CN112507789A - Construction site safety behavior monitoring working method under block chain network state - Google Patents

Construction site safety behavior monitoring working method under block chain network state Download PDF

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CN112507789A
CN112507789A CN202011211074.1A CN202011211074A CN112507789A CN 112507789 A CN112507789 A CN 112507789A CN 202011211074 A CN202011211074 A CN 202011211074A CN 112507789 A CN112507789 A CN 112507789A
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洪敏�
万里
熊榆
白金龙
胡宇
唐良艳
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Chongqing Hui Hui Information Technology Co ltd
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Abstract

The invention provides a construction site safety behavior monitoring working method in a block chain network state, which comprises the following steps: s1, acquiring construction site image data through an image acquisition module, carrying out screening operation on a construction site personnel image, acquiring a region FRAME (FRAME) image of construction site personnel characteristics, and carrying out equal-proportion image amplification; s2, after the equal-proportion building site image is amplified, a human body characteristic histogram is obtained, and safety behavior recognition is carried out on the human body characteristic histogram through a behavior monitoring model; and S3, after the identification is completed, performing cluster estimation on the building site safety behavior images, determining the building site behaviors lower than the estimated value as dangerous behaviors, and synchronizing with the block link point data.

Description

Construction site safety behavior monitoring working method under block chain network state
Technical Field
The invention relates to the field of building site computers, in particular to a working method for monitoring safety behaviors of a building site in a block chain network state.
Background
Because the building engineering construction in-process need carry out real-time supervision to on-the-spot safety, but patrol and examine through the manual work and can cause the omission, the degree of danger in the building engineering on the contrary has more been increased, although some building engineering construction main parts have set up watch-dog or camera, but only look over in real time through the control room personnel, watch-dog or camera can't independently judge safe action, and even there is the software method of action discernment, the action data of refining is inaccurate also incomplete, and the attribute of block chain node is can not falsify, synchronism and decentralized characteristics, but prior art does not have fine combination block chain attribute, it is fixed not to carry out the evidence with safe action, this just needs technical staff in the field to solve corresponding technical problem urgently.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art, and particularly provides a working method for monitoring the safety behavior of a construction site in a block chain network state.
In order to achieve the above object, the present invention provides a method for monitoring safety behavior of a construction site in a blockchain network state, comprising the following steps:
s1, acquiring construction site image data through an image acquisition module, carrying out screening operation on a construction site personnel image, acquiring a region FRAME (FRAME) image of construction site personnel characteristics, and carrying out equal-proportion image amplification;
s2, after the construction site image is amplified in equal proportion, a human body characteristic histogram is obtained, and safety behavior recognition is carried out on the human body characteristic histogram through a behavior monitoring model;
and S3, after the identification is completed, performing cluster estimation on the building site safety behavior images, determining the building site behaviors lower than the estimated value as dangerous behaviors, and synchronizing with the block link point data.
Preferably, the S1 includes:
s1-1, carrying out construction site image acquisition through an image acquisition module, and synchronously carrying out image screening preparation on block chain link points; acquiring human body dynamic behavior data by using the construction site image, and capturing the image with the human body dynamic behavior data;
s1-2, in the capturing process, the weight vector C of the human body feature image is usediExtracting personnel from the construction site image, and estimating the number of workers on the construction site through similarity discrimination, wherein M is the conditional probability of the basic characteristic a of the workers on the construction site, F is the characteristic set of the workers on the construction site, b is the behavior characteristic of the workers on the construction site, and eta is a behavior limiting factor, wherein F belongs to { helmet, bodyspeed, vest, arm, anti },
forming expected estimation relations
Figure BDA0002758748820000021
Wherein u isiRepresenting a worksite personnel performance result; i represents the number of actions taking place, including safety and hazardous actions; v. ofiRepresenting the probability of occurrence of dangerous behaviors of the staff at the construction site; l (u)i) Is a worksite personnel number weight function, which is related to the probability of dangerous behavior occurrence; q (v)i) Is a gain function of the dangerous behavior of the staff in the construction site, is an overall evaluation of the deviation of the dangerous behavior from a reference point, wiPreliminarily screening conditional probability values of the construction site personnel region frames in the construction site images, wherein beta is a construction site personnel behavior correction parameter;
the worksite personnel number weighting function represents a probabilistic measure of the number of worksite personnel acquired from the construction site image, influences the design worksite personnel safety behavior decisions by anticipating changes in the estimated relationship,
Figure BDA0002758748820000022
where S is the safety behavior expectation, Δ Pi δExtracting deviation value of safety behavior image from construction site image, delta is safety behavior deviation correction value, and superscript WiFitting parameters for safety behavior, superscript ZiAnd fitting parameters for the dangerous behaviors, wherein epsilon is a deviation correction value of the dangerous behaviors.
