Bridge dehumidification system visualization method based on multi-source data fusion

Abstract

This invention relates to the field of bridge health monitoring and operation and maintenance management technology, and particularly to a visualization method for bridge dehumidification systems based on multi-source data fusion. The method includes the following steps: S1, deploying the required sensors within the bridge dehumidification area and collecting sensor monitoring data, collecting real-time data and weather forecast data from external meteorological stations, performing timestamp alignment and denoising operations on the sensor monitoring data, the real-time data, and the weather forecast data, and outputting a spatiotemporal multidimensional fusion dataset. In this invention, by constructing a computational fluid dynamics model of the main cable microenvironment based on the porous medium assumption, and using the spatiotemporally aligned and denoised multi-source fusion data as boundary conditions to drive the full-domain flow field inversion, this invention effectively solves the technical problem that traditional point sensor monitoring cannot cover the blind areas of complex void flow channels inside the main cable, and realizes the digital reconstruction of the humidity field and airflow field in the area inside the main cable where no sensors are deployed.

Description

Technical Field

[0001] This invention relates to the field of bridge health monitoring and operation and maintenance management technology, and in particular to a visualization method for bridge dehumidification systems based on multi-source data fusion. Background Technology

[0002] The dehumidification system for bridge main cables is a crucial facility for ensuring the long-term corrosion protection of the steel wires in long-span bridges. Its operational status highly depends on the accurate monitoring and control of the humidity environment inside the main cable. However, due to the complex internal structure of the main cable, the tiny gaps between the steel wires, and the enclosed space, the internal humid and hot microenvironment exhibits a significantly non-uniform distribution, making it difficult to comprehensively acquire humidity and airflow information across the entire area using only a limited number of sensors. Therefore, a method is needed that can integrate multi-source data, reconstruct the internal microenvironment field of the main cable, and provide an intuitive visual display to support the intelligent operation and maintenance and risk warning of bridge dehumidification systems.

[0003] Existing bridge dehumidification systems primarily employ point-based sensor monitoring and threshold-triggered control, which suffers from limited monitoring coverage and blind spots in large areas within the main cable, making it difficult to promptly identify deep-seated, hidden corrosion risks. Furthermore, traditional dehumidification regulation typically relies on current humidity levels for delayed control, lacking the ability to predict future weather changes and optimize energy efficiency. This results in passive dehumidification operation, high energy consumption, and requires maintenance personnel to manually interpret large amounts of data, leading to low decision-making efficiency and hindering the rapid identification of high-risk areas and closed-loop control. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides a visualization method for bridge dehumidification systems based on multi-source data fusion. It aims to improve the existing bridge dehumidification systems, which mainly use point sensor monitoring and threshold trigger control, resulting in limited monitoring coverage and a large number of blind spots inside the main cable.

[0005] This invention provides the following technical solution: a visualization method for a bridge dehumidification system based on multi-source data fusion, comprising the following steps: S1. Deploy the required sensors inside the bridge dehumidification area and collect sensor monitoring data. Collect real-time data and weather forecast data from external meteorological stations. Perform timestamp alignment and denoising operations on the sensor monitoring data, the real-time data, and the weather forecast data, and output a spatiotemporal multidimensional fusion dataset. S2. Input the spatiotemporal multidimensional fusion dataset into a preset computational fluid dynamics model, map the spatiotemporal multidimensional fusion dataset to three-dimensional mesh nodes, calculate the humidity value and airflow vector value of the coordinate points of the main cable without sensor deployment through numerical interpolation, and output the global microenvironment field data. S3. Input the global microenvironmental field data into the metal corrosion rate calculation model to calculate the corrosion rate value of the main cable steel wire, and calculate the start-up time parameters and air volume parameters of the dehumidification equipment according to the meteorological forecast data and dehumidification energy efficiency algorithm, and output dynamic control strategy data. S4. Convert the humidity values ​​and airflow vector values ​​in the global microenvironment field data into surface visual attribute data and flow field visualization data of the three-dimensional model surface, convert the dynamic control strategy data into chart coordinate data, and output the three-dimensional rendering source data. S5. Transmit the three-dimensional rendering source data to the display terminal, modify the RGB color parameters of the three-dimensional model area that matches the global micro-environment field data according to the preset threshold judgment logic, draw the UI control layer containing the dynamic control strategy data, and output the interactive interface image. S6. Monitor the user's input signal on the interactive interface image, extract the target device identification code and operating parameter value from the input signal, encode the target device identification code and the operating parameter value into an industrial control protocol message and send it to the field controller, and output the device control signal.

[0006] Preferably, in S1, the steps of performing timestamp alignment and denoising operations on the sensor monitoring data, the real-time data, and the weather forecast data specifically include the following steps: Establish a unified reference time series with a preset sampling frequency; The sensor monitoring data, the real-time data and the weather forecast data are mapped to the unified reference time series. Interpolation operations are performed on time nodes that are missing or have inconsistent sampling frequencies in the unified reference time series to generate preliminary aligned data. The preliminary aligned data is subjected to a statistical outlier detection operation to identify abnormal data points that deviate from local statistical features. The values ​​in the neighborhood of the abnormal data points are then used for replacement and correction to generate a spatiotemporal multidimensional fusion dataset.

