Shed frame safety monitoring method and system optimized through IOT sensing and medium

Abstract

The invention discloses a shed frame safety monitoring method and system optimized through IOT sensing and a medium, and relates to the technical field of the Internet of Things, and the method comprises the steps: obtaining the building and shed frame constraint information of a target scene, and constructing a digital shed frame model; executing multi-working-condition simulation disturbance analysis based on the model, wherein the multi-working-condition simulation disturbance analysis comprises structure abnormal response and operator interference simulation; key monitoring points are determined according to an analysis result, and an optimal sensor layout scheme is obtained in combination with an optimization algorithm; and deploying a sensor and constructing a monitoring network, and carrying out shed frame safety monitoring according to a safety early warning rule. Therefore, the technical effects of improving the monitoring efficiency and the detection coverage and improving the monitoring sensitivity and accuracy are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a scaffolding safety monitoring method, system and medium optimized through IOT sensing. Background Art

[0002] Scaffolding structures are widely used in construction as supports and work platforms, and their safety directly impacts the lives and property of construction workers. However, existing technologies still lack the ability to select monitoring points, optimize sensor placement, and provide real-time early warnings. Traditional scaffolding monitoring methods rely primarily on manual inspections or fixed sensor deployments, resulting in low monitoring efficiency, limited coverage, and delayed data processing. Summary of the Invention

[0003] The present invention provides a scaffolding safety monitoring method, system and medium optimized through IOT sensing to solve the technical problems of low monitoring efficiency, imperfect coverage, poor monitoring sensitivity and accuracy in the existing technology, and achieve the technical effect of improving monitoring efficiency and detection coverage, and improving monitoring sensitivity and accuracy.

[0004] In a first aspect, the present invention provides a scaffolding safety monitoring method optimized by IOT sensing, wherein the scaffolding safety monitoring method optimized by IOT sensing comprises:

[0005] Obtain the architectural information and scaffolding constraint information of the target scene and construct a digital scaffolding model.

[0006] A multi-operating condition simulation disturbance analysis is performed based on the digital scaffolding model, wherein the multi-operating condition simulation disturbance analysis includes structural abnormal response simulation and operator interference simulation.

[0007] A set of key monitoring points is determined based on the results of multi-operating condition simulation disturbance analysis, and based on the key monitoring point set, an optimal sensor layout plan is obtained in combination with an optimization algorithm.

[0008] In combination with the optimal layout plan, sensor equipment is deployed and a monitoring sensor network is constructed, and scaffolding safety monitoring of the target scene is performed based on the monitoring sensor network and preset safety warning rules.

[0009] In a feasible implementation, obtaining the building information and scaffolding constraint information of the target scene includes:

[0010] The management database of the interactive target scene extracts the building information and scaffolding construction requirements.

[0011] Based on the scaffolding construction requirements, scaffolding structural member information and scaffolding connector information are parametrically extracted.

[0012] The scaffolding structural member information and scaffolding connecting member information are used as retrieval constraints, typical structural member constraint information and typical connecting member constraint information are obtained based on big data, and correction is performed based on a preset early warning control coefficient to obtain the scaffolding constraint information.

[0013] In a feasible implementation, building a digital scaffolding model includes:

[0014] A physical model is constructed according to the building information, the scaffolding structural member information and the scaffolding connecting member information.

[0015] The physical model is initialized according to the scaffolding constraint information, boundary conditions are set, and the digital scaffolding model is obtained.

[0016] In a feasible implementation, building a digital scaffolding model further includes:

[0017] The digital scaffolding model is analyzed to extract a minimum basic model set, wherein the minimum basic model set includes a plurality of minimum basic models that can constitute the complete digital scaffolding model.

[0018] The digital scaffolding model is pruned with a single minimum basic model as a basic unit to obtain a simplified digital framework model, wherein the simplified digital framework model includes a plurality of discrete regional framework sub-models.

[0019] In a feasible implementation, a multi-operating condition simulation disturbance analysis is performed based on the digital scaffolding model, wherein the multi-operating condition simulation disturbance analysis includes structural abnormal response simulation and operator interference simulation, including:

[0020] The process is decomposed based on the construction information to determine a process task list, wherein the process task list includes personnel information, material information and equipment information.

[0021] A dynamic disturbance point set is constructed according to the personnel information and the equipment information, wherein the dynamic disturbance point is associated with and stores dynamic disturbance characteristics.

[0022] A static disturbance point set is constructed according to the material information and the equipment information, wherein the static disturbance point is configured with static disturbance parameters.

[0023] A multi-operating condition simulation disturbance analysis is performed based on the dynamic disturbance point set and the static disturbance point set.

[0024] In a feasible implementation, performing multi-operating condition simulation disturbance analysis based on the dynamic disturbance point set and the static disturbance point set includes:

[0025] Real-time distribution constraints are defined through the process task list, and the real-time distribution constraints are combined with the digital scaffolding model to randomly distribute the static disturbance point set, and iteratively obtain the static working condition set.

[0026] The static working condition set is traversed, and a structural abnormal response simulation is performed on the digital scaffolding model to obtain a first simulation result.

[0027] The dynamic disturbance point set is randomly distributed in the digital scaffolding model, and the distribution result is randomly combined with the static working condition set to iteratively obtain the dynamic working condition set.

[0028] The dynamic working condition set is traversed, and an operator interference simulation is performed on the digital scaffolding model to obtain a second simulation result.

[0029] The first simulation result and the second simulation result are output as the multi-operating condition simulation disturbance analysis result.

