Intelligent construction management method based on data analysis

Abstract

The invention provides an intelligent construction management method based on data analysis, and relates to the technical field of information management.The intelligent construction management method comprises the steps that a dynamically-adjusted risk assessment model is formed by constructing a comprehensive risk assessment index and combining multiple factors such as the physiological state, the environment abnormity, the space congestion and the danger occurrence rate; on the basis, the assignment weights of the risk evaluation parameters are calibrated in combination with an intelligent algorithm, so that the early warning information is more accurate and has timeliness; besides, personnel scheduling and operation distribution are optimized, and personnel configuration and operation tasks can be adjusted in real time according to changes of a construction site through real-time analysis of risk indexes, so that the construction efficiency is effectively improved, and potential safety hazards are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of information management, and in particular to an intelligent construction management method based on data analysis. Background Art

[0002] At the construction site of a large high-rise building, the construction area is widely distributed, involving multiple high-risk operation areas (such as steel structure installation area, concrete pouring area, electrical installation area, etc.). The safety status of these areas changes frequently, the environmental conditions are complex, and the personnel are dense, making information sharing and real-time monitoring difficult. In order to solve these problems, in recent years, with the continuous advancement of information technology, especially the development of wearable devices, Internet of Things technology, big data analysis and artificial intelligence, construction management has gradually developed in the direction of intelligence. Some existing intelligent construction management systems, with the help of various sensors and data acquisition technologies, can collect physiological, environmental, location and other data of the construction site in real time, and conduct preliminary risk assessments. Although the application of these systems has improved the efficiency of construction management to a certain extent, there are still problems such as insufficient data integration, limited accuracy of risk assessment, and insufficient intelligent decision-making, which make it difficult to cope with complex risk situations during the construction process.

[0003] In the prior art, the publication number is CN119151458A, and the name is an intelligent construction management system and method based on data analysis. Each extracted feature data is calculated to generate a decision tree, and the decision data of all feature data are merged to construct a classifier. The classifier is used to judge the data of all personnel involved in the construction to obtain the department category of the on-site construction; build an on-site construction process chain; find the position of each construction department in the construction process chain and mark it; calculate the correlation index between adjacent departments in the construction process chain; use the calculated correlation index and construction process chain to build an on-site construction system; use the accident threshold to monitor the construction site of each department in real time and issue an early warning; the superior process department conducts a comprehensive analysis of all early warning departments.

[0004] The main shortcomings of existing technologies are first reflected in the singleness of data collection and analysis dimensions. Many existing systems rely solely on physiological data (such as heart rate, body temperature, etc.) or environmental data (such as temperature, humidity, noise, etc.) to conduct risk assessment, ignoring the mutual influence of the two and their comprehensive effects on the health of construction workers and construction safety. For example, although physiological data can reflect the physical health of construction workers, if it is not combined with environmental data (such as ambient temperature and humidity, air quality, etc.) and factors such as the spatial density of personnel, physiological data alone cannot fully assess the work risks of construction workers. In addition, most existing technologies rely on static risk assessment models and fail to make real-time dynamic adjustments based on changes in the location of construction workers and environmental changes, resulting in a lack of pertinence and timeliness in warning strategies and early warning information. These deficiencies make it impossible for existing systems to fully adapt to the rapidly changing construction environment and health of personnel, making it difficult to achieve effective control of construction risks.

[0005] The above information disclosed in the above Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an intelligent construction management method based on data analysis to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An intelligent construction management method based on data analysis, the specific steps include:

[0009] Step S1: collecting the physiological index data of the construction workers, as well as the positioning data of the construction workers and the environmental abnormality index in real time through the wearable device;

[0010] Uniquely mark multiple risky construction sites in the current construction area and collect safety status data for each risky construction site;

[0011] Step S2: Analyze the physiological index data of the construction workers and calculate the physiological state coefficient and psychological stress index;

[0012] Step S3: Analyze the environmental abnormality index data of the location where the construction workers are located, and calculate the abnormality evaluation coefficient and the space congestion index;

[0013] Step S4: Determine the adjacent risk construction points of the current construction personnel's location based on the positioning data, analyze the safety status data of these risk construction points, and calculate the hazard incidence evaluation coefficient and environmental additional load index of these risk construction points;

[0014] Step S5: Combined with the location data of the construction personnel, the physiological state coefficient, the abnormal evaluation coefficient and the risk incidence evaluation coefficient are comprehensively analyzed to construct a construction risk evaluation vector, and generate a corresponding comprehensive risk evaluation index, which includes the assigned weights of each component in the construction risk evaluation vector.

[0015] Step S6: Analyze the psychological stress index, the space crowding index and the environmental additional load index, and generate a calibration strategy for adjusting the value weight of each parameter in the comprehensive risk assessment index;

[0016] Step S7: Based on the comprehensive risk assessment index adjusted by the calibration strategy, a warning strategy is formulated and comprehensive warning information is sent to optimize personnel scheduling, job allocation and safety protection.

[0017] Compared with the prior art, the beneficial effects of the present invention are: by constructing a comprehensive risk assessment index, a dynamically adjusted risk assessment model is formed by combining multiple factors such as physiological status, environmental abnormalities, spatial crowding and hazard incidence; on this basis, the assignment weights of risk assessment parameters are calibrated in combination with intelligent algorithms, making the early warning information more accurate and timely; in addition, personnel scheduling and job allocation are also optimized. Through real-time analysis of the risk index, personnel allocation and job tasks can be adjusted in real time according to changes at the construction site, thereby effectively improving construction efficiency and reducing safety hazards. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the overall method flow of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Embodiment 1:

[0022] See also Figure 1 , the present invention provides a technical solution:

[0023] An intelligent construction management method based on data analysis, the specific steps include:

[0024] Step S1: collecting the physiological index data of the construction workers, as well as the positioning data of the construction workers and the environmental abnormality index in real time through the wearable device;

[0025] Uniquely mark multiple risky construction sites in the current construction area and collect safety status data for each risky construction site;

[0026] Step S2: Analyze the physiological index data of the construction workers and calculate the physiological state coefficient and psychological stress index;

[0027] Step S3: Analyze the environmental abnormality index data of the location where the construction workers are located, and calculate the abnormality evaluation coefficient and the space congestion index;

[0028] Step S4: Determine the adjacent risk construction points of the current construction personnel's location based on the positioning data, analyze the safety status data of these risk construction points, and calculate the hazard incidence evaluation coefficient and environmental additional load index of these risk construction points;

[0029] Step S5: Combined with the positioning data of the construction personnel, the physiological state coefficient, the abnormality evaluation coefficient and the risk incidence evaluation coefficient are comprehensively analyzed to construct a construction risk evaluation vector, and generate a corresponding comprehensive risk evaluation index, which includes the assigned weights of each component in the construction risk evaluation vector;

[0030] Step S6: Analyze the psychological stress index, the space crowding index and the environmental additional load index, and generate a calibration strategy for adjusting the value weight of each parameter in the comprehensive risk assessment index;

[0031] Step S7: Based on the comprehensive risk assessment index adjusted by the calibration strategy, a warning strategy is formulated and comprehensive warning information is sent to optimize personnel scheduling, job allocation and safety protection.

[0032] Further explanation: physiological index data include heart rate, blood oxygen saturation, body temperature, respiratory rate, instantaneous skin conductance response value used for the following physiological state coefficient calculation; and instantaneous skin conductance response value and heart rate variability used for the following psychological stress index calculation;

[0033] Environmental abnormality indicators include ambient temperature, ambient humidity, air quality index and noise level used for calculation of the following abnormality evaluation coefficients; and population density, personnel flow speed and space capacity used for calculation of the following space crowding index;

[0034] Safety status data include equipment failure rate, equipment abnormal vibration rate, equipment maintenance qualification rate used for calculation of the following hazard occurrence rate evaluation coefficient, as well as construction material stacking density, on-site lighting intensity, and abnormal rate of harmful gas concentration used for calculation of the following environmental additional load index;

[0035] Positioning data is information about the specific geographic location of construction workers at the construction site; it can be obtained as follows:

[0036] GPS coordinates: The geographic coordinates (longitude and latitude) provided by the Global Positioning System (GPS) can accurately locate the specific location of construction workers outdoors.

[0037] Indoor positioning information: In indoor or underground workplaces, use Bluetooth beacons, Wi-Fi positioning, ultra-wideband (UWB) technology or radio frequency identification (RFID) technology to accurately track the location of construction workers.

[0038] Security geo-fences: Pre-set virtual boundaries that send alerts when construction workers enter or leave these areas, ensuring they are in a safe zone.

[0039] Physiological index data includes the following parameters:

[0040] Data required for the calculation of physiological state coefficients:

[0041] Heart rate: reflects the working intensity of the heart and is used to judge the body load and fatigue level;

[0042] Basal metabolic needs: The human body needs to maintain life activities when at rest, such as breathing and maintaining body temperature, which requires the heart to continuously supply blood to the whole body. A heart rate above 60bpm helps to ensure basal metabolic needs.

[0043] For healthy adults, the normal heart rate range at rest is usually 60-100bpm, among which: Lowest value: The lower limit of resting heart rate for healthy people is 60bpm, but for some special people, a heart rate below 60bpm is also considered normal.

[0044] Special groups are athletes and people with high physical fitness: People who exercise regularly, especially endurance athletes (such as running, swimming, etc.), have stronger hearts and larger heartbeats, so their resting heart rate can be as low as 40-60bpm;

[0045] Blood oxygen saturation (SpO2): assesses whether the body is lacking oxygen and reflects health status;

[0046] Normal blood oxygen saturation: 95% to 100%. This range indicates that the blood contains sufficient oxygen to meet the needs of various organs in the human body.

[0047] Slightly low (90%-94%): indicates mild hypoxia, such as high altitude environment, mild respiratory diseases, etc.

[0048] Below 90%: Below 90%: There is severe hypoxia and you need to be alert to respiratory, cardiovascular disease or other health problems.

[0049] Below 80%: This is an acute hypoxia state, which may be life-threatening and requires emergency medical intervention.

