CN117198263A - Industrial field large space active noise reduction method - Google Patents
Industrial field large space active noise reduction method Download PDFInfo
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- CN117198263A CN117198263A CN202311216354.5A CN202311216354A CN117198263A CN 117198263 A CN117198263 A CN 117198263A CN 202311216354 A CN202311216354 A CN 202311216354A CN 117198263 A CN117198263 A CN 117198263A
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Abstract
The invention discloses a large-space active noise reduction method in the industrial field, which belongs to the technical field of noise reduction in the industrial background, and comprises the following steps: setting a target area and identifying each target source in the target area; determining corresponding target denoising equipment according to the noise data corresponding to each target source; acquiring noise data corresponding to each detection point of the target source, and identifying and extracting noise characteristics corresponding to each target source; setting corresponding limiting conditions according to the target area and each noise characteristic; obtaining a plurality of to-be-selected denoising devices based on the limiting conditions; evaluating each denoising device to be selected, obtaining a recommendation list, sending the recommendation list to a corresponding manager, selecting the corresponding recommendation device as a target denoising device by the manager, and obtaining the corresponding installation position of the target denoising device; installing target denoising equipment at a corresponding position; and monitoring target source noise in a target area in real time through target equipment, and denoising the monitored target source noise in real time.
Description
Technical Field
The invention belongs to the technical field of noise reduction in industrial background, and particularly relates to a large-space active noise reduction method in the industrial field.
Background
In an industrial working environment, noise pollution is visible everywhere, such as industrial background places like hydropower stations, a large amount of noise is often generated, the noise is liable to cause adverse effects on physical and mental health of people, and work communication is also influenced, and the noise reduction in a space field is an urgent problem to be solved in the industrial field, so that the invention provides a large-space active noise reduction method in the industrial field in order to solve the problem of noise reduction in a large space in the industrial field.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides a large-space active noise reduction method in the industrial field.
The aim of the invention can be achieved by the following technical scheme:
the large-space active noise reduction method in the industrial field comprises the following steps:
step S1: setting a target area and identifying each target source in the target area;
further, the method for determining the target source comprises the following steps:
and identifying various noise sources in the target area, evaluating all the noise sources by combining target source checking standards, and marking the noise sources meeting the evaluation requirements as target sources.
Further, the method of identifying various noise sources present in the target area includes:
establishing a target area three-dimensional model, analyzing the target area three-dimensional model to obtain a plurality of corresponding detection points, setting corresponding noise detection equipment according to the space coordinates of each detection point, and carrying out real-time noise detection through each noise detection equipment to obtain noise data corresponding to each detection point;
and analyzing the noise sources and the noise values of the noise sources at the corresponding detection points according to the obtained noise data of the monitoring points.
Further, the detection points can be distributed at various positions within the target area space.
Further, the method for evaluating each noise source comprises the following steps:
and identifying an influence level partition corresponding to each detection point, calculating a corresponding target value according to the noise value corresponding to each noise source at each detection point and the influence level partition, and marking the noise source with the target value larger than the threshold value X1 as a target source.
Further, the calculation method of the target value includes:
marking the obtained adjustment coefficients and noise values as ui and ZSi respectively according to adjustment coefficients corresponding to the influence level partition matching, wherein i=1, 2, … … and n are positive integers; the corresponding target value is calculated according to the evaluation formula epl= (Σui×zsi)/n.
Step S2: determining corresponding target denoising equipment according to the noise data corresponding to each target source;
further, the method for determining the target denoising device includes:
acquiring noise data corresponding to each detection point of the target source, and identifying and extracting noise characteristics corresponding to each target source; setting corresponding limiting conditions according to the target area and each noise characteristic; obtaining a plurality of to-be-selected denoising devices based on the limiting conditions;
evaluating each denoising device to be selected, obtaining a recommendation list, sending the recommendation list to a corresponding manager, selecting the corresponding recommendation device as a target denoising device by the manager, and obtaining the corresponding installation position of the target denoising device.
Further, the method for evaluating the denoising device to be selected comprises the following steps:
analyzing the denoising equipment to be selected to obtain performance values of the denoising equipment to be selected on each target performance and corresponding installation positions, and marking the obtained performance values as XZj, wherein j=1, 2, … … and m are positive integers respectively; removing the to-be-selected denoising model with the performance value lower than the threshold value X2, obtaining the use cost of each piece of the rest to-be-selected denoising equipment, and carrying out unit conversion on the obtained use cost to obtain a corresponding cost value; the obtained cost value is marked as CBK, the corresponding comprehensive value PLK is calculated according to the comprehensive formula PLK=b1× (Σ XZj)/n-b 2×CBK, b1 and b2 are all proportionality coefficients, the value range is 0< b1 less than or equal to 1,0< b2 less than or equal to 1, the denoising equipment to be selected with the comprehensive value lower than the threshold value X3 is removed, the rest denoising equipment to be selected is marked as recommending equipment, and all recommending equipment are ordered according to the sequence of the comprehensive value from high to low, so that a recommending list is obtained.
