CN116343449B - Safety monitoring and early warning method, device and system for construction site - Google Patents
Safety monitoring and early warning method, device and system for construction site Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 39
- 238000010276 construction Methods 0.000 title claims abstract description 35
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- 238000012545 processing Methods 0.000 claims abstract description 31
- 238000012216 screening Methods 0.000 claims abstract description 13
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
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Abstract
The invention relates to the technical field of safety monitoring, in particular to a safety monitoring early warning method, a safety monitoring early warning device and a safety monitoring early warning system for a construction site, wherein the safety monitoring early warning system comprises the following components: the data acquisition module is used for acquiring mental safety signals of each worker in the construction site through the intelligent safety helmet; the first processing module is used for screening abnormal values and extracting target effective information of the mental safety signals and determining the mental fatigue level and the concentration degree of each worker at the current moment; the second processing module is used for determining the overall mental fatigue level and the overall concentration degree corresponding to all workers in each working group through the corresponding work types and work type danger coefficients of each worker; the safety evaluation module adopts a pre-trained safety level evaluation model to determine the safety level and the safety level grade of each working group; and the early warning module determines a corresponding early warning prompt mode to prompt according to the safety level of each working group. Through the scheme, the probability of error early warning is reduced, and the processing efficiency of safety early warning is improved.
Description
Technical Field
The disclosure relates to the technical field of safety monitoring of construction sites, in particular to a safety monitoring and early warning method, device and system of a construction site.
Background
The intelligent safety helmet is built by carrying modules such as communication, safety monitoring, management and interaction on the basis of the traditional safety helmet, and the basic building method is to combine the modules such as an intelligent circuit, a sensor and a power supply with the shell of the traditional safety helmet, and generally has two methods of modifying the shell or externally binding. The brain-computer interface is a system for monitoring human brain-computer signals, and the monitoring system of the intelligent safety helmet with the brain-computer interface is in a flow form, only presents the brain-computer physiological signal index, and does not really perform the monitoring and early warning functions; on the other hand, the intelligent safety system with the monitoring and early warning functions does not carry an electroencephalogram safety helmet and has the early warning function of monitoring the mental safety state of operators. Under the large background of gradually paying attention to the safety of human engineering, a system capable of analyzing mental safety signals and achieving a good monitoring and early warning function is urgently needed.
Disclosure of Invention
In order to overcome the problems in the related art, the present disclosure provides a method, a device and a system for safety monitoring and early warning in a construction site.
According to a first aspect of embodiments of the present disclosure, there is provided a safety monitoring and early warning system for a construction site, including:
the data acquisition module is used for acquiring mental safety signals of each worker in the construction site through the intelligent safety helmet;
the first processing module is connected to the data acquisition module and is used for screening abnormal values and extracting target effective information of mental safety signals of each worker and determining mental fatigue level and concentration degree of attention of each worker at the current moment according to the target effective information;
the second processing module is connected to the first processing module and is used for determining the overall mental fatigue level and the overall concentration degree corresponding to all workers in each working group according to the mental fatigue level and the concentration degree of each worker at the current moment through the corresponding work type of each worker and the work type danger coefficient corresponding to each work type;
the safety evaluation module is connected to the second processing module and is used for determining the safety level of each working group by adopting a pre-trained safety level evaluation model according to the overall mental fatigue level and the overall concentration degree of all workers in each working group and determining a corresponding safety level according to the safety level;
The early warning module is connected to the safety evaluation module and used for determining a corresponding early warning prompting mode to prompt according to the safety level grade of each working group, wherein the safety level grade comprises a low level, a medium level and a high level.
In one embodiment, preferably, the first processing module includes:
the noise reduction unit is used for carrying out data noise reduction processing on the mental safety signals of each worker so as to obtain processed mental safety signal data;
an extraction unit for extracting target effective information from the processed mental safety signal data, wherein the target effective information comprises: time domain variability information, key time domain profile information, frequency domain power profile information, and frequency domain power variability information;
a first calculation unit for calculating the mental fatigue level of each worker at the current moment by using a pre-trained mental fatigue model according to the target effective information;
and the second calculating unit is used for calculating the concentration degree of each worker at the current moment by adopting a pre-trained concentration model according to the target effective information.
In one embodiment, the overall mental fatigue level for all workers in each work group is preferably calculated using the following first calculation formula:
Wherein F represents the overall mental fatigue level, k i Representing the work risk coefficient corresponding to the work of the ith worker, F i Representing the mental fatigue level of the ith worker;
the following second calculation formula is adopted to calculate the integral concentration degree corresponding to all workers in each working group:
wherein A represents the overall concentration, k i Representing the work risk coefficient corresponding to the work of the ith worker, A i Indicating the degree of concentration of the ith worker.
In one embodiment, preferably, the security assessment module comprises:
the training unit is used for training according to a preset training set and the fuzzy neural network model so as to obtain a safety level assessment model;
and the evaluation unit is used for taking the overall mental fatigue level and the overall concentration degree in each working group as the input of the safety level evaluation model so as to output the corresponding safety level of each working group and determine the corresponding safety level grade.