Preferably, the S1 further includes:
s1-3, dividing the obtained area frame image into continuous windows through the construction site image, carrying out amplification operation on each window containing the construction site personnel image, and calculating the construction site image window pair (N)k,Nk+1) Average value of each channel in color space; form U e (N)k,Nk+1) (ii) a The equal-scale magnification weight function is
Figure BDA0002758748820000031
Wherein, label (N)k) Obtaining a function, label (N), for a current frame of a construction site imagek+1) Obtaining a function for the next frame of the building site image, m is greater than 1, mu is an image frame constraint factor, omega1Adjusting parameters, omega, for a current frame of a construction site image2The parameters are adjusted for the next frame of the building site image,
Figure BDA0002758748820000032
wherein q is1For safety action preference coefficient, q2A risk behavior preference coefficient;
and comparing the accumulated number of the construction site personnel obtained from the current construction site image window pair, amplifying the construction site image window pair when the change is more than a threshold value T, and moving to the next construction site image window pair to continuously search the construction site personnel if the change is not more than the threshold value T.
Preferably, the S2 includes:
s2-1, acquiring the image data of the construction site personnel from the isometric enlarged image of the construction site imageBuilding a spatial histogram array
Figure BDA0002758748820000033
Q(Ij) Extraction of characteristic images for the workers at the construction site, IjFor the enlarged image of the workers on the construction site, extraction is carried out by the trisection method, the symbols are U and [ 2 ]]All represent a combination of the histograms,
carrying out accumulation summation on the square image array H to form an enhanced image; extracting texture features in the histogram array on the basis of a histogram of gray levels of the enhanced image,
Hfeature＝125[f(IX/Xn)-f(IY/Yn)]·116[f(IX/Xn)+f(IY/Yn)]
counting the gray level of the texture features of the enhanced image, and setting XnIs 35.879, YnTo 113.245, by calculating the channel image function f (I) of the X-axisX/Xn) Thereby depicting the texture of the X-axis and calculating the channel image function f (I) of the Y-axisY/Yn) Thereby characterizing the Y-axis texture.
Preferably, the S2 further includes:
s2-2, converting the texture-delineated construction site personnel image into a Grey scale graph Grey, wherein the formula is as follows:
Figure BDA0002758748820000041
the behavior monitoring model for RGB is defined as:
Figure BDA0002758748820000042
Ei,j(σ1and theta) represents given (sigma)1θ) obtaining values of the behavior quantity i and the gray level j within the window of the safety behavior of the building site personnel, wherein; sigma1To obtain adjusted distance values for security behavior pixels, Fi,j(σ2And theta) represents given (sigma)2Theta) pair of site personnel hazardsObtaining values of behavior quantity i and gray level j in a behavior window, wherein the values are obtained; sigma1In order to obtain the adjustment distance value of the dangerous behavior pixel point, the gray level is subjected to degradation processing; the distance adjustment value is equally divided and jumped according to the gray level definition; selecting theta at 0 degree, 90 degrees, 180 degrees and 270 degrees;
preferably, the S2 further includes:
s2-3, establishing a behavior limiting function DiThe safe behavior and the dangerous behavior are gradually extracted,
Figure BDA0002758748820000043
kifor secure action image frame feature sets, pi+1Detecting index phi for the next image frame feature set of dangerous behaviors through the safety behavior of the personnel in the construction siteiRespectively associated with the X-axis gray scale feature points LxAnd Y-axis gray feature point MyAfter multiplication, comparing the two through the construction site personnel characteristic set F to obtain a time vector t of the safety behavior1And a safety behavior characteristic factor u1Squaring convolution vector of safety behavior and time vector t of dangerous behavior2And a dangerous behavior characteristic factor u2Squaring the convolution vector of security behavior, where OtDensity means representing the performance of the staff at the worksite.
