Intelligent Environmental Noise Monitoring Method and System

Abstract

The present disclosure provides an intelligent environmental noise monitoring method and system, which are applied to a noise monitoring system. The noise monitoring system includes a gear assembly and a rotating rod. The rotating rod is used to support a sampling probe, and the gear assembly is used to provide resistance to the rotation of the rotating rod. The method includes: in response to detecting noise data, inputting the noise data into a denoising model for noise reduction processing to obtain target monitoring data; wherein, the noise data is time series data within a continuous time period, and the denoising model is used to evaluate the abnormality degree of each data point in the noise data based on the time fluctuation condition of the noise data. The present disclosure can achieve the operation and maintenance of the sampling probe on the rotating rod without climbing, and at the same time, use the denoising model to perform noise reduction processing on the monitored noise data, which can eliminate the noise generated during the operation and maintenance process, so that the monitoring does not need to be paused during the operation and maintenance process, thus meeting the requirement of continuous environmental noise monitoring and ensuring the safety of the operation and maintenance personnel.

Description

Technical Field

[0001] The present disclosure relates to the technical field of noise monitoring, and in particular to an intelligent environmental noise monitoring method and system. Background Art

[0002] At present, the relevant construction standards for fixed noise monitoring equipment require that the noise sampling probe in the equipment be no less than 4 meters, and the sampling probe must be operated and maintained at least once a month. In addition, if there is bad weather (for example, windy or rainy), the frequency of operation and maintenance should be appropriately increased.

[0003] Therefore, the sampling probe has a high maintenance frequency, and the operating height limit requires frequent operation of the ladder for maintenance. High-altitude operations are not only dangerous, but also easily damage equipment and tools if they are not operated properly.

[0004] In addition, during the high-altitude operation and maintenance process, the operation and maintenance personnel need to first turn on the host of the noise monitoring station, and then press the pause monitoring and start operation and maintenance button to put the noise monitoring equipment in a shutdown state and start operation and maintenance. Otherwise, artificial noise interference may be generated during the operation and maintenance process. As mentioned above, the cumbersome operation and maintenance process in high-altitude operations will increase the danger, and such an operation and maintenance method cannot meet the needs of continuous environmental noise monitoring. Summary of the invention

[0005] The present invention provides an intelligent environmental noise monitoring method and system.

[0006] In a first aspect, an embodiment of the present disclosure provides an intelligent environmental noise monitoring method, which is applied to a noise monitoring system, wherein the noise monitoring system includes a gear assembly and a rotating rod, wherein the rotating rod is used to support a sampling probe, and the gear assembly is used to provide resistance to the rotation of the rotating rod. The method includes: in response to monitored noise data, inputting the noise data into a denoising model for noise reduction processing to obtain target monitoring data; wherein the noise data is time series data within a continuous time period, and the denoising model is used to evaluate the degree of abnormality of each data point in the noise data based on the time fluctuation of the noise data.

[0007] In a second aspect, an embodiment of the present disclosure provides a noise monitoring system, which includes: a main pole; a key block, a first end of the key block being clamped with the main pole; a rotating rod, a first end of the rotating rod being rotatably connected with the first end of the main pole, the second end of the key block being inserted into the interior of the rotating rod, and the second end of the rotating rod being used to support a sampling probe; a gear assembly, used to provide resistance to the rotation of the rotating rod; and a host, used to implement the intelligent environmental noise monitoring method described in the first aspect.

[0008] In a third aspect, an embodiment of the present disclosure provides a computer-readable medium, characterized in that a computer program is stored thereon, and when the computer program is executed by a processor, the environmental noise intelligent monitoring method described in the first aspect is implemented.

[0009] In an embodiment of the present disclosure, when the sampling probe rotates with the rotating rod, the rotating rod rotates around the first end of the key block to trigger the gear assembly to provide resistance to the rotation of the rotating rod, so that the sampling probe on the rotating rod can be maintained and repaired without climbing high. At the same time, a denoising model is used to perform denoising processing on the monitored noise data. The denoising model evaluates the abnormality degree of the noise data in a continuous time period based on the time fluctuation of the noise data, and eliminates the noise generated during the maintenance process, so that it is not necessary to pause the monitoring process every time during the maintenance process, thus meeting the requirement of continuous environmental noise monitoring and ensuring the safety of maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In the drawings of the embodiments of the present disclosure:

[0011] Figure 1 It is a schematic flowchart of an environmental noise intelligent monitoring method provided by an embodiment of the present disclosure.

[0012] Figure 2 It is a schematic flowchart of the training process of the denoising model provided by an embodiment of the present disclosure.

[0013] Figure 3 It is a schematic flowchart of the noise reduction processing provided by an embodiment of the present disclosure.

[0014] Figure 4 It is a schematic structural diagram of a noise monitoring system provided by an embodiment of the present disclosure.

[0015] Figure 5 It is a schematic structural diagram of the gear assembly provided by an embodiment of the present disclosure.

[0016] Figure 6 It is a schematic diagram of the mass relationship between the key block, the rotating rod and the sampling probe provided by an embodiment of the present disclosure.

[0017] Figure 7 It is a schematic diagram of a partial structure of the noise monitoring system when the rotation angle of the rotating rod is 0° to 90° after the key block is removed, provided by an embodiment of the present disclosure.

[0018] Figure 8 It is a schematic diagram of a partial structure of the gear assembly when the rotation angle of the rotating rod is 0° to 90° after the key block is removed, provided by an embodiment of the present disclosure.

[0019] Figure 9 It is a schematic structural diagram of the noise monitoring system when the rotation angle of the rotating rod is 90° after the key block is removed, provided by an embodiment of the present disclosure.

[0020] Figure 10 This is a partial structural schematic diagram of the gear assembly when the rotation angle of the rotating rod is 90° after the key block is removed according to the embodiments provided by the present disclosure.

[0021] Figure 11 This is a structural schematic diagram of the noise monitoring system when the rotation angle of the rotating rod is 90° to 180° after the key block is removed according to the embodiments provided by the present disclosure.

