Foundation pit construction monitoring method, system, product and medium

Abstract

The invention discloses a foundation pit construction monitoring method and system, a product and a medium. The method comprises the steps that physical data and construction data of a foundation pit are obtained in real time; environment data, displacement data and geological data of the foundation pit are collected in real time according to the preset frequency; importing the physical data, the environmental data and the geological data into a BIM platform to obtain a BIM model; according to the displacement data and the construction data at the coordinates of each monitoring point, substituting the displacement data and the construction data into a preset mathematical expression to obtain a first parameter corresponding to each monitoring point; according to the first parameter of each monitoring point and a preset maximum displacement data threshold value, substituting the first parameter and the preset maximum displacement data threshold value into a mathematical expression, and calculating a construction data safety threshold value of each monitoring point; when the safety threshold monitoring point exists, simulating the construction condition at the corresponding safety threshold monitoring point in the BIM model according to the real-time construction data to obtain a simulation result; and outputting the simulation result. By implementing the technical scheme provided by the invention, the accuracy of foundation pit construction safety monitoring is improved.

Description

Technical Field

[0001] The present application relates to the field of foundation pit monitoring for construction projects, and in particular to a foundation pit construction monitoring method, system, product and medium. Background Art

[0002] Foundation pit monitoring is a key preparatory work before deep foundation pit excavation, and is of great significance for ensuring foundation pit safety and preventing collapse.

[0003] Currently, foundation pit monitoring primarily involves deploying various measurement robots or monitoring sensors around the pit to collect data on various aspects, including engineering geology, hydrogeology, and pit deformation at monitoring points. This data provides real-time insights into changes in the pit's status during construction, providing timely warnings if anomalies occur, allowing construction personnel to take appropriate measures and avoid accidents.

[0004] However, existing foundation pit inspection methods rely on deploying measurement robots or sensors to collect data. This data can reflect changes in the pit's status and issue warnings if anomalies are detected. While this holistic approach can provide a certain degree of visibility into the overall condition of the pit, it lacks in-depth assessment of specific local areas, making it difficult to detect anomalies in those areas in a timely manner. This results in low accuracy in foundation pit construction safety monitoring and can easily overlook potential safety hazards. Summary of the Invention

[0005] The present application provides a foundation pit construction monitoring method, system, product and medium for improving the accuracy of foundation pit construction safety monitoring.

[0006] In a first aspect of the present application, a foundation pit construction monitoring method is provided, the method comprising: The physical data and construction data of the foundation pit are acquired in real time; the construction data includes construction machine positioning data, construction machine driving route data, construction machine weight data and construction machine vibration data; the environmental data, displacement data and geological data of the foundation pit are collected in real time at a preset frequency; the physical data, the environmental data and the geological data are imported into the BIM platform to obtain a BIM model; the displacement data and construction data at the coordinates of each monitoring point are substituted into a preset mathematical expression to obtain a first parameter corresponding to each monitoring point; the first parameter is the value of the construction data relative to the displacement data; the mathematical expression is the data relationship between the displacement data and the construction data; the first parameter of each monitoring point and a preset maximum displacement data threshold are substituted into the mathematical expression to calculate a construction data safety threshold for each monitoring point; the construction data safety threshold is the upper limit of the construction data that each monitoring point can withstand; when a safety threshold monitoring point exists, the construction situation is simulated at the corresponding safety threshold monitoring point in the BIM model according to the real-time construction data to obtain a simulation result; the safety threshold monitoring point is the monitoring point where the real-time construction data corresponding to the monitoring point exceeds the construction data safety threshold; the simulation result is output.

[0007] In the above embodiment, the operation is based on the monitoring point, and a detailed analysis can be performed on the different actual conditions of different areas corresponding to each monitoring point, different results can be obtained for different areas, potential risks can be identified more accurately, and overall accidents caused by local problems can be avoided, thereby improving the accuracy of foundation pit construction safety monitoring.

[0008] In conjunction with some embodiments of the first aspect, in some embodiments, when a safety threshold monitoring point exists, simulating a construction situation at the safety threshold monitoring point in the BIM model based on the real-time construction data, and before obtaining the simulation result, further comprising: Collect real-time weather data; calculate the differentiation index between the real-time weather data and the preset extreme weather data; determine the real-time weather level based on the value of the differentiation index and the preset differentiation abnormal warning threshold range; the weather level is divided into normal, warning and abnormal; when the real-time weather level is warning, reduce the construction data safety threshold corresponding to all monitoring points by a first preset multiple; when the real-time weather level is abnormal, reduce the construction data safety threshold corresponding to all monitoring points by a second preset multiple; the second preset multiple value is greater than the first preset multiple.

[0009] In the above embodiment, by collecting weather data in real time and calculating its differential indicators from preset extreme weather data, weather conditions can be quantified, and the real-time weather level can be determined based on the indicator values and preset threshold ranges. When the weather is at a warning or abnormal level, the construction data safety threshold is dynamically adjusted to take weather factors into account. Lowering the threshold by a corresponding multiple can reserve more safety margins for the construction process. Under the influence of severe weather, the geological environment may change accordingly, increasing the probability of foundation pit risks under the same construction data. Fully considering weather factors can avoid accidents before the construction data exceeds the original safety threshold, thereby ensuring safe and orderly construction.

[0010] In conjunction with some embodiments of the first aspect, in some embodiments, when a safety threshold monitoring point exists, a construction situation is simulated at the safety threshold monitoring point in the BIM model based on the real-time construction data to obtain a simulation result, specifically including: When there is a safety threshold monitoring point, the simulated construction parameters are calculated based on the real-time construction data; when the real-time weather level is warning or abnormal, the real-time weather data and simulated construction parameters are mapped to the BIM model; in the BIM model, the real-time situation data of the safety threshold monitoring point is obtained as the simulation result.

[0011] In the above example, when safety threshold monitoring points are present, simulated construction parameters calculated based on real-time construction data reflect the current construction situation. Real-time weather data mapping incorporates weather factors. Combining these two within the BIM model results in more accurate simulation results.

[0012] In conjunction with some embodiments of the first aspect, in some embodiments, the real-time acquisition of physical data and construction data of the foundation pit specifically includes: Acquire construction data and position coordinate information of multiple image acquisition devices to obtain a device position coordinate matrix; combine the device position coordinate matrix, construction machine positioning data in the construction data, and construction machine driving route data, and use a path planning algorithm to set the expected driving route of the image acquisition device; determine the expected image acquisition range of the image acquisition device by combining the expected driving route and the performance data of the image acquisition device; compare the expected image acquisition range with the engineering drawing, and if a scanning blind spot exists in the engineering drawing, obtain the blind spot coordinate position of the scanning blind spot; the scanning blind spot is an area that exists in the engineering drawing but is not included in the expected image acquisition range; combine the blind spot coordinate position and the expected driving route, and use the path planning algorithm to obtain an optimized driving route; control the image acquisition device to drive according to the optimized driving route, and obtain the physical data of the foundation pit in real time.

[0013] In the above embodiment, by integrating multi-source data, a comprehensive understanding of the construction scene can be achieved, and the path planning algorithm can enable the equipment to move reasonably to ensure coverage of key construction areas; the collection range is determined to be in line with actual capabilities in combination with equipment performance; blind spots are identified by comparing drawings to avoid monitoring blind spots; the route is optimized so that the equipment can cover blind spots, thereby collecting foundation pit physical data in all directions, making foundation pit monitoring comprehensive and efficient, and providing sufficient and accurate data support for construction safety assessment and management.

[0014] In conjunction with some embodiments of the first aspect, in some embodiments, after controlling the image acquisition device to travel along the optimized driving route and acquiring physical data of the foundation pit in real time, the method further includes: The environmental quality data around each image acquisition device is acquired in real time as a real-time environmental quality data group; when there is polluted environmental data in the real-time environmental quality data group, the image acquisition mode of the image acquisition device corresponding to the polluted environmental data is switched to an infrared penetration scanning mode; the polluted environmental data is real-time environmental quality data that exceeds the normal environmental quality threshold.

