Construction site accident hidden danger management method and system based on industrial internet of things

Abstract

This application provides a method and system for managing construction site accident hazards based on the Industrial Internet of Things (IIoT), relating to the field of IIoT technology. The method includes: determining a set of key hazard management characteristic parameters; determining a preset risk assessment threshold that meets the requirements of the construction industry; configuring a correction coefficient for the preset risk assessment threshold based on factors such as workload intensity, equipment aging, and the accumulation trend of site hazards, and setting a dynamic constraint mechanism for hazards in conjunction with the set of key hazard management characteristic parameters; and simultaneously verifying the accident hazard investigation and analysis parameters based on the dynamic constraint mechanism and the on-site safety intervention mechanism, outputting a construction site accident hazard control strategy. This application solves the technical problem in existing technologies where static and isolated hazard assessment standards are severely out of sync with the dynamic and complex working conditions of construction sites, leading to untimely hazard identification and further affecting the efficiency of accident hazard control. It achieves timely hazard identification and improves the efficiency of accident hazard control.

Description

Technical Field

[0001] This application relates to the field of industrial Internet of Things (IoT) technology, specifically to a method and system for managing construction site accident hazards based on industrial IoT. Background Technology

[0002] In the field of construction site safety management, existing technologies mainly rely on pre-set fixed safety standard thresholds and periodic hazard inspections. These are often based on fixed rules or experience and lack the ability to respond in real time to changes in the site environment, resulting in the inability to promptly detect safety hazards during construction. This static and isolated approach is usually unable to adapt to the influence of dynamic factors such as equipment aging, changes in workload, and personnel behavior, thus causing untimely hazard identification, increasing the risk of accidents, and affecting the effective control of hazards.

[0003] In summary, existing technologies suffer from a technical problem where static and isolated hazard assessment criteria are severely out of sync with the dynamic and complex conditions of construction sites, leading to untimely hazard identification and further impacting the efficiency of accident hazard management. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for managing construction site accident hazards based on the Industrial Internet of Things, in order to solve the technical problem in the prior art that the static and isolated hazard judgment criteria are seriously out of touch with the dynamic and complex construction site conditions, resulting in untimely hazard identification and further affecting the efficiency of accident hazard control.

[0005] To achieve the above objectives, this application provides a method and system for managing construction site accident hazards based on the Industrial Internet of Things.

[0006] Firstly, this application provides a method for managing construction site accident hazards based on the Industrial Internet of Things (IIoT). This method is implemented through an IIoT-based construction site accident hazard management system. The method includes: using an IIoT communication gateway to connect to a management host, associating site layout factors, equipment operation factors, and personnel operation factors to determine a set of key hazard management characteristic parameters; determining a preset risk assessment threshold that meets the requirements of the construction industry based on the construction site's safety management standards and hazard investigation specifications; configuring a correction coefficient for the preset risk assessment threshold based on workload intensity, equipment aging degree, and the accumulation trend of site hazards, and setting a dynamic hazard constraint mechanism in conjunction with the set of key hazard management characteristic parameters; and synchronously verifying the accident hazard investigation and analysis parameters corresponding to the management host based on the dynamic hazard constraint mechanism and the on-site safety intervention mechanism, and outputting the construction site's accident hazard control strategy.

[0007] Optionally, the site layout factors include work area division, safety passage width, and hazardous area isolation and protection coverage data collected by site-level monitoring nodes, which are deployed in the core work area, hazardous area boundaries, and material storage yard of the construction site.

[0008] Optionally, the equipment operating factors include equipment runtime, fault alarm records, and maintenance cycle data collected based on equipment-level monitoring nodes, which are deployed in key parts of the construction machinery and electrical control boxes.

[0009] Optionally, the personnel operation factors include PPE wearing compliance rate, continuous working time, and high-altitude operation qualification data collected based on personnel-level monitoring nodes, which are deployed on safety helmets, reflective vests, and smart bracelets.

[0010] Optionally, the deformation state of the support structure, the frequency of equipment failures, and the fatigue state of personnel are used as dynamic correction factors for the preset risk judgment threshold. Risk levels are mapped to these dynamic correction factors to generate a work surface safety score. When the work surface safety score is lower than the safety threshold, the preset risk judgment threshold adjustment based on the correction coefficient is frozen, and the dynamic correction factor is incorporated into the on-site safety intervention mechanism as a trigger condition for suspending hazardous operations. Otherwise, within the permissible range of the work surface safety status, the dynamic correction factor is used as a weight constraint to apply safety limits to the correction coefficients configured based on work load intensity, equipment aging degree, and the accumulation trend of site hazards, generating a comprehensive adjustment factor. The preset risk judgment threshold is updated based on the comprehensive adjustment factor.

[0011] Optionally, the characteristics of hazard evolution and deterioration are extracted; based on the risk increment value of the hazard evolution and deterioration characteristics and the safety protection level under the current work scenario, a potential accident acceleration factor is determined; the similarity between the triggering conditions of historical accident modes in the hazard evolution and deterioration characteristics and the current hazard parameters is used to determine the potential accident transmission path; and the on-site safety intervention mechanism is set according to the potential accident acceleration factor and the potential accident transmission path.

[0012] Optionally, a historical accident case database is connected, and matching historical events are extracted by combining the hazard management feature matrix; the duration of potential hazard out of control is determined by the cumulative periodic distribution of hazards and the current hazard growth rate of the matching historical events; the scope of potential accident impact is determined based on the matching historical events; and the dynamic constraint mechanism for the hazard is set based on the duration of potential hazard out of control and the scope of potential accident impact.

[0013] Optionally, the bearing capacity limit of the hazardous work surface, the response speed of hazard investigation, and the coverage radius of emergency resources are determined through the set of key hazard management feature parameters; feature correlation analysis is performed based on the bearing capacity limit of the hazardous work surface, the response speed of hazard investigation, and the coverage radius of emergency resources to construct the hazard management feature matrix.

[0014] Optionally, the industrial IoT communication gateway sends the hazard investigation incentive signal to the site-level monitoring node, equipment-level monitoring node, and personnel-level monitoring node; when the construction site is carrying out operations, the site-level monitoring node, equipment-level monitoring node, and personnel-level monitoring node receive the hazard investigation incentive signal and use a hierarchical collaborative mechanism based on the distribution of hazard risks to perform the allocation and management of hazard monitoring tasks.

