Bridge hanging basket construction safety monitoring method, device, equipment and medium

Abstract

The invention relates to the technical field of data processing, in particular to a bridge hanging basket construction safety monitoring method and device, equipment and a medium. The method comprises the steps that by synchronously collecting core parameters such as structural stress, inclination angle and displacement and real-time wind speed data, the influence of environmental load on the stability of the hanging basket is accurately quantified, a dynamic influence evaluation model is introduced, wind speed change and structural response are subjected to coupling analysis, and the potential risk evolution trend is effectively recognized; and finally, through a machine learning prediction algorithm, a safety boundary is deduced in combination with historical data and a real-time state, and transformation from passive response to active prevention is realized. The hysteresis and subjectivity of manual inspection are reduced, a scientific basis can be provided for construction decision making under complex meteorological conditions, and the accuracy of determining the safety of hanging basket detection data is remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, equipment and medium for monitoring the safety of bridge hanging basket construction. Background Art

[0002] Basket construction is a critical step in modern bridge construction, and its safety and efficiency are crucial to the overall project progress and quality. As bridge structures evolve toward larger spans and greater complexity, safety monitoring technology for basket construction has become a key focus of the industry. Traditionally, the safety of basket construction relies primarily on manual inspections and empirical judgment. However, this approach struggles to meet the precision and real-time requirements of modern bridge construction, necessitating the development of more efficient and reliable monitoring methods.

[0003] Currently, to address safety issues during construction, the industry often uses sensor monitoring methods. By installing strain gauges, inclinometers, and other sensors on the gantry, real-time stress, inclination, and displacement data are collected to assess the gantry's condition.

[0004] However, traditional monitoring methods are usually based on fixed thresholds, which may lead to biased safety assessment results. Therefore, how to improve the accuracy of determining the safety of hanging basket inspection data has become a technical problem that needs to be solved urgently. Summary of the Invention

[0005] In order to improve the accuracy of determining the safety of hanging basket detection data, the present application provides a bridge hanging basket construction safety monitoring method, device, equipment and medium.

[0006] In the first aspect, the present application provides a method for monitoring the safety of bridge hanging basket construction, which adopts the following technical solutions: A bridge hanging basket construction safety monitoring method comprising: Obtaining current detection data and current wind speed data corresponding to the current hanging basket, wherein the current detection data includes stress, inclination, and displacement corresponding to the current hanging basket at the current moment, and the current wind speed data is the wind speed corresponding to the current cycle, wherein the current cycle starts at the current moment; Determining the degree of influence of the current wind speed data on the current detection data; Based on the current detection data, the impact degree and the current wind speed data, predict the safety situation of the current hanging basket in the current cycle, the safety situation being safe or unsafe; Based on the security situation, it is determined whether to generate security alert information.

[0007] By adopting this technical solution, by synchronously collecting core parameters such as structural stress, inclination, and displacement along with real-time wind speed data, the impact of environmental loads on the stability of the hanging basket can be accurately quantified. A dynamic impact assessment model is introduced to couple wind speed changes with structural response for analysis, effectively identifying the evolution of potential risks. Finally, through machine learning prediction algorithms, safety boundaries are deduced by combining historical data with real-time status, achieving a transition from passive response to active prevention. This not only reduces the lag and subjectivity of manual inspections, but also provides a scientific basis for construction decision-making under complex meteorological conditions, significantly improving the accuracy of determining the safety of hanging basket inspection data.

[0008] In one possible implementation, determining the degree of influence of the current wind speed data on the current detection data includes: Acquire historical data of the current hanging basket, the historical data including historical detection data corresponding to historical detection moments and historical wind speed data; Performing time domain analysis and frequency domain analysis on the historical data to obtain a time domain relationship between each detection sub-data and the wind speed data and a frequency domain eigenvector matrix corresponding to the historical data, wherein the detection sub-data is one of stress, inclination angle, and displacement; Based on the time domain relationship and the frequency domain eigenvector matrix, the degree of influence of the current wind speed data on the current detection data is determined.

[0009] In a possible implementation, performing time domain analysis and frequency domain analysis on the historical data to obtain the time domain relationship between each detection sub-data and the wind speed data and the frequency domain eigenvector matrix corresponding to the historical data includes: For each detection sub-data, the historical pouring stage, historical detection sub-data, and historical wind speed data corresponding to each historical detection moment are obtained; the historical basic sub-data corresponding to the historical pouring stage are determined; the historical detection difference between the historical basic sub-data corresponding to each historical detection moment and the corresponding historical detection sub-data is calculated, and based on the historical detection difference and the corresponding historical wind speed data corresponding to each historical detection moment, a time domain analysis is performed on the historical detection difference and the corresponding historical wind speed data to obtain a time domain function relationship with the historical detection moment as the horizontal axis and the historical ratio as the vertical axis, where the historical ratio is the ratio between the historical detection difference and the corresponding historical wind speed data; Frequency domain analysis is performed on each historical sub-data and historical wind speed data in the historical data to obtain frequency domain eigenvectors corresponding to each historical sub-data and the historical wind speed data, and the frequency eigenvectors are integrated to obtain a frequency domain eigenvector matrix corresponding to the historical data.