[0007] Preferably, in step S2, the calculation of humidity and airflow vector values ​​at sensor-free coordinate points inside the main cable using numerical interpolation to output global microenvironmental field data specifically includes the following steps: Extract the spatial coordinate parameters of the sensor's location and the values ​​of environmental physical quantities from the spatiotemporal multidimensional fusion dataset; Retrieve the boundary grid node in the preset computational fluid dynamics model that is closest to the spatial coordinate parameters, and assign the environmental physical quantity value to the boundary grid node as a boundary condition; Based on the boundary conditions, the fluid dynamics equations are solved on the internal grid nodes in the preset computational fluid dynamics model to calculate the humidity scalar value and airflow velocity vector value of the internal grid nodes. The humidity scalar value and the airflow velocity vector value are associated with the spatial coordinates of the internal grid nodes to output global microenvironment field data.

[0008] Preferably, the pre-built computational fluid dynamics model is constructed based on the porous media assumption and specifically includes the following components: Define the three-dimensional computational geometry domain that defines the physical boundary of the main cable dehumidification zone; Porous medium resistance parameters and porosity parameters characterizing the gap structure between steel wires inside the main cable; A set of governing equations describing the fluid flow state and water vapor diffusion process, wherein the set of governing equations includes at least the mass conservation equation, the momentum conservation equation, and the component transport equation.

[0009] Preferably, in step S3, the step of inputting the global microenvironmental field data into the metal corrosion rate calculation model to calculate the corrosion rate of the main cable steel wire specifically includes the following steps: By traversing the entire microenvironmental field data, the relative humidity scalar value and temperature scalar value of each three-dimensional grid node are parsed out. The pre-stored electrochemical corrosion parameters of the main cable steel wire material are retrieved, and the relative humidity scalar value and the temperature scalar value are used as input variables. A mapping operation is performed in the preset corrosion kinetic function to calculate the corrosion current density of the three-dimensional mesh node. The corrosion current density is converted and calculated with the pre-stored metal electrochemical equivalent to generate the corrosion depth per unit time of the three-dimensional mesh node, which is used as the corrosion rate value.

[0010] Preferably, in step S4, the output of the 3D rendering source data specifically includes the following steps: The global microenvironmental field data is traversed, and the humidity scalar values ​​of each grid node are mapped to a preset color gradient table to generate corresponding RGB color channel values, which are used as the surface visual attribute data. A three-dimensional vector field is constructed using the airflow velocity vector values ​​in the global microenvironment field data. The motion position sequence of the virtual tracer point in the three-dimensional vector field is calculated to generate the flow field visualization data. The dynamic control strategy data is analyzed, time dimension parameters and equipment operation dimension parameters are extracted, a two-dimensional coordinate point set is constructed, and chart coordinate data is generated. Data encapsulation is performed on the surface visual attribute data, the flow field visualization data, and the chart coordinate data to output 3D rendering source data.

[0011] Preferably, in S5, modifying the RGB color parameters of the three-dimensional model region matching the global micro-environment field data according to the preset threshold determination logic specifically includes the following steps: Extract the relative humidity value from the global microenvironment field data, compare the relative humidity value with the pre-stored graded alarm range, and determine the alarm level to which the relative humidity value belongs. Retrieve a preset color value associated with the alarm level, and use the preset color value as the target RGB color parameter; The rendering attribute data of the corresponding spatial location in the 3D rendering source data is updated using the target RGB color parameters, and the color-modified interactive interface image is output.

[0012] Preferably, in step S6, the output device control signal specifically includes the following steps: The input signal is analyzed to obtain the target device identifier and the operating parameter values; Retrieve the corresponding communication protocol configuration parameters and register address mapping table based on the target device identification code; The operating parameter values ​​are converted into formats and checked according to the requirements of the communication protocol configuration parameters, and then assembled into an industrial control protocol message. The industrial control protocol message is sent to the network address bound to the target device identifier code through the network communication interface, and the device control signal is output.

[0013] The present invention has the following beneficial effects: 1. In this invention, by constructing a computational fluid dynamics model of the microenvironment of the main cable based on the assumption of porous media, and using multi-source fusion data that has undergone spatiotemporal alignment and denoising as boundary conditions to drive the inversion of the global flow field, the technical problem of traditional point sensor monitoring being unable to cover the blind area of ​​the complex void flow channel inside the main cable is effectively solved. It realizes the digital reconstruction of the humidity field and airflow field in the area inside the main cable where no sensors are deployed, and significantly improves the perception accuracy and global coverage capability of the dehumidification system for the deep hidden corrosion risk of the main cable without significantly increasing the hardware monitoring cost.

[0014] 2. In this invention, by establishing a metal corrosion rate calculation model coupled with environmental parameters and a feedforward control algorithm based on weather forecasts, the abstract physical quantities of temperature and humidity are transformed into quantitative steel wire corrosion loss indicators. Based on this, the optimal start-up time and air volume parameters of the dehumidification equipment under future weather windows are predicted and calculated. This changes the passive mode of traditional dehumidification systems that rely solely on the current humidity threshold for lag adjustment. While ensuring the corrosion safety of the main cable steel wire throughout its entire life cycle, this invention achieves refined management of dehumidification energy consumption and optimization of system operating efficiency.