[0015] Secondly, this application also provides a construction site accident hazard management system based on the Industrial Internet of Things (IIoT), used to execute the construction site accident hazard management method based on the Industrial Internet of Things as described in the first aspect. The construction site accident hazard management system based on the Industrial Internet of Things includes: a feature parameter determination module, used to connect to a management host using an IIoT communication gateway, and associate site layout factors, equipment operation factors, and personnel operation factors to determine a set of key hazard management feature parameters; a threshold determination module, used to determine a preset risk judgment threshold that meets the requirements of the construction industry based on the safety management standards and hazard investigation specifications of the construction site; a constraint setting module, used to configure a correction coefficient for the preset risk judgment threshold based on work load intensity, equipment aging degree, and site hazard accumulation trend, and to set a dynamic hazard constraint mechanism in conjunction with the set of key hazard management feature parameters; and a synchronization verification module, used to synchronously verify the accident hazard investigation and analysis parameters corresponding to the management host based on the dynamic hazard constraint mechanism and the on-site safety intervention mechanism, and output the accident hazard control strategy for the construction site.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0017] By connecting the management host through an industrial IoT communication gateway, and associating site layout factors, equipment operation factors, and personnel operation factors at the construction site, a set of key hazard management characteristic parameters is determined. Based on the construction site's safety management standards and hazard investigation specifications, a preset risk assessment threshold that meets construction industry requirements is determined. Correction coefficients for the preset risk assessment thresholds are configured based on workload intensity, equipment aging, and the accumulation trend of site hazards. Combined with the key hazard management characteristic parameter set, a dynamic hazard constraint mechanism is set. Based on this dynamic constraint mechanism and on-site safety intervention mechanism, the accident hazard investigation and analysis parameters corresponding to the management host are synchronously verified, and an accident hazard control strategy for the construction site is output. In other words, by utilizing an industrial IoT communication gateway to comprehensively consider multiple factors at the construction site, determine a set of key hazard management characteristic parameters, set risk assessment thresholds according to safety management standards and hazard investigation specifications, configure correction coefficients, form a dynamic hazard constraint mechanism, verify the hazard investigation and analysis parameters of the management host, and output an accident hazard control strategy. This achieves dynamic, accurate, and timely hazard identification, thereby significantly improving the efficiency of accident hazard control.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the construction site accident hazard management method based on the Industrial Internet of Things (IIoT) proposed in this application.

[0021] Figure 2 This is a schematic diagram of the construction site accident hazard management system based on the Industrial Internet of Things (IIoT) of this application.

[0022] Explanation of reference numerals in the attached figures: Feature parameter determination module 11, threshold determination module 12, constraint setting module 13, and synchronization verification module 14. Detailed Implementation

[0023] This application provides a method and system for managing construction site safety hazards based on the Industrial Internet of Things (IIoT). It addresses the technical problem in existing technologies where static, isolated hazard assessment criteria are severely out of sync with the dynamic and complex conditions of construction sites, leading to untimely hazard identification and further impacting the efficiency of hazard control. By comprehensively considering various factors at the construction site, a set of key hazard management characteristic parameters is determined. Based on safety management standards and hazard investigation specifications, risk assessment thresholds are set, and correction coefficients are configured to form a dynamic hazard constraint mechanism. This mechanism verifies the hazard investigation and analysis parameters of the management host and outputs hazard control strategies. This achieves dynamic, accurate, and timely hazard identification, thereby significantly improving the efficiency of hazard control.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a method for managing construction site accident hazards based on the Industrial Internet of Things (IIoT). The method is applied to an IIoT-based construction site accident hazard management system, and specifically includes the following steps:

[0026] By using an industrial IoT communication gateway to connect to the management host, and associating site layout factors, equipment operation factors, and personnel operation factors at the construction site, a set of key hazard management characteristic parameters can be determined.

[0027] Furthermore, this application also includes the following steps: the site layout factors include work area division, safety passage width, and hazardous area isolation and protection coverage data collected based on site-level monitoring nodes, and the site-level monitoring nodes are deployed in the core work area, hazardous area boundaries, and material storage yard of the construction site.

[0028] Furthermore, this application also includes the following steps: the equipment operating factors include equipment runtime, fault alarm records, and maintenance cycle data collected based on equipment-level monitoring nodes, which are deployed in key parts of the construction machinery and electrical control boxes.

[0029] Furthermore, this application also includes the following steps: the personnel operation factors include PPE wearing compliance rate, continuous working time, and high-altitude operation qualification data collected based on personnel-level monitoring nodes, wherein the personnel-level monitoring nodes are deployed on safety helmets, reflective vests, and smart bracelets.

[0030] Specifically, IoT deployment is implemented at the construction site. Various sensors are installed in key areas such as the foundation pit support structure, construction access roads, and material hoisting areas, serving as site-level nodes. Monitoring modules are integrated or added to the power systems and control cabinets of machinery such as tower cranes, construction elevators, and large pile drivers, serving as equipment-level nodes. Smart safety helmets, wristbands, and qualification cards with built-in sensors are provided to on-site workers, serving as personnel-level nodes. All these nodes transmit the raw data streams they collect to the industrial IoT communication gateway in real time via wireless or wired methods. The gateway, acting as a protocol conversion and data aggregation center, uniformly encapsulates heterogeneous data and uploads it to the management host via the construction site network. The industrial IoT communication gateway is a network hub device deployed at the construction site, responsible for uniformly receiving, converting, and aggregating data from monitoring terminals (nodes) of different types and communication protocols, and reliably transmitting it to the management host. It is the nerve center connecting the on-site sensing layer and the cloud / local management platform. For wired nodes, such as sensors deployed on fixed structures or modules within equipment control cabinets, industrial IoT communication gateways typically provide standard industrial bus interfaces, using mature industrial protocols such as RTU / TCP for data exchange. The gateway has a built-in protocol parser capable of reading data points from these protocol frames. For wireless nodes, such as sensors deployed on mobile devices or in large areas, LoRa or NB-IoT modules can be used for access. The gateway acts as a LoRa concentrator or communicates with NB-IoT nodes via a cellular network. Devices such as smart bracelets and safety helmets often integrate Bluetooth Low Energy (BLE). The gateway can collect data from personnel nodes by using multiple BLE beacons deployed in key areas of the construction site as relays, then aggregate the data to the gateway. Personnel node qualification cards are read by card readers at specific check-in points, and the readers upload the data to the gateway via wired or Wi-Fi connections. Internally, the gateway runs corresponding protocol adapters for each access protocol, understands the frame structure of specific protocols, and extracts valid sensor ID-measurement-timestamp triplet information. All extracted triplet information is then converted into a unified data model defined within the system, i.e., normalized. The normalized data is then encapsulated into data packets suitable for standard application layer protocols used in uplink transmission.

[0031] At the construction site, multiple site-level monitoring nodes are deployed, typically installed at key locations such as work areas, hazardous area boundaries, and material storage areas. These nodes are responsible for collecting site layout data, including the division of work areas, the width of safety passages, and the isolation and protection coverage of hazardous areas. This data helps the management system assess the safety conditions of the construction site and determine whether there are any layouts that do not comply with safety regulations.

[0032] Install equipment-level monitoring nodes on key construction machinery, such as the electrical control box and critical parts of the hydraulic system of cranes, to monitor information such as equipment runtime, fault alarms, and maintenance cycles. Equipment runtime reflects the equipment's usage load; fault alarm records and maintenance cycle data help determine if there are potential fault risks. For example, if a crane has accumulated three fault alarms in the past six months, with each alarm less than 30 days apart, this data can be used to predict the probability of equipment failure and allow for timely repair or replacement.