[0050] Body Temperature: Abnormal body temperature reflects physical fatigue or health problems (such as heat stroke, hypothermia, etc.) of construction workers;

[0051] Respiratory Rate: Normal respiratory rate and abnormal respiratory changes can reflect the body's stress response;

[0052] Phasic Response / Skin Conductance Response (SCR): reflects the psychological stress of the human body, especially emotional reactions such as anxiety or stress;

[0053] The instantaneous skin conductance response value is expressed by the following formula:

[0054] SCR=G peak -G base

[0055] SCR is the instantaneous skin conductance response value; G peak It is the conductance value (μS) when the skin conductance reaches its peak under stimulation conditions. base It is the basic skin conductance value before stimulation occurs, that is, the baseline value or initial conductance value (μS).

[0056] Emotional stimulation: such as fright, anger, tension, etc., SCR rises rapidly;

[0057] Psychological stress: SCR changes are more obvious in situations with increased stress;

[0058] Concentration: When you are highly focused on a task, your SCR will increase;

[0059] Data required for the calculation of the psychological stress index:

[0060] The psychological stress index reflects the mental and emotional stress of construction workers and is assessed through the following data:

[0061] Phasic Response / Skin Conductance Response (SCR): Assess psychological stress through changes in skin conductivity. Skin conductivity increases when psychological stress increases.

[0062] Heart rate variability (HRV): refers to the change in the time interval between heart beats, which is recorded as RR interval. Lower heart rate variability is usually associated with higher stress levels. The HRV in this embodiment uses SDNN as the evaluation indicator, that is, the standard deviation of all normal RR intervals, reflecting the overall heart rate variability level.

[0063] SDNN is the standard deviation of the RR intervals and is used to assess overall HRV:

[0064]

[0065] RR i″ is the time of the i″th RR interval; is the average of all RR intervals. N″ is the total number of RR intervals.

[0066] Environmental anomaly indicators include the following parameters:

[0067] The data required for calculating the abnormal evaluation coefficient:

[0068] Air Quality Index (AQI): The concentration of pollutants in the air reflects the cleanliness of the air and directly affects the safety of the construction environment;

[0069] The Air Quality Index (AQI) is a comprehensive indicator used to quantitatively describe the impact of the concentration levels of certain major pollutants in the air on public health. The AQI is mainly based on the concentration of key pollutants in the air (such as PM2.5, PM10, SO2, NO2, O3 and CO in this example), combined with the standard limits in different stages, and is divided into corresponding indexes to reflect the cleanliness of the air.

[0070] For each pollutant, its air quality index IAQI h′ The calculation formula is:

[0071]

[0072] Among them, IAQI h′is the air quality index of the h′th pollutant, h′∈{1,2,3,4,5,6}; and {1,2,3,4,5,6} represent PM2.5, PM10, SO2, NO2, O3 and CO respectively;

[0073] C h′ is the actual measured concentration of the h′th type of pollutant.

[0074] C low It is the lower limit of the range in which the pollutant concentration lies (according to national or regional standards).

[0075] C high It is the upper limit of the range in which the pollutant concentration lies (according to national or regional standards).

[0076] I low It is the lower limit of the AQI range corresponding to the standard concentration range.

[0077] I high It is the upper limit of the AQI range corresponding to the standard concentration range.

[0078] The Air Quality Index (AQI) comprehensively reflects the overall condition of air quality at a certain moment, taking the maximum value of all monitored pollutant sub-indices, namely:

[0079] AQI=max(IAQI1,IAQI2,...,IAQI6)

[0080] Explanation: (IAQI1, IAQI2, ..., IAQI6): They are the air quality index of each pollutant.

[0081] Noise Level: A high noise environment will affect the communication, hearing and health of construction workers;

[0082] The noise level is expressed in decibels (dB) using the formula:

[0083]

[0084] Where I is the noise intensity. I0 is the baseline noise intensity; NLZY is the noise level;

[0085] Data required for the calculation of the spatial crowding index:

[0086] Assess the density and spatial distribution of people in the construction area and calculate the spatial crowding index:

[0087] Personnel Density: The number of construction workers per square meter in the construction area. A dense population will increase the risk of accidents.

[0088] Flow Speed ​​of Personnel: The speed at which construction workers move within the area, reflecting whether the space is unobstructed;

[0089] Available Space: The available space size of the construction area. Crowded space is prone to accidents.

[0090] The security status data includes the following parameters:

[0091] Data required for calculation of risk incidence evaluation coefficient:

[0092] Equipment failure rate (Failure Rate of Equipment):

[0093] Collection method: The device management platform records the cumulative usage time of each device and the total number of faults recorded at the current moment, and calculates its fault occurrence rate;

[0094] Significance: reflects the reliability of equipment and helps predict failure risks;

[0095] Abnormal Vibration Rate of Equipment;

[0096] Collection method: The vibration sensor on the equipment is used to calculate the ratio of the number of times the vibration value of the equipment exceeds the normal range to the total number of vibrations during the current monitoring period. The normal value is determined based on the manufacturer's maintenance and use manual of each equipment or the corresponding equipment use standard, which will not be elaborated on here. The current monitoring period is set within the past half hour of the construction equipment's work, and includes the current time.

[0097] Significance: reflects whether the mechanical performance of the equipment is stable and identifies potential dangers;

[0098] Equipment maintenance qualification rate (MaintenanceComplianceRate);

[0099] Data type: Maintenance pass rate of construction equipment corresponding to the current risk construction point;

[0100] Collection method: Statistics are collected through the construction equipment maintenance management record system to calculate the proportion of qualified maintenance times of the corresponding construction equipment in the planned maintenance; for example, if 5 out of 10 maintenances are qualified in the planned maintenance, the qualified rate of equipment maintenance is 0.5, i.e. 5%;

[0101] Significance: It reflects whether the equipment is maintained in time. Equipment that is not maintained as planned is more likely to fail and has higher safety risks.

[0102] Required data for calculation of environmental additional load index:

[0103] Construction material storage density (MaterialStorageDensity);

[0104] Data type: kg / m2 2 );

[0105] Collection method: Calculate the material accumulation per unit area by measuring the total area of ​​the stacking area and the actual weight of the material;

[0106] Significance: Over-dense stacking will affect stability, leading to risks such as collapse and slipping;

[0107] Lighting Intensity of Site;

[0108] Data type: Lux;

[0109] Collection method: Use a photometer to measure key areas of the construction site to obtain lighting intensity distribution data;

[0110] Significance: Inadequate lighting can cause workers to make operational errors or fail to observe dangers in time, increasing risks;

[0111] Abnormal Gas Concentration Rate of hazardous gas at risk construction sites:

[0112] Collection method: Use gas detectors to monitor the concentration of harmful gases (such as CO, H2S, and combustible gases) in the construction area at the current moment, and record any events that exceed the standard. The abnormal rate of harmful gas concentration is evaluated based on the maximum concentration value monitored, and the maximum concentration value is used as the abnormal rate of harmful gas concentration at the risk construction point.

[0113] Significance: It reflects whether there are harmful gases in the construction environment and ensures the health and safety of construction workers.

[0114] Uniquely mark multiple risk construction points in the current construction area, specifically:

[0115] The set of risky construction points in the current construction area is defined as {1, 2, ..., i, ..., n}, where i represents the index of the i-th risky construction point and n is the total number of risky construction points.

[0116] The selection of risky construction points in this embodiment is determined by the location of construction equipment in the current construction area, and the construction equipment is referred to as equipment hereinafter.

[0117] Further explanation: Analyze the physiological index data of construction workers, calculate the physiological state coefficient and psychological stress index, specifically including:

[0118] Analyze the physiological index data of the construction workers in the current monitoring period and calculate the physiological state coefficient used to evaluate the physical state; at the same time, calculate the psychological stress index of the construction workers in the current monitoring period based on these physiological index data;

[0119] The physiological state coefficient of construction workers is defined as PSC, and the calculation formula is as follows:

[0120]

[0121] Among them, w j is the weight coefficient of the jth physiological index; 0<w j <1; and

[0122] w j Based on expert experience and statistical analysis, each w j Reflects the relative importance of the corresponding indicator to the physiological state; where j∈{1,2,3,4,5}, represents heart rate, blood oxygen saturation, body temperature, respiratory rate, and instantaneous skin conductance response value respectively;

[0123] Among them, heart rate, blood oxygen saturation, body temperature, and respiratory rate are the average values ​​of the monitoring values ​​in the current monitoring time period;

[0124] X j is the actual measured value of the jth physiological index, X j ∈{HR, SpO2, BT, RH, SCR}; {HR, SpO2, BT, RH, SCR} represent heart rate, blood oxygen saturation, body temperature, respiratory rate, instantaneous skin conductance response value, and determine the normal range of each physiological index; C j is the reference value of the jth physiological index, which is used for standardization adjustment; and sets C j It is the median of the normal range corresponding to the corresponding physiological index;

[0125] Based on the current construction environment, the normal ranges of heart rate, blood oxygen saturation, body temperature, respiratory rate, and transient skin conductance response values ​​are set as follows:

[0126]

[0127] Among them, HR, SpO2, BT, RH, and SCR represent heart rate, blood oxygen saturation, body temperature, respiratory rate, and instantaneous skin conductance response value respectively; and the unit of RH is times / minute; the unit of BT is ℃; the normal ranges of HR, SpO2, BT, RH, and SCR are adjusted according to the actual application of construction personnel, and will not be elaborated;

[0128] By C jNormalize physiological indicators of different dimensions to eliminate the dimension effect;

[0129] Set the value range of the physiological state coefficient to 0<PSC<1;

[0130] When PSC is closer to 1, it means that the physiological state of the construction workers is better, and when PSC is closer to 0, it means that the physiological state of the construction workers is worse;

[0131] It is a nonlinear adjustment function used to adjust the nonlinear effects of various physiological indicators; it compresses excessive values ​​through logarithmic functions and enhances the sensitivity to low value changes;

[0132] Weight coefficient w j Add: When the weight w of the jth physiological indicator j When it increases, the indicator The contribution of w1 increases, which in turn affects the value of PSC; for example, if w1 increases, the impact of heart rate on the overall physiological state increases;

[0133] Physiological indicatorsX j Add: If the jth physiological index X j Add, nonlinear adjustment function Increase, which in turn leads to an increase in PSC;

[0134] When all physiological indicators are X j =C j hour:

[0135]

[0136] When all physiological indicators were at baseline values, PSC≈0.731, indicating a moderate physiological state.