[0015] 3. In this invention, a three-dimensional visualization interactive interface that integrates surface visual attribute mapping and flow field dynamic trajectory tracing is constructed. Threshold-based automated coloring technology is used to render complex global micro-environmental field data into an intuitive red, yellow, and green health distribution image in real time. This allows maintenance personnel to accurately locate high-risk areas within milliseconds without manually interpreting massive monitoring values. Furthermore, it enables the generation of closed-loop industrial control messages to drive field equipment based on visualized control strategies, greatly reducing the threshold for professional data understanding and improving the response speed of bridge maintenance decisions. Attached Figure Description

[0016] Figure 1 This is a flowchart of a visualization method for a bridge dehumidification system based on multi-source data fusion proposed in this invention. Detailed Implementation

[0017] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention provides a visualization method for bridge dehumidification systems based on multi-source data fusion, such as... Figure 1 As shown, it includes the following steps: S1. Deploy the required sensors inside the bridge dehumidification area and collect sensor monitoring data. Collect real-time data and weather forecast data from external meteorological stations. Perform timestamp alignment and denoising operations on the sensor monitoring data, real-time data, and weather forecast data, and output a spatiotemporal multidimensional fusion dataset. Furthermore, in S1, the timestamp alignment and denoising operations performed on sensor monitoring data, real-time data, and weather forecast data specifically include the following steps: Establish a unified reference time series with a preset sampling frequency; Sensor monitoring data, real-time data and weather forecast data are mapped to a unified reference time series. Interpolation operations are performed on time nodes that are missing or have inconsistent sampling frequencies in the unified reference time series to generate preliminary aligned data. A statistical outlier detection operation is performed on the initially aligned data to identify anomalous data points that deviate from local statistical features. The values ​​in the neighborhood of the anomalous data points are then used to replace and correct them, generating a spatiotemporal multidimensional fusion dataset.

[0019] Specifically, relative humidity data inside the main cable is acquired by an array of temperature and humidity sensors deployed at key sections inside the main cable with a sampling period of 5 minutes. Real-time temperature, humidity, and wind speed data of the external environment are acquired by an automatic weather station on the bridge deck tower with a sampling period of 1 minute. Weather forecast data for the next 24 hours is obtained through the meteorological bureau's standard API interface with an update period of 1 hour. Considering the inconsistent time resolution of the above three types of data and the possibility of electromagnetic interference noise in the transmission line, the data processing module first establishes a sampling interval. Set as a unified reference time series of 10 minutes, this series contains A standard time point.

[0020] For the monitoring data from the sensors inside the main cable with a high sampling frequency, the system maps them to a unified reference time series. For reference moments in the series that lack directly corresponding measured values... The system searches for the two most recent valid measured data points before and after a given moment, and uses a linear interpolation algorithm to calculate the humidity value at that moment. The calculation formula is as follows: ; In the formula: Indicates time in the unified reference time series The interpolated value of the relative humidity inside the main cable to be determined is expressed as a percentage (%). This indicates that in the raw sensor monitoring data, the timestamp is less than... And distance Recent Moments The corresponding measured relative humidity value; This indicates that in the raw sensor monitoring data, the timestamp is greater than... And distance Recent Moments The corresponding measured relative humidity value; , , All values ​​are in Unix timestamp format, in seconds.

[0021] For meteorological forecast data with low sampling frequency and long time span, in order to avoid step-like abrupt changes affecting the convergence of subsequent fluid models, the system uses a cubic spline interpolation algorithm to smooth and refine the hourly data to 10-minute intervals. For any two adjacent known time nodes in the forecast data sequence... and Construct a cubic polynomial function describing the change of meteorological parameters over time within the specified time interval. The calculation formula is as follows: ; In the formula: Indicates the time of interpolation The forecast values ​​of meteorological parameters at the location, specifically the ambient temperature or ambient relative humidity; This indicates the known first number in the weather forecast data. Timestamps for each time point; , , , These are all coefficients of the spline interpolation polynomial, and these coefficients are derived from known data points. and The function value at a given point, along with its first and second derivatives, and the continuity boundary conditions, are determined by solving a system of simultaneous equations.

[0022] After aligning the timeline, the system performs a denoising operation based on sliding window statistics to address outliers in the sensor data that may be caused by instantaneous vibrations of field equipment or signal transmission errors. This operation constructs a sliding window covering the past hour's data length (i.e., containing six consecutive time points). For the current moment... Main cable relative humidity monitoring value Calculate the arithmetic mean of the data within this window. with standard deviation Based on this, anomaly detection and replacement are performed. The specific calculation and detection formulas are as follows: ; In the formula: Indicates the current time Original relative humidity monitoring values ​​collected from the main cable; Indicates the nearest within the sliding window The arithmetic mean of the relative humidity monitoring values; Indicates the nearest within the sliding window The standard deviation of each relative humidity monitoring value; This represents the number of samples within the sliding window, which is 6 in this embodiment; Indicates the first in the sliding window Relative humidity values ​​at historical moments; This represents the preset outlier detection threshold coefficient, which is set to 3 in this embodiment, representing a range of 3 times the standard deviation. This represents the final relative humidity value output after noise reduction processing.

[0023] This effectively solves the problem of asynchronous multi-source heterogeneous data in the time dimension and automatically removes noise data caused by environmental interference, providing a standardized and high-quality data input foundation for subsequent fluid dynamics simulation calculations.