[0022] Figure 12 This is a partial structural schematic diagram of the gear assembly when the rotation angle of the rotating rod is 90° to 180° after the key block is removed according to the embodiments provided by the present disclosure.

[0023] Figure 13 This is a structural schematic diagram of the noise monitoring system when the monitoring state of the sampling probe 4 is restored according to the embodiments provided by the present disclosure.

[0024] Figure 14 This is another structural schematic diagram of the noise monitoring system when the monitoring state of the sampling probe 4 is restored according to the embodiments provided by the present disclosure.

[0025] Figure 15 This is a structural schematic diagram of the noise monitoring system after the key block is installed according to the embodiments provided by the present disclosure.

[0026] Reference signs:

[0027] 1 - Main vertical rod; 2 - Key block; 3 - Rotating rod; 4 - Sampling probe; 5 - Gear assembly; 51 - First gear; 511 - Damping convex block; 52 - Second gear; 53 - Third gear; 531 - Damping landslide; 6 - Host. Detailed implementation manners

[0028] To enable those skilled in the art to better understand the technical solutions of the present disclosure, the embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0029] In the following, the present disclosure will be described more fully with reference to the accompanying drawings. However, the illustrated embodiments may be embodied in different forms, and the present disclosure should not be construed as limited to the embodiments set forth below. On the contrary, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the present disclosure to those skilled in the art.

[0030] The accompanying drawings of the embodiments of the present disclosure are used to provide a further understanding of the embodiments of the present disclosure, and constitute a part of the specification. Together with the detailed embodiments, they are used to explain the present disclosure, and do not constitute a limitation to the present disclosure. By describing the detailed embodiments with reference to the accompanying drawings, the above and other features and advantages will become more apparent to those skilled in the art.

[0031] The present disclosure may be described with reference to plan views and / or cross-sectional views by means of ideal schematic diagrams of the present disclosure. Therefore, the example illustrations may be modified according to manufacturing techniques and / or tolerances.

[0032] In the case of no conflict, the embodiments of the present disclosure and the features in the embodiments may be combined with each other.

[0033] The terms used in the present disclosure are only for describing specific embodiments and are not intended to limit the present disclosure. As used in the present disclosure, the term "and / or" includes any and all combinations of one or more of the related listed items. As used in the present disclosure, the singular forms "a" and "the" are also intended to include the plural forms unless the context clearly indicates otherwise. As used in the present disclosure, the terms "comprising", "made of", specify the presence of the described features, wholes, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their groups.

[0034] Unless otherwise defined, all terms (including technical and scientific terms) used in the present disclosure have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless the present disclosure clearly so defines.

[0035] The present disclosure is not limited to the embodiments shown in the drawings, but includes modifications to the configurations formed based on the manufacturing process. Therefore, the regions illustrated in the drawings have schematic properties, and the shapes of the regions shown in the drawings illustrate the specific shapes of the regions of the elements, but are not intended to be restrictive.

[0036] In some related technologies, there are requirements for the position of the sampling probe in the fixed noise monitoring system in the relevant construction standards of the noise monitoring system. Generally, the requirement is not less than 4 meters, and there are also requirements for the operation and maintenance frequency of the sampling probe. Generally, it is required to perform operation and maintenance on it at least once a month. In addition, in case of bad weather (such as windy or rainy weather, etc.), the operation and maintenance frequency will be appropriately increased. That is, the sampling probe has the characteristics of a relatively high installation position relative to the ground and a high operation and maintenance frequency. Both the installation height and the operation and maintenance frequency of the sampling probe increase the maintenance difficulty of the equipment for the maintenance personnel.

[0037] Among them, during the operation and maintenance process, it is usually required that the noise monitoring system can continuously monitor the noise. However, the noise monitored during the existing operation and maintenance process may include both environmental noise and artificial noise generated by the operation and maintenance personnel, thus affecting the monitoring results. If the operation and maintenance personnel manually pause the work of the noise monitoring system before starting the operation and maintenance, it may exacerbate the dangerous state of high-altitude operations, and the collected data will also be non-continuous after the pause.

[0038] Therefore, there is an urgent need to provide a noise monitoring solution to address the high-altitude operation risks brought by the existing noise monitoring system and the need to continuously monitor environmental noise.

[0039] In a first aspect, an embodiment of the present disclosure provides an intelligent environmental noise monitoring method, which is applied to the host of a noise monitoring system. The noise monitoring system further includes a gear assembly and a rotating rod, where the rotating rod is used to support a sampling probe, and the gear assembly is used to provide resistance to the rotation of the rotating rod. Referring to Figure 1 , the method includes step S1.

[0040] S1. In response to detecting noise data, input the noise data into a denoising model for denoising processing to obtain target monitoring data; wherein, the noise data is time series data within a continuous time period, and the denoising model is used to evaluate the abnormality degree of each data point in the noise data based on the time fluctuation of the noise data.

[0041] Among them, the noise data is data continuously and real-time collected by the host of the noise monitoring system according to the time sequence, and the denoising model is a pre-trained model that can be used to at least remove the artificial noise generated by operation and maintenance in the noise data.

[0042] In an embodiment of the present disclosure, the denoising model is used to perform denoising processing on the detected noise data. The denoising model evaluates the abnormality degree of the noise data within a continuous time period based on the time fluctuation of the noise data to eliminate abnormal noise points generated during operation and maintenance, so that the operation and maintenance process does not need to pause the monitoring every time, thus meeting the requirement of continuously monitoring environmental noise and reducing the cumbersome operation and maintenance process in high-altitude operations, and ensuring the safety of operation and maintenance personnel.

[0043] In some embodiments, referring to Figure 2 , the training process of the denoising model includes steps S1' to S5'.

[0044] S1'. Obtain N training data; each training data includes time series noise data within a continuous time period.

[0045] S2': Take m training data from the N training data according to a preset number of times and input them into the first preset model for processing to obtain the first target preset model; 1 < m < N.