[0015] In the above embodiment, by acquiring environmental quality data in real time, environmental changes can be detected in a timely manner. Conventional image acquisition modes are susceptible to interference in polluted environments. The infrared penetration scanning mode utilizes the infrared penetration ability to effectively acquire clear image data in polluted environments such as haze and smoke, thereby ensuring the accuracy and continuity of foundation pit physical data acquisition work under harsh environmental conditions, enabling the image acquisition work of foundation pit monitoring in complex environments to be carried out stably and efficiently, providing a reliable image basis for construction safety assessment.

[0016] In conjunction with some embodiments of the first aspect, in some embodiments, after substituting the first parameter and the preset maximum displacement data threshold of each monitoring point into the mathematical expression to calculate the construction data safety threshold of each monitoring point, the method further includes: The monitoring points whose construction safety threshold is lower than the preset minimum construction safety value are marked as dangerous monitoring points; the coordinate information data of all the dangerous monitoring points are obtained as a dangerous coordinate information data group; in combination with the dangerous coordinate information data group, all the dangerous monitoring points are marked in the BIM model; the spatial distribution of the dangerous monitoring points in the BIM model is analyzed, and all the dangerous monitoring points are connected into multiple dangerous areas through spatial statistical analysis methods; the real-time device position information of all image acquisition devices is obtained; when there is real-time device position information entering the dangerous area, the image acquisition frequency of the image acquisition device entering the dangerous area is adjusted to an increased frequency; the increased frequency is the product of the image acquisition frequency of the preset image acquisition device and a preset multiple.

[0017] In the above embodiment, by marking dangerous monitoring points, construction personnel can quickly locate hidden dangers; by analyzing dangerous areas, they can grasp the overall risk distribution; and by obtaining the real-time location of the equipment and increasing the collection frequency when it enters the dangerous area, they can collect data from high-risk areas more intensively, discover potential danger signals in time, and provide sufficient basis for taking effective safety measures, making the management and control of construction safety risks more accurate and efficient, and effectively ensuring the safety of foundation pit construction.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after outputting the simulation result, it also includes: when there is a monitoring point whose corresponding displacement data exceeds a preset dangerous displacement threshold, marking the corresponding monitoring point as an accident monitoring point; obtaining the accident time, accident coordinate position data and original coordinate position data of the accident monitoring point whose corresponding displacement data exceeds the preset dangerous displacement threshold; the original coordinate position data is the coordinate position data of the accident monitoring point before the displacement data exceeds the preset dangerous displacement threshold; combining the original coordinate position data, the accident event, the accident coordinate position data and the construction data, locating the accident construction machine that caused the accident; and sending the corresponding preset number of the construction machine to a preset mobile terminal.

[0019] In the above embodiment, real-time monitoring can detect anomalies in a timely manner, obtain comprehensive accident data to provide a basis for analysis, and accurately lock the target by combining construction data for positioning. Sending numbers to mobile terminals allows management personnel to respond quickly, so that when an accident occurs, relevant personnel can quickly identify the equipment that caused the accident, take measures to reduce losses, and ensure construction safety and the smooth progress of the project.

[0020] In the second aspect, an embodiment of the present application provides a foundation pit construction monitoring system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, and the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the foundation pit construction monitoring system to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product comprising instructions. When the above-mentioned computer program product is run on a foundation pit construction monitoring system, the above-mentioned foundation pit construction monitoring system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a foundation pit construction monitoring system, the foundation pit construction monitoring system executes the method described in the first aspect and any possible implementation method of the first aspect.

[0023] It is understood that the foundation pit construction monitoring system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the foundation pit construction monitoring system provided in the embodiments of this application. Therefore, the beneficial effects that can be achieved can be referenced to the beneficial effects of the corresponding methods and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. This application operates based on monitoring points, and can conduct detailed analysis of the different actual conditions of different areas corresponding to each monitoring point, obtain different results for different areas, more accurately identify potential risks, and avoid overall accidents caused by local problems, thereby improving the accuracy of foundation pit construction safety monitoring.

[0025] 2. This application can quantify weather conditions by collecting weather data in real time and calculating its differentiation indicators from preset extreme weather data, and judge the real-time weather level based on the indicator values and the preset threshold range. When the weather is at the warning or abnormal level, the construction data safety threshold is dynamically adjusted considering weather factors. Lowering the threshold by a corresponding multiple can reserve more safety margins for the construction process. Under the influence of severe weather, the geological environment may change accordingly, and the probability of risks in the foundation pit will increase under the same construction data. Fully considering weather factors can avoid accidents caused by construction data before it exceeds the original safety threshold, thereby ensuring safe and orderly construction.

[0026] 3. This application can fully understand the construction scene by integrating multi-source data. The path planning algorithm can enable the equipment to move reasonably to ensure coverage of key construction areas; the collection range is determined to be in line with actual capabilities based on equipment performance; blind spots are found by comparing drawings to avoid monitoring blind spots; the route is optimized so that the equipment can cover blind spots, thereby collecting foundation pit physical data in all directions, making foundation pit monitoring comprehensive and efficient, and providing sufficient and accurate data support for construction safety assessment and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of a foundation pit construction monitoring method in an embodiment of the present application; Figure 2 This is another flow chart of the foundation pit construction monitoring method in the embodiment of the present application; Figure 3 This is another flow chart of the foundation pit construction monitoring method in the embodiment of the present application; Figure 4 It is an exemplary hardware structure diagram of the foundation pit construction monitoring system in the embodiment of the present application. DETAILED DESCRIPTION

[0028] The terms used in the following examples of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to and encompasses any or all possible combinations of one or more of the listed items.

[0029] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0030] In related technologies, foundation pit monitoring mainly involves deploying various monitoring equipment around the foundation pit to collect data on engineering geology, hydrogeology, and foundation pit deformation in real time to reflect the changes in the state of the foundation pit during construction. When abnormalities appear in the monitoring data, the system can issue a timely warning, providing a reference for construction personnel to take emergency measures. However, this type of monitoring method is mainly based on the analysis and evaluation of overall data. Due to the lack of in-depth monitoring of specific areas, local safety hazards are easily overlooked, thereby reducing the accuracy and reliability of foundation pit construction safety monitoring, resulting in potential risks not being discovered and handled in a timely manner, and thus affecting the safety of foundation pit construction.

[0031] In the embodiment of the present application, by acquiring the physical data and construction data of the foundation pit in real time, and combining the environmental, displacement and geological data, a BIM model is generated, thereby realizing a three-dimensional visual simulation of the construction site. By using preset mathematical expressions to calculate the first parameter and safety threshold of the monitoring point, it is possible to quantify the impact of construction data on displacement, thereby predicting potential risks. When the monitoring point exceeds the safety threshold, the simulated construction situation can be used to evaluate the impact and avoid construction accidents. By operating with the monitoring point as the main body, it is possible to conduct detailed analysis of the different actual conditions of different areas corresponding to each monitoring point, and obtain different results for different areas. Compared with the global monitoring of related technologies, this detailed monitoring method can detect local anomalies more promptly, avoid overall accidents caused by local problems, and thus improve the safety and reliability of foundation pit construction.

[0032] Figure 1 This is a flow chart of a foundation pit construction monitoring method according to an embodiment of the present application, comprising the following steps: S101, real-time acquisition of foundation pit physical data and construction data; Specifically, the construction data includes construction machine positioning data, construction machine travel route data, construction machine weight data, and construction machine vibration data.

[0033] Real-time acquisition of the physical data and construction data of the foundation pit is achieved by arranging a variety of sensors and monitoring equipment at the foundation pit construction site or using movable sensors and detection equipment, combined with Internet of Things technology, to achieve dynamic perception and data collection of the foundation pit status; for construction data, some data is obtained by receiving input construction-related data.

[0034] S102, collecting environmental data, displacement data, and geological data of the foundation pit in real time at a preset frequency; Specifically, real-time collection of environmental data, displacement data and geological data of the foundation pit is achieved by arranging a variety of sensors and monitoring equipment at the foundation pit construction site, combined with a preset collection frequency, to achieve dynamic perception and data collection of the foundation pit status.

[0035] S103, importing the physical data, the environmental data, and the geological data into the BIM platform to obtain a BIM model; BIM (Building Information Modeling) is a digital tool used in engineering design, construction, and management. It integrates all relevant information about a construction project by creating a three-dimensional digital model. Furthermore, BIM's three-dimensional visualization capabilities allow stakeholders to more intuitively understand the underlying data. In this application, combined with 4D construction simulation, it also allows for a preview of the construction process and an assessment of the impact of different construction options on foundation pit stability.