Preferably, the S3 includes:
s3-1, clustering feature sets F of building site safety behavior images in the safety behavior identification process, and after preliminary screening, performing model judgment on image data of different building site personnel feature sets C; extracting attribute values of the specific characteristic image, constructing texture information,
helmet attribute value
Figure BDA0002758748820000051
Wherein psiiFor a construction site personnel helmet wearing behavior attribute value at a certain time,
Figure BDA0002758748820000052
for wearing the safety helmet control factor in the safety behavior,
Figure BDA0002758748820000053
the helmet control factor is not worn in dangerous behaviors,
attribute value of walking speed
Figure BDA0002758748820000054
Wherein r isiFor the attribute value of the walking speed behavior of the building site personnel at a certain moment,
Figure BDA0002758748820000055
a control factor for the normal walking speed in the safe behavior,
Figure BDA0002758748820000056
an abnormal walking speed control factor in dangerous behaviors,
attribute value of reflective vest
Figure BDA0002758748820000057
Wherein gamma isiFor the staff wearing the reflective vest at a certain time,
Figure BDA0002758748820000058
in order to wear reflective vest control factors in safety activities,
Figure BDA0002758748820000059
in order to avoid wearing the reflective vest control factor in dangerous behaviors,
hand-held item attribute values
Figure BDA00027587488200000510
Wherein etaiFor the property value of the behavior of the building site personnel holding the article at a certain moment,
Figure BDA00027587488200000511
for the hand-held article control factor in security activities,
Figure BDA00027587488200000512
the control factors of the articles are not held in the dangerous behaviors,
violation retrograde attribute value
Figure BDA00027587488200000513
Wherein
Figure BDA00027587488200000514
For a worker violation retrograde behavior attribute value at a time,
Figure BDA00027587488200000515
for non-violating retrograde control factors in the security activities,
Figure BDA00027587488200000516
for the violation of the retrograde control factor in the dangerous behavior,
preferably, the S3 further includes:
s3-2, extracting the texture characteristics of the helmet attribute value, the walking speed attribute value, the reflective vest attribute value, the handheld article attribute value and the illegal converse attribute value, screening by corresponding control factors,
in optimally obtaining site personnel safety behavior image distance attributes
Figure BDA0002758748820000061
Through construction site personnel safety behavior weight vector J and safety behavior vector transposition JTAnd safety behavior extraction sample uiObtaining the distance attribute of the safety behavior image through the convolution calculation after multiplication, and performing dangerous behavior weight vector K and dangerous behavior vector transposition K on the construction site personnelTSample c taken in conjunction with dangerous behavioriThe multiplied convolution calculation obtains the distance attribute of the dangerous behavior image,
optimized safety behavior convergence function
Figure BDA0002758748820000062
And optimizing the dangerous behavior convergence function
Figure BDA0002758748820000063
S3-3, judging according to the safety behavior estimation value
Figure BDA0002758748820000064
Wherein tau is a result control operator;
if the safety behavior estimated value is higher than the safety behavior estimated value, the safety behavior is classified as safety behavior, if the safety behavior estimated value is lower than the safety behavior estimated value, the safety behavior is classified as dangerous behavior, and meanwhile, the safety behavior estimated value is synchronized at the block link point in real time.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the method comprises the steps of obtaining building site image data through an image acquisition module, carrying out screening operation on building site personnel images, obtaining regional frame images of building site personnel features, carrying out equal-proportion image amplification, extracting and identifying through a human body feature histogram, effectively measuring human body information, carrying out image clustering, judging behavior types of a building site, quickly carrying out behavior judgment, refining dangerous behaviors, and synchronizing with block chain link points.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a general schematic of the present invention;
FIG. 2 is a schematic diagram of the image generation of the present invention;
FIG. 3 is an enlarged isometric view of an image of the present invention;
fig. 4 is a schematic diagram of feature point identification according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1-4, the invention discloses a method for monitoring the safety behavior of a construction site in a blockchain network state, which comprises the following steps:
s1, acquiring construction site image data through an image acquisition module, carrying out screening operation on a construction site personnel image, acquiring a region FRAME (FRAME) image of construction site personnel characteristics, and carrying out equal-proportion image amplification;
s2, after the construction site image is amplified in equal proportion, a human body characteristic histogram is obtained, and safety behavior recognition is carried out on the human body characteristic histogram through a behavior monitoring model;
and S3, after the identification is completed, performing cluster estimation on the building site safety behavior images, determining the building site behaviors lower than the estimated value as dangerous behaviors, and synchronizing with the block link point data.