[0024] S2. Input the spatiotemporal multidimensional fusion dataset into the preset computational fluid dynamics model, map the spatiotemporal multidimensional fusion dataset to three-dimensional mesh nodes, calculate the humidity value and airflow vector value of the coordinate points of the main cable without sensor deployment through numerical interpolation, and output the global microenvironment field data. Further, in step S2, the humidity and airflow vector values ​​at the coordinate points of the main cable where no sensors are deployed are calculated through numerical interpolation, and the global microenvironmental field data is output. This specifically includes the following steps: Extract spatial coordinate parameters and environmental physical quantity values ​​of the sensor's location from the spatiotemporal multidimensional fusion dataset; Retrieve the boundary grid node in the pre-set computational fluid dynamics model that is closest to the spatial coordinate parameters, and assign the environmental physical quantity values ​​to the boundary grid node as boundary conditions; Based on boundary conditions, the fluid dynamics equations are solved on the internal grid nodes in the pre-set computational fluid dynamics model, and the humidity scalar value and airflow velocity vector value of the internal grid nodes are calculated. The humidity scalar value and the airflow velocity vector value are correlated to the spatial coordinates of the internal grid nodes to output global microenvironment field data.

[0025] Furthermore, the pre-built computational fluid dynamics model is constructed based on the porous media assumption and specifically includes the following components: Define the three-dimensional computational geometry domain that defines the physical boundary of the main cable dehumidification zone; Porous medium resistance parameters and porosity parameters characterizing the gap structure between steel wires inside the main cable; The governing equations describing the fluid flow state and water vapor diffusion process include at least the mass conservation equation, the momentum conservation equation, and the component transport equation.

[0026] Specifically, after completing the spatiotemporal alignment and cleaning of multi-source heterogeneous data and generating a spatiotemporal multidimensional fusion dataset in step S1, step S2 then uses this dataset to drive a pre-set computational fluid dynamics model for global flow field reconstruction. The pre-set computational fluid dynamics model construction process in this step is as follows: First, a three-dimensional computational geometry domain is defined. A cylindrical fluid computational domain is constructed based on the actual physical dimensions of the bridge main cable, and a spatial discrete mesh is generated using hexahedral mesh generation technology. In order to accurately characterize the micro-channel features formed by the tightly arranged high-strength steel wires inside the main cable, the system pre-calculates the permeability parameters and inertial drag coefficient of the main cable based on the porous medium assumption and using the Kozeny-Carman equation. The calculation formula is as follows: ; ; In the formula: The permeability of the porous medium in the main cable; The inertial drag coefficient of the porous medium in the main cable; The average diameter of a single steel wire inside the main cable; The porosity is the cross-sectional porosity of the main cable.

[0027] The calculated permeability parameters and inertial drag coefficients are fixed as constant parameters in the momentum equation source term of the model. During the model operation phase, the system extracts the spatial coordinate parameters of each sensor monitoring point and its corresponding environmental physical quantity values ​​from the spatiotemporal multidimensional fusion dataset output in step S1. The environmental physical quantity values ​​include inlet air pressure, temperature, and relative humidity. Since the fluid solver uses mass fraction to calculate water vapor diffusion, the system first uses the Antoine equation to calculate the saturated vapor pressure, and then converts the relative humidity data into water vapor mass fraction by combining the measured relative humidity. Next, it traverses the grid nodes on the model boundary, calculates the Euclidean distance between the sensor coordinates and the boundary node coordinates, marks the nearest boundary node as the anchor point, and assigns the converted water vapor mass fraction and air pressure to the anchor point to construct the Dirichlet boundary conditions.

[0028] Based on the defined boundary conditions and preset drag parameters, the system iteratively solves the governing equations for all grid nodes within the model. These equations include the momentum conservation equations with the introduction of Darcy-Forchheimer source terms and the component transport equations. ; ; ; In the formula: The density of the mixed humid air inside the main cable; The airflow velocity vector within the internal gap of the main cable; It is the hydrostatic pressure of the fluid; For the viscous stress tensor of the fluid; This is the momentum sink term generated by the airflow from the main cable wire bundle; is the dynamic viscosity coefficient of air; This represents the mass fraction of water vapor in the air. is the effective diffusion coefficient of water vapor in porous media.

[0029] The system solves until the residuals converge, obtaining the water vapor mass fraction and velocity vector of each internal grid node. Finally, the system uses the local pressure and temperature field data of each node to perform an inverse thermodynamic conversion, recalculating the calculated water vapor mass fraction into a relative humidity value. Finally, the system outputs global microenvironmental field data containing the three-dimensional coordinates, airflow velocity vector, and relative humidity value of each grid node.

[0030] This step, by parameterizing the microscopic geometric features of the main cable wires into the drag coefficient of the fluid dynamics model and combining it with the bidirectional mapping of thermodynamic parameters, achieves accurate inversion of the entire humid and thermal environment inside the main cable without consuming huge computing resources, filling the data gap in the sensor monitoring blind spot.

