Bridge tower section butt joint calibration system based on internet of things

Abstract

The application discloses a bridge tower segment butt joint calibration system based on the Internet of Things, and relates to the technical field of bridge tower segment measurement, comprising an analysis module and a calibration module. The analysis module sets tower segment monitoring point positions, compares and analyzes posture data of the tower segment according to design data of the tower segment to generate deviation analysis data, and presets a first determination criterion and a second determination criterion. The butt joint state of the tower segment is synchronously determined by the first determination criterion and the second determination criterion, and corresponding first determination data and second determination data are respectively output. The calibration module performs first calibration according to the first determination data and second calibration according to the second determination data. Through the arrangement of monitoring points at key stress parts of the tower segment and the combination of mechanical relations, a dynamic deviation chain is constructed to accurately determine deviation, and the first determination is performed on real-time deviation and a safety threshold. The second determination is performed on a double-standard evaluation state of a cumulative deviation change rate and a conduction range. Graded calibration ensures accuracy, improves efficiency, protects bridge safety and reduces cost.

Description

Technical Field

[0001] This invention relates to the field of bridge tower section measurement technology, and in particular to a bridge tower section docking calibration system based on the Internet of Things. Background Technology

[0002] In recent years, with the increasing proportion of long-span bridges in transportation infrastructure construction, the accuracy of the docking calibration of bridge tower sections, as core load-bearing components, directly determines the overall load-bearing capacity, wind and earthquake resistance, and long-term service safety of the bridge. Even a slight deviation in posture during the docking process of tower sections may be amplified by force transmission, leading to structural stress concentration and reduced durability. Therefore, Internet of Things (IoT) technology is gradually being integrated into the field of tower section measurement, promoting the upgrade from traditional manual inspection to real-time intelligent monitoring.

[0003] However, existing technologies still have significant shortcomings. Most systems only collect attitude data from local monitoring points on the surface of tower sections, without considering the mechanical transmission relationship of key stress-bearing parts. This makes it impossible to trace the source and transmission trajectory of deviations, resulting in ambiguous deviation location. At the same time, the judgment criteria mostly rely on the comparison of a single real-time deviation value with a fixed threshold, ignoring the dynamic rate of change of cumulative deviations and the collaborative impact of deviations on adjacent tower sections. This makes it difficult to comprehensively identify potential docking risks, leaving hidden dangers for subsequent calibration. The deficiencies of existing technologies further lead to blind and inefficient calibration operations. When faced with deviations, only local adjustment forces are applied to a single deviation node without considering the cumulative effect after deviation transmission, resulting in repeated calibrations and insufficient accuracy. Alternatively, when deviations exceed the limits, a one-size-fits-all shutdown and reset is directly adopted. There is a lack of graded response strategies based on deviation transmission gain and impact range, which delays construction progress and is difficult to adapt to docking scenarios with different risk levels. Summary of the Invention

[0004] The technical problem addressed by this invention is that existing technologies still have significant shortcomings. Most systems only collect attitude data from local monitoring points on the surface of tower sections, without considering the mechanical transmission relationship of key stress-bearing parts. This makes it impossible to trace the source and transmission trajectory of deviations, resulting in ambiguous deviation location. At the same time, the judgment criteria mostly rely on the comparison of a single real-time deviation value with a fixed threshold, ignoring the dynamic rate of change of cumulative deviations and the synergistic effect of deviations on adjacent tower sections. This makes it difficult to comprehensively identify potential docking risks, leaving hidden dangers for subsequent calibration. The deficiencies of existing technologies further lead to blind and inefficient calibration operations. When faced with deviations, only local adjustment forces are applied to a single deviation node without considering the cumulative effect after deviation transmission, resulting in repeated calibrations and insufficient accuracy. Alternatively, when deviations exceed limits, a one-size-fits-all shutdown and reset is directly adopted, lacking a graded response strategy based on deviation transmission gain and impact range. This not only delays the construction progress but also makes it difficult to adapt to docking scenarios with different risk levels.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a bridge tower section docking calibration system based on the Internet of Things includes an analysis module and a calibration module;

[0006] The analysis module sets the monitoring point positions of the tower section, compares and analyzes the attitude data of the tower section according to the design data of the tower section, generates deviation analysis data, and performs first and second judgments on the docking status of the tower section simultaneously based on the preset first judgment criteria and preset second judgment criteria, and outputs the corresponding first judgment data and second judgment data respectively.

[0007] The calibration module performs a first calibration based on the first determination data and a second calibration based on the second determination data.

[0008] As a preferred embodiment of the IoT-based bridge tower section docking calibration system of the present invention, the bridge tower section is used as the monitoring object. The monitoring points are arranged along the key stress-bearing parts of the top, bottom and side walls of the tower section. The number of monitoring points is greater than or equal to 3. The key stress-bearing parts include the connection area between the transverse diaphragm and the vertical side wall of the tower section, the edge area of ​​the side wall opening, the transition area between the side wall and the corner arc of the tower section, and the root area of ​​the connecting lug plate between the side wall and the auxiliary structure.

[0009] The design data includes the design reference attitude data of the tower section and the design location of the monitoring point. The design reference attitude data includes the design plumb line parameters along the height direction of the tower section and the design center axis parameters of the tower section itself.

[0010] Each tower section is assigned a unique number, and IoT sensing terminals are deployed at each monitoring point. The IoT sensing terminal includes a total station monitoring instrument. The total station monitoring instrument collects the geographical coordinates and attitude data of each tower section in real time according to the number of each tower section. The attitude data includes the vertical line parameters and the central axis parameters of the tower section.

[0011] The tilt angle and torsion angle of the tower section are obtained by comparing the attitude data with the design reference attitude data.

[0012] The tilt angle is the deviation angle of the vertical parameter of the tower section relative to the design vertical parameter, and the torsion angle is the rotational deviation angle of the central axis parameter of the tower section relative to the design central axis parameter.

[0013] As a preferred embodiment of the IoT-based bridge tower section docking calibration system of the present invention, wherein: the attitude data is compared and analyzed based on the design baseline attitude data of the design data to generate deviation analysis data, specifically including:

[0014] Based on the design location of each monitoring point in the design data and the mechanical transmission relationship of the key stress parts, a reference deviation transmission path for tower section connection is preset. The reference deviation transmission path includes the deviation threshold and transmission coefficient of the key node. The key node is the monitoring point corresponding to the key stress part, and each key node is assigned a unique number, which serves as the key node number.

[0015] Attitude data and corresponding time-series data of tower sections are continuously collected at a preset sampling frequency. The real-time deviation value of the attitude data of the monitoring point at each collection time is calculated. The real-time deviation value is the difference between the attitude data of the monitoring point and the corresponding design reference attitude data.

[0016] An initial threshold is preset for the real-time deviation value. Monitoring points where the real-time deviation value exceeds the initial threshold are recorded as deviation nodes. Deviation nodes that appear for the first time in the continuously collected time series data are selected and marked as source deviation points. The geographical coordinates of the source deviation points are determined, and the geographical coordinates are the location information of the source deviation points.

[0017] Using a directed graph processing algorithm, the real-time deviation value corresponding to the source deviation point is traced along the reference deviation transmission path to the adjacent key nodes. The deviation transmission gain and the accumulated real-time deviation value of each key node are calculated. The deviation transmission gain is the ratio of the real-time deviation value of the adjacent key nodes due to transmission to the initial real-time deviation value of the source deviation point.

[0018] Wherein, when the ratio is greater than 1, the deviation conduction gain is determined to be the amplification coefficient after deviation conduction; when the ratio is less than 1, the deviation conduction gain is determined to be the attenuation coefficient after deviation conduction.

[0019] A dynamic deviation transmission chain is constructed, which is a time series graph with the time axis as the horizontal axis and the real-time deviation value of the key node and the reference deviation transmission path as the vertical axis.

[0020] As a preferred embodiment of the IoT-based bridge tower section docking calibration system of the present invention, the construction process of the dynamic deviation transmission chain is as follows:

[0021] The collected time-series data is mapped to the horizontal axis of the dynamic deviation transmission chain as the time axis, and the time scale is divided according to a preset time interval that matches the sampling frequency. The collection time corresponding to the real-time deviation value of each key node is marked synchronously.

[0022] The vertical axis of the dynamic deviation transmission chain is divided into a deviation value layer and a transmission path layer. The deviation value layer maps the real-time deviation value and cumulative real-time deviation value of the key nodes to the corresponding coordinate range of the vertical axis deviation value layer according to the correspondence between the preset deviation value range and the coordinate range of the deviation value layer of the vertical axis. Then, it is associated with the corresponding time scale in a preset visualization form. The transmission path layer connects each key node with directed arrows. The arrow features are adjusted according to the deviation transmission gain. When the deviation transmission gain is greater than 1, the arrow feature represents deviation amplification. When the deviation transmission gain is less than 1, the arrow feature represents deviation attenuation. The source deviation point and the key nodes in the transmission trajectory are marked with preset colors.

[0023] Extract the timestamps corresponding to each time scale of the time axis, and bind the real-time deviation value, cumulative real-time deviation value, and the acquisition time corresponding to the real-time deviation value and cumulative real-time deviation value of the key nodes in the deviation value layer with the key node number, deviation transmission gain, and the time when the key node is transmitted in the transmission trajectory associated with the directed arrow in the transmission path layer through the timestamps to the corresponding time scale of the time axis, forming a correlation dataset of time, deviation value, and transmission path, and completing the establishment of a time correlation benchmark;

[0024] The real-time deviation values ​​and cumulative real-time deviation values ​​of the key nodes included in the deviation value layer of the associated dataset are mapped to the coordinate range of the deviation value layer on the vertical axis of the dynamic deviation transmission chain according to the preset correspondence between the deviation value range and the coordinate range of the deviation value layer on the vertical axis. The real-time deviation values ​​are labeled with a first visualization element, and the cumulative real-time deviation values ​​are labeled with a second visualization element. The spatial positions of the real-time deviation values ​​and cumulative real-time deviation values ​​are aligned with the projection of the corresponding time scale on the horizontal axis. At the same time, the starting coordinates and ending coordinates of each directed arrow in the transmission path layer data are mapped to the coordinate range of the transmission path layer on the vertical axis according to the geographical coordinates of the key nodes, thus completing the spatial coordinate mapping.

[0025] Based on the preset sampling frequency, for each new set of time-series data acquired, the steps of establishing time correlation benchmarks and performing spatial coordinate mapping are repeated to update the correlation dataset and spatial coordinate mapping relationship, synchronously refresh the first visualization element and the second visualization element of the deviation value layer and the directed arrow feature of the transmission path layer, and keep the time scale of the time axis automatically extended as the acquisition time progresses.