The S1 includes:
s1-1, carrying out construction site image acquisition through an image acquisition module, and synchronously carrying out image screening preparation on block chain link points; acquiring human body dynamic behavior data by using the construction site image, and capturing the image with the human body dynamic behavior data;
s1-2, in the capturing process, the weight vector C of the human body feature image is usediExtracting personnel from the construction site image, and estimating the quantity of the construction site personnel through similarity discrimination, wherein M is the conditional probability of the basic characteristic a of the construction site personnel, F is the construction site personnel characteristic set, b is the construction site personnel behavior characteristic, and eta is a behavior limiting factor, wherein F belongs to { helmet, bodyspeed, vest, arm, anti }Collecting characteristics of the behaviors of wearing safety helmets, walking speed, reflecting waistcoats, holding articles, illegal retrograde motion and the like;
forming expected estimation relations
Figure BDA0002758748820000081
Wherein u isiRepresenting a worksite personnel performance result; i represents the number of actions taking place, including safety and hazardous actions; v. ofiRepresenting the probability of occurrence of dangerous behaviors of the staff at the construction site; l (u)i) Is a worksite personnel number weight function, which is related to the probability of dangerous behavior occurrence; q (v)i) Is a gain function of the dangerous behavior of the staff in the construction site, is an overall evaluation of the deviation of the dangerous behavior from a reference point, wiPreliminarily screening conditional probability values of the construction site personnel region frames in the construction site images, wherein beta is a construction site personnel behavior correction parameter;
the worksite personnel number weighting function represents a probabilistic measure of the number of worksite personnel acquired from the construction site image, influences the design worksite personnel safety behavior decisions by anticipating changes in the estimated relationship,
Figure BDA0002758748820000082
where S is the safety behavior expectation, Δ Pi δExtracting deviation value of safety behavior image from construction site image, delta is safety behavior deviation correction value, and superscript WiFitting parameters for safety behavior, superscript ZiFitting parameters for the dangerous behavior, epsilon is a dangerous behavior deviation correction value,
the occurrence probability of the image safety behaviors of the construction site is influenced by judgment and evaluation of working environment by workers and habits of safety behaviors of other workers, so that the behavior targets are corrected through the dangerous behavior deviation correction values, and more accurate weight functions can be fitted.
S1-3, dividing the acquired area frame image into continuous frames by the construction site imageEach window containing an image of the worksite personnel to be magnified, computing a pair of (N) architectural site image windowsk,Nk+1) Average value of each channel in color space; form U e (N)k,Nk+1)；
The equal-scale magnification weight function is
Figure BDA0002758748820000091
Wherein, label (N)k) Obtaining a function, label (N), for a current frame of a construction site imagek+1) Obtaining a function for the next frame of the building site image, m is greater than 1, mu is an image frame constraint factor, omega1Adjusting parameters, omega, for a current frame of a construction site image2The parameters are adjusted for the next frame of the building site image,
Figure BDA0002758748820000092
wherein q is1For safety action preference coefficient, q2A risk behavior preference coefficient;
comparing the accumulated number of the construction site personnel obtained from the current construction site image window pair, amplifying the construction site image window pair when the change is more than a threshold value T, and moving to the next construction site image window pair to continuously search the construction site personnel if the change is not more than the threshold value T;
as shown in fig. 2 and 3, the S2 includes:
s2-1, acquiring the image data of the construction site personnel through the equal proportion enlarged image of the construction site image, and constructing a space histogram array
Figure BDA0002758748820000093
Q(Ij) Extraction of characteristic images for the workers at the construction site, IjFor the enlarged image of the workers on the construction site, extraction is carried out by the trisection method, the symbols are U and [ 2 ]]All represent a combination of the histograms,
carrying out accumulation summation on the square image array H to form an enhanced image; extracting texture features in the histogram array on the basis of a histogram of gray levels of the enhanced image,
Hfeature＝125[f(IX/Xn)-f(IY/Yn)]·116[f(IX/Xn)+f(IY/Yn)]
counting the gray level of the texture features of the enhanced image, and setting XnIs 35.879, YnTo 113.245, by calculating the channel image function f (I) of the X-axisX/Xn) Thereby depicting the texture of the X-axis and calculating the channel image function f (I) of the Y-axisY/Yn) Thereby depicting the Y-axis texture;