[0010] In a possible implementation, determining the degree of influence of the current wind speed data on the current detection data based on the time domain relationship and the frequency domain eigenvector matrix includes: Determining, based on the time domain relationship, an influence sub-degree of the current wind speed data on each detection sub-data, and determining, based on each influencing sub-data, a first influence degree of the current wind speed data on the current detection data; Inputting the frequency domain eigenvector matrix into a recurrent network model, and obtaining a second influence degree output by the recurrent network model; Based on the first influence degree and the second influence degree, the influence degree of the current wind speed data on the current detection data is determined.

[0011] In a possible implementation, predicting the safety condition of the current cradle in the current cycle based on the current detection data, the impact degree, and the current wind speed data includes: Obtaining a detection threshold corresponding to the current detection data, and determining whether the current detection data is within the corresponding detection threshold; If the current detection data is not within the corresponding detection threshold, it is predicted that the safety condition of the current hanging basket in the current cycle is unsafe; If the current detection data is within the corresponding detection threshold, the expected detection data of the current hanging basket in the current period is determined based on the impact degree and the current wind speed data, and the safety situation of the current hanging basket in the current period is predicted based on the expected detection data.

[0012] In a possible implementation, determining expected detection data of the current basket in a current period based on the impact degree and the current wind speed data includes: Obtain the initial BIM model corresponding to the current hanging basket, and obtain the current pouring stage corresponding to the current hanging basket, where the current pouring stage is the pouring stage of the current hanging basket in the current cycle; Inputting the current pouring stage into the initial BIM model to obtain a target BIM model; The impact degree, the current detection data and the current wind speed data are input into the target BIM model, and the detection data displayed by the target BIM model is obtained to obtain the expected detection data of the current hanging basket in the current period.

[0013] In one possible implementation, determining whether to generate security alert information based on the security situation includes: If the security situation is safe, no security alert information is generated; If the safety situation is unsafe, a safety alert message is generated and a target BIM model marked with expected detection data is displayed.

[0014] In a second aspect, the present application provides a bridge hanging basket construction safety monitoring device, which adopts the following technical solution: A bridge hanging basket construction safety monitoring device, comprising: An acquisition module is used to acquire current detection data and current wind speed data corresponding to the current hanging basket, wherein the current detection data includes stress, inclination and displacement corresponding to the current hanging basket at the current moment, and the current wind speed data is the wind speed corresponding to the current cycle, wherein the current cycle starts at the current moment; A determination module, configured to determine the degree of influence of the current wind speed data on the current detection data; a prediction module, configured to predict a safety condition of the current hanging basket in a current cycle based on the current detection data, the impact degree, and the current wind speed data, wherein the safety condition is safe or unsafe; A generating module is used to determine whether to generate security alert information based on the security situation.

[0015] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device, comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the bridge hanging basket construction safety monitoring method described in any one of the first aspects above.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium, comprising: storing a computer program that can be loaded by a processor and execute the bridge hanging basket construction safety monitoring method described in any one of the first aspects above.

[0017] In summary, this application has the following beneficial technical effects: By synchronously collecting core parameters such as structural stress, inclination, and displacement along with real-time wind speed data, the impact of environmental loads on the stability of the hanging basket is accurately quantified. A dynamic impact assessment model is introduced to couple wind speed changes with structural response for analysis, effectively identifying potential risk evolution trends. Finally, through machine learning prediction algorithms, safety boundaries are deduced by combining historical data with real-time status, enabling a transition from passive response to active prevention. This not only reduces the lag and subjectivity of manual inspections but also provides a scientific basis for construction decision-making under complex weather conditions, significantly improving the accuracy of determining the safety of hanging basket inspection data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a flow chart of a bridge hanging basket construction safety monitoring method provided in an embodiment of the present application; Figure 2 1 is a block diagram of a bridge hanging basket construction safety monitoring device provided in an embodiment of the present application; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following is combined with Figure 1 -Attached Figure 3 This application is described in further detail.

[0020] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0021] In order to facilitate understanding of the technical solutions proposed in this application, several elements that will be introduced in the description of this application are first introduced here. It should be understood that the following introduction is only for the convenience of understanding these elements, so as to understand the content of the embodiments of this application, and does not necessarily cover all possible situations.

