Intelligent deck bridge fabrication machine cluster management system based on Internet of Things

Abstract

The invention relates to the technical field of intelligent management, in particular to an intelligent deck bridge fabrication machine cluster management system based on the Internet of Things, which comprises a data acquisition module, a feature alignment module, a multi-source data analysis module, a coupling state determination module and a cluster management module. Calculating a space shielding coefficient based on a preset track of a bridge, generating a time delay compensation coefficient according to a time difference of receiving a control instruction by each bridge fabrication machine so as to calculate a dynamic collaborative deviation characteristic value, determining an abnormal bridge fabrication tendency in a bridge fabrication stage, positioning a corresponding bridge fabrication machine, determining an environmental influence characteristic value based on the environmental data, and calculating a construction management characterization value in combination with the dynamic collaborative deviation characteristic value to divide the construction state of the construction section, and sending an instruction based on the construction management characterization value to correct the control instruction data. By performing multi-source data analysis on bridge construction, the abnormal bridge fabrication machine is determined, the control data is adjusted, and the construction efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent management technology, and in particular to an intelligent bridge-building machine cluster management system based on the Internet of Things. Background Technology

[0002] With the continuous development of my country's economic construction and the constant progress of science and technology, the modern high-speed rail system has also made rapid progress in step with the nation's development. The continuous completion of various high-speed railway projects has led to the increasingly sophisticated construction technology of cantilever continuous beams. The construction technology of cantilever continuous beams for bridges is both a key focus and a challenge in bridge construction. The construction method of the upper-bearing bridge-building machine for high-speed railway continuous beams has significant advantages over rhomboid and triangular hanging baskets, forming a construction method that integrates safety, efficiency, and intelligence.

[0003] Chinese Patent Publication No. CN119358799A discloses a construction management method and apparatus for an upward-moving bridge-building machine. The method includes: after assembling and initializing the upward-moving bridge-building machine, establishing a twin model using the machine and the application scenario; optimizing the bridge hoisting path based on the twin model; establishing control constraints, using the calibrated path as the optimization target, and establishing first control parameters; collecting real-time data using wire rope tension sensors, gyroscopes, acceleration sensors, and displacement sensors installed on the machine; establishing second control parameters; and compensating the first control parameters with the second control parameters before controlling the machine to perform the bridge hoisting task. This application solves the technical problems of low hoisting accuracy and poor stability caused by the coarse path planning and the inability of control parameters to adapt to complex environmental changes in existing upward-moving bridge-building machines.

[0004] Chinese Patent Publication No. CN120912149A discloses an AI-based information management system for the entire construction cycle of a bridge-building machine. The system includes a bridge-building machine full-domain perception subsystem for collecting construction parameters during the bridge-building machine's operation; an edge processing subsystem for edge processing the construction parameters collected by the full-domain perception subsystem; a digital twin subsystem for establishing a digital twin model simulating the entire construction cycle of the bridge-building machine and synchronously mapping the edge-processed construction parameters to the digital twin model; and a safety monitoring subsystem for monitoring the operational safety of the bridge-building machine based on the digital twin model. This invention addresses the problem of weak monitoring and analysis capabilities for the construction process of cantilever bridge-building machines in existing technologies, which is detrimental to operational safety. It aims to achieve information-based management of the bridge-building machine's construction process and improve the safety and scientific rigor of continuous beam construction.

[0005] However, the following problems still exist in the existing technology.

[0006] When existing bridge-building machines are used in multi-machine cluster construction on interchange ramps, the equipment is densely packed, and the robotic arms and formwork are prone to interference. The ramp structure obstructs the monitoring blind spots, and there are significant delays in wireless transmission. The information of multi-machine collaboration is asynchronous. When encountering sudden environmental events such as strong winds and heavy fog, the above risks are amplified, which can easily lead to collision misjudgments, forcing construction to be less efficient or even interrupted. Summary of the Invention

[0007] To address these issues, this invention provides an IoT-based intelligent upper-bearing bridge-building machine cluster management system. This system aims to solve the problems of existing upper-bearing bridge-building machines operating in multi-machine clusters at interchange ramps, where the dense equipment leads to interference between the robotic arms and the formwork; ramp structure obstructions cause monitoring blind spots; wireless transmission suffers from significant delays; and multi-machine collaborative information is asynchronous. Furthermore, these risks are amplified in the event of sudden environmental events such as strong winds or heavy fog, which can easily lead to collision misjudgments and force reduced construction efficiency or even interruption.

[0008] To achieve the above objectives, the present invention provides an intelligent upper-bearing bridge-building machine cluster management system based on the Internet of Things, comprising:

[0009] The data acquisition module acquires construction data, control command data, and environmental data of several bridge-building machines within the target area. The construction data includes the real-time location of the bridge-building machine and the preset trajectory of the bridge. The environmental data includes wind speed, vibration data, and temperature difference.

