A construction safety risk dynamic early warning method and system based on multi-source perception data fusion

Abstract

The application relates to the technical field of building construction early warning, and discloses a building construction safety risk dynamic early warning method and system based on multi-source sensing data fusion, which comprises the following steps: acquiring structure state data, environment state data and personnel behavior data of a building construction process and performing pretreatment; presetting standard sensing data, determining a safety risk occurrence probability according to the standard sensing data and the pretreated multi-source sensing data; acquiring a to-be-constructed process and determining a construction risk grade; determining a comprehensive risk grade based on the safety risk occurrence probability and the construction risk grade and performing dynamic early warning. The application calculates the safety risk occurrence probability by fusing the multi-source sensing data, dynamically adjusts and classifies the early warning according to the comprehensive risk grade in combination with the construction process risk grade, and improves the accuracy and timeliness of the construction safety risk early warning.

Description

Technical Field

[0001] This invention relates to the field of construction safety early warning technology, and more specifically, to a dynamic early warning method and system for construction safety risks based on multi-source sensing data fusion. Background Technology

[0002] The construction industry is characterized by complex working environments, a concentration of high-risk processes, and high employee turnover, making it a high-risk area for workplace accidents. With the continuous expansion of construction scale and the increasing difficulty of construction, safety risks in the construction process exhibit characteristics such as multi-source, dynamic, and coupled nature. Traditional safety management models are no longer sufficient to meet the requirements of modern construction engineering for safety, real-time performance, and intelligence.

[0003] Currently, construction safety risk monitoring and early warning mainly rely on the following methods: Traditional safety management measures heavily depend on manual inspections and periodic measurements, resulting in significant problems such as large blind spots in inspections, delayed hazard identification, inadequate rectification loops, and weak accountability, failing to achieve full-time, comprehensive, and standardized control of construction sites. Existing monitoring systems often have limited functionality, relying on single parameters (such as monitoring only stress or only environmental parameters) for safety assessments, lacking the ability to comprehensively analyze multi-dimensional data. With the continuous development of IoT and sensor technologies, more and more monitoring devices are being deployed on construction sites, capable of collecting various data such as structural stress and strain, environmental temperature, humidity, wind speed, and personnel location trajectories. However, this data is often scattered across different systems, forming "data silos" and lacking effective data fusion and collaborative analysis mechanisms. Most existing safety risk assessment methods are static assessments, conducted once before or at the beginning of construction, making it difficult to adapt to the dynamic changes in risk factors during construction. Construction is a dynamic evolutionary process; different construction procedures face significantly different types and levels of risks, and static assessment results cannot reflect the real-time risk status during construction.

[0004] Therefore, there is an urgent need for a dynamic early warning method and system for construction safety risks based on multi-source sensing data fusion, so as to realize dynamic and accurate assessment and early warning of safety risks throughout the entire construction process. Summary of the Invention

[0005] In view of this, the present invention proposes a dynamic early warning method and system for construction safety risks based on multi-source sensing data fusion, aiming to solve the problems of existing construction safety risk monitoring and early warning technologies, such as single data source, difficulty in effectively integrating multi-source data, static risk assessment, and delayed and inaccurate early warning.

[0006] On the one hand, this invention proposes a dynamic early warning method for construction safety risks based on multi-source sensing data fusion, including: Acquire multi-source sensing data of the building construction process and preprocess the multi-source sensing data, which includes structural state data, environmental state data and personnel behavior data; Standard sensing data is preset, and the probability of safety risks occurring during the building construction process is determined based on the standard sensing data and preprocessed multi-source sensing data. Obtain the construction process to be carried out, and determine the construction risk level based on the construction process to be carried out; The comprehensive risk level is determined based on the probability of occurrence of the aforementioned safety risks and the level of construction risks, and dynamic early warning is carried out based on the comprehensive risk level.

[0007] Preferably, the structural state data includes: stress data and strain data of the construction structure; The environmental status data includes: wind speed data, temperature data, humidity data, and dust data; The personnel behavior data includes: personnel location data, personnel trajectory data, and safety equipment usage data.

[0008] Preferably, the preprocessing includes: timestamp calibration, format standardization, outlier removal, and missing value imputation of the multi-source sensing data.

[0009] Preferably, determining the probability of safety risks occurring during the building construction process based on the standard sensing data and preprocessed multi-source sensing data includes: The standard perception data includes standard structural status data, standard environmental status data, and standard personnel behavior data; The structural state data is compared with standard structural state data to determine the probability of deformation risk of the construction structure; the environmental state data is compared with standard environmental state data to determine the probability of environmental impact during the construction process; and the personnel behavior data is compared with standard personnel behavior data to determine the probability of personnel violations during the construction process. The probability of safety risks occurring during the construction process is determined based on the probability of deformation risk, the probability of environmental impact, and the probability of personnel violations.

[0010] Preferably, the structural condition data is compared with standard structural condition data to determine the probability of deformation risk of the construction structure, including: The standard structural condition data includes standard stress threshold and standard strain threshold; The stress data is compared with the standard stress threshold to determine the degree of stress exceeding the limit of the construction structure, and the strain data is compared with the standard strain threshold to determine the degree of strain exceeding the limit of the construction structure. Based on the stress and strain exceedance levels, the stress risk probability and strain risk probability of the construction structure are calculated respectively. Based on the stress risk probability and strain risk probability of the construction structure, the deformation risk probability of the construction structure is determined by weighted fusion.