[0022] The hanging basket is the primary equipment used in cantilever construction and can be categorized by structural form into four types: truss, cable-stayed, steel-type, and hybrid. Based on the technical requirements for concrete cantilever construction and the design drawings' requirements for the hanging basket, a comprehensive comparison of the various types of hanging baskets was conducted, including their characteristics, weight, steel type, and construction techniques. The design principles for the hanging basket include light weight, simple structure, sturdiness and stability, easy movement and assembly and disassembly, strong reusability, and minimal deformation under load. Furthermore, ample space beneath the hanging basket provides a large working surface, facilitating rebar and formwork construction operations.

[0023] The present application embodiment provides a method for monitoring the safety of bridge hanging basket construction, such as Figure 1 As shown, the method provided in the embodiment of the present application is performed by an electronic device, which can be a server or a terminal device, wherein the server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S101 to S104, wherein: Step S101: Obtain current detection data and current wind speed data corresponding to the current hanging basket.

[0024] The current detection data includes the stress, inclination, and displacement corresponding to the current hanging basket at the current moment. The current wind speed data is the wind speed corresponding to the current cycle, which starts at the current moment. The current wind speed data can be obtained by obtaining weather forecast data for the current hanging basket location, or by communicating with an on-site anemometer through electronic equipment to obtain the current wind speed.

[0025] Specifically, the electronic device establishes a communication connection with various sensors installed on the basket (such as stress sensors, tilt sensors, and displacement sensors) to read the current stress, tilt, and displacement data collected by these sensors in real time, which is the current detection data. These sensors transmit this data to the electronic device via wired or wireless means for storage and processing.

[0026] Step S102: Determine the degree of influence of the current wind speed data on the current detection data.

[0027] Among them, the degree of influence indicates the size and significance of the effect of the current wind speed data on the current detection data (stress, inclination, displacement), which is used to measure the impact of wind speed on the state of the hanging basket.

[0028] Specifically, in this embodiment, determining the degree of influence of the current wind speed data on the current detection data includes: Obtain the historical data of the current hanging basket, including the historical detection data corresponding to the historical detection time and the historical wind speed data; Perform time domain and frequency domain analysis on the historical data to obtain the time domain relationship between each detection sub-data and wind speed data, as well as the frequency domain eigenvector matrix corresponding to the historical data. The detection sub-data is one of stress, inclination, and displacement. Based on the time domain relationship and the frequency domain eigenvector matrix, the degree of influence of the current wind speed data on the current detection data is determined.

[0029] Specifically, historical data related to the current basket can be obtained from the basket database corresponding to the basket. This historical data includes historical detection data such as stress, inclination, and displacement collected by sensors installed on the basket at different historical detection moments in the past, as well as historical wind speed data recorded by the anemometer at the same moment. Furthermore, for each detection sub-data (stress, inclination, displacement), its historical data series that changes over time is compared and analyzed with the corresponding historical wind speed data series. By calculating correlation coefficients, drawing time series curves, and other methods, the changing trends and mutual relationships between the detection sub-data and wind speed data in the time dimension can be observed. For example, when the wind speed changes, how does the stress data change accordingly? Is there an immediate response or a certain delay? This can determine the time domain relationship between the detection sub-data and the wind speed data.

[0030] The entire historical data set, including historical detection data and historical wind speed data, is transformed into the frequency domain using methods such as Fourier transform. After converting the time domain data into the frequency domain, the frequency characteristics are extracted to form frequency domain eigenvectors. For different historical data records, these frequency domain eigenvectors are combined to form a frequency domain eigenvector matrix. This matrix reflects the distribution and characteristics of the historical data at different frequency components, facilitating analysis of potential connections between wind speed data and detection sub-data from a frequency perspective.

[0031] Furthermore, an impact assessment model can be established, taking the time domain relationship and the frequency domain eigenvector matrix as input. The impact assessment model first analyzes whether the change trend between the current wind speed data and the current detection data is consistent with the time domain relationship in the historical data, as well as the magnitude of the change, based on the time domain relationship. Then, the frequency domain eigenvector matrix is ​​used to compare the similarity between the frequency domain features of the current data and the frequency domain features of the historical data. If the frequency domain features of the current data are similar to the frequency domain features in the historical data that show that high wind speed has a significant impact on the detection data, it is considered that the current wind speed data has a high degree of influence on the current detection data; conversely, if the difference is large, the degree of influence is low. The impact degree output by the impact assessment model is obtained. The impact assessment model is trained using a large number of time domain relationship samples, frequency domain eigenvector matrix samples, and impact degree samples.