[0010] The feature alignment module is connected to the data acquisition module. It analyzes the construction data to determine the dynamic distance between each of the bridge-building machines, calculates the spatial interference coefficient, determines the occlusion area based on the bridge's preset trajectory, calculates the spatial occlusion coefficient, and generates a delay compensation coefficient based on the time difference between the timestamp of the control command received by each bridge-building machine and the actual execution timestamp.

[0011] A multi-source data analysis module, which is connected to the feature alignment module, calculates dynamic collaborative deviation feature values ​​based on the spatial interference coefficient, the spatial occlusion coefficient, and the time delay compensation coefficient, in order to determine abnormal bridge-building tendencies during the bridge-building stage and locate the corresponding bridge-building machine.

[0012] The coupling state determination module is connected to the data acquisition module and the multi-source data analysis module. In response to abnormal bridge-building tendency, it determines the environmental impact characteristic value based on the environmental data, and calculates the construction management characterization value in combination with the dynamic collaborative deviation characteristic value to divide the construction state of the construction section.

[0013] The cluster management module, which is connected to the multi-source data analysis module and the coupling state determination module, is used to send correction control command data to the corresponding bridge building machine based on the construction management characterization value.

[0014] Furthermore, the feature alignment module calculates the spatial interference coefficients, including:

[0015] Used to place each of the bridge-building machines in the same coordinate system and determine the coordinates of each of the bridge-building machines;

[0016] This is used to calculate the distance between each of the coordinate points and obtain the average value;

[0017] The average value is determined to be the spatial interference coefficient.

[0018] Further, the feature alignment module calculates the spatial occlusion coefficient, including,

[0019] Used to extract the coordinates of the connection node between the current construction segment and the next construction segment based on the bridge's preset trajectory;

[0020] Based on the spatial relationship between the real-time position of each bridge-building machine and the coordinates of the nodes, the occlusion area caused by the main structure of each bridge-building machine to the measurement process is simulated.

[0021] The spatial shading coefficient is used to determine the ratio of the area of ​​the shading area to the area of ​​the construction area.

[0022] Furthermore, the feature alignment module generates delay compensation coefficients, including:

[0023] Used to determine the first timestamp of each bridge-building machine receiving control commands and the second timestamp of actually starting to execute commands;

[0024] The absolute value of the difference between the second timestamp and the first timestamp is used to calculate the original time delay;

[0025] The ratio of the original delay to the reference delay is used to determine the delay compensation coefficient.

[0026] Furthermore, the multi-source data analysis module calculates dynamic collaborative deviation characteristic values, including:

[0027] The spatial interference factor is used to determine the ratio of the spatial interference coefficient to the reference spatial interference coefficient.

[0028] The spatial occlusion factor is used to determine the ratio of the spatial occlusion coefficient to the reference spatial occlusion coefficient.

[0029] The time delay compensation factor is used to determine the ratio of the time delay compensation coefficient to the reference time delay compensation coefficient.

[0030] The weighted sum of the spatial interference factor, the spatial occlusion factor, and the time delay compensation factor is used to determine the dynamic cooperative deviation characteristic value.

[0031] Furthermore, the multi-source data analysis module determines abnormal bridge-building tendencies during the bridge-building phase and locates the corresponding bridge-building machine, including:

[0032] If the dynamic coordination deviation characteristic value is greater than the dynamic coordination deviation characteristic value threshold, then the abnormal bridge-building tendency in the bridge-building stage is determined to be a strong abnormal tendency, and the corresponding bridge-building machine is located.

[0033] If the dynamic coordination deviation characteristic value is less than or equal to the dynamic coordination deviation characteristic value threshold, then the abnormal bridge-building tendency in the bridge-building stage is determined to be a weak abnormal tendency.

[0034] Furthermore, the cluster management module determines environmental impact characteristic values, including:

[0035] The ratio of the wind speed to the reference operating wind speed is used as the wind speed influence factor;

[0036] The spectral characteristics of the vibration data are used to determine the resonance influence factor by comparing it with the natural frequency of the bridge-building machine.

[0037] The temperature influence factor is used to determine the ratio of the absolute value of the temperature difference to the reference operating temperature.

[0038] The average value of the sum of the wind speed influence factor, the resonance influence factor, and the temperature influence factor after normalization is used to determine the environmental influence characteristic value.

[0039] Furthermore, the coupling state determination module calculates construction management characterization values, including:

[0040] The first construction management factor is used to determine the ratio of the dynamic coordination deviation characteristic value to the benchmark dynamic coordination deviation characteristic value.

[0041] The ratio of the environmental impact characteristic value to the benchmark environmental impact characteristic value is used to determine the second construction management factor;

[0042] This is used to determine the weighted sum of the first construction management factor and the second construction management factor as the construction management characterization value.

[0043] Furthermore, the coupling state determination module divides the construction segments into construction states, wherein,

[0044] If the construction management characterization value is greater than the construction management characterization value threshold, then the construction status of the divided construction segment is abnormal construction.

[0045] If the construction management characterization value is less than or equal to the construction management characterization value threshold, then the construction status of the divided construction segment is normal construction.

