒和后盖之间设置有防水圈，所述设备盒上部安装有收音麦克风和通讯天线，利用通讯天线建立网络管理系统，监控监测装置的状态和数据传输，所述设备盒内安装有电路板和蓄电池，所述电路板包括麦克风、数字信号处理器、内存及存储单元，建立云端存储系统，接收并存储城市中各个噪声节点自主网监测装置的噪声数据流，进而生成噪声分贝值，频率和振幅，所述电路板安装蓄电池的一侧设置电源接口，所述设备盒上与电源接口对应位置设置电源口孔。The noise monitoring device based on autonomous network includes a radio microphone, a communication antenna, a device box, a circuit board, a battery, a power interface and a back cover. The noise monitoring device based on autonomous network is provided with a device box and a back cover, and a waterproof ring is provided between the device box and the back cover. A radio microphone and a communication antenna are installed on the upper part of the device box. A network management system is established by using the communication antenna to monitor the status and data transmission of the monitoring device. A circuit board and a battery are installed in the device box. The circuit board includes a microphone, a digital signal processor, a memory and a storage unit. A cloud storage system is established to receive and store noise data streams of autonomous network monitoring devices of various noise nodes in the city, and then generate noise decibel values, frequencies and amplitudes. A power interface is provided on one side of the circuit board where the battery is installed, and a power port is provided on the device box at a position corresponding to the power interface.
作为本发明装置进一步改进，所述麦克风采用Knowles SPU0410LR5H-QB麦克风芯片，所述数字信号处理器采用Cortex-A72处理器芯片、所述内存及存储单元包括K4F8E3S4HM-MGCJ内存芯片、NAND闪存、以太网接口芯片、NVIDIA图像处理芯片、DSPC5535声纹处理识别芯片与Texas Instruments电源管理芯片。As a further improvement of the device of the present invention, the microphone adopts the Knowles SPU0410LR5H-QB microphone chip, the digital signal processor adopts the Cortex-A72 processor chip, the memory and storage unit include K4F8E3S4HM-MGCJ memory chip, NAND flash memory, Ethernet interface chip, NVIDIA image processing chip, DSPC5535 voiceprint processing and recognition chip and Texas Instruments power management chip.
本发明提供基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法，其特征在于：包括以下步骤：The present invention provides a noise source tracing optimization method of an improved Seq2Seq algorithm of a noise monitoring device based on an autonomous network, which is characterized by comprising the following steps:
步骤一、自主网的噪声监测装置部署；将自主网噪声监测装置部署到各区域噪声节点处，利用通讯天线建立网络管理系统，监控监测装置的状态和数据传输；Step 1: Deployment of noise monitoring devices in the autonomous network: deploy the noise monitoring devices in the autonomous network to the noise nodes in each area, establish a network management system using communication antennas, and monitor the status of the monitoring devices and data transmission;
步骤二、噪声数据采集以及处理；建立云端存储系统，接收并存储城市中各个噪声节点自主网监测装置的噪声数据流，进而生成噪声分贝值，频率和振幅，并通过YOLOV8网络对声音的波形图进行深度学习，形成波形噪声概率特征值，使用高振幅波动间隔作为表征噪声的表达量之一，利用间隔公式计算高振幅波动间隔特征值；Step 2: Noise data collection and processing: Establish a cloud storage system to receive and store the noise data stream of the autonomous network monitoring device of each noise node in the city, and then generate the noise decibel value, frequency and amplitude. Use the YOLOV8 network to conduct deep learning on the waveform of the sound to form the waveform noise probability characteristic value, use the high amplitude fluctuation interval as one of the expressions to characterize the noise, and use the interval formula to calculate the high amplitude fluctuation interval characteristic value;
步骤三、自主网声学声纹库构建；通过自主网监测装置获取各区域噪声节点的声学特征向量，进而构建各区域噪声节点的声学声纹库，通过线性插值与扩散公式构建出各节点之间无自主网监测装置的声学声纹库，实现全城市噪声声纹库构建；Step 3: Autonomous network acoustic voiceprint library construction: Acoustic feature vectors of noise nodes in each area are obtained through autonomous network monitoring devices, and then the acoustic voiceprint library of noise nodes in each area is constructed. The acoustic voiceprint library without autonomous network monitoring devices between nodes is constructed through linear interpolation and diffusion formulas, so as to realize the construction of noise voiceprint library for the whole city.
步骤四、改进Seq2Seq算法模型网络构建；通过改进Seq2Seq算法模型网络，去预测自主网噪声监测装置实时产生的声学向量，采用声纹损失函数给定算法模型损失，此外根据该向量匹配其隶属的声学声纹库，具体判定结果采用阈值公式进行抉择，最终完成噪声的溯源工作。Step 4. Improve the construction of Seq2Seq algorithm model network. By improving the Seq2Seq algorithm model network, predict the acoustic vector generated by the autonomous network noise monitoring device in real time, use the voiceprint loss function to give the algorithm model loss, and match the vector with the acoustic voiceprint library to which it belongs. The specific judgment result is determined by the threshold formula, and finally the noise tracing work is completed.