[0033] For on-site construction workers, personnel-level monitoring nodes are installed, such as sensors on safety helmets, reflective vests, and smart wristbands, to monitor real-time information such as PPE wearing compliance rate, continuous working time, and qualifications for working at heights. This helps the management system assess the safety of on-site personnel and identify potential safety hazards, such as unqualified personnel working at heights. Miniature pressure sensors or contact sensors are integrated into key stress points inside the safety helmet lining, such as the top and sides of the head. When the safety helmet is correctly worn and the chin strap is fastened, the sensor is triggered by continuous pressure, generating a wearing signal. Simultaneously, a low-power Bluetooth beacon can be embedded inside the helmet shell for identification and rough positioning. Magnetic contact sensors are integrated into specific locations on the reflective vest, such as the shoulder and chest buckle. When the vest is correctly worn and closed via Velcro or zipper, the circuit is connected, generating a wearing signal. A low-power Bluetooth beacon can also be integrated for identification binding. The management host receives status signals from the safety helmet and reflective vest sensors of specific personnel. PPE wearing compliance rate can be calculated in real time as the ratio of the number of correctly worn PPE items to the number of required PPE items. For example, if a safety helmet and reflective vest are required in a specific work area, the compliance rate is 100% if both sensor signals are valid; otherwise, it is 50%. The smart bracelets worn by personnel have built-in motion and heart rate sensors to continuously monitor their physiological activity. The management host uses the continuous activity data uploaded by the bracelets, such as a heart rate higher than the resting threshold and regular body movement characteristics, combined with the electronic fence information of the work area obtained through bracelet or helmet tag positioning, to comprehensively determine whether the personnel are on duty. Continuous work time is the cumulative time from the start to the end of the work, such as entering a rest area or prolonged stillness indicated by motion signals. Thresholds are set, such as automatic warnings when the continuous work time exceeds a specified limit (e.g., 8 hours). Information on each construction worker's high-altitude work qualification certificate, such as certificate number, validity period, and permitted work type, is entered into the management system upon entry and bound to a unique electronic identifier, such as an RFID chip, assigned to that worker. The electronic tag can be integrated into a tag within a smart wristband or safety helmet. The construction site is defined as the entrance or key platform of the high-altitude work area, such as scaffolding access points or suspended platforms, and fixed RFID readers are deployed. When personnel wearing smart wristbands or safety helmets enter this area, the area reader automatically reads their electronic tag and sends the tag ID to the management host. Upon receiving the ID, the management host immediately queries the associated high-altitude work qualification database to verify the validity of the certificate and determine if the current work type is within the scope of the qualification permit. The verification result—if the work is permitted / qualified, or if not, or if an alarm is triggered—is fed back in real-time to the on-site audible and visual alarms, the management personnel's terminal, and the personnel's wristband, with vibration alerts possible.

[0034] The data processing engine in the management host then begins to work, parsing and preprocessing the raw data. It doesn't simply store it, but performs deep correlation and feature extraction according to predefined rules. Based on the real-time status of the construction site, it assesses key hazard management characteristics to determine the hazard risk level and generate corresponding hazard control strategies. For example, it links sensor data from different geographical locations with a BIM (Building Information Modeling) digital construction site map to form a digital mapping of work area divisions; it accumulates the operating pulses of equipment nodes to calculate precise equipment operating time; and it analyzes the continuity of personnel node signals to determine PPE wearing compliance rate and continuous working time. These processed indicators are combined to form a set of key hazard management characteristic parameters, used to quantify the core indicator set of the construction site's safety risk status, including site, equipment, and personnel. Through the industrial IoT communication gateway, it can collect and automatically analyze on-site data in real time, monitoring factors such as equipment operation, personnel work, and site layout, identifying potential risks in advance, and providing targeted safety warnings.

[0035] Based on the safety management standards and hazard investigation specifications of the construction site, a preset risk assessment threshold that meets the requirements of the construction industry is determined.

[0036] Specifically, this involves obtaining safety management standards and hazard identification specifications for construction sites. Safety management standards stipulate the baseline requirements for safety design, mandatory safety protection measures, and statutory limits for key parameters. Hazard identification specifications typically refer to procedures, lists, and processes developed by enterprises or projects based on the aforementioned standards and their own experience to guide routine safety inspections. These specifications define what to inspect, how to inspect, when to inspect, and how to record and report findings. They represent the operational level of the standards.

[0037] All quantifiable safety control indicators are extracted from safety management standards and hazard identification specifications to obtain preset risk assessment thresholds that meet the requirements of the construction industry, serving as the baseline for automatic risk assessment. For example, the regulation that outdoor high-altitude operations should be suspended when gusts exceed level 5 (wind speed 8.0 to 10.7 m / s) is quantified into a preset wind speed threshold: a wind speed exceeding 8.0 m / s for 3 consecutive seconds triggers a high-altitude operation wind risk warning. Requirements in inspection specifications, such as monthly checks on the tightness of standard tower crane bolts, are converted into a time threshold: after 30 days since the last bolt inspection record was entered, an inspection task is automatically generated and a risk warning is displayed.

[0038] Data collected in real time by on-site monitoring equipment, such as equipment runtime, compliance of personnel wearing PPE, and whether the work area meets safety passage standards, will be compared with preset risk assessment thresholds to determine if any situation exceeds safety limits. The preset risk assessment thresholds need to be dynamically adjusted based on the actual conditions of the construction site, such as adjusting the original standards according to actual equipment usage or changes in site layout. For example, if a piece of equipment experiences frequent malfunctions, its usage time may need to be shortened or its alarm frequency increased.

[0039] By comparing real-time data with preset risk thresholds, problems can be detected before they occur, thus avoiding delayed detection due to post-incident inspections. Risk thresholds are dynamically adjusted based on the specific conditions of the construction site to ensure that management standards always meet actual needs.

[0040] By configuring the correction coefficient of the preset risk judgment threshold based on the intensity of work load, the degree of equipment aging, and the accumulation of site hazards, and combining the key hazard management characteristic parameter set, a dynamic constraint mechanism for hazards is set.

[0041] Furthermore, this application also includes the following steps: using the deformation state of the support structure, the frequency of equipment failures, and the fatigue state of personnel as dynamic correction factors for the preset risk judgment threshold; mapping the dynamic correction factors to risk levels to generate a work surface safety score; when the work surface safety score is lower than the safety threshold, freezing the preset risk judgment threshold adjustment based on the correction coefficient, and incorporating the dynamic correction factor into the on-site safety intervention mechanism as a trigger condition for suspending hazardous operations; otherwise, within the allowable range of the work surface safety status, using the dynamic correction factor as a weight constraint to apply a safety limit to the correction coefficient configured based on the intensity of the work load, the degree of equipment aging, and the accumulation trend of site hazards, generating a comprehensive adjustment factor; updating the preset risk judgment threshold based on the comprehensive adjustment factor.

[0042] Specifically, the core factor for workload intensity is personnel work factors, used to measure the task pressure and safety risk exposure level of personnel per unit time; the core factor for equipment aging is equipment operation factors, used to measure the performance degradation and failure tendency of construction machinery and equipment due to wear and tear; the core factor for the accumulation status of site hazards is site layout factors, used to measure the historical accumulation and diffusion trend of unclosed hazards in a specific spatial area. Workload intensity, equipment aging, and the accumulation status of site hazards are monitored in the medium to long term to reflect long-term trends.