[0046] S3': Input the nth training data in the N training data into the first target preset model for the first noise reduction process to determine intermediate abnormal data; n is taken in turn as an integer from 1 to N.

[0047] S4': Input the intermediate abnormal data corresponding to the nth training data into the second preset model corresponding to the (n - 1)th training data for the second noise reduction process to obtain the second preset model corresponding to the nth training data.

[0048] S5': Determine the denoising model according to the first target preset model and the second preset model corresponding to the Nth training data.

[0049] In the embodiments of the present disclosure, a part of the data randomly selected from the entire set of training data (i.e., N training data), that is, m training data, is used to train the first preset model by randomly selecting m training data multiple times, which can reduce the computational complexity of the training process. At the same time, since the m training data selected from the N training data each time are usually different, therefore, through multiple trainings, the diversity of the trained first target preset model can be increased. In some embodiments, the first preset model is the isolation forest model iForest. The trained first target preset model can effectively identify the global abnormal data points in the training data. The present disclosure does not impose special restrictions on the value of m, which can be set according to training requirements.

[0050] Based on the trained first target preset model, take the nth training data from the N training data and input it for the first noise reduction process to obtain intermediate abnormal data, which are the global abnormal data points in the nth training data identified by the first target preset model. Input the intermediate abnormal data into the second preset model corresponding to the (n - 1)th training data for the second noise reduction process to obtain the second preset model corresponding to the nth training data. The model obtained after inputting all the N training data into the second preset model for training (i.e., the second preset model corresponding to the Nth training data) is determined as the second target preset model. Thus, a denoising model is generated according to the first target preset model and the second preset model corresponding to the Nth training data. Among them, the trained second preset model can effectively identify the local abnormal data points in the training data. In some embodiments, the second preset model is the local outlier factor model LOF.

[0051] Using the second target preset model to further monitor the abnormal data points obtained after the preliminary noise reduction process of the first target preset model can more accurately judge the degree of abnormality, thereby improving the robustness of the monitoring of abnormal data points.

[0052] The embodiments of the present disclosure do not impose special restrictions on the model type of the second preset model, which may be LOF or a combination of a clustering algorithm and another iForest model.

[0053] In some embodiments, before step S1', it further includes:

[0054] Performing data preprocessing on N training data.

[0055] In the embodiments of the present disclosure, the original data matrix corresponding to N training data is normalized by formula (1), where n is the number of samples, that is, the total number of training data, and d is the feature dimension.

[0056] (1).

[0057] In formula (1), is the mean vector of each feature, satisfying , is the standard deviation vector of each feature, satisfying , represents element-wise division, is the original data matrix The value at the i-th sample and the j-th feature.

[0058] In some embodiments, step S2' includes:

[0059] For each data point in the m training data extracted each time, select the intermediate feature of each data point and its corresponding intermediate splitting point.

[0060] According to the intermediate feature and its corresponding intermediate splitting point, divide each data point into two data subsets;

[0061] For each data point in each data subset, repeat the steps of selecting the intermediate feature of each data point and its corresponding intermediate splitting point, and dividing each data point into two data subsets according to the intermediate feature and its corresponding intermediate splitting point until a preset termination condition is met, to obtain the intermediate decision tree corresponding to the m training data extracted this time; the preset termination condition is that all data points in the data subset are completely isolated or the intermediate decision tree reaches a preset maximum depth.

[0062] Generate the first target preset model according to the intermediate decision trees corresponding to the m training data extracted according to the preset number of times. Wherein, the intermediate feature at least includes the time fluctuation situation corresponding to the training data.

[0063] In an embodiment of the present disclosure, when the first preset model is an iForest model, the first preset model is composed of decision trees iTree. An intermediate decision tree is constructed for each selection of m training data. The construction process of the intermediate decision tree includes randomly selecting an intermediate feature from all features, and randomly selecting an intermediate splitting point for the intermediate feature, dividing the data set composed of each data point of the training data into two data subsets, and repeating the operations of selecting the intermediate feature, the intermediate splitting point, and the division of the data subset until the training data is completely isolated or the depth of the intermediate decision tree reaches the preset maximum depth. The m training data extracted a preset number of times can construct intermediate decision trees with the same number as the preset number of times, thereby generating the first target preset model.

[0064] In some embodiments, both the selection of the intermediate feature and the intermediate splitting point are random. In the preset number of selections of m training data, at least once the intermediate feature corresponding to the selected m training data is the time fluctuation condition corresponding to the training data.

[0065] In some embodiments, before constructing the intermediate decision tree for each extraction of m training data, it further includes: extracting the intermediate feature in the m training data, and the intermediate feature at least includes the time fluctuation condition corresponding to the training data. The embodiment of the present disclosure does not make special restrictions on the extraction method of the intermediate feature. It can be to decompose the time series of noise data in a continuous time period into trends, residuals, etc., or it can be determined by an LSTM (Long Short-Term Memory) network.

[0066] In some embodiments, referring to Figure 3 , step S1 includes steps S11 to S13.

[0067] S11. Perform a first noise reduction process on the noise data to determine the first abnormal data and the first evaluation value of each data point in the first abnormal data; the first abnormal data is determined at least based on the time fluctuation condition of the noise data.

[0068] S12. Perform a second noise reduction process on the first abnormal data to obtain the second evaluation value of each data point in the first abnormal data.

[0069] S13. According to the first evaluation value and the second evaluation value, determine the data to be excluded from the first abnormal data, and exclude the data to be excluded from the noise data to obtain the target monitoring data.

[0070] In an embodiment of the present disclosure, the first target preset model after training performs a first noise reduction process on the noise data, which is equivalent to a preliminary screening of each data point in the noise data. The data points suspected of being abnormal constitute the first abnormal data, and the first evaluation value of each data point is determined; the second target preset model is used to perform secondary monitoring on each data point in the first abnormal data to determine whether these data points can be determined as abnormal data points, so that the abnormal data points are removed from the noise data to obtain the target monitoring data. Through the first noise reduction process and the second noise reduction process, abnormal data points can be automatically identified, and it has the advantage of a low misjudgment rate. Especially in the first noise reduction process, the data points that are significantly different from other data points can be isolated earlier, and then combined with the second noise reduction process, it can ensure that the artificial noise generated by the operation and maintenance personnel is accurately removed.