In one embodiment, preferably, the security level assessment model includes a blur layer, a rule layer, a regularization layer, a follow-up layer, and a defuzzification layer;
the fuzzy layer is used for fuzzifying the overall mental fatigue level and the overall concentration degree in each working group through three membership functions respectively, and calculating and outputting a first membership value and a second membership value;
The rule layer is used for multiplying the first membership value and the second membership value output by the fuzzy layer to be used as the activation degree value of the fuzzy rule, and outputting the activation degree value;
the regularization layer is used for carrying out row regularization calculation on the activation degree value to obtain a regularized activation degree value;
the subsequent layer is used for calculating and obtaining a new membership value according to the regularized activation degree value;
the defuzzification layer is used for calculating and outputting the safety level according to the new membership value.
In one embodiment, the first and second membership values are preferably calculated using the following third calculation formula:
wherein n represents the node number, x represents the overall mental fatigue level F or the overall concentration level A, O 1，n Representing either the first membership value or the second membership value,representing membership function A n For the course of the input global mental fatigue level x or global concentration level x, i.e. membership functions, c i Sum sigma i Representing membership function shape parameters;
the triggering strength of each fuzzy rule is calculated by adopting the following fourth calculation formula:
wherein n represents the number of the fuzzy rule, w n Representing the trigger intensity of each fuzzy rule, Membership value indicating the level of global mental fatigue F, < ->A membership value representing the overall concentration a;
calculating the regularized activation degree value by using the following fifth calculation formula:
a value representing the degree of activation of the regularization;
the new membership value is calculated using the following sixth calculation formula:
wherein F represents the overall mental fatigue level, A represents the overall concentration level, p n And q n Representing the corresponding weight, r n Representing constant terms;
the safety level is calculated using the following seventh calculation formula:
s represents the safety level.
In one embodiment, preferably, the early warning module is configured to:
when the safety level of the working group is low, determining a corresponding early warning prompt mode as a first early warning prompt;
when the safety level of the working group is horizontal, determining a corresponding early warning prompt mode as a second early warning prompt;
and when the safety level of the working group is high, determining that the corresponding early warning prompt mode is a third early warning prompt.
According to a second aspect of the embodiments of the present disclosure, there is provided a safety monitoring and early warning method for a construction site, including:
collecting mental safety signals of each worker in the construction site through an intelligent safety helmet;
Screening abnormal values and extracting target effective information from mental safety signals of each worker, and determining mental fatigue level and concentration degree of each worker at the current moment according to the target effective information;
according to the mental fatigue level and the concentration degree of each worker at the current moment, determining the overall mental fatigue level and the overall concentration degree of all workers in each working group through the corresponding work types of each worker and the corresponding work type danger coefficient of each work type;
according to the overall mental fatigue level and the overall concentration degree of all workers in each working group, determining the safety level of each working group by adopting a pre-trained safety level evaluation model, and determining the corresponding safety level according to the safety level;
and determining a corresponding early warning prompt mode to prompt according to the safety level grade of each working group, wherein the safety level grade comprises a low level, a medium level and a high level.
In one embodiment, it is preferable that the abnormal value screening and the extraction of the target effective information are performed on the mental safety signal of each worker, and the mental fatigue level and the concentration degree of each worker at the current time are determined according to the target effective information, including:
Carrying out data noise reduction processing on the mental safety signals of each worker to obtain processed mental safety signal data;
extracting target effective information from the processed mental safety signal data, wherein the target effective information comprises: time domain variability information, key time domain profile information, frequency domain power profile information, and frequency domain power variability information;
according to the target effective information, calculating the mental fatigue level of each worker at the current moment by adopting a pre-trained mental fatigue model;
and calculating the concentration degree of each worker at the current moment by adopting a pre-trained concentration model according to the target effective information.
In one embodiment, the overall mental fatigue level for all workers in each work group is preferably calculated using the following first calculation formula:
wherein F represents the overall mental fatigue level, k i Representing the work risk coefficient corresponding to the work of the ith worker, F i Representing the mental fatigue level of the ith worker;
the following second calculation formula is adopted to calculate the integral concentration degree corresponding to all workers in each working group:
wherein A represents the overall concentration, k i Representing the work risk coefficient corresponding to the work of the ith worker, A i Indicating the degree of concentration of the ith worker.
In one embodiment, preferably, determining the safety level of each work group using a pre-trained safety level assessment model based on the overall mental fatigue level and the overall concentration of all workers in each work group, and determining the corresponding safety level class based on the safety level, comprises:
training according to a preset training set and a fuzzy neural network model to obtain a safety level assessment model;
the overall mental fatigue level and the overall concentration within each work group are used as inputs to the safety level assessment model to output a corresponding safety level for each work group and to determine a corresponding safety level class.