[0011] Preferably, the environmental state data is compared with standard environmental state data to determine the probability of environmental impact during the building construction process, including: The standard environmental condition data includes: standard wind speed threshold range, standard temperature threshold range, standard humidity threshold range, and standard dust threshold range; The wind speed data is compared with the standard wind speed threshold range to determine the wind speed environmental impact sub-probability; the temperature data is compared with the standard temperature threshold range to determine the temperature environmental impact sub-probability; the humidity data is compared with the standard humidity threshold range to determine the humidity environmental impact sub-probability; the dust data is compared with the standard dust threshold range to determine the dust environmental impact sub-probability. The environmental impact probability of the building construction process is determined by fusing the environmental impact subprobabilities of wind speed, temperature, humidity, and dust.

[0012] Preferably, the personnel behavior data is compared with standard personnel behavior data to determine the probability of personnel violations during the construction process, including: The standard personnel behavior data includes the standard operating area range, standard operating trajectory path, and standard safety equipment usage status; The personnel positioning data is compared with the standard operating area range to determine the probability of positioning exceeding the boundary; the personnel trajectory data is compared with the standard operating trajectory path to determine the probability of trajectory deviation; the safety equipment usage data is compared with the standard safety equipment usage status to determine the probability of protection deficiency. The probability of personnel violations during the construction process is determined by fusing the sub-probability of location boundary violation, trajectory deviation, and protection deficiency.

[0013] Preferably, the process to be constructed is obtained, and the construction risk level is determined based on the process to be constructed, including: Obtain the current construction progress, and obtain the construction procedures to be carried out based on the construction progress; Based on a pre-set process hazard level comparison table, the corresponding construction risk level is determined according to the process to be constructed; The process hazard level comparison table includes several construction processes, each with a corresponding construction risk level; the construction risk level includes low risk level and general risk level.

[0014] Preferably, the comprehensive risk level is determined based on the probability of occurrence of the safety risk and the construction risk level, including: A first probability of occurrence and a second probability of occurrence are preset, wherein the first probability of occurrence is less than the second probability of occurrence; The construction risk level is adjusted based on the relationship between the probability of occurrence of the safety risk and the first probability of occurrence and the second probability of occurrence to obtain a comprehensive risk level; If the probability of occurrence of the safety risk is less than the first probability of occurrence, then the construction risk level will not be adjusted, and the construction risk level will be set as the comprehensive risk level. If the probability of occurrence of the safety risk is greater than or equal to the first probability of occurrence, and the probability of occurrence of the safety risk is less than the second probability of occurrence, then the construction risk level is adjusted up by one level to obtain the comprehensive risk level. If the probability of occurrence of the safety risk is greater than or equal to the second probability of occurrence, the construction risk level will be adjusted upward by two levels to obtain a comprehensive risk level; wherein, the comprehensive risk level includes a low risk level, a general risk level, a relatively high risk level, and a high risk level.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention collects and preprocesses three types of data—structure, environment, and personnel—overcoming the shortcomings of scattered and isolated multi-source data. Through comparison and fusion calculation, the probability of safety risks is obtained, achieving deep fusion analysis of multi-dimensional data and improving the comprehensiveness and accuracy of risk identification. By comparing stress and strain data with corresponding thresholds, the probability of deformation risk is obtained by weighted fusion after calculating the probabilities of the two types of risks, overcoming the limitations of single-parameter judgment and providing reliable support for structural safety early warning. By comparing wind speed, temperature, humidity, and dust data with corresponding thresholds, the probability of environmental impact is obtained by fusion after calculating sub-probabilities, overcoming the one-sidedness of single-parameter monitoring and comprehensively reflecting the degree of environmental impact on construction safety. By comparing personnel positioning, trajectory, and safety equipment usage data with corresponding standards, the probability of personnel violations is obtained by fusion after calculating the three types of sub-probabilities, overcoming the shortcomings of large blind spots and difficulty in real-time capture of violations in manual supervision and inspection, and improving the level of precision in personnel safety management.

[0016] This invention determines the work procedures based on the current construction progress and obtains the corresponding risk level by referring to a work procedure hazard level comparison table. This overcomes the shortcomings of static assessments in adapting to dynamic changes in construction, achieving dynamic linkage between risk assessment and construction progress, and improving the timeliness and relevance of the assessment. By comparing the probability of safety risk occurrence with two preset probabilities, a four-level comprehensive risk level is dynamically adjusted, overcoming the problems of false alarms and missed alarms in fixed-threshold binary early warnings, and improving the accuracy and lead time of early warnings.

[0017] On the other hand, this application also provides a dynamic early warning system for construction safety risks based on multi-source sensing data fusion, used to apply the above-mentioned dynamic early warning method for construction safety risks based on multi-source sensing data fusion, including: The multi-source data acquisition module is configured to acquire multi-source sensing data of the building construction process and preprocess the multi-source sensing data, which includes structural state data, environmental state data and personnel behavior data. The risk occurrence probability determination module is configured to have preset standard sensing data, and to determine the probability of safety risks occurring in the building construction process based on the standard sensing data and preprocessed multi-source sensing data. The module corresponding to the construction process is configured to acquire the process to be constructed and determine the construction risk level based on the process to be constructed. The safety risk dynamic early warning module is configured to determine a comprehensive risk level based on the probability of occurrence of the safety risk and the construction risk level, and to provide dynamic early warning based on the comprehensive risk level.