[0071] In some embodiments, the data points suspected of being abnormal obtained after the processing of the first target preset model can also be marked.

[0072] In some embodiments, during the process of using the first target preset model to perform the first noise reduction process on the noise data, a contamination parameter can also be set. This parameter refers to the expected percentage of abnormal values of the training data in the abnormal monitoring by the first target preset model.

[0073] In some embodiments, before step S11, it further includes: performing dimensionality reduction processing on the noise data. In the embodiments of the present disclosure, no special limitation is imposed on the dimensionality reduction method, which can be PCA (Principal Component Analysis), or t-SNE (t-distributed Stochastic Neighbor Embedding).

[0074] In some embodiments, step S11 includes:

[0075] For each data point in the noise data, through the formula , calculate the average path length of the data point in at least one decision tree in the first target preset model; where is the expected value of, is the path length corresponding to the i-th decision tree, and t is the total number of decision trees;

[0076] Through the formula , calculate the first evaluation value; where is the normalization factor, is the first evaluation value.

[0077] In an embodiment of the present disclosure, a data point is input into a first target preset model to determine whether the data point is abnormal. By calculating the average path length by which the data point is isolated in each decision tree corresponding to the first target preset model, if the average path length corresponding to the data point is short, it indicates that the data point is easily isolated and has a greater probability of being an outlier; conversely, if it is not easily isolated, it has a greater probability of being a normal value.

[0078] Where H(i) is the harmonic number estimated by ln(i) + 0.5772156649 (i.e., the Euler constant γ), h(x) is the average path length by which the data point x in the first target preset model is isolated, E(h(x)) is the expected value of h(x), c(n) is the normalization factor of the average path length by which any data point in the first target preset model is isolated, n is the number of samples (i.e., the number of data points), and Score(x,n) is the outlier score (i.e., the first evaluation value) of the data point x.

[0079] When Score(x,n) approaches 1, x is an outlier; when Score(x,n) approaches 0, x is a normal value. In this solution, 0.5 can be set as the judgment threshold for outlier scoring to find outliers.

[0080] In some embodiments, formula (2) is the normalization factor. 。

[0081] (2)。

[0082] In some embodiments, 。

[0083] In some embodiments, the data point with the highest evaluation value among the data points of the noise data is selected through formula (3). samples.

[0084] (3)。

[0085] In some embodiments, step S12 includes:

[0086] For each data point in the first abnormal data, calculate the reachable distance between the data point and k adjacent data points.

[0087] Determine the local density of the data point according to the reachable distance.

[0088] Determine the second evaluation value of each data point in the first abnormal data according to a preset threshold and the ratio between the local density of the data point and the local density of the adjacent data points.

[0089] In an embodiment of the present disclosure, the reachability distance between a data point and k adjacent data points is calculated by formula (4).

[0090] (4).

[0091] Wherein, is the data point, are the k adjacent data points, and the , is the k-nearest neighbor set of the i-th data point x i , is the j-th data point in the k-nearest neighbor set of the i-th data point x i .

[0092] The local density of the data point is calculated by formula (5).

[0093] (5).

[0094] Wherein, is the k-nearest neighbor set of the i-th data point x i , is the local density of the data point x i .

[0095] The evaluation value of the data point is calculated by formula (6).

[0096] (6).

[0097] Wherein, is the evaluation value of the data point x i , is the k-nearest neighbor set of the i-th data point x i , , is the j-th data point in the k-nearest neighbor set of the i-th data point x i , is the local density of the data point , is the local density of the data point x i .

[0098] In some embodiments, the sample with the highest evaluation value among the data points of the first abnormal data is selected by formula (7). samples.

[0099] (7).

[0100] Wherein, is the set of m samples (i.e., samples) with the highest evaluation value among the data points of the first abnormal data selected, is the i-th data point, and C is the candidate sample set composed of all the first abnormal data. is the evaluation value of the i-th data point. is to sort each data point according to the evaluation value. is the ranking threshold.

[0101] In some embodiments, step S13 includes:

[0102] Normalize the first evaluation value and the second evaluation value to obtain a first intermediate evaluation value and a second intermediate evaluation value.

[0103] For each data point in the first abnormal data, according to the preset weight value, perform weighted calculation on the first evaluation value and the second evaluation value of the data point to determine the comprehensive evaluation value.

[0104] Determine the data points with the highest preset number of the comprehensive evaluation values among the data points in the first abnormal data as the data to be excluded.

[0105] In the embodiments of the present disclosure, the first evaluation value is normalized through formula (8).

[0106] (8).

[0107] Wherein, is the first intermediate evaluation value of the i-th data point. is the first evaluation value of the i-th data point. is the minimum value among the first evaluation values of each data point in the first abnormal data. is the maximum value among the first evaluation values of each data point in the first abnormal data.

[0108] The second evaluation value is normalized through formula (9).

[0109] (9).

[0110] Wherein, is the second intermediate evaluation value of the i-th data point. is the evaluation value of the data point x i of. is the minimum value among the second evaluation values of each data point in the first abnormal data. is the maximum value among the second evaluation values of each data point in the first abnormal data.

[0111] Perform weighted calculation on the first evaluation value and the second evaluation value through formula (10).

[0112] (10).

[0113] Among them, is the comprehensive evaluation value of the i-th data point, is the weight, is the first intermediate evaluation value of the i-th data point, is the second intermediate evaluation value of the i-th data point.

[0114] Preset the highest k comprehensive evaluation value data points through formula (11).

[0115] (11).

[0116] Among them, is the set of the highest k comprehensive evaluation value data points selected, is to sort each data point according to the comprehensive evaluation value.