[0031] S3. Input the full-domain microenvironmental field data into the metal corrosion rate calculation model to calculate the corrosion rate value of the main cable steel wire, and calculate the start-up time parameters and air volume parameters of the dehumidification equipment based on the meteorological forecast data and dehumidification energy efficiency algorithm, and output dynamic control strategy data. Further, in step S3, the global microenvironmental field data is input into the metal corrosion rate calculation model, and the calculation of the corrosion rate value of the main cable steel wire specifically includes the following steps: By traversing the microenvironmental field data across the entire domain, the relative humidity scalar value and temperature scalar value of each three-dimensional grid node are extracted. The pre-stored electrochemical corrosion parameters of the main cable steel wire material are retrieved, and the relative humidity scalar value and temperature scalar value are used as input variables. The mapping operation is performed in the pre-set corrosion kinetic function to calculate the corrosion current density of the three-dimensional mesh nodes. The corrosion current density is converted and calculated with the pre-stored metal electrochemical equivalent to generate the corrosion depth per unit time of the three-dimensional mesh nodes, which is used as the corrosion rate value.

[0032] Specifically, after step S2 outputs the global microenvironmental field data containing the three-dimensional coordinates, relative humidity values, and temperature values ​​of each spatial grid node inside the main cable, step S3 first performs a quantitative calculation of the corrosion rate of the main cable steel wire. The system traverses the global microenvironmental field data, extracts the relative humidity scalar value and temperature scalar value at each grid node. In order to convert the environmental parameters into corrosion risk indicators, the system retrieves the electrochemical corrosion parameters of high-strength galvanized steel wire pre-stored in the database, including the reference corrosion current density, corrosion activation energy, and material constants. The system uses the modified Arrhenius equation and power law function to construct a corrosion kinetic function, and substitutes the extracted relative humidity and temperature into the function to calculate the instantaneous corrosion current density of each grid node. The calculation formula is as follows: ; In the formula: The calculated corrosion current density at the mesh nodes; The baseline corrosion current density is set under reference conditions. The relative humidity scalar value for the grid nodes; For reference relative humidity; Humidity impact index; This represents the apparent activation energy of the steel wire corrosion reaction. It is the ideal gas constant; The temperature scalar value for the grid node; This is a reference temperature.

[0033] After obtaining the corrosion current density, the system converts it into the corrosion depth per unit time, i.e., the corrosion rate, which is commonly used in engineering, according to Faraday's law of electrolysis. The system retrieves the pre-stored electrochemical equivalents of the metal, including the molar mass of the steel wire, the charge number of the metal ions, and the density of the steel wire, and performs the following conversion calculation: ; In the formula: The calculated corrosion rate value of the main cable steel wire; Unit conversion factor; The molar mass of the steel wire material; This represents the number of electrons transferred during the reaction. It is Faraday's constant; This represents the density of the steel wire material.

[0034] After completing the corrosion risk assessment, the system generates a dynamic control strategy for the dehumidification equipment by combining meteorological forecast data. The system reads meteorological forecast data for a preset period in the future, identifies the starting point of high-risk time windows where the ambient humidity exceeds a preset threshold, and calculates the optimal start-up time and air volume of the dehumidifier based on a dehumidification energy efficiency algorithm in order to establish a drying margin before severe weather. The system constructs an optimization model with the goal of minimizing system energy consumption. It calculates the dehumidification load based on the difference between the current total moisture content inside the main cable and the target moisture content, and solves for the operating frequency and start-up advance of the air supply fan that meet the time constraints by combining the dehumidification capacity curve of the dehumidifier. The calculation logic is as follows: ; ; In the formula: The optimal air supply volume parameters were obtained through calculation; The air volume of the main cable dehumidification area; air density; This represents the current average moisture content inside the main cable. The preset target safe moisture content; The overall efficiency coefficient of the dehumidification system; This refers to the predicted time of occurrence of high humidity weather events in meteorological forecasts; The current moment; The moisture content at the dehumidifier's exhaust port; The moisture content at the dehumidifier's air outlet; Recommended start-up time parameters for the calculated dehumidifier.

[0035] The system packages and outputs the calculated corrosion rate values ​​of each grid node, the suggested start-up time parameters, and the optimal air supply parameters as dynamic control strategy data.

[0036] This step transforms invisible environmental data into quantifiable corrosion and loss indicators through an electrochemical mechanism model, and utilizes a feedforward control algorithm to achieve optimal planning of dehumidification energy consumption while ensuring the dryness and safety of the main cable, providing a scientific basis for operation and maintenance personnel to make decisions.

[0037] S4. Convert the humidity values ​​and airflow vector values ​​in the global microenvironment field data into surface visual attribute data and flow field visualization data of the three-dimensional model surface, convert the dynamic control strategy data into chart coordinate data, and output the three-dimensional rendering source data. Furthermore, in S4, outputting 3D rendering source data specifically includes the following steps: Traverse the microenvironmental field data across the entire domain, map the humidity scalar values ​​of each grid node to a preset color gradient table, and generate corresponding RGB color channel values ​​as surface visual attribute data. A three-dimensional vector field is constructed using the airflow velocity vector values ​​in the global microenvironment field data. The motion position sequence of virtual tracer points in the three-dimensional vector field is calculated to generate flow field visualization data. Analyze the dynamic control strategy data, extract time dimension parameters and equipment operation dimension parameters, construct a two-dimensional coordinate point set, and generate chart coordinate data; Perform data encapsulation on surface visual attribute data, flow field visualization data, and chart coordinate data, and output 3D rendering source data.

