Construction safety monitoring management system and method based on intelligent construction site

Abstract

The invention discloses a construction safety monitoring management system and method based on an intelligent construction site, and relates to the technical field of construction safety, and the method comprises the steps: carrying out the arrangement and classification processing of safety operation specification data, and constructing a normal operation database of construction personnel; calculating a fault threshold value of each type of construction equipment; judging each equipment fault by using the fault threshold; utilizing a linear regression algorithm to calculate and obtain a correlation function of the feature data and the operation data of each type of equipment; correlation functions of the number of faults and the operation data are calculated, and the two correlation functions are used for comprehensive analysis to obtain an optimization function of the operation data of each device; calculating an operation threshold value of operating each type of construction equipment by a constructor; real-time operation data is transmitted to the central processor for judgment; and inputting the real-time characteristic data and the fault frequency of each type of equipment into the optimization function to optimize the operation threshold value of the constructor.

Description

Technical Field

[0001] The present invention relates to the field of construction safety technology, and in particular to a construction safety monitoring management system and method based on a smart construction site. Background Art

[0002] The construction site is complex and changeable, with a large number of risk factors, such as aerial work, heavy machinery operation, electrical equipment failure, etc. If there is a lack of effective safety management, personal injury, equipment damage and other accidents are very likely to occur. Traditional safety management methods rely on manual inspections and paper reports; manual monitoring relies on on-site inspections, which makes it difficult to obtain data in real time, and information lags may lead to safety hazards not being handled in a timely manner. With the development of science and technology, image monitoring technology has become more and more mature. By deploying sensors, cameras, RFID tags and other intelligent devices on the construction site, real-time environmental data, equipment status, personnel behavior and other information on the site are collected to provide a data basis for safety monitoring. The deep integration of AI and the Internet of Things will make construction safety monitoring management more intelligent, and the system can autonomously identify, analyze and make decisions, greatly reducing human intervention. However, when detecting the behavior of construction workers on the construction site, construction workers need to operate according to the operating specifications of different equipment. When the status of the equipment changes or fails, the previous operating specifications may not apply. When construction workers still use the previous operating specifications to operate the equipment, danger and equipment failure are very likely to occur. Therefore, it is crucial to optimize, update and adjust the operating specifications of construction workers according to the status of the equipment. Summary of the invention

[0003] The purpose of the present invention is to provide a construction safety monitoring management system and method based on a smart construction site to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] A construction safety monitoring and management method based on a smart construction site, the method comprising the following steps:

[0006] S100, collecting safety operation guidelines for construction personnel of each type of construction equipment in the construction site, extracting safety operation data in the safety operation guidelines, sorting the collected safety operation specification data, and building a normal operation database for construction personnel after classification and processing;

[0007] Furthermore, the specific steps of constructing the normal operation database of construction personnel after classification processing are as follows:

[0008] S101. Collect the safety operation guides for each construction equipment and construction personnel at the construction site, calculate the contribution of each operation step in the safety operation guide of each construction equipment, extract the amount of information of each step in the safety operation guide and the number of operation errors of personnel at each step in history, and the formula is: G = X P , where G is the contribution of each step in the safety operation guide, X is the amount of information in each step, and P is the number of human operation errors in each step in history; the contribution of each step in the safety operation guide is calculated and judged, and the safety operation data S of each construction equipment with the largest contribution is extracted. Suppose the safety operation data extracted from the safety operation guide of each equipment is {S1, S2, S3, ..., S n}, S1, S2, S3, ..., S n represents the safety operation data collected for the 1st, 2nd, 3rd, ..., nth construction equipment construction personnel at the construction site, where n is a positive integer;

[0009] S102. After extracting the safety operation data of each equipment at the construction site, classify the extracted safety operation data according to the type of equipment at the construction site. There are M types of construction equipment at the construction site, and M types of corresponding safety operation data are obtained. The M types of safety operation data are used to build a normal operation database.

[0010] There are many devices on the construction site, and different equipment has different safety operation guidelines. However, the construction workers are too scattered and cannot always keep in mind safe operation. Calculating the contribution of the operating steps in the safety operation guidelines, extracting safe operation data and building a normal operation database can always remind construction workers to operate the construction equipment according to the safe operation steps to avoid dangers caused by malfunctions.

[0011] S200, collecting equipment data when construction equipment fails in history, extracting equipment features, and using the equipment features to calculate the fault threshold of each type of construction equipment; collecting feature data values ​​of each type of equipment at the construction site in real time, and using the fault threshold to judge the fault of each type of equipment;

[0012] Furthermore, the specific steps of using the fault threshold to judge each device fault are as follows:

[0013] S201, collect the equipment data of the construction equipment failure in the history {B1, B2, B3, ..., B h},

[0014] B1, B2, B3, ..., B hIt represents the data of the 1st, 2nd, 3rd, …, hth types of equipment when a construction equipment fails in the collected history; extract the data values of the h types of equipment before and after the construction equipment fails in the history, and calculate the change amount of each type of equipment data when the construction equipment fails. The formula is: Bc = Bb - Ba. In the formula, Bc represents the change amount of each type of equipment data when the construction equipment fails, Bb represents the data value of each type of equipment before the construction equipment fails, and Ba represents the data value of each type of equipment after the construction equipment fails; take the absolute value of the difference of each type of equipment data calculated, and screen out the equipment data with the largest absolute value of the difference as the characteristic data of each construction equipment when it fails.