Further, when the target denoising device does not exist, the denoising device to be selected with the highest comprehensive value is marked as the reference device, development requirements are generated according to each performance value and limiting condition of the reference device, and the generated development requirements are adjusted by corresponding management staff and then are used as the development requirements of the denoising device to develop.
Step S3: installing target denoising equipment at a corresponding position;
step S4: and monitoring target source noise in a target area in real time through target equipment, and denoising the monitored target source noise in real time.
Compared with the prior art, the invention has the beneficial effects that:
by carrying out field detection evaluation on the target area, various target sources and corresponding target data in the target area are more accurately determined, the characteristics of basically unchanged various noise sources under the corresponding background are fully combined, and more accurate analysis data is provided for subsequent active noise reduction; the target denoising device is determined according to the noise data corresponding to each target source, and the intelligent setting is most suitable for the denoising device under the current noise background, so that more accurate active denoising is realized.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort to a person skilled in the art.
Fig. 1 is a functional block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the method for actively reducing noise in a large space in the industrial field comprises the following steps:
step S1: setting a target area and identifying each target source in the target area;
an area needing active noise reduction, namely a target area, is defined in the working area, and the target area is set according to the noise reduction requirement; and identifying and evaluating various sources capable of generating noise devices, equipment and the like in the target area based on the determined target area, and marking the noise source which is evaluated to meet the active noise reduction requirement as a target source.
The specific target source determining method comprises the following steps:
establishing a target area three-dimensional model by combining a current three-dimensional modeling technology, analyzing the target area three-dimensional model to obtain a plurality of corresponding detection points, setting corresponding noise detection equipment according to the space coordinates of each detection point, and carrying out real-time noise detection through each noise detection equipment to obtain noise data corresponding to each detection point; the noise detection device is an existing device.
Analyzing noise sources and noise values of the noise sources at corresponding detection points according to the obtained noise data of the monitoring points, comprehensively evaluating the noise sources according to the noise values of the noise sources at the detection points to obtain a target value of the noise sources, and marking the noise sources with the target value larger than a threshold value X1 as target sources.
The method comprises the steps of analyzing a three-dimensional model of a target area, determining corresponding detection points, which are used for comprehensively detecting noise conditions in a necessary area, specifically establishing a corresponding detection point analysis model based on a CNN network or a DNN network, establishing a corresponding training set through a manual mode for training, wherein the training set comprises three-dimensional models set through various simulation, detection points and influence level partitions which are correspondingly arranged, the influence level partitions are arranged according to the influence of the detection points on workers, such as wall-attached positions, middle positions of working areas and the like, the influence corresponding to detection data with the same positions is different, each influence level partition is provided with a corresponding adjustment coefficient, which is used for amplifying or shrinking, and analyzing the three-dimensional model of the target area through the detection point analysis model after the training is successful to obtain space coordinate data and affiliated influence level partitions of each detection point; because neural networks are prior art in the art, the specific setup and training process is not described in detail in this disclosure.
According to the noise data of each detection point, analyzing the noise source and the noise value of each noise source at the corresponding detection point, according to various noise types included in the detected noise data, carrying out matching such as preset equipment directory and the like to determine the corresponding noise source, then according to the detection data corresponding to the detection point, analyzing the influence condition of noise characteristics such as noise types, decibels and the like of the noise source on the position of the detection point, setting the corresponding noise value by combining with target source checking standards preset by a manager, specifically, establishing a corresponding noise source analysis model based on a CNN network or a DNN network, establishing a corresponding training set in a manual mode to train, wherein the training set comprises various noise data, the noise source directory and the noise source and the noise value which are correspondingly set, and analyzing through the noise source analysis model after successful training to obtain the noise source and the noise value.
The method for comprehensively evaluating the noise value of the noise source at each detection point comprises the following steps:
acquiring a noise value and an affiliated influence level partition corresponding to each detection point, respectively marking the acquired adjustment coefficient and noise value as ui and ZSI according to the acquired adjustment coefficient corresponding to the influence level partition matching, wherein i=1, 2, … … and n, and n is a positive integer; the corresponding target value is calculated according to the evaluation formula epl= Σ (ui×zsi)/n.