[0036] Convert physical, environmental, and geological data into formats supported by the BIM platform, such as CSV, XML, or BIM-specific data exchange formats (e.g., IFC). Within the BIM software, display different types of data in their corresponding model locations. Based on the collected geological data, use BIM software to create a 3D geological model of the foundation pit. Real-time data is transmitted to the BIM platform via IoT technology, dynamically updating the model to reflect the real-time operating status of the foundation pit.

[0037] S104, substituting the displacement data and construction data at the coordinates of each monitoring point into a preset mathematical expression to obtain a first parameter corresponding to each monitoring point; Specifically, the first parameter is the value of the construction data relative to the displacement data; and the mathematical expression is the data relationship between the displacement data and the construction data.

[0038] There is a certain relationship between displacement data (such as the horizontal displacement of foundation pit monitoring points) and construction data (such as the construction load on the support structure and the vibration frequency generated by construction). By establishing mathematical expressions, we can quantify the relationship between these data and reveal the impact of construction activities on displacement.

[0039] The mathematical expression describes the relationship between displacement data and construction data in mathematical language, and then solves the first parameter, that is, the numerical contribution of construction data to displacement data, through algebraic operations.

[0040] The first parameter is a comprehensive quantitative indicator that comprehensively reflects the relationship between displacement data and construction data at each monitoring point, as well as the potential impact on construction safety. This parameter provides an intuitive understanding of how the displacement of a monitoring point is affected by construction factors under the current construction status. For example, a larger first parameter value indicates a greater impact of construction on the monitoring point and a higher potential safety risk. Conversely, a smaller first parameter value indicates a relatively small impact of construction on the monitoring point. This parameter provides a specific quantitative basis for construction safety assessments, allowing construction personnel and managers to take timely measures to ensure construction safety based on the value, such as adjusting the operating parameters of construction machinery and increasing the monitoring frequency of high-value monitoring points.

[0041] Establish the data relationship between displacement data x and construction data y in advance, y = f(x), where f is a preset function that represents the relationship between displacement and construction data. For the function f, select the appropriate mathematical expression form in advance in combination with the actual environment and physical laws of the actual foundation pit. In the linear relationship, that is, y = kx + b, k is the corresponding first parameter in the mathematical expression of the linear relationship. For the construction data y, it is actually a composite vector composed of multiple factors. If the construction load is W and the vibration frequency is f, the construction data is expressed in vector form as y = (W, f). For the above mathematical relationship, mathematical expressions such as power function relationship or trigonometric function relationship can be used. For example, the power function relationship, x = k × W a ×f b , a represents the influencing parameter of construction load on displacement data, b represents the influencing parameter of vibration frequency on displacement data, a and b are both preset known constants, and k is the corresponding first parameter; in the trigonometric function relationship, x = k × sin(cW + df), c represents the influencing parameter of construction load on displacement data, d represents the influencing parameter of vibration frequency on displacement data, c and d are both preset known constants, and k is the corresponding first parameter.

[0042] S105, substituting the first parameter and the preset maximum displacement data threshold of each monitoring point into the mathematical expression to calculate the construction data safety threshold of each monitoring point; It is understood that the construction data safety threshold is the upper limit of the construction data that each monitoring point can withstand. If the real-time construction data exceeds this threshold, the displacement data of the corresponding monitoring point will exceed the preset maximum displacement data threshold, which may cause safety issues.

[0043] Each monitoring point has a preset maximum displacement data threshold, which is the maximum allowable displacement value set based on engineering design specifications, historical data, and structural safety standards. Substituting the maximum displacement data threshold and the first parameter corresponding to each monitoring point into the mathematical expression, the upper limit of construction data that each monitoring point can withstand is obtained. In other words, if the real-time construction data exceeds the construction data safety threshold, the displacement data of the corresponding monitoring point will also exceed the preset maximum displacement data threshold.

[0044] In some embodiments of the present application, data may be missing or abnormal due to equipment failure, data transmission errors, measurement environment interference, etc. In order to ensure the continuity of the data sequence, avoid analysis deviations caused by missing data, and reduce the impact on subsequent analysis, a data filling method can be used. Among them, the data filling method includes: linear interpolation, assuming that the change of data near the missing point is linear, linear interpolation is used to fill the missing value through known adjacent data points; spline interpolation, using polynomial functions (usually quadratic or cubic polynomials) to fit data points, and filling the missing value through piecewise polynomial functions; weighted average method, based on the proximity of data points or other weight factors, the missing points are filled with weighted average. First, abnormal or missing data points are identified through statistical analysis (such as standard deviation, mean absolute deviation) or visualization methods (such as drawing a time series graph); then, according to the characteristics of the data (such as change trend, periodicity, noise level), a suitable filling method is selected; finally, the selected filling method is applied to effectively improve data quality and provide reliable support for engineering monitoring and analysis.

[0045] S106. When a safety threshold monitoring point exists, simulate the construction situation at the corresponding safety threshold monitoring point in the BIM model based on the real-time construction data to obtain a simulation result. It can be understood that the safety threshold monitoring point is the monitoring point where the real-time construction data corresponding to the monitoring point exceeds the construction data safety threshold.

[0046] In the BIM model, the construction status of construction machines at safety threshold monitoring points is simulated based on real-time construction data.

[0047] In foundation pit construction monitoring, BIM models play a key role similar to digital twins. BIM models integrate the physical data of the foundation pit (such as spatial structural parameters and internal layout information), environmental data (temperature, humidity, groundwater level, etc.), geological data (soil properties, geological stratification, geotechnical parameters), and construction data (construction machine positioning, driving routes, weight, and vibration data). This data is collected in real time from various sensors, monitoring equipment, and related systems. Through specific data interfaces and format conversion, it is unified and aggregated into the BIM model, forming a database containing comprehensive information about the foundation pit.

[0048] Leveraging BIM's 3D modeling capabilities, the integrated data is presented in an intuitive 3D model. Construction workers and managers can observe the foundation pit structure, the locations of monitoring points, and the surrounding environment from various angles, gaining a clear understanding of the pit's real-time status. For example, geological differences between different areas are displayed through changes in properties such as color and material, while the dynamic display of the location and trajectory of construction machinery provides a real-time overview of construction progress. This visual presentation significantly improves data readability and comprehension, helping to quickly identify potential issues.

[0049] Combined with 4D construction simulation technology (3D models with a time dimension), BIM models can preview the construction process. Based on real-time construction data, they simulate the operation of construction machinery at different stages and predict the impact of construction activities on foundation pit stability. For example, during large-scale mechanical excavation operations, BIM models can simulate the machinery's travel routes and operating movements, analyzing their impact on surrounding soil displacement and stress distribution. This allows for early assessment of the feasibility of construction plans and prevents safety accidents caused by unreasonable construction operations.

[0050] S107: Output the simulation result.

[0051] Simulation results include displacement changes, stress distribution, and potential safety hazards during construction. These results are generated through the analysis and calculation functions of the BIM model. Simulation results can be output in various ways, such as displaying the construction process in an animated form within the BIM model or displaying displacement changes and stress distribution data in graphical form.

[0052] In the above embodiment, the overall state of the foundation pit is presented intuitively, and the first parameter is derived based on the relationship between the displacement at the monitoring point coordinates and the construction data, clarifying the specific impact of the construction data on the displacement data at each monitoring point; the safety threshold of the construction data is calculated to provide a quantitative standard for construction safety. When the real-time construction data exceeds the threshold, the construction is simulated at the corresponding monitoring point in the BIM model and the results are output, which can intuitively show the possible consequences of the abnormal situation. The complex construction risks are broken down into each monitoring point for analysis, making the abnormality assessment more accurate. By refining the abnormal situation from the global level to each monitoring point, and conducting targeted analysis of the construction data and displacement data, the risk points can be accurately located, thereby providing a more reliable basis for construction decision-making and ensuring the safety of foundation pit construction.