[0015] S202. The characteristic data of each construction equipment when it fails in the history is

[0016] {Te1, Te2, Te3, …, Te j}, Te1, Te2, Te3, …, Te j represents the characteristic data of each construction equipment when it fails for the 1st, 2nd, 3rd, …, jth time in the collected history, where j is a positive integer; use the j characteristic data collected to calculate the failure threshold of each construction equipment. The formula is:

[0017]

[0018] In the formula, In represents the failure threshold of each construction equipment calculated, Tep represents the average value of the j characteristic data collected, Te min represents the minimum value among the j characteristic data collected, and Te max represents the maximum value among the j characteristic data collected.

[0019] S203. The characteristic data values of each equipment at the construction site collected in real time are {Tes1, Tes2, Tes3, …, Tes M}, Tes1, Tes2, Tes3, …, Tes M represents the characteristic data values of the 1st, 2nd, 3rd, …, Mth types of equipment at the construction site collected in real time, where M is a positive integer; use the failure threshold of each construction equipment calculated to judge the characteristic data values collected in real time.

[0020] When the characteristic data of the construction equipment is negative before taking the absolute value, when judging that Tes ≤ In, the construction equipment has a failure risk and a warning is issued; when Tes > In, the construction equipment is working normally.

[0021] When the characteristic data of the construction equipment is positive before taking the absolute value, when judging that Tes ≥ In, the construction equipment has a failure risk and a warning is issued; when Tes < In, the construction equipment is working normally.

[0022] Based on the equipment data of each type of construction equipment when it fails in history, feature data is extracted by searching, and the fault threshold of each type of construction equipment is calculated using the feature data. The fault threshold can be used to determine whether the equipment has failed. When there is a risk of failure, an early warning is issued to reduce the probability of equipment failure, reduce the cost required for construction, and increase construction efficiency.

[0023] S300, collecting characteristic data values ​​of each type of construction equipment when failure occurs when construction personnel have different operation data values ​​in history, and using a linear regression algorithm to calculate the correlation function between the characteristic data of each type of equipment and the operation data; collecting the number of equipment failures when each type of construction equipment fails when construction personnel have different operation data values ​​in history, calculating the correlation function between the number of failures and the operation data, and using the two correlation functions to perform a comprehensive analysis to obtain an optimization function for the operation data of each type of equipment;

[0024] Furthermore, the specific steps of using the two correlation functions to conduct a comprehensive analysis to obtain the optimization function of each device operation data are as follows:

[0025] S301, collecting characteristic data values ​​of each type of construction equipment when a failure occurs when the construction personnel have different operation data values ​​in history, extracting the characteristic data values ​​of the construction personnel in history {C1, C2, C3, ..., C k},

[0026] C1, C2, C3, ..., C k represents the 1st, 2nd, 3rd, ..., operation data values ​​of the construction personnel in the extracted history, k is a positive integer; when collecting different operation data values, the characteristic data value of each construction equipment failure is {Tec1, Tec2, Tec3, ..., Tec k}, Tec1, Tec2, Tec3,..., Tec k The characteristic data value when each construction equipment fails when the 1st, 2nd, 3rd, ..., kth operation data value collected is represented, and k is a positive integer; the characteristic data value is used as an independent variable, and the operation data value is used as a variable to draw a curve graph, and the correlation function between the operation data and the characteristic data is calculated by a linear regression algorithm as C=a×Tec+b, a1 represents the coefficient of the correlation function, and b1 represents the constant term of the correlation function;

[0027] S302, collecting the number of failures of each type of construction equipment when different operation data values ​​of construction personnel are used in history is {F1, F2, F3, ..., F k}, F1, F2, F3, ..., F kIndicates the number of equipment failures when each type of construction equipment fails when the construction personnel have the 1st, 2nd, 3rd, ..., kth operation data values ​​in the collected history; the correlation function between the number of failures and different operation data is calculated using the linear regression algorithm as C=a2×F+b2, where a2 represents the coefficient of the independent variable and b2 represents the constant term; the optimization function of each type of equipment operation data is obtained by comprehensive analysis using the two correlation functions, and the formula is: a=a1+a2, In the formula, a represents the coefficient of the optimization function, and b represents the constant term of the optimization function; the optimization function of each construction equipment is finally obtained as C=a×(Tec+F)+b.

[0028] S400, collecting operation data values ​​when construction workers make mistakes in operating each type of construction equipment in history, and using the operation data values ​​collected in history to calculate the operation threshold of each type of construction equipment operated by construction workers; setting a visual sensor in the helmet of each construction worker at the construction site, using the visual sensor in the helmet to collect the operation data of each construction worker in real time, and transmitting the operation data to the central processor for judgment;

[0029] Furthermore, the specific steps of transmitting the operation data to the central processor for judgment are as follows:

[0030] S401, collect the operation data values ​​{Cu1, Cu2, Cu3, ..., Cu r}, Cu1, Cu2, Cu3,..., Cu r represents the operation data values ​​of the 1st, 2nd, 3rd, ..., rth errors of the construction personnel in operating each type of construction equipment in the collected history, where r is a positive integer; the operation threshold of each type of construction equipment is calculated using the operation data values ​​of the errors of the construction personnel in operating the equipment in the collected history, and the formula is: Cy=Cup-Cust, where Cy represents the calculated operation threshold of each type of construction equipment, Cup represents the average value of the r collected operation data, and Cust represents the standard deviation of the r collected operation data;

[0031] S402, a visual sensor is installed in the helmet of each construction worker at the construction site, and the visual sensor in the helmet is used to collect the operation data of each construction worker in real time {Cs1, Cs2, Cs3, ..., Cs e}, Cs1, Cs2, Cs3,..., Cs eIt represents the operation data of the 1st, 2nd, 3rd, …, e-th construction workers collected in real time by the vision sensor, and uses the vision sensor to judge the types of equipment that each construction worker is operating in real time. The real-time operation data of the construction workers is judged using the operation thresholds of the corresponding equipment. When Cs≥Cy, it is judged that there is a risk of operation error for the construction worker; when Cs<Cy, it is judged that there is no risk for the construction worker's operation.

