CN116958133A - Outdoor noise dust environment monitoring method and system - Google Patents
Outdoor noise dust environment monitoring method and system Download PDFInfo
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- CN116958133A CN116958133A CN202311203174.3A CN202311203174A CN116958133A CN 116958133 A CN116958133 A CN 116958133A CN 202311203174 A CN202311203174 A CN 202311203174A CN 116958133 A CN116958133 A CN 116958133A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 67
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- 238000012545 processing Methods 0.000 claims abstract description 51
- 230000005236 sound signal Effects 0.000 claims description 62
- 230000007613 environmental effect Effects 0.000 claims description 26
- 238000003709 image segmentation Methods 0.000 claims description 14
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- 239000004973 liquid crystal related substance Substances 0.000 claims description 8
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- G06T7/0002—Inspection of images, e.g. flaw detection
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- G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
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- G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
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Abstract
The application provides a method and a system for monitoring outdoor noise dust environment, which are applied to a server, wherein the method comprises the following steps: acquiring an environment image and a standard image of each subarea in real time; wherein each sub-region constitutes a target monitoring region; respectively calculating the difference value and the absolute value of the first pixel value of each pixel point of the environment image of each subarea and the second pixel value of each pixel point of the standard image, and carrying out first marking processing on the environment image corresponding to the pixel point with the absolute value larger than the preset threshold value; selecting pixel points through a preset strategy, obtaining a contour image so as to determine an image with dust emission and performing second marking treatment; and processing the environment image processed by the second mark and the corresponding noise thereof into early warning data and synchronously transmitting the early warning data to the monitoring platform in real time. Compared with the prior art adopting the dust prediction model, the application can identify finer dust at the pixel level, and improves the identification efficiency and accuracy.
Description
Technical Field
The application relates to the field of pollution monitoring, in particular to a method and a system for monitoring outdoor noise dust environment.
Background
The pollution of the environment includes various types such as dust pollution, light pollution, noise pollution or water pollution. With the development of economic technology, the infrastructure is increased. Under the scene such as construction site, noise raise dust can bring great influence to surrounding environment. When noise occurs, the conventional dust noise detection method usually performs feature extraction on the image, and the method relies on feature recognition of dust, so that definition of characteristic information of the dust is required, for example, a dust estimation model is constructed. However, this method can only identify the dust with larger volume, but can not identify the dust with very fine dust, so the identification efficiency is low, the accuracy is low, and other technical problems exist.
Disclosure of Invention
The application provides an outdoor noise dust environment monitoring method and system, which are used for solving the technical problem of low recognition efficiency in the prior art.
In order to solve the technical problems, an embodiment of the present application provides a method for monitoring an outdoor noise dust environment, which is applied to a server, and the monitoring method includes:
acquiring environmental images of all the subareas in real time through first outdoor acquisition equipment corresponding to the subareas; obtaining standard images corresponding to all the subareas respectively through a preset database; wherein each sub-region forms a target monitoring region; each sub-area corresponds to one or more first outdoor acquisition devices;
respectively calculating difference values of first pixel values of all pixel points of the environment image of each subarea and second pixel values of all pixel points of the standard image, calculating absolute values through the difference values of all pixel points, and carrying out first marking processing on the environment image corresponding to the pixel points with the absolute values larger than a preset threshold value;
selecting pixel points in the environment image subjected to the first marking treatment through a preset pixel point selection strategy to obtain a contour image; if the number of the pixel points of the contour image is in a preset number interval, determining that dust is in the environment image, and performing second marking processing on the environment image with the dust;
acquiring a noise signal of the sub-region corresponding to the environment image subjected to the second marking processing in a noise intensity region through second outdoor acquisition equipment; the environment image processed by the second mark and the corresponding noise signal are processed into early warning data, and are synchronously transmitted to the monitoring platform in real time; the monitoring platform is in communication connection with the server.
As a preferred solution, the selecting, by a preset pixel point selection policy, a pixel point in the environmental image that is subjected to the first marking process specifically includes:
carrying out gray scale processing on the environment image subjected to the first marking processing to obtain a gray scale image;
image segmentation is carried out on the gray level image to obtain a plurality of sub gray level images;
taking the gray average value of each sub gray image as a clustering center, and carrying out clustering treatment on the gray images to obtain a plurality of pixel classes;
and determining the pixel class closest to the gray average value of the gray map corresponding to the standard image, and taking the pixel class as the selected pixel point.