[0032] Specifically, in this embodiment, the historical data is subjected to time domain analysis and frequency domain analysis to obtain the time domain relationship between each detection sub-data and the wind speed data and the frequency domain eigenvector matrix corresponding to the historical data, including: For each detection sub-data, obtain the historical pouring stage, historical detection sub-data and historical wind speed data corresponding to each historical detection moment; determine the historical basic sub-data corresponding to the historical pouring stage; calculate the historical detection difference between the historical basic sub-data and the corresponding historical detection sub-data corresponding to each historical detection moment, and based on the historical detection difference and the corresponding historical wind speed data corresponding to each historical detection moment, perform time domain analysis on the historical detection difference and the corresponding historical wind speed data to obtain a time domain function relationship with the historical inspection moment as the horizontal axis and the historical ratio as the vertical axis, where the historical ratio is the ratio between the historical detection difference and the corresponding historical wind speed data; Frequency domain analysis is performed on each historical sub-data and historical wind speed data in the historical data to obtain the frequency domain eigenvectors corresponding to each historical sub-data and historical wind speed data, and the frequency eigenvectors are integrated to obtain the frequency domain eigenvector matrix corresponding to the historical data.

[0033] Within the acquired historical data for the current hanging basket, operations are performed separately on the three detection sub-data: stress, inclination, and displacement. For each detection sub-data, the historical pouring stage information corresponding to the historical detection moment (e.g., whether it was at the beginning, middle, or end of the beam segment pouring) is extracted, along with the specific value of the detection sub-data at that moment (i.e., the historical detection sub-data), and the historical wind speed data at the same moment. Furthermore, stress is directly affected by pouring weight. The greater the pouring volume and the more uneven the distribution, the more significant the stress change. The inclination is highly sensitive to pouring eccentricity, and unilateral pouring can easily cause the hanging basket to tilt. Displacement increases linearly with pouring progress (elastic deformation). Therefore, each detection sub-data is related to the pouring stage, and the pouring weight varies in different pouring stages. Therefore, the pouring stages can be divided into initial pouring stage, mid-pouring stage, and late pouring stage. Based on the acquired historical pouring stage information and referring to pre-set rules (which can be based on engineering experience and design specifications), the historical basic sub-data corresponding to each historical pouring stage is determined. For example, for the initial pouring stage, a theoretical base value of stress, inclination, or displacement is determined as the historical basic sub-data based on the designed initial stress state and structural characteristics of the hanging basket. Since each pouring process is based on pre-set pouring instructions, each pouring process is similar or identical. The impact on each detection sub-data in each pouring stage of each pouring process is similar or identical. Therefore, the basic sub-data corresponding to each historical pouring stage can be determined based on pre-set rules.

[0034] Furthermore, for each historical detection moment, the historical basic sub-data corresponding to that moment is subtracted from the historical detection sub-data to obtain the historical detection difference. For the historical detection difference and the corresponding historical wind speed data obtained at each historical detection moment, time domain analysis methods are used, such as calculating correlation coefficients and drawing time series curves. By analyzing how these data change over time (historical detection moments), the ratio between the historical detection difference and the historical wind speed data (i.e., the historical ratio) is calculated. A function curve is then drawn with the historical detection moment as the horizontal axis and the historical ratio as the vertical axis to obtain the time domain function relationship between them. This relationship reflects the correlation between wind speed changes and relative changes in the hanging basket detection sub-data at different historical detection moments.

[0035] Furthermore, frequency domain analysis is performed on each historical detection sub-data (historical data corresponding to stress, inclination, and displacement) and historical wind speed data within the historical data. Frequency domain analysis methods such as Fourier transform are used to convert the time domain data into frequency domain data. In the frequency domain, frequency characteristics of the data, such as frequency components and energy distribution, are extracted to form a frequency domain eigenvector corresponding to each data sequence. For example, for the historical stress data sequence, Fourier transform is used to obtain characteristics such as its energy distribution at different frequencies, forming a frequency domain eigenvector. The frequency domain eigenvectors corresponding to each historical sub-data (frequency domain eigenvectors corresponding to stress, inclination, and displacement) and historical wind speed data are then arranged and combined in a specific order (e.g., according to the order of data type) to form a matrix, namely the frequency domain eigenvector matrix.

[0036] Furthermore, in this embodiment, based on the time domain relationship and the frequency domain eigenvector matrix, determining the degree of influence of the current wind speed data on the current detection data includes: Determining the influence sub-degree of the current wind speed data on each detection sub-data based on the time domain relationship, and determining a first influence degree of the current wind speed data on the current detection data based on each influencing sub-data; Inputting the frequency domain eigenvector matrix into the recurrent network model and obtaining the second influence degree output by the recurrent network model; Based on the first influence degree and the second influence degree, the influence degree of the current wind speed data on the current detection data is determined.

[0037] According to the obtained time domain relationship (i.e., the time domain function relationship with the historical detection time as the horizontal axis and the historical ratio as the vertical axis), the current wind speed data is substituted into the time domain function relationship. For each detection sub-data (stress, inclination, displacement), the change in the historical ratio corresponding to the current wind speed data is analyzed. According to the change amplitude of the historical ratio and the similarity with the change pattern in the historical data, the influence of the current wind speed data on each detection sub-data is determined. The influence of each detection sub-data is comprehensively considered, and the influence of each detection sub-data is integrated by weighted average method. Among them, stress, inclination, and displacement each have different preset weights, and the weighted average is calculated to determine the first influence of the current detection data corresponding to the current wind speed data.