[0032] The operation thresholds of each type of equipment are calculated through the operation data of construction workers' mistakes when operating equipment in history. Using the operation thresholds, the operation data of construction workers can be judged, the operation methods of construction workers can be standardized, and equipment failures or construction hazards caused by abnormal operations can be avoided, greatly reducing the construction cost and enhancing the safety of the construction site;

[0033] S500. After the central processor makes a real-time judgment on the operation data of the construction workers, extract the real-time characteristic data of each type of equipment described in S300, and extract the number of judged failure times of each type of equipment; input the real-time characteristic data and the number of failure times of each type of equipment into the optimization function to optimize the operation thresholds of the construction workers.

[0034] Furthermore, the specific steps for inputting the real-time characteristic data and the number of failure times of each type of equipment into the optimization function to optimize the operation thresholds of the construction workers are as follows:

[0035] S501. When the central processor makes a real-time judgment on the operation data of the construction workers, extract the real-time characteristic data of each type of equipment described in S300, and extract the number of judged failure times of each type of equipment. Input the extracted real-time characteristic data and the number of failure times of each type of equipment into the optimization function. The formula is:

[0036] Cop=a×(Tes+Fs)+b ；

[0037] In the formula, Cop represents the operation threshold of each type of construction equipment after optimization, Tes represents the real-time characteristic data of each type of equipment extracted, and Fs represents the number of failure times of each type of equipment extracted; use the optimized operation threshold to make a secondary judgment on the operation data of each construction worker. When Cs≥Cop, it is judged that the construction worker makes an operation error and a warning is issued; when Cs<Cop, it is judged that the construction worker's operation is normal;

[0038] S502. Use the optimized operation threshold to optimize and update the safe operation data of each type of equipment in the normal operation database.

[0039] The operation threshold is optimized using the optimization function, and the optimized operation threshold is used for secondary judgment. This avoids the situation where when the equipment fails or there is a risk, the operation is still performed according to the normal equipment operation data, which accelerates the equipment failure or creates unexpected dangers. It makes the operation of the equipment more intelligent, and the operation data is intelligently adjusted according to the equipment status, making the construction safer and more reliable.

[0040] A construction safety monitoring and management system based on a smart construction site, the construction safety monitoring and management system includes a data collection module, a database construction module, an equipment fault judgment module, a function calculation module, and a personnel operation judgment module;

[0041] The data collection module is used to collect equipment data of construction equipment and operation data of construction personnel at historical construction sites;

[0042] The database construction module is used to analyze the safety operation guide of each device, extract the safety operation data of each device, and construct a normal operation database;

[0043] The equipment fault judgment module is used to use the equipment data collected in the history when the equipment fails, extract feature data to calculate the fault threshold, and use the fault threshold to issue a fault warning for each equipment;

[0044] The function calculation module is used to calculate two correlation functions according to the historical operation data, characteristic data and fault times, and combine the two correlation functions to construct an optimization function;

[0045] The personnel operation judgment module is used to calculate the operation threshold according to the historical data, use the operation threshold to judge the operation data of the real-time construction personnel, and use the optimization function to optimize the operation threshold and then perform a secondary judgment.

[0046] The equipment fault judgment module includes a feature extraction unit, a fault threshold calculation unit and a real-time judgment unit;

[0047] The feature extraction unit is used to analyze the device data collected in the history when the failure occurs, and extract the feature data of each device when the failure occurs;

[0048] The fault threshold calculation unit is used to calculate the fault threshold of each device according to the historical characteristic data of each device;

[0049] The real-time judgment unit is used to make a real-time judgment on whether each device has a failure risk by using a failure threshold.

[0050] The function calculation module includes a characteristic data correlation function unit, a fault number correlation function unit and an optimization function unit;

[0051] The characteristic data correlation function unit is used to analyze the characteristic data when the equipment fails under different operation data in history, and calculate the correlation function using a linear regression algorithm;

[0052] The failure number correlation function unit is used to analyze the number of failures of the equipment under different operation data in history and calculate the correlation function using a linear regression algorithm;

[0053] The optimization function unit is used to perform a comprehensive analysis on the two correlation functions and combine them to generate an optimization function for each device.

[0054] The personnel operation judgment module includes an operation threshold calculation unit, an operation judgment unit and an optimization unit;

[0055] The operation threshold calculation unit is used to calculate the operation threshold according to the operation data of the equipment abnormality when the construction personnel operate the equipment in the past;

[0056] The operation judgment unit is used to collect operation data of construction personnel when operating equipment in real time, and judge the real-time operation data using the operation threshold;

[0057] The optimization unit is used to input the real-time characteristic data and the number of failures into the optimization function to calculate the optimized operation threshold, use the optimized operation threshold to make a secondary operation judgment, and optimize and update the normal operation database.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The present invention uses characteristic data to calculate the fault threshold of each construction equipment; the fault threshold can be used to judge whether the equipment has failed. When there is a risk of failure, early warning is given to reduce the probability of equipment failure, reduce the cost required for construction, and increase construction efficiency.

[0060] 2. The present invention utilizes the operation threshold to judge the operation data of the construction personnel, standardize the operation mode of the construction personnel, avoid equipment failure or construction danger caused by abnormal operation, etc., greatly reduce the construction cost, and enhance the safety of the construction site.