By carrying out field detection and evaluation on the target area, various target sources and corresponding target data in the target area are determined more accurately, the characteristics of basic invariance of various noise sources under the corresponding background are fully combined, and more accurate analysis data is provided for subsequent active noise reduction.
Step S2: determining corresponding target denoising equipment according to the noise data corresponding to each target source;
firstly, noise data corresponding to each detection point of a target source are identified and analyzed, noise characteristics corresponding to each target source are determined, and then corresponding denoising equipment is selected by combining the noise characteristics.
The specific process comprises the following steps:
presetting a noise characteristic template, wherein the noise characteristic template is used for representing which data items are included in noise characteristics, and carrying out corresponding data generation according to the corresponding data items; setting corresponding noise characteristics from a large amount of noise data based on the noise characteristic template, integrating the noise characteristics into a corresponding training set, establishing a corresponding noise characteristic model based on a CNN network or a DNN network, and training through the established training set;
acquiring noise data corresponding to each detection point of a target source, and analyzing the corresponding noise data through a noise characteristic model after successful training to acquire corresponding noise characteristics;
setting corresponding limiting conditions according to the target area and each noise characteristic, and searching the denoising equipment according to the set limiting conditions to obtain a plurality of denoising equipment to be selected; various denoising devices capable of meeting the limiting conditions, such as a range in which a denoising range needs to reach a target area, denoising of noise of each noise characteristic and the like, are obtained from the current market, and the limiting conditions are integrated and the denoising devices to be selected are searched by utilizing the current technology;
analyzing the optimal position in the target area according to the information of each denoising device to be selected and when the denoising device is installed at the position, evaluating corresponding performance values and cost values of corresponding target performances according to the actual denoising performance of each denoising device to be selected on each target performance, wherein each target performance is set based on each data item in a noise characteristic template, including corresponding denoising range evaluation, so as to avoid the situation that the denoising device is installed at the installation position
Specifically, a corresponding performance evaluation model can be established based on a CNN network or a DNN network, a corresponding training set is established for training in a manual mode, a performance value and a corresponding installation position of the denoising equipment to be selected on each target performance are obtained through analysis of the performance evaluation model after successful training, each obtained performance value is respectively marked as XZj, j represents the corresponding target performance, j=1, 2, … …, m and m are positive integers; removing the to-be-selected denoising model with the performance value lower than the threshold value X2, obtaining the use cost of each piece of the rest to-be-selected denoising equipment, including equipment cost and installation cost, carrying out unit conversion on the obtained use cost to obtain a corresponding cost value, and presetting a corresponding cost unit conversion coefficient to carry out conversion; the obtained cost value is marked as CBK, the corresponding comprehensive value PLK is calculated according to the comprehensive formula PLK=b1× (Sigma XZj)/n-b 2×CBK, b1 and b2 are both proportionality coefficients, the value range is 0< b1 less than or equal to 1,0< b2 less than or equal to 1, the denoising equipment to be selected with the comprehensive value lower than the threshold value X3 is removed, the rest denoising equipment to be selected is marked as recommending equipment, all recommending equipment are ordered according to the sequence of the comprehensive value from high to low, a recommending list is obtained, the recommending list is sent to corresponding management personnel, the corresponding recommending equipment is selected as target denoising equipment by the management personnel, and the installation position corresponding to the target denoising equipment is obtained.
In one embodiment, because there may be no target denoising device in the screening process, that is, all the existing denoising devices do not meet the requirements; based on the above considerations, the following solutions are proposed.
When the target denoising device does not exist, the to-be-selected denoising device with the highest comprehensive value is identified and marked as a reference device, target performances reaching the comprehensive requirement and target performances not reaching the standard in the reference device are analyzed according to the performance values of the reference device, development requirements are generated by combining limiting conditions, and the generated development requirements are adjusted by corresponding management staff and then are used as the development requirements of the denoising device for development.
The method comprises the steps of determining whether target performance meets the standard or not based on a difference value between a comprehensive value and a threshold value X3, determining that each performance value is unqualified after multiplying the performance value by the correction coefficient and then being lower than the threshold value X4, presetting the correction coefficients corresponding to different difference value intervals, and obtaining the corresponding correction coefficients by matching according to the corresponding difference value intervals.