[0038] The frequency-domain eigenvector matrix is ​​then input into a pre-trained recurrent network model (such as a long short-term memory (LSTM) network or a gated recurrent unit (GRU)). This recurrent network model has been trained on a large amount of historical data and has learned the relationship between the frequency-domain eigenvectors and the degree of wind speed's impact on the basket's detection data. The recurrent network model processes and analyzes the input frequency-domain eigenvector matrix. Through internal neuron calculations and parameter adjustments, it outputs a numerical value representing the second degree of impact of the current wind speed data on the current detection data. Electronic equipment then captures and records this second degree of impact output by the recurrent network model.

[0039] Furthermore, an average value of the first influence degree and the second influence degree is calculated to obtain the influence degree of the current wind speed data on the current detection data.

[0040] Step S103: Based on the current detection data, the impact degree and the current wind speed data, predict the safety situation of the current hanging basket in the current cycle.

[0041] Among them, the safety situation is safe or unsafe.

[0042] Specifically, a trained safety prediction model can be used, taking the current test data (stress, inclination, displacement), the determined impact level, and the current wind speed data as input parameters. This safety prediction model can be trained using a machine learning algorithm. Based on the input data, the model analyzes whether various basket indicators exceed safety thresholds. For example, if the stress exceeds the material's allowable stress value, or if the inclination and displacement exceed the specified safety range, and considering the significant impact of the current wind speed data, the model will predict that the basket is in an unsafe state for the current cycle. If all indicators are within the safe range, the basket is deemed safe.

[0043] In this embodiment, based on the current detection data, the impact level, and the current wind speed data, the safety status of the current hanging basket in the current cycle is predicted, including: Obtain the detection threshold corresponding to the current detection data, and determine whether the current detection data is within the corresponding detection threshold; If the current detection data is not within the corresponding detection threshold, the safety condition of the current hanging basket in the current cycle is predicted to be unsafe; If the current detection data is within the corresponding detection threshold, the expected detection data of the current hanging basket in the current period is determined based on the impact degree and the current wind speed data, and the safety situation of the current hanging basket in the current period is predicted based on the expected detection data.

[0044] The detection threshold corresponding to each current detection data (any one of stress, inclination, and displacement) is obtained from the basket database corresponding to the current basket, and the specific value of the current detection data (any one of stress, inclination, and displacement) is compared with the corresponding detection threshold to determine whether the value of the current detection data is within the range specified by the detection threshold, for example, to determine whether the current stress value is within the range of the stress detection threshold.

[0045] Furthermore, if, after comparing the current test data with the detection threshold, any value of the current test data (stress, inclination, or displacement) is found to be outside the corresponding detection threshold, the current basket is judged to be unsafe for the current cycle. This is because even if any key indicator exceeds the safe range, it may pose a threat to the structural safety of the basket. Therefore, no further analysis is required and an unsafe prediction result is directly given.

[0046] When the current test data (stress, inclination, displacement) is determined to be within the corresponding detection threshold range, the possible changes in the basket's test data (stress, inclination, displacement) during the current cycle can be predicted, thereby deriving the estimated test data. The estimated test data (estimated stress, estimated inclination, estimated displacement) is then compared with the corresponding detection threshold. If all estimated test data are within the corresponding detection threshold range, the current basket's safety status for the current cycle is predicted to be safe. If any of the estimated test data exceeds the corresponding detection threshold range, the current basket's safety status for the current cycle is predicted to be unsafe.

[0047] Specifically, in this embodiment, based on the impact degree and the current wind speed data, the expected detection data of the current hanging basket in the current cycle is determined, including: Get the initial BIM model corresponding to the current hanging basket, and get the current pouring stage corresponding to the current hanging basket. The current pouring stage is the pouring stage of the current hanging basket in the current cycle. Input the current pouring stage into the initial BIM model to obtain the target BIM model; The impact degree, current detection data and current wind speed data are input into the target BIM model, and the detection data displayed by the target BIM model is obtained to obtain the expected detection data of the current hanging basket in the current cycle.

[0048] Specifically, the initial BIM model corresponding to the current hanging basket can be obtained from the hanging basket database corresponding to the current hanging basket. The initial BIM model contains various information of the hanging basket design stage, such as structural dimensions, material properties, connection methods, etc. At the same time, the current pouring stage of the current hanging basket in the current cycle is obtained from the construction management system, and the obtained current pouring stage information is input into the initial BIM model. The initial BIM model adjusts the relevant parameters and states in the initial BIM model according to the preset rules and the characteristics of the current pouring stage. For example, the load conditions borne by the hanging basket are determined according to the current pouring stage, and the corresponding load parameters in the model are adjusted; or information such as the solidification state of the concrete in the model is updated according to the pouring progress. After these adjustments, a target BIM model that reflects the actual state of the hanging basket in the current pouring stage is generated.