[0117] In the above embodiments of the present disclosure, the denoising model is used to perform denoising processing on the monitored noise data. The denoising model evaluates the abnormal degree of the noise data in a continuous time period based on the time fluctuation of the noise data, so as to eliminate the abnormal noise points generated during the operation and maintenance process, so that the operation and maintenance process does not need to pause the monitoring every time. That is, while the noise monitoring system is normally monitoring the noise, the operation and maintenance personnel can perform operation and maintenance on the equipment, thus meeting the requirement of continuous environmental noise monitoring, reducing the cumbersome operation and maintenance process in high-altitude operations, and ensuring the safety of the operation and maintenance personnel.

[0118] In a second aspect, the embodiments of the present disclosure provide a noise monitoring system, referring to Figure 4 , which includes:

[0119] Main vertical pole 1;

[0120] Key block 2, the first end of the key block 2 is clamped with the main vertical pole 1;

[0121] Rotating rod 3, the first end of the rotating rod 3 is rotatably connected to the first end of the main vertical pole 1, the second end of the key block 2 is inserted into the inside of the rotating rod 3, and the second end of the rotating rod 3 is used to support the sampling probe 4;

[0122] Gear assembly 5, used to provide resistance to the rotation of the rotating rod 3;

[0123] Host 6, used to implement the environmental noise intelligent monitoring method described in the first aspect.

[0124] In the embodiments of the present disclosure, when the sampling probe 4 rotates with the rotating rod 3, the rotating rod 3 rotates around the first end of the main vertical rod 1 and triggers the gear assembly 5 to provide resistance to the rotation of the rotating rod 3, so that the second end of the rotating rod 3 stops rotating when it rotates close to the ground, so that the operation and maintenance personnel can perform operation and maintenance on the sampling probe 4 originally at a high place without climbing high.

[0125] In some embodiments, the main vertical rod 1 can be vertically fixed to the ground by means of embedding, or can be fixed to the ground by means of expansion screws, and the present disclosure is not limited thereto. The host 6 is fixed to the main vertical rod 1 in a wall-mounted form, or the host 6 is placed on the ground. The sampling probe 4 is used to collect noise data, and the host 6 is used to further process the collected noise data.

[0126] In some embodiments, the first end of the key block 2 can be locked to the main vertical rod 1.

[0127] It should be noted that when the key block 2 is close to the ground, is clamped to the main vertical rod 1 and inserted into the first end of the rotating rod 3, the mass of the key block 2 should be at least greater than the sum of the masses of the rotating rod 3 and the sampling probe 4. The rotating rod 3 fixed with the sampling probe 4 is perpendicular to the ground, and the first end of the rotating rod 3 is close to the ground and the second end is far from the ground. The connection between the key block 2 and the main vertical rod 1 and the rotating rod 3 can ensure that the noise monitoring system will not generate self-noise due to environmental interference (for example, wind), avoiding the influence on the monitored noise data.

[0128] As an example, referring to Figure 5 , in the case where the mass of the key block 2 is m1, the distance from the center of gravity of the key block 2 to the axis of the third gear 53 is L1, the total mass of the sampling probe 4 and the rotating rod 3 is m2, and the distance from the center of gravity of the sampling probe 4 and the rotating rod 3 to the axis of the third gear 53 is L2, when , the rotating rod 3 can be perpendicular to the ground in a form where the end (second end) where the sampling probe 4 is located is far from the ground and the first end is close to the ground, and the first end of the key block 2 can be clamped to the main vertical rod 1 and the key block 2 can be locked to the main vertical rod 1 by using a structural lock.

[0129] In some embodiments, referring to Figure 6 , the gear assembly 5 includes:

[0130] A first gear 51, the first gear 51 includes a damping convex block 511;

[0131] A second gear 52, the second gear 52 meshes with the first gear 51;

[0132] A third gear 53, the third gear 53 meshes with the second gear 52 and is fixedly connected to the rotating rod 3, the third gear 53 includes a damping landslide 531;

[0133] When the first end of the key block 2 is clamped to the first end of the main vertical rod 1 and the second end of the key block 2 is inserted into the interior of the rotating rod 3, the key block 2 limits the rotating rod 3; when the first end of the key block 2 is not clamped to the first end of the main vertical rod 1 and the second end of the key block 2 is not inserted into the interior of the rotating rod 3, the rotating rod 3 rotates around the first end of the key block 2, the third gear 53 rotates with the rotating rod 3, and the damping bump 511 of the first gear 51 contacts the damping landslide 531 of the third gear 53 and generates resistance during the rotation process.

[0134] In the embodiment of the present disclosure, the gear assembly 5 includes a first gear 51, a second gear 52, and a third gear 53 that are sequentially engaged. The damping bump 511 in the first gear 51 and the damping landslide 531 in the third gear 53 can cooperate with each other to generate a damping effect on the rotation of the gear, and thus provide resistance to the rotation of the rotating rod 3.

[0135] Compared with the related art in which the sampling probe is fixedly installed on the top of the host to obtain data. In the present disclosure, the sampling probe 4 is fixedly connected to one end of the rotating rod 3, and the other end of the rotating rod 3 is fixedly connected to the third gear 53 of the gear assembly 5. When the first end of the key block 2 is clamped to the first end of the main vertical rod 1 and the second end of the key block 2 is inserted into the interior of the rotating rod 3, under the action of the gravity of the key block 2, the rotation of the rotating rod 3 is restricted, that is, the rotating rod 3 is limited, so that the sampling probe 4 can be fixed to one end of the rotating rod 3 and away from the ground; when the first end of the key block 2 is not clamped to the first end of the main vertical rod 1 and the second end of the key block 2 is not inserted into the interior of the rotating rod 3, the rotating rod 3 rotates, and thus provides a driving force for the third gear 53. The third gear 53 rotates with the side of the rotating rod 3 where the sampling probe 4 is fixed. The rotation of the third gear 53 drives the first gear 51 and the second gear 52 to rotate in sequence, so that the damping bump 511 contacts the damping landslide 531 and generates a resistance opposite to the rotation direction of the gear during the rotation process.