In one embodiment, preferably, the security level assessment model includes a blur layer, a rule layer, a regularization layer, a follow-up layer, and a defuzzification layer;
the fuzzy layer is used for fuzzifying the overall mental fatigue level and the overall concentration degree in each working group through three membership functions respectively, and calculating and outputting a first membership value and a second membership value;
The rule layer is used for multiplying the first membership value and the second membership value output by the fuzzy layer to be used as the activation degree value of the rule, and outputting the activation degree value;
the regularization layer is used for carrying out row regularization calculation on the activation degree value to obtain a regularized activation degree value;
the subsequent layer is used for calculating and obtaining a new membership value according to the regularized activation degree value;
the defuzzification layer is used for calculating and outputting the safety level according to the new membership value.
In one embodiment, the first and second membership values are preferably calculated using the following third calculation formula:
wherein n represents the node number, x represents the overall mental fatigue level F or the overall concentration level A, O 1，n Representing either the first membership value or the second membership value,representing membership function A n For the course of the input global mental fatigue level x or global concentration level x, i.e. membership functions, c i Sum sigma i Representing membership function shape parameters;
the triggering strength of each fuzzy rule is calculated by adopting the following fourth calculation formula:
wherein n represents the number of the fuzzy rule, w n Each representation isThe triggering strength of the individual fuzzy rules is, Membership value indicating the level of global mental fatigue F, < ->A membership value representing the overall concentration a;
calculating the regularized activation degree value by using the following fifth calculation formula:
a value representing the degree of activation of the regularization;
the new membership value is calculated using the following sixth calculation formula:
wherein F represents the overall mental fatigue level, A represents the overall concentration level, p n And q n Representing the corresponding weight, r n Representing constant terms;
the safety level is calculated using the following seventh calculation formula:
s represents the safety level.
In one embodiment, preferably, determining the corresponding early warning prompting mode for prompting according to the safety level of each working group includes:
when the safety level of the working group is low, determining a corresponding early warning prompt mode as a first early warning prompt;
when the safety level of the working group is horizontal, determining a corresponding early warning prompt mode as a second early warning prompt;
and when the safety level of the working group is high, determining that the corresponding early warning prompt mode is a third early warning prompt.
According to a third aspect of embodiments of the present disclosure, there is provided a safety monitoring and early warning device for a construction site, including:
A processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
collecting mental safety signals of each worker in the construction site through an intelligent safety helmet;
screening abnormal values and extracting target effective information from mental safety signals of each worker, and determining mental fatigue level and concentration degree of each worker at the current moment according to the target effective information;
according to the mental fatigue level and the concentration degree of each worker at the current moment, determining the overall mental fatigue level and the overall concentration degree of all workers in each working group through the corresponding work types of each worker and the corresponding work type danger coefficient of each work type;
according to the overall mental fatigue level and the overall concentration degree of all workers in each working group, determining the safety level of each working group by adopting a pre-trained safety level evaluation model, and determining the corresponding safety level according to the safety level;
and determining a corresponding early warning prompt mode to prompt according to the safety level grade of each working group, wherein the safety level grade comprises a low level, a medium level and a high level.
According to a fourth aspect of the present invention there is provided a computer readable storage medium having stored thereon computer instructions which when executed by a processor perform the steps of the method of any of the embodiments of the first aspect.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
according to the embodiment of the invention, the early warning can be given according to the mental safety state of the operators, meanwhile, the instability of mental monitoring is considered, the data of a plurality of operators, namely all operators in the working group, are comprehensively analyzed, the problem of larger single data deviation is avoided, meanwhile, mental safety warning is carried out according to the working group, the scope of safety problem processing is more matched, the probability of false early warning is reduced, and the processing efficiency of safety early warning is increased.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a schematic structural diagram of a safety monitoring and early warning system for a construction site according to an exemplary embodiment.
Fig. 2 is a schematic structural diagram of a first processing module in a safety monitoring and early warning system of a construction site according to an exemplary embodiment.
Fig. 3 is a schematic structural diagram of a security assessment module in a security monitoring and early warning system of a construction site according to an exemplary embodiment.
FIG. 4 is a flow chart illustrating a method of safety monitoring and early warning at a job site, according to one exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.
Fig. 1 is a schematic structural diagram of a safety monitoring and early warning system for a construction site according to an exemplary embodiment.
As shown in fig. 1, the safety monitoring and early warning system for a construction site includes:
a data acquisition module 11 for acquiring mental safety signals of each worker in the construction site through a smart helmet;
A first processing module 12 connected to the data acquisition module for performing outlier screening and extraction of target effective information on mental safety signals of each worker, and determining mental fatigue level and concentration degree of each worker at the current time according to the target effective information;
a second processing module 13, connected to the first processing module, for determining the overall mental fatigue level and the overall concentration degree corresponding to all the workers in each work group according to the mental fatigue level and the concentration degree of each worker at the current moment, through the work type corresponding to each worker and the work type risk coefficient corresponding to each work type;
a safety assessment module 14, connected to the second processing module, for determining a safety level of each work group by using a pre-trained safety level assessment model according to the overall mental fatigue level and the overall concentration of all workers in each work group, and determining a corresponding safety level according to the safety level;
and the early warning module 15 is connected to the safety evaluation module and is used for determining a corresponding early warning prompting mode to prompt according to the safety level grade of each working group, wherein the safety level grade comprises a low level, a medium level and a high level.