[0026] As a preferred embodiment of the IoT-based bridge tower section docking calibration system of the present invention, the refreshed time axis and time scale, the first and second visualization elements of the deviation value layer, the directed arrows of the transmission path layer, and the source deviation points and key nodes in the transmission trajectory marked with preset colors are superimposed and rendered on the same canvas to form a visual time series graph. The visual time series graph dynamically displays the real-time deviation value, the cumulative real-time deviation value, and whether the real-time deviation value exceeds the corresponding first preset threshold, whether the cumulative real-time deviation value exceeds the corresponding second preset threshold, and the stable deviation value at each time scale. At the same time, it displays the transmission direction, transmission path, deviation amplification or attenuation effect, and corresponding transmission time of the key nodes connected by the directed arrows. The stable deviation value is the change amplitude of the real-time deviation value within a preset time window that does not exceed a preset stable threshold.

[0027] The peak time, stable deviation value, and transmission trajectory including transmission delay time of each key node in the dynamic deviation transmission chain are extracted, and the transmission direction of deviation from the source deviation point to the adjacent key node is determined.

[0028] As a preferred embodiment of the IoT-based bridge tower section docking calibration system of the present invention, the peak time, stable deviation value, conduction delay time, conduction trajectory, conduction direction and source deviation point location information, deviation conduction gain amplification coefficient or attenuation coefficient and cumulative real-time deviation value are correlated to generate correlation results. The correlation results include a one-to-one correspondence binding relationship between dynamic deviation parameters, static marking parameters and calculation parameters. The binding relationship is that the dynamic deviation parameter of each key node is correspondingly bound to the geographical coordinates of the source deviation point to which the dynamic deviation parameter belongs, the deviation conduction gain of the key node and the cumulative real-time deviation value.

[0029] The dynamic deviation parameters include peak time, stable deviation value, conduction trajectory and conduction direction; the static marking parameters include source deviation point location information; and the calculation parameters include deviation conduction gain and cumulative real-time deviation value.

[0030] Based on the correlation results, deviation analysis data is generated, specifically including:

[0031] The association results are directly bound to the geographic coordinates of the marked source deviation points. The geographic coordinates are extracted from the association results as the location information of the source deviation points.

[0032] Extract the conduction trajectory and conduction direction, including conduction delay time, from the association results. Count the total number of key nodes covered by the conduction trajectory, which is taken as the number of key nodes covered by the deviation. Based on the geographical coordinates of each key node in the conduction trajectory, calculate the straight-line distance from the geographical coordinates of the source deviation point to the geographical coordinates of the farthest key node in the conduction trajectory, which is taken as the spatial span of the deviation coverage. Extract the conduction delay time corresponding to each key node in the conduction trajectory, and take the maximum value of the conduction delay time as the conduction time of the deviation coverage. Combine the number of key nodes, spatial span, and conduction time to form a conduction range evaluation parameter based on the conduction trajectory and conduction direction.

[0033] Extract the amplification factor and attenuation factor of the deviation transmission gain of each key node and the cumulative real-time deviation value from the correlation results. Calculate the product of the deviation transmission gain and the cumulative real-time deviation value of each key node. The product is used as the deviation influence value of the key node. The product value corresponding to the amplification factor represents the influence after deviation amplification, and the product value corresponding to the attenuation factor represents the influence after deviation attenuation. Combine the deviation influence values ​​of all key nodes into a set including the corresponding values ​​of each node to form an influence quantification parameter based on deviation transmission gain and cumulative real-time deviation value.

[0034] The deviation analysis data includes the location information of the source deviation point, the conduction range evaluation parameters based on the conduction trajectory and conduction direction, and the influence quantification parameters based on the deviation conduction gain and the cumulative real-time deviation value.

[0035] As a preferred embodiment of the IoT-based bridge tower section docking calibration system of the present invention, the docking status of the tower sections is determined based on a preset first determination criterion, and first determination data is output, specifically including:

[0036] The first judgment criterion is preset as the matching judgment logic between the real-time deviation value of the key node at the docking point of two adjacent tower sections and the preset safety threshold. The preset safety threshold includes the deviation warning threshold and the deviation limit threshold corresponding to the key node. The real-time deviation value is the real-time deviation value of the key node at the docking point of two adjacent tower sections in the deviation analysis data.

[0037] The matching determination logic includes:

[0038] Compare the real-time deviation values ​​of each key node at the junction of two adjacent tower sections with the preset safety threshold:

[0039] If the real-time deviation value is less than the deviation warning threshold, the docking status of two adjacent tower sections is determined to be normal docking, and the first determination data is output. The first determination data includes a normal status identifier and the real-time deviation value of the corresponding key node.

[0040] If the real-time deviation value is greater than or equal to the deviation warning threshold and less than the deviation limit threshold, then the docking status of two adjacent tower sections is determined to be docking to be calibrated, and the first determination data is output. The first determination data includes the calibration status identifier, the real-time deviation value of the corresponding key node, and the difference between the real-time deviation value and the deviation warning threshold.

[0041] If the real-time deviation value is greater than or equal to the deviation limit threshold, the docking status of two adjacent tower sections is determined to be emergency intervention docking, and the first determination data is output. The first determination data includes an emergency intervention status identifier, the real-time deviation value of the corresponding key node, the difference between the real-time deviation value and the deviation limit threshold, and the source deviation point location information of the key node at the docking point of the two adjacent tower sections.

[0042] As a preferred embodiment of the IoT-based bridge tower section docking calibration system of the present invention, the docking status of the tower sections is determined based on a preset second determination criterion, and second determination data is output, specifically including:

[0043] The second judgment criterion is preset as a collaborative judgment logic of the cumulative real-time deviation change rate and the conduction range of the key nodes at the docking point of two adjacent tower sections. The cumulative real-time deviation value and the conduction range evaluation parameters are the cumulative real-time deviation value and the conduction range evaluation parameters corresponding to the key nodes at the docking point of two adjacent tower sections in the deviation analysis data. The preset second judgment criterion includes the threshold of the cumulative deviation change rate and the critical value of the conduction range at the docking point of two adjacent tower sections.

[0044] Calculate the rate of change of the cumulative real-time deviation value of each key node at the docking point of two adjacent tower sections per unit time, and determine the influence range data of the cumulative real-time deviation in the docking area of ​​the two adjacent tower sections by combining the transmission range evaluation parameters.

[0045] The collaborative determination logic includes:

[0046] If the rate of change is less than the cumulative deviation rate of change threshold and the influence range data is less than the conduction range critical value, the docking status of two adjacent tower sections is determined to be a stable docking, and second determination data is output. The second determination data includes a stable state identifier, the cumulative real-time deviation rate of change, and the influence range data.

[0047] If the rate of change is greater than or equal to the cumulative deviation rate of change threshold or the influence range data is greater than or equal to the transmission range critical value, the docking status of two adjacent tower sections is determined to be dynamic risk docking, and second determination data is output. The second determination data includes dynamic risk status identifier, cumulative real-time deviation rate of change, influence range data, and the transmission direction of deviation between two adjacent tower sections.

[0048] If the rate of change is greater than a times the cumulative deviation rate of change threshold and the range of influence is greater than b times the transmission range threshold, the docking status of two adjacent tower sections is determined to be a high-risk docking, and second determination data is output. The second determination data includes a high-risk status indicator, the cumulative real-time deviation rate of change, the range of influence data, the transmission direction of the deviation between the two adjacent tower sections, and the stable deviation value of the corresponding key node.

[0049] As a preferred embodiment of the IoT-based bridge tower section docking calibration system of the present invention, the first calibration based on the first determination data specifically includes:

[0050] If the first determination data includes a normal status indicator, the calibration operation will not be initiated;

[0051] If the first determination data includes a calibration status identifier, then based on the difference between the real-time deviation value of the key node in the first determination data and the real-time deviation value exceeding the deviation warning threshold, a linear adjustment force is applied to the corresponding key node at the docking point of two adjacent tower sections. The adjustment range is positively correlated with the difference between the real-time deviation value exceeding the deviation warning threshold until the real-time deviation value of the key node is less than the deviation warning threshold.

[0052] If the first determination data includes an emergency intervention status indicator, a shutdown command is first triggered to suspend the tower section docking operation. Then, based on the source deviation point location information, the real-time deviation value of the key node, and the difference between the real-time deviation value and the deviation limit threshold in the first determination data, the two adjacent tower sections are reset in an overall attitude, with the source deviation point as the reference and an adjustment margin of c times the difference between the real-time deviation value and the deviation limit threshold. After the reset, the real-time deviation value of the key node is re-acquired until the real-time deviation value is less than the deviation warning threshold.

[0053] As a preferred embodiment of the IoT-based bridge tower section docking calibration system of the present invention, the second calibration based on the second determination data specifically includes:

[0054] If the second determination data includes a stable state indicator, the calibration operation will not be initiated.

[0055] If the second judgment data includes a dynamic risk status identifier, then based on the cumulative real-time deviation value change rate, the influence range data, and the transmission direction of the cumulative real-time deviation value between two adjacent tower sections in the second judgment data, a compensation force is applied in the opposite direction of the transmission direction of the cumulative real-time deviation value. The compensation frequency of the compensation force is positively correlated with the cumulative real-time deviation value change rate, and the compensation range of the compensation force covers the influence range data of the cumulative real-time deviation value until the cumulative real-time deviation value change rate is less than the cumulative deviation change rate threshold.

[0056] If the second judgment data includes a high-risk status indicator, an early warning command is first triggered and the tower section docking operation is suspended. Then, based on the key node stable deviation value, cumulative real-time deviation value transmission direction and influence range data between two adjacent tower sections in the second judgment data, the stable deviation value is used as the target calibration value. Calibration force is synchronously applied to all key nodes within the influence range data in the opposite direction of the transmission direction of the cumulative real-time deviation value. The calibration force amplitude is positively correlated with the stable deviation value. After calibration, the cumulative real-time deviation value change rate and influence range data are recalculated until the cumulative real-time deviation value change rate is less than the cumulative deviation change rate threshold and the influence range data is less than the transmission range critical value.