Preferably, the calculating the difference between the first pixel value of each pixel of the environmental image of each sub-region and the second pixel value of each pixel of the standard image includes:
calculating the difference s (x, y) according to the pixel points a (x, y) of the environment image of each subarea and the pixel points b (x, y) of the standard image; wherein (x, y) is the position of the pixel point in the standard image or the environment image, and the standard image and the environment image have the same size.
As a preferred solution, the acquiring the noise signal of the sub-area corresponding to the environmental image after the second marking process in the noise intensity interval specifically includes:
acquiring an analog noise signal, an original sound signal of a corresponding subarea of the environment image processed by the second mark, and a reference sound signal in a noise-free environment;
determining a noise intensity lower limit value according to the reference sound signal; determining an upper limit value of noise intensity according to the analog noise signal; determining the noise intensity interval based on the noise intensity lower limit value and the noise intensity upper limit value;
and filtering the original sound signal to remove the sound signal outside the noise intensity interval, and performing enhancement processing on the sound signal in the noise intensity interval to obtain the noise signal.
Preferably, the determining a noise intensity lower limit value according to the reference sound signal includes:
the mean value and the variance of the reference sound signal are obtained, and the mean value and the twice variance of the reference sound signal are added to obtain the noise intensity lower limit value;
the determining the noise intensity upper limit value according to the analog noise signal comprises the following steps:
and obtaining the mean value and the variance of the analog noise signal, and adding the mean value and the three-time variance of the reference sound signal to obtain the noise intensity upper limit value.
Preferably, before the standard images corresponding to the subareas are obtained through the preset database, the method further includes:
and respectively acquiring images without dust emission of all the subareas, taking the images as standard images, and storing the standard images in the preset database.
As a preferred scheme, the images without dust emission in each subarea are respectively obtained, and the images are specifically:
acquiring historical image data; wherein the history image data comprises a plurality of history images of each sub-region;
generating a boundary box corresponding to each target object in each historical image by adopting a target identification technology; wherein the types of objects include vehicles, utility poles, equipment, and human bodies;
based on the position coordinates of the boundary frame, carrying out image segmentation processing on each target object in each historical image;
generating corner points of all targets according to the image segmentation result;
generating a long code of the object according to the generated number of the object corner points and the position coordinates of the corner points; comparing the generated long code with a standard code corresponding to the type of the target object to determine whether dust exists in the target object image;
and when the long codes generated by all the targets in the image are consistent with the standard codes corresponding to the targets, determining that dust emission does not exist in the image, and taking the image as the standard image.
Correspondingly, the embodiment of the application also provides an outdoor noise dust environment monitoring system which is applied to the server, wherein the monitoring system comprises an acquisition module, a first marking module, a second marking module and a synchronization module; wherein, the liquid crystal display device comprises a liquid crystal display device,
the acquisition module is used for respectively acquiring the environment images of the subareas in real time through the first outdoor acquisition equipment corresponding to the subareas; obtaining standard images corresponding to all the subareas respectively through a preset database; wherein each sub-region forms a target monitoring region; each sub-area corresponds to one or more first outdoor acquisition devices;
the first marking module is used for respectively calculating difference values of first pixel values of all pixel points of the environment image of each subarea and second pixel values of all pixel points of the standard image, calculating absolute values through the difference values of all pixel points, and carrying out first marking processing on the environment image corresponding to the pixel points with the absolute values larger than a preset threshold value;
the second marking module is used for selecting pixel points in the environment image subjected to the first marking processing through a preset pixel point selection strategy to obtain a contour image; if the number of the pixel points of the contour image is in a preset number interval, determining that dust is in the environment image, and performing second marking processing on the environment image with the dust;
the synchronization module is used for acquiring a noise signal of the sub-region corresponding to the environment image processed by the second mark in the noise intensity region through the second outdoor acquisition equipment; the environment image processed by the second mark and the corresponding noise signal are processed into early warning data, and are synchronously transmitted to the monitoring platform in real time; the monitoring platform is in communication connection with the server.
As a preferred solution, the second marking module selects, through a preset pixel point selection policy, a pixel point in the environmental image that is subjected to the first marking process, specifically:
the second marking module carries out gray scale processing on the environment image subjected to the first marking processing to obtain a gray scale image;
image segmentation is carried out on the gray level image to obtain a plurality of sub gray level images;
taking the gray average value of each sub gray image as a clustering center, and carrying out clustering treatment on the gray images to obtain a plurality of pixel classes;
and determining the pixel class closest to the gray average value of the gray map corresponding to the standard image, and taking the pixel class as the selected pixel point.