进一步的，所述基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法的步骤二中使用的间隔公式表示为：Furthermore, the interval formula used in step 2 of the noise source tracing optimization method of the improved Seq2Seq algorithm of the noise monitoring device based on the autonomous network is expressed as:
其中，间隔公式表示为：The interval formula is expressed as:
其中，fi为当前时刻的振幅值，ft为前向时刻的振幅值，F则表示为前向120秒振幅的平均值，α表示为异常振幅放大因子，σ则为高振幅波动间隔具体值。Among them,fi is the amplitude value at the current moment,ft is the amplitude value at the previous moment, F is the average amplitude of the previous 120 seconds, α is the abnormal amplitude amplification factor, and σ is the specific value of the high amplitude fluctuation interval.
进一步的，所述基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法的步骤三中使用的扩散公式表示为：Furthermore, the diffusion formula used in step 3 of the noise source tracing optimization method of the improved Seq2Seq algorithm of the noise monitoring device based on the autonomous network is expressed as:
其中，扩散公式表示如下：The diffusion formula is expressed as follows:
其中，χi为两个自主网噪声监测装置中间待插值向量的特征值，k1,k2为两个自主网监测到各自区域内的噪声数据的最显著特征，即自主网噪声监测装置处于交通干道与工业区，则为分贝值特征，处于商业区，则为振幅，处与居住小区则为高振幅波动间隔，处于学校区则为频率；d1,d2则分别为两个自主网噪声监测装置的其余原始特征值。Among them, χi is the eigenvalue of the interpolation vector between the two autonomous network noise monitoring devices, k1 and k2 are the most significant features of the noise data monitored by the two autonomous networks in their respective areas, that is, if the autonomous network noise monitoring device is located in a traffic artery and an industrial area, it is the decibel value feature, if it is in a commercial area, it is the amplitude, if it is in a residential area, it is the high amplitude fluctuation interval, and if it is in a school area, it is the frequency; d1 and d2 are the remaining original eigenvalues of the two autonomous network noise monitoring devices respectively.
进一步的，所述基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法的步骤四中使用的声纹损失函数和阀值公式表示为：Furthermore, the voiceprint loss function and threshold formula used in step 4 of the noise source tracing optimization method of the improved Seq2Seq algorithm of the noise monitoring device based on the autonomous network are expressed as follows:
其中，声纹损失函数表示如下：Among them, the voiceprint loss function is expressed as follows:
其中，Lo为声纹损失值，γi则为自主网监测装置实时监测到的声学数据特征向量值，即为分贝值，高振幅波动间隔、频率、振幅，波形噪声概率这五种数据组成的向量，存在两种类型即噪声与非噪声，n表明该处自主网监测装置监测的声学声纹库所包含的该类型的总数据量，pi则为自主网监测装置监测的声学声纹库中该类型第i个数据向量；Among them, Lo is the voiceprint loss value, γi is the characteristic vector value of the acoustic data monitored by the autonomous network monitoring device in real time, that is, a vector composed of five data types: decibel value, high amplitude fluctuation interval, frequency, amplitude, and waveform noise probability. There are two types, namely noise and non-noise. n indicates the total amount of data of this type contained in the acoustic voiceprint library monitored by the autonomous network monitoring device at this location,and pi is the i-th data vector of this type in the acoustic voiceprint library monitored by the autonomous network monitoring device;
其中，阈值公式表示如下：The threshold formula is as follows:
其中，yi表示为网络预测出的向量值，Yi为掌纹数据库中与其余弦距离最近的值，ω则为判断因子，通过本公式即可完成，对于预测出的向量属于哪一声学声纹库。Among them,yi represents the vector value predicted by the network,Yi is the value closest to the cosine in the palmprint database, and ω is the judgment factor. This formula can be used to determine which acoustic voiceprint library the predicted vector belongs to.
本发明提供基基于自主网的噪声监测装置及噪声溯源优化方法，通过设置自主网的噪声监测装置的设备盒和后盖，在设备盒上部安装收音麦克风和通讯天线，在设备盒内安装电路板和蓄电池，在电路板一侧设置电源接口；基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法的步骤为，步骤一、自主网的噪声监测装置部署；步骤二、噪声数据采集以及处理；步骤三、自主网声学声纹库构建；步骤四、改进Seq2Seq算法模型网络构建；基于自主网的噪声监测装置及噪声溯源优化方法自主网建立云端声纹库，采用多模型Seq2Seq噪声监测网络架构，使用余弦计算和中间声纹优先匹配原则进行噪声溯源，快速确定噪声来源；带来的好处是：The present invention provides a noise monitoring device based on an autonomous network and a noise source tracing optimization method. The device box and the back cover of the noise monitoring device of the autonomous network are provided, a radio microphone and a communication antenna are installed on the upper part of the device box, a circuit board and a storage battery are installed in the device box, and a power interface is provided on one side of the circuit board. The steps of the noise source tracing optimization method of the improved Seq2Seq algorithm based on the noise monitoring device of the autonomous network are as follows: step 1, deployment of the noise monitoring device of the autonomous network; step 2, noise data collection and processing; step 3, construction of an acoustic voiceprint library of the autonomous network; step 4, construction of an improved Seq2Seq algorithm model network; the noise monitoring device and the noise source tracing optimization method based on the autonomous network establish a cloud voiceprint library on the autonomous network, adopt a multi-model Seq2Seq noise monitoring network architecture, use cosine calculation and intermediate voiceprint priority matching principle to trace the noise source, and quickly determine the noise source; the benefits brought are:
