Construction site data acquisition method and system

Abstract

The invention relates to the technical field of construction, and discloses a construction site data acquisition method and system, and the method comprises the steps: obtaining environment impact factors through an environment state evaluation module based on rainfall, vibration intensity and pore water pressure; obtaining a consequence influence factor through a consequence influence evaluation module based on the number of influence municipal roads and high-risk pipelines; geometric risk factors are obtained based on the slope gradient and height through a body stability evaluation module; acquiring a displacement-creep matching coefficient based on the displacement rate and the creep rate through a displacement-creep matching degree evaluation module; and finally, dynamically adjusting a three-dimensional laser scanning period based on the geometric risk factor and the displacement-creep matching coefficient through an acquisition period regulation and control module. According to the method, the problem that the data acquisition frequency is not matched with the risk level in a traditional monitoring method is solved, optimal configuration of monitoring resources is realized, and the system efficiency is remarkably improved while the monitoring effect is ensured.

Description

Technical Field

[0001] This invention belongs to the field of construction technology, and in particular relates to a method and system for data acquisition at construction sites. Background Technology

[0002] In the field of safety monitoring for foundation pit and slope engineering, traditional monitoring methods rely on single-point measuring equipment such as total stations and inclinometers, which have limited monitoring range and cannot comprehensively reflect the overall deformation state of the structure. Although 3D laser scanning technology can overcome this limitation through area measurement, it faces a prominent contradiction in practical applications: the challenge of balancing acquisition frequency and operating costs. Excessively high scanning frequency generates massive amounts of redundant data, significantly increasing the system's operational burden; while excessively low scanning frequency may miss key deformation features, creating safety hazards.

[0003] Existing monitoring systems typically employ a simple control strategy based on a single parameter threshold, increasing the scanning frequency only when displacement exceeds limits. This approach has significant shortcomings: firstly, it fails to comprehensively consider multi-dimensional information such as environmental factors, structural characteristics, and the consequences of instability; secondly, it lacks effective handling of the dynamic coupling relationships between various risk factors, making it impossible to achieve intelligent optimization of monitoring resources.

[0004] Therefore, there is an urgent need in this field for an intelligent monitoring solution that can deeply integrate multi-source information and has dynamic adaptive capabilities, so as to significantly improve monitoring efficiency while ensuring safety. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for data acquisition at construction sites, in order to solve the above-mentioned problems.

[0006] This invention is implemented as follows: a construction site data acquisition system, comprising: The environmental status assessment module obtains environmental impact factors based on rainfall, vibration intensity, and pore water pressure through an environmental status model. The consequences impact assessment module, based on the number of municipal roads affected and the number of high-risk pipelines affected, obtains the consequences impact factors through the consequences impact model. The solid stability assessment module obtains geometric risk factors based on slope gradient and slope height through a solid stability model. The displacement-creep matching degree assessment module, under the current environmental impact factors and consequence impact factors, obtains the displacement-creep matching coefficient based on the slope displacement rate and creep rate through a matching model. The acquisition cycle control module, based on geometric risk factors and displacement-creep matching coefficients, outputs a target acquisition cycle through a cycle control model. When the target acquisition cycle is less than the current remaining acquisition interval, the current cycle is immediately interrupted and the target acquisition cycle is replaced with a new execution cycle.

[0007] A further technical solution is that the cycle control model is as follows: ,in, For the target acquisition period, For the maximum collection period, Minimum acquisition period These are the weighting coefficients. Geometric risk factor This is the displacement-creep matching coefficient.

[0008] A further technical solution involves obtaining geometric risk factors based on slope gradient and slope height using a solid stability model, as follows: The slope index is obtained by comparing the current slope gradient with the maximum slope gradient. The height index is obtained by comparing the current slope height with the maximum slope height. Introduce the slope index and height into the formula. Obtain geometric risk factors ,in, , The slope index is... It is an altitude index.

[0009] A further technical solution, under the current environmental impact factors and consequence impact factors, involves obtaining the displacement-creep matching coefficient based on the slope displacement rate and creep rate using a matching model. The steps are as follows: The foundation matching coefficient is obtained based on the slope displacement rate and creep rate; The basic safety status factor, as well as the current environmental impact factor and the consequence impact factor, are imported into the formula. Obtain the displacement-creep matching coefficient ,in, , Based on the matching coefficient, As environmental impact factors, As a consequence influencing factor, This is the risk adjustment coefficient.