[0046] Furthermore, the cluster management module adjusts the control command data, including,

[0047] In response to abnormal construction conditions, the adjustment range of the bridge-building machine's operating speed is positively correlated with the construction management characterization value.

[0048] Compared with existing technologies, this invention sets up a data acquisition module, a feature alignment module, a multi-source data analysis module, a coupling state determination module, and a cluster management module. It calculates spatial interference coefficients by analyzing construction data, spatial occlusion coefficients based on the bridge's preset trajectory, and generates delay compensation coefficients based on the time difference in the control commands received by each bridge-building machine. This allows for the calculation of dynamic coordination deviation characteristic values ​​to identify abnormal bridge-building tendencies during the bridge-building phase, locate the corresponding bridge-building machine, determine environmental impact characteristic values ​​based on the environmental data, and calculate construction management characterization values ​​in conjunction with the dynamic coordination deviation characteristic values. These values ​​are used to classify the construction status of construction sections, and commands are sent based on the construction management characterization values ​​to correct the control command data. This invention improves construction efficiency by performing multi-source data analysis on bridge construction, identifying abnormal bridge-building machines, and adjusting the control data of the bridge-building machines.

[0049] In particular, by calculating the spatial interference coefficient, spatial occlusion coefficient, and time delay compensation coefficient, the probability of construction anomalies caused by the spatial distribution, structural occlusion, and response delay of bridge-building machines in cluster operations is quantified. In reality, when using bridge-building machines for bridge construction, especially in multi-machine cluster collaborative operations on complex alignments such as interchange ramps, the factors leading to construction deviations or operational anomalies are often multi-source and coupled. Individually considering each influencing factor is insufficient to fully reflect the true state of cluster operations. For example, the spatial interference coefficient focuses on describing the geometric positional relationship between bridge-building machines but cannot quantify the probability of measurement anomalies caused by structural occlusion. While spatial obstruction coefficients can characterize the connectivity of measurement links in the event of blind spots or signal interruptions, they neglect timing asynchrony issues caused by signal transmission or mechanical response delays. Although time delay compensation coefficients can reflect the response speed of the control system, they lack correlation with the current relative position of the bridge-building machine and the line-of-sight environment. Based on this, this invention considers a comprehensive analysis of spatial interference coefficients, spatial obstruction coefficients, and time delay compensation coefficients to provide a data foundation for subsequent calculation of dynamic coordination deviation characteristic values. This accurately maps the coordination misalignment of multi-machine systems in complex environments, thereby improving operational efficiency while effectively ensuring the safety and synchronization accuracy of cluster construction.

[0050] In particular, by calculating the dynamic coordination deviation characteristic value through spatial interference coefficient, spatial occlusion coefficient, and time delay compensation coefficient, the three heterogeneous information types of spatial position relationship, measurement link status, and system response delay are integrated into a unified quantitative index. This characteristic value not only reflects the coordination failure of multiple bridge-building machines at the current moment, but also reveals the potential risks caused by the coupling and superposition of spatial, perception, and temporal factors. For example, when two bridge-building machines are at a moderate spatial distance but are in each other's signal blind zone and have a large control delay, even if each individual coefficient does not exceed the limit, their dynamic coordination deviation characteristic value may still increase significantly due to the coupling of multiple factors, thereby triggering an early warning and causing construction anomalies. Based on this, the present invention considers the comprehensive consideration of three factors to provide an accurate and comprehensive data foundation for subsequent identification of abnormal construction tendencies, calculation of construction management characterization values, and generation of corrective control commands, so as to improve the bridge-building speed and accuracy.

[0051] In particular, regarding abnormal bridge-building tendencies, by analyzing coupled environmental factors and dynamic coordination deviation characteristic values, construction management characterization values ​​are calculated to characterize the further abnormal impact of environmental factors on bridge construction under abnormal bridge-building tendencies. In reality, abnormal construction states are often not triggered by a single isolated factor, but are the result of the superposition and coupling of internal system deviations and external environmental disturbances. For example, when constructing high piers in strong winds, if the dynamic coordination deviation characteristic value shows a slight exceedance but does not have a substantial impact on construction, it is still within a controllable range from the perspective of coordination deviation alone. However, if the wind speed continues to rise, even if the coordination state of the bridge-building machine itself does not deteriorate further, environmental factors will have a significant impact on bridge construction. The aggravating effects of environmental factors can significantly increase the overall construction risk. In this case, focusing only on the dynamic coordination deviation characteristic value may lead to the underreporting of potential hazards. However, if environmental factors are taken into account, the construction management characterization value will increase accordingly, accurately reflecting the superimposed impact of environmental deterioration on abnormal states, thereby triggering early warnings or interventions in a timely manner. Based on this, this invention comprehensively analyzes and integrates the above-mentioned multi-source environmental factors and dynamic coordination deviation characteristic values. Through the overall assessment of environmental factors and combined with the existing degree of internal coordination deviation, the construction management characterization value is finally calculated, providing a data basis for the subsequent transmission of corrective control commands, thereby improving the bridge construction speed and accuracy. Attached Figure Description

[0052] Figure 1 A schematic diagram of the structure of the IoT-based intelligent bridge-building machine cluster management system, which is an embodiment of the invention.