s2-2, converting the texture-delineated construction site personnel image into a Grey scale graph Grey, wherein the formula is as follows:
Figure BDA0002758748820000101
the behavior monitoring model for RGB is defined as:
Figure BDA0002758748820000102
Ei,j(σ1and theta) represents given (sigma)1θ) obtaining values of the behavior quantity i and the gray level j within the window of the safety behavior of the building site personnel, wherein; sigma1To obtain adjusted distance values for security behavior pixels, Fi,j(σ2And theta) represents given (sigma)2Theta) obtaining values of the behavior quantity i and the gray level j in the construction site personnel dangerous behavior window, wherein; sigma1In order to obtain the adjustment distance value of the dangerous behavior pixel point, in order to improve the operation efficiency, the gray level is subjected to degradation processing; the distance adjustment value is equally divided and jumped according to the gray level definition; selecting theta at 0 degree, 90 degrees, 180 degrees and 270 degrees;
s2-3, establishing a behavior limiting function DiThe safe behavior and the dangerous behavior are gradually extracted,
Figure BDA0002758748820000103
kifor secure action image frame feature sets, pi+1Detecting index phi for the next image frame feature set of dangerous behaviors through the safety behavior of the personnel in the construction siteiRespectively associated with the X-axis gray scale feature points LxAnd Y-axis gray feature point MyAfter multiplication, comparing the two through the construction site personnel characteristic set F to obtain a time vector t of the safety behavior1And a safety behavior characteristic factor u1Squaring convolution vector of safety behavior and time vector t of dangerous behavior2And a dangerous behavior characteristic factor u2Squaring the convolution vector of security behavior, where OtA density average representing the behavior of the worksite personnel,
as can be seen in fig. 4, after the calculation, the corresponding trajectory of the moving person is clearly extracted and labeled.
The S3 includes:
s3-1, clustering feature sets F of building site safety behavior images in the safety behavior identification process, and after preliminary screening, performing model judgment on image data of different building site personnel feature sets C; extracting attribute values of the specific characteristic image, constructing texture information,
helmet attribute value
Figure BDA0002758748820000111
Wherein psiiFor a construction site personnel helmet wearing behavior attribute value at a certain time,
Figure BDA0002758748820000112
for wearing the safety helmet control factor in the safety behavior,
Figure BDA0002758748820000113
the helmet control factor is not worn in dangerous behaviors,
attribute value of walking speed
Figure BDA0002758748820000114
Wherein r isiFor the attribute value of the walking speed behavior of the building site personnel at a certain moment,
Figure BDA0002758748820000115
a control factor for the normal walking speed in the safe behavior,
Figure BDA0002758748820000116
an abnormal walking speed control factor in dangerous behaviors,
attribute value of reflective vest
Figure BDA0002758748820000117
Wherein gamma isiFor the staff wearing the reflective vest at a certain time,
Figure BDA0002758748820000118
in order to wear reflective vest control factors in safety activities,
Figure BDA0002758748820000119
in order to avoid wearing the reflective vest control factor in dangerous behaviors,
hand-held item attribute values
Figure BDA00027587488200001110
Wherein etaiFor the property value of the behavior of the building site personnel holding the article at a certain moment,
Figure BDA00027587488200001111
for the hand-held article control factor in security activities,
Figure BDA00027587488200001112
the control factors for the articles are not held by hands in the dangerous behaviors, the corresponding moving radius is also considered due to the influence of the length and the width of the articles held by the building site personnel, if the building site personnel hold bricks by hands, the moving radii of other building site personnel are relatively large, if the building site personnel hold reinforcing steel bars by shoulders, the moving radii of other building site personnel are relatively small, and the behavior control factors judged by the attribute values are fast in convergence, so that the dangerous behaviors are prevented;
violation retrograde attribute value
Figure BDA0002758748820000121
Wherein
Figure BDA0002758748820000122
For a worker violation retrograde behavior attribute value at a time,
Figure BDA0002758748820000123
for non-violating retrograde control factors in the security activities,
Figure BDA0002758748820000124
for the violation of the retrograde control factor in the dangerous behavior,
different construction sites have different site conditions, and different attribute values are adjusted to adapt to different site behavior identification, for example, some sites do not need to wear luminous waistcoats, and some sites do not have safe walking guide lines, so that illegal retrograde motion does not exist; adjusting the actually generated attribute value according to the actual situation;
s3-2, extracting the texture characteristics of the helmet attribute value, the walking speed attribute value, the reflective vest attribute value, the handheld article attribute value and the illegal converse attribute value, screening by corresponding control factors,