[0018] It is understood that the dynamic early warning method and system for construction safety risks based on multi-source sensing data fusion provided in this application have the same beneficial effects, and will not be elaborated here. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings: Figure 1 A flowchart illustrating a dynamic early warning method for construction safety risks based on multi-source sensing data fusion, provided in an embodiment of the present invention; Figure 2 This is a functional block diagram of a dynamic early warning system for construction safety risks based on multi-source sensing data fusion, provided in an embodiment of the present invention. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] In some embodiments of this application, see Figure 1 As shown, this embodiment provides a dynamic early warning method for construction safety risks based on multi-source sensing data fusion, including: Acquire multi-source sensing data of the building construction process and preprocess the multi-source sensing data, which includes structural state data, environmental state data and personnel behavior data; Standard sensing data is preset, and the probability of safety risks occurring during the building construction process is determined based on the standard sensing data and preprocessed multi-source sensing data. Obtain the construction process to be carried out, and determine the construction risk level based on the construction process to be carried out; The comprehensive risk level is determined based on the probability of occurrence of the aforementioned safety risks and the level of construction risks, and dynamic early warning is carried out based on the comprehensive risk level.

[0022] This invention integrates sensing data from three different sources—structure, environment, and personnel—to comprehensively cover various risk-inducing factors during construction, avoiding monitoring blind spots inherent in single-source data monitoring and effectively improving the comprehensiveness of safety risk identification. Simultaneously, it adjusts risk assessment results based on the inherent risk characteristics of the construction process, making the overall risk level determination more aligned with the actual construction progress. This enables dynamic risk warnings at different construction stages, improving the accuracy and timeliness of risk warnings and facilitating timely risk control measures by on-site management personnel to reduce the likelihood of safety accidents.

[0023] In some embodiments of this application, the structural state data includes: stress data and strain data of the construction structure; the environmental state data includes: wind speed data, temperature data, humidity data, and dust data; and the personnel behavior data includes: personnel positioning data, personnel trajectory data, and safety equipment usage data.

[0024] Understandably, detailed data classification provides a clear data source and foundation for subsequent data preprocessing, standard data comparison, and calculation of the probability of security risks, ensuring the operability and comprehensiveness of multi-source sensing data fusion analysis.

[0025] In some embodiments of this application, the preprocessing includes: timestamp calibration, format standardization, outlier removal, and missing value imputation of multi-source sensing data.

[0026] Understandably, by preprocessing the acquired multi-source sensing data, the differences in time reference, data format, data quality, and data integrity among the multi-source sensing data can be eliminated, enabling data from different sensors and acquisition devices to be fused, analyzed, and compared under a unified standard.

[0027] In some embodiments of this application, determining the probability of safety risks occurring during the construction process based on the standard sensing data and preprocessed multi-source sensing data includes: the standard sensing data including standard structural state data, standard environmental state data, and standard personnel behavior data; comparing the structural state data with the standard structural state data to determine the probability of deformation risk of the construction structure; comparing the environmental state data with the standard environmental state data to determine the probability of environmental impact during the construction process; comparing the personnel behavior data with the standard personnel behavior data to determine the probability of personnel violations during the construction process; and determining the probability of safety risks occurring during the construction process based on the deformation risk probability, environmental impact probability, and personnel violation probability.

[0028] Understandably, by breaking down safety risks into three independent dimensions—structural deformation risk, environmental impact risk, and personnel violation risk—and quantifying and assessing them separately, and then integrating them to obtain the overall probability of safety risk occurrence, a multi-faceted and comprehensive quantification of safety risks in the construction process is achieved, avoiding the one-sidedness of assessments based on a single dimension.

[0029] Specifically, standard perception data are pre-defined benchmark data used to measure the safety status of construction, including standard structural status data, standard environmental status data, and standard personnel behavior data. Standard structural status data characterizes the stress and deformation conditions that a construction structure should meet under safe conditions; standard environmental status data characterizes the range of environmental parameters allowed for construction operations; and standard personnel behavior data characterizes the behavioral boundaries and regulatory requirements for compliant operations by construction personnel.

[0030] After setting the standard sensing data, the three types of collected and preprocessed data were compared and analyzed with the corresponding standard data. Firstly, the structural state data was compared with standard structural state data. Structural state data reflects the actual stress and deformation state of the construction structure, while standard structural state data reflects the safety state of the structure within the design allowable range. By comparing the two item by item, the degree of deviation of the current structural state from the safety benchmark value can be determined, thereby determining the deformation risk probability of the construction structure. This deformation risk probability is used to quantitatively characterize the possibility of the construction structure becoming unstable, undergoing excessive deformation, or structural failure under the current load conditions. Secondly, the environmental state data was compared with standard environmental state data. Environmental state data reflects the actual environmental conditions at the construction site, such as wind speed, temperature, humidity, and dust, while standard environmental state data reflects the safe range of environmental parameters allowed for construction operations. By comparing each environmental parameter with the corresponding standard threshold range, the degree to which the current environmental conditions exceed the safe allowable range can be determined, thereby determining the environmental impact probability of the construction process. This environmental impact probability is used to quantitatively characterize the possibility that the current environmental conditions will have an adverse impact on construction safety. Thirdly, personnel behavior data is compared with standard personnel behavior data. Personnel behavior data reflects the current location, movement trajectory, and safety equipment wearing status of construction workers, while standard personnel behavior data reflects the area, prescribed routes, and safety protection requirements that construction workers should adhere to under compliant operating conditions. By comparing the actual personnel behavior data with the standard behavior data, it is possible to determine whether construction workers have violated regulations such as working outside designated areas, deviating from designated routes, or lacking protective measures, thereby determining the probability of personnel violations during the construction process. This probability of personnel violations is used to quantitatively characterize the degree to which current construction worker behavior deviates from safety regulations and the likelihood of it causing a safety accident.