[0053] Figure 2 A logic block diagram for determining abnormal bridge-building tendencies during the bridge-building stage, as shown in the embodiment of the invention.

[0054] Figure 3This is a logic block diagram illustrating the construction state of dividing construction sections according to an embodiment of the invention. Detailed Implementation

[0055] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0056] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0057] It should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0058] Please see Figure 1 , Figure 1 This is a schematic diagram of the IoT-based intelligent upper-bearing bridge-building machine cluster management system according to an embodiment of the invention. The IoT-based intelligent upper-bearing bridge-building machine cluster management system according to an embodiment of the invention includes:

[0059] The data acquisition module acquires construction data, control command data, and environmental data of several bridge-building machines within the target area. The construction data includes the real-time location of the bridge-building machine and the preset trajectory of the bridge. The environmental data includes wind speed, vibration data, and temperature difference.

[0060] The feature alignment module is connected to the data acquisition module. It analyzes the construction data to determine the dynamic distance between each of the bridge-building machines, calculates the spatial interference coefficient, determines the occlusion area based on the bridge's preset trajectory, calculates the spatial occlusion coefficient, and generates a delay compensation coefficient based on the time difference between the timestamp of the control command received by each bridge-building machine and the actual execution timestamp.

[0061] A multi-source data analysis module, which is connected to the feature alignment module, calculates dynamic collaborative deviation feature values ​​based on the spatial interference coefficient, the spatial occlusion coefficient, and the time delay compensation coefficient, in order to determine abnormal bridge-building tendencies during the bridge-building stage and locate the corresponding bridge-building machine.

[0062] The coupling state determination module is connected to the data acquisition module and the multi-source data analysis module. In response to abnormal bridge-building tendency, it determines the environmental impact characteristic value based on the environmental data, and calculates the construction management characterization value in combination with the dynamic collaborative deviation characteristic value to divide the construction state of the construction section.

[0063] The cluster management module, which is connected to the multi-source data analysis module and the coupling state determination module, is used to send correction control command data to the corresponding bridge building machine based on the construction management characterization value.

[0064] Specifically, there are no restrictions on the method of acquiring construction data. It can be obtained in real time through GPS modules, total stations, laser rangefinders, encoders, or vision recognition systems deployed at key nodes of the bridge-building machine (such as mobile outriggers, cranes, and main beam nodes). Alternatively, the position data and preset trajectory parameters in the internal registers can be directly read by communicating with the electrical control system of the bridge-building machine. Of course, those skilled in the art can also obtain the required data through other methods, which will not be elaborated here.

[0065] Specifically, there are no restrictions on the method of acquiring control command data. It can be achieved by monitoring or mirroring the industrial Ethernet, CAN bus, or wireless communication link between the cluster control center and the bridge-building machine, and capturing the original command data packets issued by the host computer through protocol parsing. Alternatively, historical command records can be read directly from the database or log file of the construction management system through interface calls. Of course, those skilled in the art can also obtain the required data through other methods, which will not be elaborated here.

[0066] Specifically, there are no restrictions on the method of acquiring environmental data. Real-time sensing can be achieved by relying on meteorological sensors, vibration accelerometers, and temperature sensors deployed on bridge piers, erected beams, or the bridge-building machine itself. Alternatively, synchronous environmental data can be obtained from the environmental monitoring system or calibration station already deployed at the construction site via a wireless network. Of course, those skilled in the art can also obtain the required data through other means, which will not be elaborated here.

[0067] It is understandable that spatial interference, spatial obstruction, and time delay are all causes of "operational delays" or "operational conflicts," and it is reasonable to integrate them into a comprehensive risk value.

[0068] Specifically, the feature alignment module calculates the spatial interference coefficients, including,

[0069] Used to place each of the bridge-building machines in the same coordinate system and determine the coordinates of each of the bridge-building machines;

[0070] This is used to calculate the distance between each of the coordinate points and obtain the average value;

[0071] The average value is determined to be the spatial interference coefficient.

[0072] Specifically, the larger the spatial interference coefficient, the more dispersed the point sets of each bridge-building machine, that is, the greater the overall distance between bridge-building machines, the higher the degree of dispersion of the corresponding spatial distribution, and the greater the probability of coordination deviation between the bridge-building machine and the construction.

[0073] Specifically, the feature alignment module calculates the spatial occlusion coefficient, including...

[0074] Used to extract the coordinates of the connection node between the current construction segment and the next construction segment based on the bridge's preset trajectory;

[0075] Based on the spatial relationship between the real-time position of each bridge-building machine and the coordinates of the nodes, the occlusion area caused by the main structure of each bridge-building machine to the measurement process is simulated.