[0043] The deformation state of the support structure typically refers to the real-time horizontal displacement rate of the top of the retaining piles (walls) or the sidewalls of deep and large foundation pits; equipment failure frequency is the number of coded failures that occur per unit time in critical construction machinery (such as tower cranes and large crawler cranes), recorded by the equipment's self-diagnostic system or sensors, such as abnormal hoisting brakes or excessive slewing positioning deviations, reflecting the immediate reliability of the equipment; personnel fatigue state refers to a comprehensive index characterized by a decline in the physiological and psychological alertness of workers, monitored and calculated by biosensors such as smart wristbands, usually based on the fusion of data such as continuous working time, heart rate variability, and body motion analysis. The deformation state of the support structure, equipment failure frequency, and personnel fatigue state are usually instantaneous states, used as indicators to reflect instantaneous risks. Large construction machinery is usually equipped with a digital control system with industrial Ethernet, whose programmable logic controller has built-in self-diagnostic functions, capable of real-time monitoring of key parameters such as motor current, hydraulic pressure, brake status, and position deviation, and generating standard fault diagnosis codes. An industrial IoT gateway, serving as the core of the equipment-level monitoring node, is installed or integrated within the equipment's electrical control cabinet. This gateway connects to the equipment's internal communication network via a CAN Ethernet interface in read-only mode. Following a pre-defined communication protocol, such as a proprietary protocol provided by the equipment manufacturer, the gateway periodically reads fault code lists, key operating parameters, and status flags from the controller, acquiring standardized fault events recognized by the equipment itself, such as the lifting overload alarm E101 and the rotary encoder malfunction E205. The data acquisition module provides opto-isolation for sensor signals and incorporates a low-pass filter circuit to suppress electromagnetic interference and signal noise. Sensors, acquisition modules, and communication equipment are all selected from high-protection-level industrial-grade products, capable of being dustproof, waterproof, and vibration-resistant, adapting to harsh construction site environments. Key monitoring points employ redundant sensor configurations, the communication protocol includes data check bits, and the gateway has data validity checking logic, such as determining whether values ​​are within physically possible ranges to eliminate abnormal data packets. Deploy a simple rule engine on the gateway or data acquisition module on the device side to preprocess the raw data locally, such as calculating valid values ​​and comparing them with thresholds. Only upload event reports when an alert or fault condition is triggered to reduce network load and invalid data.

[0044] As workload increases, worker fatigue also increases; as equipment ages, equipment failure frequency increases; and as site hazards accumulate, the risk of support structure deformation increases. Workload intensity, equipment aging, and site hazard accumulation provide the foundational methods, while support structure deformation, equipment failure frequency, and worker fatigue supplement extreme scenario handling. These two factors complement and work together to dynamically adjust safety risk assessment thresholds, addressing both long-term potential risks and immediate on-site risks. Macro-level factors drive the system to make forward-looking and gradual adaptive changes; micro-level factors ensure these changes don't escalate prematurely in critical situations and can decisively switch to a circuit breaker mode in extreme circumstances. Together, they ensure that the process of revising the preset risk assessment thresholds is both adaptable to long-term working conditions and extremely sensitive to instantaneous dangers, thus achieving the ultimate goal of dynamic safety management: optimization under normal conditions and preservation during crises.

[0045] Real-time data on support structure deformation, equipment failure frequency, and personnel fatigue status are acquired as dynamic correction factors, reflecting the impact of on-site changes on safety risks. These dynamic correction factors are mapped to risk levels, converting the raw values ​​into uniform risk levels, and a weighted safety score for the work area is calculated. If the deformation of the support structure exceeds a set safety limit (e.g., horizontal displacement exceeding 2 cm), it is mapped to a high-risk level. If the equipment failure frequency is high (e.g., three failures within the past month), it is mapped to a medium-high-risk level. If the personnel fatigue index exceeds a predetermined threshold (e.g., continuous work exceeding 12 hours with significantly reduced heart rate variability), it is mapped to a high-risk level. By combining these mapping results, a work area safety score is generated, reflecting the overall safety status of the construction area. For example, a score of 45 indicates a high risk for the work area, requiring immediate safety intervention. The safety score of the work area is a dynamic value between 0 and 100. It is obtained by weighting and standardizing the above three dynamic correction factors. It represents the quantitative level of the overall instantaneous safety status of the current work area (such as a construction section or the entire work area). The lower the score, the more urgent the risk.

[0046] The safety score of the work site is compared with a safety threshold. The safety threshold is a decision-making triage point set for this mechanism, such as 70 points. When the safety score of the work site falls below this value, it is determined that the site is in an emergency state that cannot be adapted by adjusting the standards, and mandatory intervention must be initiated.

[0047] When the safety score of the work area falls below the safety threshold, the current working condition is deemed extremely dangerous. Any relaxation or minor adjustment of the risk threshold could lead to disaster. All routine threshold dynamic adjustment calculations are immediately frozen, and the three dynamic correction factors for these hazards are directly input as the highest priority instructions into the on-site safety intervention mechanism. For example, a mandatory instruction is immediately pushed to the tower crane operator's cab and management personnel's handheld terminals in the relevant area: Given the accelerated support deformation, frequent equipment failures, and high personnel fatigue, the overall safety score is 65 points. Below the critical value of 70 points, automatic intervention is now triggered: Suspend all lifting and hoisting operations and pit edge work on this work area; personnel are requested to evacuate to a safe zone.

[0048] Conversely, if the safety score of the work surface is higher than the safety threshold, the current state is deemed acceptable for continued refined risk threshold management. In this case, the dynamic correction factors no longer act as alarms but as regulators, used as weights to apply a safety limit to the initial correction coefficient calculated from macroeconomic factors. For example, macroeconomic factors might suggest lowering the tower crane wind speed alarm threshold by 15% from 13 m / s to 11.05 m / s to address equipment aging. However, if the system checks the current risk level of the equipment failure frequency factor and finds it low, it imposes a limit constraint on this 15% reduction, ultimately generating a comprehensive adjustment factor that only lowers the threshold to 11.8 m / s. Then, the comprehensive adjustment factor updates the preset risk judgment thresholds for all relevant risks, making them more aligned with the current working conditions while ensuring safety. The comprehensive adjustment factor is a proportional coefficient used to update the preset risk judgment threshold. It is such as 0.8 or 1.2. It is a preliminary correction coefficient derived from three factors representing long-term / macro trends: workload intensity, equipment aging degree, and site hazard accumulation. It is then generated after being corrected for safety limits by a dynamic correction factor representing short-term / micro conditions.

[0049] By monitoring and adjusting dynamic correction factors in real time, the actual risk situation at the construction site can be accurately reflected, and potential safety hazards can be identified in a timely manner. When the safety score of the work surface falls below the threshold, the correction operation is automatically frozen and a safety intervention mechanism is activated to ensure that measures can be taken quickly in dangerous situations to prevent accidents from occurring.

[0050] Furthermore, this application also includes the following steps: extracting the characteristics of hazard evolution and deterioration; determining the potential accident acceleration factor based on the risk increment value of the hazard evolution and deterioration characteristics and the safety protection level under the current working scenario; determining the potential accident transmission path based on the similarity between the triggering conditions of historical accident modes in the hazard evolution and deterioration characteristics and the current hazard parameters; and setting the on-site safety intervention mechanism according to the potential accident acceleration factor and the potential accident transmission path.

[0051] Specifically, extracting the evolution and deterioration characteristics of potential hazards involves analyzing continuous monitoring data of a specific hazard to identify key patterns indicating a worsening risk level or an accelerating trend of evolution. The risk increment value is the increase in the risk indicator corresponding to the hazard's deterioration characteristics per unit time. For example, if the displacement rate of a foundation pit increases from 2 mm / hour to 3 mm / hour, the rate risk increment value is 1 mm / hour / hour.

[0052] Based on the risk increment value of the hazard evolution characteristics and the safety protection level under the current working scenario, a potential accident acceleration factor is determined. This factor measures how quickly an accident can occur under the influence of these factors. The potential accident acceleration factor quantifies the degree to which the probability and speed of a hazard evolving into an accident are amplified under current conditions. It is jointly determined by the risk increment value, which describes the speed of the hazard's own deterioration, and the safety protection level, which describes the strength of external mitigation capabilities. The weaker the protection, the larger the increment, and the higher the acceleration factor value, meaning the accident is likely to occur more quickly.