In the embodiment, early warning can be given according to the mental safety state of operators, meanwhile, the instability of mental monitoring is considered, a plurality of operators, namely, the data of all operators in a working group are comprehensively analyzed, the problem that single data deviation is large is avoided, mental safety warning is carried out according to the working group, the scope of safety problem processing is matched more, the probability of false early warning is reduced, and the processing efficiency of safety early warning is increased.
The system interaction comprises the collection of user account information and user instruction information, and provides the functions of threshold value and parameter setting for an administrator user. The authority setting comprises three levels of authorities: total administrator, user rights. The total administrator authority is all authority including all project viewing, examination and approval of lower-level use accounts, and unified allocation of total administrator accounts. The administrator account is an account used by a production place practical manager, a leading office, a security director and the like, the authority range is a single production place, the authority range is used for inquiring and counting all security conditions, communication scheduling and the like of the production place, and the authority for opening the accounts of the lower users is provided. The user authority is the authority of self-checking of the health condition of the user, communication with upper straight pipe personnel and the like, and is uniformly opened by the applicant application or an administrator. The system database stores multi-level data of personnel level, team level, production level and regional project level respectively. The visualization draws the data of the system database into a visual chart according to the user authority layering level.
Fig. 2 is a schematic structural diagram of a first processing module in a safety monitoring and early warning system of a construction site according to an exemplary embodiment.
As shown in fig. 2, in one embodiment, the first processing module 12 preferably includes:
a noise reduction unit 21 for performing data noise reduction processing on the mental safety signal of each worker to obtain processed mental safety signal data; and combining multi-mode data (sports and operation actions), screening and eliminating mental safety signals of individual layer abnormality caused by abnormal fluctuation and external noise influence, and reserving effective signals for information extraction.
An extracting unit 22 for extracting target effective information from the processed mental safety signal data, wherein the target effective information includes: time domain variability information, key time domain profile information, frequency domain power profile information, and frequency domain power variability information;
according to the calculation requirement, extracting useful data in the mental safety signals according to the time domain and the frequency domain respectively, wherein the useful data mainly comprises time domain variability and a key time domain map; frequency domain power spectrum, frequency domain power variability index, mental fatigue level F of worker at current moment is calculated using pre-trained fatigue and attention algorithm i And degree of concentration A i . According to the calculation requirement, extracting useful data in the mental safety signals according to the time domain and the frequency domain respectively, wherein the useful data mainly comprises time domain variability and a key time domain map; frequency domain power spectrum, frequency domain power variability index, mental fatigue level F of worker at current moment is calculated using pre-trained fatigue and attention algorithm i And degree of concentration A i 。
A first calculation unit 23 for calculating the mental fatigue level of each worker at the current moment using a pre-trained mental fatigue model according to the target effective information;
and a second calculation unit 24 for calculating the concentration degree of each worker at the current moment by using a pre-trained concentration model according to the target effective information.
According to the work danger coefficient set by an expert, the worker with the larger danger coefficient has larger weight, the mental safety data of the individual level of the worker are summarized and calculated to the level of a work group, and the division of the work group can calculate the overall mental fatigue level F and the concentration degree A of the work according to logics such as work types, groups and the like.
In one embodiment, the overall mental fatigue level for all workers in each work group is preferably calculated using the following first calculation formula:
Wherein F represents the overall mental fatigue level, k i Representing the work risk coefficient corresponding to the work of the ith worker, F i Representing the mental fatigue level of the ith worker;
the following second calculation formula is adopted to calculate the integral concentration degree corresponding to all workers in each working group:
wherein A represents the overall concentration, k i Representing the work risk coefficient corresponding to the work of the ith worker, A i Indicating the degree of concentration of the ith worker.
Fig. 3 is a schematic structural diagram of a security assessment module in a security monitoring and early warning system of a construction site according to an exemplary embodiment.
As shown in fig. 3, in one embodiment, the security assessment module 14 preferably includes:
the training unit 31 is configured to perform training according to a preset training set and the fuzzy neural network model, so as to obtain a security level assessment model;
and automatically training a fuzzy neural network model through a real training set of a construction site by using a fuzzy neural network ANFIS algorithm, and evaluating the safety level. A training set is first constructed by collecting site-real data for 20 time periods. And (3) normalizing the data by using the mental fatigue level F and the attention level A obtained by the calculation in the previous step and adopting a MinMaxScaler normalization algorithm, converting F, A into a value ranging from 0 to 1, scoring the safety level S of the construction site, wherein the score is between 0 and 1, and the macroscopic safety level is higher as the score is higher. And taking the F, A after normalization processing as an input x of an algorithm, and taking the corresponding S as an output y of the algorithm to construct a training set. The training algorithm adopts a hybrid training method in an ANFIS tool box by using a hybrid training algorithm, 15 data points are randomly selected from 20 samples to serve as training data, the other 5 data points serve as a test set, the training error receiving range is set to be 0.001, and the maximum training times are set to be 2000.