[0031] After obtaining the probability of deformation risk, the probability of environmental impact, and the probability of personnel violations separately, these three probabilities are integrated to calculate the overall probability of safety risks occurring during the construction process. These three probabilities reflect the safety risks faced at the construction site from three different dimensions: structural safety, environmental conditions, and personnel behavior. The integrated calculation comprehensively reflects the overall safety risk level of the construction site under the coupled effects of multiple factors.

[0032] In some embodiments of this application, comparing the structural state data with standard structural state data to determine the deformation risk probability of the construction structure includes: the standard structural state data includes a standard stress threshold and a standard strain threshold; comparing the stress data with the standard stress threshold to determine the stress exceeding limit of the construction structure, and comparing the strain data with the standard strain threshold to determine the strain exceeding limit of the construction structure; calculating the stress risk probability and strain risk probability of the construction structure based on the stress exceeding limit and strain exceeding limit, respectively; and determining the deformation risk probability of the construction structure by weighted fusion based on the stress risk probability and strain risk probability of the construction structure.

[0033] Understandably, by comparing stress data and strain data with their respective standard thresholds, calculating stress risk probability and strain risk probability respectively, and then obtaining a comprehensive deformation risk probability through weighted fusion, the one-sidedness of judging structural deformation risk based on only a single parameter (stress or strain) in existing technologies is overcome, and a comprehensive quantitative assessment of the deformation risk of construction structures based on two parameters is achieved.

[0034] Specifically, the standard stress threshold and standard strain threshold are determined based on the design parameters, material performance parameters, and construction specifications of the construction structure.

[0035] Specifically, calculating the stress risk probability and strain risk probability of the construction structure includes: obtaining the time series sequences of stress and strain values ​​of the construction structure within a preset time window; calculating stress statistical characteristic values ​​based on the stress value time series sequences, including the stress mean, stress standard deviation, and stress maximum; calculating strain statistical characteristic values ​​based on the strain value time series sequences, including the strain mean, strain standard deviation, and strain maximum; determining the stress risk probability based on the degree of deviation between the stress statistical characteristic values ​​and the standard stress threshold; and determining the strain risk probability based on the degree of deviation between the strain statistical characteristic values ​​and the standard strain threshold.

[0036] Determining the stress risk probability specifically includes: setting a lower and upper stress warning threshold, which together constitute a standard stress threshold range; when the stress value of the construction structure falls within the standard stress threshold range, the stress state of the monitoring point is determined to be normal, and the stress risk probability is 0; when the stress value of the construction structure exceeds the standard stress threshold range, the ratio of the excess to the corresponding limit stress value of the monitoring point is calculated as the stress exceedance ratio of the monitoring point; based on the stress exceedance ratio, the stress risk probability of the construction structure is calculated using a preset stress risk probability mapping function, which is a monotonically increasing function.

[0037] Determining the strain risk probability specifically includes: setting a lower strain warning threshold and an upper strain warning threshold, which together constitute a standard strain threshold range; when the strain value of the construction structure falls within the standard strain threshold range, the strain state of the monitoring point is determined to be normal, and the strain risk probability is 0; when the strain value of the construction structure exceeds the standard strain threshold range, the ratio of the excess to the ultimate strain value of the construction structure is calculated as the strain exceedance ratio of the monitoring point; based on the strain exceedance ratio, the strain risk probability of the monitoring point is calculated through a preset strain risk probability mapping function, which is a monotonically increasing function.

[0038] In some embodiments of this application, the environmental state data is compared with standard environmental state data to determine the environmental impact probability of the building construction process. This includes: the standard environmental state data includes: standard wind speed threshold range, standard temperature threshold range, standard humidity threshold range, and standard dust threshold range; comparing the wind speed data with the standard wind speed threshold range to determine the wind speed environmental impact sub-probability; comparing the temperature data with the standard temperature threshold range to determine the temperature environmental impact sub-probability; comparing the humidity data with the standard humidity threshold range to determine the humidity environmental impact sub-probability; comparing the dust data with the standard dust threshold range to determine the dust environmental impact sub-probability; and fusing the wind speed environmental impact sub-probability, temperature environmental impact sub-probability, humidity environmental impact sub-probability, and dust environmental impact sub-probability to determine the environmental impact probability of the building construction process.

[0039] Understandably, by calculating the environmental impact sub-probabilities corresponding to each environmental parameter separately and then fusing them to obtain the comprehensive environmental impact probability, the problem of one-sided environmental impact assessment caused by monitoring only a single environmental parameter in the existing technology is overcome, and a comprehensive quantitative assessment of the coupled impact of multiple environmental factors on the construction site is achieved.

[0040] Specifically, the standard environmental condition data includes standard wind speed threshold range, standard temperature threshold range, standard humidity threshold range, and standard dust threshold range. Each threshold range is determined based on building construction safety specifications, construction procedure requirements, and environmental tolerance parameters of the construction structure. Specifically, determining the wind speed environmental impact sub-probability includes: setting a standard wind speed threshold range, which consists of a lower wind speed threshold and an upper wind speed threshold. The upper wind speed threshold is determined based on the safe wind speed limits for construction crane operations and high-altitude workers; obtaining the current measured wind speed value; when the measured wind speed value falls within the standard wind speed threshold range, the wind speed is considered normal, and the wind speed environmental impact sub-probability is 0; when the measured wind speed value is greater than the upper wind speed threshold, the ratio of the wind speed exceedance to the upper wind speed threshold is calculated as the wind speed exceedance ratio, and this ratio is mapped to the wind speed environmental impact sub-probability through a preset environmental risk probability mapping function; when the measured wind speed value is less than the lower wind speed threshold, the low wind speed is determined to have no environmental impact on construction, and the wind speed environmental impact sub-probability is 0.