[0010] A further technical solution involves the step of obtaining the basic safety state factor based on slope displacement rate and creep rate: The current slope displacement rate is compared with the displacement rate threshold, and the upper limit of the min function is set to 1 to obtain the slope displacement rate index. The current creep rate is compared with the creep rate threshold, and the upper limit is set to 1 using the min function to obtain the creep rate exponent. Import the slope displacement rate exponent and creep rate exponent into the formula. Obtain the basic matching coefficient ,in, , This is the slope displacement rate index. The creep rate index. To avoid dividing by zero for extremely small positive numbers.

[0011] A further technical solution involves obtaining environmental impact factors based on rainfall, vibration intensity, and pore water pressure using an environmental state model, as follows: The rainfall index is obtained by comparing the current rainfall with the maximum designed rainfall intensity. The vibration intensity index is obtained by comparing the current vibration intensity with the maximum allowable vibration intensity. The pore water pressure index is obtained by comparing the current pore water pressure with the critical pore water pressure. The rainfall index, vibration intensity index, and pore water pressure index are imported into the formula. Obtaining environmental impact factors ,in, , This is a rainfall index. The vibration intensity index, The pore water pressure index, , and All are weighting coefficients.

[0012] A further technical solution involves obtaining the consequence impact factor through a consequence impact model based on the number of municipal roads and the number of high-risk pipelines: The road impact index is obtained by comparing the current number of affected municipal roads with the maximum number of roads to be considered, and then using a min function with a maximum limit of 1. The pipeline impact index is obtained by comparing the current number of high-risk pipelines with the maximum number of pipelines to be considered, and then using a min function with an upper limit of 1. Import the road impact index and pipeline impact index into the formula. Obtain the impact factors of the consequences ,in, , The road impact index, Pipeline impact index.

[0013] A method for collecting data at a construction site includes the following steps: Environmental impact factors are obtained through an environmental state model based on rainfall, vibration intensity, and pore water pressure. Based on the number of municipal roads and high-risk pipelines affected, the consequence impact factors are obtained through a consequence impact model. Geometric risk factors are obtained based on slope gradient and slope height using a volumetric stability model. Under the current environmental impact factors and consequences impact factors, the displacement-creep matching coefficient is obtained through a matching model based on the slope displacement rate and creep rate. Based on geometric risk factors and displacement-creep matching coefficients, a target acquisition cycle is output through a cycle control model. When the target acquisition cycle is less than the current remaining acquisition interval, the current cycle is immediately interrupted and the target acquisition cycle is replaced with a new execution cycle.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a method and system for data acquisition at construction sites. By establishing an intelligent evaluation system that integrates multi-dimensional parameters, this invention achieves dynamic optimization of the 3D laser scanning work cycle. Compared to traditional single-threshold control methods, this solution comprehensively considers multiple parameters, including environmental factors, consequences, structural stability, and deformation compatibility. Through the coupled calculation of displacement-creep matching coefficients and environmental risk factors, the acquisition frequency accurately matches the actual risk level. The collaborative control mechanism of geometric risk factors and matching coefficients ensures timely monitoring under high-risk conditions while avoiding resource waste during low-risk periods. The closed-loop control architecture ensures the system can quickly respond to sudden risks, significantly improving the timeliness and accuracy of monitoring and early warning, and providing a more intelligent and efficient monitoring solution for slope engineering safety. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0016] Figure 1 The present invention provides a flowchart of a construction site data acquisition system. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In existing technologies, safety monitoring of foundation pits and slopes mainly relies on single-point measuring devices such as total stations and inclinometers. These devices have limited monitoring ranges and cannot comprehensively reflect the overall deformation state of the structure. Although three-dimensional laser scanning technology can extend the monitoring range through area measurement, there is a contradiction between acquisition frequency and operating costs in practical applications: high-frequency scanning generates redundant data, increasing the system burden, while low-frequency scanning may miss key deformation features. Existing monitoring systems typically adopt control strategies based on a single parameter threshold, such as adjusting the frequency only when the displacement exceeds the limit. This method does not comprehensively consider the coupling relationship between environmental factors, structural characteristics, and instability consequences, resulting in low efficiency in the allocation of monitoring resources.

[0019] To address the aforementioned issues, the inventors discovered that traditional methods cannot effectively handle the dynamic interactive effects of multidimensional risk factors. For example, rainfall infiltration and construction vibrations can accelerate slope displacement, and the degree of matching between displacement rate and creep rate directly affects the assessment of structural stability. Analysis revealed the need to establish a multidimensional collaborative assessment mechanism that integrates environmental conditions, consequences, and structural stability parameters, and enables real-time adjustment of the data acquisition cycle based on deformation dynamic characteristics. This approach overcomes the limitations of single-parameter control, forming a closed-loop control logic from risk identification to response decision-making.