The model has two inputs (F, A) and outputs a single value S,each input is passed through three membership functions A 1i ,A 2i ,A 3i Blurring is performed to represent three levels of high, medium, and low in the safe performance of the time period at the construction site, respectively, wherein the first subscript indicates the order of three membership functions and the second i indicates two inputs. ANFIS can learn fuzzy logic adaptively from the dataset of (F, a) similar to that used in human reasoning, namely:
If F is A 11 and A is A 12 then S＝p 1 F+q 1 A+r 1
an evaluation unit 32 for taking the overall mental fatigue level and the overall concentration level within each work group as inputs to the safety level evaluation model to output a corresponding safety level for each work group and to determine a corresponding safety level.
In one embodiment, preferably, the security level assessment model includes a blur layer, a rule layer, a regularization layer, a follow-up layer, and a defuzzification layer;
the fuzzy layer is used for fuzzifying the overall mental fatigue level and the overall concentration degree in each working group through three membership functions respectively, and calculating and outputting a first membership value and a second membership value;
membership functions fuzzify classical set theory, indicating the degree to which the input meets the mathematical properties required for a certain set.
In one embodiment, the first and second membership values are preferably calculated using the following third calculation formula:
wherein n represents the node number, x represents the overall mental fatigue level F or the overall concentration level A, O 1，n Representing either the first membership value or the second membership value,representing membership function A n For the course of the input global mental fatigue level x or global concentration level x, i.e. membership functions, c i Sum sigma i Representing membership function shape parameters;
the triggering strength of each fuzzy rule is calculated by adopting the following fourth calculation formula:
wherein n represents the number of the fuzzy rule, w n Representing the trigger intensity of each fuzzy rule,membership value indicating the level of global mental fatigue F, < ->A membership value representing the overall concentration a;
calculating the regularized activation degree value by using the following fifth calculation formula:
a value representing the degree of activation of the regularization;
the new membership value is calculated using the following sixth calculation formula:
wherein F represents the overall mental fatigue level, A represents the overall concentration level, p n And q n Representing the corresponding weight, r n Representing constant terms;
the safety level is calculated using the following seventh calculation formula:
S represents the safety level.
In one embodiment, the pre-warning module 15 is preferably configured to:
when the safety level of the working group is low, determining a corresponding early warning prompt mode as a first early warning prompt;
when the safety level of the working group is horizontal, determining a corresponding early warning prompt mode as a second early warning prompt;
and when the safety level of the working group is high, determining that the corresponding early warning prompt mode is a third early warning prompt.
And sending out early warning information according to the calculated safety level S in the specific time period according to the low, medium and high levels. When the medium or low level security is presented, the early warning information can be sent out to inform the manager of the related authority, and the corresponding key security personnel can be informed in the forms of machine telephone, short message and the like.
In one embodiment, preferably, the method further comprises: the mobile phone gathers the safety precaution information and stores the safety precaution information in a specified database, and periodically analyzes the safety accidents of the stored safety precaution information, outputs a safety analysis report and assists in improving the safety capability of the system.
FIG. 4 is a flow chart illustrating a method of safety monitoring and early warning at a job site, according to one exemplary embodiment.
As shown in fig. 4, according to a second aspect of the embodiments of the present disclosure, there is provided a safety monitoring and early warning method for a construction site, including:
step S401, collecting mental safety signals of each worker in the construction site through an intelligent safety helmet;
step S402, screening abnormal values and extracting target effective information of mental safety signals of each worker, and determining mental fatigue level and concentration degree of each worker at the current moment according to the target effective information;
step S403, according to the mental fatigue level and the concentration degree of each worker at the current moment, determining the overall mental fatigue level and the overall concentration degree of all workers in each working group through the work types corresponding to each worker and the work type danger coefficient corresponding to each work type;
step S404, determining the safety level of each working group by adopting a pre-trained safety level evaluation model according to the overall mental fatigue level and the overall concentration degree of all workers in each working group, and determining a corresponding safety level according to the safety level;
step S405, determining a corresponding early warning prompt mode to prompt according to the safety level grade of each working group, wherein the safety level grade comprises a low level, a medium level and a high level.
In one embodiment, it is preferable that the abnormal value screening and the extraction of the target effective information are performed on the mental safety signal of each worker, and the mental fatigue level and the concentration degree of each worker at the current time are determined according to the target effective information, including:
carrying out data noise reduction processing on the mental safety signals of each worker to obtain processed mental safety signal data;
extracting target effective information from the processed mental safety signal data, wherein the target effective information comprises: time domain variability information, key time domain profile information, frequency domain power profile information, and frequency domain power variability information;
according to the target effective information, calculating the mental fatigue level of each worker at the current moment by adopting a pre-trained mental fatigue model;
and calculating the concentration degree of each worker at the current moment by adopting a pre-trained concentration model according to the target effective information.
In one embodiment, the overall mental fatigue level for all workers in each work group is preferably calculated using the following first calculation formula:
wherein F represents the overall mental fatigue level, k i Representing the work risk coefficient corresponding to the work of the ith worker, F i Representing the mental fatigue level of the ith worker;
the following second calculation formula is adopted to calculate the integral concentration degree corresponding to all workers in each working group:
wherein A represents the overall concentration, k i Representing the work risk coefficient corresponding to the work of the ith worker, A i Indicating the degree of concentration of the ith worker.