[0076] The spatial shading coefficient is used to determine the ratio of the area of ​​the shading area to the area of ​​the construction area.

[0077] Specifically, the spatial occlusion coefficient is used to quantify the severity of occlusion caused by the main structure of the bridge-building machine to measurement signals (such as GPS, laser ranging, visual observation, etc.). The larger the coefficient, the more severe the occlusion, which may lead to a larger measurement blind zone, causing a coordination deviation between the bridge-building machine and the construction, affecting the construction accuracy or safety.

[0078] Specifically, no limitation is made to the simulation method. In practice, the occlusion area of ​​the bridge-building machine's main structure on the measurement can be simulated by ray tracing, geometric projection, occlusion analysis based on three-dimensional models, or point cloud data occlusion detection. Of course, those skilled in the art can also use other methods, as long as the required data can be obtained. This will not be elaborated further.

[0079] Specifically, the feature alignment module generates delay compensation coefficients, including:

[0080] Used to determine the first timestamp of each bridge-building machine receiving control commands and the second timestamp of actually starting to execute commands;

[0081] The absolute value of the difference between the second timestamp and the first timestamp is used to calculate the original time delay;

[0082] The ratio of the original delay to the reference delay is used to determine the delay compensation coefficient.

[0083] Specifically, there are no restrictions on the method of obtaining the second timestamp. For example, in implementation, the timestamp can be collected and determined by a high-definition camera set in the execution instruction area. Any reasonable method will suffice, and will not be elaborated further here.

[0084] Specifically, the baseline delay is calculated in advance by acquiring delay data from several normal construction operations without dynamic adjustments, and determining the average of each delay data as the baseline delay.

[0085] Specifically, the time delay compensation coefficient represents the total lag time between the issuance of the command and the execution of the action. The larger the time delay compensation coefficient, the longer the reaction time, that is, the slower the response speed, and the greater the probability of coordination deviation between the bridge building machine and the construction.

[0086] Specifically, by calculating the spatial interference coefficient, spatial occlusion coefficient, and time delay compensation coefficient, the probability of construction anomalies caused by the spatial distribution, structural occlusion, and response delay of bridge-building machines in cluster operations is quantified. In reality, when using bridge-building machines for bridge construction, especially in multi-machine cluster collaborative operations on complex alignments such as interchange ramps, the factors leading to construction deviations or operational anomalies are often multi-source and coupled. Individually considering each influencing factor is insufficient to fully reflect the true state of cluster operations. For example, the spatial interference coefficient focuses on describing the geometric positional relationship between bridge-building machines but cannot quantify the probability of construction anomalies caused by structural occlusion. Measurement blind spots or signal interruptions can be addressed by spatial occlusion coefficients, which characterize the connectivity of measurement links but neglect timing asynchrony issues caused by signal transmission or mechanical response delays. While time delay compensation coefficients reflect the response speed of the control system, they lack correlation with the current relative position of the bridge-building machine and the line-of-sight environment. Therefore, this invention considers a comprehensive analysis of spatial interference coefficients, spatial occlusion coefficients, and time delay compensation coefficients to provide a data foundation for subsequent calculation of dynamic coordination deviation characteristics. This accurately maps the coordination misalignment of multi-machine systems in complex environments, thereby improving operational efficiency while effectively ensuring the safety and synchronization accuracy of cluster construction.

[0087] Specifically, the multi-source data analysis module calculates dynamic collaborative deviation characteristic values, including,

[0088] The spatial interference factor is used to determine the ratio of the spatial interference coefficient to the reference spatial interference coefficient.

[0089] The spatial occlusion factor is used to determine the ratio of the spatial occlusion coefficient to the reference spatial occlusion coefficient.

[0090] The time delay compensation factor is used to determine the ratio of the time delay compensation coefficient to the reference time delay compensation coefficient.

[0091] The weighted sum of the spatial interference factor, the spatial occlusion factor, and the time delay compensation factor is used to determine the dynamic cooperative deviation characteristic value.

[0092] Specifically, the reference spatial interference coefficient is calculated in advance. Several historical spatial interference coefficients corresponding to the completion of construction tasks and the adjustment of construction are obtained in advance, and the mean of each historical spatial interference coefficient is determined as the reference spatial interference coefficient.

[0093] Specifically, the baseline spatial occlusion coefficient is calculated in advance. Several historical spatial occlusion coefficients corresponding to the completion of construction tasks and the adjustment of construction are obtained in advance, and the average value of each historical spatial occlusion coefficient is determined as the baseline spatial occlusion coefficient.

[0094] Specifically, the benchmark time delay compensation coefficient is calculated in advance. Several historical time delay compensation coefficients corresponding to the completion of construction tasks and the adjustment of construction are obtained in advance, and the average of each historical time delay compensation coefficient is determined as the benchmark time delay compensation coefficient.

[0095] Specifically, the sum of the weight coefficients of the spatial interference factor, the spatial occlusion factor, and the time delay compensation factor is 1. When configuring the weights, considering that time delay can directly reflect the coordination deviation of the bridge construction state, the weight coefficients of the spatial interference factor and the spatial occlusion factor are both set to 0.3, and the weight coefficient of the time delay compensation factor is set to 0.4.