[0020] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0021] like Figure 1 As shown, a construction site data acquisition system according to an embodiment of the present invention includes: The environmental status assessment module obtains environmental impact factors based on rainfall, vibration intensity, and pore water pressure through an environmental status model. The consequences impact assessment module, based on the number of municipal roads affected and the number of high-risk pipelines affected, obtains the consequences impact factors through the consequences impact model. The solid stability assessment module obtains geometric risk factors based on slope gradient and slope height through a solid stability model. The displacement-creep matching degree assessment module, under the current environmental impact factors and consequence impact factors, obtains the displacement-creep matching coefficient based on the slope displacement rate and creep rate through a matching model. The acquisition cycle control module, based on geometric risk factors and displacement-creep matching coefficients, outputs a target acquisition cycle through a cycle control model. When the target acquisition cycle is less than the current remaining acquisition interval, the current cycle is immediately interrupted and the target acquisition cycle is replaced with a new execution cycle.

[0022] The environmental status assessment module assesses environmental impacts by integrating parameters of precipitation infiltration, construction disturbance, and soil saturation. This can be implemented using a multi-sensor data fusion algorithm to quantify the combined effects of the external environment on slope stability. The consequence impact assessment module assesses potential losses based on the degree of impact on municipal infrastructure. This can be implemented using a weighted statistical model to reflect the social and economic risks that may arise from project instability. The intrinsic stability assessment module assesses inherent risks based on slope geometry. This can be implemented using a nonlinear geometric model to characterize the stability differences of different slope structures. The displacement-creep matching degree assessment module analyzes the coordination of deformation rates. This can be implemented using a dynamic time warping algorithm to determine the trend of structural deformation. The data acquisition cycle control module dynamically optimizes the data acquisition frequency. This can be implemented using an adaptive control algorithm to balance monitoring accuracy and system resource consumption.

[0023] Specifically, the system uses an environmental condition assessment module to monitor rainfall, vibration intensity, and pore water pressure in real time, weighting these three factors to calculate an environmental impact factor that reflects the immediate impact of the external environment on slope stability. The consequence impact assessment module counts the number of affected municipal roads and underground pipelines, generating a consequence impact factor using a maximum value function to quantify the scale of secondary disasters that may result from engineering instability. The intrinsic stability assessment module calculates a geometric risk factor based on the geometric relationship between slope gradient and height using a square root function, characterizing the inherent risk level of the slope structure. The displacement-creep matching degree assessment module, under the constraints of environmental and consequence factors, compares the dynamic trends of displacement rate and creep rate, generating a matching coefficient through difference calculation. The data acquisition cycle control module inputs the geometric risk factor and matching coefficient into the control model, dynamically calculates the optimal data acquisition cycle, immediately interrupts the current cycle, and replaces the target data acquisition cycle with a new execution cycle, forming a closed-loop control from risk perception to execution response.

[0024] Compared with existing technologies, this scheme overcomes the limitations of single threshold control through a multi-dimensional parameter fusion mechanism. Traditional methods only monitor displacement exceeding limits, while this scheme simultaneously considers the coupled effects of environmental infiltration, construction disturbance, structural morphology, and instability consequences. It uses a dynamic matching coefficient to reflect changes in deformation compatibility, making the data acquisition cycle adjustment more consistent with the actual risk evolution. Existing technologies employ fixed weight allocation; this scheme uses a risk adjustment coefficient to achieve dynamic weighting of environmental and consequence impacts, enhancing the system's adaptability to complex working conditions.

[0025] Through the above technical solutions, this application effectively solves the problems of limited monitoring range and imbalanced data acquisition frequency. By fusing and evaluating multi-source data, the system can comprehensively perceive the multidimensional influencing factors of slope stability, avoiding the omission of key risk characteristics. Based on dynamic matching coefficients and adaptive control models, optimal allocation of monitoring resources is achieved, reducing data redundancy while ensuring the accuracy of safe monitoring. A closed-loop control mechanism ensures rapid response under abnormal conditions, enhancing the system's ability to handle sudden risks.

[0026] Preferably, the periodic regulation model is as follows: in, The target acquisition period refers to the monitoring data acquisition interval after dynamic adjustment based on real-time risk. Specifically, it can be constrained by a preset maximum and minimum period range to balance the data acquisition frequency and system load. The maximum data collection period refers to the longest monitoring interval allowed by the system, which can be set to 24 hours. The minimum acquisition cycle refers to the shortest monitoring interval allowed by the system, which can be set to 1 hour, for example. These are weighting coefficients used to adjust the contribution ratio of the geometric risk factor and the displacement-creep matching coefficient to the cycle adjustment. The specific values ​​can be set through historical data analysis or expert experience. Greater than or equal to 0 and less than or equal to 1; The geometric risk factor characterizes the inherent stability risk of the slope structure, which can be obtained through normalized calculation of slope gradient and height. The displacement-creep matching coefficient reflects the dynamic deformation compatibility of the slope, and can be calculated through the matching relationship between displacement rate and creep rate.