As a preferred solution, the first marking module calculates a difference between a first pixel value of each pixel of the environmental image of each sub-region and a second pixel value of each pixel of the standard image, specifically:
the first marking module calculates the difference value s (x, y) according to the pixel points a (x, y) of the environment image of each subarea and the pixel points b (x, y) of the standard image; wherein (x, y) is the position of the pixel point in the standard image or the environment image, and the standard image and the environment image have the same size.
As a preferred solution, the synchronization module obtains a noise signal of the sub-area corresponding to the environmental image after the second marking process in the noise intensity interval, specifically:
the synchronization module acquires an analog noise signal, an original sound signal of the corresponding subarea of the environment image processed by the second mark and a reference sound signal in a noise-free environment;
determining a noise intensity lower limit value according to the reference sound signal; determining an upper limit value of noise intensity according to the analog noise signal; determining the noise intensity interval based on the noise intensity lower limit value and the noise intensity upper limit value;
and filtering the original sound signal to remove the sound signal outside the noise intensity interval, and performing enhancement processing on the sound signal in the noise intensity interval to obtain the noise signal.
Preferably, the synchronization module determines a noise intensity lower limit value according to the reference sound signal, including:
the synchronization module obtains the mean value and the variance of the reference sound signal, and adds the mean value and the twice variance of the reference sound signal to obtain the noise intensity lower limit value;
the synchronization module determines a noise intensity upper limit value according to the analog noise signal, and comprises:
the synchronization module obtains the mean value and the variance of the analog noise signal, and adds the mean value and the triple variance of the reference sound signal to obtain the noise intensity upper limit value.
As a preferred solution, the monitoring system further includes a standard image setting module, where the standard image setting module is configured to, before the standard images corresponding to the sub-areas are acquired through a preset database:
and respectively acquiring images without dust emission of all the subareas, taking the images as standard images, and storing the standard images in the preset database.
As a preferred solution, the standard image setting module respectively obtains images without dust emission in each sub-area, and the images are specifically:
the standard image setting module acquires historical image data; wherein the history image data comprises a plurality of history images of each sub-region;
generating a boundary box corresponding to each target object in each historical image by adopting a target identification technology; wherein the types of objects include vehicles, utility poles, equipment, and human bodies;
based on the position coordinates of the boundary frame, carrying out image segmentation processing on each target object in each historical image;
generating corner points of all targets according to the image segmentation result;
generating a long code of the object according to the generated number of the object corner points and the position coordinates of the corner points; comparing the generated long code with a standard code corresponding to the type of the target object to determine whether dust exists in the target object image;
and when the long codes generated by all the targets in the image are consistent with the standard codes corresponding to the targets, determining that dust emission does not exist in the image, and taking the image as the standard image.
Compared with the prior art, the embodiment of the application has the following beneficial effects:
the embodiment of the application provides a method and a system for monitoring outdoor noise dust environment, which are applied to a server, wherein the monitoring method comprises the following steps: acquiring environmental images of all the subareas in real time through first outdoor acquisition equipment corresponding to the subareas; obtaining standard images corresponding to all the subareas respectively through a preset database; wherein each sub-region forms a target monitoring region; each sub-area corresponds to one or more first outdoor acquisition devices; respectively calculating difference values of first pixel values of all pixel points of the environment image of each subarea and second pixel values of all pixel points of the standard image, calculating absolute values through the difference values of all pixel points, and carrying out first marking processing on the environment image corresponding to the pixel points with the absolute values larger than a preset threshold value; selecting pixel points in the environment image subjected to the first marking treatment through a preset pixel point selection strategy to obtain a contour image; if the number of the pixel points of the contour image is in a preset number interval, determining that dust is in the environment image, and performing second marking processing on the environment image with the dust; acquiring a noise signal of the sub-region corresponding to the environment image subjected to the second marking processing in a noise intensity region through second outdoor acquisition equipment; the environment image processed by the second mark and the corresponding noise signal are processed into early warning data, and are synchronously transmitted to the monitoring platform in real time; the monitoring platform is in communication connection with the server. According to the embodiment of the application, the environment image corresponding to the pixel points with the absolute value larger than the preset threshold value is subjected to the first marking processing, and the dust is determined according to the number of the pixel points of the contour image.
Drawings
Fig. 1: the application provides a flow diagram of one embodiment of an outdoor noise dust environment detection method.