1、基于自主网的噪声监测装置及噪声溯源优化方法能通过部署自主网噪声监测装置覆盖城市各个区域，并借助云端存储和深度学习技术，实现了全面监测城市噪声的能力。这使得城市管理部门可以及时获得全城市范围内的噪声数据，并能够实时响应噪声事件，从而更好地保护居民的健康和生活质量；1. The noise monitoring device based on autonomous network and the noise source tracing optimization method can cover all areas of the city by deploying the noise monitoring device of autonomous network, and realize the ability of comprehensive monitoring of urban noise with the help of cloud storage and deep learning technology. This enables the city management department to obtain noise data across the city in a timely manner and respond to noise events in real time, so as to better protect the health and quality of life of residents;
2、基于自主网的噪声监测装置及噪声溯源优化方法通过构建声学声纹库和引入多模型Seq2Seq噪声监测网络，能够更准确地追溯噪声的来源；这对于采取有针对性的治理措施至关重要，有助于城市管理部门迅速识别和解决噪声污染问题；2. The noise monitoring device based on the autonomous network and the noise source tracing optimization method can trace the source of noise more accurately by constructing an acoustic voiceprint library and introducing a multi-model Seq2Seq noise monitoring network; this is crucial for taking targeted governance measures and helps urban management departments to quickly identify and solve noise pollution problems;
3、基于自主网的噪声监测装置及噪声溯源优化方法引入高振幅波动间隔作为噪声特征之一，并提供了间隔公式来定量描述这一特征；区别于传统的噪声特征通常侧重于分贝值、频率等参数，高振幅波动间隔作为一个新的表征噪声的量化特征，可以为噪声监测和溯源提供更多信息，有助于更准确地区分不同来源和类型的噪声；3. The noise monitoring device and noise source tracing optimization method based on the autonomous network introduces the high amplitude fluctuation interval as one of the noise characteristics, and provides an interval formula to quantitatively describe this feature; different from the traditional noise characteristics which usually focus on parameters such as decibel value and frequency, the high amplitude fluctuation interval as a new quantitative feature to characterize noise can provide more information for noise monitoring and tracing, and help to more accurately distinguish different sources and types of noise;
4、基于自主网的噪声监测装置及噪声溯源优化方法使用一种创新的噪声监测网络架构，采用Seq2Seq模型，并提出了中间声纹优先匹配原则；这一架构允许将实时收集到的声学数据输入到算法网络中，通过向量匹配来确定噪声的来源。中间声纹优先匹配原则进一步提高了匹配的准确性，有助于迅速且可靠地确定噪声源。4. The noise monitoring device based on the autonomous network and the noise source tracing optimization method use an innovative noise monitoring network architecture, adopt the Seq2Seq model, and propose the intermediate voiceprint priority matching principle; this architecture allows the real-time collected acoustic data to be input into the algorithm network, and the source of the noise is determined by vector matching. The intermediate voiceprint priority matching principle further improves the matching accuracy and helps to quickly and reliably determine the noise source.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明整体结构示意图；Fig. 1 is a schematic diagram of the overall structure of the present invention;
图2为本发明基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法流程图；FIG2 is a flow chart of a noise source tracing optimization method of an improved Seq2Seq algorithm of a noise monitoring device based on an autonomous network according to the present invention;
图3为本发明基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法声音波形图示意图；3 is a schematic diagram of a sound waveform of a noise source tracing optimization method of an improved Seq2Seq algorithm of a noise monitoring device based on an autonomous network according to the present invention;
图4为本发明基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法声学声纹库示意图；FIG4 is a schematic diagram of an acoustic voiceprint library of a noise source tracing optimization method of an improved Seq2Seq algorithm of a noise monitoring device based on an autonomous network of the present invention;
图5为本发明基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法算法网络架构示意图。FIG5 is a schematic diagram of the algorithm network architecture of the noise source tracing optimization method of the improved Seq2Seq algorithm of the noise monitoring device based on the autonomous network of the present invention.
图中标记为：1、收音麦克风；2、通讯天线；3、设备盒；4、电路板；5、蓄电池；6、电源接口；7、后盖。The components marked in the figure are: 1. Radio microphone; 2. Communication antenna; 3. Equipment box; 4. Circuit board; 5. Battery; 6. Power interface; 7. Back cover.