[0057] The beneficial effects of this invention are as follows: By setting up monitoring points at key stress-bearing parts of the tower section and combining them with mechanical transmission relationships, a dynamic deviation transmission chain is constructed to accurately trace the source, transmission path, and degree of influence of deviations. This solves the problem of ambiguous deviation positioning in existing technologies. A dual standard is adopted, using real-time deviation values ​​matched with preset safety thresholds for the first judgment and cumulative deviation change rate and transmission range for the second judgment. This achieves a comprehensive and accurate assessment of the docking status, avoiding the one-sidedness of a single judgment. The calibration process is implemented in stages based on the judgment results. Operation is not initiated in normal conditions, linear adjustment is performed in the calibration state, and shutdown and targeted compensation are performed as needed in emergency or high-risk states. This ensures calibration accuracy while avoiding blind operation that delays the construction period, effectively improving the efficiency and quality of tower section docking, ensuring the long-term service safety of the bridge structure, and reducing construction costs and risks. Attached Figure Description

[0058] Figure 1 This is a basic flowchart of a bridge tower section docking calibration system based on the Internet of Things, provided as an embodiment of the present invention. Detailed Implementation

[0059] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0060] Example, refer to Figure 1 As an embodiment of the present invention, a bridge tower section docking calibration system based on the Internet of Things is provided, including an analysis module and a calibration module;

[0061] The analysis module sets the monitoring point locations of the tower section, compares and analyzes the attitude data of the tower section based on the design data of the tower section, generates deviation analysis data, and simultaneously performs first and second judgments on the docking status of the tower section according to the preset first judgment criteria and preset second judgment criteria, and outputs the corresponding first judgment data and second judgment data respectively.

[0062] The calibration module performs a first calibration based on the first judgment data and a second calibration based on the second judgment data.

[0063] In one embodiment, the system includes an analysis module and a calibration module. The analysis module first plans and deploys the monitoring points for each tower section. Then, using the design reference attitude (including the design plumb line parameters along the tower section height direction and the design center axis parameters of the tower section itself) and the design positions of the monitoring points in the tower section design data as references, it performs a multi-dimensional comparative analysis of the real-time collected tower section attitude data (including the actual plumb line parameters and the actual center axis parameters of the tower section) with the aforementioned design data. This generates deviation analysis data including the source and degree of deviation. Subsequently, based on a preset first judgment standard (threshold judgment for real-time deviation values) and a second judgment standard (coordinated judgment for the dynamics and transmission range of cumulative deviation), it performs a synchronous two-dimensional judgment on the tower section docking status, outputting clearly defined values. The calibration module uses first and second judgment data to determine the docking status level (e.g., normal, awaiting calibration, emergency intervention). Based on the judgment data, it performs differentiated calibration. The first calibration (e.g., linear adjustment, overall attitude reset) is performed based on the first judgment data, and the second calibration (e.g., reverse direction compensation, multi-node synchronous calibration) is performed based on the second judgment data to address the dynamic risks of cumulative deviations. This collaborative logic of analysis-judgment-calibration avoids the problems of missed judgments and misjudgments under a single judgment standard, improves the comprehensiveness and accuracy of tower section docking status assessment, and reduces efficiency losses caused by blind adjustment through precise matching of calibration operations with deviation characteristics. This effectively ensures the docking accuracy, construction efficiency, and structural safety of bridge tower sections, and adapts to the needs of prefabricated and intelligent bridge construction.

[0064] The bridge tower section is used as the monitoring object. The monitoring points are set up along the top, bottom and key stress parts of the side wall of the tower section. The number of monitoring points is greater than or equal to 3. The key stress parts include the connection area between the transverse diaphragm and the vertical side wall of the tower section, the edge area of ​​the opening of the side wall, the transition area between the side wall and the corner arc of the tower section, and the root area of ​​the connecting ear plate between the side wall and the auxiliary structure.

[0065] The design data includes the design reference attitude data of the tower section and the design location of the monitoring point. The design reference attitude data includes the design plumb line parameters along the height direction of the tower section and the design center axis parameters of the tower section itself.

[0066] Each tower section is assigned a unique number, and IoT sensing terminals are deployed at each monitoring point. The IoT sensing terminals include a total station monitor. The total station monitor, corresponding to the number of each tower section, collects the geographical coordinates and attitude data of the tower section in real time. The attitude data includes the vertical line parameters and the central axis parameters of the tower section.

[0067] The tilt angle and torsion angle of the tower section are obtained by comparing the attitude data with the design baseline attitude data;

[0068] The tilt angle is the angle of deviation of the vertical parameter of the tower section from the design vertical parameter, and the torsion angle is the angle of rotational deviation of the central axis parameter of the tower section from the design central axis parameter.

[0069] In one embodiment, a bridge tower section is used as the monitoring object. Monitoring points are arranged along the key stress-bearing parts of the top, bottom, and sidewalls of the tower section. Specifically, the key stress-bearing parts are: the connection area between the transverse diaphragm and the vertical sidewall of the tower section, which is a critical node for the transmission of longitudinal and lateral forces and is prone to deformation due to uneven stress; the edge area of ​​the opening in the sidewall, where the opening weakens the overall structural integrity and stress concentration is easily formed at the edge; the rounded transition area between the sidewall and the tower section at the corner, where abrupt changes in geometry lead to stress concentration and is a sensitive area for torsional deviation; and the root area of ​​the connecting ear plate between the sidewall and the auxiliary structure, where the ear plate bears the load of the auxiliary structure and the root is the starting point of force transmission, which is prone to local deformation. Furthermore, the number of monitoring points should not be less than three. Based on the geometric principle that three points determine a plane, the spatial posture of the tower section can be accurately described. The monitoring points must be reasonably distributed. The distance between monitoring points in the same area where the diaphragm connects to the side wall should not be less than 0.5m and not more than 1.5m. The monitoring points at the edge of the side wall opening should be 30-50cm from the edge of the opening. The monitoring points in the corner arc transition area should be arranged every 50-80cm along the arc. The monitoring points at the root of the ear plate should be 20-40cm from the root of the ear plate. At the same time, the horizontal distance between monitoring points in different key stress-bearing parts should not be less than 1 / 3 of the length of the shorter side of the tower section cross-section. For example, a 25m long precast tower section (section...) The tower section has a short side length of 3m. One point can be set at the center of the top, one point 50cm from the edge of the support on the corresponding side wall, and one point 80cm from the top and bottom of the diaphragm connecting area. This meets the quantity and distance requirements. The required design data includes the tower section's design reference attitude data (including the design plumb line parameters along the tower section's height and the design center axis parameters of the tower section itself) and the design locations of the monitoring points. During implementation, each tower section is first assigned a unique number (e.g., T1, T2…Tn). Then, IoT sensing terminals, including a total station monitoring device, are deployed at each monitoring point. The total station monitoring device is linked to the tower section number to collect real-time data on the tower section's position. The system collects coordinate and attitude data (including the vertical parameters of the tower section and the central axis parameters of the tower section). Then, it compares the collected attitude data with the design reference attitude data to obtain the tilt angle and torsion angle of the tower section. The tilt angle is the deviation angle of the actual vertical parameter of the tower section relative to the design vertical parameter. For example, if the actual vertical parameter of a tower section deviates 2° to the left relative to the design vertical parameter, this 2° is the tilt angle. The torsion angle is the rotational deviation angle of the actual central axis parameter of the tower section relative to the design central axis parameter. For example, if the actual central axis of a tower section rotates 1.5° counterclockwise around the height direction relative to the design central axis, this 1.5° is the torsion angle.

[0070] The attitude data is compared and analyzed with the design baseline attitude data based on the design data to generate deviation analysis data, specifically including:

[0071] Based on the design location of each monitoring point in the design data and the mechanical transmission relationship of key stress parts, the reference deviation transmission path of tower section connection is preset. The reference deviation transmission path includes the deviation threshold and transmission coefficient of key nodes. Key nodes are the monitoring points corresponding to key stress parts, and each key node is assigned a unique number, which serves as the key node number.

[0072] Attitude data and corresponding time-series data of tower sections are continuously collected at a preset sampling frequency. The real-time deviation value of the attitude data of the monitoring point at each collection time is calculated. The real-time deviation value is the difference between the attitude data of the monitoring point and the corresponding design reference attitude data.

[0073] An initial threshold is preset for the real-time deviation value. Monitoring points whose real-time deviation values ​​exceed the initial threshold are recorded as deviation nodes. The first deviation node to appear in the continuously collected time series data is selected and marked as the source deviation point. The geographical coordinates of the source deviation point are determined, and the geographical coordinates are the location information of the source deviation point.

[0074] By using a directed graph processing algorithm, the real-time deviation value corresponding to the source deviation point is traced along the reference deviation transmission path to the adjacent critical nodes. The deviation transmission gain and the accumulated real-time deviation value of each critical node are calculated. The deviation transmission gain is the ratio of the real-time deviation value of the adjacent critical nodes due to transmission to the initial real-time deviation value of the source deviation point.

[0075] When the ratio is greater than 1, the deviation conduction gain is determined to be the amplification factor after deviation conduction; when the ratio is less than 1, the deviation conduction gain is determined to be the attenuation factor after deviation conduction.

[0076] A dynamic deviation transmission chain is constructed, which is a time series graph with the time axis as the horizontal axis and the real-time deviation values ​​of key nodes and the reference deviation transmission path as the vertical axis.

[0077] In one embodiment, the process of generating deviation analysis data based on design data is as follows: First, combining the design locations of each monitoring point and the mechanical transmission relationship of key stress-bearing parts (such as the force transmission path from the connection area between the diaphragm and the side wall to the corner transition area), the benchmark deviation transmission path for tower section docking is preset, including the deviation threshold of key nodes (i.e., key stress-bearing monitoring points) (e.g., the threshold of the node in the diaphragm connection area is set to 1mm, based on the stress sensitivity of this part) and the transmission coefficient (the transmission coefficient is set to a range of 0.8-1.2 based on the mechanical simulation results of similar tower sections), and assigning a unique number to each key node (such as N1, N2...Nn). Then, according to the preset sampling frequency (the preset sampling frequency is 10Hz, set to adapt to the dynamic response speed of tower section attitude data changes during tower section docking construction, so as to accurately capture real-time deviations), attitude data and time series data are continuously collected, and the actual deviation of each monitoring point at each moment is calculated. The time deviation value (i.e., the difference between the actual attitude data and the design reference attitude data) is used to set an initial threshold for the real-time deviation value (the initial threshold is set to 0.5mm, based on the minimum allowable deviation accuracy for tower section docking). Monitoring points that exceed the threshold are recorded as deviation nodes. The first deviation node to appear in the continuous time sequence is selected as the source deviation point (e.g., if node N3 first shows a deviation of 0.6mm at a certain moment, it is marked as the source deviation point), and its geographical coordinates are recorded. The propagation trajectory of the source deviation value to the adjacent node is traced along the reference path through a directed graph processing algorithm. The deviation propagation gain (the ratio of the deviation value of the adjacent node to the initial deviation value of the source, e.g., if the deviation of node N4 is 0.9mm, the gain is 1.5, which is determined as the amplification factor) and the cumulative real-time deviation value (e.g., if the cumulative deviation of node N4 reaches 1.8mm within 10 seconds) are calculated. Finally, a dynamic deviation propagation chain is constructed with the time axis as the horizontal axis and the deviation values ​​of key nodes and the propagation path as the vertical axis.