[0137] The psychological stress index of construction workers is defined as PSI, and the calculation formula is as follows:

[0138]

[0139] Among them, λ1 and λ2 are the weight coefficients of each parameter; they are used to adjust the influence of SCR and SDNN on PSI respectively; λ1,λ2>0;

[0140] λ1 and λ2 are determined based on statistical regression analysis and expert evaluation, or by using the entropy weight method, which will not be described in detail;

[0141] SDNN is an indicator of heart rate variability; it is expressed using the standard deviation of all normal RR intervals;

[0142] C SCR and C SDNN are the benchmark values ​​of SCR and SDNN, respectively, used for standardization adjustment;

[0143] C SCR and C SDNN According to the actual monitoring data setting; this embodiment C SCR =5, C SDNN =50ms;

[0144] Normalize indicators of different dimensions through benchmark values ​​to eliminate the impact of dimensions;

[0145] The f(SCR) function tends to 1 as the SCR increases, reflecting the decreasing growth rate of increasing pressure;

[0146] f(SDNN) function decreases when SDNN increases, reflecting the decrease of psychological stress;

[0147] The weight coefficient λ1 increases:

[0148] The enhanced influence of SCR in PSI reflects the higher dependence of psychological stress on SCR changes;

[0149] The weight coefficient λ2 increases:

[0150] The increase in the influence of SDNN in PSI reflects a stronger inverse relationship between psychological stress and SDNN;

[0151] By adjusting the values ​​of λ1 and λ2, PSI is limited to the interval (0,1);

[0152] In this embodiment, λ1=ln(9), λ2=ln(9);

[0153] So that when Through function design, ensure that 0<PSI<1; the PSI value range is the interval (0,1);

[0154] When PSI approaches 1, it means that the psychological stress of the construction workers is greater, the more nervous the construction workers are, and the more decompression measures need to be taken;

[0155] The closer the PSI is to 0, the lower the psychological stress of the construction workers is and the better their emotional state is.

[0156] Further explanation: Analyze the abnormal environment index data of the construction workers' location, calculate the abnormal evaluation coefficient and space crowding index, specifically including:

[0157] The abnormal environment index of the construction workers' location in the current monitoring period is analyzed, and the abnormal evaluation coefficient used to evaluate the abnormal degree of the current construction location area is calculated; at the same time, based on these abnormal environment indexes, the spatial crowding index of the construction workers in the current monitoring period is calculated;

[0158] The abnormal evaluation coefficient of the construction worker's position is defined as AEC, and the calculation formula is as follows:

[0159]

[0160] Among them, AEC is the anomaly evaluation coefficient, which is used to quantify the degree of environmental anomaly in the current construction location area;

[0161] Based on the logistic transformation, the value range of AEC is set in the interval (0, 1);

[0162] The closer the AEC value is to 1, the higher the degree of environmental abnormality is, and the closer the AEC value is to 0, the lower the degree of environmental abnormality is.

[0163] α k is the weight coefficient of the kth environmental anomaly index, k∈{1, 2, 3, 4}, and k∈{1, 2, 3, 4} represent the ambient temperature, ambient humidity, air quality index and noise level respectively; 0<α k <1, and

[0164] Based on expert experience and statistical analysis, each α k Reflects the relative importance of the corresponding indicator to the degree of environmental abnormality;

[0165] X k is the actual measured value of the kth environmental anomaly indicator; X k ∈{TEMP, HUM, AQI, NLZY}; TEMP, HUM, AQI, NLZY represent ambient temperature, ambient humidity, air quality index and noise level respectively;

[0166] 1. Ambient temperature (Temperature, TEMP);

[0167] 2. Humidity (HUM);

[0168] 3. Air Quality Index (AQI);

[0169] 4. Noise level (NoiseLevel, NLZY);

[0170] In this embodiment, the normal operating ranges of TEMP, HUM, AQI, and NLZY are respectively:

[0171]

[0172] The normal working ranges of the above TEMP, HUM, AQI, and NLZY are adjusted according to the actual test data and will not be elaborated on here;

[0173] M k is the average value or benchmark value of the kth environmental anomaly index; M k It is set according to the normal working range of each indicator, and M k It can also be the industry standard value of each indicator, which will not be described in detail;

[0174] D k is the standard deviation of the kth environmental anomaly index; D k It is set according to the actual monitoring data and used for standardization adjustment to ensure the comparability of the contribution of each indicator to the abnormal evaluation;

[0175] β k is the exponential adjustment factor of the kth environmental anomaly index, and 1≤β k ≤3;

[0176] β k Based on the sensitivity of each indicator to the degree of abnormality, β k The higher the value, the k The larger the nonlinear impact of the enhancement index;

[0177] Is X k Standardization is used to eliminate the impact of dimension. Through standardization, the data of different indicators are made comparable and reflect the degree of deviation from the benchmark value.

[0178] It is to adjust the index of the standardized indicators; enhance the nonlinear contribution of the indicators to the degree of abnormality, and reflect the different degrees of influence of different indicators on abnormal evaluation;

[0179] The logical reasoning for the parameter changes included in the AEC calculation formula is as follows:

[0180] α k Add: When the weight α of the kth environmental anomaly indicator k When it increases, the indicator The influence of increases, which in turn affects the value of AEC;

[0181] Environmental abnormality index X k Increase:

[0182] When X k Increase and above M k , Increase, Further amplification leads to Increase, and then AEC increases, reflecting the increase in the degree of environmental abnormality;

[0183] On the contrary, when X k Reduced and below M k , reduce, The corresponding decrease leads to Decrease, AEC decreases, reflecting the decrease in the degree of environmental abnormality;

[0184] The spatial crowding index of the construction workers' location is defined as SCI, and the calculation formula is as follows:

[0185]

[0186] The value range of SCI is set to (0, 1); m (PD, FS, AS) and v m (PD, FS, AS) are sub-functions;

[0187] The closer the SCI is to 1, the higher the degree of spatial crowding where the construction workers are located; this means that the greater the density of people in the construction area, the slower the flow speed, or the less space capacity, the higher the probability of accident risk, and more evacuation or optimization of spatial layout measures are needed;

[0188] The closer the SCI is to 0, the lower the degree of spatial crowding where the construction workers are located; the more evenly distributed and smoothly the personnel flow in the construction area, and the more sufficient the space capacity, the lower the change in construction efficiency and accident risk reduction;

[0189] The personnel density, personnel flow speed, and space capacity are denoted as PD, FS, and AS, respectively;

[0190] 1. Personnel Density (PD);

[0191] 2. Flow Speed ​​of Personnel (FS);

[0192] 3. Available Space (AS);

[0193] The calculation formula for population density is defined as:

[0194]

[0195] Where PD is the population density (unit: person / square meter); N pr is the total number of construction workers in the construction area (unit: person); SZAS is the total area of ​​the construction area (unit: square meters).

[0196] The formula for calculating the personnel flow rate is defined as:

[0197]

[0198] Where FS is the flow speed of personnel (unit: m / s). DZY is the total distance moved by construction personnel in a period of time (unit: m). TZt is the total time in the corresponding period of time (unit: seconds);

[0199] The calculation formula for defining space capacity is:

[0200] AS=A total -A obs

[0201] AS is the space capacity (unit: square meters); A total is the total area of ​​the construction area (unit: square meters); A obs is the area occupied or covered by obstacles in the construction area (unit: square meters); and the occupied area A obs Includes area occupied by equipment, storage areas, structural components, etc.

[0202] γ m and δ m are the weight coefficients of each sub-function in the numerator and denominator respectively; γ m and δ m They are used to adjust the influence of each sub-function in the numerator and denominator respectively; γ m and δ m The value range of is (0, 1), and

[0203] γ m and δ m Based on expert experience and statistical analysis, each γ m and δ m Reflects the relative importance of the corresponding sub-function to SCI;

[0204] PD is the density of the construction workers at their location; the unit is the number of construction workers per square meter; 0≤PD≤10 people / ㎡;

[0205] The higher the PD, the higher the density of people in the space where the construction workers are located, and the greater the potential risk of accidents;

[0206] FS is the flow speed of personnel; the unit is the moving distance per minute (m / min); 0≤FS≤5m / min;

[0207] The higher the FS, the faster the flow of people and the higher the degree of spatial unobstructedness;

[0208] AS is the space capacity; the unit is square meters; 10≤AS≤1000㎡;

[0209] The larger the AS is, the larger the space capacity is and the more space there is for people to move around;

[0210] CPD , C FS , C AS They represent the benchmark values ​​of personnel density, personnel flow speed and space capacity, respectively, and are used for standardization adjustment;

[0211] C PD , C FS , C AS Set according to actual monitoring data; normalize indicators of different dimensions through benchmark values ​​to eliminate dimension effects; θ m is the exponential adjustment factor of the space capacity, 1≤θ m ≤3;

[0212] h m (PD, FS, AS) is a nonlinear combination function that comprehensively considers population density, population flow speed and space capacity; through the combination of logarithmic function, exponential function and power function, it reflects the comprehensive impact of increased population density, reduced flow speed and reduced space capacity on the degree of spatial crowding;

[0213] v m (PD, FS, AS) is a nonlinear function that combines trigonometric functions and inverse tangent functions, which is used to adjust the influence of the denominator. Through trigonometric functions and inverse tangent functions, it smoothly reflects the influence of population density, flow speed and space capacity on the degree of spatial crowding, avoiding the mutual cancellation of parameters.

[0214] Weight coefficient γ m and δ m Increase: When the weight coefficient γ m or δ m When it increases, the contribution of the corresponding sub-function to SCI increases, which in turn affects the value of SCI.

[0215] For the changes of spatial indicators PD, FS, AS:

[0216] Increase in population density PD: Increase, h m (PD, FS, AS) increases, the numerator increases, and the denominator Also increase;

[0217] Increase of personnel flow rate FS: Reduce, h m (PD, FS, AS) decreases, the numerator decreases; the denominator Reduce, denominator v m (PD, FS, AS) decreased overall, and the impact on SCI was complex;

[0218] Space capacity AS increases: Increase, h m (PD, FS, AS) increases, the numerator increases; the denominator Increase, the denominator increases.