具体实施方式DETAILED DESCRIPTION
下面结合附图与具体实施方式对本发明作进一步详细描述：The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments:
如图1所示，所示为基基于自主网的噪声监测装置，包括有收音麦克风1、通讯天线2、设备盒3、电路板4、蓄电池5、电源接口6和后盖，其特征在于：所示基于自主网的噪声监测装置设置有设备盒3和后盖7，所示设备盒3和后盖7之间设置有防水圈，设备盒3和后盖7采用螺栓安装，安装完成后具备较好的防水性，可以室外露天安装使用，所示设备盒3上部安装有收音麦克风1和通讯天线2，收音麦克风1和通讯天线2采用防水设计，可避免室外安装时水气对其造成影响，通讯天线2采用无线通讯，与云端设备连接传输数据，利用通讯天线建立网络管理系统，监控监测装置的状态和数据传输，所示设备盒3内安装有电路板4和蓄电池5，所述电路板包括麦克风、数字信号处理器、内存及存储单元，建立云端存储系统，接收并存储城市中各个噪声节点自主网监测装置的噪声数据流，进而生成噪声分贝值，频率和振幅，所示电路板4安装蓄电池5的一侧设置电源接口6，所示设备盒3上与电源接口6对应位置设置电源口孔，通过电源接口6对蓄电池5和电路板4提供电源，蓄电池5也可单独对电路板提供电源。As shown in Figure 1, the noise monitoring device based on the autonomous network includes a radio microphone 1, a communication antenna 2, a device box 3, a circuit board 4, a battery 5, a power interface 6 and a back cover, and is characterized in that: the noise monitoring device based on the autonomous network is provided with a device box 3 and a back cover 7, a waterproof ring is provided between the device box 3 and the back cover 7, the device box 3 and the back cover 7 are installed with bolts, and have good waterproof properties after installation, and can be installed and used outdoors, a radio microphone 1 and a communication antenna 2 are installed on the upper part of the device box 3, the radio microphone 1 and the communication antenna 2 are waterproof, which can avoid the influence of water vapor on them when installed outdoors, and the communication antenna 2 adopts wireless communication, and The cloud device is connected to transmit data, and a network management system is established using a communication antenna to monitor the status of the monitoring device and data transmission. A circuit board 4 and a battery 5 are installed in the device box 3 shown. The circuit board includes a microphone, a digital signal processor, a memory and a storage unit. A cloud storage system is established to receive and store the noise data stream of the autonomous network monitoring device of each noise node in the city, and then generate the noise decibel value, frequency and amplitude. A power interface 6 is set on one side of the circuit board 4 where the battery 5 is installed, and a power port is set at a position corresponding to the power interface 6 on the device box 3 shown. Power is provided to the battery 5 and the circuit board 4 through the power interface 6, and the battery 5 can also provide power to the circuit board alone.
本发明所述麦克风采用Knowles SPU0410LR5H-QB麦克风芯片，所述数字信号处理器采用Cortex-A72处理器芯片、所述内存及存储单元包括K4F8E3S4HM-MGCJ内存芯片、NAND闪存、以太网接口芯片、NVIDIA图像处理芯片、DSPC5535声纹处理识别芯片与TexasInstruments电源管理芯片。The microphone of the present invention adopts the Knowles SPU0410LR5H-QB microphone chip, the digital signal processor adopts the Cortex-A72 processor chip, the memory and storage unit include K4F8E3S4HM-MGCJ memory chip, NAND flash memory, Ethernet interface chip, NVIDIA image processing chip, DSPC5535 voiceprint processing and recognition chip and Texas Instruments power management chip.
如图2所示，所示基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法流程图，所示基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法具体包括以下步骤：As shown in FIG2 , a flow chart of a noise source tracing optimization method of an improved Seq2Seq algorithm of a noise monitoring device based on an autonomous network is shown. The noise source tracing optimization method of an improved Seq2Seq algorithm of a noise monitoring device based on an autonomous network specifically includes the following steps:
步骤一、自主网的噪声监测装置部署；将自主网噪声监测装置部署到各区域噪声节点处，利用通讯天线建立网络管理系统，监控监测装置的状态和数据传输；其中值得注意的是，具体部署位置需要根据环境来确定；即首先要保证城市的关键部位该装置均部署到位，包括交通干道、商业区、工业区、居住小区和学校区；其中上述自主网噪声监测装置需放置在建筑物屋顶、电线杆或交通信号灯杆上，以充分的捕捉噪声信息；在确保每个监测点稳固安全的情况后，配置数据通信模块，并对其进行数据质量控制的校准和测试，以确保传感器产生准确的噪声数据；最终，建立网络管理系统，监控监测装置的状态和数据传输，设置警报机制，以便在自主网的噪声监测装置出现问题时及时采取措施；Step 1: Deployment of noise monitoring devices in the autonomous network; deploy the noise monitoring devices in the autonomous network to the noise nodes in each area, establish a network management system using communication antennas, and monitor the status and data transmission of the monitoring devices; it is worth noting that the specific deployment location needs to be determined according to the environment; that is, first of all, it is necessary to ensure that the devices are deployed in key parts of the city, including traffic arteries, commercial areas, industrial areas, residential areas and school areas; the above-mentioned noise monitoring devices in the autonomous network need to be placed on the roofs of buildings, telephone poles or traffic signal poles to fully capture noise information; after ensuring that each monitoring point is stable and safe, configure the data communication module, and perform calibration and testing on the data quality control to ensure that the sensor generates accurate noise data; finally, establish a network management system to monitor the status and data transmission of the monitoring devices, and set up an alarm mechanism so that timely measures can be taken when problems occur in the noise monitoring devices of the autonomous network;