[0038] Specifically, after completing the corrosion rate calculation and dynamic control strategy generation in step S3, step S4 is mainly responsible for converting the complex physical field data into rendering source data that the graphics rendering engine can directly recognize. The system first traverses the global microenvironment field data generated in step S2, extracts the relative humidity value of each grid node as a scalar field data source, and in order to intuitively display the humidity distribution differences inside the main cable, the system presets a color gradient table consisting of blue corresponding to low humidity and red corresponding to high humidity. This table contains several key humidity nodes and their corresponding RGB color values. For any grid node... The system uses a linear interpolation algorithm to measure its relative humidity value. The mapping to specific RGB color channel values ​​is calculated using the following formula: ; In the formula: For the calculated mesh nodes The target color vector contains three components: red (R), green (G), and blue (B). For grid nodes The relative humidity value; For color gradient tables, not greater than And closest The lower bound humidity node value; For color gradient tables greater than And closest The upper bound humidity node value; This is the preset RGB color vector corresponding to the lower bound humidity node; This is the preset RGB color vector corresponding to the upper bound humidity node.

[0039] The system combines the calculated RGB values ​​of all mesh nodes to generate surface visual attribute data, which is used to drive the subsequent surface shading of the 3D model.

[0040] Next, the system constructs a discrete three-dimensional vector field using the airflow velocity vector values ​​output in step S2, and sets several virtual tracer points at the air inlet position. To simulate the flow trajectory of the airflow in the main cable gap, the system uses a fourth-order Runge-Kutta integral algorithm to calculate the sequence of motion positions of the virtual tracer points in the vector field. Assuming the position of the virtual tracer point at a certain moment is... The integration time step is Then the position at the next moment The calculation formula is as follows: ; ; ; ; ; In the formula: The virtual tracer point spatial coordinate vector corresponding to the current integration step; Coordinates in a three-dimensional vector field The velocity vector at a given location is obtained by interpolating the velocity values ​​of neighboring grid nodes; The time step of the virtual particle's motion; These are all intermediate slope vectors in the algorithm process.

[0041] The system repeatedly performs the above calculations until the preset trajectory length is reached, generating flow field visualization data containing a series of spatial coordinate points.

[0042] Simultaneously, the system analyzes the dynamic control strategy data output in step S3, extracts the dehumidifier's recommended air volume parameters and equipment start-up time parameters that change over time, constructs a two-dimensional coordinate point set with the time axis as the horizontal axis and the equipment operating parameter values ​​as the vertical axis, serializes and encapsulates the generated surface visual attribute data, flow field visualization data, and chart coordinate data according to a predefined JSON data structure, and outputs three-dimensional rendering source data.

[0043] This step transforms abstract engineering calculation results into a visual graphical data structure, realizing a standardized mapping from physical field data to computer graphics data, and providing the necessary data foundation for the subsequent intuitive presentation of the internal microenvironment and control strategies of the main cable on the terminal screen.

[0044] S5. Transmit the 3D rendering source data to the display terminal, modify the RGB color parameters of the 3D model area that matches the global micro-environment field data according to the preset threshold judgment logic, draw the UI control layer containing dynamic adjustment strategy data, and output the interactive interface image. Furthermore, in S5, modifying the RGB color parameters of the 3D model region that matches the global micro-environment field data according to the preset threshold judgment logic specifically includes the following steps: Extract the relative humidity value from the microenvironmental field data of the entire domain, compare the relative humidity value with the pre-stored graded alarm range, and determine the alarm level to which the relative humidity value belongs. Retrieve preset color values ​​associated with alarm levels and use the preset color values ​​as target RGB color parameters; Update the rendering attribute data of the corresponding spatial location in the 3D rendering source data using the target RGB color parameters, and output the interactive interface image with modified color.

[0045] Specifically, after generating the 3D rendering source data containing surface visual attributes, flow field visualization data, and chart coordinate data in step S4, step S5 mainly performs the final image synthesis and interactive logic processing in the graphics processing unit of the display terminal. After receiving the 3D rendering source data, in order to enable maintenance personnel to quickly identify high-risk corrosion areas inside the main cable, the system starts a threshold-based color judgment and modification program. The system traverses the relative humidity values ​​in the data packet that correspond one-to-one with the 3D model grid nodes and defines them as variables to be judged. The system calls the pre-stored graded alarm strategy, which contains several humidity ranges defined by the corrosion critical humidity threshold. For each grid node... relative humidity value The system calculates the corresponding alarm level color vector using a piecewise function. The calculation formula is as follows: ; In the formula: The calculated target RGB color parameters will override the original color attributes generated in step S4; For the currently processed grid node The relative humidity value; The preset threshold for a Level 1 yellow alert is, for example, 40%. The preset level 2 red alarm threshold, for example, is 60%; A preset color vector representing a safe state, such as green; A preset color vector representing the warning status, such as yellow; A preset color vector, such as red, to represent a critical alarm state.