[0049] Furthermore, the previously determined impact of the current wind speed data on the current test data, the current test data (stress, inclination, displacement), and the current wind speed data are input into the target BIM model. The target BIM model utilizes its internal mechanical analysis and simulation modules to simulate the changes in the basket's operating state during the current cycle under the influence of the current wind speed and impact. Through calculation and analysis, the target BIM model derives the predicted values ​​of the basket's test data (stress, inclination, displacement) under these conditions and displays these predicted values. Electronic equipment captures this displayed test data as the predicted test data for the current cycle. The target BIM model is trained using a large amount of historical test data, historical wind speed data, and historical impact levels.

[0050] Step S104: Based on the security situation, determine whether to generate security alert information.

[0051] Specifically, a judgment is made based on the predicted safety status. If the safety status is "unsafe," a corresponding safety alert message is generated. This alert message may include the basket number, current problems (such as excessive stress or abnormal tilt angle), and the degree of danger. The alert message is sent to relevant construction managers and safety officers via display screens, SMS notifications, and voice alarms. If the safety status is "safe," no safety alert message is generated, and the basket's status continues to be monitored in real time.

[0052] In this embodiment, determining whether to generate security alert information based on the security situation includes: If the security situation is safe, no security alert message is generated; If the safety situation is unsafe, a safety alert message is generated and the target BIM model marked with expected detection data is displayed.

[0053] After completing the safety prediction for the current basket during the current cycle, the resulting safety status results are checked. If the safety status is displayed as "safe," meaning all predicted test data (stress, inclination, displacement) are within the corresponding detection thresholds, the basket is safe for the current cycle and presents no significant safety risks. At this point, no actions that would generate safety alerts are performed; instead, the basket status continues to be monitored in real time, acquiring and analyzing new test data according to the established process and frequency.

[0054] If the safety inspection result is "unsafe," it means that at least one of the expected test data (stress, inclination, or displacement) exceeds the corresponding detection threshold, indicating that the current gantry presents a safety hazard within the current cycle. At this point, a safety alert message containing relevant key information is generated based on the preset alert format and content template. This information may include the gantry's identification (such as its number), a specific description of the unsafe condition (for example, stress exceeding a threshold by X% or inclination exceeding the allowable range), the current expected test data (specific stress, inclination, and displacement values), possible risk consequences, and recommended countermeasures. The previously generated target BIM model is simultaneously retrieved and the relevant expected test data is annotated on the target BIM model. For example, information such as stress values ​​exceeding the threshold or abnormal inclination angles is annotated at the corresponding parts of the gantry structure, visually demonstrating the specific location and extent of the gantry's unsafe condition. The target BIM model with the annotated expected test data is then presented to relevant construction managers and safety officers via the electronic device's display or a connected display terminal, enabling them to quickly understand the gantry's safety status and make decisions.

[0055] The embodiment of the present application provides a method for monitoring the safety of bridge hanging basket construction. By synchronously collecting core parameters such as structural stress, inclination, displacement, and real-time wind speed data, the impact of environmental loads on the stability of the hanging basket is accurately quantified. A dynamic impact assessment model is introduced to couple wind speed changes with structural responses for analysis, effectively identifying the evolution trend of potential risks. Finally, through a machine learning prediction algorithm, the safety boundary is deduced by combining historical data with real-time status, achieving a transition from passive response to active prevention. This not only reduces the lag and subjectivity of manual inspections, but also provides a scientific basis for construction decision-making under complex meteorological conditions, significantly improving the accuracy of determining the safety of hanging basket detection data.

[0056] The above embodiment introduces a bridge basket construction safety monitoring method from the perspective of method flow, and the following embodiment introduces a bridge basket construction safety monitoring device from the perspective of virtual module or virtual unit. For details, please refer to the following embodiment.

[0057] See also Figure 2 The bridge hanging basket construction safety monitoring device 20 may specifically include: an acquisition module 201, a determination module 202, a prediction module 203 and a generation module 204, wherein: A bridge hanging basket construction safety monitoring device 20, comprising: Acquisition module 201 is used to obtain current detection data and current wind speed data corresponding to the current hanging basket. The current detection data includes the stress, inclination and displacement corresponding to the current hanging basket at the current moment. The current wind speed data is the wind speed corresponding to the current cycle. The current cycle starts at the current moment. Determination module 202, for determining the degree of influence of current wind speed data on current detection data; Prediction module 203, for predicting the safety status of the current hanging basket in the current cycle based on the current detection data, the impact degree and the current wind speed data, and the safety status is safe or unsafe; The generating module 204 is configured to determine whether to generate security alert information based on the security situation.