[0041] Specifically, determining the temperature environment impact sub-probability includes: setting a standard temperature threshold range, which consists of a lower temperature threshold and an upper temperature threshold, determined based on the curing temperature requirements of construction materials and the comfort requirements of construction personnel; obtaining the current measured temperature value; when the measured temperature value falls within the standard temperature threshold range, the temperature state is determined to be normal, and the temperature environment impact sub-probability is 0; when the measured temperature value exceeds the standard temperature threshold range, the difference between the measured temperature value and the nearest boundary value of the standard temperature threshold range is calculated, and the ratio of the difference to the width of the standard temperature threshold range is calculated as the temperature exceedance ratio; and mapping the temperature exceedance ratio to the temperature environment impact sub-probability through a preset environmental risk probability mapping function.

[0042] Specifically, determining the humidity environmental impact sub-probability includes: setting a standard humidity threshold range, which consists of a lower humidity threshold and an upper humidity threshold, determined based on the humidity requirements for concrete curing of construction structures and the humidity requirements for safe operation of electrical equipment; obtaining the measured humidity value at the current moment; when the measured humidity value falls within the standard humidity threshold range, the humidity state is determined to be normal, and the humidity environmental impact sub-probability is 0; when the measured humidity value exceeds the standard humidity threshold range, the difference between the measured humidity value and the nearest boundary value of the standard humidity threshold range is calculated, and the ratio of the difference to the width of the standard humidity threshold range is calculated as the humidity exceedance ratio; and mapping the humidity exceedance ratio to the humidity environmental impact sub-probability through a preset environmental risk probability mapping function.

[0043] Specifically, determining the environmental impact sub-probability of dust pollution includes: setting a standard dust pollution threshold, which is the upper limit threshold for dust concentration, determined based on dust emission standards for construction sites and respiratory health and safety limits for construction workers; obtaining the measured dust concentration at the current moment; when the measured dust concentration is less than or equal to the upper limit threshold, the dust pollution state is determined to be normal, and the environmental impact sub-probability of dust pollution is 0; when the measured dust concentration is greater than the upper limit threshold, the ratio of the dust concentration exceeding the upper limit to the upper limit threshold is calculated as the dust exceedance ratio; and mapping the dust exceedance ratio to the environmental impact sub-probability of dust pollution through a preset environmental risk probability mapping function.

[0044] Specifically, determining the environmental impact probability of the construction process includes: obtaining the current construction procedure; determining the environmental weight coefficients for each environmental factor based on the construction procedure, including wind speed weight coefficient, temperature weight coefficient, humidity weight coefficient, and dust weight coefficient, with the sum of all weight coefficients being 1; multiplying the wind speed environmental impact sub-probability by the wind speed weight coefficient to obtain the wind speed weighted probability; multiplying the temperature environmental impact sub-probability by the temperature weight coefficient to obtain the temperature weighted probability; multiplying the humidity environmental impact sub-probability by the humidity weight coefficient to obtain the humidity weighted probability; multiplying the dust environmental impact sub-probability by the dust weight coefficient to obtain the dust weighted probability; and summing the wind speed weighted probability, temperature weighted probability, humidity weighted probability, and dust weighted probability to obtain the environmental impact probability of the construction process.

[0045] In some embodiments of this application, the personnel behavior data is compared with standard personnel behavior data to determine the probability of personnel violations during the construction process. This includes: the standard personnel behavior data includes the standard operating area range, the standard operating trajectory path, and the standard safety equipment usage status; the personnel positioning data is compared with the standard operating area range to determine the positioning boundary crossing sub-probability; the personnel trajectory data is compared with the standard operating trajectory path to determine the trajectory deviation sub-probability; the safety equipment usage data is compared with the standard safety equipment usage status to determine the protection deficiency sub-probability; and the positioning boundary crossing sub-probability, trajectory deviation sub-probability, and protection deficiency sub-probability are fused to determine the probability of personnel violations during the construction process.

[0046] Understandably, by calculating the sub-probabilities corresponding to each violation separately and then integrating them to obtain the comprehensive probability of personnel violations, the shortcomings of traditional manual supervision methods, which make it difficult to comprehensively and in real time capture personnel violations, are overcome, and intelligent, multi-dimensional quantitative assessment of the safety behavior of construction workers is achieved.

[0047] Specifically, standard personnel behavior data includes the scope of standard operating areas, standard operating routes, and the usage status of standard safety equipment. The scope of standard operating areas is determined based on the construction zoning plan and the hazardous area isolation zone. The standard operating routes are determined based on the established operating routes of the construction procedures. The usage status of standard safety equipment is determined based on the construction safety operating procedures. Specifically, determining the probability of location boundary violation includes: setting a standard operating area (SOP) range, which includes the authorized work area for each construction worker and an electronic fence for prohibited hazardous areas; acquiring the location coordinates of each construction worker in real time and determining whether the personnel location coordinates are within the authorized work area; when the personnel location coordinates are within the authorized work area, the location status is determined to be normal, and the probability of location boundary violation is 0; when the personnel location coordinates exceed the authorized work area and enter the prohibited hazardous area, the shortest distance between the personnel location coordinates and the boundary of the authorized work area is calculated, and the ratio of the shortest distance to a preset hazardous buffer distance is calculated as the location boundary violation degree value; based on the location boundary violation degree value, the probability of location boundary violation is calculated through a preset personnel violation probability mapping function, and the larger the location boundary violation degree value, the higher the probability of location boundary violation.