[0078] The process of constructing the dynamic deviation transmission chain is as follows:

[0079] The collected time-series data is mapped to the horizontal axis of the dynamic deviation transmission chain as the time axis, and the time scale is divided according to the preset time interval matched with the sampling frequency. The collection time corresponding to the real-time deviation value of each key node is marked synchronously.

[0080] The vertical axis of the dynamic deviation transmission chain is divided into a deviation value layer and a transmission path layer. The deviation value layer maps the real-time deviation value and cumulative real-time deviation value of the key nodes to the corresponding coordinate range of the vertical axis deviation value layer according to the correspondence between the preset deviation value range and the coordinate range of the deviation value layer of the vertical axis. Then, it is associated with the corresponding time scale in a preset visualization form. The transmission path layer connects each key node with directed arrows. The arrow features are adjusted according to the deviation transmission gain. When the deviation transmission gain is greater than 1, the arrow feature represents deviation amplification. When the deviation transmission gain is less than 1, the arrow feature represents deviation attenuation. The source deviation point and the key nodes in the transmission trajectory are marked with preset colors.

[0081] Extract the timestamps corresponding to each time scale of the time axis, and bind the real-time deviation value, cumulative real-time deviation value, and the acquisition time corresponding to the real-time deviation value and cumulative real-time deviation value of the key nodes in the deviation value layer with the key node number, deviation transmission gain, and the time when the key node is transmitted in the transmission trajectory associated with the directed arrow in the transmission path layer through the timestamps and the corresponding time scales of the time axis to form a correlation dataset of time, deviation value and transmission path, and complete the establishment of time correlation benchmark;

[0082] The real-time deviation values ​​and cumulative real-time deviation values ​​of key nodes included in the deviation value layer of the associated dataset are mapped to the vertical axis deviation value layer coordinate range according to the preset correspondence between the deviation value range and the coordinate range of the deviation value layer on the vertical axis of the dynamic deviation transmission chain. The real-time deviation values ​​are labeled with the first visualization element, and the cumulative real-time deviation values ​​are labeled with the second visualization element. The spatial positions of the real-time deviation values ​​and cumulative real-time deviation values ​​are aligned with the projection of the corresponding time scale on the horizontal axis. At the same time, the starting coordinates and ending coordinates of each directed arrow in the transmission path layer data are mapped to the coordinate range of the transmission path layer on the vertical axis according to the geographical coordinates of the key nodes, thus completing the spatial coordinate mapping.

[0083] Based on the preset sampling frequency, for each new set of time-series data acquired, the steps of establishing a time correlation benchmark and performing spatial coordinate mapping are repeated to update the correlation dataset and spatial coordinate mapping relationship. The first visualization element and the second visualization element of the deviation value layer and the directed arrow feature of the transmission path layer are refreshed synchronously, and the time scale of the time axis is kept to automatically extend as the acquisition time progresses.

[0084] In one embodiment, the construction process of the dynamic deviation transmission chain specifically involves mapping time-series data to the horizontal time axis, dividing the time scale according to a 10Hz sampling frequency corresponding to a 0.1-second interval (the preset time interval is set to 0.1 seconds, based on a 1:1 match with the sampling frequency, ensuring real-time capture of deviation changes while avoiding excessively dense time scales that would lead to cluttered graphs, balancing real-time performance and visualization clarity), and marking the acquisition time of the real-time deviation value at each node. The vertical axis is divided into a deviation value layer and a transmission path layer. The deviation value layer is based on a preset deviation value range of 0-1mm corresponding to 0-5 units on the vertical axis, and a deviation value range of 1-2mm corresponding to 5-10 units (the preset deviation value ranges are set to 0-1mm and 1-2mm, based on the premise that the deviation value range covers the common deviation range of tower section docking, and every 5 units corresponds to 1mm deviation, facilitating rapid reading of deviation values ​​and avoiding reading errors caused by uneven coordinate intervals). The real-time deviation values ​​(using...) are then... The red dots and cumulative real-time deviation values ​​(marked with blue broken lines) are mapped to corresponding intervals and associated with time scales. The transmission path layer uses directed arrows to connect key nodes. The arrows are either thickened to 2pt (for gain > 1, the 2pt line width is chosen because it creates a clear difference from the standard 1pt line width, visually representing the deviation amplification effect and avoiding visual confusion) or thinned to 0.8pt (for gain < 1, the 0.8pt line width is chosen because it is thinner than the standard line width, clearly representing the deviation attenuation trend). The source deviation point is marked with a solid yellow dot (yellow is chosen because it is a commonly used warning color in engineering, allowing for quick location of the deviation origin and facilitating priority handling of source problems). Transmission nodes are marked with hollow green dots (green contrasts sharply with yellow, and the hollow design distinguishes it from the core attribute of the source deviation point, clearly presenting non-source nodes on the deviation transmission path). The timestamp is extracted (format: yyyy-MM-dd). The HH:mm:ss.SSS millisecond-level timestamp is set to ensure high-precision alignment of time-series data, avoiding deviations and mismatches caused by insufficient time precision. It is especially suitable for multi-node synchronous acquisition scenarios. The deviation value, acquisition time, and the node number, gain, and conduction time associated with the arrow are bound by the timestamp to form a time-deviation-path associated dataset, completing the time association benchmark. Then, the deviation value is mapped to the vertical axis deviation value layer to ensure that the red dot, blue broken line, and time scale are perfectly aligned in the horizontal direction, avoiding misjudgments caused by spatial misalignment that lead to mismatch between the deviation value and the corresponding time, and ensuring the accuracy of data reading. At the same time, according to the local coordinate system geographic coordinates of the nodes, a local coordinate system with the center of the bottom of the tower section as the origin, the X-axis along the length of the tower section, and the Y-axis perpendicular to the side wall is adopted to replace the global coordinate system, simplifying coordinate mapping calculations and improving path mapping accuracy. The arrow start and end points are mapped to the conduction path layer to complete the spatial coordinate mapping. Every time new data is acquired (the time interval is synchronized with the sampling frequency, i.e., 0.The refresh rate is set at 1 second per update, consistent with the data acquisition frequency to ensure real-time updates of the dynamic deviation transmission chain and prevent data lag from affecting construction decisions. The above steps are repeated to update the dataset and mapping relationships, synchronously refreshing the red dots, blue broken lines of the deviation value layer, and the arrow features of the transmission path layer, with a refresh delay not exceeding 50ms (the 50ms setting is to control the refresh delay within an acceptable, imperceptible lag range to avoid construction personnel misjudging the current deviation status due to slow map updates). Simultaneously, the time axis automatically extends with the acquisition time.

[0085] The refreshed timeline and time scale, the first and second visualization elements of the deviation value layer, the directed arrows of the transmission path layer, and the source deviation points and key nodes in the transmission trajectory marked with preset colors are overlaid and rendered on the same canvas to form a visual time series graph. The visual time series graph dynamically displays the real-time deviation value, the cumulative real-time deviation value, and whether the real-time deviation value exceeds the corresponding first preset threshold, whether the cumulative real-time deviation value exceeds the corresponding second preset threshold, and the stable deviation value at each time scale. At the same time, it displays the transmission direction, transmission path, deviation amplification or attenuation effect, and corresponding transmission time of the real-time deviation value along the key nodes connected by the directed arrows. The stable deviation value is the change of the real-time deviation value within the preset time window that does not exceed the preset stable threshold.

[0086] Extract the peak time, stable deviation value, and transmission trajectory including transmission delay time of each key node from the dynamic deviation transmission chain, and determine the transmission direction of the deviation from the source deviation point to the adjacent key node.

[0087] The peak time, stable deviation value, conduction delay time, conduction trajectory, conduction direction and source deviation point location information, deviation conduction gain amplification factor or attenuation factor and cumulative real-time deviation value are correlated to generate correlation results. The correlation results include a one-to-one correspondence between dynamic deviation parameters, static labeling parameters and calculation parameters. The correlation relationship is that the dynamic deviation parameter of each key node is bound to the geographical coordinates of the source deviation point to which the dynamic deviation parameter belongs, the deviation conduction gain of the key node and the cumulative real-time deviation value.

[0088] Dynamic deviation parameters include peak time, stable deviation value, conduction trajectory and conduction direction; static marker parameters include source deviation point location information; calculation parameters include deviation conduction gain and cumulative real-time deviation value.

[0089] Deviation analysis data is generated based on the correlation results, specifically including:

[0090] The association results directly bind the geographic coordinates of the marked source deviation points. The geographic coordinates are extracted from the association results as the location information of the source deviation points.

[0091] Extract the transmission trajectory and transmission direction, including transmission delay time, from the correlation results. Count the total number of key nodes covered by the transmission trajectory. The total number is taken as the number of key nodes covered by the deviation. Based on the geographical coordinates of each key node in the transmission trajectory, calculate the straight-line distance from the geographical coordinates of the source deviation point to the geographical coordinates of the farthest key node in the transmission trajectory. The straight-line distance is taken as the spatial span of the deviation coverage. Extract the transmission delay time corresponding to each key node in the transmission trajectory. Take the maximum value of the transmission delay time as the transmission time of the deviation coverage. Combine the number of key nodes, spatial span, and transmission time to form the transmission range evaluation parameters based on the transmission trajectory and transmission direction.

[0092] Extract the amplification factor and attenuation factor of the deviation transmission gain of each key node and the cumulative real-time deviation value from the correlation results. Calculate the product of the deviation transmission gain and the cumulative real-time deviation value of each key node. The product is used as the deviation influence value of the key node. The amplification factor corresponds to the product value to represent the influence after deviation amplification, and the attenuation factor corresponds to the product value to represent the influence after deviation attenuation. Combine the deviation influence values ​​of all key nodes into a set including the corresponding values ​​of each node to form an influence quantification parameter based on deviation transmission gain and cumulative real-time deviation value.

[0093] Deviation analysis data includes the location information of the source deviation point, the conduction range evaluation parameters based on the conduction trajectory and conduction direction, and the influence quantification parameters based on the deviation conduction gain and the cumulative real-time deviation value.