[0046] The system updates the vertex color buffer stored in the video memory using the calculated target RGB color parameters, and instructs the rendering engine to execute the shader program to map the modified color data onto the surface of the 3D main cable model. At the same time, the system loads the 2D graphics rendering layer, reads the chart coordinate data generated in step S4, plots the dehumidifier start-up time and air volume parameters as a dynamic trend curve, and generates UI controls that include start, stop, and parameter setting functions. Finally, the graphics rendering engine mixes the 3D scene layer and the 2D UI control layer using frame buffers to output a complete interactive interface image.

[0047] This step uses an automated threshold coloring mechanism to transform complex three-dimensional humidity field data into an intuitive health distribution map, enabling maintenance personnel to locate corrosion risk points in milliseconds without needing to interpret specific values. Combined with visualized control strategy data, they can directly complete equipment management operations on the interface.

[0048] S6. Monitor the user's input signals on the interactive interface image, extract the target device identification code and operating parameter values ​​from the input signals, encode the target device identification code and operating parameter values ​​into industrial control protocol messages and send them to the field controller, and output device control signals.

[0049] Furthermore, in S6, the output device control signal specifically includes the following steps: Analyze the input signal to obtain the target device identification code and operating parameter values; Retrieve the corresponding communication protocol configuration parameters and register address mapping table based on the target device identification code; The operating parameter values ​​are converted into formats and check codes are calculated according to the requirements of the communication protocol configuration parameters, and then assembled into industrial control protocol messages. The industrial control protocol message is sent to the network address bound to the target device identification code through the network communication interface, and the device control signal is output.

[0050] Specifically, after step S5 displays an interactive interface image to the user that includes the main cable corrosion risk status and suggested dehumidifier start-up time and air volume parameters, step S6 is responsible for responding to the operation instructions of the maintenance personnel and converting them into control electrical signals for the physical equipment. The system detects touch or mouse click events on the display terminal screen in real time. When the maintenance personnel click the parameter confirmation button on the interface or manually modify the dehumidifier operating settings, the system captures the input signal.

[0051] The parsing module decodes the input signal and extracts the target device identification code and specific operating parameter values. For example, the system identifies from the signal that the user intends to control the dehumidifier numbered 03 located at the mid-span of the main cable and sets the target air volume to the optimal air volume value calculated in step S3.

[0052] The system uses the extracted target device identifier as an index key to search the pre-set communication configuration database and obtain the communication protocol type, network IP address, port number and register address mapping table corresponding to the device. Taking the Modbus protocol commonly used in industrial sites as an example, the system determines the holding register address corresponding to the operating parameter value according to the mapping table and converts the decimal physical quantity value into the hexadecimal data format specified by the protocol.

[0053] To ensure the accuracy of control command transmission in complex industrial electromagnetic environments, the system performs a cyclic redundancy check (CRC) operation on the original message frame containing the address code, function code, and data segment. The formula for calculating the checksum is as follows: ; In the formula: The calculated check polynomial is the check value that is ultimately appended to the end of the message. The polynomial formed by the original message frame data; To shift the original message left Position, among which It is usually 16, corresponding to the CRC-16 standard; This is a preset generator polynomial, such as 0x8005 in the Modbus protocol; This is the modulo-2 division operator.

[0054] The system appends the calculated checksum to the end of the original message frame to assemble a complete industrial control protocol message. Then, the system calls the network communication interface to establish a TCP or UDP connection with the field programmable logic controller and sends the protocol message to the specific network address bound to the target device identifier. After the field controller receives and parses the message, it drives the frequency converter or relay to change the actual operating state of the dehumidification equipment.

[0055] This step bridges the final gap between digital twin decision-making and physical device execution, ensuring that the control strategy generated based on multiphysics simulation can be applied accurately and safely to the real bridge dehumidification system, thus achieving closed-loop control.

[0056] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A visualization method for bridge dehumidification systems based on multi-source data fusion, characterized in that, Includes the following steps: S1. Deploy the required sensors inside the bridge dehumidification area and collect sensor monitoring data. Collect real-time data and weather forecast data from external meteorological stations. Perform timestamp alignment and denoising operations on the sensor monitoring data, the real-time data, and the weather forecast data, and output a spatiotemporal multidimensional fusion dataset. S2. Input the spatiotemporal multidimensional fusion dataset into a preset computational fluid dynamics model, map the spatiotemporal multidimensional fusion dataset to three-dimensional mesh nodes, calculate the humidity value and airflow vector value of the coordinate points of the main cable without sensor deployment through numerical interpolation, and output the global microenvironment field data. S3. Input the global microenvironmental field data into the metal corrosion rate calculation model to calculate the corrosion rate value of the main cable steel wire, and calculate the start-up time parameters and air volume parameters of the dehumidification equipment according to the meteorological forecast data and dehumidification energy efficiency algorithm, and output dynamic control strategy data. S4. Convert the humidity values ​​and airflow vector values ​​in the global microenvironment field data into surface visual attribute data and flow field visualization data of the three-dimensional model surface, convert the dynamic control strategy data into chart coordinate data, and output the three-dimensional rendering source data. S5. Transmit the three-dimensional rendering source data to the display terminal, modify the RGB color parameters of the three-dimensional model area that matches the global micro-environment field data according to the preset threshold judgment logic, draw the UI control layer containing the dynamic control strategy data, and output the interactive interface image. S6. Monitor the user's input signal on the interactive interface image, extract the target device identification code and operating parameter value from the input signal, encode the target device identification code and the operating parameter value into an industrial control protocol message and send it to the field controller, and output the device control signal.

