CN114062212A - Building dust monitoring and adjusting system and method - Google Patents
Building dust monitoring and adjusting system and method Download PDFInfo
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- CN114062212A CN114062212A CN202111360604.3A CN202111360604A CN114062212A CN 114062212 A CN114062212 A CN 114062212A CN 202111360604 A CN202111360604 A CN 202111360604A CN 114062212 A CN114062212 A CN 114062212A
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Abstract
The invention relates to a building dust monitoring and adjusting system and method, wherein the method comprises the following steps: denoising the original dust concentration data to obtain dust concentration data; constructing a correlation matrix according to the dust concentration data; obtaining a data fusion weight coefficient according to the correlation matrix; carrying out weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result; and when the dust concentration fusion result is greater than a preset threshold value, spraying water mist to the dust area. According to the invention, the original dust concentration data is subjected to denoising and weighted fusion processing, so that the monitored dust concentration data is more accurate, the subsequent dust removal efficiency is improved, the dust pollution on a construction site is eliminated, and the health of constructors is ensured.
Description
Technical Field
The invention relates to the technical field of dust treatment, in particular to a building dust monitoring and adjusting system and method.
Background
In recent years, with the rapid development of economy, the proportion of the construction industry in national economy is increasing, and the scale of construction is also increasing. The construction is a production movement performed by people to construct various building products in a certain space and time according to a specific design blueprint by using various building materials and mechanical equipment. The construction includes all production processes from construction preparation, earth breaking and engineering completion acceptance, but in the construction process of a construction site, a large amount of dust and dust can be generated inevitably, the dust can cause serious pollution to the environment of the construction site and can also greatly harm the personal health of constructors, so that a corresponding dust collection or dust treatment system is required to be adopted to absorb the dust generated on the construction site.
However, the existing dust treatment system is limited in structure and design, and the dust monitoring precision is not high, so that the dust removal effect is not ideal, and the dust removal requirement of a construction site cannot be met.
Disclosure of Invention
In order to solve the above problems, embodiments of the present invention provide a system and a method for monitoring and adjusting building dust, which aim to solve the problem that the existing dust processing system is not high in dust monitoring precision, resulting in an unsatisfactory dust removal effect.
A method for building dust monitoring and conditioning, comprising:
step 1: acquiring original dust concentration data acquired by each dust sensor;
step 2: denoising the original dust concentration data to obtain dust concentration data;
and step 3: constructing a correlation matrix according to the dust concentration data;
and 4, step 4: obtaining a data fusion weight coefficient according to the correlation matrix;
and 5: carrying out weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result;
step 6: judging whether the dust concentration fusion result is greater than a preset threshold value or not;
and 7: and when the dust concentration fusion result is larger than a preset threshold value, spraying water mist to the dust area.
Preferably, the step 3: constructing a correlation matrix according to the dust concentration data, wherein the correlation matrix comprises the following steps:
step 3.1: obtaining a correlation function according to the dust concentration data; wherein the relevance function is:
wherein q isijIndicating degree of associationFunction, XiIndicating the dust concentration, X, collected by the ith sensorjRepresents the dust concentration collected by the jth sensor, and i, j is 1,2, Λ n;
step 3.2: constructing a correlation matrix according to the correlation function; wherein the relevancy matrix is:
preferably, the step 4: obtaining a data fusion weight coefficient according to the correlation matrix, including:
step 4.1: calculating a maximum eigenvalue of the correlation matrix and an eigenvector corresponding to the maximum eigenvalue;
step 4.2: the formula is adopted:
obtaining a data fusion weight coefficient; wherein r isiIs the value of the ith element of the feature vector.
Preferably, the step 5: and performing weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result, wherein the method comprises the following steps:
the formula is adopted:
X＝W1X1+W2X2+Λ+WnXn
and obtaining a dust concentration fusion result.