步骤二、噪声数据采集以及处理；建立云端存储系统，接收并存储城市中各个噪声节点自主网监测装置的噪声数据流，进而生成噪声分贝值，频率和振幅，并通过YOLOV8网络对声音的波形图进行深度学习，形成波形噪声概率特征值，使用高振幅波动间隔作为表征噪声的表达量之一，利用间隔公式计算高振幅波动间隔特征值；对云端存储系统中各个噪声节点的原始噪声数据进行处理，以消除环境干扰和异常值；如图3所示基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法声音波形图示意图，所示正常声音优化完成后的波形图与噪声波形图不同，方法采用中值滤波器，以减少高频噪声和异常值；此外对于所有的缺失值，为了增加整体模块的反应速度，本次申请直接采用前向填充的方法，即直接使用前一个时间点的值来填充缺失值；此外给每个噪声数据来源打上标签，便于后续溯源能够快速定位；在本步骤中需要保持噪声数据的可比性以及一致性，因此首先将存储的数据进行单位转换，即将所有的声压级数据转换为分贝，并统一采样频率与采样位数，此外对所有数据采取标准差缩放操作，以确保数据在尺度上的一致；Step 2: Noise data collection and processing; establish a cloud storage system to receive and store the noise data stream of the autonomous network monitoring device of each noise node in the city, and then generate the noise decibel value, frequency and amplitude, and use the YOLOV8 network to perform deep learning on the sound waveform to form the waveform noise probability characteristic value, use the high amplitude fluctuation interval as one of the expressions to characterize the noise, and use the interval formula to calculate the high amplitude fluctuation interval characteristic value; process the original noise data of each noise node in the cloud storage system to eliminate environmental interference and outliers; as shown in Figure 3, the noise source tracing optimization method of the improved Seq2Seq algorithm of the noise monitoring device based on the autonomous network is shown in the sound waveform diagram, The waveform of normal sound after optimization is different from that of noise. The method uses a median filter to reduce high-frequency noise and outliers. In addition, for all missing values, in order to increase the response speed of the overall module, this application directly adopts the forward filling method, that is, directly using the value of the previous time point to fill the missing value. In addition, each noise data source is labeled to facilitate the subsequent traceability and rapid location. In this step, the comparability and consistency of the noise data need to be maintained. Therefore, the stored data is first converted to units, that is, all sound pressure level data is converted to decibels, and the sampling frequency and sampling bit number are unified. In addition, all data are subjected to standard deviation scaling to ensure the consistency of the data in scale.
对于整体声学数据的特征考虑包括分贝值，高振幅波动间隔，频率，振幅，波形噪声概率；其中分贝值、频率、振幅这三种直接作为特征值x1，x2，x3；本次申请中首次提出高振幅波动间隔作为表征噪声的表达量之一，高振幅波动间隔特征可采用间隔公式给定；The characteristics of the overall acoustic data include decibel value, high amplitude fluctuation interval, frequency, amplitude, and waveform noise probability; among them, decibel value, frequency, and amplitude are directly used as characteristic values x1 , x2 , and x3 ; this application proposes for the first time that high amplitude fluctuation interval is used as one of the expression quantities to characterize noise, and the high amplitude fluctuation interval feature can be given by the interval formula;
其中，间隔公式表示为：The interval formula is expressed as:
其中，fi为当前时刻的振幅值，ft为前向时刻的振幅值，F则表示为前向120秒振幅的平均值，α表示为异常振幅放大因子，σ则为高振幅波动间隔具体值；Among them,fi is the amplitude value at the current moment,ft is the amplitude value at the previous moment, F is the average value of the amplitude in the previous 120 seconds, α is the abnormal amplitude amplification factor, and σ is the specific value of the high amplitude fluctuation interval;
在上述公式中，首先通过公式(1)计算出两分钟内振幅的平均值，然后利用公式(2)对于当前振幅值进行判定，当前振幅值属于设定范围内，则给定高振幅波动间隔特征为0，而当前振幅值超过设定范围内，则设定高振幅波动间隔为异常振幅放大因子，当前振动幅值及平均幅值的有关值；In the above formula, the average value of the amplitude within two minutes is first calculated by formula (1), and then the current amplitude value is judged by formula (2). If the current amplitude value is within the set range, the high amplitude fluctuation interval feature is given as 0, and if the current amplitude value exceeds the set range, the high amplitude fluctuation interval is set as the abnormal amplitude amplification factor, the current vibration amplitude and the relevant value of the average amplitude;
对于声音的波形图进行深度学习，即图像分类；采用YOLOV8网络架构，进行图像分类迁移学习，其中分类目标为是否为噪声波形图，当为噪声波形图时，即判定为1，否则判定为0；此外本次申请中噪声波形图的数量相较于正常波形图是较少的，因此本次申请首次提出采用噪声波形图拼接技术，以进行数据扩展；即通过将有限的噪声波形图进行裁剪，拼接，进而形成新的噪声波形图；当该网络训练完成后，其对于每一条声音数据的波形图的噪声波形图网络置信度给定位该条数据的波形噪声概率特征值；Perform deep learning on the waveform of the sound, i.e., image classification; use the YOLOV8 network architecture to perform image classification transfer learning, where the classification target is whether it is a noise waveform. If it is a noise waveform, it is judged as 1, otherwise it is judged as 0; in addition, the number of noise waveforms in this application is less than that of normal waveforms, so this application proposes for the first time to use noise waveform splicing technology to expand data; that is, by cutting and splicing limited noise waveforms, a new noise waveform is formed; when the network training is completed, the network confidence of the noise waveform of each waveform of the sound data gives the waveform noise probability characteristic value of the data;