[0096] Understandably, in actual calculations, in order to ensure the uniformity of data dimensions and the operability of the data, the spatial interference factor, spatial occlusion factor and time delay compensation factor are normalized to map them to the same numerical range (e.g., [0,1]), thereby eliminating the scale differences caused by the different original physical meanings of each factor, and ensuring that the dynamic cooperative deviation characteristic value obtained by subsequent weighted summation can objectively reflect the degree of comprehensive deviation.

[0097] Specifically, the dynamic coordination deviation characteristic value is calculated by using spatial interference coefficient, spatial occlusion coefficient, and time delay compensation coefficient. This integrates three types of heterogeneous information—spatial position relationship, measurement link status, and system response delay—into a unified quantitative index. This characteristic value not only reflects the coordination failure of multiple bridge-building machines at the current moment, but also reveals the potential risks caused by the coupling and superposition of spatial, sensing, and temporal factors. For example, when two bridge-building machines are at a moderate spatial distance but are in each other's signal blind zones and have significant control delays, even if none of the individual coefficients exceed the limits, their dynamic coordination deviation characteristic value may still increase significantly due to the coupling of multiple factors, thereby triggering an early warning and causing construction anomalies. Based on this, the present invention considers the comprehensive consideration of these three factors to provide an accurate and comprehensive data foundation for subsequent identification of abnormal construction tendencies, calculation of construction management characterization values, and generation of corrective control commands, thereby improving the bridge-building speed and accuracy.

[0098] Please see Figure 2 , Figure 2This is a logic block diagram illustrating the determination of abnormal bridge-building tendencies during the bridge-building stage, as per an embodiment of the invention. Specifically, the multi-source data analysis module determines abnormal bridge-building tendencies during the bridge-building stage and locates the corresponding bridge-building machine, including...

[0099] If the dynamic coordination deviation characteristic value is greater than the dynamic coordination deviation characteristic value threshold, then the abnormal bridge-building tendency in the bridge-building stage is determined to be a strong abnormal tendency, and the corresponding bridge-building machine is located.

[0100] If the dynamic coordination deviation characteristic value is less than or equal to the dynamic coordination deviation characteristic value threshold, then the abnormal bridge-building tendency in the bridge-building stage is determined to be a weak abnormal tendency.

[0101] Specifically, the dynamic collaborative deviation characteristic value threshold characterizes the boundary value of construction deviation of the bridge building machine. It is calculated in advance by obtaining the historical dynamic collaborative deviation characteristic values ​​corresponding to several completed construction tasks and construction adjustments. The product of the mean of each historical dynamic collaborative deviation characteristic value and the deviation accuracy is determined as the dynamic collaborative deviation characteristic value threshold. The deviation accuracy is selected in the range [0.8, 1.0]. In implementation, considering construction risks and accuracy, the deviation accuracy is determined to be 0.9.

[0102] Specifically, the steps for locating the corresponding bridge-building machine are as follows.

[0103] The abnormal bridge-building tendency in response to the bridge-building stage is a strong abnormal tendency. The spatial interference coefficient, spatial occlusion coefficient and time delay compensation coefficient of all bridge-building machines are traversed.

[0104] Calculate the deviation of each coefficient from the corresponding benchmark value; determine the bridge-building machine whose sum of deviation values ​​is greater than the preset deviation value;

[0105] The preset deviation value is obtained by pre-traversing the system. The maximum value of the spatial interference coefficient, spatial occlusion coefficient, and time delay compensation coefficient corresponding to the completion of the construction task and the construction adjustment is obtained in advance. The sum of the deviations between each maximum value and the corresponding reference value is determined as the preset deviation value.

[0106] Specifically, the ratio of the wind speed to the reference operating wind speed is used as the wind speed influence factor;

[0107] The spectral characteristics of the vibration data are used to determine the resonance influence factor by comparing it with the natural frequency of the bridge-building machine.

[0108] The temperature influence factor is used to determine the ratio of the absolute value of the temperature difference to the reference operating temperature.

[0109] The average value of the sum of the wind speed influence factor, the resonance influence factor, and the temperature influence factor after normalization is used to determine the environmental influence characteristic value.

[0110] Specifically, the benchmark operating wind speed is calculated in advance by obtaining historical wind speeds under normal construction conditions several times in advance, and determining the average of each historical wind speed as the benchmark operating wind speed.

[0111] Specifically, there are no restrictions on how to obtain the inherent frequency of the bridge-building machine. For example, it can be obtained through the bridge-building machine's operation manual or determined through historical operating frequencies, which will not be elaborated further.

[0112] Specifically, the benchmark operating temperature is calculated in advance by obtaining historical temperatures under several normal construction conditions and determining the average of these historical temperatures as the benchmark operating temperature.