[0136] Among them, setting the damping bump 511 and the damping landslide 531 in the gear assembly 5 can make the rotating rod 3 stop rotating when the sampling probe 4 rotates to be close to the ground, avoiding excessive rotation due to inertia, so that the sampling probe 4 will not be damaged, and the maintenance personnel can also access the sampling probe 4 without working at height for maintenance.

[0137] Next, the working principle of the noise monitoring system will be described:

[0138] Refer to Figure 4, when the first end of the key block 2 is clamped to the first end of the main vertical rod 1 and the second end of the key block 2 is inserted into the interior of the rotating rod 3, the key block 2 restricts the rotation of the rotating rod 3. One end of the rotating rod 3 where the sampling probe 4 is fixed is far from the ground, and the rotating rod 3 is perpendicular to the ground and in a stationary state. Refer to Figure 6 It can be seen that at this time, the gears do not rotate and the damping convex block 511 does not contact the third gear 53.

[0139] As a specific form of the embodiment of the present disclosure, the maintenance personnel remove the key block 2 and provide an initial velocity to the rotating rod 3. The rotating rod 3 will no longer be able to maintain the state of being perpendicular to the ground when the first end of the key block 2 is clamped to the first end of the main vertical rod 1 and the second end of the key block 2 is inserted into the interior of the rotating rod 3, and drives the third gear 53 to perform a circular rotational motion. At this time, the first end of the key block 2 is not clamped to the first end of the main vertical rod 1 and the second end of the key block 2 is not inserted into the interior of the rotating rod 3. The rotation of the rotating rod 3 can provide a driving force for the third gear 53, and the rotation of the third gear 53 drives the rotation of the first gear 51 and the second gear 52 in sequence. The embodiment of the present disclosure does not impose special restrictions on the rotation direction of the rotating rod 3, which can be a left turn or a right turn.

[0140] After removing the key block 2, the rotation process of the rotating rod 3 can be divided into 2 stages. For the mass of the rotating rod 3 being m, the length of the rotating rod 3 being L, and the given initial velocity being V0, the processes of these two stages are further described as follows:

[0141] 1) Refer to Figure 7 and 9 , the sampling probe 4 at the second end of the rotating rod 3 rotates along an arc of 0 to 1 / 4 circumference with the first end of the rotating rod 3 as the center and the rod length as the radius, that is, the rotating rod 3 rotates from 0 to 90°. Refer to Figure 8 and 10 , during the process, the damping convex block 511 does not contact the damping landslide 531, so this rotation process is a process of undamped accelerated motion.

[0142] During this rotation process, the minimum value of the rotation speed is the initial velocity V0, and the maximum value of the rotation speed is the velocity V1 when the rotating rod 3 is parallel to the ground (that is, when the rotating rod 3 rotates 90°).

[0143] When the rotating rod 3 is perpendicular to the ground, the center of gravity of the rotating rod 3 is located at the bottom end , the linear velocity of the top end (i.e., the second end) of the rotating rod 3 is V0, so the initial potential energy at this time is , and the initial kinetic energy is .

[0144] When the rotating rod 3 is parallel to the ground, taking the bottom end as the zero potential energy position, the center of gravity height of the rotating rod 3 is 0 and the velocity is V1, so the initial potential energy at this time is , with an initial kinetic energy of .

[0145] Furthermore, in an ideal state (i.e., without external forces acting), the total mechanical energy is conserved. Therefore, the initial mechanical energy is equal to the final mechanical energy, and the relationship between the initial mechanical energy and the final mechanical energy is shown by formula (12).

[0146] (12).

[0147] It can be seen from formula (12) that the maximum value of the rotational speed of the rotating rod 3 depends on the initial velocity V0 and the rod length L. When the initial velocity is large, the maximum value of the rotational speed is also large. If the initial velocity is less than the critical value, the rotating rod 3 may not be able to move to be parallel to the ground.

[0148] In some embodiments, a first threshold is set for the rotational speed of the rotating rod 3. If the rotational speed of the rotating rod 3 is greater than the first threshold, the sampling probe 4 on the rotating rod 3 will be damaged. In some embodiments, the value range of the first threshold is 0 - 3 m / s.

[0149] 2) Referring to Figure 11 , the sampling probe 4 at the second end of the rotating rod 3 rotates along an arc of 1 / 4 to 1 / 2 of a circumference with the first end of the rotating rod 3 as the center and the rod length as the radius, that is, the rotating rod 3 rotates from 90 to 180°. Referring to Figure 12 , during the process, the damping bump 511 contacts the damping landslide 531, so this rotation process is a damped deceleration motion process.

[0150] During this rotation process, since the damping landslide 531 in contact with the damping bump 511 is a landslide structure, the damping coefficient is larger the closer it is to 180°, and the damping coefficient approaches infinity at 180°. Therefore, the rotating rod 3 will rotate and stop between 90 and 180°.

[0151] As another specific form of the embodiment of the present disclosure, referring to Figures 13 to 15 , after the maintenance personnel complete the maintenance process on the sampling probe 4, the monitoring state of the sampling probe 4 is restored, that is, the vertical ground state of the rotating rod 3 when the first end of the key block 2 is clamped to the first end of the main vertical rod 1 is restored. At this time, the rotating rod 3 is pushed so that the damping bump 511 is pushed out of the damping area corresponding to the damping landslide 531, the key block 2 is inserted into the bottom of the rotating rod 3, and the rotating rod 3 makes a circular rotational motion until the end of the rotating rod 3 where the sampling probe 4 is fixed is far from the ground, and the rotating rod 3 is in a stationary state perpendicular to the ground, and at the same time, the first end of the key block 2 is locked to the main vertical rod 1.