[0027] Specifically, the periodic control model generates a composite risk index by linearly weighting geometric risk factors and displacement-creep matching coefficients, and maps this index to a preset maximum and minimum period range. When the geometric risk factor increases or the displacement-creep matching coefficient decreases, the target acquisition period is automatically shortened to increase the monitoring frequency; conversely, the acquisition period is extended. Weighting coefficients are used to dynamically adjust the influence of geometric risk and dynamic deformation response on periodic decisions. For example, when the slope is under sudden environmental changes such as rainfall, the weight of the displacement-creep matching coefficient can be increased. The model transforms multidimensional risk parameters into periodic adjustment quantities, ensuring that the monitoring frequency dynamically matches the risk level.

[0028] Compared to existing technologies, traditional methods typically trigger periodic adjustments based solely on a single parameter threshold (such as displacement exceeding limits), failing to simultaneously consider the coupling relationship between the inherent structural risks and dynamic deformation characteristics of slopes. This proposed solution, however, achieves a multi-dimensional quantitative assessment of slope stability risks by introducing a composite calculation of geometric risk factors and displacement-creep matching coefficients. Furthermore, by dynamically allocating the influence weights of the two types of risk factors through weighting coefficients, it can adapt to monitoring needs under different working conditions. For example, it prioritizes responding to dynamic deformation anomalies during heavy rain, while focusing on inherent structural risks under stable working conditions.

[0029] Through the above technical solution, this application solves the problem of rigid monitoring cycle adjustment caused by the reliance on a single parameter threshold in traditional monitoring systems. By integrating geometric structural risk and dynamic deformation response, dynamic optimization of the acquisition cycle is achieved. When the slope structural risk increases or the displacement-creep coordination deteriorates, the acquisition cycle is automatically shortened to capture key deformation data; when the risk decreases, the cycle is extended to reduce redundant data. This avoids the resource waste caused by high-frequency scanning and prevents potential safety hazards missed by low-frequency monitoring, significantly improving monitoring efficiency and the timeliness of risk warnings.

[0030] Preferably, the step of obtaining the geometric risk factor based on the slope gradient and slope height through the ontological stability model is as follows: The slope index is obtained by comparing the current slope gradient with the maximum slope gradient. The height index is obtained by comparing the current slope height with the maximum slope height. Introduce the slope index and height into the formula. Obtain geometric risk factors .

[0031] The geometric risk factor refers to the calculation result of the slope index and height index combined using the geometric mean formula. Specifically, the square root operation can be used to perform nonlinear processing on the product of the two parameters to balance the combined effect of the two parameters on stability. ; The slope index is the ratio of the actual slope to the maximum slope. It can be achieved by dividing real-time measurement data by a preset threshold. It reflects the degree to which the current slope is close to the critical state. The maximum slope value refers to the maximum slope value allowed in the engineering design. It can be achieved by using the threshold specified in the geological survey report or structural design code. It is used to characterize the extreme slope conditions of the slope within the safe range. The height index is the ratio of the actual height to the maximum height. It can be achieved by standardizing sensor data and preset parameters. It is used to characterize the weight of the slope height on stability. The maximum slope height refers to the upper limit of the vertical height of the slope allowed by the project. It can be determined by geotechnical analysis or engineering safety standards. It is used to eliminate the interference of different project scales on the height evaluation.

[0032] Specifically, by comparing the slope gradient and height to their corresponding maximum values, parameters with different dimensions can be converted into standardized indices, eliminating evaluation biases caused by differences in project scale. For example, when the actual slope reaches 80% of the maximum slope value, the slope index is calculated as 0.8, directly reflecting its approach to the design limit; when the actual height is 60% of the maximum height value, the height index is calculated as 0.6, reflecting the potential risk of height to stability. Furthermore, the two indices are fused using a geometric mean formula; for example, the square root of the product of the slope index 0.8 and the height index 0.6 yields 0.692. This avoids the extreme values ​​of a single parameter dominating the calculation results and strengthens the balanced influence of the two parameters through nonlinear calculations. The resulting geometric risk factor is constrained within the range of 0 to 1 and can be directly used in the dynamic control model for subsequent data collection cycles.

