A method and system for monitoring construction environment

Abstract

The present invention relates to the technical field of data processing, and particularly relates to a method and system for monitoring the construction environment. The method includes: obtaining the original noise data of a construction site, where the original noise data includes multiple noise audio collected at multiple monitoring positions on the construction site; calculating the signal discrimination degree of different noise audio; calculating the influence degree of irrelevant noise in the original noise data based on the signal discrimination degree; and performing noise monitoring on the construction site according to the influence degree of irrelevant noise and the noise influence degree of different noise audio. The present invention can solve the technical problem that the monitoring during the construction environment monitoring process is easily interfered by the surrounding environmental noise, resulting in inaccurate monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a method and system for monitoring the construction environment. Background Art

[0002] With the acceleration of the urbanization process, construction activities are frequent, and environmental problems at the construction site are becoming increasingly prominent. Construction not only involves a large amount of resource consumption and energy use, but may also cause pollution such as noise, vibration, dust and wastewater to the surrounding environment. These environmental problems not only affect the safety and efficiency of the construction site, but may also have a negative impact on the quality of life of the surrounding residents, and even lead to social contradictions. Therefore, it is particularly important to implement effective monitoring of the construction environment. Among them, the construction noise has a greater impact on the surrounding environment, so it is necessary to monitor the noise during the construction process.

[0003] In the prior art, monitoring the noise at the construction site only measures the noise level during the construction process through a decibel meter. When the noise exceeds a certain threshold, it is considered that there is noise pollution. However, because there are various noises in the environment, the monitored noise sources may not all come from the construction at the construction site, and may also come from the surrounding environmental noise, such as car horns, etc., which will interfere with the monitoring of the construction site noise, resulting in inaccurate monitoring. Summary of the Invention

[0004] In order to solve the technical problem that the monitoring of the construction environment is easily interfered by the surrounding environmental noise, resulting in inaccurate monitoring, the purpose of the present invention is to provide a method and system for monitoring the construction environment, and the specific technical solutions adopted are as follows:

[0005] In the first aspect, the present invention provides a method for monitoring the construction environment, and the method includes:

[0006] Obtain the original noise data of the construction site, where the original noise data includes a plurality of noise audio collected at a plurality of monitoring positions on the construction site;

[0007] Calculate the signal discrimination degree of different noise audio;

[0008] Based on the signal discrimination degree, calculate the influence degree of irrelevant noise in the original noise data;

[0009] According to the influence degree of the irrelevant noise and the noise influence degree of different noise audio, perform noise monitoring on the construction site.

[0010] Optionally, the calculating the signal discrimination degree of different noise audio includes:

[0011] Determine the probability that the data range where each maximum point in the different noise audio is located is an abnormal sound source;

[0012] Calculate the noise performance degree of the data range where each maximum point is located based on the probability;

[0013] Calculate the signal discrimination degree of the different noise audio according to the noise performance degree of the data range where each maximum point is located.

[0014] Optionally, the determining the probability that the data range where each maximum point in the different noise audio is located is an abnormal sound source includes:

[0015] Determine the maximum point data of each noise audio, where the maximum point data includes the maximum points contained in the noise audio, the amplitude of each maximum point, the amplitudes of the neighboring data points corresponding to each maximum point, the maximum amplitude of each noise audio, and the number of the neighboring data points;

[0016] Calculate the probability that the data range where each maximum point in each noise audio is located is an abnormal sound source based on the maximum point data.

[0017] Optionally, the calculating the noise performance degree of the data range where each maximum point is located based on the probability includes:

[0018] Determine the maximum points corresponding to the probability greater than the first preset threshold as abnormal sound source points;

[0019] Obtain the abnormal sound source point data of each abnormal sound source point, where the abnormal sound source point data includes the time value, amplitude, and the probability of the abnormal sound source point;

[0020] Calculate the noise performance degree of the data range where each abnormal sound source point in each noise audio is located based on the abnormal sound source point data of the different noise audio.

[0021] Optionally, the calculating the noise performance degree of the data range where each abnormal sound source point in each noise audio is located based on the abnormal sound source point data of the different noise audio includes:

[0022] Determine the different noise audio as the current noise audio in turn, and execute the calculation process of the noise performance degree of the data range where the first abnormal sound source point in the current noise audio is located;

[0023] Wherein, the noise performance degree calculation process includes:

[0024] Based on the time value of the first abnormal sound source point on the current noisy audio, determine a second abnormal sound source point on other noisy audio of the multiple noisy audios that is at the same time sequence point as the first abnormal sound source point and has the smallest interval distance;

[0025] According to the abnormal sound source point data of the first abnormal sound source point and the abnormal sound source point data of the second abnormal sound source point, calculate the noise performance degree of the data range where the first abnormal sound source point is located on the current noisy audio.

[0026] Optionally, the calculating the signal discrimination degrees of different noisy audios according to the noise performance degrees of the data ranges where each maximum value point is located includes:

[0027] Perform empirical mode decomposition on different noisy audios to obtain the component signal data of each noisy audio, where the component signal data includes the IMF component signals obtained by decomposing the noisy audio and the number of the IMF component signals;

[0028] Based on the component signal data of different noisy audios, the variances of different noisy audios, and the noise performance degrees of the data ranges where each abnormal sound source point is located on the noisy audio, calculate the signal discrimination degrees of different noisy audios.

[0029] Optionally, the calculating the influence degree of irrelevant noise in the original noise data based on the signal discrimination degree includes:

[0030] Calculate the Pearson coefficients of different noisy audios;

[0031] Based on the Pearson coefficients and the signal discrimination degree, calculate the influence degree of irrelevant noise in the original noise data.

[0032] Optionally, the performing noise monitoring on the construction site according to the influence degree of irrelevant noise and the noise influence degrees of different noisy audios includes:

[0033] Calculate the covariance of different noisy audios and use the covariance as the noise influence degree of the noisy audio;

[0034] Calculate the covariance mean of multiple noisy audios in the original noise data with respect to the covariance;

[0035] Based on the covariance mean and the influence degree of irrelevant noise, calculate the construction site noise influence degree;

[0036] Perform noise monitoring on the construction site according to the construction site noise influence degree.

[0037] Optionally, the noise monitoring of the construction site according to the degree of influence of the construction site noise includes:

[0038] Determine whether the degree of influence of the construction site noise is greater than a second preset threshold;

[0039] If so, it is determined that there is significant construction noise at the construction site;

[0040] If not, it is determined that the construction noise at the construction site is in a normal state.

[0041] On the other hand, the present invention also provides a construction environment monitoring system, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of a construction environment monitoring method as described in any one of the foregoing.

[0042] The present invention has the following beneficial effects: Through the technical solution provided by the present invention, after obtaining the original noise data of multiple noise audio collected at multiple monitoring positions on the construction site, the signal discrimination of different noise audio can be calculated first; then, based on the signal discrimination, the influence degree of irrelevant noise in the original noise data can be calculated; finally, according to the influence degree of irrelevant noise and the noise influence degree of different noise audio, noise monitoring of the construction site is carried out. By calculating the influence degree of irrelevant noise in the original noise data, when performing noise monitoring on the original noise data, the interference of irrelevant noise on the environmental noise of the construction site can be eliminated based on the influence degree of irrelevant noise, thereby improving the monitoring accuracy of the construction environment.

[0043] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present invention. Other features and advantages of the present invention will be described in detail in the subsequent specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0045] Figure 1 It is a flowchart of a construction environment monitoring method provided by an embodiment of the present invention;

[0046] Figure 2 It is an example schematic diagram of an application scenario provided by an embodiment of the present invention;

[0047] Figure 3 A schematic flowchart of a method for monitoring the construction environment provided by another embodiment of the present invention. Detailed implementation manners

[0048] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following describes in detail a method and system for monitoring the construction environment according to the present invention, its specific implementation manners, structures, features and effects in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0050] The following specifically describes the specific solution of a method for monitoring the construction environment provided by the present invention in combination with the accompanying drawings.

[0051] Please refer to Figure 1 , which shows a method flowchart of a method for monitoring the construction environment provided by an embodiment of the present invention. The method includes the following steps:

[0052] Step 110: Obtain the original noise data of the construction site, where the original noise data includes multiple noise audio collected at multiple monitoring positions on the construction site.

[0053] In a specific application scenario, as Figure 2 shown, decibel meters can be installed at different monitoring positions on the construction site, and the original noise data of the construction site can be collected in real time by using multiple decibel meters. The original noise data can include multiple noise audio collected at multiple monitoring positions, and the noise audio can be a data sequence. Since the construction noise at the construction site will affect the surrounding residents, it is necessary to collect and dynamically monitor the original noise data in real time so that when there is a large construction noise in the monitoring, the construction noise can be processed in a timely and effective manner.

[0054] Step 120: Calculate the signal discrimination degrees of different noise audio.

[0055] When analyzing and monitoring the original noise data during the construction process at a construction site, there can be various sources of noise, such as roads next to the construction site, surrounding schools, surrounding shopping malls, etc. Therefore, the original noise data collected may also be a superposition of multiple noise signals. When monitoring the original noise data of a construction site, when there are other interfering noises outside the construction site, the noise recognition system is very likely to identify it as the construction noise of the construction site and then give an alarm. In order to accurately identify the noise at the construction site, the type of sound source and whether it appears as noise can be identified by analyzing the audio signals collected at different monitoring positions, and the signal differentiation from other noises is relatively high, so as to identify the irrelevant noises other than the construction site noise.

[0056] Step 130: Calculate the influence degree of the irrelevant noise in the original noise data based on the signal differentiation.

[0057] Based on the signal differentiation of the noise audio obtained from the above calculation, the influence degree of the noise is further obtained. Because the differentiation of the noise audio represents the difference in sound under the same monitoring conditions, the greater the difference, the more it indicates the influence of other sound sources, so the possibility of abnormal noise is greater.

[0058] Step 140: Conduct noise monitoring on the construction site according to the influence degree of the irrelevant noise and the noise influence degree of different noise audios.

[0059] For the embodiments of the present disclosure, after obtaining the influence degree of the irrelevant noise and the noise influence degree of different noise audios, the influence degree of the construction site noise after removing the irrelevant noise can be comprehensively calculated based on the influence degree of the irrelevant noise and the noise influence degree of different noise audios. Then, the construction environment monitoring is carried out based on the influence degree of the construction site noise after removing the irrelevant noise.

[0060] In summary, according to a construction environment monitoring method provided by the present invention, after obtaining the original noise data including multiple noise audios collected at multiple monitoring positions on the construction site, the signal differentiation of different noise audios can be first calculated; then, based on the signal differentiation, the influence degree of the irrelevant noise in the original noise data is calculated; finally, the construction site is subjected to noise monitoring according to the influence degree of the irrelevant noise and the noise influence degree of different noise audios. By calculating the influence degree of the irrelevant noise in the original noise data, when monitoring the original noise data, the interference of the irrelevant noise on the environmental noise of the construction site can be eliminated based on the influence degree of the irrelevant noise, thereby improving the monitoring accuracy of the construction environment.

[0061] Based on Figure 1The illustrated embodiment, as a refinement and extension of the above - mentioned embodiment, in order to fully illustrate the specific implementation process of the method in this embodiment, this embodiment provides the specific method as shown in Figure 3 as follows. Figure 3 Based on Figure 1 the illustrated embodiment. As shown in Figure 3 the method includes the following steps:

[0062] Step 310: Obtain the original noise data of the construction site. The original noise data includes multiple noise audio signals collected at multiple monitoring positions on the construction site.

[0063] For the embodiments of the present disclosure, the steps of the embodiments can refer to the relevant descriptions in step 110 of the embodiments, and specific limitations are not made here.

[0064] Step 320: Determine the degree of possibility that the data range where each maximum point in different noise audio signals is an abnormal sound source.

[0065] When analyzing and monitoring the original noise data during the construction process of the construction site, there are various sources of noise. Therefore, the collected noise data is also a superposition of multiple signals. In order to accurately identify the noise on the construction site, by analyzing the audio signals collected at different monitoring positions, the type of the sound source and whether it is manifested as noise are identified, so as to identify the construction site noise. However, when monitoring the noise, it will be affected by other sounds. For example, the honking sound of cars on the road next to the construction site is relatively sharp, and the noise recognition system is very likely to identify it as a noise signal and then give an alarm. Because the car honking sound generally shows a sharp and short - duration noise audio signal, the possible abnormal sound sources in the original noise data are obtained according to the change of the original noise data. First, all the maximum points in each noise audio signal are obtained, and then according to the difference between each maximum point and its neighborhood data, the degree of possibility that the data range where each maximum point is an abnormal sound source is obtained.

[0066] Correspondingly, for the embodiments of the present disclosure, determining the degree of possibility that the data range where each maximum point in different noise audio signals is an abnormal sound source in step 320 may include the following steps:

[0067] Step 320 - 1: Determine the maximum point data of each noise audio signal. The maximum point data includes the maximum points included in the noise audio signal, the amplitude of each maximum point, the amplitudes of the corresponding neighborhood data points of each maximum point, the maximum amplitude of each noise audio signal, and the number of neighborhood data points.

[0068] Step 320 - 2: Calculate the degree of possibility that the data range where each maximum point in each noise audio signal is an abnormal sound source based on the maximum point data.

[0069] For the embodiments of the present disclosure, the maximum point data can be substituted into the first calculation formula, and the possibility degree that the data range where each maximum point is located in each noisy audio is an abnormal sound source can be calculated based on the first calculation formula. Among them, the formula features of the first calculation formula are described as follows:

[0070]

[0071] In the formula, represents the possibility degree that the data range where the -th maximum point is located in the -th noisy audio is an abnormal sound source, represents the maximum amplitude in the -th noisy audio, represents the amplitude of the -th maximum point in the -th noisy audio, represents the amplitude of the -th neighboring data point of the -th maximum point in the -th noisy audio, represents the number of neighboring data points, represents the linear normalization function. represents the difference between the maximum amplitude in the -th noisy audio and the amplitude of the -th maximum point in the -th noisy audio, representing the abnormality degree of the current data point; represents the difference between the -th maximum point and its neighboring data points. It should be noted that when calculating the difference from the neighboring data points, the calculation is performed one by one from the -th data point to the left and right until the maximum value is obtained and the iterative calculation is no longer performed.

[0072] Step 330: Calculate the noise performance degree of the data range where each maximum point is located based on the possibility degree.

[0073] Correspondingly, for the embodiments of the present disclosure, calculating the noise performance degree of the data range where each maximum point is located based on the possibility degree in step 330 may include the following steps:

[0074] Step 330-1: Determine the maximum points corresponding to the possibility degree greater than the first preset threshold as abnormal sound source points.

[0075] After calculating the possible degree of the abnormal sound source for the data range where each maximum point is located through step 320-2 of the embodiment, the abnormal sound source points can be screened out from multiple maximum points based on this possible degree. Specifically, the possible degree of the data range where each maximum point is located as the abnormal sound source can be compared with a first preset threshold, and the maximum points corresponding to the possible degree greater than the first preset threshold are determined as the abnormal sound source points. And when obtains the maximum value, the th maximum point and the data range formed by its neighborhood data points are the current maximum point data range. Among them, the value of the first preset threshold can be set according to the actual application scenario. For example, it can be set to , and no specific limitation is made here.

[0076] Step 330-2: Obtain the abnormal sound source point data of each abnormal sound source point. The abnormal sound source point data includes the time value, amplitude, and possible degree of the abnormal sound source point.

[0077] Step 330-3: Based on the abnormal sound source point data of different noise audio, calculate the noise performance degree of the data range where each abnormal sound source point is located on each noise audio.

[0078] For the embodiments of the present disclosure, after determining the abnormal sound source points from the maximum points according to step 330-1 of the embodiment, based on the abnormal sound source point data of different noise audio, the differences between different noise audio can be compared to obtain the noise performance degree of the data range where each abnormal sound source point is located.

[0079] In a specific application scenario, different noise audio can be sequentially determined as the current noise audio, and the abnormal sound source point data of the abnormal sound source points on the current noise audio is substituted into the second calculation formula. Based on the second calculation formula, the noise performance degree of the data range where each abnormal sound source point is located on each noise audio is calculated. Among them, the formula feature description of the second calculation formula is:

[0080]

[0081] In the formula, represents the noise performance degree of the data range where the th abnormal sound source point is located in the nd noise audio, represents the time value of the th abnormal sound source point in the nd noise audio, represents the th abnormal sound source point in the nd noise audio, represents the th abnormal sound source point in the The data range where an abnormal sound source point is located represents the probability degree of the abnormal sound source. Indicates the th noise audio, and the data range where the th abnormal sound source point is located represents the probability degree of the abnormal sound source. Indicates the th noise audio, and the amplitude of the th abnormal sound source point. Indicates the th noise audio, and the amplitude of the th abnormal sound source point. Indicates the exponential function with the natural constant as the base. The th abnormal sound source point is the time value of the first abnormal sound source point (i.e., the th noise audio) on the current noise audio. On other noise audios of multiple noise audios, the second abnormal sound source point (i.e., the th abnormal sound source point) that is at the same time sequence point as the first abnormal sound source point and has the smallest interval distance is determined. The th noise audio is the noise audio where the th abnormal sound source point is located. The smaller the distance between two noise audios at the same time sequence point, the more likely it indicates the same sound source. Then, during the monitoring process of the noise monitoring device, the more likely it is to monitor the same noise. Indicates that the smaller the distance between two noise audios at the same time sequence point, the more likely it indicates the same sound source. Then, during the monitoring process of the noise monitoring device, the more likely it is to monitor the same noise. Indicates that on the premise of , the abnormal degree comparison of these two signals is calculated. The closer the ratio is to 1, the more similar these two signal sources are, and thus the more likely they are to be abnormal sound sources. Indicates the amplitude difference of the noise audio. Because the transmission of sound has attenuation, when collecting sound data, the decibel meters are distributed in different directions. Therefore, when there is a difference between the two, it indicates that the sound source may be caused by a car horn.

[0082] Correspondingly, step 330-3 of the embodiment may include the following steps: sequentially determining different noise audios as the current noise audio, and executing the calculation process of the noise performance degree of the data range where the first abnormal sound source point on the current noise audio is located; wherein, the noise performance degree calculation process includes: based on the time value of the first abnormal sound source point on the current noise audio, determining the second abnormal sound source point on other noise audios of multiple noise audios that is at the same time sequence point as the first abnormal sound source point and has the smallest interval distance; calculating the noise performance degree of the data range where the first abnormal sound source point on the current noise audio is located according to the abnormal sound source point data of the first abnormal sound source point and the abnormal sound source point data of the second abnormal sound source point.

[0083] Step 340: Calculate the signal discrimination of different noisy audio according to the noise performance degree of the data range where each maximum point is located.

[0084] According to the noise performance degree of the data range where each abnormal sound source point is located on the noisy audio obtained from the above calculation, and then compare the changes of different component signals on the same noisy audio to obtain the discrimination of the noisy audio. Since different noisy audio are affected by noise to different degrees and the noise sources are also different, the changes of the obtained noisy audio are different. However, for the monitoring of construction site noise, because the decibel meters are distributed inside the construction site and are relatively evenly distributed, the sound monitoring of the construction site is synchronous. Therefore, calculate the signal discrimination according to the differences of the noisy audio, and its calculation formula is as follows:

[0085]

[0086] In the formula, represents the signal discrimination of the th noisy audio, represents the number of IMF component signals of the th noisy audio, represents the number of IMF component signals of the th noisy audio, represents the DTW value between the th IMF component signal and the th IMF component signal in the th noisy audio and the th IMF component signal in the represents the minimum value between the number of IMF component signals of the th noisy audio and the number of IMF component signals of the th noisy audio, represents the variance of the th noisy audio, represents the variance of the th noisy audio, represents a hyperparameter with a value of 0.01 to prevent the denominator from being 0. represents the noise performance degree of the data range where the th maximum point is located in the th noisy audio, represents the number of maximum points. represents the th noisy audio and the Differences in the number of IMF component signals of strip noise audio. The IMF component signals are obtained by decomposing using the Empirical Mode Decomposition (EMD) algorithm. Since the EMD algorithm decomposes signals according to different frequencies of the signals, when the difference between two signals is smaller, the difference in the number of component signals obtained by decomposition is smaller. On the contrary, the greater the difference between the two component signals, the greater the difference in the number of component signals. Indicates the th IMF component signal in the strip noise audio and the th IMF component signal in the strip noise audio. Its DTW value represents the similarity of two data sequences. The smaller the DTW value, the more similar the two data sequences are. Therefore, the similarity of the component signals of the two strip noise audios is calculated here. Indicates the difference in the variances of the two strip noise audios. The greater the difference, the more likely there are differences in the noise sources of the two strip noise audios.

[0087] Correspondingly, for the embodiments of the present disclosure, calculating the signal discrimination of different strip noise audios according to the noise performance degree of the data range where each maximum point is located in step 340 may include the following steps:

[0088] Step 340-1: Perform empirical mode decomposition on different strip noise audios to obtain the component signal data of each strip noise audio. The component signal data includes the IMF component signals obtained by decomposing the strip noise audio and the number of IMF component signals.

[0089] Step 340-2: Calculate the signal discrimination of different strip noise audios based on the component signal data of different strip noise audios, the variances of different strip noise audios, and the noise performance degree of the data range where each abnormal sound source point is located on the strip noise audio.

[0090] Step 350: Calculate the influence degree of irrelevant noise in the original noise data based on the signal discrimination.

[0091] After calculating the signal discrimination of different strip noise audios according to step 340 of the above embodiments, the influence degree of irrelevant noise in the original noise data can be further calculated. Since the discrimination of the strip noise audio represents the difference in sounds under the same monitoring conditions, when the difference is greater, it indicates that there are other sound source influences, so the possibility of abnormal noise is greater.

[0092] Correspondingly, for the embodiments of the present disclosure, calculating the influence degree of irrelevant noise in the original noise data based on the signal discrimination in step 350 may include the following steps:

[0093] Step 350-1: Calculate the Pearson coefficient of different noisy audio.

[0094] Step 350-2: Calculate the influence degree of irrelevant noise in the original noise data based on the Pearson coefficient and signal discrimination.

[0095] For the embodiments of the present disclosure, based on the Pearson coefficient and signal discrimination, the influence degree of irrelevant noise can be obtained according to the change of noisy audio, and its calculation formula is as follows:

[0096]

[0097] In the formula, represents the influence degree of irrelevant noise, represents the th noisy audio and the th noisy audio, and the Pearson coefficient is normalized to represent the correlation between two data sequences.

[0098] Step 360: Conduct noise monitoring on the construction site according to the influence degree of irrelevant noise and the noise influence degree of different noisy audio.

[0099] For the embodiments of the present disclosure, after calculating the influence degree of irrelevant noise in the original noise data according to Step 350 of the above embodiments, based on the influence degree of irrelevant noise and the noise influence degree of different noisy audio, the influence degree of the construction site noise after removing irrelevant noise can be comprehensively calculated. Then, based on the influence degree of the construction site noise after removing irrelevant noise, the construction environment monitoring is carried out. Because the influence degree of noise is certain, the covariance of each noisy audio is calculated to represent the noise influence degree of the noisy audio, and then the covariance of each noisy audio is balanced . Furthermore, the influence degree of the construction site noise is: . After that, noise monitoring can be carried out on the construction site according to the influence degree of the construction site noise.

[0100] For the embodiments of the present disclosure, Step 360 of conducting noise monitoring on the construction site according to the influence degree of irrelevant noise and the noise influence degree of different noisy audio may include the following steps:

[0101] Step 360-1: Calculate the covariance of different noisy audio and use the covariance as the noise influence degree of the noisy audio.

[0102] Step 360-2: Calculate the covariance mean of multiple noisy audio in the original noise data with respect to the covariance.

[0103] Step 360-3: Calculate the impact degree of construction site noise based on the covariance mean and the impact degree of irrelevant noise.

[0104] Step 360-4: Conduct noise monitoring on the construction site according to the impact degree of construction site noise.

[0105] For the embodiments of the present disclosure, the impact degree of construction site noise can be compared with a second preset threshold. When it is determined that the impact degree of construction site noise Y is greater than the second preset threshold it is determined that there is significant construction noise at the construction site. When it is determined that the impact degree of construction site noise Y is less than or equal to the second preset threshold it is determined that the construction noise at the construction site is in a normal state. Among them, the value of the second preset threshold can be set according to the actual application scenario. For example, it can be set to and specific limitations are not provided here.

[0106] Correspondingly, step 360-4 of the embodiment can include the following steps: Determine whether the impact degree of construction site noise is greater than the second preset threshold; if so, determine that there is significant construction noise at the construction site; if not, determine that the construction noise at the construction site is in a normal state.

[0107] In summary, for the technical solution in the present application, after obtaining the original noise data of multiple noise audio collected at multiple monitoring positions on the construction site, the signal discrimination of different noise audio can be calculated first; then, based on the signal discrimination, the impact degree of irrelevant noise in the original noise data can be calculated; finally, according to the impact degree of irrelevant noise and the impact degree of noise of different noise audio, noise monitoring is carried out on the construction site. By calculating the impact degree of irrelevant noise in the original noise data, when monitoring the original noise data, the interference of irrelevant noise on the environmental noise of the construction site can be eliminated based on the impact degree of irrelevant noise, thereby improving the monitoring accuracy of the construction environment.

[0108] Based on the same inventive concept as the above method, an embodiment of the present invention also provides a construction environment monitoring system, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above methods for a construction environment monitoring method.

[0109] In summary, an embodiment of the present invention provides a construction environment monitoring method and system. By calculating the impact degree of irrelevant noise in the original noise data, when monitoring the original noise data, the interference of irrelevant noise on the environmental noise of the construction site can be eliminated based on the impact degree of irrelevant noise, thereby improving the monitoring accuracy of the construction environment.

[0110] It should be noted that the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the specific embodiments of this specification have been described. Further, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0111] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0112] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for monitoring the construction environment, characterized in that, The method includes: Obtaining the original noise data of a construction site, where the original noise data includes multiple noise audio frequencies collected at multiple monitoring positions on the construction site; Calculating the signal discrimination degrees of different noise audio frequencies; Calculating the influence degree of irrelevant noise in the original noise data based on the signal discrimination degrees; Performing noise monitoring on the construction site according to the influence degree of the irrelevant noise and the noise influence degrees of different noise audio frequencies; The calculating the signal discrimination degrees of different noise audio frequencies includes: Determining the possibility degree that the data range where each maximum point in different noise audio frequencies is an abnormal sound source; Calculating the noise performance degree of the data range where each maximum point is located based on the possibility degree; Calculating the signal discrimination degrees of different noise audio frequencies according to the noise performance degrees of the data ranges where each maximum point is located; The calculating the noise performance degree of the data range where each maximum point is located based on the possibility degree includes: Determining the maximum points corresponding to the possibility degrees greater than a first preset threshold as abnormal sound source points; Obtaining the abnormal sound source point data of each abnormal sound source point, where the abnormal sound source point data includes the time value, amplitude, and possibility degree of the abnormal sound source point; Calculating the noise performance degree of the data range where each abnormal sound source point on each noise audio frequency is located based on the abnormal sound source point data of different noise audio frequencies; The calculating the noise performance degree of the data range where each abnormal sound source point on each noise audio frequency is located based on the abnormal sound source point data of different noise audio frequencies includes: Sequentially determining different noise audio frequencies as the current noise audio frequency, and executing the calculation process of the noise performance degree of the data range where the first abnormal sound source point on the current noise audio frequency is located; Wherein, the noise performance degree calculation process includes: Based on the time value of the first abnormal sound source point on the current noise audio frequency, determining a second abnormal sound source point on other noise audio frequencies of the multiple noise audio frequencies that is at the same time sequence point as the first abnormal sound source point and has the smallest interval distance; Calculating the noise performance degree of the data range where the first abnormal sound source point on the current noise audio frequency is located according to the abnormal sound source point data of the first abnormal sound source point and the abnormal sound source point data of the second abnormal sound source point.

2. The building construction environment monitoring method according to claim 1, characterized in that, The determining the possibility degree that the data range where each maximum point in different noise audio frequencies is an abnormal sound source includes: Determining the maximum point data of each noise audio frequency, where the maximum point data includes the maximum points included in the noise audio frequency, the amplitude of each maximum point, the amplitudes of the neighboring data points corresponding to each maximum point, the maximum amplitude of each noise audio frequency, and the number of neighboring data points; Calculating the possibility degree that the data range where each maximum point in each noise audio frequency is an abnormal sound source based on the maximum point data.

3. The method according to claim 1, characterized in that, The calculating the signal discrimination degrees of different noise audio frequencies according to the noise performance degrees of the data ranges where each maximum point is located includes: Perform empirical mode decomposition on different pieces of the noise audio to obtain the component signal data of each piece of the noise audio, where the component signal data includes the IMF component signals obtained by decomposing the noise audio and the number of the IMF component signals; Based on the component signal data of different pieces of the noise audio, the variances of different pieces of the noise audio, and the noise manifestation degrees of each abnormal sound source point on the noise audio within the data range, calculate the signal discrimination degrees of different pieces of the noise audio.

4. The building construction environment monitoring method according to claim 1, characterized in that The calculating the influence degree of the irrelevant noise in the original noise data based on the signal discrimination degree includes: Calculate the Pearson coefficients of different pieces of the noise audio; Based on the Pearson coefficients and the signal discrimination degree, calculate the influence degree of the irrelevant noise in the original noise data.

5. The building construction environment monitoring method according to claim 1, characterized in that The performing noise monitoring on the construction site according to the influence degree of the irrelevant noise and the noise influence degrees of different pieces of the noise audio includes: Calculate the covariance of different pieces of the noise audio and use the covariance as the noise influence degree of the noise audio; Calculate the covariance mean of multiple pieces of the noise audio in the original noise data with respect to the covariance; Based on the covariance mean and the influence degree of the irrelevant noise, calculate the construction site noise influence degree; Perform noise monitoring on the construction site according to the construction site noise influence degree.

6. The building construction environment monitoring method according to claim 5, wherein The performing noise monitoring on the construction site according to the construction site noise influence degree includes: Judge whether the construction site noise influence degree is greater than a second preset threshold; If so, determine that there is significant construction noise at the construction site; If not, determine that the construction noise at the construction site is in a normal state.

7. A building construction environment monitoring system, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the computer program is executed by a processor, it implements the steps of a method for monitoring a construction environment according to any one of claims 1-6.

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