[0113] Specifically, averaging the wind speed influence factor, resonance influence factor, and temperature influence factor is to comprehensively assess the overall impact of environmental factors and eliminate the randomness of a single indicator.

[0114] Specifically, the coupling state determination module calculates construction management characterization values, including:

[0115] The first construction management factor is used to determine the ratio of the dynamic coordination deviation characteristic value to the benchmark dynamic coordination deviation characteristic value.

[0116] The ratio of the environmental impact characteristic value to the benchmark environmental impact characteristic value is used to determine the second construction management factor;

[0117] This is used to determine the weighted sum of the first construction management factor and the second construction management factor as the construction management characterization value.

[0118] Specifically, the reference dynamic coordination deviation characteristic value is the dynamic coordination deviation characteristic value corresponding to the reference spatial interference coefficient, the reference spatial occlusion coefficient, and the reference time delay compensation coefficient.

[0119] Specifically, the baseline environmental impact characteristic value is calculated in advance. Several historical environmental impact characteristic values ​​under normal construction conditions are obtained in advance, and the average value of each historical environmental impact characteristic value is determined as the baseline environmental impact characteristic value.

[0120] Specifically, the sum of the weight coefficients of the first construction management factor and the second construction management factor is 1. When configuring the weights, considering the role of the environment in promoting construction deviations, the weight coefficient of the first construction management factor is set to 0.6 and the weight coefficient of the second construction management factor is set to 0.4.

[0121] Specifically, regarding abnormal bridge-building tendencies, this study analyzes coupled environmental factors and dynamic coordination deviation characteristic values ​​to calculate construction management characterization values. These values ​​characterize the further abnormal impact of environmental factors on bridge construction under abnormal bridge-building tendencies. In reality, abnormal construction states are often not triggered by a single isolated factor, but rather are the result of the superposition and coupling of internal system deviations and external environmental disturbances. For example, when constructing high piers in strong winds, if the dynamic coordination deviation characteristic value shows a slight exceedance but does not have a substantial impact on construction, it is still within a controllable range from the perspective of coordination deviation alone. However, if the wind speed continues to rise, even if the coordination state of the bridge-building machine itself does not deteriorate further, the environmental factors will further influence the construction. The exacerbating effects of various factors can significantly increase the overall construction risk. In this case, focusing only on the dynamic coordination deviation characteristic value may lead to the underreporting of potential hazards. However, if environmental factors are taken into account, the construction management characterization value will increase accordingly, accurately reflecting the cumulative impact of environmental deterioration on abnormal states, thereby triggering timely warnings or interventions. Based on this, this invention comprehensively analyzes and integrates the aforementioned multi-source environmental factors and dynamic coordination deviation characteristic values. Through an overall assessment of environmental factors and combined with the existing degree of internal coordination deviation, the construction management characterization value is finally calculated, providing a data basis for the subsequent issuance of corrective control commands to improve the bridge construction rate and accuracy.

[0122] Please see Figure 3 , Figure 3 This is a logic block diagram illustrating the construction state of dividing construction sections according to an embodiment of the invention. Specifically, the coupling state determination module divides the construction sections into construction states, wherein...

[0123] If the construction management characterization value is greater than the construction management characterization value threshold, then the construction status of the divided construction segment is abnormal construction.

[0124] If the construction management characterization value is less than or equal to the construction management characterization value threshold, then the construction status of the divided construction segment is normal construction.

[0125] Specifically, the construction management characterization value threshold represents the boundary of the data that needs to be sent for correction control instructions. It is calculated in advance. Several historical construction management characterization values ​​that enable normal construction through correction control instructions are obtained in advance. The product of the mean of each historical construction management characterization value and the adjustment precision is determined as the historical construction management characterization value threshold. The adjustment precision is selected in the range [0.8, 1.0]. In practice, in order to improve the effectiveness and efficiency of construction, the adjustment precision is determined to be 0.9.

[0126] Specifically, the cluster management module adjusts control command data, including...

[0127] In response to abnormal construction conditions, the adjustment range of the bridge-building machine's operating speed is positively correlated with the construction management characterization value.

[0128] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0129] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart bridge-building machine cluster management system based on the Internet of Things, characterized in that, include: The data acquisition module acquires construction data, control command data, and environmental data of several bridge-building machines within the target area. The construction data includes the real-time location of the bridge-building machine and the preset trajectory of the bridge. The environmental data includes wind speed, vibration data, and temperature difference. The feature alignment module is connected to the data acquisition module. It analyzes the construction data to determine the dynamic distance between each of the bridge-building machines, calculates the spatial interference coefficient, determines the occlusion area based on the bridge's preset trajectory, calculates the spatial occlusion coefficient, and generates a delay compensation coefficient based on the time difference between the timestamp of the control command received by each bridge-building machine and the actual execution timestamp. A multi-source data analysis module, which is connected to the feature alignment module, calculates dynamic collaborative deviation feature values ​​based on the spatial interference coefficient, the spatial occlusion coefficient, and the time delay compensation coefficient, in order to determine abnormal bridge-building tendencies during the bridge-building stage and locate the corresponding bridge-building machine. The coupling state determination module is connected to the data acquisition module and the multi-source data analysis module. In response to abnormal bridge-building tendency, it determines the environmental impact characteristic value based on the environmental data, and calculates the construction management characterization value in combination with the dynamic collaborative deviation characteristic value to divide the construction state of the construction section. The cluster management module, which is connected to the multi-source data analysis module and the coupling state determination module, is used to send correction control command data to the corresponding bridge building machine based on the construction management characterization value.