[0058] In one possible implementation of the embodiment of the present application, when determining the degree of influence of the current wind speed data on the current detection data, the determination module 202 is specifically configured to: Obtain the historical data of the current hanging basket, including the historical detection data corresponding to the historical detection time and the historical wind speed data; Perform time domain and frequency domain analysis on the historical data to obtain the time domain relationship between each detection sub-data and wind speed data, as well as the frequency domain eigenvector matrix corresponding to the historical data. The detection sub-data is one of stress, inclination, and displacement. Based on the time domain relationship and the frequency domain eigenvector matrix, the degree of influence of the current wind speed data on the current detection data is determined.

[0059] In one possible implementation of the embodiment of the present application, when the determination module 202 performs time domain analysis and frequency domain analysis on the historical data to obtain the time domain relationship between each detection sub-data and the wind speed data and the frequency domain eigenvector matrix corresponding to the historical data, it is specifically configured to: For each detection sub-data, obtain the historical pouring stage, historical detection sub-data and historical wind speed data corresponding to each historical detection moment; determine the historical basic sub-data corresponding to the historical pouring stage; calculate the historical detection difference between the historical basic sub-data and the corresponding historical detection sub-data corresponding to each historical detection moment, and based on the historical detection difference and the corresponding historical wind speed data corresponding to each historical detection moment, perform time domain analysis on the historical detection difference and the corresponding historical wind speed data to obtain a time domain function relationship with the historical inspection moment as the horizontal axis and the historical ratio as the vertical axis, where the historical ratio is the ratio between the historical detection difference and the corresponding historical wind speed data; Frequency domain analysis is performed on each historical sub-data and historical wind speed data in the historical data to obtain the frequency domain eigenvectors corresponding to each historical sub-data and historical wind speed data, and the frequency eigenvectors are integrated to obtain the frequency domain eigenvector matrix corresponding to the historical data.

[0060] In one possible implementation of the embodiment of the present application, when determining the degree of influence of the current wind speed data on the current detection data based on the time domain relationship and the frequency domain eigenvector matrix, the determination module 202 is specifically configured to: Determining the influence sub-degree of the current wind speed data on each detection sub-data based on the time domain relationship, and determining a first influence degree of the current wind speed data on the current detection data based on each influencing sub-data; Inputting the frequency domain eigenvector matrix into the recurrent network model and obtaining the second influence degree output by the recurrent network model; Based on the first influence degree and the second influence degree, the influence degree of the current wind speed data on the current detection data is determined.

[0061] In one possible implementation of the embodiment of the present application, the prediction module 203 is specifically configured to: Obtain the detection threshold corresponding to the current detection data, and determine whether the current detection data is within the corresponding detection threshold; If the current detection data is not within the corresponding detection threshold, the safety condition of the current hanging basket in the current cycle is predicted to be unsafe; If the current detection data is within the corresponding detection threshold, the expected detection data of the current hanging basket in the current period is determined based on the impact degree and the current wind speed data, and the safety situation of the current hanging basket in the current period is predicted based on the expected detection data.

[0062] In one possible implementation of the embodiment of the present application, the prediction module 203 is specifically configured to: Get the initial BIM model corresponding to the current hanging basket, and get the current pouring stage corresponding to the current hanging basket. The current pouring stage is the pouring stage of the current hanging basket in the current cycle. Input the current pouring stage into the initial BIM model to obtain the target BIM model; The impact degree, current detection data and current wind speed data are input into the target BIM model, and the detection data displayed by the target BIM model is obtained to obtain the expected detection data of the current hanging basket in the current cycle.

[0063] In one possible implementation of the embodiment of the present application, when the generation module 204 determines whether to generate security alert information based on the security situation, it is specifically configured to: If the security situation is safe, no security alert message is generated; If the safety situation is unsafe, a safety alert message is generated and the target BIM model marked with expected detection data is displayed.

[0064] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0065] See also Figure 3 , the embodiment of the present application also introduces an electronic device from the perspective of a physical device, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that in actual applications, the number of transceivers 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0066] Processor 301 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0067] Bus 302 may include a path for transmitting information between the above components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0068] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0069] The memory 303 is used to store application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0070] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and can also be servers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0071] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0072] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0073] The above are only some of the implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for monitoring the safety of bridge hanging basket construction, characterized in that: include: Obtaining current detection data and current wind speed data corresponding to the current hanging basket, wherein the current detection data includes stress, inclination, and displacement corresponding to the current hanging basket at the current moment, and the current wind speed data is the wind speed corresponding to the current cycle, wherein the current cycle starts at the current moment; Determining the degree of influence of the current wind speed data on the current detection data; Based on the current detection data, the impact degree and the current wind speed data, predict the safety situation of the current hanging basket in the current cycle, the safety situation being safe or unsafe; Based on the security situation, it is determined whether to generate security alert information.