步骤三、自主网声学声纹库构建；通过自主网监测装置获取各区域噪声节点的声学特征向量，进而构建各区域噪声节点的声学声纹库，通过线性插值与扩散公式构建出各节点之间无自主网监测装置的声学声纹库，实现全城市噪声声纹库构建；如图4所示，所示为本发明基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法声学声纹库示意图；所示在步骤一以及步骤二中，完成了自主网噪声装置的部署以及噪声数据的特征提取给定，本步骤中，则需要完成城市各区域各地点的声学声纹库构建；需要注意的是，本次申请中声学声纹库除了针对城市各区域各地点自主网噪声监测装置进行构建，还需要进行分类，即该声纹库中分别存放着各个地点的正常声音的特征数据包括分贝值、高振幅波动间隔、频率、振幅和波形噪声概率；对于噪声数据，还需构建上述根据城市中各区域各地点自主网噪声监测装置的噪声声纹库，即此时构建了两个声学声纹库，这两种声学声纹库内里数据均为各个节点的自主网噪声监测设备的数据，但第一种声学声纹库只包含正常声音，而第二种声学声纹库只包含各个节点的噪声特征数据；此外本次申请提出采用扩散公式构建一种基于自主网噪声监测装置分散节点及区域特征的全城市声纹库；Step 3: Construct an acoustic voiceprint library of the autonomous network; obtain the acoustic feature vectors of the noise nodes in each area through the autonomous network monitoring device, and then construct an acoustic voiceprint library of the noise nodes in each area, and construct an acoustic voiceprint library without an autonomous network monitoring device between each node through linear interpolation and diffusion formulas, so as to realize the construction of the noise voiceprint library of the whole city; as shown in Figure 4, it is a schematic diagram of the acoustic voiceprint library of the noise tracing optimization method of the improved Seq2Seq algorithm of the noise monitoring device based on the autonomous network of the present invention; as shown in step 1 and step 2, the deployment of the autonomous network noise device and the feature extraction of the noise data are completed. Given, in this step, it is necessary to complete the construction of the acoustic voiceprint library of each location in each area of the city; it should be noted that in this application, the acoustic voiceprint library is not only for each location in the city, but also for each location in the city. The autonomous network noise monitoring devices at various locations in the region are constructed and classified, that is, the characteristic data of normal sounds at various locations are stored in the voiceprint library, including decibel value, high amplitude fluctuation interval, frequency, amplitude and waveform noise probability; for noise data, it is also necessary to construct the above-mentioned noise voiceprint library based on the autonomous network noise monitoring devices at various locations in the city, that is, two acoustic voiceprint libraries are constructed at this time, and the data in these two acoustic voiceprint libraries are the data of the autonomous network noise monitoring equipment of each node, but the first acoustic voiceprint library only contains normal sounds, while the second acoustic voiceprint library only contains the noise characteristic data of each node; in addition, this application proposes to use a diffusion formula to construct a city-wide voiceprint library based on the scattered nodes and regional characteristics of the autonomous network noise monitoring device;
其中，扩散公式表示如下：The diffusion formula is expressed as follows:
其中，χi为两个自主网噪声监测装置中间待插值向量的特征值，k1,k2为两个自主网监测到各自区域内的噪声数据的最显著特征，即自主网噪声监测装置处于交通干道与工业区，则为分贝值特征，处于商业区，则为振幅，处与居住小区则为高振幅波动间隔，处于学校区则为频率；d1,d2则分别为两个自主网噪声监测装置的其余原始特征值；Among them, χi is the eigenvalue of the vector to be interpolated between the two autonomous network noise monitoring devices, k1, k2 are the most significant features of the noise data monitored by the two autonomous networks in their respective areas, that is, if the autonomous network noise monitoring device is located in the main traffic road and industrial area, it is the decibel value feature, if it is in the commercial area, it is the amplitude, if it is in the residential area, it is the high amplitude fluctuation interval, and if it is in the school area, it is the frequency; d1, d2 are the remaining original eigenvalues of the two autonomous network noise monitoring devices respectively;
通过公式(3)即可完成所有自主网噪声监测装置分散节点及区域特征的全城市声纹库构建，例如当一个自主网噪声监测装置处于学校区，另外一个自主网噪声监测装置处于工业区时，对于这两个自主网中间区域的节点进行插值填充，五项特征数据中分贝值、高振幅波动间隔、频率、振幅和波形噪声概率，分贝值直接填充为工业园区的分贝值，频率则直接填充学校的频率，其余特征值高振幅波动间隔，振幅，波形噪声概率则采用两个区域的线性插值；Formula (3) can be used to complete the construction of the city-wide voiceprint library of all autonomous network noise monitoring devices' scattered nodes and regional characteristics. For example, when one autonomous network noise monitoring device is in the school area and another autonomous network noise monitoring device is in the industrial area, the nodes in the middle area of the two autonomous networks are interpolated and filled. Among the five feature data, decibel value, high amplitude fluctuation interval, frequency, amplitude and waveform noise probability, the decibel value is directly filled with the decibel value of the industrial park, and the frequency is directly filled with the frequency of the school. The remaining feature values, high amplitude fluctuation interval, amplitude, and waveform noise probability, are linearly interpolated between the two areas.