Preferably, the step 2: denoising the original dust concentration data to obtain dust concentration data, comprising:
step 2.1: performing multiple wavelet transformation on the original dust concentration data to obtain multiple wavelet coefficients;
step 2.2: obtaining a denoising threshold value according to the wavelet coefficient;
step 2.3: denoising the wavelet coefficient by using the denoising threshold value to obtain a denoised wavelet coefficient;
step 2.4: and performing wavelet inverse transformation on the denoised wavelet coefficient to obtain denoised dust concentration data.
Preferably, the step 2.2: obtaining a denoising threshold according to the wavelet coefficient, including:
the formula is adopted:
obtaining a denoising threshold value; wherein ω isjIs the wavelet coefficient of the jth wavelet transform, and k is the total number of wavelet coefficients.
Preferably, the step 2.3: denoising the wavelet coefficient by using the denoising threshold value to obtain a denoised wavelet coefficient, comprising:
the formula is adopted:
denoising the wavelet coefficient to obtain a denoised wavelet coefficient; wherein,
representing the denoised wavelet coefficients, sgn () is a sign function.
Preferably, in step 2.4: after the wavelet inverse transformation is performed on the denoised wavelet coefficient to obtain the denoised dust concentration data, the method further comprises the following steps:
step 2.5: calculating a contact value between the current dust concentration data and the dust concentration data collected in a preset time period; the calculation formula of the relation value is as follows:
wherein, YiDenotes the dust concentration at time i, Y0Indicating the dust concentration at the present momentDegree;
step 2.6: judging whether the relation value is larger than an abnormal threshold value or not;
step 2.7: and when the relation value is larger than the abnormal threshold value, removing the corresponding current dust concentration data as an abnormal value.
The invention also provides a building dust monitoring and adjusting system, which comprises:
the original data acquisition module is used for acquiring original dust concentration data acquired by each dust sensor;
the denoising module is used for denoising the original dust concentration data to obtain dust concentration data;
the relevancy matrix building module is used for building a relevancy matrix according to the dust concentration data;
the weight coefficient calculation module is used for obtaining a data fusion weight coefficient according to the incidence matrix;
the weighted fusion module is used for carrying out weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result;
the judging module is used for judging whether the dust concentration fusion result is greater than a preset threshold value or not;
and the water mist spraying module is used for spraying water mist to the dust area when the dust concentration fusion result is greater than a preset threshold value.
Preferably, the denoising module includes:
the wavelet transformation unit is used for performing wavelet transformation on the original dust concentration data for multiple times to obtain multiple wavelet coefficients;
the denoising threshold value calculating unit is used for obtaining a denoising threshold value according to the wavelet coefficient;
the denoising unit is used for denoising the wavelet coefficient by using the denoising threshold value to obtain a denoised wavelet coefficient;
and the wavelet inverse transformation unit is used for performing wavelet inverse transformation on the denoised wavelet coefficient to obtain denoised dust concentration data.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to a building dust monitoring and adjusting system and method, wherein the method comprises the following steps: denoising the original dust concentration data to obtain dust concentration data; constructing a correlation matrix according to the dust concentration data; obtaining a data fusion weight coefficient according to the correlation matrix; carrying out weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result; and when the dust concentration fusion result is greater than a preset threshold value, spraying water mist to the dust area. According to the invention, the original dust concentration data is subjected to denoising and weighted fusion processing, so that the monitored dust concentration data is more accurate, the subsequent dust removal efficiency is improved, the dust pollution on a construction site is eliminated, and the health of constructors is ensured.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method for building dust monitoring and conditioning in an embodiment provided by the present invention;
fig. 2 is a schematic diagram of a building dust monitoring and conditioning system in an embodiment of the invention.
Detailed Description
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The embodiment of the invention aims to provide a building dust monitoring and adjusting system and method, and aims to solve the problem that the existing dust processing system is not high in dust monitoring precision and causes an unsatisfactory dust removal effect.