[0033] Compared to existing technologies, traditional methods typically rely on a single geometric parameter or empirical threshold for stability assessment, such as monitoring only whether the slope exceeds a preset value, while ignoring the synergistic effect of height changes on structural instability. This proposed solution, through standardized processing and mathematical fusion of two parameters, can more comprehensively reflect the combined impact of slope geometry on stability. For example, when the slope approaches a critical value, even at lower heights, the geometric risk factor will still significantly increase, triggering more intensive data collection.

[0034] Through the above technical solution, this application solves the problem of insufficient quantitative assessment of slope geometric characteristics in the prior art. By establishing a standardized two-parameter fusion model, it provides an objective basis for the structural status of the monitoring cycle adjustment.

[0035] Preferably, under the current environmental impact factors and consequence impact factors, the steps for obtaining the displacement-creep matching coefficient based on the slope displacement rate and creep rate using a matching model are as follows: The foundation matching coefficient is obtained based on the slope displacement rate and creep rate; The basic safety status factor, as well as the current environmental impact factor and the consequence impact factor, are imported into the formula. Obtain the displacement-creep matching coefficient .

[0036] The basic matching coefficient is a structural deformation compatibility index calculated based on the relationship between displacement rate and creep rate. Specifically, it can be achieved by normalizing the difference between the displacement rate index and the creep rate index, quantifying the degree of deviation between displacement and creep rate. The basic matching coefficient reflects the degree of matching between displacement and creep rate. , Based on the matching coefficient, As an environmental impact factor, it characterizes the combined effects of rainfall, vibration, and pore water pressure on slope stability; As a consequence impact factor, it reflects the degree of harm that engineering instability poses to municipal roads and pipelines; This is a risk adjustment coefficient used to adjust the strength of the suppression of the matching coefficient by external risks. Specifically, it can be implemented using a preset empirical value or a dynamic adjustment algorithm. Greater than or equal to 0 and less than 1.

[0037] Specifically, the slope displacement rate and creep rate are first acquired through real-time monitoring. These are then compared to preset thresholds and normalized to calculate the basic matching coefficient. This coefficient reflects the coordination between changes in displacement and creep rate; when their trends are consistent, the coefficient approaches 1. Subsequently, the maximum value among environmental impact factors and consequence impact factors is used as a risk correction term, which, combined with a risk adjustment coefficient, dynamically adjusts the basic matching coefficient. When environmental or consequence risks increase, the risk correction term reduces the displacement-creep matching coefficient, triggering more frequent data collection. This calculation method preserves the inherent correlation between displacement and creep rate while simultaneously correcting the matching coefficient through external risk factors, enabling monitoring frequency adjustments to synchronously respond to sudden environmental changes and escalating potential hazards.

[0038] Compared to existing technologies, traditional methods adjust monitoring frequency solely based on displacement exceeding thresholds, neglecting the coupled influence of environmental factors and instability consequences. Existing technologies typically analyze the relationship between displacement rate and creep rate using static threshold comparisons, failing to reflect the dynamic matching degree between the two. This scheme quantifies the coordinated changes in displacement and creep rate by constructing a basic matching coefficient and introduces environmental and consequence risk factors for dynamic correction, ensuring that monitoring frequency adjustment simultaneously considers both internal structural deformation characteristics and external risk conditions. While existing technologies often employ simple superposition for risk correction, this scheme highlights dominant risk factors through maximum value calculations and achieves precise control of risk impact by combining adjustable coefficients.

[0039] Through the above technical solution, this application effectively solves the frequency adjustment deviation problem caused by traditional monitoring methods neglecting the influence of environmental and consequence risks on the displacement-creep relationship. By dynamically coupling structural deformation characteristics with external risk factors, the displacement-creep matching coefficient can accurately reflect the current comprehensive risk level, providing a reliable basis for optimizing the monitoring cycle. When encountering heavy rainfall or increased risk of damage to surrounding pipelines, the system automatically reduces the matching coefficient and shortens the acquisition cycle to promptly capture potentially dangerous deformations. When the environment is stable and the consequence risk is low, the acquisition cycle is appropriately extended to reduce data redundancy. This dynamic adjustment mechanism optimizes system resource utilization while ensuring monitoring accuracy.

[0040] Preferably, the step of obtaining the basic safety state factor based on the slope displacement rate and creep rate is as follows: The current slope displacement rate is compared with the displacement rate threshold, and the upper limit of the min function is set to 1 to obtain the slope displacement rate index. The current creep rate is compared with the creep rate threshold, and the upper limit is set to 1 using the min function to obtain the creep rate exponent. Import the slope displacement rate exponent and creep rate exponent into the formula. Obtain the basic matching coefficient .