2. The bridge hanging basket construction safety monitoring method according to claim 1 is characterized in that: Determining the degree of influence of the current wind speed data on the current detection data includes: Acquire historical data of the current hanging basket, the historical data including historical detection data corresponding to historical detection moments and historical wind speed data; Performing time domain analysis and frequency domain analysis on the historical data to obtain a time domain relationship between each detection sub-data and the wind speed data and a frequency domain eigenvector matrix corresponding to the historical data, wherein the detection sub-data is one of stress, inclination angle, and displacement; Based on the time domain relationship and the frequency domain eigenvector matrix, the degree of influence of the current wind speed data on the current detection data is determined.

3. The bridge hanging basket construction safety monitoring method according to claim 2 is characterized in that: Performing time domain analysis and frequency domain analysis on the historical data to obtain the time domain relationship between each detection sub-data and the wind speed data and the frequency domain eigenvector matrix corresponding to the historical data, including: For each detection sub-data, the historical pouring stage, historical detection sub-data, and historical wind speed data corresponding to each historical detection moment are obtained; the historical basic sub-data corresponding to the historical pouring stage are determined; the historical detection difference between the historical basic sub-data corresponding to each historical detection moment and the corresponding historical detection sub-data is calculated, and based on the historical detection difference and the corresponding historical wind speed data corresponding to each historical detection moment, a time domain analysis is performed on the historical detection difference and the corresponding historical wind speed data to obtain a time domain function relationship with the historical detection moment as the horizontal axis and the historical ratio as the vertical axis, where the historical ratio is the ratio between the historical detection difference and the corresponding historical wind speed data; Frequency domain analysis is performed on each historical sub-data and historical wind speed data in the historical data to obtain frequency domain eigenvectors corresponding to each historical sub-data and the historical wind speed data, and the frequency eigenvectors are integrated to obtain a frequency domain eigenvector matrix corresponding to the historical data.

4. The bridge hanging basket construction safety monitoring method according to claim 3 is characterized in that: The determining, based on the time domain relationship and the frequency domain eigenvector matrix, the degree of influence of the current wind speed data on the current detection data includes: Determining, based on the time domain relationship, an influence sub-degree of the current wind speed data on each detection sub-data, and determining, based on each influencing sub-data, a first influence degree of the current wind speed data on the current detection data; Inputting the frequency domain eigenvector matrix into a recurrent network model, and obtaining a second influence degree output by the recurrent network model; Based on the first influence degree and the second influence degree, the influence degree of the current wind speed data on the current detection data is determined.

5. The bridge hanging basket construction safety monitoring method according to any one of claims 1 to 4, characterized in that: The predicting of the safety condition of the current hanging basket in the current cycle based on the current detection data, the impact degree, and the current wind speed data includes: Obtaining a detection threshold corresponding to the current detection data, and determining whether the current detection data is within the corresponding detection threshold; If the current detection data is not within the corresponding detection threshold, it is predicted that the safety condition of the current hanging basket in the current cycle is unsafe; If the current detection data is within the corresponding detection threshold, the expected detection data of the current hanging basket in the current period is determined based on the impact degree and the current wind speed data, and the safety situation of the current hanging basket in the current period is predicted based on the expected detection data.

6. The bridge hanging basket construction safety monitoring method according to claim 5 is characterized in that: The determining, based on the impact degree and the current wind speed data, expected detection data of the current hanging basket in the current cycle includes: Obtain the initial BIM model corresponding to the current hanging basket, and obtain the current pouring stage corresponding to the current hanging basket, where the current pouring stage is the pouring stage of the current hanging basket in the current cycle; Inputting the current pouring stage into the initial BIM model to obtain a target BIM model; The impact degree, the current detection data and the current wind speed data are input into the target BIM model, and the detection data displayed by the target BIM model is obtained to obtain the expected detection data of the current hanging basket in the current period.

7. The bridge hanging basket construction safety monitoring method according to claim 6 is characterized in that: The determining whether to generate security alert information based on the security situation includes: If the security situation is safe, no security alert information is generated; If the safety situation is unsafe, a safety alert message is generated and a target BIM model marked with expected detection data is displayed.

8. A bridge hanging basket construction safety monitoring device, characterized in that: include: An acquisition module is used to acquire current detection data and current wind speed data corresponding to the current hanging basket, wherein the current detection data includes stress, inclination and displacement corresponding to the current hanging basket at the current moment, and the current wind speed data is the wind speed corresponding to the current cycle, wherein the current cycle starts at the current moment; a determination module, configured to determine the degree of influence of the current wind speed data on the current detection data; a prediction module, configured to predict a safety condition of the current hanging basket in a current cycle based on the current detection data, the impact degree, and the current wind speed data, wherein the safety condition is safe or unsafe; A generating module is used to determine whether to generate security alert information based on the security situation.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the bridge hanging basket construction safety monitoring method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the bridge hanging basket construction safety monitoring method according to any one of claims 1 to 7.

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