[0219] In order to verify the validity and superiority of the anomaly evaluation coefficient (AEC) and spatial crowding index (SCI) formula designed in this application document, the following test preparation and implementation process was carried out; the test selected a large construction site as the experimental site, which was divided into multiple construction areas, and each area monitored its environmental anomaly index and spatial crowding index in different time periods; the test time span was one week, and the data collection frequency was once an hour to reflect the status of construction workers in the current location in a small time period;

[0220] The test preparation includes:

[0221] 1. Equipment installation and calibration: Install environmental monitoring equipment and personnel flow monitoring equipment in each construction area; environmental monitoring equipment includes temperature sensors, humidity sensors, air quality detectors and noise monitors to ensure that each sensor accurately measures relevant indicators within the normal working range; personnel flow monitoring equipment includes infrared sensors and video analysis systems to monitor personnel density and flow speed in real time;

[0222] 2. Benchmark value setting: According to relevant national standards and construction industry specifications, set the benchmark values ​​M of various environmental abnormality indicators and space indicators. k and C PD , C FS , C AS ;

[0223] The reference values ​​of ambient temperature are set at 25°C, humidity at 50%, AQI at 100, and noise level at 70dB; the reference values ​​of population density are 5 people / ㎡, the reference values ​​of population flow speed are 2.5 meters / minute, and the reference value of space capacity is 500㎡;

[0224] 3. Determination of parameter weights: Determine the weight coefficient α of each parameter through expert evaluation and historical data analysis k and γ m and δ m ; The weight coefficients of ambient temperature and AQI are relatively high, at 0.3 and 0.25 respectively, while the weight coefficients of humidity and noise level are 0.2 and 0.25; in the spatial crowding index, the weight coefficients of population density, flow speed and space capacity are set at 0.4, 0.35 and 0.25 respectively;

[0225] The detailed implementation process includes:

[0226] Data collection: During the test, the ambient temperature TEMP, ambient humidity HUM, air quality index AQI, noise level NLZY, personnel density PD, personnel flow speed FS and space capacity AS of each construction area are recorded every hour; all data are collected and stored in real time through an automated system to ensure data accuracy and timeliness;

[0227] For each monitoring period, the standardized value of each environmental anomaly index is calculated, and then the index is adjusted, and finally the AEC value is calculated by the logistic function;

[0228] For each monitoring period, the nonlinear combination of personnel density, flow speed and space capacity is calculated, and the SCI value is finally obtained through adjustment of the weight coefficient;

[0229] By comparing the AEC and SCI values ​​of each construction area at different time periods, the impact of environmental anomalies and space congestion on construction safety and efficiency is analyzed; at the same time, the AEC and SCI values ​​are correlated with actual construction accidents or personnel health conditions to verify the effectiveness and predictive ability of the formula;

[0230] Result verification and optimization:

[0231] According to the preliminary test results, the weight coefficient and index adjustment factor were adjusted and the formula parameters were optimized to further improve the evaluation accuracy and sensitivity of AEC and SCI. Finally, a set of optimal parameter combinations was determined so that the formula can more accurately reflect the actual situation of the construction site and has a higher prediction and early warning ability.

[0232] Through the above test preparation and implementation process, the innovation and advantages of the AEC and SCI formulas designed in the present invention in practical applications are verified; the following is a test data record table showing the environmental anomaly indicators and space crowding indicators of different construction areas within a certain monitoring period, as well as the calculated AEC and SCI values;

[0233] Table 1 Validation study of anomaly evaluation coefficient (AEC) and spatial crowding index (SCI):

[0234]

[0235]

[0236] Data description:

[0237] 1. Environmental abnormality indicators:

[0238] The ambient temperature, humidity, AQI and noise level data of each area reflect the environmental conditions of different construction areas during the monitoring period;

[0239] 2. Spatial crowding index:

[0240] The personnel density, flow speed and space capacity data of each area reflect the space usage of the construction site;

[0241] 3. Abnormal Evaluation Coefficient (AEC):

[0242] The values ​​calculated by the formula range from 0.48 to 0.92, reflecting the different degrees of environmental anomaly in each region; Region 4 has the highest AEC, indicating that its environmental anomaly is the most serious and requires special attention and corresponding improvement measures;

[0243] 4. Spatial Crowding Index (SCI):

[0244] The calculated value ranges from 0.60 to 0.95, reflecting the differences in the degree of spatial crowding in each area. Area 4 has the highest SCI, indicating that its spatial crowding is extremely high and there are great safety hazards. It is recommended to optimize the distribution of personnel and space planning.

[0245] Analysis of test results:

[0246] It can be seen from the test data that by applying the AEC and SCI formulas designed by the present invention, the degree of environmental anomaly and spatial congestion at the construction site can be effectively quantified; compared with the traditional simple weighted method, the formula of the present invention takes into account the complex relationship between multiple nonlinear factors and parameters, and the evaluation results are more accurate and sensitive; for example, area four shows higher abnormal values ​​in ambient temperature, humidity, AQI and noise level, resulting in its AEC and SCI values ​​being significantly higher than other areas, which can provide timely warnings and take necessary safety measures, significantly improving the safety management level of the construction site; in addition, although area five has a lower degree of environmental anomaly, it has a larger spatial capacity and a lower SCI value, indicating that the personnel distribution in this area is reasonable and the safety is relatively high.

[0247] Further explanation: Based on the positioning data, the adjacent risk construction points of the current construction personnel are determined, and the safety status data of these risk construction points are analyzed to calculate the hazard occurrence rate evaluation coefficient and environmental additional load index of these risk construction points, including:

[0248] Receive and analyze the safety status data of these risky construction sites in real time to generate a risk occurrence rate evaluation coefficient for evaluating the probability of danger occurring at these risky construction sites;

[0249] Based on the safety status data, the environmental additional load index of these risky construction points in the current monitoring period is calculated;

[0250] Define the coordinates of the i-th risk construction point as P i (x i ,yi ), where: x i is the horizontal coordinate of the ith risk construction point in the construction coordinate system; i is the ordinate of the i-th risk construction point in the construction coordinate system;

[0251] The specific geographical location information of the construction personnel's current location is represented by the positioning data as U p (x p ,y p ), where: x p is the horizontal coordinate of the construction personnel in the construction coordinate system; p It is the vertical coordinate of the construction personnel in the construction coordinate system;

[0252] Definition of the impact scope of risk construction points:

[0253] Assume that the failure impact range of the construction equipment corresponding to the i-th risk construction point is a circular area, and its impact range is from the center point P i (x i ,y i ) and the fault impact radius RY i Indicates, specifically:

[0254]

[0255] Among them, RY i Represents the fault impact radius of the i-th risk construction point; the specific value needs to be determined according to the equipment characteristics and sensor monitoring range, and the unit is meter;

[0256] Equipment characteristics are: set according to the risk type (such as vibration, explosion, toxic gas diffusion, etc.) generated by the construction equipment, or adjusted through historical statistical data and industry standards. In this embodiment, the following examples are given:

[0257] The impact range of large mechanical equipment vibration: 30 to 50 meters;

[0258] The impact range of toxic gas leakage: 10 meters to 20 meters.

[0259] The judgment logic of whether the current location of the construction personnel is affected is as follows:

[0260] Determine the current location U of the construction personnel p (x p ,y p ) is affected by the i-th risk construction point:

[0261]

[0262] If the above conditions are met, it means that the current construction worker is within the influence range of the i-th risk construction point, and the construction point is marked as an “adjacent risk construction point” related to the construction worker;

[0263] In order to more clearly express the logic of "construction personnel location data" and "risk construction point impact range", follow the steps below to achieve it:

[0264] Location collection of construction equipment risk points:

[0265] Determine the specific location of each construction equipment through GPS, RFID or other positioning technology of the construction equipment i (x i ,y i );

[0266] The coordinates of each risk construction point and the radius of influence RY i Store in database;

[0267] Collection of construction workers’ current location:

[0268] Use the positioning terminal worn by the construction workers (such as the positioning chip on the helmet, mobile phone GPS, etc.) to obtain the current location of the construction workers in real time p (x p ,y p );

[0269] Calculate the distance between construction workers and each risk construction point:

[0270] For each risk construction point, calculate the Euclidean distance between the personnel position and the point.

[0271] Screening of adjacent risk construction sites:

[0272] If d is satisfied i ≤RY i , then add the i-th risk construction point to the set of adjacent risk construction points S p ;

[0273] Traverse all n risk construction points and obtain the set S of adjacent risk construction points related to the current construction worker's position p ={i1,i2,...,i m′},{i1,i2,...,i m′} is contained in {1,2,…,i,…,n}, where {i1,i2,…,i m′} is the index of each risk construction point in the set of adjacent risk construction points, m′ is the total number of risk construction points in the set; and define i j′ ∈{i1,i2,...,i m′}, and j′∈{1,2,…,m′};

[0274] Visualize the location data of each risky construction site and construction workers:

[0275] Mark the risky construction points (represented by circles to indicate the affected area) and the locations of construction personnel (represented by dots) on the geographical distribution map of the construction area.

[0276] The boundaries of the impact area of ​​each risky construction site are colored to indicate the risk level (such as green, yellow, and red).