[0061] 3. The present invention optimizes the operation threshold through the optimization function, and adjusts the operation threshold according to the real-time characteristic data and the number of failures of each device, so as to avoid the situation where, when the equipment fails or there is a risk, the operation is still performed according to the operation data of the normal equipment, which accelerates the failure of the equipment or creates unexpected dangers; the operation of the equipment is made more intelligent, and the operation data is intelligently adjusted according to the equipment status, making the construction safer and more reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a module distribution diagram of a construction safety monitoring and management system based on a smart construction site according to the present invention;

[0063] Figure 2 This is a schematic diagram of the steps of a construction safety monitoring and management method based on a smart construction site of the present invention. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Example: Figure 1-Figure 2 As shown, the present invention provides a technical solution.

[0066] A construction safety monitoring and management method based on a smart construction site, the method comprising the following steps:

[0067] S100, collecting safety operation guidelines for construction personnel of each type of construction equipment in the construction site, extracting safety operation data in the safety operation guidelines, sorting the collected safety operation specification data, and building a normal operation database for construction personnel after classification and processing;

[0068] The specific steps to build the normal operation database of construction personnel after classification processing are as follows:

[0069] S101. Collect the safety operation guides for each construction equipment and construction personnel at the construction site, calculate the contribution of each operation step in the safety operation guide of each construction equipment, extract the amount of information of each step in the safety operation guide and the number of operation errors of personnel at each step in history, and the formula is: G = X P , where G is the contribution of each step in the safety operation guide, X is the amount of information in each step, and P is the number of human operation errors in each step in history; the contribution of each step in the safety operation guide is calculated and judged, and the safety operation data S of each construction equipment with the largest contribution is extracted. Suppose the safety operation data extracted from the safety operation guide of each equipment is {S1, S2, S3, ..., S n}, S1, S2, S3, ..., S n represents the safety operation data collected for the 1st, 2nd, 3rd, ..., nth construction equipment construction personnel at the construction site, where n is a positive integer;

[0070] S102. After extracting the safety operation data of each equipment at the construction site, classify the extracted safety operation data according to the type of equipment at the construction site. There are M types of construction equipment at the construction site, and M types of corresponding safety operation data are obtained. The M types of safety operation data are used to build a normal operation database.

[0071] There are many devices on the construction site, and different equipment has different safety operation guidelines. However, the construction workers are too scattered and cannot always keep in mind safe operation. Calculating the contribution of the operating steps in the safety operation guidelines, extracting safe operation data and building a normal operation database can always remind construction workers to operate the construction equipment according to the safe operation steps to avoid dangers caused by malfunctions.

[0072] S200, collecting equipment data when construction equipment fails in history, extracting equipment features, and using the equipment features to calculate the fault threshold of each type of construction equipment; collecting feature data values ​​of each type of equipment at the construction site in real time, and using the fault threshold to judge the fault of each type of equipment;

[0073] The specific steps for judging each device fault using the fault threshold are as follows:

[0074] S201, collect the equipment data of the construction equipment failure in the history {B1, B2, B3, ..., B h},

[0075] B1, B2, B3, ..., B h Indicates the 1st, 2nd, 3rd, ..., hth types of equipment data collected in the history when the construction equipment fails; extracts the data values ​​of the h types of equipment data before and after the construction equipment fails in the history, and calculates the change of each type of equipment data when the construction equipment fails, the formula is: Bc=Bb-Ba, where Bc represents the change of each type of equipment data when the construction equipment fails, Bb represents the data value of each type of equipment data before the construction equipment fails, and Ba represents the data value of each type of equipment data after the construction equipment fails; takes the absolute value of the calculated difference of each type of equipment data, and selects the equipment data with the largest absolute value of the difference as the characteristic data of each type of construction equipment failure;

[0076] S202, collecting the characteristic data of each construction equipment failure in history

[0077] {Te1, Te2, Te3, ..., Te j}, Te1, Te2, Te3,..., Te jIt represents the characteristic data when each construction equipment fails for the 1st, 2nd, 3rd, …, jth time in the collected history, where j is a positive integer; the failure threshold of each construction equipment is calculated using the j collected characteristic data, and the formula is:

[0078]

[0079] In the formula, In represents the failure threshold of each construction equipment calculated, Tep represents the average value of the j collected characteristic data, Te min represents the minimum value among the j collected characteristic data, Te max represents the maximum value among the j collected characteristic data;

[0080] S203. The characteristic data values of each device at the construction site are collected in real time as {Tes1, Tes2, Tes3, …, Tes M}, and Tes1, Tes2, Tes3, …, Tes M represent the characteristic data values of the 1st, 2nd, 3rd, …, Mth devices at the construction site collected in real time, where M is a positive integer; the failure threshold of each construction equipment calculated is used to judge the characteristic data values collected in real time.

[0081] When the characteristic data of the construction equipment is negative before taking the absolute value, if Tes ≤ In is judged, there is a risk of failure for the construction equipment, and a warning is issued; when Tes > In, the construction equipment is working normally.

[0082] When the characteristic data of the construction equipment is positive before taking the absolute value, if Tes ≥ In is judged, there is a risk of failure for the construction equipment, and a warning is issued; when Tes < In, the construction equipment is working normally.

[0083] According to the equipment data when each construction equipment fails in history, the characteristic data is extracted by searching, and the failure threshold of each construction equipment is calculated using the characteristic data; the failure of the equipment can be judged through the failure threshold. When there is a risk of failure, a warning is given in advance, the probability of the equipment failing is reduced, the cost required for construction is reduced, and the construction efficiency is increased.