in optimally obtaining site personnel safety behavior image distance attributes
Figure BDA0002758748820000125
Through construction site personnel safety behavior weight vector J and safety behavior vector transposition JTAnd safety behavior extraction sample uiObtaining the distance attribute of the safety behavior image through the convolution calculation after multiplication, and performing dangerous behavior weight vector K and dangerous behavior vector transposition K on the construction site personnelTSample c taken in conjunction with dangerous behavioriThe multiplied convolution calculation obtains the distance attribute of the dangerous behavior image,
since the corresponding behavior attribute has been determined in S3-1, the behavior is subjected to attribute determination through the step S3-2, and the distance attribute is used to determine a critical value of the security behavior image, that is, whether the corresponding picture attribute reaches the degree of security behavior determination, and is called a distance attribute;
optimized safety behavior convergence function
Figure BDA0002758748820000126
And optimizing the dangerous behavior convergence function
Figure BDA0002758748820000127
S3-3, judging according to the safety behavior estimation value
Figure BDA0002758748820000131
Wherein tau is a result control operator;
if the safety behavior estimated value is higher than the safety behavior estimated value, the safety behavior is classified as safety behavior, if the safety behavior estimated value is lower than the safety behavior estimated value, the safety behavior is classified as dangerous behavior, and meanwhile, the safety behavior estimated value is synchronized at the block link point in real time.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (8)

1. A construction site safety behavior monitoring working method under a block chain network state is characterized by comprising the following steps:
s1, acquiring construction site image data through an image acquisition module, carrying out screening operation on a construction site personnel image, acquiring a region FRAME (FRAME) image of construction site personnel characteristics, and carrying out equal-proportion image amplification;
s2, after the construction site image is amplified in equal proportion, a human body characteristic histogram is obtained, and safety behavior recognition is carried out on the human body characteristic histogram through a behavior monitoring model;
and S3, after the identification is completed, performing cluster estimation on the building site safety behavior images, determining the building site behaviors lower than the estimated value as dangerous behaviors, and synchronizing with the block link point data.
2. The method for monitoring the safety behavior of the construction site under the condition of blockchain network as claimed in claim 1, wherein said S1 comprises:
s1-1, carrying out construction site image acquisition through an image acquisition module, and synchronously carrying out image screening preparation on block chain link points; acquiring human body dynamic behavior data by using the construction site image, and capturing the image with the human body dynamic behavior data;
s1-2, in the capturing process, the weight vector C of the human body feature image is usediExtracting personnel from the construction site image, and estimating the number of workers on the construction site through similarity discrimination, wherein M is the conditional probability of the basic characteristic a of the workers on the construction site, F is the characteristic set of the workers on the construction site, b is the behavior characteristic of the workers on the construction site, and eta is a behavior limiting factor, wherein F belongs to { helmet, bodyspeed, vest, arm, anti },
forming expected estimation relations
Figure FDA0002758748810000011
Wherein u isiRepresenting a worksite personnel performance result; i represents the number of actions taking place, including safety and hazardous actions; v. ofiRepresenting the probability of occurrence of dangerous behaviors of the staff at the construction site; l (u)i) Is a worksite personnel number weight function, which is related to the probability of dangerous behavior occurrence; q (v)i) Is a gain function of the dangerous behavior of the staff in the construction site, is an overall evaluation of the deviation of the dangerous behavior from a reference point, wiPreliminarily screening conditional probability values of the construction site personnel region frames in the construction site images, wherein beta is a construction site personnel behavior correction parameter;
the worksite personnel number weighting function represents a probabilistic measure of the number of worksite personnel acquired from the construction site image, influences the design worksite personnel safety behavior decisions by anticipating changes in the estimated relationship,
Figure FDA0002758748810000021
where S is the safety behavior expectation, Δ Pi δExtracting deviation value of safety behavior image from construction site image, delta is safety behavior deviation correction value, and superscript WiFitting parameters for safety behavior, superscript ZiAnd fitting parameters for the dangerous behaviors, wherein epsilon is a deviation correction value of the dangerous behaviors.