[0053] The process involves acquiring the triggering conditions of historical accident patterns within the characteristics of hazard evolution and deterioration, including equipment operating status, personnel work behavior, and environmental conditions. The current hazard's parameters are then compared with the historical accident pattern database to determine potential accident propagation paths. For example, suppose a historical record is found: a project experienced a partial collapse in a silty clay layer after a displacement rate exceeding 3.0 mm / h and continuous rainfall. If the current parameters show an 85% similarity to the triggering conditions of this historical pattern, the most likely potential accident propagation path is determined to be the partial collapse, and further deductions can be made regarding the potential impact on adjacent tower crane foundations.

[0054] Based on potential accident accelerators and potential accident propagation paths, an on-site safety intervention mechanism is established. This mechanism comprises a series of measures and strategies implemented when potential safety risks are encountered at the construction site, including suspending operations, strengthening safety protection, and notifying relevant personnel for handling. For example, if a work area is in a high-risk state, operations are immediately stopped to prevent accidents; on-site safety protection measures are increased, such as reinforcing support structures, adding safety warning signs, and dispatching more safety personnel; frequently malfunctioning equipment is repaired, and overly fatigued personnel are given rest to ensure their good working condition; the evolution of potential hazards is monitored in real time through sensors and monitoring systems, and an alarm is issued when the hazard reaches a certain level.

[0055] By analyzing the evolution and deterioration characteristics of potential hazards, accident accelerators, and transmission paths, it is possible to identify hazards that may lead to major accidents in advance, providing early warnings for safety intervention. Once a potentially high-risk condition is detected, on-site safety intervention mechanisms can be activated immediately to prevent accidents from occurring or escalating. By dynamically adjusting safety intervention measures, changes at the construction site can be precisely addressed, optimizing resource allocation and risk control.

[0056] Furthermore, this application also includes the following steps: connecting to a historical accident case database and extracting matching historical events by combining the hazard management feature matrix; determining the duration of potential hazard out-of-control based on the hazard accumulation cycle distribution and current hazard growth rate of the matching historical events; determining the scope of potential accident impact based on the matching historical events; and setting the dynamic constraint mechanism for the hazard based on the duration of potential hazard out-of-control and the scope of potential accident impact.

[0057] Furthermore, this application also includes the following steps: determining the bearing capacity limit, hazard investigation response speed, and emergency resource coverage radius parameters of the hazardous work surface through the key hazard management feature parameter set; and constructing the hazard management feature matrix by performing feature correlation analysis based on the bearing capacity limit, hazard investigation response speed, and emergency resource coverage radius parameters of the hazardous work surface.

[0058] Specifically, based on the set of key hazard management characteristic parameters, the bearing capacity limit of the hazardous work surface, the hazard investigation response speed, and the emergency resource coverage radius parameters are determined. The bearing capacity limit of the hazardous work surface is the maximum operational complexity and risk exposure that can be simultaneously withstood within a specific time and space range, while ensuring safety. This includes scaffolding load, platform load, foundation pit slope stability, and structural bearing capacity. The hazard investigation response speed is the average or theoretically shortest time from the initial identification or sensor triggering of a potential hazard to the arrival of responsible personnel on-site to confirm and initiate formal handling procedures. It reflects the efficiency of the safety management process and the agility of resource allocation, typically measured in minutes. The emergency resource coverage radius is the furthest distance from a fixed emergency material storage point (such as an emergency warehouse) or mobile emergency unit that can effectively deliver its core emergency resources to the site within an acceptable response time.

[0059] Based on the bearing capacity limit of hazardous work areas, hazard investigation response speed, and emergency resource coverage radius, feature correlation analysis is performed to analyze the interrelationships between these parameters and construct a hazard management feature matrix. For example, a high bearing capacity limit (work saturation) usually leads to a decrease in response speed (because safety personnel resources are diluted); a slow response speed must be compensated by a shorter emergency coverage radius (i.e., closer resource points), otherwise the risk exposure will be huge; if the coverage radius is insufficient, the allowable bearing capacity limit of the work area must be proactively reduced to prevent risks. Based on these correlation rules, the current bearing capacity limit index, response speed, coverage radius, and the correlation strength between them are woven together into a structured dataset, namely the current state row of the hazard management feature matrix. At the same time, each case in the historical database has also been transformed into a matrix row with the same structure. By calculating the similarity between the current row and the historical case rows, such as cosine similarity, the best matching historical scenario is found, thereby providing a basis for prediction and decision-making. The hazard management feature matrix is ​​a structured and digital data model or table. Each row represents a specific historical accident case or a current real-time operation scenario; each column represents a feature parameter, such as the bearing limit, response speed, and coverage radius mentioned above, as well as other derived features that may be extracted from the set of key hazard management feature parameters, such as average hazard density and maximum risk level.

[0060] By employing data connectivity technology, the characteristics of potential hazards at the current construction site are compared with relevant data in a historical accident case database. This database contains a large number of construction accident records, including the specific circumstances of the accidents, the characteristics of the hazards involved, and the consequences. Each accident record includes the following information: the time, location, project type, accident level, direct and indirect causes; a quantified hazard feature vector, such as geological conditions, support structure type, equipment status parameters, personnel work behavior indicators, and environmental monitoring data; and a description of the accident consequences, including the scope of impact, the degree of loss, and the emergency response process. Matching the characteristics of potential hazards at the current construction site with the historical accident case database identifies similar hazard events as matching historical events. Matching historical events helps analyze past accident patterns, providing a reference for current hazard management and ensuring the early identification of potential dangers. From the monitoring data of the current construction site, isomorphic feature parameters are extracted from the database and standardized in the same way to generate a real-time feature vector representing the current state. The current real-time feature vector is compared with the feature vectors of all historical cases in the case library. Cosine similarity is used, which calculates the cosine of the angle between the two vectors in multidimensional space; the closer the value is to 1, the higher the similarity. A preset similarity threshold, such as 0.85, is used; all historical cases exceeding this threshold are listed as matching historical events. Multiple high-similarity cases are then comprehensively analyzed as the primary reference for prediction and decision-making.

[0061] By analyzing and matching historical events, the cumulative periodic distribution of potential hazards and the current growth rate of potential hazards are extracted. The cumulative periodic distribution of potential hazards represents the periodic data accumulated by hazards during construction; the current growth rate of potential hazards is the rate at which they grow over a certain period, typically measuring the rate at which a potential problem develops into a serious problem. Through continuous observation of the hazard evolution process, combined with the cumulative periodicity and growth rate of historical events, the time required for a hazard to go from accumulation to loss of control is predicted, yielding the potential hazard loss-of-control duration. The potential hazard loss-of-control duration is the estimated time remaining before the current hazard crosses the safety threshold and evolves into an uncontrolled accident.