Fig. 2: the application provides a structural schematic diagram of one embodiment of an outdoor noise raise dust environment detection system.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, fig. 1 is a schematic diagram showing an outdoor noise dust environment monitoring method according to an embodiment of the present application, which is applied to a server, and the monitoring method includes steps S1 to S4; wherein, the liquid crystal display device comprises a liquid crystal display device,
step S1, respectively acquiring environmental images of all subareas in real time through first outdoor acquisition equipment corresponding to all subareas; obtaining standard images corresponding to all the subareas respectively through a preset database; wherein each sub-region forms a target monitoring region; each sub-region corresponds to one or more first outdoor acquisition devices.
In this embodiment, before step S1, the target monitoring area may be divided into a plurality of sub-areas, and numbered sequentially according to 1, 2, 3. And in each sub-area one or more first outdoor acquisition devices are provided, including but not limited to cameras, cameras or video recorders, etc.
Therefore, when the first outdoor acquisition equipment fails, the corresponding subarea can be rapidly determined by utilizing the corresponding relation between the failure equipment and the corresponding subarea, and corresponding remedial measures are made for the situation of the failure, so that rapid reaction is realized. In addition, the target monitoring area is divided into a plurality of subareas, so that when the area of the target monitoring area is large, the effective monitoring and quick positioning of noise dust recognition are improved by monitoring and managing the subareas respectively and still realizing the efficient noise dust recognition and monitoring.
Further, before the standard images corresponding to the subareas are obtained through the preset database, the method further includes: and respectively acquiring images without dust emission of all the subareas, taking the images as standard images, and storing the standard images in the preset database. Each sub-region may correspond to one or more standard images.
Specifically, history image data may be acquired; wherein the history image data includes a plurality of history images of each sub-region, and the history images can be images in the past one month period or images in the past two or three months period.
Generating a boundary box corresponding to each object in each historical image by adopting an object identification technology (an existing object identification algorithm can be adopted); wherein the types of objects include vehicles, utility poles, equipment, and humans.
And based on the position coordinates of the boundary frame, carrying out image segmentation processing on each target object in each historical image, separating each target object from the image, reducing invalid information in the image, and preparing for subsequent dust recognition.
Generating corner points of all targets according to the image segmentation result; wherein each object corresponds to a plurality of corner points.
Generating a long code of the object according to the generated number of the object corner points and the position coordinates of the corner points; comparing the generated long code with a standard code corresponding to the type of the target object to determine whether dust exists in the target object image;
and when the long codes generated by all the targets in the image are consistent with the standard codes corresponding to the targets, determining that dust emission does not exist in the image, and taking the image as the standard image. For example, an object may have four-digit corner points or only two-digit corner points depending on the algorithm variability. The higher the accuracy of the algorithm, or the higher the quality of the image itself, the higher the number of corner points that can be identified, whereas the number of corner points identified is relatively low. As an example of the present embodiment, when the number of corner points is a four-bit number, the four-bit number may be the first four bits of the long code, and then the subsequent bits of the long code may be represented by the position coordinates of the corner points. Standard codes with different digits can be preset according to different types of targets or different sizes of images, so that whether dust is generated in the images is determined by judging consistency between long codes generated by the targets and the standard codes.
And S2, respectively calculating the difference value of the first pixel value of each pixel point of the environment image of each subarea and the second pixel value of each pixel point of the standard image, calculating the absolute value through the difference value of each pixel point, and carrying out first marking processing on the environment image corresponding to the pixel point with the absolute value larger than the preset threshold value.
In this embodiment, the difference between the first pixel value of each pixel of the environmental image of each sub-region and the second pixel value of each pixel of the standard image is specifically:
the difference s (x, y) is calculated according to the pixel point a (x, y) of the environment image of each subarea and the pixel point b (x, y) of the standard image.
Wherein (x, y) is the position of the pixel point in the standard image or the environment image, and the standard image and the environment image have the same size. The positions of the pixel points of each environment image are the same as those of the pixel points of the standard image, so that the difference value can be obtained for each pixel point one by one.
Then, an absolute value is calculated for each difference value, and the environment image corresponding to the pixel point with the absolute value larger than the preset threshold value is subjected to first marking processing. At this time, the difference between the environment image subjected to the first marking process and the standard image is large, and the environment images are screened out, so that the environment images can be further processed in the subsequent step S3.
Step S3, selecting pixel points in the environment image subjected to the first marking treatment through a preset pixel point selection strategy to obtain a contour image; if the number of the pixels of the contour image is in a preset number interval, determining that dust is in the environment image, and performing second marking processing on the environment image with the dust.