[0084] S300. Collect the characteristic data values when each construction equipment fails with different operation data values of construction workers in history, and calculate the correlation function between the characteristic data and the operation data of each equipment using the linear regression algorithm; collect the failure times of each construction equipment when different operation data values of construction workers occur in history, calculate the correlation function between the failure times and the operation data, and comprehensively analyze the two correlation functions to obtain the optimization function of the operation data of each equipment.

[0085] The specific steps of using two correlation functions to conduct comprehensive analysis to obtain the optimization function of each device operation data are as follows:

[0086] S301, collecting characteristic data values ​​of each type of construction equipment when a failure occurs when the construction personnel have different operation data values ​​in history, extracting the different operation data values ​​of the construction personnel in history {C1, C2, C3, ..., C k},

[0087] C1, C2, C3, ..., C k represents the 1st, 2nd, 3rd, ..., operation data values ​​of the construction personnel in the extracted history, k is a positive integer; when collecting different operation data values, the characteristic data value of each construction equipment failure is {Tec1, Tec2, Tec3, ..., Tec k}, Tec1, Tec2, Tec3,..., Tec k The characteristic data value when each construction equipment fails when the 1st, 2nd, 3rd, ..., kth operation data value collected is represented, and k is a positive integer; the characteristic data value is used as an independent variable, and the operation data value is used as a variable to draw a curve graph, and the correlation function between the operation data and the characteristic data is calculated by a linear regression algorithm as C=a×Tec+b, a1 represents the coefficient of the correlation function, and b1 represents the constant term of the correlation function;

[0088] S302, collecting the number of failures of each type of construction equipment when different operation data values ​​of construction personnel are used in history is {F1, F2, F3, ..., F k}, F1, F2, F3, ..., F k Indicates the number of equipment failures when each type of construction equipment fails when the construction personnel have the 1st, 2nd, 3rd, ..., kth operation data values ​​in the collected history; the correlation function between the number of failures and different operation data is calculated using the linear regression algorithm as C=a2×F+b2, where a2 represents the coefficient of the independent variable and b2 represents the constant term; the optimization function of each type of equipment operation data is obtained by comprehensive analysis using the two correlation functions, and the formula is: a=a1+a2, In the formula, a represents the coefficient of the optimization function, and b represents the constant term of the optimization function; the optimization function of each construction equipment is finally obtained as C=a×(Tec+F)+b.

[0089] S400, collecting operation data values ​​when construction workers make mistakes in operating each type of construction equipment in history, and using the operation data values ​​collected in history to calculate the operation threshold of each type of construction equipment operated by construction workers; setting a visual sensor in the helmet of each construction worker at the construction site, using the visual sensor in the helmet to collect the operation data of each construction worker in real time, and transmitting the operation data to the central processor for judgment;

[0090] The specific steps for transmitting the operation data to the central processor for judgment are as follows:

[0091] S401. Collect the operation data values when construction workers make mistakes in operating each construction equipment in history as {Cu1, Cu2, Cu3,..., Cu r}, where Cu1, Cu2, Cu3,..., Cu r represent the operation data values of the 1st, 2nd, 3rd,..., rth mistakes of construction workers in operating each construction equipment in the collected history, and r is a positive integer; calculate the operation threshold of each construction equipment by using the operation data values of construction workers making mistakes in operating equipment in the collected history. The formula is: Cy = Cup - Cust, where Cy represents the calculated operation threshold of each construction equipment, Cup represents the average value of the r operation data collected, and Cust represents the standard deviation of the r operation data collected;

[0092] S402. Set visual sensors in the safety helmets of each construction worker at the construction site. Use the visual sensors in the safety helmets to collect the operation data of each construction worker in real time as {Cs1, Cs2, Cs3,..., Cs e}, where Cs1, Cs2, Cs3,..., Cs e represent the operation data of the 1st, 2nd, 3rd,..., e construction workers collected in real time by using the visual sensors, and use the visual sensors to judge the types of equipment operated by each construction worker in real time. Use the operation threshold of the corresponding equipment to judge the real-time operation data of the construction workers. When Cs ≥ Cy, it is judged that there is a risk of mistakes in the operation of the construction workers; when Cs < Cy, it is judged that there is no risk in the operation of the construction workers.

[0093] By calculating the operation threshold of each equipment through the operation data of construction workers making mistakes in operating equipment in history, the operation data of construction workers can be judged by using the operation threshold, standardizing the operation methods of construction workers, avoiding equipment failures or construction hazards caused by abnormal operations, greatly reducing the construction cost, and enhancing the safety of the construction site;

[0094] S500. After the central processor makes a real-time judgment on the operation data of construction workers, extract the real-time characteristic data of each equipment described in S300, and extract the judged failure times of each equipment; input the real-time characteristic data and failure times of each equipment into the optimization function to optimize the operation threshold of construction workers.

[0095] The specific steps for inputting the real-time characteristic data and failure times of each equipment into the optimization function to optimize the operation threshold of construction workers are as follows:

[0096] S501. When the central processor makes real-time judgments on the operation data of construction workers, extract the real-time characteristic data of each type of equipment described in S300, and extract the number of times each type of equipment has been judged to have failed. Input the extracted real-time characteristic data and number of failures of each type of equipment into the optimization function. The formula is:

[0097] Cop = a×(Tes + Fs)+b ；

[0098] In the formula, Cop represents the operation threshold of each construction equipment after optimization, Tes represents the real-time characteristic data of each type of equipment extracted, and Fs represents the number of times each type of equipment has failed. Use the optimized operation threshold to make a secondary judgment on the operation data of each construction worker. When Cs≥Cop, it is judged that the construction worker has made an operation error and a warning is issued; when Cs < Cop, it is judged that the construction worker's operation is normal;

[0099] S502. Use the optimized operation threshold to optimize and update the safety operation data of each type of equipment in the normal operation database.