[0053] During the foundation pit construction process, when encountering abnormal weather (such as extreme weather approaching), the existing technology, when considering the safety of foundation pit construction, fails to fully consider the reduction of the foundation pit bearing capacity under severe weather conditions, resulting in an increase in the frequency of construction risks. However, by adopting the method provided by this application, the weather level can be judged after collecting weather data in real time. Once the weather level is in an early warning or abnormal state, the construction data safety threshold of all monitoring points will be automatically lowered, and the real-time weather data and simulated construction parameters will be mapped to the BIM model for simulation. This enables construction personnel to adjust construction strategies in a timely manner according to weather changes, effectively reduce construction risks under severe weather conditions, and ensure that foundation pit construction is carried out safely and orderly.

[0054] like Figure 2 FIG. 1 is another flow chart of the foundation pit construction monitoring method provided in an embodiment of the present application, comprising the following steps: S201, acquiring physical data and construction data of the foundation pit in real time; S202, collecting environmental data, displacement data and geological data of the foundation pit in real time at a preset frequency; S203, importing the physical data, the environmental data, and the geological data into the BIM platform to obtain a BIM model; S204: Substituting the displacement data and construction data at the coordinates of each monitoring point into a preset mathematical expression to obtain a first parameter corresponding to each monitoring point; S205, substituting the first parameter and the preset maximum displacement data threshold of each monitoring point into the mathematical expression to calculate the construction data safety threshold of each monitoring point; S206, collecting real-time weather data; Real-time weather data can be obtained through a variety of channels, including but not limited to weather stations, satellite remote sensing, sensor monitoring, and weather forecast services.

[0055] S207, calculating a differentiation index between the real-time weather data and preset extreme weather data; Specifically, we select appropriate differentiation metrics to measure the difference between real-time weather data and preset extreme weather data. Common differentiation metrics include mean square error (MSE), mean absolute error (MAE), and relative error.

[0056] The calculated differentiation index is used to assess how close the current weather conditions are to the preset extreme weather data. If the differentiation index exceeds the preset threshold, it indicates that the current weather conditions are approaching or have reached extreme weather conditions.

[0057] S208. Determine the real-time weather level based on the value of the differentiation indicator and the preset differentiation abnormal warning threshold range; specifically, the weather level is divided into normal, warning, and abnormal.

[0058] The preset differential anomaly warning threshold range is pre-set based on historical data and meteorological standards. This threshold range determines whether the difference between real-time weather data and preset extreme weather data is within the normal range. When the differential indicator value exceeds this range, it indicates abnormal weather conditions.

[0059] The real-time weather level is determined based on the value of the differentiation indicator and the preset threshold range. Weather levels are generally divided into three levels: normal, warning, and abnormal. Normal: When the value of the differentiation indicator is within the preset normal range, the current weather conditions are normal. Warning: When the value of the differentiation indicator exceeds the normal range but does not reach the abnormal range, the current weather conditions are approaching extreme weather conditions. Abnormal: When the value of the differentiation indicator exceeds the abnormal range, the current weather conditions are approaching or reaching extreme weather conditions, and immediate action is required.

[0060] S209: When the real-time weather level is a warning, the construction data safety threshold corresponding to all monitoring points is reduced by a first preset multiple; The first preset multiple is set based on historical data and meteorological standards. This multiple is used in weather warning situations to lower the construction data safety threshold to ensure construction safety, as abnormal weather may increase the risk of accidents.

[0061] If the weather level is a warning, the system automatically lowers the construction data safety thresholds for all monitoring points to the original preset multiple. The adjusted construction data safety thresholds are updated in the BIM model to ensure that the model reflects the latest safety thresholds.

[0062] S210: When the real-time weather level is abnormal, the construction data safety threshold corresponding to all monitoring points is reduced by a second preset multiple; It can be understood that the second preset multiple value is greater than the first preset multiple. Since the real-time weather is worse when the weather level is abnormal than when the weather level is warning, the construction data safety threshold is lowered by the first preset multiple compared to when the weather level is warning. When the weather level is bad, the reduction multiple of the construction data safety threshold is greater, that is, the construction data safety threshold is smaller when the weather level is bad, so as to ensure construction safety.

[0063] The second preset multiple is set based on historical data and meteorological standards. This multiple is used in severe weather conditions to lower the construction data safety threshold to ensure construction safety, as abnormal weather may increase the risk of accidents.

[0064] If the weather level is abnormal, the system automatically reduces the construction data safety threshold of all monitoring points to the second preset multiple. The adjusted construction data safety threshold is updated in the BIM model to ensure that the model reflects the latest safety threshold.

[0065] In the above steps S206-S210, the construction data safety threshold is dynamically adjusted according to the weather conditions, so that the foundation pit construction can respond to different weather conditions more flexibly and ensure the safety of construction in severe weather.

[0066] S211. When safety threshold monitoring points exist, simulated construction parameters are calculated based on the real-time construction data. Safety threshold monitoring points are those where the corresponding real-time construction data exceeds the safety threshold for construction data. These monitoring points are marked in the BIM model. Real-time construction data is obtained, and the specific location of the construction machine in the BIM model is determined based on the positioning data of the construction machine. The construction machine's travel path in the foundation pit is simulated based on the machine's travel route data. The load impact of the construction machine on the foundation pit is calculated based on the machine's weight data. The vibration impact of the construction machine on the foundation pit is calculated based on the machine's vibration data.

[0067] Specifically, real-time construction data is acquired, including the construction machine's positioning data, route data, weight data, and vibration data. Based on the construction machine's positioning data, the specific location of the construction machine in the BIM model is determined. Based on the construction machine's route data, the construction machine's path within the foundation pit is simulated. Based on the construction machine's weight data, the load impact of the construction machine on the foundation pit is calculated. Based on the construction machine's vibration data, the vibration impact of the construction machine on the foundation pit is calculated. Combining these processing results, simulated construction parameters are calculated, including the load and vibration impact of the construction machine at different locations.

[0068] S212: When the real-time weather level is warning or abnormal, map the real-time weather data and simulated construction parameters to the BIM model; Specifically, when the real-time weather level is warning or abnormal, the coordinate information of the real-time weather data and simulated construction parameters is matched with the coordinate domain set in the BIM model. The real-time weather data and simulated construction parameters are organized using timestamps to align with the time base of the BIM model. This ensures the accuracy and consistency of the data over time.

[0069] In some embodiments of the present application, if the real-time weather conditions are detected as typhoons or rainstorms, the acquisition and mapping of real-time weather data is particularly important. The system can obtain high-precision real-time weather data through satellite remote sensing and weather station data, and match it with the construction parameters in the BIM model. For example, during a typhoon, the system can monitor wind speed and direction in real time, map this data to the BIM model, and generate a dynamic model to help construction personnel adjust construction plans in a timely manner and avoid safety accidents caused by extreme weather.

[0070] S213. In the BIM model, obtain real-time data of the safety threshold monitoring point as a simulation result; Specifically, the BIM model precisely assigns coordinates to each safety threshold monitoring point. Using these coordinates, the monitoring system can quickly and accurately locate the specific location of the target monitoring point within the BIM model's vast data architecture.

[0071] After locating the safety threshold monitoring points, the BIM model needs to obtain real-time data. The BIM model establishes a stable data transmission link with various sensors to achieve real-time data collection.

[0072] After obtaining the original real-time data, data processing is required because these data cannot be directly used to generate simulation results. The actual collected data may have problems such as noise interference, outliers, and inconsistent formats. To address these situations, the system will use data cleaning technology to set a reasonable data range, eliminate data with obvious errors or outside the normal fluctuation range, and remove duplicate data records to ensure the accuracy and uniqueness of the data. For missing data, interpolation algorithms such as linear interpolation and spline interpolation will be used to supplement them, and the missing values will be estimated based on the changing trends of the existing data. After cleaning and supplementing, the data must also be formatted according to the specific data format requirements of the BIM model so that it can be effectively recognized and processed by the model.

[0073] After completing the data processing phase, the system generates simulation results using the BIM model. The BIM model combines the relationship between construction data and displacement data derived from previous calculations, as well as the historical and current real-time data of the monitoring point, to simulate and predict the future state of the monitoring point. For example, based on the current vibration data of the construction machine and the existing displacement trend of the monitoring point, the displacement change of the monitoring point can be predicted over a period of time; or based on the weight and travel route data of the construction machine, the stress distribution changes in the soil around the monitoring point can be analyzed.