As a preferred embodiment, the selecting, by a preset pixel point selection policy, a pixel point in the environmental image that is subjected to the first marking process specifically includes:
carrying out gray scale processing on the environment image subjected to the first marking processing to obtain a gray scale image;
image segmentation is carried out on the gray level image to obtain a plurality of sub gray level images;
taking the gray average value of each sub gray image as a clustering center, and carrying out clustering treatment on the gray images to obtain a plurality of pixel classes;
and determining the pixel class closest to the gray average value of the gray map corresponding to the standard image, and taking the pixel class as the selected pixel point. In this embodiment, K-means clustering may be used to obtain K pixel classes. By performing the division processing on the gradation image, k sub-gradation images can be obtained. Then, according to the average value of the gray values, the average value is used as a clustering center, and then, the pixels of the gray image are clustered, so that each pixel point is clustered into k pixel classes.
And then, calculating the pixel class with the closest gray average value of the gray image corresponding to the standard image through the Euclidean distance, and selecting the pixel point in the pixel class. Thereby obtaining a contour image according to the selected pixel points. If the number of the pixels of the contour image is in a preset number interval, determining that dust is in the environment image, and performing second marking processing on the environment image with the dust.
The preset number interval is set to consider that the quality of the pictures obtained by shooting may be different for different devices or different subareas. In addition, the dust position in the image may be moved, which causes a difference in real-time distance from the image acquisition device, so that the final result of the selected pixel point may also have a certain degree of difference, and therefore, a fault tolerance interval is set for the pixel point.
S4, acquiring a noise signal of the sub-region corresponding to the environment image processed by the second mark in a noise intensity region through second outdoor acquisition equipment; the environment image processed by the second mark and the corresponding noise signal are processed into early warning data, and are synchronously transmitted to the monitoring platform in real time; the monitoring platform is in communication connection with the server.
In this embodiment, the second outdoor collection device may include, but is not limited to, a sound sensor or microphone. The obtaining of the noise signal of the corresponding subarea of the environment image after the second marking processing in the noise intensity interval specifically comprises the following steps:
acquiring an analog noise signal, an original sound signal of a corresponding subarea of the environment image processed by the second mark, and a reference sound signal in a noise-free environment;
determining a noise intensity lower limit value according to the reference sound signal; determining an upper limit value of noise intensity according to the analog noise signal; determining the noise intensity interval based on the noise intensity lower limit value and the noise intensity upper limit value; and filtering the original sound signal to remove the sound signal outside the noise intensity interval, and performing enhancement processing on the sound signal in the noise intensity interval to obtain the noise signal.
Preferably, the determining a noise intensity lower limit value according to the reference sound signal includes:
the mean value and the variance of the reference sound signal are obtained, and the mean value and the twice variance of the reference sound signal are added to obtain the noise intensity lower limit value;
the determining the noise intensity upper limit value according to the analog noise signal comprises the following steps:
and obtaining the mean value and the variance of the analog noise signal, and adding the mean value and the three-time variance of the reference sound signal to obtain the noise intensity upper limit value. According to the embodiment of the application, the noise intensity interval is determined according to the set upper limit value and the set lower limit value of the noise intensity. The noise intensity interval is set, the sound signals outside the interval can be filtered, the sound signals in the interval are enhanced, the enhanced noise signals are obtained, the purity of the noise signals is ensured, and doping of other ineffective environmental sounds is avoided.
The outdoor noise raise dust environment monitoring method provided by the embodiment is applied to a server, and the server is in communication connection or electric connection with the first outdoor acquisition equipment, the second outdoor acquisition equipment and the monitoring platform. The result of the environmental monitoring includes the environmental image processed by the second marker and its corresponding noise signal. The monitoring result is processed into the early warning data and is synchronously transmitted to the monitoring platform in real time, so that the noise dust-raising condition of the environment condition can be monitored by related personnel in real time, and quick response can be made.