[0041] Among them, the basic matching coefficient refers to the parameter that quantifies the coordination between displacement and creep rate through mathematical formula. Specifically, it can be realized by a composite calculation formula of absolute difference and relative total amount, which is used to reflect the degree of matching between the two rate change trends. , The slope displacement rate index is a ratio calculated by comparing the measured displacement rate with a preset threshold. Specifically, it can be calculated by comparing real-time monitoring data with the displacement rate limit in the engineering safety specifications, and is used to characterize the relative degree of displacement development. The creep rate index is calculated by comparing the measured creep rate with a preset threshold. Specifically, it can be achieved by comparing the creep data collected by sensors with the values ​​calculated by the geomechanical model. It is used to characterize the relative degree of creep development. To avoid dividing by zero for extremely small positive numbers.

[0042] Specifically, the slope displacement rate index and creep rate index are standardized by using a threshold ratio to eliminate the influence of different dimensions on the calculation results. A min function is used to limit the index amplitude, effectively suppressing the interference of extreme monitoring values ​​on the calculation process. The standardized indexes are then substituted into a formula containing an absolute difference term. When the displacement and creep rate indices are close, the numerator difference approaches zero, and the foundation matching coefficient approaches 1, indicating ideal deformation compatibility. When the two rate indices differ significantly, the foundation matching coefficient approaches 0, reflecting an increased risk of deformation instability. A minimum positive number is introduced into the formula. This avoids the abnormal situation where the denominator is zero when both the displacement rate exponent and the creep rate exponent are zero, thus ensuring the stability of the calculation process.

[0043] Compared with existing technologies, traditional methods rely solely on a single rate parameter and a fixed threshold for exceeding limits, failing to capture the dynamic correlation between displacement and creep rate. This proposed solution, by establishing a quantified model matching the two rates, can identify potential risk states where displacement and creep development are not coordinated, overcoming the inaccuracy of the single-parameter threshold method in identifying complex deformation modes.

[0044] Through the above technical solution, this application can accurately identify the dynamic matching relationship between slope displacement and creep rate, providing core parameters reflecting the deformation coordination state for data acquisition cycle adjustment. This technical solution effectively avoids the problem of mis-adjustment of monitoring frequency caused by the judgment of a single parameter exceeding the limit, and significantly reduces the waste of resources caused by unnecessary data acquisition while ensuring the ability to capture key deformation features.

[0045] Preferably, the step of obtaining environmental impact factors based on rainfall, vibration intensity, and pore water pressure through an environmental state model is as follows: The rainfall index is obtained by comparing the current rainfall with the maximum designed rainfall intensity. The vibration intensity index is obtained by comparing the current vibration intensity with the maximum allowable vibration intensity. The pore water pressure index is obtained by comparing the current pore water pressure with the critical pore water pressure. The rainfall index, vibration intensity index, and pore water pressure index are imported into the formula. Obtaining environmental impact factors .

[0046] in, , The maximum design rainfall intensity is the upper limit of rainfall intensity set during the engineering structure design. It can be determined through regional meteorological data statistics or engineering flood control standards and is used to measure the impact of extreme precipitation on slope seepage pressure. The vibration intensity index refers to the maximum allowable vibration intensity, which is the vibration safety threshold generated by the operation of construction equipment. It can be determined by foundation bearing capacity testing or structural vibration response analysis and is used to characterize the disturbance boundary of mechanical operation on the soil structure. The critical pore water pressure index refers to the critical value of pore water pressure when soil undergoes shear failure. It can be obtained through geotechnical tests or numerical simulations and is used to determine the risk of weakening of soil shear strength. , and These are all weighting coefficients, which are adjustment parameters reflecting the contribution of different environmental parameters to the overall risk. They can be determined using expert scoring methods or historical data regression analysis, and are used to dynamically adjust the priority of multiple environmental factors. , and All are greater than or equal to 0 and less than or equal to 1, and .

[0047] Specifically, this scheme first compares three environmental parameters—rainfall, vibration intensity, and pore water pressure—with their corresponding engineering safety thresholds to eliminate dimensional differences and convert them into standardized indices. For example, when the actual rainfall reaches 80% of the design maximum, the rainfall index is quantified as 0.8. Then, a weighted summation formula is used to merge the three indices into a single environmental impact factor, where the weighting coefficients can be dynamically configured according to the engineering stage or geological conditions. In areas prone to heavy rainfall, the rainfall weight can be increased to enhance the response to precipitation risks; during periods of intensive mechanical construction, the vibration intensity weight is increased to enhance the sensitivity of vibration disturbance monitoring. This multi-parameter coupled assessment mechanism can reflect the comprehensive effect of combined environmental factors on slope stability in real time, providing precise quantitative basis for data acquisition frequency adjustment.