步骤四、改进Seq2Seq算法模型网络构建；通过改进Seq2Seq算法模型网络，去预测自主网噪声监测装置实时产生的声学向量，采用声纹损失函数给定算法模型损失，此外根据该向量匹配其隶属的声学声纹库，具体判定结果采用阈值公式进行抉择，最终完成噪声的溯源工作；如图5所示，所示为本发明基于自主网的噪声监测装置的改进Seq2Seq算法的噪声溯源优化方法算法网络架构示意图；所示改进的Seq2Seq算法模型网络构建对实时监测到的声学数据进行判定，该网络输入的特征项为自主网噪声监测装置监测到10个连续时刻的声学数据，其输出项也为一个向量值，用该向量去匹配步骤三中的声学声纹库，寻找与其最近似的向量，损失值即为预测向量与最贴近向量之间的余弦距离，并采用声纹损失函数给定；Step 4, improve the Seq2Seq algorithm model network construction; by improving the Seq2Seq algorithm model network, predict the acoustic vector generated in real time by the autonomous network noise monitoring device, use the voiceprint loss function to give the algorithm model loss, and in addition, match the acoustic voiceprint library to which the vector belongs according to the vector, and use the threshold formula to make a decision on the specific judgment result, and finally complete the noise tracing work; as shown in Figure 5, it is a schematic diagram of the algorithm network architecture of the noise tracing optimization method of the improved Seq2Seq algorithm based on the noise monitoring device of the autonomous network of the present invention; the improved Seq2Seq algorithm model network construction judges the acoustic data monitored in real time, and the feature item of the network input is the acoustic data monitored by the autonomous network noise monitoring device at 10 consecutive moments, and its output item is also a vector value, which is used to match the acoustic voiceprint library in step three to find the vector closest to it, and the loss value is the cosine distance between the predicted vector and the closest vector, and is given by the voiceprint loss function;
其中，声纹损失函数表示如下：Among them, the voiceprint loss function is expressed as follows:
其中，Lo为声纹损失值，γi则为自主网监测装置实时监测到的声学数据特征向量值，即为分贝值，高振幅波动间隔、频率、振幅，波形噪声概率这五种数据组成的向量，存在两种类型即噪声与非噪声，n表明该处自主网监测装置监测的声学声纹库所包含的该类型的总数据量，pi则为自主网监测装置监测的声学声纹库中该类型第i个数据向量；Among them, Lo is the voiceprint loss value, γi is the characteristic vector value of the acoustic data monitored by the autonomous network monitoring device in real time, that is, a vector composed of five data types: decibel value, high amplitude fluctuation interval, frequency, amplitude, and waveform noise probability. There are two types, namely noise and non-noise. n indicates the total amount of data of this type contained in the acoustic voiceprint library monitored by the autonomous network monitoring device at this location, andpi is the i-th data vector of this type in the acoustic voiceprint library monitored by the autonomous network monitoring device;
其中装置监测初始值如下表所示：The initial values of the device monitoring are shown in the following table:
针对搭建的声学声纹库提出的一种多模型Seq2Seq噪声监测网络架构，即首先将自主网噪声监测装置实时收集到的声学数据输入到算法网络架构中，其次对其输出向量进行匹配，对于网络输出的向量与声纹库中的向量匹配原则选用余弦计算，并提出一种中间声纹的优先匹配原则，具体流程为将算法网络预测出的向量，首先与不同区域自主网噪声装置中间差值的点进行匹配，当满足阈值公式时，即确定该噪声来源于这两个自主网噪声监测装置附近，进而在与这两个自主网噪声监测装置的声学声纹库进行详细匹配，查看具体属于哪个自主网噪声监测装置，最后根据自主网噪声监测装置确定噪声来源；当中间插值数据与预测出的向量无法匹配即满足不了阈值公式时，即将该向量值与各个地点的自主网声学声纹库的噪声向量进行强匹配，查看是否存在满足阈值公式的声学声纹库向量；当都不存在时，即判定为正常声音A multi-model Seq2Seq noise monitoring network architecture is proposed for the acoustic voiceprint library. First, the acoustic data collected by the autonomous network noise monitoring device in real time is input into the algorithm network architecture, and then its output vector is matched. The cosine calculation is used for the matching principle between the vector output by the network and the vector in the voiceprint library, and a priority matching principle for the intermediate voiceprint is proposed. The specific process is to match the vector predicted by the algorithm network with the point of the middle difference of the autonomous network noise device in different areas. When the threshold formula is met, it is determined that the noise comes from the vicinity of the two autonomous network noise monitoring devices, and then a detailed match is performed with the acoustic voiceprint library of the two autonomous network noise monitoring devices to check which autonomous network noise monitoring device it belongs to. Finally, the source of the noise is determined according to the autonomous network noise monitoring device. When the intermediate interpolation data cannot match the predicted vector, that is, it cannot meet the threshold formula, the vector value is strongly matched with the noise vector of the autonomous network acoustic voiceprint library at each location to check whether there is an acoustic voiceprint library vector that meets the threshold formula; when none of them exists, it is determined to be a normal sound.
其中，阈值公式表示如下：The threshold formula is as follows:
其中，yi表示为网络预测出的向量值，Yi为掌纹数据库中与其余弦距离最近的值，ω则为判断因子，通过本公式即可完成，预测出的向量属于哪一声学声纹库。Among them,yi represents the vector value predicted by the network,Yi is the value closest to the cosine in the palmprint database, and ω is the judgment factor. This formula can be used to determine which acoustic voiceprint library the predicted vector belongs to.