Correspondingly, referring to fig. 2, the embodiment of the application further provides an outdoor noise dust environment monitoring system, which is applied to a server, wherein the monitoring system comprises an acquisition module 101, a first marking module 102, a second marking module 103 and a synchronization module 104; wherein, the liquid crystal display device comprises a liquid crystal display device,
the acquiring module 101 is configured to acquire, in real time, an environmental image of each sub-area through a first outdoor acquisition device corresponding to each sub-area; obtaining standard images corresponding to all the subareas respectively through a preset database; wherein each sub-region forms a target monitoring region; each sub-area corresponds to one or more first outdoor acquisition devices;
the first marking module 102 is configured to calculate a difference value of a first pixel value of each pixel of the environmental image of each sub-region and a second pixel value of each pixel of the standard image, calculate an absolute value according to the difference value of each pixel, and perform a first marking process on the environmental image corresponding to the pixel whose absolute value is greater than a preset threshold;
the second marking module 103 is configured to select, according to a preset pixel point selection policy, a pixel point in the environmental image that is subjected to the first marking process, so as to obtain a contour image; if the number of the pixel points of the contour image is in a preset number interval, determining that dust is in the environment image, and performing second marking processing on the environment image with the dust;
the synchronization module 104 is configured to obtain, by using a second outdoor acquisition device, a noise signal of a sub-area corresponding to the environmental image that is subjected to the second marking process in a noise intensity interval; the environment image processed by the second mark and the corresponding noise signal are processed into early warning data, and are synchronously transmitted to the monitoring platform in real time; the monitoring platform is in communication connection with the server.
As a preferred solution, the second marking module 103 selects, through a preset pixel point selection policy, a pixel point in the environmental image that is subjected to the first marking process, specifically:
the second marking module 103 performs gray scale processing on the environment image subjected to the first marking processing to obtain a gray scale image;
image segmentation is carried out on the gray level image to obtain a plurality of sub gray level images;
taking the gray average value of each sub gray image as a clustering center, and carrying out clustering treatment on the gray images to obtain a plurality of pixel classes;
and determining the pixel class closest to the gray average value of the gray map corresponding to the standard image, and taking the pixel class as the selected pixel point.
Preferably, the first marking module 102 calculates a difference between a first pixel value of each pixel of the environmental image of each sub-region and a second pixel value of each pixel of the standard image, specifically:
the first marking module 102 calculates the difference s (x, y) according to the pixel point a (x, y) of the environmental image of each sub-region and the pixel point b (x, y) of the standard image; wherein (x, y) is the position of the pixel point in the standard image or the environment image, and the standard image and the environment image have the same size.
As a preferred solution, the synchronization module 104 obtains a noise signal of the sub-area corresponding to the environmental image after the second marking process in the noise intensity interval, specifically:
the synchronization module 104 acquires an analog noise signal, an original sound signal of the corresponding subarea of the environment image processed by the second mark, and a reference sound signal in a noise-free environment;
determining a noise intensity lower limit value according to the reference sound signal; determining an upper limit value of noise intensity according to the analog noise signal; determining the noise intensity interval based on the noise intensity lower limit value and the noise intensity upper limit value;
and filtering the original sound signal to remove the sound signal outside the noise intensity interval, and performing enhancement processing on the sound signal in the noise intensity interval to obtain the noise signal.
Preferably, the synchronization module 104 determines a noise intensity lower limit value according to the reference sound signal, including:
the synchronization module 104 obtains the mean value and the variance of the reference sound signal, and adds the mean value and the twice variance of the reference sound signal to obtain the noise intensity lower limit value;
the synchronization module 104 determines a noise intensity upper limit according to the analog noise signal, including:
the synchronization module 104 obtains the mean and variance of the analog noise signal, and adds the mean and the triple variance of the reference sound signal to obtain the noise intensity upper limit value.
As a preferred solution, the monitoring system further includes a standard image setting module, where the standard image setting module is configured to, before the standard images corresponding to the sub-areas are acquired through a preset database:
and respectively acquiring images without dust emission of all the subareas, taking the images as standard images, and storing the standard images in the preset database.
As a preferred solution, the standard image setting module respectively obtains images without dust emission in each sub-area, and the images are specifically:
the standard image setting module acquires historical image data; wherein the history image data comprises a plurality of history images of each sub-region;
generating a boundary box corresponding to each target object in each historical image by adopting a target identification technology; wherein the types of objects include vehicles, utility poles, equipment, and human bodies;
based on the position coordinates of the boundary frame, carrying out image segmentation processing on each target object in each historical image;
generating corner points of all targets according to the image segmentation result;
generating a long code of the object according to the generated number of the object corner points and the position coordinates of the corner points; comparing the generated long code with a standard code corresponding to the type of the target object to determine whether dust exists in the target object image;
and when the long codes generated by all the targets in the image are consistent with the standard codes corresponding to the targets, determining that dust emission does not exist in the image, and taking the image as the standard image.