The development requirements are generated by combining the limiting conditions, namely an initial development requirement is generated according to the advantages and disadvantages of the development requirements and the limiting conditions, corresponding reference equipment is marked, the development requirements can be generated by utilizing the prior art, for example, a corresponding development analysis model is established on the basis of a CNN network or a DNN network, a corresponding training set is established in a manual mode for training, and the development analysis model after successful training is used for analyzing to obtain the corresponding development requirements.
Step S3: installing target denoising equipment at a corresponding position;
step S4: and monitoring target source noise in a target area in real time through target equipment, and denoising the monitored target source noise in real time.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. The large-space active noise reduction method in the industrial field is characterized by comprising the following steps of:
step S1: setting a target area and identifying each target source in the target area;
step S2: determining corresponding target denoising equipment according to the noise data corresponding to each target source;
acquiring noise data corresponding to each detection point of the target source, and identifying and extracting noise characteristics corresponding to each target source; setting corresponding limiting conditions according to the target area and each noise characteristic; obtaining a plurality of to-be-selected denoising devices based on the limiting conditions;
evaluating each denoising device to be selected, obtaining a recommendation list, sending the recommendation list to a corresponding manager, selecting the corresponding recommendation device as a target denoising device by the manager, and obtaining the corresponding installation position of the target denoising device;
step S3: installing target denoising equipment at a corresponding position;
step S4: and monitoring target source noise in a target area in real time through target equipment, and denoising the monitored target source noise in real time.
2. The method for actively reducing noise in a large space in an industrial field according to claim 1, wherein the method for determining a target source comprises:
and identifying various noise sources in the target area, evaluating all the noise sources by combining target source checking standards, and marking the noise sources meeting the evaluation requirements as target sources.
3. The method for actively reducing noise in a large space in an industrial field according to claim 2, wherein the method for identifying various noise sources present in the target area comprises:
establishing a target area three-dimensional model, analyzing the target area three-dimensional model to obtain a plurality of corresponding detection points, setting corresponding noise detection equipment according to the space coordinates of each detection point, and carrying out real-time noise detection through each noise detection equipment to obtain noise data corresponding to each detection point;
and analyzing the noise sources and the noise values of the noise sources at the corresponding detection points according to the obtained noise data of the monitoring points.
4. A method of active noise reduction in large volumes in the industrial field according to claim 3, wherein the detection points are distributed at various locations within the target area space.
5. A method of large space active noise reduction in the industrial field according to claim 3, wherein the method of evaluating each noise source comprises:
and identifying an influence level partition corresponding to each detection point, calculating a corresponding target value according to the noise value corresponding to each noise source at each detection point and the influence level partition, and marking the noise source with the target value larger than the threshold value X1 as a target source.
6. The method for actively reducing noise in a large space in an industrial field according to claim 5, wherein the calculating method of the target value comprises:
marking the obtained adjustment coefficients and noise values as ui and ZSi respectively according to adjustment coefficients corresponding to the influence level partition matching, wherein i=1, 2, … … and n are positive integers; the corresponding target value is calculated according to the evaluation formula epl= (Σui×zsi)/n.
7. The method for actively reducing noise in a large space in an industrial field according to claim 1, wherein the method for evaluating the denoising apparatus to be selected comprises:
analyzing the denoising equipment to be selected to obtain performance values of the denoising equipment to be selected on each target performance and corresponding installation positions, and marking the obtained performance values as XZj, wherein j=1, 2, … … and m are positive integers respectively;
removing the to-be-selected denoising model with the performance value lower than the threshold value X2, obtaining the use cost of each piece of the rest to-be-selected denoising equipment, and carrying out unit conversion on the obtained use cost to obtain a corresponding cost value; marking the obtained cost value as CBK;
calculating corresponding comprehensive values PLK according to a comprehensive formula PLK=b1× (Sigma XZj)/n-b 2×CBK, wherein b1 and b2 are proportionality coefficients, and the value range is 0< b1 less than or equal to 1, and 0< b2 less than or equal to 1;
and removing the to-be-selected denoising equipment with the comprehensive value lower than the threshold value X3, marking the rest to-be-selected denoising equipment as recommending equipment, and sequencing the recommending equipment according to the sequence from high to low of the comprehensive value to obtain a recommending list.
8. The method for actively reducing noise in a large space in an industrial field according to claim 7, wherein when there is no target denoising device, the denoising device to be selected with the highest comprehensive value is marked as a reference device, development requirements are generated according to each performance value and limiting condition of the reference device, and the generated development requirements are adjusted by corresponding management personnel and then developed as denoising device development requirements.
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