[0094] In one embodiment, the generation process of the visualized time series graph and deviation analysis data is as follows: First, the refreshed time axis (divided into 0.1-second intervals according to a 10Hz sampling frequency), the first and second visualization elements of the deviation value layer (red dots, marking real-time deviation values) and the second visualization element (blue broken line, marking cumulative real-time deviation values), the directed arrows of the transmission path layer (thick 2pt indicates gain >1, thin 0.8pt indicates gain <1), and the nodes marked with preset colors (yellow solid dots indicate source deviation points, green hollow dots indicate transmission nodes) are superimposed and rendered on the same canvas to form a visualized time series graph. This graph dynamically displays the real-time deviation values ​​(e.g., deviation of 0.7mm at t=5s for node N3) and the cumulative real-time deviation values ​​(e.g., cumulative deviation of 1.8mm at t=10s for node N4) of key nodes at each time scale, and marks whether the real-time deviation exceeds the first preset threshold (the first preset threshold is set to 1mm, based on the minimum allowable value for tower section docking). The system displays the following parameters: small deviation accuracy of 0.5mm with 0.5mm safety redundancy to avoid false alarms due to instrument fluctuations; whether the cumulative real-time deviation value exceeds the second preset threshold (the second preset threshold is set to 2mm, based on the fact that it is lower than the allowable cumulative deviation of 2.5mm for precast tower section assembly in the technical specifications for highway bridge and culvert construction, with 0.5mm adjustment space reserved); and stable deviation value (the preset stable threshold is judged by the fact that the change range does not exceed 0.1mm within the preset time window, the preset stable threshold is set to 0.1mm, based on the fact that it matches the conventional measurement accuracy (±0.1mm) of the total station monitoring instrument in the IoT sensing terminal, the preset time window is set to 5 minutes, based on the engineering practice experience of bridge tower section docking construction). It also displays the real-time deviation along the direction of arrow transmission (e.g., N3→N4→N5), transmission path, deviation amplification effect, attenuation effect (e.g., the arrow from N3 to N4 is thickened and magnified) and the corresponding transmission time (e.g., the deviation of N3 is transmitted to N4 in t=5s).

[0095] Subsequently, the peak time of real-time deviation (e.g., node N4 reaches a peak of 1.2mm at t=8s), stable deviation value (e.g., N4 eventually stabilizes at 0.9mm), and the transmission trajectory including the transmission delay time (e.g., a delay of 0.3s from N3 to N4) of each key node are extracted from the dynamic deviation transmission chain. The transmission direction of the deviation from the source deviation point to the adjacent key node (e.g., N3→N4) is determined. Then, the dynamic deviation parameters of the above-mentioned peak time, stable deviation value, transmission trajectory, and transmission direction are bound one-to-one with the static labeling parameters of the geographical coordinates of the source deviation point (e.g., coordinates of N3 X=100.2m, Y=50.3m), and the calculation parameters of deviation transmission gain (e.g., gain of 1.5 for N4) and cumulative real-time deviation value (e.g., cumulative 1.8mm for N4) to generate correlation results (e.g., node N4 is bound to the source N3 coordinates, gain of 1.5, and cumulative 1.8mm). Based on the correlation results, the deviation is generated. The differential analysis data directly extracts the geographic coordinates of the marked source deviation points from the correlation results as the source deviation point location information. It also extracts the transmission trajectory and transmission direction, including the transmission delay time. The total number of key nodes covered by the trajectory (e.g., 3) is counted as the number of nodes covered by the deviation. The straight-line distance (e.g., 8m) from the source deviation point to the farthest node (e.g., N5) is calculated as the spatial span. The maximum transmission delay time (e.g., 0.5s) is taken as the transmission time. These parameters are combined to form the transmission range assessment parameters. The deviation transmission gain and cumulative real-time deviation value of each key node are extracted. The product of the two is calculated as the deviation impact value (e.g., 1.5×1.8=2.7 for node N4, the amplification factor corresponds to the product to represent the amplified impact). The impact values ​​of all nodes are combined to form the impact quantification parameters. The final deviation analysis data includes the above-mentioned source deviation point location information, transmission range assessment parameters, and impact quantification parameters.

[0096] The docking status of the tower sections is determined based on a preset first judgment criterion, and the first judgment data is output, specifically including:

[0097] The first judgment criterion is preset as the matching judgment logic between the real-time deviation value of the key node at the docking point of two adjacent tower sections and the preset safety threshold. The preset safety threshold includes the deviation warning threshold and the deviation limit threshold of the corresponding key node, and the real-time deviation value is the real-time deviation value of the corresponding key node at the docking point of two adjacent tower sections in the deviation analysis data.

[0098] The matching logic includes:

[0099] Compare the real-time deviation values ​​of each key node at the junction of two adjacent tower sections with the preset safety threshold:

[0100] If the real-time deviation value is less than the deviation warning threshold, the docking status of the two adjacent tower sections is determined to be normal docking, and the first judgment data is output. The first judgment data includes the normal status indicator and the real-time deviation value of the corresponding key node.

[0101] If the real-time deviation value is greater than or equal to the deviation warning threshold and less than the deviation limit threshold, the docking status of two adjacent tower sections is determined to be docking to be calibrated, and the first judgment data is output. The first judgment data includes the calibration status identifier, the real-time deviation value of the corresponding key node, and the difference between the real-time deviation value and the deviation warning threshold.

[0102] If the real-time deviation value is greater than or equal to the deviation limit threshold, the docking status of the two adjacent tower sections is determined to be emergency intervention docking, and the first judgment data is output. The first judgment data includes the emergency intervention status identifier, the real-time deviation value of the corresponding key node, the difference between the real-time deviation value and the deviation limit threshold, and the source deviation point location information of the key node at the docking point of the two adjacent tower sections.

[0103] In one embodiment, when making a first determination on the docking status of adjacent bridge tower sections based on a preset first determination criterion, the first determination criterion is first defined as the matching determination logic between the real-time deviation value of the key node at the docking point of two adjacent tower sections and a preset safety threshold. The preset safety threshold includes a deviation warning threshold and a deviation limit threshold. The deviation warning threshold directly references the 1mm setting of the first preset threshold (the two values ​​are set the same but have different functions. The first preset threshold is used to mark whether the real-time deviation exceeds the safety line in the visualized time series graph, which is a display layer benchmark. The deviation warning threshold is used for the graded docking status in this determination logic, which is a decision layer benchmark. The setting basis is based on the tower section docking allowable... The minimum deviation accuracy is 0.5mm, with a safety redundancy of 0.5mm, totaling 1mm, to avoid false alarms due to instrument fluctuations. The deviation limit threshold is set at 1.5mm (based on the principle of being 0.5mm higher than the deviation warning threshold to form a clear graded judgment gradient, and lower than the upper limit of 2mm for emergency intervention of precast tower section docking in the highway bridge and culvert construction technical specifications, reserving 0.5mm for emergency adjustment space). The real-time deviation value is taken from the real-time deviation value of the corresponding key node of the docking of adjacent tower sections in the deviation analysis data. The specific judgment process takes the docking of adjacent precast tower sections T2 and T3 as an example, selecting the key nodes N4 (the connection area between the transverse diaphragm and the vertical sidewall) and N5 (the opening of the sidewall) at the docking point. Using the hole edge area as the judgment object, if the real-time deviation value of N4 is 0.8mm (less than the deviation warning threshold of 1mm), the docking status of T2 and T3 is judged as normal docking, and the first judgment data output includes the normal status indicator and the real-time deviation value of N4 of 0.8mm. If the real-time deviation value of N5 is 1.2mm (greater than or equal to the deviation warning threshold of 1mm and less than the deviation limit threshold of 1.5mm), the docking status is judged as docking under calibration, and the first judgment data output includes the calibration under calibration status indicator, the real-time deviation value of N5 of 1.2mm, and the difference of 0.2mm between this value and the deviation warning threshold. If the real-time deviation value of N4 is 1.6mm... If m (greater than or equal to the deviation limit threshold of 1.5mm) is used, the docking status is determined to be emergency intervention docking. The first judgment data output includes the emergency intervention status indicator, the real-time deviation value of N4 (1.6mm), the difference between this value and the deviation limit threshold of 0.1mm, and the location information of the source deviation point corresponding to N4 (such as geographical coordinates X=100.5m, Y=50.6m). This first judgment logic, through hierarchical thresholds and precise data output, can avoid misjudgment or omission caused by a single threshold, and can quickly locate the source deviation point in an emergency to guide targeted intervention, effectively ensuring the accuracy and safety of tower section docking construction, while providing clear targets and data support for subsequent calibration operations.

[0104] The docking status of the tower sections is determined based on a preset second determination criterion, and the second determination data is output, specifically including:

[0105] The second judgment criterion is preset as a collaborative judgment logic of the cumulative real-time deviation change rate and the conduction range of the key nodes at the docking point of two adjacent tower sections. The cumulative real-time deviation value and the conduction range evaluation parameters are the cumulative real-time deviation value and the conduction range evaluation parameters of the key nodes at the docking point of two adjacent tower sections in the deviation analysis data. The preset second judgment criterion includes the threshold of the cumulative deviation change rate and the critical value of the conduction range at the docking point of two adjacent tower sections.

[0106] Calculate the rate of change of the cumulative real-time deviation value of each key node at the docking point of two adjacent tower sections per unit time, and determine the influence range of the cumulative real-time deviation in the docking area of ​​the two adjacent tower sections by combining the transmission range assessment parameters.

[0107] The collaborative decision-making logic includes:

[0108] If the rate of change is less than the cumulative deviation rate of change threshold and the influence range data is less than the conduction range critical value, the docking status of the two adjacent tower sections is determined to be a stable docking, and the second judgment data is output. The second judgment data includes the stable state indicator, the cumulative real-time deviation rate of change and the influence range data.

[0109] If the rate of change is greater than or equal to the cumulative deviation rate of change threshold or the influence range data is greater than or equal to the transmission range critical value, the docking status of two adjacent tower sections is determined to be dynamic risk docking, and the second judgment data is output. The second judgment data includes the dynamic risk status identifier, the cumulative real-time deviation rate of change, the influence range data, and the transmission direction of the deviation between the two adjacent tower sections.

[0110] If the rate of change is greater than a times the cumulative deviation rate of change threshold and the range of influence is greater than b times the critical value of the transmission range, the docking status of the two adjacent tower sections is determined to be a high-risk docking, and the second judgment data is output. The second judgment data includes the high-risk status indicator, the cumulative real-time deviation rate of change, the range of influence data, the transmission direction of the deviation between the two adjacent tower sections, and the stable deviation value of the corresponding key node.