[0074] S214: When the displacement data corresponding to a monitoring point exceeds a preset dangerous displacement threshold, mark the corresponding monitoring point as an accident monitoring point; During foundation pit construction monitoring, displacement data is a key indicator of pit safety. The preset dangerous displacement threshold is determined based on a combination of factors, including engineering design standards, geological conditions, and past construction experience. This threshold represents the maximum allowable displacement at a monitoring point during normal construction and stable conditions.

[0075] Specifically, as the system collects displacement data from the foundation pit at a preset frequency, it compares the displacement data at each monitoring point against a preset dangerous displacement threshold in real time. This comparison is implemented through program logic programmed into the monitoring system. The program continuously reads data from the displacement sensors at each monitoring point and compares it against the pre-set dangerous displacement threshold stored in the system database.

[0076] Once the displacement data of a monitoring point is found to be greater than the preset dangerous displacement threshold, the monitoring system will trigger the marking mechanism. The system will find the record corresponding to the monitoring point in its internal data storage structure and add a special mark to the record to indicate that this monitoring point has become an accident monitoring point. At the same time, the system will also synchronize the marking information to the relevant visual interface (such as the BIM model interface) to remind construction workers and managers in an intuitive way. For example, in the BIM model, the accident monitoring point may be marked in red or highlighted with a flashing icon to facilitate quick identification and attention of staff.

[0077] S215: Obtain the accident time, accident coordinate position data, and original coordinate position data of the accident monitoring point corresponding to the displacement data exceeding the preset dangerous displacement threshold; Specifically, the original coordinate position data is the coordinate position data of the accident monitoring point before the displacement data exceeds a preset dangerous displacement threshold.

[0078] During the foundation pit construction process, the monitoring system is always in operation, continuously monitoring the displacement data of each monitoring point and comparing it in real time with the pre-set dangerous displacement threshold. This preset dangerous displacement threshold takes into account multiple factors such as the foundation pit design requirements, the geological conditions in which it is located, and past extensive engineering experience. It represents the maximum value allowed for the displacement of the monitoring point while the foundation pit is safe. Once the system detects that the displacement data of a monitoring point exceeds the preset dangerous displacement threshold, it will quickly make a judgment, mark the monitoring point as an accident monitoring point, and immediately trigger the data acquisition program. This judgment and triggering process relies on the logical algorithm pre-programmed within the monitoring system.

[0079] When the system identifies a monitoring point as an accident monitoring point, it simultaneously records the current time information. The system is equipped with a timing module connected to a high-precision clock, which is extremely accurate, capable of accuracy down to the second or even millisecond level. This time recording adheres to international standard time formats, facilitating quick query of the accident's occurrence time. It also facilitates the temporal integration of this time information with other relevant data during data analysis, providing strong support for comprehensive accident analysis.

[0080] S216: Locate the accident construction machine that caused the accident by combining the original coordinate position data, the accident event, the accident coordinate position data, and the construction data; Specifically, data correlation and preliminary analysis must first be performed to clarify the functions and interrelationships of various data points. The original coordinate location data records the location of the accident monitoring point before its displacement exceeded the danger threshold, while the accident coordinate location data represents the actual location of the monitoring point at the moment of the accident. Combining these two sets of data yields the displacement trajectory of the monitoring point. The accident event data includes key information such as the specific time and type of accident. Construction data contains detailed information on the location, route, weight, and vibration of construction machinery, providing the basis for locating the accident machinery. After data analysis is complete, the accident time must be accurately matched to the timestamps in the construction data. By searching the construction data for records closest to the time of the accident, construction machinery operating around the time of the accident can be identified, effectively narrowing the scope of subsequent investigation.

[0081] Secondly, analysis is conducted based on the displacement trajectory and the construction machine's travel path. Based on the displacement trajectory obtained at the accident monitoring point, the changes in displacement direction and distance are further analyzed. If a construction machine's route before the accident was close to the accident monitoring point, and its travel direction is correlated with the displacement direction of the monitoring point, for example, if the construction machine was traveling toward the monitoring point and the monitoring point's displacement direction also pointed in the direction of the machine's travel, the suspicion that the construction machine caused the accident is significantly increased.

[0082] Next, the impact of construction machine operating parameters on displacement must be considered. First, analyze the weight data of the construction machines, as heavier machines exert greater pressure on the ground during operation, which can cause the surrounding soil to shift. If a heavy construction machine is found operating near the accident monitoring point, and its weight exceeds the estimated bearing capacity of the soil in that area, this machine is highly likely to have been a contributing factor to the accident. At the same time, focus should be placed on the vibration data of the construction machines, as vibration can loosen the soil and cause displacement. This can be helpful when the vibration frequency and amplitude of a construction machine are observed to be synchronized or correlated with the displacement changes at the accident monitoring point. For example, if the machine vibration is intense, the displacement of the monitoring point will be significantly increased.

[0083] Finally, a comprehensive assessment is made to identify the construction machine that caused the accident. The analysis results obtained from the above various aspects are comprehensively evaluated. Each suspected construction machine is scored or weighted based on multiple factors, such as the degree of correlation between displacement trajectories, the impact of weight, and the strength of vibration, to determine its level of suspicion. Based on this, a process of elimination is used, and repeated comparative analysis and numerical ranking of suspicions are performed to ultimately determine the construction machine that caused the accident.

[0084] S217: Send the corresponding preset number of the construction machine to the preset mobile terminal.

[0085] In the early stages of construction, in order to facilitate the management and identification of various types of construction machines, each machine will be assigned a unique preset number and stored in the system's database together with detailed information about the machine, such as model, affiliated unit, and responsible construction area.

[0086] Specifically, once the construction machine causing the accident is located, the monitoring system retrieves the machine's corresponding preset number from the database. Using previously identified data about the construction machine, such as the time and location of the accident, and its role in the incident, the system searches the database to locate the machine's record and retrieves the preset number. This number is then sent to a pre-set mobile device, allowing the user to quickly identify the machine causing the accident and take timely countermeasures.

[0087] In steps S214-S217, real-time displacement data from a pit monitoring point is monitored. Once the displacement exceeds a preset dangerous displacement threshold, the monitoring point is marked as an accident point. The relevant time and coordinate data are then acquired, combined with construction data to locate the construction machine involved. Finally, the machine's location is sent to a pre-set mobile terminal. This allows construction managers to quickly identify the key factors that caused the accident and take timely countermeasures.

[0088] Steps S201-S205 and Figure 1 Steps S101 to S105 in the illustrated embodiment are similar, and reference may be made to the description of steps S101 to S105 , which will not be repeated here.

[0089] In the above embodiment, multi-source data of the foundation pit is collected in real time and imported into the BIM platform to build a model, the relevant parameters and safety thresholds of each monitoring point are calculated, the thresholds are dynamically adjusted in combination with real-time weather data, the construction is simulated to obtain results, and the accident machine is located and the relevant personnel are notified when an abnormality occurs. This enables the risks in the foundation pit construction process to be accurately identified and controlled. On the one hand, the real-time collection of multi-source data and the construction of the BIM model realize a comprehensive and intuitive presentation of the foundation pit construction status, providing a rich and accurate basis for subsequent analysis, and quantifying the impact of construction on the foundation pit by calculating parameters and thresholds; on the other hand, the threshold is adjusted dynamically according to the weather, which improves the adaptability of the construction safety standards, and when displacement anomalies occur, the accident machine can be located and the relevant personnel can be notified, so that timely measures can be taken to avoid the expansion of the accident. Overall, the above embodiment accurately detects anomalies in the foundation pit construction process, comprehensively guarantees the safety of the foundation pit construction, and reduces the probability of accidents.

[0090] During foundation pit construction, when the accident tolerance of some areas is lower than normal, if effective measures are not taken, it will be difficult to conduct comprehensive and detailed monitoring of the dangerous areas, and potential safety hazards may not be discovered in time. However, by using the method provided by this application, after determining the dangerous area, by obtaining the real-time location information of the image acquisition device, once the device enters the dangerous area, its image acquisition frequency will be automatically adjusted to increase the frequency. This can more closely monitor the situation in the dangerous area, capture possible anomalies in a timely manner, and provide strong support for ensuring the safety of foundation pit construction.