Compared with the prior art, the embodiment of the application has the following beneficial effects:
the embodiment of the application provides a method and a system for monitoring outdoor noise dust environment, which are applied to a server, wherein the monitoring method comprises the following steps: acquiring environmental images of all the subareas in real time through first outdoor acquisition equipment corresponding to the subareas; obtaining standard images corresponding to all the subareas respectively through a preset database; wherein each sub-region forms a target monitoring region; each sub-area corresponds to one or more first outdoor acquisition devices; respectively calculating difference values of first pixel values of all pixel points of the environment image of each subarea and second pixel values of all pixel points of the standard image, calculating absolute values through the difference values of all pixel points, and carrying out first marking processing on the environment image corresponding to the pixel points with the absolute values larger than a preset threshold value; selecting pixel points in the environment image subjected to the first marking treatment through a preset pixel point selection strategy to obtain a contour image; if the number of the pixel points of the contour image is in a preset number interval, determining that dust is in the environment image, and performing second marking processing on the environment image with the dust; acquiring a noise signal of the sub-region corresponding to the environment image subjected to the second marking processing in a noise intensity region through second outdoor acquisition equipment; the environment image processed by the second mark and the corresponding noise signal are processed into early warning data, and are synchronously transmitted to the monitoring platform in real time; the monitoring platform is in communication connection with the server. According to the embodiment of the application, the environment image corresponding to the pixel points with the absolute value larger than the preset threshold value is subjected to the first marking processing, and the dust is determined according to the number of the pixel points of the contour image.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present application, and are not to be construed as limiting the scope of the application. It should be noted that any modifications, equivalent substitutions, improvements, etc. made by those skilled in the art without departing from the spirit and principles of the present application are intended to be included in the scope of the present application.
Claims (10)
1. An outdoor noise dust environment monitoring method, which is characterized by being applied to a server, comprises the following steps:
acquiring environmental images of all the subareas in real time through first outdoor acquisition equipment corresponding to the subareas; obtaining standard images corresponding to all the subareas respectively through a preset database; wherein each sub-region forms a target monitoring region; each sub-area corresponds to one or more first outdoor acquisition devices;
respectively calculating difference values of first pixel values of all pixel points of the environment image of each subarea and second pixel values of all pixel points of the standard image, calculating absolute values through the difference values of all pixel points, and carrying out first marking processing on the environment image corresponding to the pixel points with the absolute values larger than a preset threshold value;
selecting pixel points in the environment image subjected to the first marking treatment through a preset pixel point selection strategy to obtain a contour image; if the number of the pixel points of the contour image is in a preset number interval, determining that dust is in the environment image, and performing second marking processing on the environment image with the dust;
acquiring a noise signal of the sub-region corresponding to the environment image subjected to the second marking processing in a noise intensity region through second outdoor acquisition equipment; the environment image processed by the second mark and the corresponding noise signal are processed into early warning data, and are synchronously transmitted to the monitoring platform in real time; the monitoring platform is in communication connection with the server.
2. The outdoor noise raise dust environment monitoring method according to claim 1, wherein the selecting the pixel point in the environment image subjected to the first marking process by the preset pixel point selecting strategy specifically comprises:
carrying out gray scale processing on the environment image subjected to the first marking processing to obtain a gray scale image;
image segmentation is carried out on the gray level image to obtain a plurality of sub gray level images;
taking the gray average value of each sub gray image as a clustering center, and carrying out clustering treatment on the gray images to obtain a plurality of pixel classes;
and determining the pixel class closest to the gray average value of the gray map corresponding to the standard image, and taking the pixel class as the selected pixel point.
3. The outdoor noise raise dust environment monitoring method according to claim 1, wherein the step of respectively calculating the difference between the first pixel value of each pixel point of the environment image of each sub-region and the second pixel value of each pixel point of the standard image is specifically as follows:
calculating the difference s (x, y) according to the pixel points a (x, y) of the environment image of each subarea and the pixel points b (x, y) of the standard image; wherein (x, y) is the position of the pixel point in the standard image or the environment image, and the standard image and the environment image have the same size.
4. The outdoor noise raise dust environment monitoring method according to claim 1, wherein the acquiring the noise signal of the sub-area corresponding to the environment image processed by the second marking in the noise intensity interval specifically comprises:
acquiring an analog noise signal, an original sound signal of a corresponding subarea of the environment image processed by the second mark, and a reference sound signal in a noise-free environment;
determining a noise intensity lower limit value according to the reference sound signal; determining an upper limit value of noise intensity according to the analog noise signal; determining the noise intensity interval based on the noise intensity lower limit value and the noise intensity upper limit value;
and filtering the original sound signal to remove the sound signal outside the noise intensity interval, and performing enhancement processing on the sound signal in the noise intensity interval to obtain the noise signal.