[0100] Optimize the operation threshold using the optimization function and make a secondary judgment using the optimized operation threshold, avoiding the situation where when the equipment fails or there are risks, the operation is still carried out according to the operation data of normal equipment, accelerating the failure of the equipment or causing accidental danger; making the operation of the equipment more intelligent, intelligently adjusting the operation data according to the equipment status, and making the construction safer and more reliable.

[0101] A construction safety monitoring and management system based on a smart construction site. The construction safety monitoring and management system includes a data collection module, a database construction module, an equipment failure judgment module, a function calculation module, and a personnel operation judgment module;

[0102] The data collection module is used to collect the equipment data of construction equipment and the operation data of construction workers at the construction site in history;

[0103] The database construction module is used to analyze the safety operation guidelines of each type of equipment, extract the safety operation data of each type of equipment, and construct a normal operation database;

[0104] The equipment failure judgment module is used to use the equipment data when the equipment fails in the collected history, extract the characteristic data to calculate the failure threshold, and use the failure threshold to give a failure warning for each type of equipment;

[0105] The function calculation module is used to calculate two correlation functions respectively according to the operation data, characteristic data and number of failures in history, and combine the two correlation functions to construct an optimization function;

[0106] The personnel operation judgment module is used to calculate the operation threshold according to the historical data, use the operation threshold to judge the operation data of the real-time construction personnel, and use the optimization function to optimize the operation threshold and then perform a secondary judgment.

[0107] The equipment fault judgment module includes a feature extraction unit, a fault threshold calculation unit and a real-time judgment unit;

[0108] The feature extraction unit is used to analyze the device data collected in the history when the failure occurs, and extract the feature data when each device fails;

[0109] The fault threshold calculation unit is used to calculate the fault threshold of each device according to the historical characteristic data of each device;

[0110] The real-time judgment unit is used to make a real-time judgment on whether each device has a failure risk by using a failure threshold.

[0111] The function calculation module includes a characteristic data correlation function unit, a fault number correlation function unit and an optimization function unit;

[0112] The characteristic data correlation function unit is used to analyze the characteristic data when the equipment fails under different operation data in history, and calculate the correlation function using a linear regression algorithm;

[0113] The failure number correlation function unit is used to analyze the number of failures of the equipment under different operation data in history and calculate the correlation function using a linear regression algorithm;

[0114] The optimization function unit is used to perform a comprehensive analysis on the two correlation functions and combine them to generate an optimization function for each device.

[0115] The personnel operation judgment module includes an operation threshold calculation unit, an operation judgment unit and an optimization unit;

[0116] The operation threshold calculation unit is used to calculate the operation threshold according to the operation data of the equipment abnormality when the construction personnel operate the equipment in the past;

[0117] The operation judgment unit is used to collect operation data of construction personnel when operating equipment in real time, and judge the real-time operation data using the operation threshold;

[0118] The optimization unit is used to input the real-time characteristic data and the number of failures into the optimization function to calculate the optimized operation threshold, use the optimized operation threshold to make a secondary operation judgment, and optimize and update the normal operation database.

[0119] Embodiment 1: Analyze a construction site at a certain location, generate a normal operation database for each device, and enable construction personnel to operate the device according to the normal operation database to complete the construction;

[0120] Taking the mixer at the construction site as an example, the characteristic data of the mixer is extracted as the vibration frequency, and the operation data of the construction workers is the mixing speed;

[0121] The calculated fault threshold of the mixer is 1KHz, and the operating threshold is 20r / min;

[0122] The optimization function is calculated based on the characteristic data, operation data and number of failures in history as C = 1.9 × (Tec + F) + 4;

[0123] The real-time collected operation data of the construction personnel is 18r / min, and it is judged that the construction personnel's operation is normal; the real-time vibration frequency is extracted as 1.1KHz and the number of faults is 3; the operation threshold is 11.79 after optimization using the optimization function; the secondary judgment shows that the construction personnel's operation is abnormal and there is a risk of error.

[0124] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A construction safety monitoring and management method based on a smart construction site, characterized in that: The method includes the following steps: S100. Collect the safety operation guidelines for construction personnel for each construction equipment at the construction site, extract the safety operation data from the safety operation guidelines, sort out the collected safety operation specification data, and construct a normal operation database for construction personnel after classification processing; S200. Collect the equipment data when the construction equipment fails in history, extract the equipment characteristics, and calculate the failure threshold for each construction equipment using the equipment characteristics; Real-time collect the characteristic data values of each equipment at the construction site, and use the failure threshold to judge the failure of each equipment; S300. Collect the characteristic data values when each construction equipment fails with different operation data values of construction personnel in history, and calculate the correlation function between the characteristic data and operation data of each equipment using the linear regression algorithm; Collect the number of failures of each construction equipment when the construction personnel have different operation data values in history, calculate the correlation function between the number of failures and operation data, and use the two correlation functions for comprehensive analysis to obtain the optimization function of the operation data of each equipment; S400. Collect the operation data values when the construction personnel make mistakes in operating each construction equipment in history, and calculate the operation threshold for the construction personnel to operate each construction equipment using the operation data values collected in history; Set a visual sensor in the safety helmet of each construction worker at the construction site, use the visual sensor in the safety helmet to collect the operation data of each construction worker in real time, and transmit the operation data to the central processor for judgment; S500. After the central processor makes a real-time judgment on the operation data of the construction personnel, extract the real-time characteristic data of each equipment described in S300, and extract the number of failures of each equipment after judgment; Input the real-time characteristic data and the number of failures of each equipment into the optimization function to optimize the operation threshold of the construction personnel.