[0091] like Figure 3 FIG. 1 is another flow chart of the foundation pit construction monitoring method provided in an embodiment of the present application, comprising the following steps: S301, obtaining construction data and position coordinate information of multiple image acquisition devices to obtain a device position coordinate matrix; Specifically, obtaining the location coordinates of multiple image acquisition devices requires the use of surveying technology. If the image acquisition device is installed in a fixed location, a total station or GPS measuring instrument can be used to measure the angle, distance, and other parameters between the device and known control points, and then calculate the coordinates of the image acquisition device using surveying principles. If the image acquisition device is mobile, in addition to using the above measurement methods to obtain the initial installation location coordinates, it is also necessary to combine the device's own positioning module (such as a GPS module or indoor positioning tag) to update the location information in real time.

[0092] S302, combining the device position coordinate matrix, the construction machine positioning data in the construction data, and the construction machine driving route data, using a path planning algorithm to set an expected driving route for the image acquisition device; Specifically, the equipment location coordinate matrix details the spatial location of each image acquisition device, the construction machine positioning data identifies the real-time location of construction equipment, and the construction machine route data presents its movement trajectory. Combined, these data provide a clear understanding of equipment distribution and construction dynamics within the construction area.

[0093] Select an appropriate path planning algorithm. Common path planning algorithms include Dijkstra's algorithm and A's algorithm. In this scenario, the current location of the image acquisition device is the starting point, and the area or point that requires key monitoring based on construction data is the end point.

[0094] Path planning is performed using an algorithm. The integrated data is fed into the selected path planning algorithm, which then performs calculations based on predefined rules and objectives. During this calculation, the positional relationships between image acquisition devices and the operational status of construction machinery are comprehensively considered to avoid collisions between devices while ensuring that the devices cover critical construction areas and acquire comprehensive and valuable monitoring data. Ultimately, the algorithm outputs the expected route for the image acquisition devices.

[0095] S303: Determine an expected image acquisition range of the image acquisition device based on the expected driving route and performance data of the image acquisition device; The expected route, derived from a pre-planned path planning algorithm, defines the trajectory of the image acquisition equipment within the excavation construction area. The performance data for the image acquisition equipment includes parameters such as viewing angle, resolution, and lens focal length, which directly determine the device's imaging capabilities and coverage.

[0096] Specifically, the image acquisition equipment collects data at various locations along the expected route, based on the collection locations determined along the route. Based on the route settings, multiple key collection points can be identified, distributed across key areas of the foundation pit to ensure comprehensive monitoring of construction progress.

[0097] The acquisition range is calculated based on device performance data. For example, if the device's angle of view is α, at a given acquisition point, a circle is drawn with the device as the center and its maximum acquisition distance as the radius. The area within this circle that is within an angle of ±α / 2 with the device's central axis is the acquisition range for that acquisition point. Performance parameters such as resolution and lens focal length affect image clarity and effective acquisition distance, which in turn influence the acquisition range.

[0098] Finally, by combining the acquisition ranges of all acquisition points, we arrive at the expected image acquisition range for the image acquisition device. This range takes into account both the device's movement path, ensuring dynamic monitoring of the construction area, and the device's performance, ensuring the collected image data meets the needs of construction monitoring.

[0099] S304: Compare the expected image acquisition range with the engineering drawing, and if a scanning blind spot exists in the engineering drawing, obtain the blind spot coordinate position of the scanning blind spot; It can be understood that the scanning blind area is an area that exists in the engineering drawing but is not included in the expected image acquisition range.

[0100] Specifically, prepare the data for the intended image acquisition range and the engineering drawing data. The intended image acquisition range is typically presented as a set of coordinates or specific graphic data. The engineering drawing is a standard document for foundation pit design, containing detailed information about each part of the pit. Because the two data formats may differ, format conversion is required to enable comparative analysis on the same platform. For example, convert the CAD format data of the engineering drawing into a vector graphics format compatible with the intended image acquisition range data.

[0101] Using graphics processing software or specialized analysis tools, the expected image acquisition range is overlaid and compared with the engineering drawing. During this comparison, the software checks pixel by pixel or coordinate by coordinate point to see if the area on the engineering drawing is within the expected image acquisition range. If an area on the engineering drawing is found to be outside the expected image acquisition range, a scanning blind spot is identified.

[0102] Once the blind area is determined, the tool automatically identifies the boundary coordinates of the area. For regularly shaped blind areas, the boundary coordinates can be obtained through simple geometric calculations; for irregular shapes, complex edge detection algorithms are used to determine the precise boundary coordinates.

[0103] S305: Combine the blind spot coordinates and the expected driving route with a path planning algorithm to obtain an optimized driving route; select an appropriate path planning algorithm. Common path planning algorithms such as Dijkstra's algorithm and A's algorithm can be used for this task.

[0104] In this scenario, the starting point could be a location on the image acquisition device's intended route close to the blind spot, and the end point could be the center of the blind spot or a key monitoring point. The evaluation function takes into account factors such as the distance and time cost of the move, as well as possible obstacles.

[0105] Specifically, the data of the blind spot coordinate position and the expected driving route are input into the selected path planning algorithm. Based on the input data, the algorithm will calculate multiple possible paths from the starting point to the end point, and sort these paths according to the evaluation function. During the calculation process, the algorithm will automatically avoid existing obstacles (such as buildings, construction equipment, etc.) to ensure the feasibility of the path. In the end, the algorithm will output an optimal path, which is the optimization result of the expected driving route after considering the scanning blind spot. Through such optimization, the image acquisition equipment can cover the scanning blind spot on the basis of completing the original monitoring task, so as to achieve more comprehensive and accurate foundation pit construction monitoring.

[0106] In some embodiments of the present application, when a temporary obstacle suddenly appears at the construction site, such as an unexpected pipeline maintenance area, the obstacle location data is re-incorporated into the path planning algorithm, and the point close to the obstacle in the original optimized route is used as a new starting point to replan a path around the obstacle to reach the blind spot, ensuring that monitoring is not affected.

[0107] S306: Control the image acquisition device to travel along the optimized driving route and acquire physical data of the foundation pit in real time; Specifically, first, a control connection is established. Through wireless communication technologies such as Wi-Fi, 4G or 5G, a reliable data transmission channel is built between the image acquisition device and the control center. Corresponding communication modules are set up on the device side and the control center respectively, and the parameters are configured to ensure that both parties can carry out stable two-way communication. Then, the control center sends the optimized driving route information to the image acquisition device. This information is encoded in a specific data format and contains detailed instructions such as the coordinates of each key node of the route, driving speed, turning angle, etc. After receiving the instructions, the image acquisition device decodes them through the built-in processor to parse out the driving route and control parameters.

[0108] The image acquisition device then uses its own navigation and drive systems to begin driving according to the interpreted instructions. If the device is equipped with GPS or other positioning systems, it will obtain its own position information in real time and compare it with the coordinates on the optimized driving route. Automatic control algorithms will then adjust the driving direction and speed to ensure that it follows the planned route.

[0109] During the driving process, various sensors on the image acquisition equipment start working to collect physical data of the foundation pit in real time.

[0110] In the above steps S301-S306, because the integration of multi-source data can fully understand the construction status, the path planning algorithm ensures the reasonable movement of the image acquisition equipment, and the acquisition range is determined in combination with the equipment performance to meet the actual capabilities, the blind spots can be found by comparing the drawings, and the optimization of the route can make up for the loopholes. Therefore, the physical data of the foundation pit can be collected in all directions, providing sufficient and accurate data support for construction safety assessment and management.

[0111] S307. Real-time environmental quality data surrounding each image acquisition device is acquired, forming a real-time environmental quality data set. The image acquisition devices are equipped with various specialized environmental monitoring sensors. For air quality monitoring, these sensors include laser dust sensors for detecting particulate matter concentration and electrochemical gas sensors for detecting harmful gas levels. These sensors operate based on specific physical or chemical principles. For example, a laser dust sensor utilizes the principle of laser scattering. When laser light strikes particulate matter in the air, it generates scattered light. By detecting the intensity and angle of the scattered light, the particle concentration can be calculated.