Referring to fig. 1, a method for monitoring and adjusting building dust includes:
s1: acquiring original dust concentration data acquired by each dust sensor;
s2: denoising the original dust concentration data to obtain dust concentration data;
further, S2 specifically includes:
s2.1: performing multiple wavelet transformation on the original dust concentration data to obtain multiple wavelet coefficients;
the wavelet transform algorithm is a data processing technology of a space transform domain, wavelet coefficients with different sizes can be generated by decomposing original sensor network data through a certain scale, the decomposition times of the wavelet transform are related to noise, generally speaking, the larger the noise is, the more the decomposition times are, and the specific decomposition times can be adjusted according to actual conditions.
S2.2: obtaining a denoising threshold value according to the wavelet coefficient;
specifically, a formula is adopted:
obtaining a denoising threshold value; wherein ω isjIs the wavelet coefficient of the jth wavelet transform, and k is the total number of wavelet coefficients.
It should be noted that the denoising threshold m of the present invention can also be adjusted according to the noise removal situation to fit the actual situation. In the present invention, m is preferable0Wherein B is selected from the range of 0-1, m0Is the adjusted denoising threshold value.
S2.3: denoising the wavelet coefficient by using a denoising threshold value to obtain a denoised wavelet coefficient;
specifically, a formula is adopted:
denoising the wavelet coefficient to obtain a denoised wavelet coefficient; wherein,
representing the denoised wavelet coefficients, sgn () is a sign function.
The invention can basically remove the wavelet coefficient with noise by performing threshold processing on all wavelet coefficients through the formula.
S2.4: and performing wavelet inverse transformation on the denoised wavelet coefficient to obtain denoised dust concentration data.
The invention adopts the inverse wavelet transform to reconstruct the denoised wavelet coefficient, thereby not only eliminating the noise in the sensor transmission process, but also improving the data quality of the sensor network.
Further, after S2.4, the method further includes:
s2.5: calculating a contact value between the current dust concentration data and the dust concentration data collected in a preset time period; the formula for calculating the relation value is:
wherein, YiDenotes the dust concentration at time i, Y0Indicating the dust concentration at the current moment;
s2.6: judging whether the contact value is larger than an abnormal threshold value or not;
according to the invention, abnormal dust concentration data is removed by comparing the abnormal threshold value with the connection value, so that the problem that the measured value acquired by the sensor at a certain moment is greatly deviated from the actual value due to the influence of self parameters or environmental factors of the sensor can be eliminated, and the accuracy of the dust concentration data is further improved.
S2.7: and when the relation value is larger than the abnormal threshold value, removing the corresponding current dust concentration data as an abnormal value.
In practical application, the fusion algorithm of data mainly comprises a weighted average algorithm, a kalman filtering method and the like. However, they have two problems in practical application, one is that most algorithms presuppose data that obey normal distribution, but a lot of experiments have shown that most of the meter measurement data do not conform to normal distribution, but are between uniform and normal. In order to solve the problems, the invention provides a fusion algorithm based on the relevance matrix, which only utilizes the information contained in the data, avoids the assumption of normal distribution and can accurately fuse the data.
S3: constructing a correlation matrix according to the dust concentration data;
s3 specifically includes:
s3.1: obtaining a correlation function according to the dust concentration data; wherein the relevance function is:
wherein q isijRepresenting a function of relevance, XiIndicating the dust concentration, X, collected by the ith sensorjRepresents the dust concentration collected by the jth sensor, and i, j is 1,2, Λ n;
s3.2: constructing a correlation matrix according to the correlation function; wherein, the incidence matrix is:
s4: obtaining a data fusion weight coefficient according to the correlation matrix;
preferably, S4 specifically includes:
s4.1: calculating the maximum eigenvalue of the correlation matrix and the eigenvector corresponding to the maximum eigenvalue, wherein R is [ R ]1r2 Λ rn]T；
S4.2: the formula is adopted:
obtaining a data fusion weight coefficient; wherein r isiIs the value of the ith element of the feature vector R.
S5: carrying out weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result;
specifically, a formula is adopted:
X＝W1X1+W2X2+Λ+WnXn
and obtaining a dust concentration fusion result.