3. The work method for monitoring safety behavior of construction site under the condition of blockchain network as claimed in claim 2, wherein said S1 further comprises:
s1-3, dividing the obtained area frame image into continuous windows through the construction site image, carrying out amplification operation on each window containing the construction site personnel image, and calculating the construction site image window pair (N)k,Nk+1) Average value of each channel in color space; form U e (N)k,Nk+1) (ii) a The equal-scale magnification weight function is
Figure FDA0002758748810000022
Wherein, label (N)k) Obtaining a function, label (N), for a current frame of a construction site imagek+1) Obtaining a function for the next frame of the building site image, m is greater than 1, mu is an image frame constraint factor, omega1Adjusting parameters, omega, for a current frame of a construction site image2The parameters are adjusted for the next frame of the building site image,
Figure FDA0002758748810000023
wherein q is1For safety action preference coefficient, q2A risk behavior preference coefficient;
and comparing the accumulated number of the construction site personnel obtained from the current construction site image window pair, amplifying the construction site image window pair when the change is more than a threshold value T, and moving to the next construction site image window pair to continuously search the construction site personnel if the change is not more than the threshold value T.
4. The method for monitoring the safety behavior of the construction site under the condition of blockchain network as claimed in claim 1, wherein said S2 comprises:
s2-1, acquiring the image data of the construction site personnel through the equal proportion enlarged image of the construction site image, and constructing a space histogram array
Figure FDA0002758748810000031
Q(Ij) Extraction of characteristic images for the workers at the construction site, IjFor the enlarged image of the workers on the construction site, extraction is carried out by the trisection method, the symbols are U and [ 2 ]]All represent a combination of the histograms,
carrying out accumulation summation on the square image array H to form an enhanced image; extracting texture features in the histogram array on the basis of a histogram of gray levels of the enhanced image,
Hfeature＝125[f(IX/Xn)-f(IY/Yn)]·116[f(IX/Xn)+f(IY/Yn)]counting the gray level of the texture features of the enhanced image, and setting XnIs 35.879, YnTo 113.245, by calculating the channel image function f (I) of the X-axisX/Xn) Thereby depicting the texture of the X-axis and calculating the channel image function f (I) of the Y-axisY/Yn) Thereby characterizing the Y-axis texture.
5. The method for monitoring the safety behavior of the construction site under the condition of blockchain network as claimed in claim 1, wherein said S2 further comprises:
s2-2, converting the texture-delineated construction site personnel image into a Grey scale graph Grey, wherein the formula is as follows:
Figure FDA0002758748810000032
the behavior monitoring model for RGB is defined as:
Figure FDA0002758748810000033
Ei,j(σ1and theta) represents given (sigma)1θ) obtaining values of the behavior quantity i and the gray level j within the window of the safety behavior of the building site personnel, wherein; sigma1To obtain adjusted distance values for security behavior pixels, Fi,j(σ2And theta) represents given (sigma)2Theta) obtaining values of the behavior quantity i and the gray level j in the construction site personnel dangerous behavior window, wherein; sigma1In order to obtain the adjustment distance value of the dangerous behavior pixel point, the gray level is subjected to degradation processing; the distance adjustment value is equally divided and jumped according to the gray level definition; theta is selected from 0 degree, 90 degrees, 180 degrees and 270 degrees.
6. The work method for monitoring safety behavior of construction site under the condition of blockchain network as claimed in claim 5, wherein said S2 further comprises:
s2-3, establishing a behavior limiting function DiThe safe behavior and the dangerous behavior are gradually extracted,
Figure FDA0002758748810000041
kifor secure action image frame feature sets, pi+1Detecting index phi for the next image frame feature set of dangerous behaviors through the safety behavior of the personnel in the construction siteiRespectively associated with the X-axis gray scale feature points LxAnd Y-axis gray feature point MyAfter multiplication, comparing the result with the construction site personnel characteristic set F to obtain the time of safety behaviorInter-vector t1And a safety behavior characteristic factor u1Squaring convolution vector of safety behavior and time vector t of dangerous behavior2And a dangerous behavior characteristic factor u2Squaring the convolution vector of security behavior, where OtDensity means representing the performance of the staff at the worksite.