[0030] In a feasible implementation, a set of key monitoring points is determined based on the results of multi-operating condition simulation disturbance analysis, including:

[0031] According to a preset stress control limit, stress concentration points are extracted from the first simulation result and the second simulation result, and the output is a stress concentration point set.

[0032] According to the preset vibration control limit, vibration significant points are extracted from the second simulation result to obtain a vibration significant point set.

[0033] The stress concentration point set and the vibration significant point set are marked and merged, and density-based cluster analysis is performed to determine multiple cluster centers.

[0034] The plurality of cluster centers are serially output as the key monitoring point set.

[0035] In a feasible implementation, based on the set of key monitoring points, an optimal sensor deployment plan is obtained in combination with an optimization algorithm, including:

[0036] The key monitoring point set is used as the selection space to generate an initial layout plan set.

[0037] An optimization objective function is constructed with the goal of minimizing the total number of sensors, maximizing the sensor coverage of each point, and balancing sensor coverage.

[0038] The initial layout solution set is iteratively optimized in combination with the optimization objective function until the preset optimization termination constraint is satisfied, and the optimal layout solution is output.

[0039] In a second aspect, the present invention further provides a scaffolding safety monitoring system optimized by IOT sensing, wherein the scaffolding safety monitoring system optimized by IOT sensing comprises:

[0040] The digital scaffolding model construction module is used to obtain the architectural information and scaffolding constraint information of the target scene and construct a digital scaffolding model.

[0041] A multi-operating condition analysis module is used to perform multi-operating condition simulation disturbance analysis based on the digital scaffolding model, wherein the multi-operating condition simulation disturbance analysis includes structural abnormal response simulation and operator interference simulation.

[0042] The layout optimization module is used for the key monitoring point set determination module, which is used to determine the key monitoring point set according to the multi-working condition simulation disturbance analysis results, and based on the key monitoring point set, combine the optimization algorithm to obtain the optimal layout plan of the sensors.

[0043] The safety monitoring execution module is used to deploy sensor equipment and build a monitoring sensor network in combination with the optimal layout plan, and perform scaffolding safety monitoring of the target scene based on the monitoring sensor network and preset safety warning rules.

[0044] In a third aspect, the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the scaffolding safety monitoring method optimized by IOT sensing provided by the present invention.

[0045] The present invention discloses a scaffolding safety monitoring method, system and medium optimized by IOT sensing, comprising: obtaining architectural information and scaffolding constraint information of a target scene, and constructing a corresponding digital scaffolding model; performing multi-working condition simulation disturbance analysis based on the model, wherein the analysis includes structural abnormality response simulation and operator interference simulation; determining a set of key monitoring points according to the analysis results, and generating an optimal sensor layout plan in combination with an optimization algorithm; deploying sensor equipment according to the layout plan, constructing a monitoring sensor network, and implementing safety monitoring of the scaffolding in the target scene in combination with preset safety warning rules. The scaffolding safety monitoring method, system and medium optimized by IOT sensing disclosed in the present invention solve the technical problems of low monitoring efficiency, incomplete coverage, and poor monitoring sensitivity and accuracy, and achieve the technical effects of improving monitoring efficiency and detection coverage, and improving monitoring sensitivity and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a flow chart of the scaffolding safety monitoring method optimized by IOT sensing according to the present invention.

[0047] Figure 2 This is a structural diagram of the scaffolding safety monitoring system optimized by IOT sensing in the present invention.

[0048] Description of the accompanying drawings: digital scaffolding model construction module 11, multi-working condition analysis module 12, layout optimization module 13, safety monitoring execution module 14. DETAILED DESCRIPTION

[0049] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0050] Example 1, as Figure 1 The flowchart of the scaffolding safety monitoring method optimized by IOT sensing of the present invention is shown below. The scaffolding safety monitoring method optimized by IOT sensing includes:

[0051] S100: Acquire architectural information and scaffolding constraint information of the target scene and construct a digital scaffolding model.

[0052] Specifically, we first collected architectural information for the target scene, including structural design drawings, material specifications, and construction schedules. We also collected scaffolding requirements and constraints, such as scaffolding type, size, and load-bearing capacity. Together, these architectural and scaffolding constraint information provided all the data needed to construct a digital model of the scaffolding in the target scene.

[0053] Furthermore, the above-mentioned building information and scaffolding constraint information are combined with digital modeling software (such as BIM) to construct a digital scaffolding model that can reflect the structural condition and mechanical properties of the scaffolding in the target scene. In other words, the digital scaffolding model not only includes the physical structure of the scaffolding, but also its mechanical properties and constraints.

[0054] The digital scaffolding model constructed through the above steps serves as the foundation for the entire monitoring solution, providing a precise virtual scenario for subsequent simulation analysis and sensor deployment. This model allows for targeted simulation of structural responses under different operating conditions, thereby identifying key monitoring points and ensuring targeted and effective monitoring, avoiding the blind selection of monitoring points found in traditional methods.

[0055] In some embodiments, obtaining the building information and scaffolding constraint information of the target scene includes:

[0056] The management database of the interactive target scene extracts the building information and scaffolding construction requirements; based on the scaffolding construction requirements, parameterized extraction of scaffolding structural member information and scaffolding connector information is performed; using the scaffolding structural member information and scaffolding connector information as retrieval constraints, typical structural member constraint information and typical connector constraint information are obtained based on big data, and corrections are performed based on preset early warning control coefficients to obtain the scaffolding constraint information.