[0048] Compared to existing technologies, traditional methods typically monitor only a single environmental parameter or employ a fixed weighting model, failing to accurately capture the risk evolution patterns under the coupled effects of multiple factors. For example, existing technologies may only trigger monitoring frequency adjustments based on rainfall thresholds, neglecting the synergistic effects of vibration disturbance and pore water pressure. This solution, through a dynamic weighting mechanism and multi-parameter normalization processing, achieves the quantitative superposition and priority adaptation of different environmental risk factors, making the environmental impact assessment results more closely reflect the complex changes in actual engineering scenarios.

[0049] Through the above technical solution, this application effectively solves the problems of single-dimensional environmental factor assessment and lack of parameter coupling relationships in traditional monitoring methods. By establishing a dynamic weight allocation model for multiple environmental parameters, the comprehensive impact of composite environmental factors on slope stability can be accurately quantified, ensuring that the data collection frequency keeps pace with the real-time environmental risk level, and avoiding monitoring lag or resource waste caused by isolated parameter assessment.

[0050] Preferably, the step of obtaining the consequence impact factor through a consequence impact model based on the number of municipal roads and the number of high-risk pipelines is as follows: The road impact index is obtained by comparing the current number of affected municipal roads with the maximum number of roads to be considered, and then using a min function with a maximum limit of 1. The pipeline impact index is obtained by comparing the current number of high-risk pipelines with the maximum number of pipelines to be considered, and then using a min function with an upper limit of 1. Import the road impact index and pipeline impact index into the formula. Obtain the impact factors of the consequences .

[0051] in, , The road impact index, with the maximum number of roads to be considered, refers to a pre-set threshold for the number of municipal roads affected. This threshold can be determined through historical accident data analysis or engineering specifications and is used to standardize the actual number of impacts. For the pipeline impact index, the maximum number of pipelines to consider refers to a predefined upper limit on the number of high-risk pipelines affected, which can be set according to the pipeline importance classification standard to eliminate dimensional differences in different engineering scenarios. The min function limit operation means that when the actual number of impacts exceeds the preset maximum value, the value is forced to be 1 to avoid distortion of the index calculation due to extreme cases. The maximum value calculation rule means that the larger value is selected from the road and pipeline impact indices as the consequence impact factor to highlight the most serious potential impact direction.

[0052] Specifically, when slope instability may affect surrounding municipal facilities, the actual number of affected municipal roads is first compared to a preset threshold. If the actual value exceeds the threshold, a value of 1 is taken as the road impact index. Similarly, the number of high-risk pipelines is processed in the same way to obtain the pipeline impact index. By taking the maximum of the two values ​​to generate a consequence impact factor, the most adverse impact of slope instability on municipal infrastructure can be dynamically reflected. This factor serves as an input parameter for subsequent data collection cycle adjustments, ensuring that the system prioritizes monitoring frequency for high-risk targets when resources are limited.

[0053] Compared to existing technologies, traditional methods assess the severity of consequences simply by summing up the number of affected facilities, without considering the risk differences among different types of infrastructure. This solution standardizes data by pre-setting a maximum acceptable number and uses the maximum value calculation to highlight key risk sources, solving the problem of weighting multi-dimensional influencing factors and avoiding the ineffective consumption of monitoring resources on non-critical targets.

[0054] Through the above technical solution, this application achieves accurate quantitative assessment of the impact of slope instability, effectively identifies the risk scenarios that pose the greatest threat to municipal roads or high-risk pipelines, provides an objective basis for dynamically adjusting the three-dimensional laser scanning frequency, and reduces data redundancy while ensuring the monitoring accuracy of key facilities.

[0055] As a preferred embodiment of the technical solution of the present invention, a method for acquiring construction site data applied to the construction site data acquisition system is provided, comprising the following steps: Environmental impact factors are obtained through an environmental state model based on rainfall, vibration intensity, and pore water pressure. Based on the number of municipal roads and high-risk pipelines affected, the consequence impact factors are obtained through a consequence impact model. Geometric risk factors are obtained based on slope gradient and slope height using a volumetric stability model. Under the current environmental impact factors and consequences impact factors, the displacement-creep matching coefficient is obtained through a matching model based on the slope displacement rate and creep rate. Based on geometric risk factors and displacement-creep matching coefficients, a target acquisition cycle is output through a cycle control model. When the target acquisition cycle is less than the current remaining acquisition interval, the current cycle is immediately interrupted and the target acquisition cycle is replaced with a new execution cycle.