[0152] For the rotating rod 3 with a mass of m, a length of L, and a given initial velocity of V0, the rotation process of the rotating rod 3 is further described as follows:

[0153] Under the gravitational force of the key block 2, the change in potential energy in the system is converted into the rotational kinetic energy of the rotating rod 3, providing sufficient initial potential energy to cause the rotating rod 3 to rotate. This initial potential energy mainly comes from the gravitational potential energy of the rotating rod 3 and the key block 2.

[0154] For the rotating rod 3: The mass of the rotating rod 3 is m, and the total length is L. Initially, the rotating rod 3 is stationary and perpendicular or inclined to the ground. The center of gravity of the rotating rod 3 is at the midpoint of the rod. Therefore, the initial potential energy is ; where is the vertical distance from the center of gravity to the fulcrum; further, since the initial inclination angle of the rotating rod 3 is , so , and the potential energy of the rotating rod 3 is .

[0155] For the key block 2: The mass of the key block 2 is M, and the distance from the fulcrum is l. Therefore, its initial potential energy is .

[0156] Therefore, the total initial potential energy is .

[0157] By providing initial kinetic energy for the rotating rod 3, the rotating rod 3 and the sampling probe 4 can move upward in a circular motion. The rotational kinetic energy for moving upward in a circular motion is provided by the moment of inertia and the angular velocity , that is .

[0158] Among them, the moment of inertia of the rotating rod 3 about the fulcrum is , and the moment of inertia of the key block 2 is . Therefore, the total moment of inertia is .

[0159] By satisfying the relationship between potential energy and kinetic energy, to overcome damping such as frictional force and air resistance, the rotating rod 3 and the sampling probe 4 start to move upward in a circular motion.

[0160] Initially, the angular velocity , so the total initial energy is the potential energy. Based on the conservation of energy, the potential energy is converted into kinetic energy: , that is .

[0161] According to the relationship between kinetic energy and total energy, the relationship between the angular velocity and the initial inclination angle is formula (13).

[0162] (13).

[0163] As another specific form of the embodiment of the present disclosure, when restoring the monitoring state of the sampling probe 4, that is, restoring the vertical ground state of the rotating rod 3 when the first end of the key block 2 is clamped to the first end of the main vertical rod 1, the rotation speed of the rotating rod 3 is set to be 0-3 m / s. According to the angular velocity calculation formula , where r is the distance from the sampling probe 4 to the rotation center, the maximum value of the angular velocity can be determined as , and the initial inclination angle is .

[0164] In the above embodiment of the present disclosure, by providing the damping bump 511 and the damping landslide 531 in the gear assembly 5, the rotating rod 3 can be stopped from rotating when the sampling probe 4 rotates close to the ground, avoiding excessive rotation due to inertia, thus preventing damage to the sampling probe 4. Maintenance personnel can access the sampling probe 4 for maintenance without performing high-altitude operations.

[0165] In a third aspect, an embodiment of the present disclosure provides a computer-readable medium, on which a computer program is stored, and when the computer program is executed by a processor, the environmental noise intelligent monitoring method described in the first aspect is implemented.

[0166] It should be noted that this computer-readable medium corresponds to the above environmental noise intelligent monitoring method. All implementation manners in the above method embodiments are applicable to the embodiments of this computer-readable medium and can achieve the same technical effects.

[0167] In order to enable those skilled in the art to more clearly understand the technical solutions provided by the embodiments of the present disclosure, the following through specific embodiments, the technical solutions provided by the embodiments of the present disclosure are described in detail:

[0168] Example 1

[0169] Exemplarily, as a specific form of the embodiment of the present disclosure, when maintenance personnel arrive beside the noise monitoring system, the maintenance process for the noise monitoring system includes:

[0170] Confirm whether there are new fixed sources or other situations within a certain range (for example, 200 meters) around that may affect the monitoring results of the noise data. If there are any abnormalities, give priority to dealing with the surrounding monitoring environment.

[0171] Open the limit of the key block 2, remove the key block 2, provide an initial speed to the rotating rod 3, the rotating rod 3 makes a rotational motion, and when the sampling probe 4 in the rotating rod 3 rotates close to the ground, remove the windproof ball kit and put on the sound calibrator.

[0172] Operate the sound calibrator to make it emit sound, monitor the device within 1 minute after the calibrator emits sound, and perform automatic calibration.

[0173] When the linked alarm light indicates that the calibration is completed, check whether the wind protection kit is contaminated and needs to be cleaned or replaced. If maintenance is required, clean it in a timely manner. After cleaning, install the wind protection kit back to the top of the rotating rod.

[0174] Push the rotating rod 3 out of the damping area, insert the key block 2 into the first end of the rotating rod 3, and the rotating rod 3 makes a circular motion. When the sampling probe 4 is perpendicular to the ground and the sampling probe 4 is far from the ground, limit and lock the key block 2 in the main vertical rod 1.

[0175] After checking that there are no remaining operation and maintenance items at the site, the sound calibration operation and maintenance is completed.

[0176] Among them, the processor is a device with data processing capabilities, including but not limited to a central processing unit (CPU), etc.; the memory is a device with data storage capabilities, including but not limited to a random access memory (RAM, more specifically such as SDRAM, DDR, etc.), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory (FLASH); the I / O interface (read / write interface) is connected between the processor and the memory and can realize the information interaction between the memory and the processor, including but not limited to a data bus (Bus), etc.

[0177] Those of ordinary skill in the art can understand that all or some of the steps, systems, and functional modules / units in the devices disclosed above can be implemented as software, firmware, hardware, and their appropriate combinations.

[0178] In the hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component can have multiple functions, or a function or step can be executed by several physical components in cooperation.