[0111] In one embodiment, when making a second judgment on the docking status of adjacent bridge tower sections based on a preset second judgment criterion, the second judgment criterion is first defined as a collaborative judgment logic of the cumulative real-time deviation change rate and the transmission range of the key nodes at the docking point of two adjacent tower sections. The required cumulative real-time deviation value and transmission range evaluation parameters are all taken from the data of the corresponding key nodes at the docking point of adjacent tower sections in the deviation analysis data. The core parameters of the preset second judgment criterion include the cumulative deviation change rate threshold and the transmission range critical value. The cumulative deviation change rate threshold is set to 0.1 mm / min (the setting is based on matching the conventional measurement accuracy of the total station monitoring instrument in the IoT sensing terminal, and is lower than the acceptable engineering experience value for slow deviation changes during tower section docking construction). 0.2 mm / min (to avoid misjudgment triggered by short-term minor fluctuations), the critical value of the conduction range is set at 5m (based on the conventional span of the single-segment docking area of ​​the precast tower section, distinguishing between local deviation and cross-regional diffusion risk). In the collaborative judgment logic, coefficient 'a' is set to 1.5 times (based on forming a reasonable gradient with the dynamic risk change rate threshold), and coefficient 'b' is set to 1.2 times (based on the spatial safety redundancy of the precast tower section docking area, ensuring that high risk is only judged when the affected area data exceeds the critical value by a certain proportion, avoiding misjudgment due to minor fluctuations). In specific implementation, first calculate the rate of change of the cumulative real-time deviation value of key nodes (such as N4 and N5) at the docking points of adjacent tower sections (such as T2 and T3) per unit time. The calculation method is as follows: The cumulative real-time deviation value of the same key node recorded at two consecutive moments is selected from the deviation analysis data. The cumulative real-time deviation value of the later moment is subtracted from the cumulative real-time deviation value of the earlier moment, and then divided by the time interval between these two moments. The time interval is preset to 1 minute (the setting is based on the conventional cycle of deviation trend monitoring in construction and is consistent with the unit mm / min of the cumulative deviation change rate threshold). Then, the influence range data of the cumulative real-time deviation is determined by combining the transmission range assessment parameters. Taking the key node N4 of the T2 and T3 docking as an example, if the cumulative real-time deviation value of N4 at the 10th minute is 1.0 mm and at the 11th minute is 1.08 mm, the value is calculated in the above way using ( (1.08mm-1.0mm)÷1min=0.08mm / min, yielding a change rate of 0.08mm / min (less than 0.1mm / min), and the influence range of its cumulative deviation is 3m (less than 5m). Therefore, the docking state of T2 and T3 is determined to be stable docking. The second judgment data output includes the stable state indicator, the cumulative real-time deviation change rate of 0.08mm / min, and the influence range data of 3m. If the cumulative real-time deviation value of N5 is 1.1mm in the 12th minute and 1.22mm in the 13th minute, the calculation is (1.22mm-1.1mm)÷1min=0.12mm / min, yielding a change rate of 0.12mm / min (greater than or equal to 0.1mm / min).If the rate of change is 1 mm / min and the affected area is 4 m (less than 5 m), it is considered a dynamic risk connection. The second set of output judgment data includes the dynamic risk status indicator, the rate of change of 0.12 mm / min, the affected area data of 4 m, and the direction of transmission of the cumulative real-time deviation value from N5 to N6. If the cumulative real-time deviation value of N4 is 1.3 mm at the 15th minute and 1.45 mm at the 16th minute, the calculation is 1.45 mm - 1.3 mm ÷ 1 min = 0.15 mm / min, resulting in a rate of change of 0.12 mm / min (i.e., 1.5 times 0.1 mm / min), and the affected area data expands to 6 m (i.e., 1.2 times 5 m). If the deviation is greater than or equal to 0.15 mm / min, it is considered a high-risk connection. The second set of judgment data includes a high-risk status indicator, a change rate of 0.15 mm / min, an influence range of 6 m, the direction of deviation propagation from N4 to N5, and the final stable deviation value of 0.9 mm at N4. This second judgment logic, through the synergistic evaluation of dynamic change rate and spatial diffusion range, avoids the risk omission caused by looking at only a single parameter (such as the hidden diffusion risk of low change rate but wide range), and can identify the dynamic development trend of deviation in advance. This provides adjustment direction (reverse propagation direction) and intervention priority (high risk priority) for subsequent calibration operations, effectively improving the foresight and accuracy of risk prediction in tower section connection construction.

[0112] The first calibration based on the first determination data specifically includes:

[0113] If the first determination data includes a normal status indicator, the calibration operation will not be initiated;

[0114] If the first determination data includes a status identifier to be calibrated, then based on the difference between the real-time deviation value of the key node in the first determination data and the real-time deviation value exceeding the deviation warning threshold, a linear adjustment force is applied to the corresponding key node at the docking point of two adjacent tower sections. The adjustment range is positively correlated with the difference between the real-time deviation value and the deviation warning threshold until the real-time deviation value of the key node is less than the deviation warning threshold.

[0115] If the first judgment data includes an emergency intervention status indicator, a shutdown command is first triggered to suspend the tower section docking operation. Then, based on the source deviation point location information, the real-time deviation value of the key node, and the difference between the real-time deviation value and the deviation limit threshold in the first judgment data, the two adjacent tower sections are reset in an overall attitude with the source deviation point as the reference and an adjustment margin of c times the difference between the real-time deviation value and the deviation limit threshold. After the reset, the real-time deviation value of the key node is re-acquired until the real-time deviation value is less than the deviation warning threshold.

[0116] In one embodiment, when performing the first calibration of the docking status of adjacent bridge tower sections based on the first determination data, taking the docking calibration of adjacent precast tower sections T2 and T3 as an example, the specific operation is as follows: If the first determination data includes a normal state indicator (e.g., the real-time deviation value of the key node N4 at the docking of T2 and T3 is 0.8mm, which is less than the deviation warning threshold of 1mm; the setting of the deviation warning threshold of 1mm and the deviation limit threshold of 1.5mm meets the requirement of the clause in the Technical Specification for Construction of Highway Bridges and Culverts (JTG / T 3650-2020) that the allowable deviation value of a single assembly of precast steel tower sections is less than or equal to 2mm, and the classification is further refined within the allowable range of the specification to improve the judgment accuracy), then no calibration operation is initiated, and the current docking status is maintained. If the first determination data includes a state indicator to be calibrated (e.g., the real-time deviation value of the key node N5 is 1.2mm, and the difference exceeding the deviation warning threshold of 1mm is 0.2mm), then based on the real-time deviation value of the key node and the difference exceeding the warning threshold, a linear adjustment force is applied to node N5 at the docking of T2 and T3 through a hydraulic adjustment device. The difference between the linear adjustment force and the difference exceeding the warning threshold is... The correspondence between the values ​​is determined by the quantitative formula F=k·Δd (where F represents the linear adjustment force applied by the hydraulic adjustment device to the key node, in kilonewtons (kN); Δd represents the difference between the real-time deviation value of the key node and the deviation warning threshold, i.e., 0.2mm for node N5, in millimeters (mm); k represents the stiffness coefficient of the hydraulic adjustment device, in kilonewtons / millimeter (kN / mm), and its value needs to be combined with the tower section material (such as Q345 steel, the elastic modulus of Q345 steel is 206GPa and Poisson's ratio is 0.3, providing a material mechanical parameter benchmark for stiffness coefficient calculation), the model of the hydraulic device, etc. For example, a double-acting hydraulic cylinder, specifically a double 50-ton double-acting hydraulic cylinder, is selected, matched with a hydraulic pump station with a working pressure of 63MPa to ensure that the output force accuracy meets the millimeter-level adjustment requirements. The force characteristics of the docking node are determined through mechanical simulation. In this embodiment, k is preset to 5kN / mm. The adjustment range is positively correlated with the difference between the over-warning threshold and the adjustment range. (In this case, when Δd=0.2mm, an adjustment force of F=5kN / mm×0.2mm=1kN is applied, which can push the N5 node to reduce the deviation by 0.2mm. If Δd=0.3mm, then an adjustment force of F=5kN / mm×0.3mm=1.5kN is applied.) Adjustment force (corresponding to a reduction of deviation by 0.3mm to ensure adjustment accuracy) is continuously adjusted until the real-time deviation value of the re-acquired N5 node is less than 1mm. If the first judgment data includes an emergency intervention status indicator (such as the real-time deviation value of the critical node N4 being 1.6mm, exceeding the deviation limit threshold of 1.5mm by 0.1mm, and the geographical coordinates of the bound source deviation point being X=100.5m and Y=50.6m), then a shutdown command is first triggered to suspend the tower section docking operation to prevent the deviation from further expanding. Then, based on the source deviation point, the deviation is adjusted according to 1.2 times the difference between the real-time deviation value and the limit threshold (c is set to 1).2. The setup is based on reserving an adjustment margin to offset the deformation and rebound of the tower section structure, matching the elastic recovery characteristics of materials commonly found in prefabricated tower section connection construction. This reserved adjustment margin (i.e., a reset amplitude planned with a margin of 0.1mm × 1.2 = 0.12mm) is used to perform overall attitude reset of T2 and T3 through multiple sets of synchronous adjustment devices (each set employs a configuration of dual 50-ton double-acting hydraulic cylinders + a 63MPa hydraulic pump station, and the PLC control system achieves a multi-cylinder displacement synchronization accuracy of less than or equal to 0.05mm, ensuring the consistency of displacement of each node during overall attitude reset). After reset, the real-time deviation value of node N4 is collected again via an IoT sensing terminal. This reset and collection process is repeated until the real-time deviation value of this node is less than 1mm.

[0117] The second calibration based on the second determination data specifically includes:

[0118] If the second determination data includes a stable state indicator, the calibration operation will not be initiated.

[0119] If the second judgment data includes a dynamic risk status indicator, then based on the cumulative real-time deviation value change rate, the influence range data, and the transmission direction of the cumulative real-time deviation value between two adjacent tower sections in the second judgment data, a compensation force is applied in the opposite direction of the transmission direction of the cumulative real-time deviation value. The compensation frequency of the compensation force is positively correlated with the cumulative real-time deviation value change rate, and the compensation range of the compensation force covers the influence range data of the cumulative real-time deviation value until the cumulative real-time deviation value change rate is less than the cumulative deviation change rate threshold.

[0120] If the second judgment data includes a high-risk status indicator, an early warning command is first triggered and the tower section docking operation is suspended. Then, based on the key node stable deviation value, cumulative real-time deviation value transmission direction and influence range data between two adjacent tower sections in the second judgment data, the stable deviation value is used as the target calibration value. Calibration force is synchronously applied to all key nodes within the influence range data in the opposite direction of the transmission direction of the cumulative real-time deviation value. The calibration force amplitude is positively correlated with the stable deviation value. After calibration, the cumulative real-time deviation value change rate and influence range data are recalculated until the cumulative real-time deviation value change rate is less than the cumulative deviation change rate threshold and the influence range data is less than the transmission range critical value.