In one embodiment, preferably, determining the safety level of each work group using a pre-trained safety level assessment model based on the overall mental fatigue level and the overall concentration of all workers in each work group, and determining the corresponding safety level class based on the safety level, comprises:
training according to a preset training set and a fuzzy neural network model to obtain a safety level assessment model;
the overall mental fatigue level and the overall concentration within each work group are used as inputs to the safety level assessment model to output a corresponding safety level for each work group and to determine a corresponding safety level class.
In one embodiment, preferably, the security level assessment model includes a blur layer, a rule layer, a regularization layer, a follow-up layer, and a defuzzification layer;
The fuzzy layer is used for fuzzifying the overall mental fatigue level and the overall concentration degree in each working group through three membership functions respectively, and calculating and outputting a first membership value and a second membership value;
the rule layer is used for multiplying the first membership value and the second membership value output by the fuzzy layer to be used as the activation degree value of the rule, and outputting the activation degree value;
the regularization layer is used for carrying out row regularization calculation on the activation degree value to obtain a regularized activation degree value;
the subsequent layer is used for calculating and obtaining a new membership value according to the regularized activation degree value;
the defuzzification layer is used for calculating and outputting the safety level according to the new membership value.
In one embodiment, the first and second membership values are preferably calculated using the following third calculation formula:
wherein n represents the node number, x represents the overall mental fatigue level F or the overall concentration level A, O 1，n Representing either the first membership value or the second membership value,representing membership function A n For the course of the input global mental fatigue level x or global concentration level x, i.e. membership functions, c i Sum sigma i Representing membership function shape parameters;
the triggering strength of each fuzzy rule is calculated by adopting the following fourth calculation formula:
wherein n represents the number of the fuzzy rule, w n Representing the trigger intensity of each fuzzy rule,membership value indicating the level of global mental fatigue F, < ->A membership value representing the overall concentration a;
calculating the regularized activation degree value by using the following fifth calculation formula:
/>
a value representing the degree of activation of the regularization;
the new membership value is calculated using the following sixth calculation formula:
wherein F represents the overall mental fatigue level, A represents the overall concentration level, p n And q n Representing the corresponding weight, r n Representing constant terms;
the safety level is calculated using the following seventh calculation formula:
s represents the safety level.
In one embodiment, preferably, determining the corresponding early warning prompting mode for prompting according to the safety level of each working group includes:
when the safety level of the working group is low, determining a corresponding early warning prompt mode as a first early warning prompt;
when the safety level of the working group is horizontal, determining a corresponding early warning prompt mode as a second early warning prompt;
And when the safety level of the working group is high, determining that the corresponding early warning prompt mode is a third early warning prompt.
According to a third aspect of embodiments of the present disclosure, there is provided a safety monitoring and early warning device for a construction site, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
collecting mental safety signals of each worker in the construction site through an intelligent safety helmet;
screening abnormal values and extracting target effective information from mental safety signals of each worker, and determining mental fatigue level and concentration degree of each worker at the current moment according to the target effective information;
according to the mental fatigue level and the concentration degree of each worker at the current moment, determining the overall mental fatigue level and the overall concentration degree of all workers in each working group through the corresponding work types of each worker and the corresponding work type danger coefficient of each work type;
according to the overall mental fatigue level and the overall concentration degree of all workers in each working group, determining the safety level of each working group by adopting a pre-trained safety level evaluation model, and determining the corresponding safety level according to the safety level;
And determining a corresponding early warning prompt mode to prompt according to the safety level grade of each working group, wherein the safety level grade comprises a low level, a medium level and a high level.
According to a fourth aspect of the present invention there is provided a computer readable storage medium having stored thereon computer instructions which when executed by a processor perform the steps of the method of any of the embodiments of the first aspect.
It is further understood that the term "plurality" in this disclosure means two or more, and other adjectives are similar thereto. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. The singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It is further understood that the terms "first," "second," and the like are used to describe various information, but such information should not be limited to these terms. These terms are only used to distinguish one type of information from another and do not denote a particular order or importance. Indeed, the expressions "first", "second", etc. may be used entirely interchangeably. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure.