[0277] Define the i-th adjacent risk construction point set j′ The hazard occurrence rate evaluation coefficient of a risk construction point is The calculation formula is as follows:

[0278]

[0279] in, is the i j′ Equipment failure rate at each risk construction site; reflects the reliability of the equipment;

[0280] is the i j′ The abnormal vibration rate of equipment at each risk construction point reflects the stability of the mechanical performance of the equipment;

[0281] is the i j′ Equipment maintenance qualification rate at each risk construction site; reflects the maintenance status of the equipment;

[0282] is the i j′ The ambient temperature of each risk construction site affects the performance and stability of the equipment;

[0283] is the i j′ The environmental humidity of each risk construction site affects the stability of construction;

[0284] a1, a2, a3 are weight coefficients of corresponding parameters, respectively; they correspond to the influence of equipment failure rate, equipment abnormal vibration rate and equipment maintenance qualification rate on the risk occurrence rate; the value range of a1, a2, a3 is the interval (0, 1), and a1 + a2 + a3 = 1;

[0285] η1 is the sensitivity coefficient of ambient temperature to the risk occurrence rate, and the value range of η1 is in the interval (0,1); T0 is the temperature reference value, and T0 is set to the optimal operating temperature of the construction equipment corresponding to the current risk construction point;

[0286] for Explanation of the numerator: The equipment failure rate, equipment abnormal vibration rate and equipment maintenance qualification rate are combined together through linear weighting to reflect the comprehensive impact of multiple factors on the risk occurrence rate;

[0287] In the large-scale high-rise building construction site applied in this embodiment, determine the i j′ The value ranges of equipment failure rate, equipment abnormal vibration rate, and equipment maintenance pass rate of the construction equipment corresponding to each risk construction point are as follows:

[0288] Equipment failure rate

[0289] Value range: (0,10) times / thousand hours;

[0290] Value selection rules: The accumulated usage time and number of failures of each construction equipment are recorded on the equipment management platform and calculated; abnormal vibration rate of the equipment

[0291] Value range: (0,5) times / hour;

[0292] Value selection rules: Monitor the vibration of the equipment through the vibration sensor and record the number of vibrations exceeding the normal value;

[0293] Equipment maintenance pass rate

[0294] Value range: (0,1);

[0295] Value selection rules: The proportion of equipment maintenance completed as planned is counted through the maintenance management system;

[0296] Weight coefficients a1, a2, a3: set to: a1 = 0.4, a2 = 0.3, a3 = 0.3 respectively;

[0297] Sensitivity coefficient η1: set to η1 = 0.1;

[0298] Temperature reference value T0: set to 25℃;

[0299] set up The valid value range of is the interval (0,1);

[0300] when The closer it is to 0, the more j′ The lower the probability of danger occurring at each risk construction site, the safer the construction environment;

[0301] when The closer it is to 1, the j′ The higher the probability of danger occurring at a risk construction site, the higher the priority of the safety measures to be taken;

[0302] Dynamically adjust the impact range of risky construction points:

[0303] Dynamically adjust the radius of the affected area based on the real-time status of the construction equipment corresponding to each risk construction point (such as vibration, gas leakage status, etc.) Improve the accuracy and timeliness of the assessment. Vibration and gas leakage status correspond to different impact ranges, so their impact range radius It is adjusted dynamically, so I won’t go into details.

[0304] Equipment failure rate When it increases, it will cause Increase;

[0305] Abnormal vibration rate of equipment When it increases, it will cause Increase;

[0306] Equipment maintenance pass rate When lowered, The value will increase, which will lead to Increase;

[0307] A low maintenance pass rate indicates that the equipment has not been maintained in a timely and effective manner, increasing the possibility of failure and danger;

[0308] when When it decreases, the denominator decreases. Increase;

[0309] High temperatures exceeding the optimal operating temperature will aggravate equipment failures and affect construction safety;

[0310] Ambient humidity Increase, leading to Increase, the denominator increases, Reduce.

[0311] Define the i-th adjacent risk construction point set j′ The environmental additional load index of each risk construction point is: The calculation formula is as follows:

[0312]

[0313] in, After standardization, the i j′ The stacking density of construction materials at each risk construction site is The value range is in the interval (0,1); it reflects the density of material accumulation;

[0314] is the first j′ The on-site lighting intensity of each risk construction point, The output value is in the interval (0,1); reflecting the lighting conditions;

[0315] After standardization, the i j′ Abnormal concentration rate of harmful gases at risk construction sites; The value range is between (0,1); it reflects the risk of harmful gas leakage;

[0316] After standardization, the i j′ The density of people at each risk construction site; The value range is in the interval (0,1); it affects the degree of spatial congestion at the construction site;

[0317] b1, b2 and b3 are weight coefficients of corresponding parameters, which correspond to the influence of the stacking density of construction materials, the intensity of on-site lighting and the abnormal rate of harmful gas concentration on the environmental additional load respectively; the value range of b1, b2 and b3 is (0,1), and b1+b2+b3=1;

[0318] θ1 is an adjustment factor used to control the effect of lighting intensity on the environmental additional load index. The value range of θ1 is [0,π / 4];

[0319] against Explanation of the numerator: The stacking density of construction materials, on-site lighting intensity and abnormal rate of harmful gas concentration are combined through linear weighting to reflect the comprehensive impact of multiple factors on the additional environmental load;

[0320] In the large-scale high-rise building construction site used in this embodiment, the on-site lighting intensity is determined according to the specific construction specifications and actual requirements of the site to determine the lighting intensity range that meets the specific use scenario. The on-site lighting intensity of the general construction area is set to: 300-500Lux;

[0321] Set the on-site lighting intensity for delicate operations or safety-critical areas to: 500-1600Lux;

[0322] And according to the above practical application environment, standardization is carried out to make The output value is limited to the interval (0,1);

[0323] Such adjustments can not only more realistically reflect the needs of the site, but also improve the accuracy and applicability of the formula design. To determine the specific applicable lighting intensity range, you can first refer to the relevant construction safety standards, such as the "Construction Engineering Construction Site Safety Technical Specifications" or "Architectural Lighting Design Standards".

[0324] Use a photometer to measure lighting intensity data at key areas of the construction site;

[0325] Monitor the concentration of harmful gases through gas detectors and record the number of times the concentration exceeds the limit;

[0326] Count the number of construction workers per unit area through positioning data;

[0327] The weight coefficients b1, b2 and b3 are set as: b1 = 0.4, b2 = 0.3, b3 = 0.3;

[0328] For the adjustment factor θ1, it is set as: Nonlinear effects for regulating lighting intensity;

[0329] set up The valid range of is the interval (0,1];

[0330] when The closer it is to 0, the more j′ The lighter the environmental burden of each risk construction site, the safer the construction environment;

[0331] when The closer it is to 1, the heavier the environmental burden of the risky construction site, the higher the safety risk, and the higher the priority of the improvement measures to be taken.

[0332] Construction material stacking density When it increases, it will cause Increase; The increase in stacking density will increase the probability of risk such as material collapse and slipping, increasing safety hazards;

[0333] On-site lighting intensity Increase: By The adjustment affects the size of the denominator; increasing the lighting intensity helps improve construction safety and reduce operational errors;

[0334] Abnormal rate of harmful gas concentration When it increases, it will cause Increase; the increase in the concentration of harmful gases will increase the health risks of workers and requires timely ventilation and treatment;

[0335] Personnel density at the i-th risk construction site Increase:

[0336] Through the denominator Increase, leading to Reduce.

[0337] Further explanation: In combination with the positioning data of the construction personnel, the physiological state coefficient, the abnormal evaluation coefficient and the danger occurrence rate evaluation coefficient are comprehensively analyzed to construct a construction risk evaluation vector, which specifically includes:

[0338] The construction risk assessment vector is defined to consist of the following three main components:

[0339] V=[PSC,AEC,R avg ]

[0340] Among them, PSC is the physiological state coefficient of the construction workers; AEC is the abnormal evaluation coefficient; R avg is the average hazard occurrence rate evaluation coefficient in the set of adjacent risk construction points;

[0341] For the average risk rate evaluation coefficient R avg Calculation instructions:

[0342] According to the set of adjacent risk construction points S p ={i1,i2,…,i m′}, where m′ is the total number of adjacent risk construction points, and the risk incidence evaluation coefficient of each construction point is

[0343]

[0344] in,

[0345] V = [PSC, AEC, R avg ], each component is assigned a different weight to form an overall risk assessment; the weights of each component are set to c1, c2 and c3, and the values ​​of c1, c2 and c3 are all in the interval (0,1); and c1+c2+c3=1;

[0346] Based on the current location U of the construction personnel p (x p ,y p ), and define the comprehensive risk assessment index as The calculation formula is as follows:

[0347]

[0348] in The valid value range is (0,1).

[0349] c1, c2 and c3 are determined according to the actual needs of construction safety management. This embodiment recommends the following weight distribution: c1, c2 and c3 can be determined by combining the entropy weight method and the fuzzy analytic hierarchy process (FAHP), which will not be described in detail;

[0350] Physiological state coefficient (PSC): c1 = 0.3;

[0351] Environmental anomaly evaluation coefficient (AEC): c2 = 0.4;

[0352] Average risk rate evaluation coefficient c3=0.3;

[0353] The above weight distribution reflects the significant impact of environmental anomalies on construction risks, while taking into account the individual physiological state of construction workers and the danger of surrounding risk construction sites.

[0354] Further explanation: the calibration strategy for providing weights for each parameter in the comprehensive risk assessment index includes:

[0355] The comprehensive risk assessment index is initially set to c1=c2=c3; and c1, c2 and c3 after adjustment by the calibration strategy are defined as c1′, c2′, c3′ respectively; the calibration strategy is as follows:

[0356] Set PSI, SCI and The judgment thresholds are PSI th ,SCI th ,

[0357] In this embodiment, PSI th ,SCI th , Take the corresponding PSI, SCI and The median of the range of PSI th ,SCI th , The determined values ​​are adaptively adjusted according to the actual application scenario and are not limited to a single value;

[0358] When PSI>PSI th When , it means that the construction workers are under great psychological pressure and need to take many decompression measures;

[0359] When SCI>SCI th When , it means that the space where the construction workers are located is highly crowded;

[0360] when When , it means that the risk construction point i j′ The environmental burden is heavy and the priority for improvement measures is high; PSI, SCI and And the corresponding judgment threshold Calculate the ratio and get

[0361] like When only one ratio is greater than 1, the assignment weight greater than 1 is increased, and the other two assignment weights less than or equal to 1 are decreased by the same value;

[0362] like When any two ratios are greater than 1, the two assignment weights greater than 1 are increased by the same amount, and the remaining assignment weight less than or equal to 1 is reduced;

[0363] like When all ratios are greater than 1, no adjustment is made to the assignment weights.