2. A construction safety monitoring and management method based on a smart construction site according to claim 1, characterized in that: The specific steps for constructing the normal operation database for construction personnel after classification processing in S100 are as follows: S101. Collect the safety operation guides for each construction equipment and construction personnel at the construction site, calculate the contribution of each operation step in the safety operation guide of each construction equipment, extract the amount of information of each step in the safety operation guide and the number of operation errors of personnel at each step in history, and the formula is: G = X P , where G is the contribution of each step in the safety operation guide, X is the amount of information in each step, and P is the number of human operation errors in each step in history; the contribution of each step in the safety operation guide is calculated and judged, and the safety operation data S of each construction equipment with the largest contribution is extracted. Suppose the safety operation data extracted from the safety operation guide of each equipment is {S1, S2, S3, ..., S n }, S1, S2, S3, ..., S n represents the safety operation data collected for the 1st, 2nd, 3rd, ..., nth construction equipment construction personnel at the construction site, where n is a positive integer; S102. After extracting the safety operation data of each equipment at the construction site, classify the extracted safety operation data according to the types of construction equipment at the construction site. Assume that there are M types of construction equipment at the construction site, and correspondingly obtain M types of safety operation data, and construct a normal operation database using the M types of safety operation data.

3. The construction safety monitoring and management method based on a smart construction site according to claim 1 is characterized in that: The specific steps for judging the failure of each equipment using the failure threshold in S200 are as follows: S201, collect the equipment data of the construction equipment failure in the history {B1, B2, B3, ..., B h }, B1, B2, B3, ..., B h Indicates the 1st, 2nd, 3rd, ..., hth types of equipment data collected in the history when the construction equipment fails; extracts the data values ​​of the h types of equipment data before and after the construction equipment fails in the history, and calculates the change of each type of equipment data when the construction equipment fails, the formula is: Bc=Bb-Ba, where Bc represents the change of each type of equipment data when the construction equipment fails, Bb represents the data value of each type of equipment data before the construction equipment fails, and Ba represents the data value of each type of equipment data after the construction equipment fails; takes the absolute value of the calculated difference of each type of equipment data, and selects the equipment data with the largest absolute value of the difference as the characteristic data of each type of construction equipment failure; S202, collect the characteristic data of each construction equipment when it fails in the history {Te1, Te2, Te3, ..., Te j }, Te1, Te2, Te3,..., Te j It represents the characteristic data of the 1st, 2nd, 3rd, ..., jth failure of each construction equipment in the history, where j is a positive integer. The fault threshold of each construction equipment is calculated using the j collected characteristic data, and the formula is: In the formula, In represents the calculated fault threshold of each construction equipment, Tep represents the average value of the j characteristic data collected, and Te min Represents the minimum value of the j feature data collected, Te max Represents the maximum value among the j feature data collected; S203, collect the characteristic data value of each equipment on the construction site in real time {Tes1, Tes2, Tes3, ..., Tes M }, Tes1, Tes2, Tes3,..., Tes M Indicates the characteristic data values ​​of the first, second, third, ..., M types of equipment at the construction site collected in real time, where M is a positive integer; the characteristic data values ​​collected in real time are judged using the calculated fault threshold of each type of construction equipment; When the characteristic data of the construction equipment is negative before taking the absolute value, if Tes ≤ In, it is judged that the construction equipment has a failure risk and an early warning is issued; if Tes > In, the construction equipment is working normally; When the characteristic data of the construction equipment is positive before taking the absolute value, if Tes ≥ In, it is judged that the construction equipment has a failure risk and an early warning is issued; if Tes < In, the construction equipment is working normally.

4. The construction safety monitoring and management method based on a smart construction site according to claim 1 is characterized in that: The specific steps for obtaining the optimization function of the operation data of each equipment through comprehensive analysis using the two correlation functions in S300 are as follows: S301, collecting characteristic data values ​​of each type of construction equipment when a failure occurs when the construction personnel have different operation data values ​​in history, extracting the characteristic data values ​​of the construction personnel in history {C1, C2, C3, ..., C k }, C1, C2, C3, ..., C k represents the 1st, 2nd, 3rd, ..., operation data values ​​of the construction personnel in the extracted history, k is a positive integer; when collecting different operation data values, the characteristic data value of each construction equipment failure is {Tec1, Tec2, Tec3, ..., Tec k }, Tec1, Tec2, Tec3,..., Tec k The characteristic data value when each construction equipment fails when the 1st, 2nd, 3rd, ..., kth operation data value collected is represented, and k is a positive integer; the characteristic data value is used as an independent variable, and the operation data value is used as a variable to draw a curve graph, and the correlation function between the operation data and the characteristic data is calculated by a linear regression algorithm as C=a×Tec+b, a1 represents the coefficient of the correlation function, and b1 represents the constant term of the correlation function; S302, collecting the number of failures of each type of construction equipment when different operation data values ​​of construction personnel are used in history is {F1, F2, F3, ..., F k }, F1, F2, F3, ..., F k Indicates the number of equipment failures when each type of construction equipment fails when the construction personnel have the 1st, 2nd, 3rd, ..., kth operation data values ​​in the collected history; the correlation function between the number of failures and different operation data is calculated using the linear regression algorithm as C=a2×F+b2, where a2 represents the coefficient of the independent variable and b2 represents the constant term; the optimization function of each type of equipment operation data is obtained by comprehensive analysis using the two correlation functions, and the formula is: a=a1+a2, In the formula, a represents the coefficient of the optimization function, and b represents the constant term of the optimization function; the optimization function of each construction equipment is finally obtained as C=a×(Tec+F)+b.