[0057] Specifically, the management database is used to store and manage information related to construction projects. For example, the management database includes detailed building design drawings, construction plans, material specifications, and other data. For example, in a high-rise construction project, the management database stores detailed building design drawings (including each floor's layout, load requirements, and construction schedule). By interacting with this database, the building information required for scaffolding construction, such as building height, load distribution per floor, and construction phase, can be extracted.

[0058] Specifically, parametric extraction is used to extract the specific values ​​of specific indicator parameters from the original data, thereby obtaining key information about the scaffolding's structural components and connectors, such as size, material, and load capacity. The typical structural component and connector constraint information obtained through parametric extraction is the typical constraint conditions for structural components and connectors in different scenarios. This typical constraint condition may include the mechanical properties, connection methods, and load limits of the structural components and connectors used in the target scenario.

[0059] Specifically, the scaffolding requirements specify the conditions that must be met to construct a scaffolding in the target scenario. For example, these conditions include the specifications, quantities, and installation locations of the required structural and connecting parts. Furthermore, based on the extracted scaffolding requirements, parametric methods can be used to extract key information about scaffolding structural parts (such as vertical poles and horizontal bars) and connecting parts (such as fasteners and bolts), including parameters such as size, material, and load-bearing capacity. For example, in a bridge construction project, the scaffolding requirements stipulate that the spacing between vertical poles should be 1.5 meters, the spacing between horizontal bars should be 1.2 meters, and the material should be Q235 steel.

[0060] Furthermore, using the extracted scaffolding structural and connector information as search criteria, the corresponding typical structural and connector constraint information is searched in the big data. This information is then modified based on preset early warning control coefficients to ultimately obtain scaffolding constraint information suitable for the target scenario. The distribution of typical structural and connector constraint information defines the typical performance of the structural and connector components, such as maximum load-bearing capacity and seismic resistance.

[0061] Specifically, the early warning control coefficient is used to adjust and calibrate the sensitivity of the early warning, thereby ensuring the accuracy and reliability of the early warning. The early warning control coefficient can be adjusted according to different construction scenarios and risk levels. For example, the higher the safety requirements of the target scenario, the corresponding early warning control coefficient can be set to a smaller value less than 1, thereby ensuring that the alarm begins with a larger safety margin.

[0062] In some embodiments, constructing a digital scaffolding model includes:

[0063] A physical model is constructed according to the building information, the scaffolding structural member information and the scaffolding connector information; the physical model is initialized according to the scaffolding constraint information, boundary conditions are set, and the digital scaffolding model is obtained.

[0064] Specifically, the physical model is a simulation basic model established based on the original structural data, which has geometric shape, mechanical performance parameters (such as elastic modulus, yield strength) and connection topology between components.

[0065] Specifically, boundary conditions refer to the constraints imposed on the model, such as fixed supports, load application areas, displacement restrictions, etc., which are used to simulate the mechanical state in the actual construction environment; the digital scaffolding model is a physical model that reflects the actual construction scaffolding structure after initialization and boundary condition setting, and is used for simulation analysis and monitoring point identification.

[0066] Specifically, first, a spatial structural framework is constructed based on building information (such as construction area dimensions and reserved hole positions), scaffolding structural components (such as vertical poles, horizontal bars, and diagonal bars), and connection information (such as snap-on and bolt-type node components and their shapes and strengths) to form a physical model. The physical model contains the geometric parameters (such as length, cross-section, and material) and mechanical properties (such as elastic modulus and density) of each component, and the connection relationship between components (such as hinged or rigid connection). Then, the construction constraints of the target scene (such as fixed end points, moving boundaries, and external wind loads) are applied to the physical model as boundary inputs, thereby further limiting the calculation scope and mechanical analysis dimensions (such as static load, vibration, or buckling analysis). Finally, the parameters are input into the structural simulation software (such as ANSYS, Abaqus, or BIM) to complete the physical modeling after boundary initialization and output a complete digital scaffolding model.

[0067] The above method and steps ensure the accuracy and reliability of the constructed physical model by inputting detailed building information and scaffolding component information. After initializing the boundary conditions, the digital scaffolding model can more realistically simulate the mechanical state in the construction environment, which is conducive to more scientific identification of key monitoring points and improves the pertinence and efficiency of the monitoring system.

[0068] In some embodiments, constructing the digital scaffolding model further includes:

[0069] The digital scaffolding model is analyzed to extract a minimum basic model set, wherein the minimum basic model set includes a plurality of minimum basic models that can constitute the complete digital scaffolding model; and the digital scaffolding model is pruned using a single minimum basic model as a basic unit to obtain a simplified digital framework model, wherein the simplified digital framework model includes a plurality of discrete regional framework sub-models.

[0070] Specifically, the minimum basic model set is the basic structural form extracted from the complex digital scaffolding model that can independently constitute a local force-bearing unit. It has geometric closure and local mechanical stability. Through the arrangement and combination of these units, a complete digital scaffolding model or any part of the digital scaffolding model can be formed. In other words, each minimum basic model represents a basic module in the scaffolding structure composed of structural parts and connecting parts.

[0071] Specifically, first, the digital scaffolding model is decomposed into a graph structure using topological structure analysis and mechanical stability constraints, and a plurality of minimum basic models that can constitute independent force-bearing units are extracted according to the component connection relationship and force path; for example, the minimum basic model may include a standard symmetrical unit (such as a tic-tac-toe lattice structure) or an asymmetric reinforcement unit (such as a special-shaped diagonal brace structure); then, the minimum basic model is selected as the basic unit to traverse and analyze the entire digital scaffolding model, remove those details and redundant parts that have little impact on the overall structure of the model, retain the key structural features, and finally obtain a streamlined digital framework model. For example, the model parts that are all minimum basic models within the neighborhood range can be removed (the simulation performance of this part can be described by a unified mathematical model or pattern), and the relatively differentiated model parts are retained to form a streamlined framework model for rapid analysis and simulation.