Base of the inventionDynamically distributing corresponding optimal data fusion weight coefficients W to the sensors according to the data measured by each sensor in real time in the relevance matrixi(i ═ 1,2, …, n) to obtain the final dust concentration fusion result, the result can be closer to the true value.
S6: judging whether the dust concentration fusion result is greater than a preset threshold value or not;
s7: and when the dust concentration fusion result is greater than a preset threshold value, spraying water mist to the dust area.
Furthermore, the invention can adopt an electromagnetic water spray head or a dust removing device and the like to remove dust in the dust area.
According to the invention, the original dust concentration data is subjected to denoising and weighted fusion processing, so that the monitored dust concentration data is more accurate, the subsequent dust removal efficiency is improved, the dust pollution on a construction site is eliminated, and the health of constructors is ensured.
The above describes in detail a process flow for monitoring and regulating building dust, which can also be implemented by a corresponding system, and the structure and function of which are described in detail below.
Referring to fig. 2, the present invention further provides a building dust monitoring and adjusting system, including:
the original data acquisition module is used for acquiring original dust concentration data acquired by each dust sensor;
the de-noising module is used for de-noising the original dust concentration data to obtain dust concentration data;
the relevancy matrix building module is used for building a relevancy matrix according to the dust concentration data;
the weight coefficient calculation module is used for obtaining a data fusion weight coefficient according to the association degree matrix;
the weighted fusion module is used for carrying out weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result;
the judging module is used for judging whether the dust concentration fusion result is greater than a preset threshold value or not;
and the water mist spraying module is used for spraying water mist to the dust area when the dust concentration fusion result is greater than a preset threshold value.
Preferably, the denoising module includes:
the wavelet transformation unit is used for performing wavelet transformation on the original dust concentration data for multiple times to obtain multiple wavelet coefficients;
the denoising threshold value calculating unit is used for obtaining a denoising threshold value according to the wavelet coefficient;
the denoising unit is used for denoising the wavelet coefficient by using a denoising threshold value to obtain a denoised wavelet coefficient;
and the wavelet inverse transformation unit is used for performing wavelet inverse transformation on the denoised wavelet coefficient to obtain denoised dust concentration data.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention relates to a building dust monitoring and adjusting system and method, wherein the method comprises the following steps: denoising the original dust concentration data to obtain dust concentration data; constructing a correlation matrix according to the dust concentration data; obtaining a data fusion weight coefficient according to the correlation matrix; carrying out weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result; and when the dust concentration fusion result is greater than a preset threshold value, spraying water mist to the dust area. According to the invention, the original dust concentration data is subjected to denoising and weighted fusion processing, so that the monitored dust concentration data is more accurate, the subsequent dust removal efficiency is improved, the dust pollution on a construction site is eliminated, and the health of constructors is ensured.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present invention, and the present invention shall be covered by the claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A monitoring and adjusting method for building dust is characterized by comprising the following steps:
step 1: acquiring original dust concentration data acquired by each dust sensor;
step 2: denoising the original dust concentration data to obtain dust concentration data;
and step 3: constructing a correlation matrix according to the dust concentration data;
and 4, step 4: obtaining a data fusion weight coefficient according to the correlation matrix;
and 5: carrying out weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result;
step 6: judging whether the dust concentration fusion result is greater than a preset threshold value or not;
and 7: and when the dust concentration fusion result is larger than a preset threshold value, spraying water mist to the dust area.
2. A building dust monitoring and adjusting method according to claim 1, wherein the step 3: constructing a correlation matrix according to the dust concentration data, wherein the correlation matrix comprises the following steps:
step 3.1: obtaining a correlation function according to the dust concentration data; wherein the relevance function is:
wherein q isijRepresenting a function of relevance, XiIndicating the dust concentration, X, collected by the ith sensorjRepresents the dust concentration collected by the jth sensor, and i, j is 1,2, Λ n;
step 3.2: constructing a correlation matrix according to the correlation function; wherein the relevancy matrix is:
3. a building dust monitoring and adjusting method according to claim 2, characterized in that the step 4: obtaining a data fusion weight coefficient according to the correlation matrix, including:
step 4.1: calculating a maximum eigenvalue of the correlation matrix and an eigenvector corresponding to the maximum eigenvalue;
step 4.2: the formula is adopted:
obtaining a data fusion weight coefficient; wherein r isiIs the value of the ith element of the feature vector.