5. The construction safety monitoring and management method based on a smart construction site according to claim 1 is characterized in that: The specific steps for transmitting the operation data to the central processor for judgment in S400 are as follows: S401, collect the operation data values ​​{Cu1, Cu2, Cu3, ..., Cu r }, Cu1, Cu2, Cu3,..., Cu r represents the operation data values ​​of the 1st, 2nd, 3rd, ..., rth errors of the construction personnel in operating each type of construction equipment in the collected history, where r is a positive integer; the operation threshold of each type of construction equipment is calculated using the operation data values ​​of the errors of the construction personnel in operating the equipment in the collected history, and the formula is: Cy=Cup-Cust, where Cy represents the calculated operation threshold of each type of construction equipment, Cup represents the average value of the r collected operation data, and Cust represents the standard deviation of the r collected operation data; S402. Set up visual sensors in the safety helmets of each construction worker at the construction site, and use the visual sensors in the safety helmets to collect the operation data of each construction worker in real time as {Cs1, Cs2, Cs3, ..., Cs e}, where Cs1, Cs2, Cs3, ..., Cs e represent the operation data of the 1st, 2nd, 3rd, ..., e-th construction workers collected in real time by the visual sensors, and use the visual sensors to judge the types of equipment that each construction worker is operating in real time. Use the operation thresholds of the corresponding equipment to judge the real-time operation data of the construction workers. When Cs≥Cy, it is judged that there is a risk of operation error for the construction worker; when Cs<Cy, it is judged that there is no risk for the construction worker's operation.

6. A construction safety monitoring and management method based on a smart construction site according to claim 4, characterized in that: The specific steps for inputting the real-time characteristic data and the number of failures of each device into the optimization function in the S500 to optimize the operation threshold of the construction personnel are as follows: S501. When the central processor makes a real-time judgment on the operation data of the construction personnel, extract the real-time characteristic data of each device described in S300, and extract the number of failures of each device after judgment. Input the extracted real-time characteristic data and the number of failures of each device into the optimization function. The formula is: Cop = a×(Tes + Fs) + b; In the formula, Cop represents the operation threshold of each construction device after optimization, Tes represents the real-time characteristic data of each device extracted, and Fs represents the number of failures of each device extracted. Use the optimized operation threshold to make a secondary judgment on the operation data of each construction personnel. When Cs≥Cop, it is judged that the construction personnel operate incorrectly and a warning is issued; when Cs<Cop, it is judged that the construction personnel operate normally; S502. Use the optimized operation threshold to optimize and update the safety operation data of each device in the normal operation database.

7. A construction safety monitoring and management system based on a smart construction site, characterized by: The construction safety monitoring and management system includes a data collection module, a database construction module, a device failure judgment module, a function calculation module, and a personnel operation judgment module; The data collection module is used to collect the device data of construction equipment and the operation data of construction personnel at the construction site in history; The database construction module is used to analyze the safety operation guidelines of each device, extract the safety operation data of each device, and construct a normal operation database; The device failure judgment module is used to use the device data when the device fails in the collected history, extract the characteristic data to calculate the failure threshold, and use the failure threshold to give a failure warning to each device; The function calculation module is used to calculate two correlation functions respectively according to the operation data, characteristic data and the number of failures in history, and combine the two correlation functions to construct an optimization function; The personnel operation judgment module is used to calculate the operation threshold according to the historical data, use the operation threshold to judge the operation data of the real-time construction personnel, and make a secondary judgment after optimizing the operation threshold using the optimization function.

8. The construction safety monitoring and management system based on a smart construction site according to claim 7 is characterized by: The device failure judgment module includes a feature extraction unit, a failure threshold calculation unit, and a real-time judgment unit; The feature extraction unit is used to analyze the device data when the device fails in the collected history, and extract the characteristic data when each device fails; The failure threshold calculation unit is used to calculate the failure threshold of each device according to the historical characteristic data of each device; The real-time judgment unit is used to use the failure threshold to make a real-time judgment on whether each device has a failure risk.

9. The construction safety monitoring and management system based on a smart construction site according to claim 7 is characterized by: The function calculation module includes a characteristic data correlation function unit, a failure times correlation function unit, and an optimization function unit; The characteristic data correlation function unit is used to analyze the characteristic data when the device fails under different operation data in history, and calculate the correlation function using the linear regression algorithm; The failure number correlation function unit is used to analyze the number of failures of the equipment under different operation data in history and calculate the correlation function using a linear regression algorithm; The optimization function unit is used to perform a comprehensive analysis on the two correlation functions and combine them to generate an optimization function for each device.

10. The construction safety monitoring and management system based on a smart construction site according to claim 7, characterized in that: The personnel operation judgment module includes an operation threshold calculation unit, an operation judgment unit and an optimization unit; The operation threshold calculation unit is used to calculate the operation threshold according to the operation data of the equipment abnormality when the construction personnel operate the equipment in the past; The operation judgment unit is used to collect operation data of construction personnel when operating equipment in real time, and judge the real-time operation data using the operation threshold; The optimization unit is used to input the real-time characteristic data and the number of failures into the optimization function to calculate the optimized operation threshold, use the optimized operation threshold to make a secondary operation judgment, and optimize and update the normal operation database.

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