[0072] Through the above-mentioned model pruning, redundant or unimportant parts of the model can be removed, and key structural features can be retained to make the model more streamlined and efficient, which helps to reduce the computational burden and enable simulation analysis to be completed faster.

[0073] S200: performing a multi-operating condition simulation disturbance analysis based on the digital scaffolding model, wherein the multi-operating condition simulation disturbance analysis includes a structural abnormal response simulation and an operator interference simulation.

[0074] Specifically, the digital scaffolding model simulates a variety of possible operating conditions, including varying load conditions, construction phases, and environmental factors. For example, the abnormal structural response simulation simulates deformation and stress distribution under extreme loads; the worker interference simulation simulates the impact of worker activities on structural stability. This multi-condition simulation and disturbance analysis helps identify potential structural risk points, providing a scientific basis for sensor deployment and ensuring the comprehensiveness and sensitivity of the monitoring system.

[0075] In some embodiments, a multi-operating condition simulation disturbance analysis is performed based on the digital scaffolding model, wherein the multi-operating condition simulation disturbance analysis includes structural abnormal response simulation and operator interference simulation, including:

[0076] Based on the building information, the process is decomposed to determine a process task list, wherein the process task list includes personnel information, material information and equipment information; based on the personnel information and the equipment information, a dynamic disturbance point set is constructed, wherein the dynamic disturbance point is associated with and stores dynamic disturbance characteristics; based on the material information and the equipment information, a static disturbance point set is constructed, wherein the static disturbance point is configured with static disturbance parameters; and based on the dynamic disturbance point set and the static disturbance point set, a multi-working condition simulation disturbance analysis is performed.

[0077] Specifically, dynamic disturbance points refer to disturbance source points caused by variable factors such as personnel movement and equipment operation during the construction process, which are characterized by temporal and spatial position variability; static disturbance points refer to fixed disturbance source points usually formed by factors such as material stacking and structural occupancy, which are stable and predictable.

[0078] Specifically, the system first analyzes building information to obtain basic data such as project structure, construction progress, and spatial layout. Combined with process standards and project node arrangements, it automatically or semi-automatically decomposes the process steps and generates a task list (such as a bill of quantities) covering the entire construction cycle, providing a detailed personnel and material information foundation for subsequent analysis. For example, this task list includes construction worker ID, entry and exit times, task location, required equipment number, usage period, required material types, and storage areas.

[0079] Specifically, after obtaining the process task list, the dynamic risk locations in the construction site are identified through the construction personnel's path planning data and equipment operation trajectory data, and a dynamic disturbance point set is constructed, where each disturbance point is set with disturbance characteristic parameters, such as disturbance frequency, impact range, duration, interaction probability, etc.; for example, a construction elevator covers the working area of ​​a certain floor in a specified time period, forming an interference peak; construction personnel pass through the corridor densely, forming a concentrated vibration source, etc.

[0080] Specifically, based on the material stacking plan and equipment footprint, static obstacle factors are extracted to conduct a spatial analysis of the construction site to identify static disturbance points. Each static disturbance point is configured with disturbance parameters, including space occupied volume, mass, permanence, and whether it is movable. For example, a batch of formwork materials (110 kg) stacked on the walkway area of ​​a scaffolding for three days poses a fixed disturbance risk within a specific time period.

[0081] Furthermore, the aforementioned disturbance point data is integrated to define different working conditions. By varying the distribution and intensity of the disturbance points, multiple rounds of virtual construction simulation are conducted within the digital scaffolding model. Performance indicators such as stress, vibration, stability, and load-bearing capacity under different disturbance combinations are analyzed to identify potential locations of abnormal structural responses. Simulation techniques such as finite element analysis (FEA) and multibody dynamics (MBD) can also be used.

[0082] By integrating disturbance data into the digital scaffolding model and conducting multiple rounds of virtual construction simulation, a quantitative assessment of the stress and vibration response of the scaffolding structure under complex disturbance scenarios was achieved. This helps to accurately identify potential risk areas and high-response nodes in the structure for disturbance early warning and control, thereby improving construction safety assurance capabilities and response efficiency.

[0083] In some implementations, performing multi-operating condition simulation disturbance analysis based on the dynamic disturbance point set and the static disturbance point set includes:

[0084] Real-time distribution constraints are defined through the process task list, and the static disturbance point set is randomly distributed in the digital scaffolding model in combination with the real-time distribution constraints to iteratively obtain a static operating condition set; the static operating condition set is traversed to perform structural abnormal response simulation in the digital scaffolding model to obtain a first simulation result; the dynamic disturbance point set is randomly distributed in the digital scaffolding model, and the distribution results are randomly combined with the static operating condition set to iteratively obtain a dynamic operating condition set; the dynamic operating condition set is traversed to perform operator interference simulation in the digital scaffolding model to obtain a second simulation result; the first simulation result and the second simulation result are output as the multi-operating condition simulation disturbance analysis result.

[0085] Specifically, real-time distribution constraints are spatiotemporal constraints imposed on the distribution of personnel, equipment, and materials based on a specific time period or construction phase, which are used to reflect the dynamic laws and constraint rules of the construction site. For example, workers cannot be concentrated in a high-risk area within the same time period, different types of work cannot appear at the same time and location, or a certain equipment can only operate in a specific construction area.