[0056] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A construction site data acquisition system, characterized in that, include: The environmental status assessment module obtains environmental impact factors based on rainfall, vibration intensity, and pore water pressure through an environmental status model. The consequences impact assessment module, based on the number of municipal roads affected and the number of high-risk pipelines affected, obtains the consequences impact factors through the consequences impact model. The solid stability assessment module obtains geometric risk factors based on slope gradient and slope height through a solid stability model. The displacement-creep matching degree assessment module, under the current environmental impact factors and consequence impact factors, obtains the displacement-creep matching coefficient based on the slope displacement rate and creep rate through a matching model. The acquisition cycle control module, based on geometric risk factors and displacement-creep matching coefficients, outputs a target acquisition cycle through a cycle control model. When the target acquisition cycle is less than the current remaining acquisition interval, the current cycle is immediately interrupted and the target acquisition cycle is replaced with a new execution cycle.

2. The construction site data acquisition system according to claim 1, characterized in that, The cycle regulation model is as follows: ,in, For the target acquisition period, For the maximum acquisition period, Minimum acquisition period These are the weighting coefficients. Geometric risk factor This is the displacement-creep matching coefficient.

3. The construction site data acquisition system according to claim 2, characterized in that, The steps for obtaining geometric risk factors based on slope gradient and slope height using a bulk stability model are as follows: The slope index is obtained by comparing the current slope gradient with the maximum slope gradient. The height index is obtained by comparing the current slope height with the maximum slope height. Introduce the slope index and height into the formula. Obtain geometric risk factors ,in, , The slope index is... It is an altitude index.

4. The construction site data acquisition system according to claim 2, characterized in that, Under the current environmental and consequence impact factors, the steps to obtain the displacement-creep matching coefficient based on slope displacement rate and creep rate using a matching model are as follows: The foundation matching coefficient is obtained based on the slope displacement rate and creep rate; The basic safety status factor, as well as the current environmental impact factor and consequence impact factor, are imported into the formula. Obtain the displacement-creep matching coefficient ,in, , Based on the matching coefficient, As environmental impact factors, As a consequence influencing factor, This is the risk adjustment coefficient.

5. The construction site data acquisition system according to claim 4, characterized in that, The steps for obtaining the basic safety state factor based on slope displacement rate and creep rate are as follows: The current slope displacement rate is compared with the displacement rate threshold, and the upper limit of the min function is set to 1 to obtain the slope displacement rate index. The current creep rate is compared with the creep rate threshold, and the upper limit is set to 1 using the min function to obtain the creep rate exponent. Import the slope displacement rate exponent and creep rate exponent into the formula. Obtain the basic matching coefficient ,in, , This is the slope displacement rate index. The creep rate index. To avoid dividing by zero for extremely small positive numbers.

6. The construction site data acquisition system according to claim 4, characterized in that, The steps for obtaining environmental impact factors based on rainfall, vibration intensity, and pore water pressure using an environmental state model are as follows: The rainfall index is obtained by comparing the current rainfall with the maximum designed rainfall intensity. The vibration intensity index is obtained by comparing the current vibration intensity with the maximum allowable vibration intensity. The pore water pressure index is obtained by comparing the current pore water pressure with the critical pore water pressure. The rainfall index, vibration intensity index, and pore water pressure index are imported into the formula. Obtaining environmental impact factors ,in, , This is a rainfall index. The vibration intensity index, The pore water pressure index, , and All are weighting coefficients.

7. The construction site data acquisition system according to claim 4, characterized in that, The steps for obtaining the consequence impact factors based on the number of municipal roads and high-risk pipelines using a consequence impact model are as follows: The road impact index is obtained by comparing the current number of affected municipal roads with the maximum number of roads to be considered, and then using a min function with a maximum limit of 1. The pipeline impact index is obtained by comparing the current number of high-risk pipelines with the maximum number of pipelines to be considered, and then using a min function with an upper limit of 1. Import the road impact index and pipeline impact index into the formula. Obtain the impact factors of the consequences ,in, , The road impact index, Pipeline impact index.

8. A method for collecting data at a construction site, characterized in that, Includes the following steps: Environmental impact factors are obtained through an environmental state model based on rainfall, vibration intensity, and pore water pressure. Based on the number of municipal roads and high-risk pipelines affected, the consequence impact factors are obtained through a consequence impact model. Geometric risk factors are obtained based on slope gradient and slope height using a volumetric stability model. Under the current environmental impact factors and consequences impact factors, the displacement-creep matching coefficient is obtained through a matching model based on the slope displacement rate and creep rate. Based on geometric risk factors and displacement-creep matching coefficients, a target acquisition cycle is output through a cycle control model. When the target acquisition cycle is less than the current remaining acquisition interval, the current cycle is immediately interrupted and the target acquisition cycle is replaced with a new execution cycle.

Images