[0062] For example, the current feature matrix of section D (high bearing capacity index, medium response speed, and accelerating displacement) was compared with the case database. A historical case with a similarity of 91% was matched: a subway deep foundation pit project, under conditions of silty clay, anchor cable stress loss, and continuous rainfall, experienced a 15-meter-long and 3-meter-deep soil slip between piles, which took 38 hours from displacement acceleration to collapse. Data from this case showed that in the 38 hours before the collapse, the displacement rate exceeded 2.0 mm / h; in the first 20 hours, the rate increased to 3.5 mm / h; and in the last 2 hours, the rate sharply increased to over 10 mm / h. The current displacement rate of section D is 2.5 mm / h and is accelerating, with the acceleration highly consistent with the characteristics of the historical case in the first 20 hours, but the current absolute rate is slightly higher than the historical average. Through a fitting algorithm, the current evolution trajectory is determined to be approximately 1.2 times faster than the historical case. Therefore, the predicted duration of the potential uncontrolled hazard from the current state to the historical collapse threshold is approximately 38 hours / 1.2 hours. Rounding down and issuing a warning: Instability is expected to occur within 30-34 hours.

[0063] Based on the actual damage range, projectile distance, and secondary disaster chain recorded in historical events, the potential impact range of an accident is determined, i.e., the spatial area that may be affected, and the personnel and critical equipment that may be affected. A dynamic constraint mechanism for the potential hazard is set up based on the duration of its potential loss of control and the potential impact range, including time constraints, spatial constraints, and resource constraints. For example, if the predicted loss of control time is only 24 hours, the monitoring frequency of the relevant area is automatically increased from once per hour to once per 15 minutes, and a countdown rule is set to automatically escalate the warning if it is not effectively controlled after 12 hours. Based on the predicted impact range, a pre-impact zone is dynamically delineated and locked within an electronic fence, prohibiting non-emergency personnel and equipment from entering, and evacuation routes for personnel and equipment within this zone are planned in advance. The system automatically checks whether the coverage radius of emergency resources covers the predicted impact zone; if not, a resource pre-allocation command is triggered to deploy materials to a nearby temporary point in advance.

[0064] By matching historical accident data and analyzing the growth rate of potential hazards, we can accurately predict when hazards might get out of control and the scope of their potential impact, thus providing early warnings of risks. The dynamic hazard control mechanism can automatically adjust safety management strategies based on changes in hazard conditions, ensuring that effective intervention measures are taken before danger occurs to prevent accidents.

[0065] Based on the aforementioned dynamic constraint mechanism for hidden dangers and on-site safety intervention mechanism, the accident hazard investigation and analysis parameters corresponding to the management host are synchronously verified, and the accident hazard control strategy for the construction site is output.

[0066] Specifically, for the dynamic constraint mechanism for hidden dangers and the on-site safety intervention mechanism, the accident hazard investigation and analysis parameters corresponding to the management host are synchronously verified. The on-site safety intervention mechanism refers to taking a series of practical intervention measures, such as adjusting work schedules, strengthening on-site inspections, and allocating emergency resources, according to preset safety management rules and emergency response procedures when safety hazards or accidents occur at the construction site. The aim is to quickly and effectively resolve safety hazards and prevent accidents from occurring or escalating. Accident hazard investigation and analysis parameters are various data and indicators used to assess the risk of hidden dangers at the construction site, including the type, location, severity, growth rate, and duration of uncontrolled hazard.

[0067] When the hazard identification and analysis parameters are simultaneously verified and potential risks are identified, corresponding control strategies are output based on the type, severity, and risk level of the hazard. The formulation of these control strategies is based on real-time monitoring data and optimized using parameters such as historical accident experience and hazard management characteristic matrices, ensuring that the strategies accurately address the actual situation on-site. The dynamic hazard constraint mechanism and on-site safety intervention mechanism act as filters and correctors to verify the original hazard identification and analysis parameters.

[0068] Check whether the two mechanisms point to the same risk direction and control intent. For example, if both indicate that the area is at extremely high risk and requires strict measures, the check passes, enhancing the confidence in the decision. If potential conflicts exist, such as the dynamic constraint mechanism recommending downgraded construction while the intervention mechanism requires immediate suspension, following the principle of safety first, the stricter intervention mechanism's instructions are adopted as the basis for action. Modify the original analysis parameters using the specific rules within the mechanisms. For example, if the original analysis predicts loss of control after 28 hours, but the dynamic constraint mechanism provides a 30-34 hour range based on more accurate historical matching, the latter is adopted as the more reliable prediction parameter.

[0069] Based on the verified results, a final accident hazard management strategy is generated and output. This strategy integrates macro-level constraints and micro-level interventions into a coherent, orderly, and phased task list, including clear management objectives, step-by-step handling measures, designated responsible persons and resources, specific timeframes and spatial scopes, and contingency plans for different scenarios. The goal of the accident hazard management strategy is to prevent accidents at the construction site through effective management and control of potential risks.

[0070] By verifying and analyzing hazard identification parameters in real time, we ensure the timely detection and handling of safety hazards at construction sites, preventing accidents. When a hazard exceeds a safety threshold, a safety intervention mechanism is automatically triggered, and intervention measures are adjusted according to the hazard's risk level to ensure a rapid and effective emergency response. By outputting precise accident hazard control strategies, safety management at construction sites can allocate resources more efficiently, avoid resource waste, and improve emergency response capabilities.

[0071] Furthermore, this application also includes the following steps: sending the hazard investigation incentive signal to the site-level monitoring node, equipment-level monitoring node, and personnel-level monitoring node through the industrial IoT communication gateway; when carrying out work activities at the construction site, the site-level monitoring node, equipment-level monitoring node, and personnel-level monitoring node receive the hazard investigation incentive signal and use a hierarchical collaborative mechanism based on the distribution of hazard risks to perform the allocation and management of hazard monitoring tasks.

[0072] Specifically, the industrial IoT communication gateway will issue hazard investigation incentive signals on a regular or as-needed basis, based on the safety management requirements, hazard investigation plans, and work progress at the construction site. These incentive signals can be for scheduled hazard investigation tasks or for sending emergency commands when certain sudden hazards are detected. Through the industrial IoT gateway, signals are sent from the management host to different monitoring nodes at the construction site.

[0073] Hazard identification incentive signals are instructions or prompts issued by the management host to motivate on-site monitoring nodes to conduct hazard identification activities. These signals can be timed instructions, emergency warnings, or safety task scheduling, with the aim of ensuring that hazard identification work is carried out on time and as required, and that hazards are identified and addressed promptly. Site-level monitoring nodes are deployed in key areas of the construction site and are responsible for monitoring the site layout, work areas, and safety conditions of hazardous areas, such as monitoring the width of safety passages and the isolation of hazardous areas. Equipment-level monitoring nodes are mainly used to monitor the operating status of construction machinery and equipment, such as equipment failure, equipment load, and working hours, to ensure that equipment operates within safe load limits. Personnel-level monitoring nodes are responsible for monitoring the health status and safety protection measures of workers, such as compliance with personal protective equipment wearing, worker fatigue levels, and working hours.

[0074] When work activities are carried out at the construction site, site-level monitoring nodes, equipment-level monitoring nodes, and personnel-level monitoring nodes receive hazard investigation incentive signals. In other words, after receiving the hazard investigation incentive signal, these monitoring nodes will begin hazard investigation work according to preset task requirements. Through real-time data analysis, the distribution of hazard risks in different areas of the construction site is identified. For example, the risk of equipment aging may be concentrated in equipment areas, the risk of personnel fatigue may be concentrated in areas with long working hours, and site layout hazards may be concentrated in specific high-risk work areas.