[0086] Specifically, the static operating condition set is a set of disturbance states generated based on the distribution of static disturbance points, ignoring the dynamic movement of personnel or equipment. It is used to simulate the response of the structure under various initial or steady-state disturbance conditions. The dynamic operating condition set is based on the static operating condition set and introduces dynamic disturbance points (such as worker behavior and mobile equipment paths) to generate a set of composite disturbance state combinations. It is used to reflect the response characteristics of the scaffolding under the superposition of complex disturbance factors during actual construction.

[0087] Specifically, personnel, equipment, and material information from the process task list is first extracted. Combined with the work schedule and area division information, a distribution constraint model encompassing time, space, and work objects is established. For example, a certain type of lifting equipment can only enter Section C of the scaffolding area between 12:00 PM and 2:00 PM, or material transportation routes can be set to avoid high-density work areas. Then, through methods such as Monte Carlo simulation and Latin hypercube sampling, static disturbance points are randomly placed in the digital scaffolding model over multiple rounds, combined with real-time distribution constraints. This generates a representative and comprehensive set of static working conditions, each of which represents a possible construction layout state (step state of the static disturbance source).

[0088] Specifically, for each static operating condition, structural mechanics simulation methods (such as finite element analysis) are used to simulate the stress distribution, displacement response, critical node forces, and deformation of the scaffolding under the static disturbance state, outputting a first set of simulation results. For example, the first set of simulation results includes: when materials are concentrated in the north area (coordinates X, Z), a lateral deformation trend is generated, and a high stress concentration occurs at the front node S1.

[0089] Specifically, using the same principles as above, a set of dynamic disturbance points, such as personnel movement paths and equipment operation points, is randomly distributed within the digital scaffolding model. This set of dynamic disturbance points is then randomly combined with a set of static operating conditions, and after multiple iterations, a dynamic operating condition set is generated. For example, in a bridge construction project, a set of dynamic disturbance points, such as construction personnel movement paths and crane operation points, is randomly distributed and combined with a set of static operating conditions (such as material storage points) to generate multiple dynamic operating condition sets, each representing a different set of personnel and equipment activities.

[0090] Furthermore, according to each dynamic working condition set, the transient impact, periodic disturbance or loading anomaly caused by the workers entering the key area, operating machinery, and concentrated actions on the scaffolding structure are simulated, and the worker interference simulation is performed to analyze the vibration response and stability of the scaffolding structure under dynamic loads, and the simulation results are recorded to obtain the second simulation results.

[0091] By building real-time distributed constraints based on the process task list and then conducting combined modeling and simulation of static and dynamic disturbance points, it is possible to construct multi-condition disturbance scenarios covering various construction behavior characteristics, providing high-precision and high-reliability data support for scaffolding safety assessment, construction optimization and risk control.

[0092] S300: Determine a set of key monitoring points according to the results of the multi-operating condition simulation disturbance analysis, and obtain an optimal sensor layout plan based on the key monitoring point set in combination with an optimization algorithm.

[0093] Specifically, by analyzing the results of multi-condition simulation disturbance analysis, we can find the points where the structural response is most significant, which corresponds to the set of key monitoring points that need to be monitored; then, using optimization algorithms such as genetic algorithms and particle swarm optimization, we can calculate the optimal sensor layout plan with the goal of minimizing the number of sensors and maximizing monitoring coverage and balance.

[0094] By implementing a scientific and optimal placement strategy, comprehensive and accurate monitoring of the scaffolding structure can be achieved with a limited number of sensors, thereby improving the accuracy and reliability of safety monitoring. The optimization algorithm minimizes the number of sensors while ensuring monitoring coverage and sensitivity, helping to reduce the hardware cost of the monitoring system and the resource consumption of data processing.

[0095] In some embodiments, determining a set of key monitoring points based on the multi-operating condition simulation disturbance analysis results includes:

[0096] According to the preset stress control limit, stress concentration points are extracted from the first simulation result and the second simulation result, and the output is a stress concentration point set; according to the preset vibration control limit, vibration significant points are extracted from the second simulation result to obtain a vibration significant point set; the stress concentration point set and the vibration significant point set are marked and merged, and density-based clustering analysis is performed to determine multiple cluster centers; and the multiple cluster centers are serially output as the key monitoring point set.

[0097] Specifically, the stress control limit refers to a pre-set stress threshold value, which is used to determine whether the stress of the structure under load exceeds the safe range. If it exceeds, the corresponding position can be set as the stress concentration point (the point in the structure where the stress is significantly higher than the surrounding area); the vibration control limit is a set of pre-set vibration control index values, which is used to determine whether the vibration of the structure under dynamic load exceeds the safe range.

[0098] Specifically, first, in the multi-condition simulation disturbance analysis results, based on the preset stress control limit, points where stress exceeds the threshold are screened out to form a stress concentration point set. For example, if the preset stress control limit is 200MPa, then the simulation results under static and dynamic conditions are analyzed to extract points where stress exceeds 200MPa to form a stress concentration point set. Similarly, in the simulation analysis results of dynamic conditions, based on the preset vibration control limit, points where vibration amplitude exceeds the threshold are screened out to form a vibration-significant point set. For example, if the preset vibration control limit is 0.5g, then points where vibration amplitude exceeds 0.5g are extracted to form a vibration-significant point set.