[0112] To ensure real-time data, these sensors are connected to the image acquisition device's built-in data processing module, which provides real-time data acquisition and preliminary processing capabilities. The sensors transmit the monitored environmental quality data to the data processing module in the form of electrical or digital signals. The module rapidly collects data at a preset sampling frequency, such as once per second or several times per minute, and performs simple filtering to remove outliers caused by factors such as sensor noise.

[0113] After preliminary processing, the data is transmitted to the data receiving end in real time via the device's communication module using wireless communication technology. On the server side, the data from each image acquisition device is aggregated and integrated, arranged by device ID and chronological order, to form a complete real-time environmental quality data set.

[0114] S308: When polluted environment data exists in the real-time environmental quality data group, switching the image acquisition mode of the image acquisition device corresponding to the polluted environment data to an infrared penetration scanning mode; It can be understood that the polluted environment data is real-time environmental quality data that exceeds the normal environmental quality threshold.

[0115] Specifically, the real-time environmental quality data set continuously receives data from the environmental monitoring sensors surrounding each image acquisition device. The system pre-sets normal environmental quality thresholds, covering the normal ranges for various environmental parameters, including air quality. Once new data enters the system, it is immediately compared with the corresponding normal environmental quality thresholds. For example, if the concentration of particulate matter in the air exceeds the set maximum allowable value, or the content of harmful gases exceeds the normal standard, the system will determine that the data is polluted.

[0116] Once the system identifies polluted environmental data, it quickly identifies the image acquisition device that generated it. This is achieved by using the device identification information carried during data transmission. Each image acquisition device carries a unique identification code when transmitting data, allowing the system to accurately locate the device. Once identification is complete, the control center issues a command to the corresponding image acquisition device to switch image acquisition modes.

[0117] After the image acquisition device receives the switching command, its built-in control system will switch the image acquisition mode from conventional mode to infrared penetration scanning mode according to the preset program. At the hardware level, the device will adjust the internal optical path and sensor parameters to enable the camera to emit and receive infrared rays. At the software level, the relevant drivers and image processing algorithms will also be adjusted accordingly to meet the needs of infrared imaging. The infrared penetration scanning mode utilizes the characteristics of infrared rays. It can penetrate obstacles and pollutants in polluted environments such as haze and smoke, obtain clearer and more accurate foundation pit image data, and ensure the normal progress of construction monitoring work in harsh environments.

[0118] S309, collecting environmental data, displacement data, and geological data of the foundation pit in real time at a preset frequency; S310, importing the physical data, the environmental data, and the geological data into a BIM platform to obtain a BIM model; S311, according to the displacement data and construction data at the coordinates of each monitoring point, substitute into a preset mathematical expression to obtain a first parameter corresponding to each monitoring point; S312, substituting the first parameter of each monitoring point and the preset maximum displacement data threshold into the mathematical expression to calculate the construction data safety threshold of each monitoring point; S313: Mark the monitoring point where the construction safety threshold is lower than the preset minimum construction safety value as a dangerous monitoring point; The preset minimum construction safety value is a fixed value determined based on multiple factors such as engineering design standards, construction specifications, and past experience with similar projects. It serves as an important criterion for measuring the safety status of monitoring points.

[0119] Specifically, the construction safety threshold of each monitoring point is compared against a preset minimum safety threshold. Once the monitoring system detects that a monitoring point's construction safety threshold falls below the preset minimum safety threshold, a marking process is immediately initiated. Each monitoring point has a corresponding record in the system's database, containing information such as its location coordinates, number, and real-time monitoring data. If a monitoring point is identified as dangerous, the system adds a special mark to the corresponding record.

[0120] S314, obtaining coordinate information data of all the dangerous monitoring points as a dangerous coordinate information data group; Specifically, within the entire foundation pit construction monitoring system, all data associated with each monitoring point, including real-time monitoring data, calculated construction safety thresholds, and coordinate information, is stored in a structured database. Once dangerous monitoring points are identified through preliminary comparisons, the monitoring system searches the database for these points. The database management system extracts the coordinate information fields and organizes them according to a specific format.

[0121] The sorted coordinate information will be arranged and combined in a specific order (for example, according to the monitoring point number from small to large, or according to the order of location distribution in the foundation pit area) to form an ordered data set, namely the dangerous coordinate information data group.

[0122] S315. Mark all the dangerous monitoring points in the BIM model based on the dangerous coordinate information data set; Specifically, the coordinate data format in the hazard coordinate information data set must be compatible with the coordinate system and data format used by the BIM model. BIM models are usually built based on a specific geographic information system (GIS) framework and have their own established coordinate system. If the coordinate format of the hazard coordinate information data set is different, such as using a different projection method or coordinate accuracy, it is necessary to use professional geospatial data processing tools to perform coordinate conversion and data format adjustment. Through the coordinate conversion algorithm, the coordinates in the data set are converted into coordinate values in a coordinate system consistent with the BIM model to ensure data compatibility.

[0123] After format adaptation is complete, the hazard coordinate data set is imported into the BIM model using the interface or data import function provided by the BIM software. The spatial data processing engine within the BIM software will use the imported coordinate information to accurately locate the corresponding position in the 3D model space.

[0124] After successfully locating hazardous monitoring points in the BIM model, they can be marked using the visualization capabilities of the BIM software. Unique visual styles can be set for these hazardous monitoring points, such as using eye-catching colors (e.g., red), specific icons (e.g., triangular warning signs), and detailed attribute information tags, such as the monitoring point number, hazard level, and current construction safety threshold.

[0125] S316: Analyze the spatial distribution of the dangerous monitoring points in the BIM model, and connect all the dangerous monitoring points into multiple dangerous areas through a spatial statistical analysis method; Specifically, the coordinate information of the dangerous monitoring points in the BIM model is collected. This information has been accurately entered into the model when the dangerous monitoring points were marked. The coordinates of each dangerous monitoring point correspond to the actual location of the foundation pit.

[0126] Select an appropriate spatial statistical analysis method to perform clustering calculations and input the collected coordinate information of dangerous monitoring points into the selected clustering algorithm.

[0127] Finally, the clustering results are visualized. In the BIM model, dangerous monitoring points belonging to the same cluster are connected with polygons or other closed shapes to form intuitive danger zones. Different danger zones can be distinguished by different colors or fill styles.

[0128] S317. Obtain real-time device location information of all image acquisition devices; The image acquisition device is equipped with a positioning module. Common positioning modules include the Global Positioning System (GPS) module, the Beidou Satellite Navigation System (BDS) module, or Wi-Fi or Bluetooth-based positioning tags used in indoor environments. These positioning modules determine the device's location by receiving satellite signals (such as GPS and BDS) or surrounding wireless signals (such as Wi-Fi and Bluetooth).

[0129] S318. When there is real-time device location information entering the dangerous area, the image acquisition frequency of the image acquisition device entering the dangerous area is adjusted to an increased frequency; Specifically, the increase frequency is the product of the image acquisition frequency of the preset image acquisition device and a preset multiple.

[0130] The monitoring system continuously receives real-time location information from each image acquisition device. This information is compared in real time with previously defined danger zone boundaries. Using spatial analysis algorithms, such as point-in-polygon detection, the system determines whether the image acquisition device's real-time location is within the danger zone.

[0131] Once the monitoring system determines that an image acquisition device has entered a hazardous area, it triggers a frequency adjustment instruction generator. The system retrieves two parameters from the preset parameter configuration file: the base acquisition frequency and the preset multiplier for the preset image acquisition device. The preset base acquisition frequency is pre-set based on normal construction monitoring needs, while the preset multiplier is determined based on factors such as the hazardous area's risk level and construction safety requirements. This determines the degree to which the acquisition frequency should be increased within the hazardous area.

[0132] Then, the system multiplies the obtained basic acquisition frequency with the preset multiple to obtain the value of the increased frequency.

[0133] Finally, the system sends frequency adjustment instructions to the image acquisition device entering the dangerous area through the wireless communication network.

[0134] In the above steps S313-S318, by marking dangerous monitoring points, hidden dangers can be located; by analyzing dangerous areas, the risk distribution can be grasped from an overall perspective; by increasing the collection frequency when the equipment enters the dangerous area, data can be collected more intensively, and potential danger signals can be captured in time, providing sufficient basis for taking effective safety measures, thereby ensuring the safety of foundation pit construction.