[0121] In one embodiment, when performing a second calibration on the docking status of adjacent bridge tower sections based on the second determination data, taking the docking calibration of adjacent precast tower sections T2 and T3 as an example, the specific operation is as follows: If the second determination data includes a stable state indicator (e.g., the cumulative real-time deviation change rate of the key node N4 of the docking between T2 and T3 is 0.08 mm / min, which is less than the cumulative deviation change rate threshold of 0.1 mm / min, and the influence range data is 3 m, which is less than the transmission range critical value of 5 m), then no calibration operation is initiated, and the current docking status is maintained. If the second determination data includes a dynamic risk state indicator (e.g., the cumulative real-time deviation change rate of the key node N5 is 0.12 mm / min, which is greater than or equal to 0.1 mm / min), then... If the affected area is 4m (less than 5m) and the cumulative real-time deviation is transmitted in the direction of N5→N6, then based on the aforementioned rate of change, affected area data, and transmission direction, a hydraulic compensation device applies a compensation force to the associated nodes within N5 and the affected area along the reverse transmission direction (N6→N5). The compensation frequency of the compensation force is positively correlated with the rate of change, and this relationship is determined by the quantitative formula f=10×r (where f is the compensation frequency in Hertz (Hz), r is the rate of change of the cumulative real-time deviation value in millimeters per minute (mm / min), and the coefficient 10 is set based on the normal response frequency range (0.5-5Hz) of the matching hydraulic compensation device to ensure that the compensation speed is synchronized with the deviation change speed). For example, 0.12mm... The rate of change per minute corresponds to a compensation frequency of 1.2 Hz, ensuring that the compensation speed matches the rate of change of the deviation. The compensation range data completely covers the 4m influence range data. Compensation force is continuously applied and the rate of change is monitored in real time until the cumulative real-time deviation value of N5 is less than 0.1 mm / min. If the second judgment data includes a high-risk status indicator (e.g., the cumulative real-time deviation value of critical node N4 is 0.15 mm / min, which is 1.5 times greater than 0.1 mm / min; the influence range data is 6m, which is 1.2 times greater than 5m; the cumulative real-time deviation transmission direction is N4→N5; and the corresponding critical node stable deviation value is 0.9 mm), then an audible and visual warning command is triggered first, and the tower section docking operation is suspended to prevent further deviation. The process proceeds step by step, and based on the stable deviation value, transmission direction, and influence range data, with 0.9 mm as the target calibration value, a multi-node synchronous calibration system is used to synchronously apply calibration forces to the critical nodes N4 and N5 within a 6 m influence range along the reverse transmission direction (N5→N4). The calibration force amplitude is positively correlated with the stable deviation value, and the relationship between the two is determined by the quantitative formula F=10×d (where F is the calibration force amplitude in kilonewtons (kN), d is the stable deviation value of the critical node in millimeters (mm), and the coefficient 10 is set based on the stiffness characteristics of the tower section material (Q345 steel, elastic modulus 206GPa) to ensure that the calibration force can effectively offset the stable deviation while avoiding damage to the tower section structure due to excessive force). For example, 0.A stable deviation of 9mm corresponds to a calibration force of 9kN, ensuring that the calibration force matches the target deviation. After calibration, the cumulative real-time deviation change rate and influence range data are recalculated. The calibration and calculation steps are repeated until the change rate is less than 0.1mm / min and the influence range data is less than 5m. This second calibration strategy uses a graded response of dynamic compensation and synchronous calibration. In dynamic risk scenarios, compensation force is precisely applied in the opposite direction of transmission to prevent the deviation from continuing to expand. In high-risk scenarios, synchronous calibration is performed with the stable deviation value as the target to prevent the deviation from spreading across nodes. This approach balances construction efficiency and effectively controls the dynamic development trend of the deviation, further ensuring the long-term stability and construction safety of the tower section connection.

[0122] This invention addresses the problem of ambiguous deviation location in existing technologies by deploying monitoring points at key stress points of the tower section and combining them with mechanical transmission relationships to construct a dynamic deviation transmission chain that accurately traces the source, transmission path, and degree of impact of deviations. It employs a dual standard: matching real-time deviation values ​​with preset safety thresholds for the first judgment, and coordinating the cumulative deviation change rate with the transmission range for the second judgment. This achieves a comprehensive and accurate assessment of the docking status, avoiding the one-sidedness of a single judgment. The calibration process is implemented in stages based on the judgment results: operation is not initiated under normal conditions, linear adjustment is performed under conditions awaiting calibration, and shutdown and targeted compensation are provided as needed in emergency or high-risk conditions. This ensures calibration accuracy while avoiding blind operation that delays the construction period, effectively improving the efficiency and quality of tower section docking, guaranteeing the long-term service safety of the bridge structure, and reducing construction costs and risks.

[0123] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A bridge tower section docking calibration system based on the Internet of Things, characterized in that, Includes analysis and calibration modules; The analysis module sets the monitoring point positions of the tower section, compares and analyzes the attitude data of the tower section according to the design data of the tower section, generates deviation analysis data, and performs first and second judgments on the docking status of the tower section simultaneously based on the preset first judgment criteria and preset second judgment criteria, and outputs the corresponding first judgment data and second judgment data respectively. The attitude data is compared and analyzed with the design baseline attitude data based on the design data to generate deviation analysis data, specifically including: Based on the design location of each monitoring point in the design data and the mechanical transmission relationship of key stress parts, a reference deviation transmission path for tower section connection is preset. The reference deviation transmission path includes the deviation threshold and transmission coefficient of key nodes. The key nodes are the monitoring points corresponding to the key stress parts, and each key node is assigned a unique number, which serves as the key node number. Attitude data and corresponding time-series data of tower sections are continuously collected at a preset sampling frequency. The real-time deviation value of the attitude data of the monitoring point at each collection time is calculated. The real-time deviation value is the difference between the attitude data of the monitoring point and the corresponding design reference attitude data. An initial threshold is preset for the real-time deviation value. Monitoring points where the real-time deviation value exceeds the initial threshold are recorded as deviation nodes. Deviation nodes that appear for the first time in the continuously collected time series data are selected and marked as source deviation points. The geographical coordinates of the source deviation points are determined, and the geographical coordinates are the location information of the source deviation points. Using a directed graph processing algorithm, the real-time deviation value corresponding to the source deviation point is traced along the reference deviation transmission path to the adjacent key nodes. The deviation transmission gain and the accumulated real-time deviation value of each key node are calculated. The deviation transmission gain is the ratio of the real-time deviation value of the adjacent key nodes due to transmission to the initial real-time deviation value of the source deviation point. Wherein, when the ratio is greater than 1, the deviation conduction gain is determined to be the amplification coefficient after deviation conduction; when the ratio is less than 1, the deviation conduction gain is determined to be the attenuation coefficient after deviation conduction. A dynamic deviation transmission chain is constructed, which is a time series graph with the time axis as the horizontal axis and the real-time deviation value and the reference deviation transmission path of the key node as the vertical axis. The docking status of the tower sections is determined based on a preset second determination criterion, and the second determination data is output, specifically including: The second judgment criterion is preset as a collaborative judgment logic of the cumulative real-time deviation change rate and the conduction range of the key nodes at the docking point of two adjacent tower sections. The cumulative real-time deviation value and the conduction range evaluation parameters are the cumulative real-time deviation value and the conduction range evaluation parameters of the key nodes at the docking point of two adjacent tower sections in the deviation analysis data. The preset second judgment criterion includes the cumulative deviation change rate threshold and the conduction range critical value of the docking point of two adjacent tower sections. Calculate the rate of change of the cumulative real-time deviation value of each key node at the docking point of two adjacent tower sections per unit time, and determine the influence range data of the cumulative real-time deviation in the docking area of ​​the two adjacent tower sections by combining the transmission range evaluation parameters. The calibration module performs a first calibration based on the first determination data and a second calibration based on the second determination data.

2. The bridge tower section docking calibration system based on the Internet of Things as described in claim 1, characterized in that: The bridge tower section is used as the monitoring object. The monitoring points are arranged along the key stress-bearing parts of the top, bottom and side walls of the tower section. The number of monitoring points is greater than or equal to 3. The key stress-bearing parts include the connection area between the transverse diaphragm and the vertical side wall of the tower section, the edge area of ​​the opening in the side wall, the transition area between the side wall and the corner arc of the tower section, and the root area of ​​the connecting ear plate between the side wall and the auxiliary structure. The design data includes the design reference attitude data of the tower section and the design location of the monitoring point. The design reference attitude data includes the design plumb line parameters along the height direction of the tower section and the design center axis parameters of the tower section itself. Each tower section is assigned a unique number, and IoT sensing terminals are deployed at each monitoring point. The IoT sensing terminal includes a total station monitoring instrument. The total station monitoring instrument collects the geographical coordinates and attitude data of each tower section in real time according to the number of each tower section. The attitude data includes the vertical line parameters and the central axis parameters of the tower section. The tilt angle and torsion angle of the tower section are obtained by comparing the attitude data with the design reference attitude data. The tilt angle is the deviation angle of the vertical parameter of the tower section relative to the design vertical parameter, and the torsion angle is the rotational deviation angle of the central axis parameter of the tower section relative to the design central axis parameter.