[0099] Furthermore, the stress concentration point set and the vibration significant point set are merged, and the merged point set is subjected to density-based cluster analysis to determine multiple cluster centers. The above-mentioned multiple cluster centers represent multiple areas or points in the scaffolding structure that need to be monitored. Then, the preset sorting rules of the determined cluster centers are output to form a key monitoring point set. For example, the key monitoring point set is output by sorting according to the severity of stress and vibration. The serialized key monitoring point set is used to guide the order of sensor deployment to ensure that more important locations are deployed first under the premise of limited sensors.

[0100] In some embodiments, based on the set of key monitoring points, an optimal sensor deployment plan is obtained in combination with an optimization algorithm, including:

[0101] The key monitoring point set is used as the selection space to generate an initial layout plan set; an optimization objective function is constructed with the goals of minimizing the total number of sensors, maximizing the sensor coverage of each point, and balancing sensor coverage; the initial layout plan set is iteratively optimized in combination with the optimization objective function until the preset optimization termination constraints are met, and the optimal layout plan is output.

[0102] Specifically, the system first generates multiple initial placement schemes within a set of key monitoring points, based on a set of key monitoring points and combining them with established rules (e.g., selecting 1-2 monitoring points for every 5 points). Different initial placement schemes include different combinations of sensor numbers and locations. Preferably, the initial placement schemes are generated using a heuristic strategy (e.g., prioritizing the point with the highest stress value) or random sampling.

[0103] Specifically, the optimization objective function includes: minimizing the total number of sensors to reduce costs, maximizing the coverage of each point to ensure that high-risk points are perceived, and achieving optimal coverage balance to avoid over-dense deployment in a certain area and omissions in other areas; for example, maximizing the coverage of each point corresponds to the presence of at least one IOT sensor in the neighborhood of each key monitoring point; optimal coverage balance corresponds to the fact that if an IOT sensor is simultaneously located in the neighborhood of multiple monitoring points, the smaller the standard deviation of the distance from the sensor to the corresponding multiple monitoring points, the better.

[0104] Furthermore, an optimization algorithm (such as genetic algorithm, particle swarm optimization, etc.) is used in combination with the optimization objective function to perform iterative optimization of the initial layout plan set until the preset optimization termination constraints are met (such as the maximum number of iterations, the result of the consecutive preset number of iterations is stable, or the preset function target value is achieved), and then the optimal layout plan in the iterative process is output, which uses the least number of sensors while meeting the coverage and balance requirements.

[0105] S400: Deploy sensor equipment and build a monitoring sensor network in combination with the optimal deployment plan, and perform scaffolding safety monitoring of the target scene based on the monitoring sensor network and preset safety warning rules.

[0106] Specifically, according to the optimal layout plan, sensor equipment is installed on the actual scaffolding structure to form a complete monitoring sensor network covering the target scene scaffolding; then, based on the constructed monitoring sensor network, combined with IOT technology, the monitoring data is transmitted to the monitoring system in real time and compared with the preset safety warning rules. When the comparison finds that the monitoring data exceeds the preset safety threshold, an early warning signal is automatically issued to remind construction personnel to take corresponding measures.

[0107] The above steps deploy an accurate, comprehensive and reasonable detection sensor network through the optimal layout plan determined above, and timely discover and deal with potential safety hazards through real-time monitoring and intelligent early warning, thereby helping to ensure the safety of the construction process.

[0108] Optionally, when construction conditions or scaffolding structures change (e.g., when construction nodes or stages change, causing the scaffolding to change), the monitoring points and layout plan can be quickly re-determined by adjusting the model and simulation parameters, while facilitating the addition of new monitoring functions or sensor types in the future.

[0109] In summary, the scaffolding safety monitoring method optimized by IOT sensing provided by the present invention has the following technical effects:

[0110] By acquiring the architectural information and scaffolding constraint information of the target scene, a corresponding digital scaffolding model is constructed; based on this model, a multi-operating condition simulation disturbance analysis is performed, which includes structural abnormal response simulation and operator interference simulation; a set of key monitoring points is determined based on the analysis results, and an optimal sensor layout plan is generated in combination with an optimization algorithm; sensor equipment is deployed based on this layout plan, a monitoring sensor network is constructed, and combined with preset safety warning rules, safety monitoring of the scaffolding in the target scene is implemented, thereby achieving the technical effect of improving monitoring efficiency and detection coverage, and enhancing monitoring sensitivity and accuracy.

[0111] Example 2, as Figure 2This is a schematic diagram of the structure of the scaffolding safety monitoring system optimized by IOT sensing in the present invention. For example, Figure 1 The flowchart of the scaffolding safety monitoring method optimized by IOT sensing in the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0112] Based on the same concept as the scaffolding safety monitoring method optimized by IOT sensing in the above embodiment, the present invention also provides a scaffolding safety monitoring system optimized by IOT sensing, including:

[0113] The digital scaffolding model building module 11 is used to obtain the architectural information and scaffolding constraint information of the target scene and build a digital scaffolding model.

[0114] The multi-operating condition analysis module 12 is configured to perform multi-operating condition simulation disturbance analysis based on the digital scaffolding model, wherein the multi-operating condition simulation disturbance analysis includes structural abnormal response simulation and operator interference simulation.

[0115] The layout optimization module 13 is used for the key monitoring point set determination module, which is used to determine the key monitoring point set according to the multi-working condition simulation disturbance analysis results, and obtain the optimal layout plan of the sensors based on the key monitoring point set in combination with the optimization algorithm.