[0364] like Only When the ratio is greater than 1, the expression formulas of c1′, c2′, and c3′ are as follows:

[0365]

[0366] like Only When the ratio is greater than 1, the expression formulas of c1′, c2′, and c3′ are as follows:

[0367]

[0368] like Only When the ratio is greater than 1, the expression formulas of c1′, c2′, and c3′ are as follows:

[0369]

[0370] like Simultaneous existence and When the ratio is greater than 1, the expression formulas of c1′, c2′, and c3′ are as follows:

[0371]

[0372] like Simultaneous existence and When the ratio is greater than 1, the expression formulas of c1′, c2′, and c3′ are as follows:

[0373]

[0374] like Simultaneous existence and When the ratio is greater than 1, the expression formulas of c1′, c2′, and c3′ are as follows:

[0375]

[0376] like Simultaneous existence and and When the ratio is greater than 1,

[0377] Further explanation: after the calibration strategy is adjusted, the comprehensive risk assessment index is used to formulate warning strategies and send comprehensive warning information to optimize personnel scheduling, job allocation and safety protection, including:

[0378] Provide construction personnel and management personnel with different levels of warning strategies based on the actual value range of each component in the construction risk assessment vector; and send comprehensive early warning information to construction management personnel or superior process departments based on the warning strategies;

[0379] Comprehensive early warning information is used to optimize personnel scheduling, job allocation and safety protection measures at the construction site to reduce construction risks; ensure the physical and mental health of construction workers and the safety of the construction site;

[0380] The comprehensive risk assessment index after calibration strategy adjustment is set as The calculation formula is as follows:

[0381]

[0382] right The risk level is divided according to the value range of to formulate the following different warning strategies:

[0383] Low risk Construction workers are in a safe state and only need to implement the existing monitoring strategy, which is conventional monitoring; resource allocation and early warning response costs can be reduced;

[0384] At low risk, 0.7<PSC<1, 0<AEC<0.35, 0<R avg <0.37;

[0385] Medium risk Construction workers face potential risks and need to pay more attention and take protective measures; early warning is needed to reduce the possibility of accidents;

[0386] At medium risk, 0.45<PSC≤7, 0.35≤AEC<0.65, 0.37≤R avg <0.66;

[0387] High risk Construction workers are in danger and need to take early warning response and protective measures immediately; prioritize ensuring the safety of construction workers and take intervention measures;

[0388] At high risk, 0<PSC≤0.45, 0.65≤AEC<1, 0.66≤R avg <1;

[0389] PSC, AEC and R for each risk level above avgAdjustments are made according to the changes in application scenarios. The numerical values ​​are initially limited by the expert group through empirical knowledge, and are jointly verified by the entropy weight method and the fuzzy analytical hierarchy process (FAHP) in turn to determine the final value range. The specific verification steps are not repeated here.

[0390] According to the above risk levels, the following adaptive warning strategies are generated for construction workers and management personnel respectively:

[0391] Individual warning strategies for construction workers: The following smart terminals are wearable devices;

[0392] Low risk

[0393] A green prompt box will display "good status" on the smart terminal (such as a helmet or watch) worn by the construction workers, suggesting that they continue to work in the current state;

[0394] Using low-power Bluetooth technology, low-level risk warning information is sent through devices worn by construction workers;

[0395] Medium risk

[0396] A yellow warning box will be displayed on the smart terminal indicating "potential risks" with specific suggestions. The specific suggestions include staying focused and paying attention to the safety of the surrounding environment.

[0397] Combined with voice assistant, it reminds construction workers to check their own protective measures. If there is no response, the system will automatically notify the management personnel; high risk

[0398] A red warning box will be displayed on the smart terminal indicating "high risk", and an emergency voice alarm will be issued, requiring construction workers to stop work immediately and go to a safe area;

[0399] Combined with wireless networks, sound alarm devices are deployed around construction workers to trigger safety warnings for the entire area;

[0400] Comprehensive warning strategy for construction managers:

[0401] Low risk

[0402] The status is only recorded in the construction management system, and no warning information is sent;

[0403] Technical implementation: Store real-time status data in the database and generate a green status report on the management platform;

[0404] Medium risk

[0405] Send yellow warning information to construction management personnel, recommending that they focus on monitoring relevant areas and arrange additional inspections;

[0406] Technical implementation: Push via SMS or APP, with risk analysis report, including PSC、AEC、R avg Specific values ​​and visual charts;

[0407] High risk

[0408] Send red alert information to construction management personnel and superior process departments, with additional emergency response suggestions, including but not limited to personnel evacuation and immediate suspension of high-risk operations;

[0409] Technical implementation: Send real-time high-definition video streams and key data analysis reports through the 5G network to assist remote decision-making;

[0410] Send comprehensive early warning information through the construction management platform; specifically including:

[0411] By integrating risk level data and the real-time status of the construction site, comprehensive early warning information is generated for the construction management platform to optimize personnel scheduling, work allocation and safety protection measures;

[0412] Generation of early warning information:

[0413] Consolidate the following key data into standardized information packages:

[0414] Comprehensive risk assessment index

[0415] Risk levels include low risk, medium risk, and high risk;

[0416] PSC、AEC、R avg Real-time data;

[0417] The current location information of the construction personnel p (x p ,y p );

[0418] Sending of early warning information:

[0419] Through the intelligent construction management platform, the information is sent to the following recipients:

[0420] Construction managers: receive specific risk warnings and optimization suggestions;

[0421] Superior process department: Receive risk distribution reports of the global construction area for decision support;

[0422] On-site execution team: receive early warning notifications through broadcast or smart terminals;

[0423] Technical implementation of information sending:

[0424] Use edge computing technology to process data in real time on site to reduce latency;

[0425] Centralized management and storage of risk data through cloud computing platforms;

[0426] Use blockchain technology to ensure the authenticity and tamper-proofness of information transmission;

[0427] Optimize personnel scheduling and job allocation based on early warning information;

[0428] Comprehensive early warning information is used to optimize personnel scheduling, work allocation, and safety protection measures at the construction site in real time:

[0429] For optimizing staff scheduling:

[0430] When the risk level of the area where the construction workers' positioning data is located is high, experienced construction workers are deployed first and the density of people in the area is reduced; experienced construction workers are divided according to their years of work or professional skill level. The longer the years of work or the higher the professional skill level, the richer the corresponding experience;

[0431] Crowd density control:

[0432] For high-risk areas, the target population density is defined as

[0433]

[0434] in: is the current population density;

[0435] μ1 is the density reduction coefficient, and the value range of μ1 is set to 0.2≤μ1≤0.5;

[0436] The safety personnel density threshold is set based on national or industry standards;

[0437] Indicates from and Select the smallest value as the adjusted target population density;

[0438] Technical implementation: Based on the real-time data analysis of the positioning system, the optimal scheduling plan is generated and sent to the management platform;

[0439] For optimizing job distribution:

[0440] Break down tasks in high-risk areas into multiple subtasks and assign them to adjacent areas with medium or low risks;

[0441] Technical implementation: Combine the task allocation algorithm and historical construction data to automatically generate a task allocation plan;

[0442] Specifically, the tasks in high-risk areas are decomposed into several subtasks, with the goal of minimizing the workload in high-risk areas and balancing the workload in medium-risk and low-risk areas, so that the workload ratio between medium-risk and low-risk areas is maintained at 3:7;

[0443] Task allocation plan generation: Based on the decomposition results and combined with historical construction data, the task allocation plan is automatically generated and notified to the construction personnel in the relevant areas through the intelligent construction management platform.

[0444] For optimizing safety protection measures:

[0445] Deploy additional security protection facilities in high-risk areas, including isolation fences and mobile monitoring equipment.

[0446] Technical implementation: Combined with the Internet of Things technology, remote control of the start and stop of construction equipment;

[0447] Safety protection facility deployment rules:

[0448] In high-risk areas, each S unit A monitoring device and an isolation fence are deployed per square meter, and the number of deployments is N deploy The calculation is as follows:

[0449]

[0450] Where S A is the area of ​​the high-risk area, in m 2 , S unit The standard area covered by a single device, in m 2 .

[0451] Remote control rules:

[0452] When in high-risk areas When the alarm is raised, the remote stop signal is automatically triggered to force the construction equipment to shut down.

[0453] Example 2: To fully verify the comprehensive risk assessment index The experiment was carried out in a simulated construction environment to test the feasibility and effect optimization of warning strategy formulation and early warning information sending. The experiment mainly reflects the dynamic risk characteristics of the construction site and the health status of construction workers through a variety of data collection schemes, thereby forming comprehensive evaluation data and optimizing warning strategies.

[0454] The experimental area is divided into three risk levels (low-risk area, medium-risk area and high-risk area), and 10 typical construction workers are selected to cover different work cycles and environmental scenarios; the experiment mainly includes the following processes:

[0455] 1. Experimental equipment and data collection:

[0456] Physiological monitoring equipment: Each construction worker is equipped with a health monitoring device (model: HR-MonitorPro, etc.) to record their heart rate (beats / min), blood oxygen concentration (%) and body temperature (℃), respiratory rate, and instantaneous skin conductance response value in real time; standardized into the physiological state coefficient PSC through the following formula:

[0457]

[0458] Environmental monitoring: collect ambient temperature, ambient humidity, air quality index and noise level data through environmental sensors (model: Env-SensorX) to calculate the abnormal evaluation coefficient AEC;

[0459] Risk construction site monitoring: equipment failure rate, equipment abnormal vibration rate, equipment maintenance pass rate, and the average risk occurrence rate evaluation coefficient R is calculated by the formula avg :

[0460]

[0461] 2. Set the following comprehensive risk assessment index, and the calculation formula is:

[0462]

[0463] 3. Warning strategy implementation and evaluation:

[0464] The risk index is divided into risk levels, which trigger green prompts, yellow warnings and red warnings respectively, and the effects of personnel status, density adjustment, etc. after the warning strategy are recorded;

[0465] Additional protection measures were deployed for high-risk areas, including regional isolation and dynamic monitoring, to test the security effects after optimization. The following are the specific data and results collected in the experiment;

[0466] Table 2 Demonstration study of comprehensive risk assessment index:

[0467]

[0468]

[0469] Analysis and conclusion:

[0470] 1. Initial risk analysis: Construction workers A6, A7, A9 and A10 in high-risk areas They reached 0.72, 0.81, 0.68 and 0.86 respectively, forming a clear contrast with the medium and low risk areas; indicating that the system can effectively distinguish the characteristics of different risk areas and trigger warning strategies at different levels;

[0471] 2. Effect of adjusting personnel density: The scheduling plan for high-risk areas significantly reduced the personnel density, with an average reduction of 6 people / 100 square meters in each area, reducing the potential accident risk caused by excessive personnel density in high-risk areas;

[0472] 3. Task allocation optimization effect: By adjusting tasks in high-risk areas to medium- and low-risk areas, the amount of tasks is reduced by an average of 30%, which improves the scientific nature and dynamic adaptability of specific task allocation;

[0473] This proves that this technical solution can significantly reduce construction risks and improve operation efficiency through real-time risk assessment and early warning strategy optimization, and has obvious application value;

[0474] Further comparative analysis of the key data in the table revealed that the parameters differed significantly between different regions and construction personnel. The changes in these parameters not only clarified the specific risk level of the construction site, but also provided an important basis for formulating reasonable warning strategies and optimizing them. The following focuses on analyzing the warning strategies and their optimization effects for each risk interval in combination with the table data, while also explaining the creativity and novelty of the invention.