3. The bridge tower section docking calibration system based on the Internet of Things as described in claim 2, characterized in that: The construction process of the dynamic deviation transmission chain is as follows: The collected time-series data is mapped to the horizontal axis of the dynamic deviation transmission chain as the time axis, and the time scale is divided according to a preset time interval that matches the sampling frequency. The collection time corresponding to the real-time deviation value of each key node is marked synchronously. The vertical axis of the dynamic deviation transmission chain is divided into a deviation value layer and a transmission path layer. The deviation value layer maps the real-time deviation value and cumulative real-time deviation value of the key nodes to the corresponding coordinate range of the vertical axis deviation value layer according to the correspondence between the preset deviation value range and the coordinate range of the deviation value layer of the vertical axis. Then, it is associated with the corresponding time scale in a preset visualization form. The transmission path layer connects each key node with directed arrows. The arrow features are adjusted according to the deviation transmission gain. When the deviation transmission gain is greater than 1, the arrow feature represents deviation amplification. When the deviation transmission gain is less than 1, the arrow feature represents deviation attenuation. The source deviation point and the key nodes in the transmission trajectory are marked with preset colors. Extract the timestamps corresponding to each time scale of the time axis, and bind the real-time deviation value, cumulative real-time deviation value, and the acquisition time corresponding to the real-time deviation value and cumulative real-time deviation value of the key nodes in the deviation value layer with the key node number, deviation transmission gain, and the time when the key node is transmitted in the transmission trajectory associated with the directed arrow in the transmission path layer through the timestamps to the corresponding time scale of the time axis, forming a correlation dataset of time, deviation value, and transmission path, and completing the establishment of a time correlation benchmark; The real-time deviation values ​​and cumulative real-time deviation values ​​of the key nodes included in the deviation value layer of the associated dataset are mapped to the coordinate range of the deviation value layer on the vertical axis of the dynamic deviation transmission chain according to the preset correspondence between the deviation value range and the coordinate range of the deviation value layer on the vertical axis. The real-time deviation values ​​are labeled with a first visualization element, and the cumulative real-time deviation values ​​are labeled with a second visualization element. The spatial positions of the real-time deviation values ​​and cumulative real-time deviation values ​​are aligned with the projection of the corresponding time scale on the horizontal axis. At the same time, the starting coordinates and ending coordinates of each directed arrow in the transmission path layer data are mapped to the coordinate range of the transmission path layer on the vertical axis according to the geographical coordinates of the key nodes, thus completing the spatial coordinate mapping. Based on the preset sampling frequency, for each new set of time-series data acquired, the steps of establishing time correlation benchmarks and performing spatial coordinate mapping are repeated to update the correlation dataset and spatial coordinate mapping relationship, synchronously refresh the first visualization element and the second visualization element of the deviation value layer and the directed arrow feature of the transmission path layer, and keep the time scale of the time axis automatically extended as the acquisition time progresses.

4. The bridge tower section docking calibration system based on the Internet of Things as described in claim 3, characterized in that: The refreshed timeline and time scale, the first and second visualization elements of the deviation value layer, the directed arrows of the transmission path layer, and the source deviation points and key nodes in the transmission trajectory marked with preset colors are superimposed and rendered on the same canvas to form a visual time series graph. The visual time series graph dynamically displays the real-time deviation value, the cumulative real-time deviation value, and whether the real-time deviation value exceeds the corresponding first preset threshold, whether the cumulative real-time deviation value exceeds the corresponding second preset threshold, and the stable deviation value at each time scale. At the same time, it displays the transmission direction, transmission path, deviation amplification or attenuation effect, and corresponding transmission time of the key nodes connected by the directed arrows. The stable deviation value is the change of the real-time deviation value within a preset time window that does not exceed a preset stable threshold. The peak time, stable deviation value, and transmission trajectory including transmission delay time of each key node in the dynamic deviation transmission chain are extracted, and the transmission direction of deviation from the source deviation point to the adjacent key node is determined.

5. The bridge tower section docking calibration system based on the Internet of Things as described in claim 4, characterized in that: The peak time, stable deviation value, conduction delay time, conduction trajectory, conduction direction and source deviation point location information, deviation conduction gain amplification factor or attenuation factor and cumulative real-time deviation value are correlated to generate correlation results. The correlation results include a one-to-one correspondence binding relationship between dynamic deviation parameters, static labeling parameters and calculation parameters. The binding relationship is that the dynamic deviation parameter of each key node is bound to the geographical coordinates of the source deviation point to which the dynamic deviation parameter belongs, the deviation conduction gain of the key node and the cumulative real-time deviation value. The dynamic deviation parameters include peak time, stable deviation value, conduction trajectory and conduction direction; the static marking parameters include source deviation point location information; and the calculation parameters include deviation conduction gain and cumulative real-time deviation value. Based on the correlation results, deviation analysis data is generated, specifically including: The association results are directly bound to the geographic coordinates of the marked source deviation points. The geographic coordinates are extracted from the association results as the location information of the source deviation points. Extract the conduction trajectory and conduction direction, including conduction delay time, from the association results. Count the total number of key nodes covered by the conduction trajectory, which is taken as the number of key nodes covered by the deviation. Based on the geographical coordinates of each key node in the conduction trajectory, calculate the straight-line distance from the geographical coordinates of the source deviation point to the geographical coordinates of the farthest key node in the conduction trajectory, which is taken as the spatial span of the deviation coverage. Extract the conduction delay time corresponding to each key node in the conduction trajectory, and take the maximum value of the conduction delay time as the conduction time of the deviation coverage. Combine the number of key nodes, spatial span, and conduction time to form a conduction range evaluation parameter based on the conduction trajectory and conduction direction. Extract the amplification factor and attenuation factor of the deviation transmission gain of each key node and the cumulative real-time deviation value from the correlation results. Calculate the product of the deviation transmission gain and the cumulative real-time deviation value of each key node. The product is used as the deviation influence value of the key node. The product value corresponding to the amplification factor represents the influence after deviation amplification, and the product value corresponding to the attenuation factor represents the influence after deviation attenuation. Combine the deviation influence values ​​of all key nodes into a set including the corresponding values ​​of each node to form an influence quantification parameter based on deviation transmission gain and cumulative real-time deviation value. The deviation analysis data includes the location information of the source deviation point, the conduction range evaluation parameters based on the conduction trajectory and conduction direction, and the influence quantification parameters based on the deviation conduction gain and the cumulative real-time deviation value.

6. The bridge tower section docking calibration system based on the Internet of Things as described in claim 5, characterized in that: The docking status of the tower sections is determined based on a preset first judgment criterion, and the first judgment data is output, specifically including: The first judgment criterion is preset as the matching judgment logic between the real-time deviation value of the key node at the docking point of two adjacent tower sections and the preset safety threshold. The preset safety threshold includes the deviation warning threshold and the deviation limit threshold corresponding to the key node. The real-time deviation value is the real-time deviation value of the key node at the docking point of two adjacent tower sections in the deviation analysis data. The matching determination logic includes: Compare the real-time deviation values ​​of each key node at the junction of two adjacent tower sections with the preset safety threshold: If the real-time deviation value is less than the deviation warning threshold, the docking status of two adjacent tower sections is determined to be normal docking, and the first determination data is output. The first determination data includes a normal status identifier and the real-time deviation value of the corresponding key node. If the real-time deviation value is greater than or equal to the deviation warning threshold and less than the deviation limit threshold, then the docking status of two adjacent tower sections is determined to be docking to be calibrated, and the first determination data is output. The first determination data includes the calibration status identifier, the real-time deviation value of the corresponding key node, and the difference between the real-time deviation value and the deviation warning threshold. If the real-time deviation value is greater than or equal to the deviation limit threshold, the docking status of two adjacent tower sections is determined to be emergency intervention docking, and the first determination data is output. The first determination data includes an emergency intervention status identifier, the real-time deviation value of the corresponding key node, the difference between the real-time deviation value and the deviation limit threshold, and the source deviation point location information of the key node at the docking point of the two adjacent tower sections.

7. The bridge tower section docking calibration system based on the Internet of Things as described in claim 6, characterized in that: The collaborative determination logic includes: If the rate of change is less than the cumulative deviation rate of change threshold and the influence range data is less than the conduction range critical value, the docking status of two adjacent tower sections is determined to be a stable docking, and second determination data is output. The second determination data includes a stable state identifier, the cumulative real-time deviation rate of change, and the influence range data. If the rate of change is greater than or equal to the cumulative deviation rate of change threshold or the influence range data is greater than or equal to the transmission range critical value, the docking status of two adjacent tower sections is determined to be dynamic risk docking, and second determination data is output. The second determination data includes dynamic risk status identifier, cumulative real-time deviation rate of change, influence range data, and the transmission direction of deviation between two adjacent tower sections. If the rate of change is greater than a times the cumulative deviation rate of change threshold and the range of influence is greater than b times the transmission range threshold, the docking status of two adjacent tower sections is determined to be a high-risk docking, and second determination data is output. The second determination data includes a high-risk status indicator, the cumulative real-time deviation rate of change, the range of influence data, the transmission direction of the deviation between the two adjacent tower sections, and the stable deviation value of the corresponding key node.

8. The bridge tower section docking calibration system based on the Internet of Things as described in claim 7, characterized in that: The first calibration based on the first determination data specifically includes: If the first determination data includes a normal status indicator, the calibration operation will not be initiated; If the first determination data includes a calibration status identifier, then based on the difference between the real-time deviation value of the key node in the first determination data and the real-time deviation value exceeding the deviation warning threshold, a linear adjustment force is applied to the corresponding key node at the docking point of two adjacent tower sections. The adjustment range is positively correlated with the difference between the real-time deviation value exceeding the deviation warning threshold until the real-time deviation value of the key node is less than the deviation warning threshold. If the first determination data includes an emergency intervention status indicator, a shutdown command is first triggered to suspend the tower section docking operation. Then, based on the source deviation point location information, the real-time deviation value of the key node, and the difference between the real-time deviation value and the deviation limit threshold in the first determination data, the two adjacent tower sections are reset in an overall attitude, with the source deviation point as the reference and an adjustment margin of c times the difference between the real-time deviation value and the deviation limit threshold. After the reset, the real-time deviation value of the key node is re-acquired until the real-time deviation value is less than the deviation warning threshold.

9. The bridge tower section docking calibration system based on the Internet of Things as described in claim 8, characterized in that: The second calibration based on the second determination data specifically includes: If the second determination data includes a stable state indicator, the calibration operation will not be initiated. If the second judgment data includes a dynamic risk status identifier, then based on the cumulative real-time deviation value change rate, the influence range data, and the transmission direction of the cumulative real-time deviation value between two adjacent tower sections in the second judgment data, a compensation force is applied in the opposite direction of the transmission direction of the cumulative real-time deviation value. The compensation frequency of the compensation force is positively correlated with the cumulative real-time deviation value change rate, and the compensation range of the compensation force covers the influence range data of the cumulative real-time deviation value until the cumulative real-time deviation value change rate is less than the cumulative deviation change rate threshold. If the second judgment data includes a high-risk status indicator, an early warning command is first triggered and the tower section docking operation is suspended. Then, based on the key node stable deviation value, cumulative real-time deviation value transmission direction and influence range data between two adjacent tower sections in the second judgment data, the stable deviation value is used as the target calibration value. Calibration force is synchronously applied to all key nodes within the influence range data in the opposite direction of the transmission direction of the cumulative real-time deviation value. The calibration force amplitude is positively correlated with the stable deviation value. After calibration, the cumulative real-time deviation value change rate and influence range data are recalculated until the cumulative real-time deviation value change rate is less than the cumulative deviation change rate threshold and the influence range data is less than the transmission range critical value.

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