[0048] Specifically, when determining the sub-probability of location crossing the boundary, a correction is also made based on the duration of the personnel crossing the boundary. This includes: obtaining the continuous duration for which the personnel's location coordinates exceed the authorized work area; setting a threshold for the tolerance duration of crossing the boundary; when the continuous duration is less than or equal to the threshold, no probability correction is made; when the continuous duration is greater than the threshold, the ratio of the excess duration to the threshold is calculated as a time penalty factor; and the sub-probability of location crossing the boundary is multiplied by the time penalty factor to obtain the corrected sub-probability of location crossing the boundary.

[0049] Specifically, determining the trajectory deviation sub-probability includes: setting a standard operating trajectory path, which includes preset movement routes and preset work stop points corresponding to each construction process; acquiring personnel trajectory data within a preset time window to form the actual movement trajectory; calculating the trajectory deviation distance between the actual movement trajectory and the standard operating trajectory path, where the trajectory deviation distance is the spatial Euclidean distance between the actual position and the corresponding position on the standard trajectory path at the same moment; setting a trajectory deviation tolerance threshold, where the trajectory state is determined to be normal and the trajectory deviation sub-probability is 0 when the trajectory deviation distance is less than or equal to the trajectory deviation tolerance threshold; when the trajectory deviation distance is greater than the trajectory deviation tolerance threshold, calculating the difference between the deviation distance and the trajectory deviation tolerance threshold, and calculating the ratio of the difference to the width of the area where the standard operating trajectory path is located, as the trajectory deviation degree value; and calculating the trajectory deviation sub-probability based on the trajectory deviation degree value using a preset personnel violation probability mapping function.

[0050] Determining the sub-probability of protective equipment deficiency specifically includes: setting standard safety equipment usage states, including standard values ​​for helmet wearing, safety rope wearing, and reflective vest wearing; acquiring the actual safety equipment usage states of construction personnel through image recognition sensors or equipment wearing sensors, including actual helmet wearing, safety rope wearing, and reflective vest wearing; comparing the actual safety equipment usage states with the standard safety equipment usage states item by item to generate a deficiency judgment result for each protective equipment; when the actual state matches the standard state, it is judged as not missing; otherwise, it is judged as missing; counting the number of missing protective equipment items and calculating the ratio of the number of missing items to the total number of protective equipment items as the protective deficiency rate; and calculating the protective deficiency sub-probability based on the protective deficiency rate using a preset personnel violation probability mapping function.

[0051] In some embodiments of this application, obtaining the construction process to be constructed and determining the construction risk level based on the construction process to be constructed includes: obtaining the current construction progress and obtaining the construction process to be constructed according to the construction progress; determining the corresponding construction risk level based on the construction process to be constructed according to a pre-set process hazard level comparison table; the process hazard level comparison table includes several construction processes, and each construction process has a corresponding construction risk level; the construction risk level includes a low risk level and a general risk level.

[0052] Understandably, by pre-linking and standardizing construction procedures with risk levels, the construction risk level can be dynamically switched according to the construction progress, enabling risk assessment to match the actual construction stage and overcoming the problem that existing static assessment methods cannot reflect the differences in risk between different procedures.

[0053] Specifically, this involves obtaining the current construction progress. Construction progress reflects the amount of work completed and the current stage of construction in the actual project. It can be obtained by reading actual progress information entered into the construction management system, querying construction log records, or utilizing progress monitoring equipment installed at the construction site. Construction progress is typically described in the form of time milestones, percentage of work completed, or specific construction stages.

[0054] Based on the current construction progress, the next construction steps to be performed, i.e., the pending construction steps, are determined. The construction process of a building project consists of multiple steps unfolding sequentially according to a certain logical order, with different construction progresses corresponding to different pending construction steps. For example, in the foundation construction stage, pending construction steps may include earthwork excavation, subgrade construction, rebar tying, formwork installation, and concrete pouring; in the main structure construction stage, pending construction steps may include column rebar tying, beam and slab formwork installation, concrete pouring, and curing; in the decoration and finishing stage, pending construction steps may include plastering, painting, and curtain wall installation. By comparing the current construction progress with the pre-prepared construction schedule, one or more pending construction steps that will be implemented at the current moment can be determined.

[0055] After determining the construction procedures, the corresponding construction risk level is determined based on a pre-set procedure hazard level comparison table. The procedure hazard level comparison table is a configuration table developed in advance based on a comprehensive assessment of the inherent hazard types, the probability of hazard occurrence, and the severity of accident consequences of different construction procedures. This comparison table includes all or major construction procedures involved in the construction process, and each construction procedure is assigned a corresponding construction risk level.

[0056] Specifically, the construction risk levels in the process hazard level comparison table include two levels: low risk and moderate risk. The low risk level applies to construction processes with few hazards, low accident rates, and minor consequences, such as site leveling, material handling, and general masonry work. The moderate risk level applies to construction processes with a certain number of hazards, a moderate accident rate, or relatively serious consequences, such as rebar tying, formwork installation, and concrete pouring. It should be noted that the construction risk levels here are based solely on the inherent risks of the process itself and have not yet been adjusted for real-time safety risk occurrence probabilities, providing an initial benchmark for subsequent dynamic adjustments to the overall risk level.