[0075] Based on the analysis of the distribution of potential hazards and risks, the system automatically assigns hazard identification tasks to different monitoring nodes. For example, site-level monitoring nodes are responsible for monitoring the layout and safety facilities of the work area to ensure that hazardous areas are effectively isolated; equipment-level monitoring nodes focus on the operating status, failure frequency, and maintenance cycle of equipment to ensure that equipment is maintained on time and operates within safe limits; and personnel-level monitoring nodes monitor the safety protection measures and fatigue status of workers to prevent accidents caused by fatigue or inadequate protection.

[0076] Site-level, equipment-level, and personnel-level monitoring nodes not only independently perform hazard monitoring tasks but also need to collaborate. For example, when a site-level monitoring node detects non-compliant personnel protective measures within a hazardous area, it sends a warning to the personnel-level monitoring node via an IoT gateway, reminding workers to wear appropriate protective equipment. Hazard monitoring task allocation and management refers to the rational distribution of monitoring tasks to different monitoring nodes based on the hazard situation at the construction site. By classifying and grading hazards, tasks are assigned to appropriate nodes to ensure the comprehensiveness and efficiency of hazard investigation.

[0077] By analyzing the distribution of potential hazards and employing a hierarchical collaboration mechanism, construction sites can quickly and accurately identify and resolve hazards in high-risk areas, ensuring comprehensive and thorough safety work. The hierarchical collaboration mechanism ensures mutual cooperation and information sharing among various monitoring nodes, making hazard identification not merely an isolated task but a globally collaborative effort, thus improving the overall efficiency of safety management. Through automated hazard identification incentive signals and task allocation, the system responds promptly to changes in on-site safety hazards and effectively allocates resources, ensuring the rapid implementation of hazard identification and safety measures.

[0078] In summary, the construction site accident hazard management method based on the Industrial Internet of Things provided in this application has the following technical effects:

[0079] By connecting the management host through an industrial IoT communication gateway, and associating site layout factors, equipment operation factors, and personnel operation factors at the construction site, a set of key hazard management characteristic parameters is determined. Based on the construction site's safety management standards and hazard investigation specifications, a preset risk assessment threshold that meets construction industry requirements is determined. Correction coefficients for the preset risk assessment thresholds are configured based on workload intensity, equipment aging, and the accumulation trend of site hazards. Combined with the key hazard management characteristic parameter set, a dynamic hazard constraint mechanism is set. Based on this dynamic constraint mechanism and on-site safety intervention mechanism, the accident hazard investigation and analysis parameters corresponding to the management host are synchronously verified, and an accident hazard control strategy for the construction site is output. In other words, by comprehensively considering multiple factors at the construction site, a set of key hazard management characteristic parameters is determined. Based on safety management standards and hazard investigation specifications, risk assessment thresholds are set, and correction coefficients are configured to form a dynamic hazard constraint mechanism. This mechanism verifies the hazard investigation and analysis parameters of the management host and outputs an accident hazard control strategy. This achieves dynamic, accurate, and timely hazard identification, thereby significantly improving the efficiency of accident hazard control.

[0080] Example 2: Based on the same inventive concept as the construction site accident hazard management method based on the Industrial Internet of Things in Example 1, this application also provides a construction site accident hazard management system based on the Industrial Internet of Things. Please refer to the appendix. Figure 2 The construction site accident hazard management system based on the Industrial Internet of Things includes:

[0081] The feature parameter determination module 11 is used to connect to the management host using an industrial IoT communication gateway, associate site layout factors, equipment operation factors, and personnel operation factors at the construction site, and determine the set of key hidden danger management feature parameters; the threshold determination module 12 is used to determine a preset risk judgment threshold that meets the requirements of the construction industry based on the safety management standards and hidden danger investigation specifications of the construction site; the constraint setting module 13 is used to configure the correction coefficient of the preset risk judgment threshold through the intensity of work load, the degree of equipment aging, and the accumulation trend of site hidden dangers, and set a dynamic constraint mechanism for hidden dangers in combination with the set of key hidden danger management feature parameters; the synchronous verification module 14 is used to synchronously verify the accident hidden danger investigation and analysis parameters corresponding to the management host based on the dynamic constraint mechanism for hidden dangers and the on-site safety intervention mechanism, and output the accident hidden danger control strategy for the construction site.

[0082] Furthermore, the feature parameter determination module 11 in the construction site accident hazard management system based on the Industrial Internet of Things is also used for: the site layout factors include work area division, safety passage width, and hazardous area isolation and protection coverage data collected by site-level monitoring nodes, and the site-level monitoring nodes are deployed in the core work area, hazardous area boundary and material storage yard of the construction site.

[0083] Furthermore, the feature parameter determination module 11 in the construction site accident hazard management system based on the Industrial Internet of Things is also used for: the equipment operation factors include equipment running time, fault alarm records, and maintenance cycle data collected by equipment-level monitoring nodes, and the equipment-level monitoring nodes are deployed in key parts of construction machinery and electrical control boxes.

[0084] Furthermore, the feature parameter determination module 11 in the construction site accident hazard management system based on the Industrial Internet of Things is also used for: the personnel operation factors include PPE wearing compliance rate, continuous working time, and high-altitude operation qualification data collected by personnel-level monitoring nodes, and the personnel-level monitoring nodes are deployed on safety helmets, reflective vests, and smart bracelets.

[0085] Furthermore, the constraint setting module 13 in the construction site accident hazard management system based on the Industrial Internet of Things is also used to: use the deformation state of the support structure, the frequency of equipment failure, and the fatigue state of personnel as dynamic correction factors for the preset risk judgment threshold; map the dynamic correction factors to risk levels to generate a work surface safety score; when the work surface safety score is lower than the safety threshold, freeze the preset risk judgment threshold adjustment based on the correction coefficient, and incorporate the dynamic correction factor into the on-site safety intervention mechanism as a trigger condition for suspending dangerous operations; otherwise, within the allowable range of the work surface safety status, use the dynamic correction factor as a weight constraint to apply a safety limit to the correction coefficient configured based on the workload intensity, equipment aging degree, and the accumulation trend of site hazards, and generate a comprehensive adjustment factor; update the preset risk judgment threshold based on the comprehensive adjustment factor.

[0086] Furthermore, the constraint setting module 13 in the construction site accident hazard management system based on the Industrial Internet of Things is also used for: extracting hazard evolution and deterioration characteristics; determining potential accident acceleration factors based on the risk increment value of the hazard evolution and deterioration characteristics and the safety protection level under the current work scenario; determining potential accident transmission paths based on the similarity between the triggering conditions of historical accident modes in the hazard evolution and deterioration characteristics and the current hazard parameters; and setting the on-site safety intervention mechanism according to the potential accident acceleration factors and the potential accident transmission paths.

[0087] Furthermore, the constraint setting module 13 in the construction site accident hazard management system based on the Industrial Internet of Things is also used to: connect to the historical accident case library and extract matching historical events by combining the hazard management feature matrix; determine the duration of potential hazard out of control based on the cumulative periodic distribution of the hazard and the current hazard growth rate of the matching historical events; determine the scope of potential accident impact based on the matching historical events; and set the dynamic constraint mechanism for the hazard based on the duration of potential hazard out of control and the scope of potential accident impact.

[0088] Furthermore, the constraint setting module 13 in the construction site accident hazard management system based on the Industrial Internet of Things is also used to: determine the bearing limit of the dangerous working surface, the response speed of hazard investigation, and the emergency resource coverage radius parameters through the key hazard management feature parameter set; and perform feature correlation analysis based on the bearing limit of the dangerous working surface, the response speed of hazard investigation, and the emergency resource coverage radius parameters to construct the hazard management feature matrix.