4. A building dust monitoring and conditioning method according to claim 3, characterized in that the step 5: and performing weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result, wherein the method comprises the following steps:
the formula is adopted:
X＝W1X1+W2X2+Λ+WnXn
and obtaining a dust concentration fusion result.
5. A building dust monitoring and adjusting method according to claim 1, wherein the step 2: denoising the original dust concentration data to obtain dust concentration data, comprising:
step 2.1: performing multiple wavelet transformation on the original dust concentration data to obtain multiple wavelet coefficients;
step 2.2: obtaining a denoising threshold value according to the wavelet coefficient;
step 2.3: denoising the wavelet coefficient by using the denoising threshold value to obtain a denoised wavelet coefficient;
step 2.4: and performing wavelet inverse transformation on the denoised wavelet coefficient to obtain denoised dust concentration data.
6. A building dust monitoring and conditioning method according to claim 5, characterized in that the step 2.2: obtaining a denoising threshold according to the wavelet coefficient, including:
the formula is adopted:
obtaining a denoising threshold value; wherein ω isjIs the wavelet coefficient of the jth wavelet transform, and k is the total number of wavelet coefficients.
7. A building dust monitoring and conditioning method according to claim 6, characterized in that the step 2.3: denoising the wavelet coefficient by using the denoising threshold value to obtain a denoised wavelet coefficient, comprising:
the formula is adopted:
denoising the wavelet coefficient to obtain a denoised wavelet coefficient; wherein,
representing the denoised wavelet coefficients, sgn () is a sign function.
8. A building dust monitoring and conditioning method according to claim 5, characterized in that in step 2.4: after the wavelet inverse transformation is performed on the denoised wavelet coefficient to obtain the denoised dust concentration data, the method further comprises the following steps:
step 2.5: calculating a contact value between the current dust concentration data and the dust concentration data collected in a preset time period; the calculation formula of the relation value is as follows:
wherein, YiDenotes the dust concentration at time i, Y0Indicating the dust concentration at the current moment;
step 2.6: judging whether the relation value is larger than an abnormal threshold value or not;
step 2.7: and when the relation value is larger than the abnormal threshold value, removing the corresponding current dust concentration data as an abnormal value.
9. A building dust monitoring and regulating system, comprising:
the original data acquisition module is used for acquiring original dust concentration data acquired by each dust sensor;
the denoising module is used for denoising the original dust concentration data to obtain dust concentration data;
the relevancy matrix building module is used for building a relevancy matrix according to the dust concentration data;
the weight coefficient calculation module is used for obtaining a data fusion weight coefficient according to the incidence matrix;
the weighted fusion module is used for carrying out weighted fusion on the dust concentration data according to the data fusion weight coefficient to obtain a dust concentration fusion result;
the judging module is used for judging whether the dust concentration fusion result is greater than a preset threshold value or not;
and the water mist spraying module is used for spraying water mist to the dust area when the dust concentration fusion result is greater than a preset threshold value.
10. The building dust monitoring and adjusting system as claimed in claim 9, wherein the de-noising module comprises:
the wavelet transformation unit is used for performing wavelet transformation on the original dust concentration data for multiple times to obtain multiple wavelet coefficients;
the denoising threshold value calculating unit is used for obtaining a denoising threshold value according to the wavelet coefficient;
the denoising unit is used for denoising the wavelet coefficient by using the denoising threshold value to obtain a denoised wavelet coefficient;
and the wavelet inverse transformation unit is used for performing wavelet inverse transformation on the denoised wavelet coefficient to obtain denoised dust concentration data.
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