[0057] In some embodiments of this application, determining a comprehensive risk level based on the probability of occurrence of the safety risk and the construction risk level includes: pre-setting a first probability of occurrence and a second probability of occurrence, wherein the first probability of occurrence is less than the second probability of occurrence; adjusting the construction risk level according to the relationship between the probability of occurrence of the safety risk and the first and second probabilities of occurrence to obtain a comprehensive risk level; if the probability of occurrence of the safety risk is less than the first probability of occurrence, then the construction risk level is not adjusted, and the construction risk level is set as the comprehensive risk level; if the probability of occurrence of the safety risk is greater than or equal to the first probability of occurrence, and the probability of occurrence of the safety risk is less than the second probability of occurrence, then the construction risk level is adjusted up one level to obtain a comprehensive risk level; if the probability of occurrence of the safety risk is greater than or equal to the second probability of occurrence, then the construction risk level is adjusted up two levels to obtain a comprehensive risk level; wherein, the comprehensive risk level includes a low risk level, a general risk level, a relatively high risk level, and a high risk level.

[0058] Understandably, by using the probability of safety risks occurring in real time as a dynamic adjustment factor to dynamically correct the construction risk level determined based on the inherent risks of the process, the organic combination of static process risks and dynamic monitoring risks is achieved. This enables the final comprehensive risk level to simultaneously reflect the inherent dangers of the process and the real-time safety status, solving the problem that the risk level in the existing technology is fixed and cannot be dynamically adjusted with real-time monitoring data. At the same time, through the division of three-level early warning intervals and the differentiated adjustment of corresponding levels, refined graded early warning is achieved.

[0059] Specifically, the comprehensive risk level is determined based on the probability of safety risk occurrence and the level of construction risk, which is achieved through the following method: A first probability of occurrence and a second probability of occurrence are pre-set, where the first probability of occurrence is less than the second probability of occurrence. The first and second probabilities of occurrence are two thresholds used to classify the probability levels of safety risks, dividing the range of probability values ​​into three different intervals, each corresponding to a different level of risk. These two thresholds can be flexibly set according to the project type, construction environment, and safety management requirements. For example, the first probability of occurrence can be set to 0.3, the second probability to 0.7, or other values ​​can be set based on actual engineering experience.

[0060] After setting the first and second probabilities of occurrence, the construction risk level is adjusted based on the relationship between the safety risk occurrence probability and the first and second probabilities, resulting in a comprehensive risk level. The comprehensive risk level is determined by the adjusted construction risk level and includes four levels: low risk, moderate risk, relatively high risk, and high risk. These four levels are progressively higher, with the risk level increasing sequentially. After adjustment, the comprehensive risk level incorporates both the inherent risk attributes of the construction process itself and the dynamic safety status reflected by real-time multi-source sensing data. This allows for a more comprehensive and accurate reflection of the current actual risk level at the construction site, providing a reliable basis for subsequent dynamic early warning.

[0061] On the other hand, see Figure 2 As shown, this application also provides a dynamic early warning system for construction safety risks based on multi-source sensing data fusion, used to apply the above-mentioned dynamic early warning method for construction safety risks based on multi-source sensing data fusion, including: The multi-source data acquisition module is configured to acquire multi-source sensing data of the building construction process and preprocess the multi-source sensing data, which includes structural state data, environmental state data and personnel behavior data. The risk occurrence probability determination module is configured to have preset standard sensing data, and to determine the probability of safety risks occurring in the building construction process based on the standard sensing data and preprocessed multi-source sensing data. The module corresponding to the construction process is configured to acquire the process to be constructed and determine the construction risk level based on the process to be constructed. The safety risk dynamic early warning module is configured to determine a comprehensive risk level based on the probability of occurrence of the safety risk and the construction risk level, and to provide dynamic early warning based on the comprehensive risk level.

[0062] This invention effectively solves the problems of single data source, insufficient integration, static assessment and delayed warning in existing construction safety risk early warning technologies by using a series of technical means such as multi-source sensing data fusion, multi-dimensional risk comprehensive quantification, dynamic correlation of construction procedures and refined risk level classification and adjustment. It realizes dynamic and accurate assessment and early warning of safety risks throughout the construction process, and significantly improves the safety management level of construction sites.

[0063] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0065] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0066] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0067] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A dynamic early warning method for construction safety risks based on multi-source sensing data fusion, characterized in that, include: Acquire multi-source sensing data of the building construction process and preprocess the multi-source sensing data, which includes structural state data, environmental state data and personnel behavior data; Standard sensing data is preset, and the probability of safety risks occurring during the building construction process is determined based on the standard sensing data and preprocessed multi-source sensing data. Obtain the construction process to be carried out, and determine the construction risk level based on the construction process to be carried out; The comprehensive risk level is determined based on the probability of occurrence of the aforementioned safety risks and the level of construction risks, and dynamic early warning is carried out based on the comprehensive risk level.

2. The method for dynamic early warning of construction safety risks based on multi-source sensing data fusion as described in claim 1, characterized in that, The structural status data includes: stress data and strain data of the constructed structure; The environmental status data includes: wind speed data, temperature data, humidity data, and dust data; The personnel behavior data includes: personnel location data, personnel trajectory data, and safety equipment usage data.

3. The method for dynamic early warning of construction safety risks based on multi-source sensing data fusion as described in claim 1, characterized in that, The preprocessing includes: timestamp calibration, format standardization, outlier removal, and missing value imputation of multi-source sensing data.