[0089] Furthermore, the construction site accident hazard management system based on the Industrial Internet of Things also includes: sending hazard investigation incentive signals to the site-level monitoring nodes, equipment-level monitoring nodes, and personnel-level monitoring nodes through the Industrial Internet of Things communication gateway; when work activities are carried out at the construction site, the site-level monitoring nodes, equipment-level monitoring nodes, and personnel-level monitoring nodes receive the hazard investigation incentive signals and use a hierarchical collaborative mechanism based on hazard risk distribution to perform the allocation and management of hazard monitoring tasks.

[0090] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The construction site accident hazard management method and specific examples based on the Industrial Internet of Things in the aforementioned Embodiment 1 are also applicable to the construction site accident hazard management system based on the Industrial Internet of Things in this embodiment. Through the foregoing detailed description of the construction site accident hazard management method based on the Industrial Internet of Things, those skilled in the art can clearly understand the construction site accident hazard management system based on the Industrial Internet of Things in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0091] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0092] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for managing construction site accident hazards based on the Industrial Internet of Things, characterized in that, include: By using an industrial IoT communication gateway to connect to the management host, and associating site layout factors, equipment operation factors, and personnel operation factors at the construction site, a set of key hazard management characteristic parameters can be determined. Based on the safety management standards and hazard investigation specifications of the construction site, a preset risk assessment threshold that meets the requirements of the construction industry is determined; By considering factors such as workload intensity, equipment aging, and the accumulation of site hazards, a correction coefficient for the preset risk assessment threshold is configured, and a dynamic constraint mechanism for hazards is set in conjunction with the set of key hazard management characteristic parameters. Based on the aforementioned dynamic constraint mechanism for hidden dangers and on-site safety intervention mechanism, the accident hazard investigation and analysis parameters corresponding to the management host are synchronously verified, and the accident hazard control strategy for the construction site is output.

2. The method for managing construction site accident hazards based on the Industrial Internet of Things as described in claim 1, characterized in that, The method includes: The deformation state of the support structure, the frequency of equipment failure, and the fatigue state of personnel are used as dynamic correction factors for the preset risk judgment threshold. The dynamic correction factor is mapped to a risk level to generate a work surface safety score; When the safety score of the work surface is lower than the safety threshold, the adjustment of the preset risk judgment threshold based on the correction coefficient is frozen, and the dynamic correction factor is incorporated into the on-site safety intervention mechanism as a trigger condition for suspending hazardous operations. Otherwise, within the permissible range of the safety status of the work site, the dynamic correction factor is used as a weight constraint to limit the correction coefficient configured based on the intensity of the work load, the degree of equipment aging, and the accumulation of site hazards, thereby generating a comprehensive adjustment factor. The preset risk assessment threshold is updated based on the comprehensive adjustment factor.

3. The method for managing construction site accident hazards based on the Industrial Internet of Things as described in claim 2, characterized in that, The method further includes incorporating the dynamic correction factor into the on-site safety intervention mechanism: Extract the characteristics of the evolution and deterioration of hidden dangers; Based on the risk increment value of the aforementioned hazard evolution and deterioration characteristics, and combined with the safety protection level under the current working scenario, potential accident acceleration factors are determined; The potential accident propagation path is determined by the similarity between the triggering conditions of historical accident patterns and the current hazard parameters in the aforementioned hazard evolution and deterioration characteristics. Based on the potential accident acceleration factor and the potential accident transmission path, the on-site safety intervention mechanism is set up.

4. The method for managing construction site accident hazards based on the Industrial Internet of Things as described in claim 1, characterized in that, The method, which considers factors such as site layout, equipment operation, and personnel operations at the construction site, includes: The site layout factors include data on the division of work areas, width of safety passages, and coverage of hazardous areas based on data collected from site-level monitoring nodes. These site-level monitoring nodes are deployed in the core work area, hazardous area boundaries, and material storage yards of the construction site.

5. The method for managing construction site accident hazards based on the Industrial Internet of Things as described in claim 4, characterized in that, The method further includes: The equipment operation factors include equipment runtime, fault alarm records, and maintenance cycle data collected based on equipment-level monitoring nodes, which are deployed in key parts of construction machinery and electrical control boxes.

6. The method for managing construction site accident hazards based on the Industrial Internet of Things as described in claim 5, characterized in that, The method further includes: The personnel operation factors include PPE wearing compliance rate, continuous working time, and high-altitude operation qualification data collected based on personnel-level monitoring nodes, which are deployed on safety helmets, reflective vests, and smart bracelets.

7. The method for managing construction site accident hazards based on the Industrial Internet of Things as described in claim 6, characterized in that, The method includes: The industrial IoT communication gateway sends the hazard investigation incentive signal to the site-level monitoring node, equipment-level monitoring node, and personnel-level monitoring node. When work activities are carried out at the construction site, the site-level monitoring nodes, equipment-level monitoring nodes, and personnel-level monitoring nodes receive the hazard investigation incentive signals and use a hierarchical collaborative mechanism based on hazard risk distribution to perform the allocation and management of hazard monitoring tasks.

8. The method for managing construction site accident hazards based on the Industrial Internet of Things as described in claim 1, characterized in that, Based on the aforementioned set of key hazard management characteristic parameters, a dynamic constraint mechanism for hazards is established. The method includes: Connect to the historical accident case database and extract matching historical events by combining the hazard management feature matrix; The duration of potential hazards out of control is determined by the cumulative periodic distribution of hazards in the historical matching events and the current hazard growth rate. Based on the matched historical events, the scope of potential accident impact is determined; A dynamic constraint mechanism for the potential hazard is set based on the duration of its uncontrolled deterioration and the scope of its impact.

9. The method for managing construction site accident hazards based on the Industrial Internet of Things as described in claim 8, characterized in that, The method further includes extracting and matching historical events using a hazard management feature matrix. The bearing capacity limit of the hazardous work surface, the response speed of hazard investigation, and the coverage radius of emergency resources are determined by the set of key hazard management characteristic parameters. Based on the bearing capacity limit of the hazardous work surface, the response speed of hazard investigation, and the coverage radius of emergency resources, feature correlation analysis is performed to construct the hazard management feature matrix.

10. A construction site accident hazard management system based on the Industrial Internet of Things, characterized in that, The steps for implementing the construction site accident hazard management method based on the Industrial Internet of Things (IIoT) according to any one of claims 1 to 9, wherein the construction site accident hazard management system based on the Industrial Internet of Things includes: The feature parameter determination module is used to connect to the management host using an industrial IoT communication gateway, associate site layout factors, equipment operation factors, and personnel operation factors at the construction site, and determine the set of key hidden danger management feature parameters. The threshold determination module is used to determine a preset risk judgment threshold that meets the requirements of the construction industry, based on the safety management standards and hidden danger investigation specifications of the construction site. The constraint setting module is used to configure the correction coefficient of the preset risk judgment threshold by considering factors such as workload intensity, equipment aging degree, and the accumulation status of site hazards, and to set a dynamic constraint mechanism for hazards in combination with the set of key hazard management feature parameters. The synchronous verification module is used to synchronously verify the accident hazard investigation and analysis parameters corresponding to the management host based on the dynamic constraint mechanism for hidden dangers and the on-site safety intervention mechanism, and output the accident hazard control strategy for the construction site.

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