It will be further understood that although operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any adaptations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (7)
1. A safety monitoring and early warning system for a construction site, comprising:
the data acquisition module is used for acquiring mental safety signals of each worker in the construction site through the intelligent safety helmet;
the first processing module is connected to the data acquisition module and is used for screening abnormal values and extracting target effective information of mental safety signals of each worker and determining mental fatigue level and concentration degree of attention of each worker at the current moment according to the target effective information;
the second processing module is connected to the first processing module and is used for determining the overall mental fatigue level and the overall concentration degree corresponding to all workers in each working group according to the mental fatigue level and the concentration degree of each worker at the current moment through the corresponding work type of each worker and the work type danger coefficient corresponding to each work type;
the safety evaluation module is connected to the second processing module and is used for determining the safety level of each working group by adopting a pre-trained safety level evaluation model according to the overall mental fatigue level and the overall concentration degree of all workers in each working group and determining a corresponding safety level according to the safety level;
The early warning module is connected to the safety evaluation module and used for determining a corresponding early warning prompting mode to prompt according to the safety level grade of each working group, wherein the safety level grade comprises a low level, a medium level and a high level;
wherein the security assessment module comprises:
the training unit is used for training according to a preset training set and the fuzzy neural network model so as to obtain a safety level assessment model;
an evaluation unit for taking the overall mental fatigue level and the overall concentration level within each work group as inputs of the safety level evaluation model to output a corresponding safety level for each work group and to determine a corresponding safety level;
the safety level assessment model comprises a blurring layer, a rule layer, a regularization layer, a subsequent layer and a defuzzification layer;
the fuzzy layer is used for fuzzifying the overall mental fatigue level and the overall concentration degree in each working group through three membership functions respectively, and calculating and outputting a first membership value and a second membership value;
the rule layer is used for multiplying the first membership value and the second membership value output by the fuzzy layer to be used as the activation degree value of the fuzzy rule, and outputting the activation degree value;
The regularization layer is used for carrying out row regularization calculation on the activation degree value to obtain a regularized activation degree value;
the subsequent layer is used for calculating and obtaining a new membership value according to the regularized activation degree value;
the defuzzification layer is used for calculating and outputting the safety level according to the new membership value;
the first membership value and the second membership value are calculated by adopting the following third calculation formula:
wherein n represents the node number, x represents the overall mental fatigue level F or the overall concentration level A, O 1，n Representing either the first membership value or the second membership value,representing membership function A n For the course of the input global mental fatigue level x or global concentration level x, i.e. membership functions, c i Sum sigma i Representing membership function shape parameters;
the triggering strength of each fuzzy rule is calculated by adopting the following fourth calculation formula:
wherein n represents the number of the fuzzy rule, w n Representing the trigger intensity of each fuzzy rule,membership value indicating the level of global mental fatigue F, < ->A membership value representing the overall concentration a;
calculating the regularized activation degree value by using the following fifth calculation formula:
A value representing the degree of activation of the regularization;
the new membership value is calculated using the following sixth calculation formula:
wherein F represents the overall mental fatigue level, A represents the overall concentration level, p n And q n Representing the corresponding weight, r n Representing constant terms;
the safety level is calculated using the following seventh calculation formula:
s represents the safety level.
2. The job site safety monitoring and early warning system according to claim 1, wherein the first processing module comprises:
the noise reduction unit is used for carrying out data noise reduction processing on the mental safety signals of each worker so as to obtain processed mental safety signal data;
an extraction unit for extracting target effective information from the processed mental safety signal data, wherein the target effective information comprises: time domain variability information, key time domain profile information, frequency domain power profile information, and frequency domain power variability information;
a first calculation unit for calculating the mental fatigue level of each worker at the current moment by using a pre-trained mental fatigue model according to the target effective information;
and the second calculating unit is used for calculating the concentration degree of each worker at the current moment by adopting a pre-trained concentration model according to the target effective information.
3. The safety monitoring and early warning system for a construction site according to claim 1, wherein the following first calculation formula is adopted to calculate the overall mental fatigue level corresponding to all workers in each work group:
wherein F represents the overall mental fatigue level, k i Representing the work risk coefficient corresponding to the work of the ith worker, F i Representing the mental fatigue level of the ith worker;
the following second calculation formula is adopted to calculate the integral concentration degree corresponding to all workers in each working group:
wherein A represents the overall concentration, k i Representing the work risk coefficient corresponding to the work of the ith worker, A i Indicating the degree of concentration of the ith worker.
4. The job site safety monitoring and early warning system according to claim 1, wherein the early warning module is configured to:
when the safety level of the working group is low, determining a corresponding early warning prompt mode as a first early warning prompt;
when the safety level of the working group is horizontal, determining a corresponding early warning prompt mode as a second early warning prompt;
and when the safety level of the working group is high, determining that the corresponding early warning prompt mode is a third early warning prompt.