[0116] The safety monitoring execution module 14 is used to deploy sensor equipment and build a monitoring sensor network in combination with the optimal layout plan, and perform scaffolding safety monitoring of the target scene based on the monitoring sensor network and preset safety warning rules.

[0117] In some embodiments, the digital scaffolding model building module 11 includes:

[0118] The building and scaffolding information extraction unit is used to interact with the management database of the target scene and extract the building information and scaffolding construction requirements.

[0119] The scaffolding structure and connection part information extraction unit is used to parametrically extract the scaffolding structure and connection part information based on the scaffolding construction requirements.

[0120] The scaffolding constraint information acquisition unit is used to use the scaffolding structural member information and the scaffolding connecting member information as retrieval constraints, acquire typical structural member constraint information and typical connecting member constraint information based on big data, and perform correction based on a preset early warning control coefficient to acquire the scaffolding constraint information.

[0121] In some embodiments, the digital scaffolding model building module 11 includes:

[0122] A physical model building unit is used to build a physical model according to the building information, the scaffolding structure information and the scaffolding connector information.

[0123] The digital scaffolding model initialization unit is configured to initialize the physical model according to the scaffolding constraint information, set boundary conditions, and obtain the digital scaffolding model.

[0124] In some embodiments, the digital scaffolding model building module 11 further includes:

[0125] The minimum basic model set extraction unit is used to analyze the digital scaffolding model and extract a minimum basic model set, wherein the minimum basic model set includes a plurality of minimum basic models that can constitute the complete digital scaffolding model.

[0126] The digital framework model simplification element is used to prune the digital scaffolding model with a single minimum basic model as the basic unit to obtain the simplified digital framework model, wherein the simplified digital framework model includes multiple discrete regional framework sub-models.

[0127] In some embodiments, the multi-operating condition analysis module 12 includes:

[0128] The process task list determination unit is used to perform process decomposition based on the construction information and determine the process task list, wherein the process task list includes personnel information, material information and equipment information.

[0129] The dynamic disturbance point set construction unit is used to construct a dynamic disturbance point set according to the personnel information and the device information, wherein the dynamic disturbance point is associated with and stores dynamic disturbance characteristics.

[0130] The static disturbance point set construction unit is used to construct a static disturbance point set according to the material information and the equipment information, wherein the static disturbance point is configured with static disturbance parameters.

[0131] The multi-operating condition simulation disturbance analysis execution unit is used to perform multi-operating condition simulation disturbance analysis according to the dynamic disturbance point set and the static disturbance point set.

[0132] In some implementations, the multi-operating condition simulation disturbance analysis execution unit in the multi-operating condition analysis module 12 includes:

[0133] The static working condition set acquisition unit is used to define real-time distribution constraints through the process task list, and randomly distribute the static disturbance point set in the digital scaffolding model in combination with the real-time distribution constraints to iteratively acquire the static working condition set.

[0134] The first simulation result acquisition unit is used to traverse the static working condition set, perform structural abnormal response simulation on the digital scaffolding model, and acquire a first simulation result.

[0135] The dynamic working condition set acquisition unit is used to randomly distribute the dynamic disturbance point set in the digital scaffolding model, and randomly combine the distribution result with the static working condition set to iteratively acquire the dynamic working condition set.

[0136] The second simulation result acquisition unit is used to traverse the dynamic working condition set, perform operator interference simulation on the digital scaffolding model, and obtain a second simulation result.

[0137] The multi-operating condition simulation disturbance analysis result output unit is used to output the first simulation result and the second simulation result as the multi-operating condition simulation disturbance analysis result.

[0138] In some embodiments, the layout optimization module 13 includes:

[0139] The stress concentration point set extraction unit is used to extract stress concentration points from the first simulation result and the second simulation result according to a preset stress control limit, and output them as a stress concentration point set.

[0140] The vibration significant point set acquisition unit is used to extract vibration significant points from the second simulation result according to a preset vibration control limit to acquire a vibration significant point set.

[0141] The cluster analysis and key monitoring point set determination unit is used to mark and merge the stress concentration point set and the vibration significant point set, and perform density-based cluster analysis to determine multiple cluster centers.

[0142] The key monitoring point set output unit is used to serially output multiple cluster centers as the key monitoring point set.

[0143] In some embodiments, the layout optimization module 13 includes:

[0144] The initial layout plan set generating unit is used to generate an initial layout plan set with the key monitoring point set as the selection space.

[0145] The optimization objective function construction unit is used to construct the optimization objective function with the goals of minimizing the total number of sensors, maximizing the coverage of sensors at each point, and balancing sensor coverage.

[0146] The optimal layout solution optimization unit is used to iteratively optimize the initial layout solution set in combination with the optimization objective function until the preset optimization termination constraint is met, and output the optimal layout solution.

[0147] In the third embodiment, the present invention further provides a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the scaffolding safety monitoring method optimized by IOT sensing in the embodiment of the present invention, thereby realizing the above-mentioned scaffolding safety monitoring method optimized by IOT sensing.

[0148] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A scaffolding safety monitoring method optimized by IOT sensing, characterized in that: include: Obtain the architectural information and scaffolding constraint information of the target scene and construct a digital scaffolding model; Performing multi-operating condition simulation disturbance analysis based on the digital scaffolding model, wherein the multi-operating condition simulation disturbance analysis includes structural abnormal response simulation and operator interference simulation; Determine a set of key monitoring points based on the results of multi-condition simulation disturbance analysis, and obtain the optimal sensor layout plan based on the key monitoring point set and combined with the optimization algorithm; In combination with the optimal layout plan, sensor equipment is deployed and a monitoring sensor network is constructed, and scaffolding safety monitoring of the target scene is performed based on the monitoring sensor network and preset safety warning rules.