2. The IoT-based intelligent bridge-building machine cluster management system according to claim 1, characterized in that, The feature alignment module calculates the spatial interference coefficients, including: This is used to place each of the bridge-building machines in the same coordinate system and determine the geometric center of each of the bridge-building machines as the coordinate point; This is used to calculate the distance between each of the coordinate points and obtain the average value; The average value is determined to be the spatial interference coefficient.

3. The IoT-based intelligent bridge-building machine cluster management system according to claim 2, characterized in that, The feature alignment module calculates the spatial occlusion coefficient, including: Used to extract the coordinates of the connection node between the current construction segment and the next construction segment based on the bridge's preset trajectory; Based on the spatial relationship between the real-time position of each bridge-building machine and the coordinates of the nodes, the occlusion area caused by the main structure of each bridge-building machine to the measurement process is simulated. The spatial shading coefficient is used to determine the ratio of the area of ​​the shading area to the area of ​​the construction area.

4. The IoT-based intelligent bridge-building machine cluster management system according to claim 1, characterized in that, The feature alignment module generates delay compensation coefficients, including: Used to determine the first timestamp of each bridge-building machine receiving control commands and the second timestamp of actually starting to execute commands; The absolute value of the difference between the second timestamp and the first timestamp is used to calculate the original time delay; The ratio of the original delay to the reference delay is used to determine the delay compensation coefficient.

5. The IoT-based intelligent bridge-building machine cluster management system according to claim 1, characterized in that, The multi-source data analysis module calculates the dynamic collaborative deviation characteristic value. include, The spatial interference factor is used to determine the ratio of the spatial interference coefficient to the reference spatial interference coefficient. The spatial occlusion factor is used to determine the ratio of the spatial occlusion coefficient to the reference spatial occlusion coefficient. The time delay compensation factor is used to determine the ratio of the time delay compensation coefficient to the reference time delay compensation coefficient. The weighted sum of the spatial interference factor, the spatial occlusion factor, and the time delay compensation factor is used to determine the dynamic cooperative deviation characteristic value.

6. The IoT-based intelligent bridge-building machine cluster management system according to claim 1, characterized in that, The multi-source data analysis module determines abnormal bridge-building tendencies during the bridge-building phase and locates the corresponding bridge-building machine, including... If the dynamic coordination deviation characteristic value is greater than the dynamic coordination deviation characteristic value threshold, then the abnormal bridge-building tendency in the bridge-building stage is determined to be a strong abnormal tendency, and the corresponding bridge-building machine is located. If the dynamic coordination deviation characteristic value is less than or equal to the dynamic coordination deviation characteristic value threshold, then the abnormal bridge-building tendency in the bridge-building stage is determined to be a weak abnormal tendency.

7. The IoT-based intelligent bridge-building machine cluster management system according to claim 1, characterized in that, The cluster management module determines environmental impact characteristic values, including: The ratio of the wind speed to the reference operating wind speed is used as the wind speed influence factor; The spectral characteristics of the vibration data are used to determine the resonance influence factor by comparing it with the natural frequency of the bridge-building machine. The temperature influence factor is used to determine the ratio of the absolute value of the temperature difference to the reference operating temperature. The average value of the sum of the wind speed influence factor, the resonance influence factor, and the temperature influence factor after normalization is used to determine the environmental influence characteristic value.

8. The IoT-based intelligent bridge-building machine cluster management system according to claim 1, characterized in that, The coupling state determination module calculates construction management characterization values, including: The first construction management factor is used to determine the ratio of the dynamic coordination deviation characteristic value to the benchmark dynamic coordination deviation characteristic value. The ratio of the environmental impact characteristic value to the benchmark environmental impact characteristic value is used to determine the second construction management factor; This is used to determine the weighted sum of the first construction management factor and the second construction management factor as the construction management characterization value.

9. The IoT-based intelligent bridge-building machine cluster management system according to claim 1, characterized in that, The coupling state determination module divides the construction segment construction state, wherein, If the construction management characterization value is greater than the construction management characterization value threshold, then the construction status of the divided construction segment is abnormal construction. If the construction management characterization value is less than or equal to the construction management characterization value threshold, then the construction status of the divided construction segment is normal construction.

10. The IoT-based intelligent bridge-building machine cluster management system according to claim 9, characterized in that, The cluster management module adjusts control command data, including: In response to abnormal construction conditions, the adjustment range of the bridge-building machine's operating speed is positively correlated with the construction management characterization value.

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