5. The safety monitoring and early warning method for the construction site is characterized by comprising the following steps of:
collecting mental safety signals of each worker in the construction site through an intelligent safety helmet;
screening abnormal values and extracting target effective information from mental safety signals of each worker, and determining mental fatigue level and concentration degree of each worker at the current moment according to the target effective information;
according to the mental fatigue level and the concentration degree of each worker at the current moment, determining the overall mental fatigue level and the overall concentration degree of all workers in each working group through the corresponding work types of each worker and the corresponding work type danger coefficient of each work type;
according to the overall mental fatigue level and the overall concentration degree of all workers in each working group, determining the safety level of each working group by adopting a pre-trained safety level evaluation model, and determining the corresponding safety level according to the safety level;
determining a corresponding early warning prompt mode to prompt according to the safety level grade of each working group, wherein the safety level grade comprises a low level, a medium level and a high level;
Wherein, according to the corresponding whole mental fatigue level and the whole concentration degree of all workers in each working group, adopting a pre-trained safety level assessment model to determine the safety level of each working group, and determining the corresponding safety level according to the safety level, comprising:
training according to a preset training set and a fuzzy neural network model to obtain a safety level assessment model;
taking the overall mental fatigue level and the overall concentration level in each working group as the input of the safety level assessment model to output the corresponding safety level of each working group and determine the corresponding safety level grade;
the safety level assessment model comprises a blurring layer, a rule layer, a regularization layer, a subsequent layer and a defuzzification layer;
the fuzzy layer is used for fuzzifying the overall mental fatigue level and the overall concentration degree in each working group through three membership functions respectively, and calculating and outputting a first membership value and a second membership value;
the rule layer is used for multiplying the first membership value and the second membership value output by the fuzzy layer to be used as the activation degree value of the fuzzy rule, and outputting the activation degree value;
The regularization layer is used for carrying out row regularization calculation on the activation degree value to obtain a regularized activation degree value;
the subsequent layer is used for calculating and obtaining a new membership value according to the regularized activation degree value;
the defuzzification layer is used for calculating and outputting the safety level according to the new membership value;
the first membership value and the second membership value are calculated by adopting the following third calculation formula:
wherein n represents the node number, x represents the overall mental fatigue level F or the overall concentration level A, O 1，n Representing either the first membership value or the second membership value,representing membership function A n For the course of the input global mental fatigue level x or global concentration level x, i.e. membership functions, c i Sum sigma i Representing membership function shape parameters;
the triggering strength of each fuzzy rule is calculated by adopting the following fourth calculation formula:
wherein n represents the number of the fuzzy rule, w n Representing the trigger intensity of each fuzzy rule,membership value indicating the level of global mental fatigue F, < ->A membership value representing the overall concentration a;
calculating the regularized activation degree value by using the following fifth calculation formula:
A value representing the degree of activation of the regularization;
the new membership value is calculated using the following sixth calculation formula:
wherein F represents the overall mental fatigue level, A represents the overall concentration level, p n And q n Representing the corresponding weight, r n Representing constant terms;
the safety level is calculated using the following seventh calculation formula:
s represents the safety level.
6. A safety monitoring and early warning device for a construction site, the device comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
collecting mental safety signals of each worker in the construction site through an intelligent safety helmet;
screening abnormal values and extracting target effective information from mental safety signals of each worker, and determining mental fatigue level and concentration degree of each worker at the current moment according to the target effective information;
according to the mental fatigue level and the concentration degree of each worker at the current moment, determining the overall mental fatigue level and the overall concentration degree of all workers in each working group through the corresponding work types of each worker and the corresponding work type danger coefficient of each work type;
According to the overall mental fatigue level and the overall concentration degree of all workers in each working group, determining the safety level of each working group by adopting a pre-trained safety level evaluation model, and determining the corresponding safety level according to the safety level;
determining a corresponding early warning prompt mode to prompt according to the safety level grade of each working group, wherein the safety level grade comprises a low level, a medium level and a high level;
wherein, according to the corresponding whole mental fatigue level and the whole concentration degree of all workers in each working group, adopting a pre-trained safety level assessment model to determine the safety level of each working group, and determining the corresponding safety level according to the safety level, comprising:
training according to a preset training set and a fuzzy neural network model to obtain a safety level assessment model;
taking the overall mental fatigue level and the overall concentration level in each working group as the input of the safety level assessment model to output the corresponding safety level of each working group and determine the corresponding safety level grade;
the safety level assessment model comprises a blurring layer, a rule layer, a regularization layer, a subsequent layer and a defuzzification layer;
The fuzzy layer is used for fuzzifying the overall mental fatigue level and the overall concentration degree in each working group through three membership functions respectively, and calculating and outputting a first membership value and a second membership value;
the rule layer is used for multiplying the first membership value and the second membership value output by the fuzzy layer to be used as the activation degree value of the fuzzy rule, and outputting the activation degree value;
the regularization layer is used for carrying out row regularization calculation on the activation degree value to obtain a regularized activation degree value;
the subsequent layer is used for calculating and obtaining a new membership value according to the regularized activation degree value;
the defuzzification layer is used for calculating and outputting the safety level according to the new membership value;
the first membership value and the second membership value are calculated by adopting the following third calculation formula:
wherein n represents the node number, x represents the overall mental fatigue level F or the overall concentration level A, O 1，n Representing either the first membership value or the second membership value,representing membership function A n For the course of the input global mental fatigue level x or global concentration level x, i.e. membership functions, c i Sum sigma i Representing membership function shape parameters;
the triggering strength of each fuzzy rule is calculated by adopting the following fourth calculation formula:
wherein n represents the number of the fuzzy rule, w n Representing the trigger intensity of each fuzzy rule,membership value indicating the level of global mental fatigue F, < ->A membership value representing the overall concentration a;
calculating the regularized activation degree value by using the following fifth calculation formula:
a value representing the degree of activation of the regularization;
the new membership value is calculated using the following sixth calculation formula:
wherein F represents the overall mental fatigue level, A represents the overall concentration level, p n And q n Representing the corresponding weight, r n Representing constant terms;
the safety level is calculated using the following seventh calculation formula:
s represents the safety level.
7. A computer readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the steps of the method of any of claims 1-4.
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