4. The method for dynamic early warning of construction safety risks based on multi-source sensing data fusion according to claim 2, characterized in that, The probability of safety risks occurring during the construction process is determined based on the standard sensing data and the preprocessed multi-source sensing data, including: The standard perception data includes standard structural status data, standard environmental status data, and standard personnel behavior data; The structural condition data is compared with standard structural condition data to determine the probability of deformation risk of the construction structure. The environmental condition data is compared with standard environmental condition data to determine the probability of environmental impact during the building construction process; The personnel behavior data is compared with standard personnel behavior data to determine the probability of personnel violations during the construction process; The probability of safety risks occurring during the construction process is determined based on the probability of deformation risk, the probability of environmental impact, and the probability of personnel violations.

5. The method for dynamic early warning of construction safety risks based on multi-source sensing data fusion according to claim 4, characterized in that, The structural condition data is compared with standard structural condition data to determine the probability of deformation risk of the construction structure, including: The standard structural condition data includes standard stress threshold and standard strain threshold; The stress data is compared with the standard stress threshold to determine the degree of stress exceeding the limit of the construction structure, and the strain data is compared with the standard strain threshold to determine the degree of strain exceeding the limit of the construction structure. Based on the stress and strain exceedance levels, the stress risk probability and strain risk probability of the construction structure are calculated respectively. Based on the stress risk probability and strain risk probability of the construction structure, the deformation risk probability of the construction structure is determined by weighted fusion.

6. The method for dynamic early warning of construction safety risks based on multi-source sensing data fusion according to claim 4, characterized in that, The environmental condition data is compared with standard environmental condition data to determine the probability of environmental impact during the building construction process, including: The standard environmental condition data includes: standard wind speed threshold range, standard temperature threshold range, standard humidity threshold range, and standard dust threshold range; The wind speed data is compared with the standard wind speed threshold range to determine the wind speed environmental impact sub-probability; the temperature data is compared with the standard temperature threshold range to determine the temperature environmental impact sub-probability; the humidity data is compared with the standard humidity threshold range to determine the humidity environmental impact sub-probability; the dust data is compared with the standard dust threshold range to determine the dust environmental impact sub-probability. The environmental impact probability of the building construction process is determined by fusing the environmental impact subprobabilities of wind speed, temperature, humidity, and dust.

7. The method for dynamic early warning of construction safety risks based on multi-source sensing data fusion according to claim 4, characterized in that, The personnel behavior data is compared with standard personnel behavior data to determine the probability of personnel violations during the construction process, including: The standard personnel behavior data includes the standard operating area range, standard operating trajectory path, and standard safety equipment usage status; The personnel positioning data is compared with the standard operating area range to determine the probability of positioning exceeding the boundary; the personnel trajectory data is compared with the standard operating trajectory path to determine the probability of trajectory deviation; the safety equipment usage data is compared with the standard safety equipment usage status to determine the probability of protection deficiency. The probability of personnel violations during the construction process is determined by fusing the sub-probability of location boundary violation, trajectory deviation, and protection deficiency.

8. The method for dynamic early warning of construction safety risks based on multi-source sensing data fusion according to claim 1, characterized in that, Obtain the construction process to be carried out, and determine the construction risk level based on the construction process to be carried out, including: Obtain the current construction progress, and obtain the construction procedures to be carried out based on the construction progress; Based on a pre-set process hazard level comparison table, the corresponding construction risk level is determined according to the process to be constructed; The process hazard level comparison table includes several construction processes, each with a corresponding construction risk level; the construction risk level includes low risk level and general risk level.

9. The method for dynamic early warning of construction safety risks based on multi-source sensing data fusion according to claim 8, characterized in that, The comprehensive risk level is determined based on the probability of occurrence of the aforementioned safety risks and the level of construction risks, including: A first probability of occurrence and a second probability of occurrence are preset, wherein the first probability of occurrence is less than the second probability of occurrence; The construction risk level is adjusted based on the relationship between the probability of occurrence of the safety risk and the first probability of occurrence and the second probability of occurrence to obtain a comprehensive risk level; If the probability of occurrence of the safety risk is less than the first probability of occurrence, then the construction risk level will not be adjusted, and the construction risk level will be set as the comprehensive risk level. If the probability of occurrence of the safety risk is greater than or equal to the first probability of occurrence, and the probability of occurrence of the safety risk is less than the second probability of occurrence, then the construction risk level is adjusted up by one level to obtain the comprehensive risk level. If the probability of occurrence of the safety risk is greater than or equal to the second probability of occurrence, the construction risk level will be adjusted upward by two levels to obtain a comprehensive risk level; wherein, the comprehensive risk level includes a low risk level, a general risk level, a relatively high risk level, and a high risk level.

10. A dynamic early warning system for construction safety risks based on multi-source sensing data fusion, used to apply the dynamic early warning method for construction safety risks based on multi-source sensing data fusion as described in any one of claims 1-9, characterized in that, include: The multi-source data acquisition module is configured to acquire multi-source sensing data of the building construction process and preprocess the multi-source sensing data, which includes structural state data, environmental state data and personnel behavior data. The risk occurrence probability determination module is configured to have preset standard sensing data, and to determine the probability of safety risks occurring in the building construction process based on the standard sensing data and preprocessed multi-source sensing data. The module corresponding to the construction process is configured to acquire the process to be constructed and determine the construction risk level based on the process to be constructed. The safety risk dynamic early warning module is configured to determine a comprehensive risk level based on the probability of occurrence of the safety risk and the construction risk level, and to provide dynamic early warning based on the comprehensive risk level.

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