5. The outdoor noise rise environment monitoring method of claim 4 wherein said determining a noise intensity lower limit from said reference sound signal comprises:
the mean value and the variance of the reference sound signal are obtained, and the mean value and the twice variance of the reference sound signal are added to obtain the noise intensity lower limit value;
the determining the noise intensity upper limit value according to the analog noise signal comprises the following steps:
and obtaining the mean value and the variance of the analog noise signal, and adding the mean value and the three-time variance of the reference sound signal to obtain the noise intensity upper limit value.
6. The outdoor noise raised dust environment monitoring method according to any one of claims 1 to 5, wherein before the standard images corresponding to the subareas are obtained through a preset database, the method further comprises:
and respectively acquiring images without dust emission of all the subareas, taking the images as standard images, and storing the standard images in the preset database.
7. The outdoor noise dust environment monitoring system is characterized by being applied to a server, and comprises an acquisition module, a first marking module, a second marking module and a synchronization module; wherein, the liquid crystal display device comprises a liquid crystal display device,
the acquisition module is used for respectively acquiring the environment images of the subareas in real time through the first outdoor acquisition equipment corresponding to the subareas; obtaining standard images corresponding to all the subareas respectively through a preset database; wherein each sub-region forms a target monitoring region; each sub-area corresponds to one or more first outdoor acquisition devices;
the first marking module is used for respectively calculating difference values of first pixel values of all pixel points of the environment image of each subarea and second pixel values of all pixel points of the standard image, calculating absolute values through the difference values of all pixel points, and carrying out first marking processing on the environment image corresponding to the pixel points with the absolute values larger than a preset threshold value;
the second marking module is used for selecting pixel points in the environment image subjected to the first marking processing through a preset pixel point selection strategy to obtain a contour image; if the number of the pixel points of the contour image is in a preset number interval, determining that dust is in the environment image, and performing second marking processing on the environment image with the dust;
the synchronization module is used for acquiring a noise signal of the sub-region corresponding to the environment image processed by the second mark in the noise intensity region through the second outdoor acquisition equipment; the environment image processed by the second mark and the corresponding noise signal are processed into early warning data, and are synchronously transmitted to the monitoring platform in real time; the monitoring platform is in communication connection with the server.
8. The outdoor noise raise dust environment monitoring system of claim 7, wherein the second marking module selects the pixel point in the environment image processed by the first marking according to a preset pixel point selection strategy, specifically:
the second marking module carries out gray scale processing on the environment image subjected to the first marking processing to obtain a gray scale image;
image segmentation is carried out on the gray level image to obtain a plurality of sub gray level images;
taking the gray average value of each sub gray image as a clustering center, and carrying out clustering treatment on the gray images to obtain a plurality of pixel classes;
and determining the pixel class closest to the gray average value of the gray map corresponding to the standard image, and taking the pixel class as the selected pixel point.
9. The outdoor noise raised dust environment monitoring system according to claim 7, wherein the synchronization module obtains a noise signal of the sub-area corresponding to the environmental image processed by the second marking in the noise intensity interval, specifically:
the synchronization module acquires an analog noise signal, an original sound signal of the corresponding subarea of the environment image processed by the second mark and a reference sound signal in a noise-free environment;
determining a noise intensity lower limit value according to the reference sound signal; determining an upper limit value of noise intensity according to the analog noise signal; determining the noise intensity interval based on the noise intensity lower limit value and the noise intensity upper limit value;
and filtering the original sound signal to remove the sound signal outside the noise intensity interval, and performing enhancement processing on the sound signal in the noise intensity interval to obtain the noise signal.
10. The outdoor noise rise environment monitoring system of claim 9 wherein said synchronization module determines a noise intensity lower limit based on said reference sound signal, comprising:
the synchronization module obtains the mean value and the variance of the reference sound signal, and adds the mean value and the twice variance of the reference sound signal to obtain the noise intensity lower limit value;
the synchronization module determines a noise intensity upper limit value according to the analog noise signal, and comprises:
the synchronization module obtains the mean value and the variance of the analog noise signal, and adds the mean value and the triple variance of the reference sound signal to obtain the noise intensity upper limit value.
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