7. The method for monitoring the safety behavior of the construction site under the condition of blockchain network as claimed in claim 1, wherein said S3 comprises:
s3-1, clustering feature sets F of building site safety behavior images in the safety behavior identification process, and after preliminary screening, performing model judgment on image data of different building site personnel feature sets C; extracting attribute values of the specific characteristic image, constructing texture information,
helmet attribute value
Figure FDA0002758748810000042
Wherein psiiFor a construction site personnel helmet wearing behavior attribute value at a certain time,
Figure FDA0002758748810000043
for wearing the safety helmet control factor in the safety behavior,
Figure FDA0002758748810000044
the helmet control factor is not worn in dangerous behaviors,
attribute value of walking speed
Figure FDA0002758748810000051
Wherein r isiFor the attribute value of the walking speed behavior of the building site personnel at a certain moment,
Figure FDA0002758748810000052
a control factor for the normal walking speed in the safe behavior,
Figure FDA0002758748810000053
an abnormal walking speed control factor in dangerous behaviors,
attribute value of reflective vest
Figure FDA0002758748810000054
Wherein gamma isiFor the staff wearing the reflective vest at a certain time,
Figure FDA0002758748810000055
in order to wear reflective vest control factors in safety activities,
Figure FDA0002758748810000056
in order to avoid wearing the reflective vest control factor in dangerous behaviors,
hand-held item attribute values
Figure FDA0002758748810000057
Wherein etaiFor the property value of the behavior of the building site personnel holding the article at a certain moment,
Figure FDA0002758748810000058
for the hand-held article control factor in security activities,
Figure FDA0002758748810000059
the control factors of the articles are not held in the dangerous behaviors,
violation retrograde attribute value
Figure FDA00027587488100000510
Wherein
Figure FDA00027587488100000511
For a worker violation retrograde behavior attribute value at a time,
Figure FDA00027587488100000512
for non-violating retrograde control factors in the security activities,
Figure FDA00027587488100000513
and the control factor is the illegal reverse running control factor in the dangerous behavior.
8. The work method for monitoring safety behavior of construction site under the condition of blockchain network as claimed in claim 7, wherein said S3 further comprises:
s3-2, extracting the texture characteristics of the helmet attribute value, the walking speed attribute value, the reflective vest attribute value, the handheld article attribute value and the illegal converse attribute value, screening by corresponding control factors,
in optimally obtaining site personnel safety behavior image distance attributes
Figure FDA00027587488100000514
Through construction site personnel safety behavior weight vector J and safety behavior vector transposition JTAnd safety behavior extraction sample uiObtaining the distance attribute of the safety behavior image through the convolution calculation after multiplication, and performing dangerous behavior weight vector K and dangerous behavior vector transposition K on the construction site personnelTSample c taken in conjunction with dangerous behavioriThe multiplied convolution calculation obtains the distance attribute of the dangerous behavior image,
optimized safety behavior convergence function
Figure FDA0002758748810000061
And optimizing the dangerous behavior convergence function
Figure FDA0002758748810000062
S3-3, judging according to the safety behavior estimation value
Figure FDA0002758748810000063
Wherein the content of the first and second substances,tau is a result control operator;
if the safety behavior estimated value is higher than the safety behavior estimated value, the safety behavior is classified as safety behavior, if the safety behavior estimated value is lower than the safety behavior estimated value, the safety behavior is classified as dangerous behavior, and meanwhile, the safety behavior estimated value is synchronized at the block link point in real time.
CN202011211074.1A 2020-11-03 2020-11-03 Construction site safety behavior monitoring working method under block chain network state Withdrawn CN112507789A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115664006A (en) * 2022-10-21 2023-01-31 江苏东港能源投资有限公司 Increment distribution network intelligence management and control integration platform
CN116311081A (en) * 2023-05-12 2023-06-23 天津医科大学 Medical laboratory monitoring image analysis method and system based on image recognition

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115664006A (en) * 2022-10-21 2023-01-31 江苏东港能源投资有限公司 Increment distribution network intelligence management and control integration platform
CN116311081A (en) * 2023-05-12 2023-06-23 天津医科大学 Medical laboratory monitoring image analysis method and system based on image recognition
CN116311081B (en) * 2023-05-12 2023-08-22 天津医科大学 Medical laboratory monitoring image analysis method and system based on image recognition

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