以上所述，仅是本发明的较佳实施例而已，并非是对本发明作任何其他形式的限制，而依据本发明的技术实质所作的任何修改或等同变化，仍属于本发明所要求保护的范围。The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2023116825357 | 2023-12-08 | ||
| CN202311682535 | 2023-12-08 |
| Publication Number | Publication Date |
|---|---|
| CN117928722A CN117928722A (en) | 2024-04-26 |
| CN117928722Btrue CN117928722B (en) | 2024-11-05 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410092943.5AActiveCN117928722B (en) | 2023-12-08 | 2024-01-23 | Noise monitoring device based on autonomous network and noise source tracing optimization method |
| Country | Link |
|---|---|
| CN (1) | CN117928722B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN120354115B (en)* | 2025-06-23 | 2025-08-26 | 江苏省环境监测中心 | Multi-city noise automatic monitoring data analysis method and system based on time period classification |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104697625A (en)* | 2013-12-04 | 2015-06-10 | 苏州水天堂软件科技有限公司 | Noise data acquisition device |
| CN108801448A (en)* | 2018-07-09 | 2018-11-13 | 滨州职业学院 | A kind of urban traffic noise monitoring system of intelligent terminal monitoring |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101235215B1 (en)* | 2010-10-29 | 2013-02-20 | 경북대학교 산학협력단 | Apparatus and method for eliminating noise |
| JP6489163B2 (en)* | 2017-06-22 | 2019-03-27 | 株式会社Ｊｖｃケンウッド | Noise reduction apparatus, noise reduction method, and program. |
| JP6604649B1 (en)* | 2017-12-27 | 2019-11-13 | 株式会社Ｐｈｏｔｏ ｅｌｅｃｔｒｏｎ Ｓｏｕｌ | Sample inspection apparatus and sample inspection method |
| CN108489604B (en)* | 2018-05-30 | 2023-05-02 | 河北工业大学 | Self-driven urban noise measuring device and using method thereof |
| CN111505710B (en)* | 2020-05-06 | 2021-08-13 | 西安交通大学 | A seismic signal extraction method of high-speed railway source based on equally spaced spectral features |
| CN113851119A (en)* | 2021-09-22 | 2021-12-28 | 杭州电子科技大学 | An urban noise recognition method based on Min-Max single-class deep neural network |
| CN115262440A (en)* | 2022-07-18 | 2022-11-01 | 山西路桥第四工程有限公司 | Noise reduction barrier for road and bridge construction |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104697625A (en)* | 2013-12-04 | 2015-06-10 | 苏州水天堂软件科技有限公司 | Noise data acquisition device |
| CN108801448A (en)* | 2018-07-09 | 2018-11-13 | 滨州职业学院 | A kind of urban traffic noise monitoring system of intelligent terminal monitoring |
| Publication number | Publication date |
|---|---|
| CN117928722A (en) | 2024-04-26 |
| Publication | Publication Date | Title |
|---|---|---|
| CN118051827A (en) | Power grid fault prediction method based on deep learning | |
| CN117928722B (en) | Noise monitoring device based on autonomous network and noise source tracing optimization method | |
| CN103325205B (en) | Based on inside fire Forecasting Methodology and the system of radial base neural net | |
| CN111311869A (en) | A fire safety monitoring method, system and cloud platform based on regional alarm model | |
| CN115145788B (en) | A method and device for generating detection data for intelligent operation and maintenance system | |
| CN120142960B (en) | Battery module early abnormality warning method and system integrating time series knowledge graph | |
| CN119252040B (en) | Road and bridge pavement environment monitoring and early warning method and system | |
| CN116973945A (en) | Interference detection method and system based on GNSS data of intelligent terminal | |
| CN119916140B (en) | A method for quickly locating distribution network faults based on artificial intelligence | |
| CN118859800A (en) | A smart manufacturing monitoring method and system based on big data | |
| CN118964878A (en) | Method and system for generating meteorological disaster prevention and mitigation plans based on big data | |
| CN119202887A (en) | Photovoltaic station monitoring method, device, medium and equipment based on multimodal fusion | |
| CN111047079B (en) | Wind power plant wind speed time series prediction method and system | |
| CN116244600A (en) | Method, system and equipment for constructing GIS intermittent discharge mode identification model | |
| CN119835726A (en) | Cluster head election method for improving LEACH algorithm and LEACH routing protocol improvement method | |
| CN119783356A (en) | Intelligent monitoring method and system for water conservancy project data based on digital twin | |
| CN118606680A (en) | An intelligent monitoring and assessment method for environmental chemical pollutants | |
| CN114338088A (en) | Evaluation algorithm and evaluation system for network security level of transformer substation power monitoring system | |
| CN118228030A (en) | Satellite-ground cooperation-based disaster weather prediction method and system | |
| Rajó et al. | Flexible hybrid edge computing IoT architecture for low-cost bird songs detection system | |
| CN120528949B (en) | Ocean mobile distributed detection network construction method | |
| CN119246699B (en) | Sliding surface characteristic monitoring method and system based on acoustic emission | |
| CN116169788B (en) | Distribution network communication management system based on Internet of Things | |
| CN120786405A (en) | Simple networking type real-time monitoring system and device for base station signals AI | |
| LU600883B1 (en) | A Heterogeneous Graph Spatio-temporal Traffic Flow Prediction System and Method Based on RGCN-Wavenet Model |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |