Building construction environment monitoring method and system

Abstract

The invention relates to the technical field of data processing, in particular to a building construction environment monitoring method and system, and the method comprises the steps: obtaining the original noise data of a building construction site, the original noise data comprising a plurality of noise audios collected at a plurality of monitoring positions on the building construction site; calculating the signal distinction degree of different noise audios; calculating the influence degree of irrelevant noise in the original noise data based on the signal discrimination degree; and performing noise monitoring on the building construction site according to the influence degree of the irrelevant noise and the noise influence degree of the different noise audios. According to the invention, a technical problem of inaccurate monitoring caused by easy interference of surrounding environment noise in a building construction environment monitoring process can be solved.

Description

Technical Field

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

[0002] With the acceleration of urbanization, construction activities are frequent, and environmental problems on construction sites are becoming increasingly prominent. Construction not only involves a large amount of resource consumption and energy use, but may also cause noise, vibration, dust, wastewater and other pollution 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 surrounding residents and even cause social conflicts. Therefore, it is particularly important to implement effective construction environment monitoring. Among them, construction noise has a greater impact on the surrounding environment, so it is necessary to monitor the noise during construction.

[0003] In the prior art, monitoring construction site noise is simply done by measuring the noise level during construction using a decibel meter. When the noise exceeds a certain threshold, it is considered that noise pollution exists. However, since there are many types of noise in the environment, the monitored noise sources may not all come from construction sites, but may also come from surrounding environmental noise, such as car horns, which will interfere with construction site noise monitoring, resulting in inaccurate monitoring. Summary of the invention

[0004] In order to solve the technical problem that the construction environment monitoring process is easily disturbed by the surrounding environmental noise, resulting in inaccurate monitoring, the purpose of the present invention is to provide a construction environment monitoring method and system, and the technical scheme adopted is as follows: In a first aspect, the present invention provides a method for monitoring a construction environment, the method comprising: Acquire original noise data of a construction site, wherein the original noise data includes a plurality of noise audios collected from a plurality of monitoring locations on the construction site; Calculating signal distinctions of different noise audios; Based on the signal discrimination, calculating the influence degree of irrelevant noise in the original noise data; Noise monitoring is performed on the construction site according to the influence degree of the irrelevant noise and the noise influence degree of different noise audio frequencies.

[0005] Optionally, the calculating signal distinguishability of different noise audios includes: Determine the possibility that the data range where each maximum value point in the different noise audios is located is an abnormal sound source; Calculate the noise performance degree of the data range where each of the maximum value points is located based on the possibility degree; The signal distinction of different noise audios is calculated according to the noise performance degree of the data range where each maximum value point is located.

[0006] Optionally, the determining the possibility that the data range where each maximum point in the different noise audios is located is an abnormal sound source includes: Determine the maximum point data of each of the noise audios, the maximum point data including the maximum points contained in the noise audios, the amplitude of each of the maximum points, the amplitude of the neighborhood data points corresponding to each of the maximum points, the maximum amplitude of each of the noise audios, and the number of the neighborhood data points; The possibility that the data range where each of the maximum points in each of the noise audios is located is an abnormal sound source is calculated based on the maximum point data.

[0007] Optionally, the calculating, based on the possibility degree, the noise performance degree of the data range where each of the maximum value points is located includes: Determine the maximum point corresponding to the possibility degree greater than the first preset threshold as the abnormal sound source point; Acquire abnormal sound source point data of each abnormal sound source point, wherein the abnormal sound source point data includes a time value, an amplitude and a possible degree of the abnormal sound source point; Based on the abnormal sound source point data of different noise audios, the noise performance degree of the data range where each abnormal sound source point on each noise audio is located is calculated.

[0008] Optionally, the calculating the noise performance degree of the data range where each abnormal sound source point on each noise audio is located based on the abnormal sound source point data of different noise audios includes: Determine the different noise audios as current noise audios respectively and in turn, and perform a noise performance degree calculation process for the data range where the first abnormal sound source point on the current noise audio is located; The noise performance degree calculation process includes: Based on the time value of the first abnormal sound source point on the current noise audio, determine a second abnormal sound source point on other noise audios of the multiple noise audios that is at the same time sequence point as the first abnormal sound source point and has the smallest interval distance; The noise representation degree of the data range where the first abnormal sound source point is located on the current noise audio is calculated 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.

[0009] Optionally, calculating the signal distinction of different noise audios according to the noise performance degree of the data range where each of the maximum value points is located includes: Performing empirical mode decomposition on different noise audios to obtain component signal data of each noise audio, wherein the component signal data includes an IMF component signal obtained by decomposing the noise audio and the number of the IMF component signals; The signal discrimination of different noise audios is calculated based on the component signal data of different noise audios, the variance of different noise audios and the noise performance degree of the data range where each abnormal sound source point on the noise audio is located.

[0010] Optionally, the calculating, based on the signal discrimination, the influence degree of irrelevant noise in the original noise data includes: Calculating the Pearson coefficient of different noise audio frequencies; Based on the Pearson coefficient and the signal discrimination, the influence degree of irrelevant noise in the original noise data is calculated.

[0011] Optionally, the noise monitoring of the construction site according to the influence degree of the irrelevant noise and the noise influence degree of different noise audio frequencies includes: Calculating the covariance of different noise audios, and using the covariance as the noise influence degree of the noise audio; Calculating a covariance mean of a plurality of the noise audios in the original noise data with respect to the covariance; Calculating the impact degree of construction site noise based on the covariance mean and the impact degree of the irrelevant noise; Noise monitoring is performed on the construction site according to the degree of noise impact at the construction site.

[0012] Optionally, the performing noise monitoring on the construction site according to the noise impact degree of the construction site includes: Determining whether the impact degree of the construction site noise is greater than a second preset threshold; If yes, it is determined that there is a large construction noise at the construction site; If not, it is determined that the construction noise of the construction site is in a normal state.

[0013] On the other hand, the present invention also provides a construction environment monitoring system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the steps of a construction environment monitoring method as described in any one of the foregoing items are implemented.

[0014] The present invention has the following beneficial effects: through the technical solution provided by the present invention, after obtaining the original noise data including multiple noise audios collected from multiple monitoring positions on the construction site, the signal discrimination of different noise audios can be calculated first; then based on the signal discrimination, the influence degree of irrelevant noise in the original noise data is calculated; finally, according to the influence degree of irrelevant noise and the noise influence degree of different noise audios, the construction site is noise monitored. By calculating the influence degree of irrelevant noise in the original noise data, the present invention can eliminate the interference of irrelevant noise on the environmental noise of the construction site based on the influence degree of irrelevant noise when performing noise monitoring on the original noise data, thereby improving the monitoring accuracy of the construction environment.

[0015] 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 following specific implementation section. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of a flow chart of a construction environment monitoring method provided by an embodiment of the present invention; Figure 2 An example schematic diagram of an application scenario provided by an embodiment of the present invention; Figure 3 A schematic flow chart of a construction environment monitoring method provided by another embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a construction environment monitoring method and system proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0020] A specific scheme of a construction environment monitoring method provided by the present invention is described in detail below with reference to the accompanying drawings.

[0021] See also Figure 1 , which shows a method flow chart of a construction environment monitoring method provided by an embodiment of the present invention, the method comprising the following steps: Step 110: Acquire original noise data of the construction site, where the original noise data includes multiple noise audios collected from multiple monitoring locations on the construction site.

[0022] In specific application scenarios, such as Figure 2 As shown, decibel meters can be installed at different monitoring locations of a construction site, and multiple decibel meters can be used to collect original noise data of the construction site in real time. The original noise data may include multiple noise audios collected at multiple monitoring locations, and the noise audios may be data sequences. Because the construction noise of a 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, the construction noise can be promptly and effectively processed.

[0023] Step 120: Calculate signal distinctions of different noise audios.

[0024] When analyzing and monitoring the original noise data during the construction process of a construction site, there may be many sources of noise, such as the highway next to the construction site, surrounding schools, surrounding shopping malls, etc. Therefore, the original noise data collected may also be the 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 likely to identify it as construction noise of the construction site and then issue an alarm. In order to accurately identify the noise of a construction site, the audio signals collected at different monitoring locations can be analyzed to identify the type of sound source and whether it is noise, and the signal differentiation from other noises is high, so as to identify irrelevant noise other than the construction site noise.

[0025] Step 130: Calculate the influence of irrelevant noise in the original noise data based on the signal discrimination.

[0026] The signal discrimination of the noise audio obtained by the above calculation can then be used to obtain the degree of noise influence. Because the discrimination of the noise audio represents the difference in sound under the same monitoring conditions, the greater the difference, the more likely it is that there are other sound sources affecting the sound, and therefore the greater the possibility that it is abnormal noise.

[0027] Step 140: noise monitoring is performed on the construction site according to the influence degree of irrelevant noise and the noise influence degree of different noise audio frequencies.

[0028] For the embodiment of the present disclosure, after obtaining the influence degree of 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 irrelevant noise and the noise influence degree of different noise audios. Then, the construction environment monitoring is performed based on the influence degree of the construction site noise after removing the irrelevant noise.

[0029] In summary, according to a construction environment monitoring method provided by the present invention, after obtaining the original noise data of multiple noise audios collected from multiple monitoring positions on the construction site, the signal discrimination of different noise audios can be first calculated; then, based on the signal discrimination, the influence degree of irrelevant noise in the original noise data is calculated; finally, according to the influence degree of irrelevant noise and the noise influence degree of different noise audios, the construction site is noise monitored. By calculating the influence degree of irrelevant noise in the original noise data, the present invention can eliminate the interference of irrelevant noise on the environmental noise of the construction site based on the influence degree of irrelevant noise when performing noise monitoring on the original noise data, thereby improving the monitoring accuracy of the construction environment.

[0030] based on Figure 1 The embodiment shown is a refinement and extension of the above embodiment. In order to fully illustrate the specific implementation process of the method of this embodiment, this embodiment provides the following Figure 3 The specific method shown. Figure 3 based on Figure 1 The embodiment shown. Figure 3 As shown, the method comprises the following steps: Step 310: Acquire original noise data of the construction site, where the original noise data includes multiple noise audios collected from multiple monitoring locations on the construction site.

[0031] For the embodiments of the present disclosure, the embodiment steps may refer to the relevant description in embodiment step 110, and no specific limitation is given here.

[0032] Step 320: Determine the possibility that the data range where each maximum point in the different noise audios is located is an abnormal sound source.

[0033] When analyzing and monitoring the original noise data during the construction process of the construction site, there are many sources of noise, so the noise data collected is also a superposition of multiple signals. In order to accurately identify the noise of the construction site, the type of sound source is identified by analyzing the audio signals collected at different monitoring positions to determine whether it is noise, so as to identify the noise of the construction site. However, when monitoring the noise, it will be affected by other sounds, such as the sound of car horns on the road next to the construction site. The sound of the horn is relatively sharp, and the noise recognition system is likely to identify it as a noise signal and then issue an alarm. Because the sound of a car horn is generally sharp and short-lasting noise audio, the possible abnormal sound source in the original noise data is obtained according to the change of the original noise data. First, all the maximum points in each noise audio are obtained, and then the difference between each maximum point and its neighboring data is used to obtain the possible degree that the data range where each maximum point is located is an abnormal sound source.

[0034] Accordingly, for the embodiment of the present disclosure, determining the possibility that the data range where each maximum point in different noise audio is located is an abnormal sound source in step 320 may include the following steps: Step 320-1, determine the maximum point data of each noise audio, the maximum point data includes the maximum points contained in the noise audio, the amplitude of each maximum point, the amplitude of the neighborhood data point corresponding to each maximum point, the maximum amplitude of each noise audio, and the number of neighborhood data points.

[0035] Step 320-2: Calculate the possibility that the data range where each maximum point is located in each noise audio is an abnormal sound source based on the maximum point data.

[0036] For the disclosed embodiment, the maximum value point data can be substituted into the first calculation formula, and the possibility of the data range where each maximum value point in each noise audio is located being an abnormal sound source is calculated based on the first calculation formula. Among them, the formula feature description of the first calculation formula is: In the formula, Indicates Noise audio The data range where the maximum value point is located is the possible degree of the abnormal sound source. Indicates The maximum amplitude in the noise audio. Indicates Noise audio The amplitude of the maximum point, Indicates Noise audio The maximum point The amplitude of the neighboring data points, represents the number of neighborhood data points, represents the linear normalization function. Indicates The maximum amplitude of the noise audio is Noise audio The amplitude difference of the maximum points indicates the abnormality of the current data point; Represents the jth maximum point and its neighboring data points It should be noted that when calculating the difference with the neighboring data points, The data points are calculated one by one to the left and right until No more iterative calculations are performed when the maximum value is obtained.

[0037] Step 330: Calculate the noise performance level of the data range where each maximum point is located based on the degree of possibility.

[0038] Accordingly, for the embodiment of the present disclosure, the step 330 of calculating the noise performance degree of the data range where each maximum value point is located based on the possibility degree may include the following steps: Step 330-1: Determine a maximum point whose corresponding possibility degree is greater than a first preset threshold as an abnormal sound source point.

[0039] After calculating the probability that the data range where each maximum point is located is an abnormal sound source through step 320-2 of the embodiment, the abnormal sound source point can be screened out from multiple maximum points based on the probability. Specifically, the probability that the data range where each maximum point is located is an abnormal sound source can be compared with the first preset threshold, and the maximum point whose corresponding probability is greater than the first preset threshold is determined as an abnormal sound source point, and when When the maximum value is obtained, The data range formed by the maximum point and its neighboring data points is the current maximum point data range. The value can be set according to the actual application scenario, such as , no specific limitation is given here.

[0040] Step 330 - 2 : Acquire abnormal sound source point data of each abnormal sound source point, where the abnormal sound source point data includes the time value, amplitude, and possible degree of the abnormal sound source point.

[0041] Step 330 - 3 : Based on the abnormal sound source point data of different noise audios, the noise performance level of the data range where each abnormal sound source point on each noise audio is located is calculated.

[0042] For the embodiment of the present disclosure, after the abnormal sound source point is determined in the maximum point according to step 330-1 of the embodiment, the noise performance degree of the data range where each abnormal sound source point is located can be obtained by comparing the differences between different noise audios based on the abnormal sound source point data of different noise audios.

[0043] In a specific application scenario, different noise audios can be determined as current noise audios in turn, and the abnormal sound source point data of the abnormal sound source point on the current noise audio is substituted into the second calculation formula, and the noise performance degree of the data range where each abnormal sound source point on each noise audio is located is calculated based on the second calculation formula. Among them, the formula feature description of the second calculation formula is: In the formula, Indicates Noise audio The noise performance level of the data range where the abnormal sound source point is located, Indicates Noise audio The time value of the abnormal sound source point, Indicates Noise audio An abnormal sound source point, Indicates Noise audio The data range where the abnormal sound source point is located is the possible degree of the abnormal sound source. Indicates Noise audio The data range where the abnormal sound source point is located is the possible degree of the abnormal sound source. Indicates Noise audio The amplitude of the abnormal sound source point, Indicates Noise audio The amplitude of the abnormal sound source point, represents an exponential function with a natural constant as the base. The abnormal sound source point is based on the current noise audio (i.e. The first abnormal sound source point (i.e. abnormal sound source point), determine the second abnormal sound source point (i.e., the first abnormal sound source point) at the same time sequence point as the first abnormal sound source point and with the smallest interval distance on other noise audios of the multiple noise audios. abnormal sound source point). The noise audio is The noise audio at the location of the abnormal sound source. The smaller the distance between two noise audios at the same time point, the more likely it is that they are from the same sound source. In this case, the noise monitoring device is more likely to detect the same noise during the monitoring process. Indicated in The abnormality of the two signals is calculated and compared under the premise that the ratio is closer to 1, the more similar the two signal sources are, and therefore the more likely it is an abnormal sound source. It indicates the amplitude difference of noise audio. Because the transmission of sound is attenuated, when collecting sound data, the decibel meters are distributed in different directions. Therefore, when there is a difference between the two, it means that the sound source may be caused by a car horn.

[0044] Correspondingly, step 330-3 of the embodiment may include the following steps: determining different noise audios as the current noise audios respectively and in turn, and executing a noise performance degree calculation process for 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 a second abnormal sound source point on other noise audios of multiple noise audios that is at the same time point as the first abnormal sound source point and has the smallest interval distance; calculating the noise performance degree for the data range where the first abnormal sound source point on the current noise audio is located based on 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.

[0045] Step 340: Calculate the signal distinction of different noise audios according to the noise performance degree of the data range where each maximum value point is located.

[0046] According to the above calculation, the noise performance degree of each abnormal sound source point on the noise audio is obtained in the data range, and then the changes of different component signals on the same noise audio are compared to obtain the discrimination of the noise audio. Because different noise audio is affected by noise to different degrees and the noise sources are also different, the changes of the noise audio obtained are different. However, for the monitoring of construction site noise, because the decibel meters are distributed inside the construction site and the distribution is relatively uniform, the sound monitoring of the construction site is synchronized. Therefore, the signal discrimination is calculated based on the difference in noise audio. The calculation formula is as follows: In the formula, Indicates Signal differentiation of noise audio, Indicates The number of IMF component signals of the noise audio. Indicates The number of IMF component signals of the noise audio. Indicates Noise audio The IMF component signal is Noise audio DTW value of the IMF component signal, Indicates The number of IMF component signals of the noise audio is The minimum value of the IMF component signal number of the noise audio, Indicates The variance of the noise audio, Indicates The variance of the noise audio, It represents a hyperparameter with a value of 0.01 to prevent the denominator from being 0. Indicates Noise audio The degree of noise performance of the data range where the maximum point is located, Indicates the number of maximum points. Indicates Noise audio and The difference in the number of IMF component signals between the noise audio. The IMF component signals are decomposed using the Empirical Mode Decomposition (EMD) algorithm. Because the EMD algorithm decomposes the signal based on the different frequencies of the signal, the smaller the difference between the two signals, the smaller the difference in the number of component signals obtained by decomposition. In other words, the greater the difference between the two component signals, the greater the difference in the number of component signals. Indicates Noise audio The IMF component signal is Noise audio The DTW value of the IMF component signal indicates the similarity of the 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 noise audios is calculated here. It represents the difference in the variance of two noise audios. The larger the difference is, the more likely the noise sources of the two noise audios are different.

[0047] Accordingly, for the embodiment of the present disclosure, calculating the signal distinction of different noise audios according to the noise performance degree of the data range where each maximum value point is located in step 340 may include the following steps: Step 340 - 1 , perform empirical mode decomposition on different noise audios to obtain component signal data of each noise audio, where the component signal data includes IMF component signals obtained by decomposing the noise audio and the number of IMF component signals.

[0048] Step 340 - 2 : Calculate the signal discrimination of different noise audios based on the component signal data of different noise audios, the variance of different noise audios, and the noise performance degree of the data range where each abnormal sound source point on the noise audio is located.

[0049] Step 350: Calculate the influence of irrelevant noise in the original noise data based on the signal discrimination.

[0050] After calculating the signal discrimination of different noise audio signals according to step 340 of the above embodiment, the influence of irrelevant noise in the original noise data can be further calculated. Because the discrimination of noise audio signals represents the difference of sounds under the same monitoring conditions, the greater the difference, the more likely it is that there are other sound sources, and therefore the greater the possibility that it is abnormal noise.

[0051] Accordingly, for the embodiment of the present disclosure, the step 350 of calculating the influence of irrelevant noise in the original noise data based on the signal discrimination may include the following steps: Step 350 - 1 : Calculate the Pearson coefficients of different noise audios.

[0052] Step 350 - 2 : Calculate the influence of irrelevant noise in the original noise data based on the Pearson coefficient and the signal discrimination.

[0053] For the embodiment of the present disclosure, the influence degree of irrelevant noise can be obtained according to the change of the noise audio based on the Pearson coefficient and the signal discrimination, and the calculation formula is as follows: In the formula, Indicates the influence of irrelevant noise. Indicates The noise audio and The Pearson coefficient of the noise audio is normalized to represent the correlation between the two data series.

[0054] Step 360: noise monitoring is performed on the construction site according to the influence degree of irrelevant noise and the noise influence degree of different noise audio frequencies.

[0055] For the embodiment of the present disclosure, after calculating the influence degree of irrelevant noise in the original noise data according to step 350 of the above embodiment, 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 performed based on the influence degree of the construction site noise after removing the irrelevant noise. Because the influence degree of the noise is certain, the covariance of each noise audio is calculated. To indicate the noise impact of the noise audio, and then the covariance of each noise audio Seeking Balance . Then the impact of construction site noise is: Afterwards, noise monitoring can be carried out on the construction site according to the degree of noise impact on the construction site.

[0056] For the embodiment of the present disclosure, the noise monitoring of the construction site according to the influence degree of irrelevant noise and the noise influence degree of different noise audio in step 360 may include the following steps: Step 360 - 1 : Calculate the covariance of different noise audios, and use the covariance as the noise influence degree of the noise audio.

[0057] Step 360 - 2 : Calculate the covariance mean of the covariance of the multiple noise audios in the original noise data.

[0058] Step 360 - 3 : Calculate the impact degree of construction site noise based on the covariance mean and the impact degree of irrelevant noise.

[0059] Step 360-4: noise monitoring is performed on the construction site according to the degree of noise impact of the construction site.

[0060] For the embodiment of the present disclosure, the construction site noise impact degree can be compared with the second preset threshold value. When it is determined that the construction site noise impact degree Y is greater than the second preset threshold value, When it is determined that there is a large construction noise at the construction site; when it is determined that the impact degree Y of the construction site noise is less than or equal to the second preset threshold When the construction noise of the construction site is determined to be in a normal state. The value can be set according to the actual application scenario, such as , no specific limitation is given here.

[0061] Accordingly, step 360-4 of the embodiment may include the following steps: determining whether the degree of impact of the construction site noise is greater than a second preset threshold; if so, determining that there is a large construction noise at the construction site; if not, determining that the construction noise at the construction site is in a normal state.

[0062] In summary, the technical solution in the present application, after obtaining the original noise data including multiple noise audios collected from multiple monitoring positions on the construction site, can first calculate the signal discrimination of different noise audios; then calculate the influence of irrelevant noise in the original noise data based on the signal discrimination; finally, according to the influence of irrelevant noise and the noise influence of different noise audios, perform noise monitoring on the construction site. By calculating the influence of irrelevant noise in the original noise data, the present invention can eliminate the interference of irrelevant noise on the environmental noise of the construction site based on the influence of irrelevant noise when performing noise monitoring on the original noise data, thereby improving the monitoring accuracy of the construction environment.

[0063] 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, the steps of any one of the above-mentioned construction environment monitoring methods are implemented.

[0064] In summary, the embodiments of the present invention provide a method and system for monitoring a construction environment. The present invention calculates the influence degree of irrelevant noise in the original noise data. When performing noise monitoring on the original noise data, the present invention can eliminate the interference of irrelevant noise on the environmental noise of the construction site based on the influence degree of irrelevant noise, thereby improving the monitoring accuracy of the construction environment.

[0065] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0067] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A construction environment monitoring method, characterized in that: The method comprises: Acquire original noise data of a construction site, wherein the original noise data includes a plurality of noise audios collected from a plurality of monitoring locations on the construction site; Calculating signal distinctions of different noise audios; Based on the signal discrimination, calculating the influence degree of irrelevant noise in the original noise data; Performing noise monitoring on the construction site according to the influence degree of the irrelevant noise and the noise influence degree of different noise audio frequencies; The calculating the signal distinguishability of different noise audios comprises: Determine the possibility that the data range where each maximum value point in the different noise audios is located is an abnormal sound source; Calculate the noise performance degree of the data range where each of the maximum value points is located based on the possibility degree; The signal distinction of different noise audios is calculated according to the noise performance degree of the data range where each maximum value point is located.

2. The construction environment monitoring method according to claim 1, characterized in that: The determining of the possibility that the data range where each maximum value point in the different noise audios is located is an abnormal sound source includes: Determine the maximum point data of each of the noise audios, the maximum point data including the maximum points contained in the noise audios, the amplitude of each of the maximum points, the amplitude of the neighborhood data points corresponding to each of the maximum points, the maximum amplitude of each of the noise audios, and the number of the neighborhood data points; The possibility that the data range where each of the maximum points in each of the noise audios is located is an abnormal sound source is calculated based on the maximum point data.

3. The construction environment monitoring method according to claim 1, characterized in that: The calculating, based on the possibility, the noise performance degree of the data range where each of the maximum value points is located comprises: Determine the maximum point corresponding to the possibility degree greater than the first preset threshold as the abnormal sound source point; Acquire abnormal sound source point data of each abnormal sound source point, wherein the abnormal sound source point data includes a time value, an amplitude and a possible degree of the abnormal sound source point; Based on the abnormal sound source point data of different noise audios, the noise performance degree of the data range where each abnormal sound source point on each noise audio is located is calculated.

4. The construction environment monitoring method according to claim 3, characterized in that: The step of calculating the noise performance degree of a data range where each abnormal sound source point on each noise audio is located based on the abnormal sound source point data of different noise audios comprises: Determine the different noise audios as current noise audios respectively and in turn, and perform a noise performance degree calculation process for the data range where the first abnormal sound source point on the current noise audio is located; The noise performance degree calculation process includes: Based on the time value of the first abnormal sound source point on the current noise audio, determine a second abnormal sound source point on other noise audios of the multiple noise audios that is at the same time sequence point as the first abnormal sound source point and has the smallest interval distance; The noise representation degree of the data range where the first abnormal sound source point is located on the current noise audio is calculated 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.

5. The method according to claim 3, characterized in that: The calculating the signal distinction of different noise audios according to the noise performance degree of the data range where each of the maximum value points is located includes: Performing empirical mode decomposition on different noise audios to obtain component signal data of each noise audio, wherein the component signal data includes an IMF component signal obtained by decomposing the noise audio and the number of the IMF component signals; The signal discrimination of different noise audios is calculated based on the component signal data of different noise audios, the variance of different noise audios and the noise performance degree of the data range where each abnormal sound source point on the noise audio is located.

6. The construction environment monitoring method according to claim 1, characterized in that: The calculating, based on the signal discrimination, the influence degree of irrelevant noise in the original noise data comprises: Calculating the Pearson coefficient of different noise audio frequencies; Based on the Pearson coefficient and the signal discrimination, the influence degree of irrelevant noise in the original noise data is calculated.

7. The construction environment monitoring method according to claim 1, characterized in that: The noise monitoring of the construction site according to the influence degree of the irrelevant noise and the noise influence degree of different noise audio frequencies includes: Calculating the covariance of different noise audios, and using the covariance as the noise influence degree of the noise audio; Calculating a covariance mean of a plurality of the noise audios in the original noise data with respect to the covariance; Calculating the impact degree of construction site noise based on the covariance mean and the impact degree of the irrelevant noise; Noise monitoring is performed on the construction site according to the degree of noise impact at the construction site.

8. The construction environment monitoring method according to claim 7, characterized in that: The noise monitoring of the construction site according to the noise impact degree of the construction site includes: Determining whether the impact degree of the construction site noise is greater than a second preset threshold; If yes, it is determined that there is a large construction noise at the construction site; If not, it is determined that the construction noise of the construction site is in a normal state.

9. A construction environment monitoring system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the computer program is executed by a processor, the steps of a construction environment monitoring method as described in any one of claims 1 to 8 are implemented.

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