2. The visualization method for a bridge dehumidification system based on multi-source data fusion according to claim 1, characterized in that, In S1, timestamp alignment and noise reduction operations are performed on the sensor monitoring data, the real-time data, and the weather forecast data, specifically including the following steps: Establish a unified reference time series with a preset sampling frequency; The sensor monitoring data, the real-time data and the weather forecast data are mapped to the unified reference time series. Interpolation operations are performed on time nodes that are missing or have inconsistent sampling frequencies in the unified reference time series to generate preliminary aligned data. The preliminary aligned data is subjected to a statistical outlier detection operation to identify abnormal data points that deviate from local statistical features. The values ​​in the neighborhood of the abnormal data points are then used for replacement and correction to generate a spatiotemporal multidimensional fusion dataset.

3. The visualization method for a bridge dehumidification system based on multi-source data fusion according to claim 1, characterized in that, In step S2, the humidity and airflow vector values ​​at the coordinate points of the main cable where no sensors are deployed are calculated through numerical interpolation, and the global microenvironmental field data is output. This specifically includes the following steps: Extract the spatial coordinate parameters of the sensor's location and the values ​​of environmental physical quantities from the spatiotemporal multidimensional fusion dataset; Retrieve the boundary grid node in the preset computational fluid dynamics model that is closest to the spatial coordinate parameters, and assign the environmental physical quantity value to the boundary grid node as a boundary condition; Based on the boundary conditions, the fluid dynamics equations are solved for the internal grid nodes in the preset computational fluid dynamics model, and the humidity scalar value and airflow velocity vector value of the internal grid nodes are calculated. The humidity scalar value and the airflow velocity vector value are associated with the spatial coordinates of the internal grid nodes to output global microenvironment field data.

4. The visualization method for a bridge dehumidification system based on multi-source data fusion according to claim 3, characterized in that, The pre-built computational fluid dynamics model is based on the porous media assumption and includes the following components: Define the three-dimensional computational geometry domain that defines the physical boundary of the main cable dehumidification zone; Porous medium resistance parameters and porosity parameters characterizing the gap structure between steel wires inside the main cable; A set of governing equations describing the fluid flow state and water vapor diffusion process, wherein the set of governing equations includes at least the mass conservation equation, the momentum conservation equation, and the component transport equation.

5. The visualization method for a bridge dehumidification system based on multi-source data fusion according to claim 1, characterized in that, In step S3, the global microenvironmental field data is input into the metal corrosion rate calculation model, and the calculation of the corrosion rate value of the main cable steel wire specifically includes the following steps: By traversing the entire microenvironmental field data, the relative humidity scalar value and temperature scalar value of each three-dimensional grid node are parsed out. The pre-stored electrochemical corrosion parameters of the main cable steel wire material are retrieved, and the relative humidity scalar value and temperature scalar value are used as input variables. A mapping operation is performed in the pre-set corrosion kinetic function to calculate the corrosion current density of the three-dimensional mesh node. The corrosion current density is converted and calculated with the pre-stored metal electrochemical equivalent to generate the corrosion depth per unit time of the three-dimensional mesh nodes, which is used as the corrosion rate value.

6. The visualization method for a bridge dehumidification system based on multi-source data fusion according to claim 1, characterized in that, In S4, the output of the 3D rendering source data specifically includes the following steps: The global microenvironmental field data is traversed, and the humidity scalar values ​​of each grid node are mapped to a preset color gradient table to generate corresponding RGB color channel values, which are used as the surface visual attribute data. A three-dimensional vector field is constructed using the airflow velocity vector values ​​in the global microenvironment field data. The motion position sequence of the virtual tracer point in the three-dimensional vector field is calculated to generate the flow field visualization data. The dynamic control strategy data is analyzed, time dimension parameters and equipment operation dimension parameters are extracted, a two-dimensional coordinate point set is constructed, and chart coordinate data is generated. Data encapsulation is performed on the surface visual attribute data, the flow field visualization data, and the chart coordinate data to output 3D rendering source data.

7. The visualization method for a bridge dehumidification system based on multi-source data fusion according to claim 1, characterized in that, In S5, modifying the RGB color parameters of the three-dimensional model region that matches the global micro-environment field data according to the preset threshold judgment logic specifically includes the following steps: Extract the relative humidity value from the global microenvironment field data, compare the relative humidity value with the pre-stored graded alarm range, and determine the alarm level to which the relative humidity value belongs. Retrieve a preset color value associated with the alarm level, and use the preset color value as the target RGB color parameter; The rendering attribute data of the corresponding spatial location in the 3D rendering source data is updated using the target RGB color parameters, and the color-modified interactive interface image is output.

8. The visualization method for a bridge dehumidification system based on multi-source data fusion according to claim 1, characterized in that, In S6, the output device control signal specifically includes the following steps: The input signal is analyzed to obtain the target device identifier and the operating parameter values; Retrieve the corresponding communication protocol configuration parameters and register address mapping table based on the target device identifier code; The operating parameter values ​​are converted into formats and checked according to the requirements of the communication protocol configuration parameters, and then assembled into an industrial control protocol message. The industrial control protocol message is sent to the network address bound to the target device identifier code through the network communication interface, and the device control signal is output.

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