[0475] Low risk range

[0476] The data is shown as follows:

[0477] The comprehensive risk indexes of construction workers A1 and A3 are 0.28 and 0.26 respectively, both lower than 0.3, which are in the low risk range.

[0478] Influencing factors: PSC is large (such as A1 is 0.85, A3 is 0.75), while AEC and average risk rate evaluation coefficient are small (such as A1, AEC = 0.2, R avg =0.1).

[0479] Warning strategy:

[0480] Develop a green warning strategy based on low-risk characteristics:

[0481] 1. Construction workers A1 and A3 are only sent green prompt messages through the devices they wear, such as "Construction status is good, continue to maintain the current status."

[0482] 2. No adjustments are made to staff density or task allocation to avoid wasting resources.

[0483] Parameter relationship analysis and technical effects:

[0484] Due to the large PSC, the construction workers are in good physiological condition, while AEC and R avg Smaller, making Keep it in low range.

[0485] According to the formula:

[0486]

[0487] When PSC increases by 10% (e.g. from 0.75 to 0.825), The results show that the health status of construction workers has a significant impact on the risk index.

[0488] By only prompting warnings, green zone construction safety risks are reduced by 100%.

[0489] Reducing resource burden and interference from low-priority tasks improves the response efficiency of the early warning system.

[0490] Medium risk range

[0491] The data is shown as follows:

[0492] The comprehensive risk indexes of construction workers A2, A4, A5 and A8 were 0.31, 0.58, 0.62 and 0.39 respectively, all of which were in the medium risk range.

[0493] Influencing factors: Compared with low-risk areas, AEC and R avg All of them increased (such as A5, AEC = 0.45, R avg =0.50), but the PSC remains moderate (such as A5, PSC = 0.70).

[0494] Warning strategy:

[0495] Formulate a yellow warning strategy based on the characteristics of the medium risk zone:

[0496] 1. Send yellow warning information to construction workers, such as "potential risk, please pay special attention to the environment and construction status", and recommend wearing appropriate protective equipment.

[0497] 2. Make appropriate adjustments to the population density in specific areas (e.g. in area A4, adjust the population density from 14 people / 100 m2 to 12 people / 100 m2).

[0498] 3. Appropriately reduce the amount of task allocation (for example, in the A5 area, the task amount is adjusted from 32t to 28t) to reduce the load on high-risk workstations.

[0499] Parameter relationship analysis and technical effects:

[0500] The medium risk range is mainly composed of AEC and R avg For example:

[0501] When the construction environment abnormal AEC increases by 20%, such as from 0.45 to 0.54), then An increase of 9.8%, indicating that environmental abnormalities are the key factor leading to increased risks.

[0502] Compared with PSC, AEC and R avg The impact of the increase in risk index shows a nonlinear expansion trend.

[0503] By adjusting the personnel density and task allocation, the average exposure risk of construction workers was reduced by 20%-30% (for example, after reducing from 14 people / 100㎡ to 12 people / 100㎡, the risk in the area has been reduced).

[0504] The targeted yellow warning scheme reduced potential safety hazards in accident-occurring areas and increased construction efficiency by 15%.

[0505] High-risk zone

[0506] The data is shown as follows:

[0507] The risk indexes of construction workers A6, A7, A9 and A10 were 0.72, 0.81, 0.68 and 0.86 respectively, all of which were considered high risk.

[0508] Influencing factors: the above-mentioned environmental abnormality coefficient of the construction workers (such as A7, AEC = 0.70) and the average risk incidence evaluation coefficient (such as A10, R avg =0.70) is in the high value range, while PSC is low (such as A10, PSC = 0.55).

[0509] Warning strategy:

[0510] Develop a red warning strategy based on the characteristics of the high-risk area:

[0511] 1. Prioritize sending a red alert to construction workers and forcibly notify them to stop working. For example, construction workers A7 and A10 need to evacuate to a safe area immediately.

[0512] 2. Deploy additional safety facilities (such as isolation fences and mobile monitoring equipment) in high-risk areas, and send comprehensive early warning reports to construction managers.

[0513] 3. Substantial adjustments were made to tasks and personnel density (e.g. in the A10 area, the density was reduced from 22 people / 100 m2 to 14 people / 100 m2 and the task volume was reduced by 30%).

[0514] Parameter relationship analysis and technical effects:

[0515] The main feature of the high-risk range is the cumulative effect of all indicators, especially AEC and R avg The key to fluctuations is the main factor. For example:

[0516] When R avg Increase by 10% (e.g. from 0.70 to 0.77) It will rise by 6.2%, indicating that within the high-risk range, any increase in danger signals will amplify the overall risk.

[0517] A decrease in PSC (e.g. from 0.60 to 0.55) will further amplify the risk posed by an individual's health status. The increase can reach 12%-15%.

[0518] Quantifying the effect of technology:

[0519] After adjustment, the population density in high-risk areas decreased by 36.4% (from 22 people / 100 m2 to 14 people / 100 m2), significantly reducing the exposure possibility of people in dangerous environments.

[0520] After stopping some work in high-risk areas, the risk exposure rate was reduced by 40%-50%.

[0521] The timeliness of the emergency red warning reduced the probability of potential accidents by more than 50%, achieving significant safety protection effects.

[0522] 1. Dynamic multi-parameter fusion optimization:

[0523] Compared with the traditional single risk monitoring method, the present invention integrates PSC, AEC, R avg Multiple parameters are used to comprehensively assess the risks of the construction environment and personnel status. This multi-factor comprehensive calculation can dynamically adapt to the needs of different scenarios and greatly improve the accuracy of protection strategies.

[0524] 2. Hierarchical and sophisticated warning mechanism:

[0525] The present invention is based on The hierarchical distribution of risks (low risk, medium risk, high risk) has led to the formulation of differentiated warning strategies, from green prompts to red alerts, combined with density adjustment and task optimization, which has greatly reduced resource waste. In addition, the rapid control and early warning information dissemination in high-risk areas can significantly reduce work-related accidents.

[0526] Subsequent data quantification showed that the overall construction safety of the present invention was improved by more than 50%, the construction efficiency was increased by 15%-20%, and the accident rate was reduced by 40%-60%, which is of great value to the intelligent risk management of the construction industry.

[0527] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0528] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0529] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0530] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An intelligent construction management method based on data analysis, characterized in that: The specific steps include: Step S1: collecting the physiological index data of the construction workers, as well as the positioning data of the construction workers and the environmental abnormality index in real time through the wearable device; Uniquely mark multiple risky construction sites in the current construction area and collect safety status data for each risky construction site; Step S2: Analyze the physiological index data of the construction workers to calculate the physiological state coefficient and the psychological stress index respectively; Step S3: analyzing the environmental abnormality index data of the location where the construction workers are located to calculate the abnormality evaluation coefficient and the space congestion index respectively; Step S4: Determine the adjacent risk construction points of the current construction personnel's location based on the positioning data, analyze the safety status data of these risk construction points, and calculate the hazard occurrence rate evaluation coefficients and environmental additional load indexes of these risk construction points respectively; Step S5: Combined with the positioning data of the construction personnel, the physiological state coefficient, the abnormality evaluation coefficient and the risk incidence evaluation coefficient are comprehensively analyzed to construct a construction risk evaluation vector, and generate a corresponding comprehensive risk evaluation index, which includes the assigned weights of each component in the construction risk evaluation vector; Step S6: Analyze the psychological stress index, the space crowding index and the environmental additional load index, and generate a calibration strategy for adjusting the value weight of each parameter in the comprehensive risk assessment index; Step S7: Based on the comprehensive risk assessment index adjusted by the calibration strategy, a warning strategy is formulated and comprehensive warning information is sent to optimize personnel scheduling, job allocation and safety protection.

2. The intelligent construction management method based on data analysis according to claim 1 is characterized by: Uniquely mark multiple risk construction points in the current construction area, specifically: The set of risky construction points in the current construction area is defined as {1, 2, ..., i, ..., n}, where i represents the index of the i-th risky construction point and n is the total number of risky construction points; The physiological state coefficient of construction workers is defined as PSC, and the calculation formula is as follows: Among them, w j is the weight coefficient of the jth physiological index; 0<w j <1; and Each w j Reflects the relative importance of the corresponding indicator to the physiological state; where j∈{1,2,3,4,5}, represents heart rate, blood oxygen saturation, body temperature, respiratory rate, and instantaneous skin conductance response value respectively; X j is the actual measured value of the jth physiological index, X j ∈{HR, SpO2, BT, RH, SCR}; {HR, SpO2, BT, RH, SCR} represent heart rate, blood oxygen saturation, body temperature, respiratory rate, and instantaneous skin conductance response value, respectively; C j is the baseline value of the jth physiological index, which is used for standardization adjustment; Set the value range of the physiological state coefficient to 0<PSC<1; When PSC is closer to 1, it means that the physiological state of the construction workers is better, and when PSC is closer to 0, it means that the physiological state of the construction workers is worse; The psychological stress index of construction workers is defined as PSI, and the calculation formula is as follows: Among them, λ1 and λ2 are the weight coefficients of each parameter; SDNN is the heart rate variability index; C SCR and C SDNN are the benchmark values ​​of SCR and SDNN, respectively, used for standardization adjustment; by adjusting the values ​​of λ1 and λ2, PSI is limited to the interval (0,1), and the values ​​of λ1 and λ2 are both in the interval (0,1), and λ1+λ2=1; When PSI approaches 1, it means that the psychological stress of the construction workers is greater, the more nervous the construction workers are, and the more decompression measures need to be taken; The closer the PSI is to 0, the lower the psychological stress of the construction workers is and the better their emotional state is.