2. The scaffolding safety monitoring method optimized by IOT sensing according to claim 1, characterized in that: Obtain the target scene's building information and scaffolding constraint information, including: Interact with the target scene management database to extract the building information and scaffolding requirements; Based on the scaffolding construction requirements, parametrically extract scaffolding structural member information and scaffolding connector information; The scaffolding structural member information and scaffolding connecting member information are used as retrieval constraints, typical structural member constraint information and typical connecting member constraint information are obtained based on big data, and correction is performed based on a preset early warning control coefficient to obtain the scaffolding constraint information.

3. The scaffolding safety monitoring method optimized by IOT sensing according to claim 2, characterized in that: Build a digital scaffolding model, including: Constructing a physical model based on the building information, the scaffolding structure information, and the scaffolding connector information; The physical model is initialized according to the scaffolding constraint information, boundary conditions are set, and the digital scaffolding model is obtained.

4. The scaffolding safety monitoring method optimized by IOT sensing according to claim 3, characterized in that: Building a digital scaffolding model also includes: Analyzing the digital scaffolding model to extract a minimum basic model set, wherein the minimum basic model set includes a plurality of minimum basic models that can constitute the complete digital scaffolding model; The digital scaffolding model is pruned with a single minimum basic model as a basic unit to obtain a simplified digital framework model, wherein the simplified digital framework model includes a plurality of discrete regional framework sub-models.

5. The scaffolding safety monitoring method optimized by IOT sensing according to claim 3, characterized in that: Performing a multi-operating condition simulation disturbance analysis based on the digital scaffolding model, wherein the multi-operating condition simulation disturbance analysis includes structural abnormal response simulation and operator interference simulation, including: Decomposing the process based on the construction information to determine a process task list, wherein the process task list includes personnel information, material information, and equipment information; Constructing a dynamic disturbance point set according to the personnel information and the device information, wherein the dynamic disturbance point is associated with and stores dynamic disturbance characteristics; Constructing a static disturbance point set according to the material information and the equipment information, wherein the static disturbance point is configured with a static disturbance parameter; A multi-operating condition simulation disturbance analysis is performed based on the dynamic disturbance point set and the static disturbance point set.

6. The scaffolding safety monitoring method optimized by IOT sensing according to claim 5, characterized in that: Performing multi-operating condition simulation disturbance analysis according to the dynamic disturbance point set and the static disturbance point set, including: By using the process task list, a real-time distribution constraint is defined, and the real-time distribution constraint is combined with the digital scaffolding model to randomly distribute the static disturbance point set, and iteratively obtain a static working condition set; Traversing the static working condition set, performing structural abnormal response simulation on the digital scaffolding model, and obtaining a first simulation result; Randomly distributing the dynamic disturbance point set in the digital scaffolding model, and randomly combining the distribution result with the static working condition set to iteratively obtain the dynamic working condition set; Traversing the dynamic working condition set, performing an operator interference simulation on the digital scaffolding model, and obtaining a second simulation result; The first simulation result and the second simulation result are output as the multi-operating condition simulation disturbance analysis result.

7. The scaffolding safety monitoring method optimized by IOT sensing according to claim 6, characterized in that: The key monitoring point set is determined based on the results of multi-operating condition simulation disturbance analysis, including: Extracting stress concentration points distributed in the first simulation result and the second simulation result according to a preset stress control limit, and outputting the stress concentration point set; Extracting vibration significant points from the second simulation results according to a preset vibration control limit to obtain a vibration significant point set; Marking and merging the stress concentration point set and the vibration significant point set, and performing density-based cluster analysis to determine multiple cluster centers; The plurality of cluster centers are serially output as the key monitoring point set.

8. The scaffolding safety monitoring method optimized by IOT sensing according to claim 7, characterized in that: Based on the key monitoring point set, the optimal sensor layout plan is obtained in combination with the optimization algorithm, including: Taking the key monitoring point set as the selection space, generating an initial layout plan set; The optimization objective function is constructed with the goal of minimizing the total number of sensors, maximizing the coverage of sensors at each point, and balancing sensor coverage; The initial layout solution set is iteratively optimized in combination with the optimization objective function until the preset optimization termination constraint is satisfied, and the optimal layout solution is output.

9. The scaffolding safety monitoring system optimized by IOT sensing is characterized by: The method for monitoring scaffolding safety through IOT sensing optimization according to any one of claims 1 to 8 comprises: A digital scaffolding model building module is used to obtain the architectural information and scaffolding constraint information of the target scene and build a digital scaffolding model; A multi-operating condition analysis module, configured to perform multi-operating condition simulation disturbance analysis based on the digital scaffolding model, wherein the multi-operating condition simulation disturbance analysis includes structural abnormal response simulation and operator interference simulation; A layout optimization module is used for determining a set of key monitoring points based on the results of multi-condition simulation disturbance analysis, and based on the set of key monitoring points, an optimal layout plan for sensors is obtained in combination with an optimization algorithm; The safety monitoring execution module is used to deploy sensor equipment and build a monitoring sensor network in combination with the optimal layout plan, and perform scaffolding safety monitoring of the target scene based on the monitoring sensor network and preset safety warning rules.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the scaffolding safety monitoring method optimized by IOT sensing as described in any one of claims 1 to 8 is implemented.

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