[0179] Some or all of the physical components may be implemented as software executed by a processor such as a central processing unit (CPU), a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transitory medium). As is well known to those of ordinary skill in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM, more specifically SDRAM, DDR, etc.), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory (FLASH), or other magnetic disk storage; compact disc read only memory (CD-ROM), digital versatile disc (DVD), or other optical disk storage; magnetic cassettes, tapes, magnetic disk storage or other magnetic storage; and any other medium that can be used to store the desired information and that can be accessed by a computer. Additionally, as is well known to those of ordinary skill in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0180] The present disclosure has disclosed example embodiments, and although specific terms have been employed, they are used only and should be interpreted only for general illustrative purposes and not for purposes of limitation. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in connection with a particular embodiment may be used alone or in combination with features, characteristics, and / or elements described in connection with other embodiments, unless otherwise expressly stated. Accordingly, those skilled in the art will understand that various forms and details may be changed without departing from the scope of the present disclosure as set forth by the appended claims.

Claims

1. An intelligent environmental noise monitoring method, characterized in that: A host applied to a noise monitoring system, the noise monitoring system further comprising a gear assembly and a rotating rod, the rotating rod being used to support a sampling probe, the gear assembly being used to provide resistance to the rotation of the rotating rod, the method comprising: In response to noise data being monitored, the noise data is input into a denoising model for denoising to obtain target monitoring data; wherein the noise data is time series data within a continuous time period, and the denoising model is used to evaluate the degree of abnormality of each data point in the noise data based on the time fluctuation of the noise data; The step of inputting the noise data into a denoising model for denoising to obtain target monitoring data includes: Performing a first noise reduction process on the noise data to determine first abnormal data and a first evaluation value of each data point in the first abnormal data; the first abnormal data is determined based at least on a time fluctuation of the noise data; Performing a second noise reduction process on the first abnormal data to obtain a second evaluation value of each data point in the first abnormal data; According to the first evaluation value and the second evaluation value, data to be eliminated is determined from the first abnormal data, and the data to be eliminated is eliminated from the noise data to obtain target monitoring data.

2. The method according to claim 1, characterized in that: The training process of the denoising model includes: Obtain N training data; each training data includes time series noise data in a continuous time period; Take m training data from the N training data according to a preset number of times and input them into the first preset model for processing to obtain a first target preset model; 1<m<N; Inputting the nth training data among the N training data into the first target preset model for first noise reduction processing to determine the intermediate abnormal data; n is sequentially selected from integers from 1 to N; Inputting the intermediate abnormal data corresponding to the nth training data into the second preset model corresponding to the n-1th training data for second noise reduction processing to obtain the second preset model corresponding to the nth training data; A denoising model is determined according to the first target preset model and the second preset model corresponding to the Nth training data.

3. The method according to claim 2, characterized in that: The step of taking m training data from the N training data according to a preset number of times and inputting them into a first preset model for processing to obtain a first target preset model includes: For each data point in the m training data extracted each time, select the intermediate features of each data point and its corresponding intermediate split point; According to the intermediate features and their corresponding intermediate split points, each data point is divided into two data subsets; For each data point in each data subset, repeatedly perform the steps of selecting the intermediate features of each data point and the corresponding intermediate split points, and dividing each data point into two data subsets according to the intermediate features and the corresponding intermediate split points, until a preset termination condition is met, and an intermediate decision tree corresponding to the m training data extracted this time is obtained; the preset termination condition is that each data point in the data subset is completely isolated or the intermediate decision tree reaches a preset maximum depth; wherein the intermediate features at least include the time fluctuation of each data point; A first target preset model is generated according to the intermediate decision tree corresponding to the m training data extracted a preset number of times.

4. The method according to claim 1, characterized in that: The performing a first noise reduction process on the noise data to determine first abnormal data and a first evaluation value of each data point in the first abnormal data includes: For each data point in the noise data, the formula , calculating the average path length of the data point in at least one decision tree in the first target preset model; wherein, for The expected value of is the path length corresponding to the i-th decision tree, and t is the total number of decision trees; By formula , calculate the first evaluation value; wherein, is the standardization factor.

5. The method according to claim 1, characterized in that: The performing a second noise reduction process on the first abnormal data to obtain a second evaluation value of each data point in the first abnormal data includes: For each data point in the first abnormal data, calculating the reachable distance between the data point and k adjacent data points; Determining a local density of the data points according to the reachable distance; A second evaluation value of each data point in the first abnormal data is determined according to a preset threshold and a ratio between a local density of the data point and a local density of the adjacent data points.

6. The method according to claim 1, characterized in that: The step of determining the data to be removed from the first abnormal data according to the first evaluation value and the second evaluation value includes: Normalizing the first evaluation value and the second evaluation value to obtain a first intermediate evaluation value and a second intermediate evaluation value; For each data point in the first abnormal data, a first evaluation value and a second evaluation value of the data point are weightedly calculated according to a preset weight value to determine a comprehensive evaluation value; A preset number of data points in the first abnormal data with the highest comprehensive evaluation values ​​are determined as data to be eliminated.

7. A noise monitoring system, characterized in that: It includes: Main pole; A key block, a first end of which is clamped with the main vertical pole; A rotating rod, wherein the first end of the rotating rod is rotatably connected to the first end of the main vertical rod, the second end of the key block is inserted into the interior of the rotating rod, and the second end of the rotating rod is used to support the sampling probe; a gear assembly, used to provide resistance to the rotation of the rotating rod; A host, used to implement the environmental noise intelligent monitoring method described in any one of claims 1 to 6.

8. The system according to claim 7, characterized in that: The gear assembly comprises: a first gear, the first gear comprising a damping bump; a second gear, the second gear meshing with the first gear; a third gear, the third gear meshing with the second gear and fixedly connected to the rotating rod, the third gear comprising a damping slide; When the first end of the key block is clamped on the first end of the main pole and the second end of the key block is inserted into the interior of the rotating rod, the key block limits the rotating rod; when the first end of the key block is not clamped on the first end of the main pole and the second end of the key block is not inserted into the interior of the rotating rod, the rotating rod rotates around the first end of the key block, the third gear rotates with the rotating rod, and the damping protrusion of the first gear contacts the damping slope of the third gear and generates resistance during the rotation process.

9. A computer-readable medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the method for intelligent monitoring of environmental noise described in any one of claims 1 to 6 is implemented.

Images