3. The intelligent construction management method based on data analysis according to claim 2 is characterized by: Analyze the abnormal environmental index data of the construction workers' location and calculate the abnormal evaluation coefficient and space crowding index, including: The abnormal evaluation coefficient of the construction worker's position is defined as AEC, and the calculation formula is as follows: Among them, AEC is the anomaly evaluation coefficient, which is used to quantify the degree of environmental anomaly in the current construction location area; And set the value range of AEC to the interval (0, 1); The closer the AEC value is to 1, the higher the degree of environmental abnormality is, and the closer the AEC value is to 0, the lower the degree of environmental abnormality is. α k is the weight coefficient of the kth environmental anomaly index, k∈{1, 2, 3, 4}, and k∈{1, 2, 3, 4} represent the ambient temperature, ambient humidity, air quality index and noise level respectively; 0<α k <1, and X k is the actual measured value of the kth environmental anomaly indicator; X k ∈{TEMP, HUM, AQI, NLZY}; TEMP, HUM, AQI, NLZY represent ambient temperature, ambient humidity, air quality index and noise level respectively; M k is the average value of the kth environmental anomaly index; D k is the standard deviation of the kth environmental anomaly index; β k is the exponential adjustment factor of the kth environmental anomaly index, and 1≤β k ≤3; The spatial crowding index of the construction workers' location is defined as SCI, and the calculation formula is as follows: The population density, population flow speed, and space capacity are recorded as PD, FS, and AS respectively; the value range of SCI is set to (0, 1); h m (PD, FS, AS) and v m (PD, FS, AS) are sub-functions; The closer the SCI is to 1, the higher the degree of spatial crowding at the location of the construction workers; The closer the SCI is to 0, the lower the degree of spatial crowding at the location of the construction workers; γ m and δ m are the weight coefficients of each sub-function in the numerator and denominator respectively; γ m and δ m The value range of is (0, 1), and C PD , C FS , C AS Respectively represent the benchmark values ​​of personnel density, personnel flow speed and space capacity, which are used for standardization adjustment; θ m is the exponential adjustment factor of the space capacity, 1≤θ m ≤3.

4. The intelligent construction management method based on data analysis according to claim 3 is characterized by: Based on the positioning data, the adjacent risk construction points of the current construction personnel are determined, and the safety status data of these risk construction points are analyzed to calculate the hazard incidence evaluation coefficient and environmental additional load index of these risk construction points, including: Define the coordinates of the i-th risk construction point as P i (x i ,y i ), where: x i is the horizontal coordinate of the ith risk construction point in the construction coordinate system; i is the ordinate of the ith risk construction point in the construction coordinate system; and each risk construction point corresponds to the corresponding construction equipment; The specific geographical location information of the construction personnel's current location is represented by the positioning data as U p (x p ,y p ), where: x p is the horizontal coordinate of the construction personnel in the construction coordinate system; p It is the vertical coordinate of the construction personnel in the construction coordinate system; Definition of the impact scope of risk construction points: Assume that the failure impact range of the construction equipment corresponding to the i-th risk construction point is a circular area, and its impact range is from the center point P i (x i ,y i ) and the fault impact radius RY i Indicates, specifically: Among them, RY i Indicates the fault impact radius of the i-th risk construction point; the judgment logic of whether the current position of the construction personnel is affected is set as follows: For the impact range of each risk construction point, calculate the Euclidean distance d between the construction personnel position and each risk construction point i , d i The calculation formula is Screen the following adjacent risk construction sites: If d is satisfied i ≤RY i , then add the i-th risk construction point to the set of adjacent risk construction points S p ; Traverse all n risk construction points and obtain the set S of adjacent risk construction points related to the current construction worker's position p ={i1,i2,...,i m′ },{i1,i2,...,i m′ } is contained in {1,2,…,i,…,n}, where {i1,i2,…,i m′ } is the index of each risk construction point in the set of adjacent risk construction points, m′ is the total number of risk construction points in the set of adjacent risk construction points; and define i j′ ∈{i1,i2,…,i m′ }, and j′∈{1,2,…,m′}; Define the i-th adjacent risk construction point set j′ The hazard occurrence rate evaluation coefficient of a risk construction point is The calculation formula is as follows: in, is the i j′ Equipment failure rate at each risk construction site; is the i j′ Abnormal vibration rate of equipment at each risk construction site; is the i j′ Equipment maintenance qualification rate at risk construction sites; is the i j′ The ambient temperature of each risk construction point; is the i j′ Environmental humidity at each risk construction site; a1, a2, a3 are weight coefficients of corresponding parameters respectively; the value ranges of a1, a2, a3 are all in the interval (0,1), and a1+a2+a3=1; η1 is the sensitivity coefficient of ambient temperature to the risk occurrence rate, and the value range of η1 is in the interval (0,1); T0 is the temperature reference value, and T0 is set to the optimal operating temperature of the construction equipment corresponding to the current risk construction point; set up The valid value range of is the interval (0,1); when The closer it is to 0, the more j′ The lower the probability of danger occurring at each risk construction site, the safer the construction environment; when The closer it is to 1, the j′ The higher the probability of danger occurring at a risk construction site, the higher the priority of the safety measures to be taken; Define the i-th adjacent risk construction point set j′ The environmental additional load index of each risk construction point is: The calculation formula is as follows: in, After standardization, the i j′ The stacking density of construction materials at each risk construction site is The value range is in the interval (0,1); is the first j′ The on-site lighting intensity of each risk construction point, The output value is in the interval (0,1); After standardization, the i j′ Abnormal concentration rate of harmful gases at risk construction sites; The value range is in the interval (0,1); After standardization, the i j′ The density of people at each risk construction site; The value range is in the interval (0,1); b1, b2 and b3 are weight coefficients of corresponding parameters, the value range of b1, b2 and b3 are all (0,1), and b1+b2+b3=1; θ1 is an adjustment factor used to control the effect of lighting intensity on the environmental additional load index. The value range of θ1 is [0,π / 4]; set up The valid range of is the interval (0,1]; when The closer it is to 0, the j′ The lighter the environmental burden of each risk construction site, the safer the construction environment; when The closer it is to 1, the heavier the environmental burden of the risky construction site, the higher the safety risk, and the higher the priority of the improvement measures to be taken.

5. The intelligent construction management method based on data analysis according to claim 4 is characterized in that: Combined with the positioning data of the construction personnel, the physiological state coefficient, the abnormal evaluation coefficient and the danger occurrence rate evaluation coefficient are comprehensively analyzed to construct a construction risk evaluation vector, which specifically includes: The construction risk assessment vector is defined to consist of the following three main components: V=[PSC,AEC,R avg ] Among them, PSC is the physiological state coefficient of the construction workers; AEC is the abnormal evaluation coefficient; R avg is the average hazard occurrence rate evaluation coefficient in the set of adjacent risk construction points; For the average risk rate evaluation coefficient R avg Calculation instructions: According to the set of adjacent risk construction points S p ={i1,i2,…,i m′ }, where m′ is the total number of adjacent risk construction points, and the risk incidence evaluation coefficient of each construction point is in, V = [PSC, AEC, R avg ], each component is assigned a different weight to form an overall risk assessment; the weights of each component are set to c1, c2 and c3, and the values ​​of c1, c2 and c3 are all in the interval (0,1); and c1+c2+c3=1; Based on the current location U of the construction personnel p (x p ,y p ), and define the comprehensive risk assessment index as The calculation formula is as follows: in The valid value range is (0,1).

6. The intelligent construction management method based on data analysis according to claim 5 is characterized by: Provide a calibration strategy for assigning weights to each parameter in the comprehensive risk assessment index, including: The initial setting of the comprehensive risk assessment index is c1=c2=c3; and c1, c2 and c3 after adjustment by the calibration strategy are defined as c1′, c2′, c3′ respectively; the specific calibration strategy is as follows: Set PSI, SCI and The judgment thresholds are PSI th ,SCI th , PSI, SCI and And the corresponding judgment threshold Calculate the ratio and get like When only one ratio is greater than 1, the assignment weight greater than 1 is increased, and the other two assignment weights less than or equal to 1 are decreased by the same value; like When any two ratios are greater than 1, the two assignment weights greater than 1 are increased by the same amount, and the remaining assignment weight less than or equal to 1 is reduced; like When all ratios in are greater than 1, no adjustment is made to the assignment weights; The above adjustment settings need to define c1′+c2′+c3′=1.

7. The intelligent construction management method based on data analysis according to claim 6 is characterized by: The comprehensive risk assessment index adjusted by the calibration strategy is used to formulate warning strategies and send comprehensive warning information to optimize personnel scheduling, work allocation and safety protection, including: Provide construction personnel and management personnel with different levels of warning strategies; and send comprehensive early warning information to construction management personnel or superior process departments based on the warning strategies; The comprehensive risk assessment index after calibration strategy adjustment is set as The calculation formula is as follows: right The risk level is divided according to the value range of to formulate the following different warning strategies: Low risk Construction workers are in a safe state and existing monitoring strategies are implemented; Medium risk Construction workers face potential risks and need to pay more attention and take protective measures; High risk Construction workers are in danger and need to take early warning response and protective measures immediately; According to the above risk levels, the following adaptive warning strategies are generated for construction workers and management personnel respectively: Individual warning strategies for construction workers: The following smart terminals are wearable devices; Low risk The construction workers will see a green prompt box on their smart terminals that say "good status", suggesting they continue working in the current state. Medium risk A yellow warning box will be displayed on the smart terminal indicating "potential risks" with specific suggestions; High risk A red warning box will be displayed on the smart terminal indicating "high risk", and an emergency voice alarm will be issued, requiring construction workers to stop work immediately and go to a safe area; Comprehensive warning strategy for construction managers: Low risk The status is only recorded in the construction management system, and no warning information is sent; Medium risk Send yellow warning information to construction management personnel, recommending that they focus on monitoring relevant areas and arrange additional inspections; High risk Send red alert information to construction management personnel and superior process departments, with additional emergency response suggestions, including but not limited to personnel evacuation and immediate suspension of high-risk operations; Send comprehensive early warning information through the construction management platform; Comprehensive early warning information is used to optimize the personnel dispatch, work allocation and safety protection measures at the construction site in real time, specifically: For optimizing staff scheduling: When the risk level of the area where the construction personnel's location data is located is high risk, construction personnel are deployed first and the density of personnel in the area is reduced; For optimizing job distribution: Decompose the tasks in high-risk areas into multiple subtasks and assign them to adjacent risk construction sites with medium or low risks; For optimized safety protection measures: Deploy additional security protection facilities in high-risk areas, including isolation fences and mobile monitoring equipment.

Images