[0135] S319: When a safety threshold monitoring point exists, simulate the construction situation at the corresponding safety threshold monitoring point in the BIM model based on the real-time construction data to obtain a simulation result; S320: Output the simulation result.

[0136] Steps S309-S312, S319-S320 and Figure 1 In the illustrated embodiment, steps S102 to S107 are similar, and reference may be made to the description of steps S102 to S107 , which will not be repeated here.

[0137] In the above embodiment, through multi-dimensional collection of foundation pit data, all kinds of information in the construction process are comprehensively covered, providing a rich data basis for construction safety assessment; using algorithms to plan the image acquisition equipment route, comparative analysis to clarify the scanning blind spots and optimize the route; real-time monitoring of environmental quality to switch the acquisition mode, to ensure stable data acquisition even in complex environments; calculating safety thresholds to mark dangerous monitoring points and analyze dangerous areas, which can accurately locate high-risk areas; adjusting the acquisition frequency according to the location of the equipment, so that monitoring resources can be concentrated on dangerous areas and safety hazards can be captured in a timely manner. Using algorithms to plan the image acquisition equipment route can effectively cover key construction areas based on the actual construction situation. Comparative analysis to determine scanning blind spots and optimize routes fills the monitoring gaps, improves the comprehensiveness of monitoring, and thus improves the accuracy of foundation pit construction safety monitoring.

[0138] The following introduces an exemplary foundation pit construction monitoring system 400 provided in an embodiment of the present application. Figure 4 It is a schematic diagram of an exemplary hardware structure of the foundation pit construction monitoring system 400 provided in an embodiment of the present application.

[0139] In some embodiments, the foundation pit construction monitoring system 400 includes a computer device. The computer device includes a processor, a memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with other external terminals or servers via a network connection. In some embodiments, the network interface can be a wired network interface, and in some embodiments, the network interface can also be a wireless network interface. When the computer program is executed by the processor, the method in the embodiment of the present application is implemented.

[0140] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0141] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0142] As used in the above embodiments, the term “when…” may be interpreted to mean “if…” or “after…” or “in response to determining…” or “in response to detecting…”, depending on the context. Similarly, the phrases “upon determining…” or “if (stated condition or event) is detected” may be interpreted to mean “if determining…” or “in response to determining…” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0143] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk).

[0144] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A foundation pit construction monitoring method, characterized in that: include: Real-time acquisition of physical data and construction data of the foundation pit; the construction data includes construction machine positioning data, construction machine driving route data, construction machine weight data, and construction machine vibration data; Collect environmental data, displacement data and geological data of foundation pit in real time according to preset frequency; Importing the physical data, the environmental data, and the geological data into a BIM platform to obtain a BIM model; Substituting the displacement data and the construction data at each monitoring point coordinate into a preset mathematical expression, a first parameter corresponding to each monitoring point is obtained; the first parameter is the value of the construction data relative to the displacement data; and the mathematical expression is the data relationship between the displacement data and the construction data; Substituting the first parameter and the preset maximum displacement data threshold of each monitoring point into the mathematical expression, the construction data safety threshold of each monitoring point is calculated; The construction data safety threshold is the upper limit of the construction data that each monitoring point can withstand; When a safety threshold monitoring point exists, the construction situation is simulated at the corresponding safety threshold monitoring point in the BIM model based on the real-time construction data to obtain a simulation result; the safety threshold monitoring point is when the real-time construction data corresponding to the monitoring point exceeds the construction data safety threshold; The simulation results are output.

2. The method according to claim 1, characterized in that In the case where a safety threshold monitoring point exists, simulating a construction situation at the safety threshold monitoring point in the BIM model according to the real-time construction data, and before obtaining a simulation result, further comprising: Collect real-time weather data; Calculating a differentiation index between the real-time weather data and preset extreme weather data; Determine the real-time weather level based on the values of the differentiation indicators and the preset differentiation abnormal warning threshold range; the weather level is divided into normal, warning and abnormal; When the real-time weather level is a warning, the construction data safety threshold corresponding to all monitoring points is reduced by a first preset multiple; When the real-time weather level is abnormal, the construction data safety threshold corresponding to all monitoring points is reduced by a second preset multiple; the second preset multiple is greater than the first preset multiple.

3. The method according to claim 2, characterized in that When a safety threshold monitoring point exists, simulating the construction situation at the corresponding safety threshold monitoring point in the BIM model based on the real-time construction data to obtain a simulation result specifically includes: When there is a safety threshold monitoring point, the simulated construction parameters are calculated based on the real-time construction data; When the real-time weather level is warning or abnormal, mapping the real-time weather data and the simulated construction parameters into the BIM model; In the BIM model, the real-time situation data of the safety threshold monitoring point is obtained as a simulation result.

4. The method according to claim 1, wherein The real-time acquisition of physical data and construction data of the foundation pit specifically includes: Acquire construction data and position coordinate information of multiple image acquisition devices to obtain a device position coordinate matrix; Using a path planning algorithm, the device position coordinate matrix, the construction machine positioning data in the construction data, and the construction machine driving route data are combined to set an expected driving route of the image acquisition device; Determining an expected image acquisition range of the image acquisition device based on the expected driving route and performance data of the image acquisition device; Comparing the expected image acquisition range with the engineering drawing, and obtaining the blind spot coordinate position of the scanning blind spot if there is a scanning blind spot in the engineering drawing; the scanning blind spot is an area that exists in the engineering drawing but is not in the expected image acquisition range; Combining the blind spot coordinate position and the expected driving route with a path planning algorithm to obtain an optimized driving route; The image acquisition device is controlled to travel along the optimized travel route and obtain physical data of the foundation pit in real time.

5. The method according to claim 4, characterized in that After the image acquisition device is controlled to travel along the optimized travel route and physical data of the foundation pit is acquired in real time, the method further includes: Acquire the environmental quality data around each image acquisition device in real time, which is a real-time environmental quality data group; When there is polluted environment data in the real-time environment quality data group, the image acquisition mode of the image acquisition device corresponding to the polluted environment data is switched to the infrared penetration scanning mode; the polluted environment data is real-time environment quality data that exceeds the normal environment quality threshold.

6. The method according to claim 1, wherein After substituting the first parameter and the preset maximum displacement data threshold of each monitoring point into the mathematical expression to calculate the construction data safety threshold of each monitoring point, the method further includes: Marking a monitoring point where the construction safety threshold is lower than a preset minimum construction safety value as a dangerous monitoring point; Acquiring coordinate information data of all the dangerous monitoring points as a dangerous coordinate information data group; In combination with the dangerous coordinate information data set, marking all the dangerous monitoring points in the BIM model; Analyze the spatial distribution of the dangerous monitoring points in the BIM model, and connect all the dangerous monitoring points into multiple dangerous areas through spatial statistical analysis methods; Obtain real-time device location information of all image acquisition devices; When there is real-time device location information entering the dangerous area, the image acquisition frequency of the image acquisition device entering the dangerous area is adjusted to an increased frequency; the increased frequency is the product of the image acquisition frequency of the preset image acquisition device and a preset multiple.

7. The method according to claim 1, characterized in that After outputting the simulation results, the method further includes: When the displacement data corresponding to a monitoring point exceeds a preset dangerous displacement threshold, the corresponding monitoring point is marked as an accident monitoring point; Obtaining the accident time, accident coordinate position data, and original coordinate position data of the accident monitoring point corresponding to the displacement data exceeding the preset dangerous displacement threshold; the original coordinate position data is the coordinate position data of the accident monitoring point before the displacement data exceeds the preset dangerous displacement threshold; combining the original coordinate position data, the accident event, the accident coordinate position data, and the construction data to locate the accident construction machine that caused the accident; The corresponding preset number of the construction machine is sent to a preset mobile terminal.

8. A foundation pit construction monitoring system, characterized in that: The foundation pit construction monitoring system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the foundation pit construction monitoring system to execute the method described in any one of claims 1-7.

9. A computer program product comprising instructions, characterized in that When the computer program product is run on a foundation pit construction monitoring system, the foundation pit construction monitoring system is enabled to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the foundation pit construction monitoring system, the foundation pit construction monitoring system is caused to execute the method according to any one of claims 1 to 7.

Images