Bridge intelligent dehumidification system operation method based on environmental parameter sensing

Abstract

This invention relates to a method for operating an intelligent bridge dehumidification system based on environmental parameter sensing, comprising the following steps: First, collecting the temperature and humidity of the bridge's main cable, dehumidification airflow, and external environmental parameters to form environmental data; then, preprocessing and fusing the environmental data to generate the main cable's environmental status; next, based on the main cable's environmental status data and an environmental status prediction model, predicting environmental changes within a prediction time window to generate environmental status prediction data; generating operating parameters and timing strategies for the dehumidification execution unit according to dehumidification control rules; then controlling the operation of the dehumidification execution unit and continuously updating the environmental data; finally, in the event of communication or control anomalies, switching between centralized and local control according to mode switching rules and synchronizing data. This invention collects the temperature and humidity of the main cable, dehumidification airflow, and external environmental parameters, and preprocesses, fuses, predicts, and regulates the data to achieve continuous sensing and closed-loop control of the main cable's environment, improving the humidity lag and discontinuous control problems of manual inspection or single-point monitoring.

Description

Technical Field

[0001] This invention relates to the field of bridge dehumidification technology, and in particular to an operation method for an intelligent bridge dehumidification system based on environmental parameter sensing. Background Technology

[0002] With the large-scale construction and long-term service of long-span bridges, especially steel structure bridges such as suspension bridges and cable-stayed bridges, the steel wires inside the main cables are in a relatively closed environment for extended periods, making them susceptible to corrosion due to changes in humidity, temperature, and external climate conditions. To slow down the corrosion of the main cable wires and improve the durability of the bridge structure, a main cable dehumidification system is usually installed in engineering projects. This system continuously or intermittently supplies dry air into the main cable to reduce the humidity level inside the cable.

[0003] Traditional bridge main cable dehumidification methods mostly rely on manual inspection or single-point monitoring. Due to the lack of unified perception and dynamic prediction of multi-source environmental parameters, the response to humidity changes inside the main cable is lagging and the control is discontinuous. Summary of the Invention

[0004] To overcome the above shortcomings, this invention provides an operation method for a bridge intelligent dehumidification system based on environmental parameter perception. It aims to improve the problem that traditional bridge main cable dehumidification mostly relies on manual inspection or single-point monitoring, which easily leads to delayed response and discontinuous control of humidity changes inside the main cable.

[0005] This invention provides the following technical solution: a method for operating an intelligent bridge dehumidification system based on environmental parameter sensing, comprising the following steps:

[0006] S1. Collect humidity and temperature parameters inside the main cable of the bridge, collect the operating parameters of the dehumidifying airflow, and collect the external environmental parameters of the bridge site to form corresponding environmental parameter collection data.

[0007] S2. The environmental parameter acquisition data is preprocessed, and the environmental parameter acquisition data from different sources are fused to generate environmental status data that characterizes the internal environmental state of the main cable.

[0008] S3. Based on the environmental status data, predict the changes in the internal environmental status of the main cable according to the preset environmental status prediction model and prediction time window, and generate environmental status prediction data.

[0009] S4. Based on the environmental state prediction data, generate a dehumidification control strategy including the operating parameters and operating sequence of the dehumidification execution unit according to the preset dehumidification control rules.

[0010] S5. Control the operation of the dehumidification execution unit according to the dehumidification control strategy, and continuously collect and update environmental parameter data during the dehumidification execution process;

[0011] S6. When a communication or control anomaly is detected, the system switches between a preset centralized control mode and a local control mode according to preset mode switching rules, and performs local storage or synchronization processing on the running data.

[0012] By adopting the above technical solution, by collecting humidity parameters, temperature parameters, dehumidification airflow operation parameters, and external environmental parameters of the bridge main cable, and by preprocessing, fusing, predicting, and controlling the collected environmental parameter data, continuous perception and closed-loop control of the internal environmental state of the main cable can be achieved. This improves the problem that traditional bridge main cable dehumidification mostly relies on manual inspection or single-point monitoring, which lacks unified perception and dynamic prediction of multi-source environmental parameters, resulting in delayed response and discontinuous control of humidity changes inside the main cable.

[0013] Furthermore, in S1, the steps of collecting the operating parameters of the dehumidified airflow and collecting the external environmental parameters of the bridge site to form corresponding environmental parameter collection data include:

[0014] Data acquisition units are installed in the supply air duct and return air duct of the dehumidification execution unit to acquire operating parameters that characterize the operating status of the dehumidification airflow.

[0015] The operating parameters are associated with the corresponding time information to generate dehumidification airflow operating parameter data;

[0016] Obtain the external environmental parameters of the bridge site area and collect the data of the external environmental parameters;

[0017] The dehumidification airflow operation parameter data, along with the external environmental parameters and the humidity and temperature parameters inside the main cable of the bridge, are uniformly collected to form environmental parameter acquisition data.

[0018] Furthermore, in S2, the step of preprocessing the environmental parameter acquisition data includes:

[0019] The collected raw environmental parameter data is denoised to remove transient outliers and measurement errors.

[0020] Missing data is filled by linear interpolation or nearest neighbor substitution.

[0021] Data from different acquisition frequencies are sampled synchronously over time to unify them to the same time step.

[0022] The preprocessed data is categorized and stored according to parameter type to generate a standardized environmental parameter dataset.

[0023] Furthermore, in S2, the step of fusing environmental parameter acquisition data from different sources includes:

[0024] The preprocessed data from sensors inside the main cable, dehumidification airflow sensors, and external meteorological data are uniformly identified and time-aligned.

[0025] Perform weighted averaging or filtering fusion on parameters of the same type;

[0026] Different types of parameters are combined and processed according to logical relationships, such as the relationship between temperature and humidity, and the relationship between air supply parameters and environmental parameters.

[0027] Output a set of environmental status data to characterize the internal environment of the main cable, forming a unified data interface.

[0028] Furthermore, in S3, the step of predicting the changes in the internal environmental state of the main cable according to the preset environmental state prediction model and prediction time window includes:

[0029] The internal temperature, humidity, and air supply parameters of the main cable are extracted from the environmental condition data and organized into an input data sequence according to the time series.

[0030] According to the preset prediction time window, the input data sequence is segmented to form sliding time window data;

[0031] The sliding time window data is input into the preset environmental state prediction model to calculate the change value of the internal environmental state of the main cable within the prediction time window.

[0032] The calculated future internal temperature, humidity, and air supply parameters of the main cable are combined according to time series to form the environmental state prediction data set, and output for subsequent dehumidification control strategy generation.

[0033] Furthermore, in S4, the step of generating a dehumidification control strategy, including the operating parameters and operating sequence of the dehumidification execution unit, according to preset dehumidification control rules, includes:

[0034] Use the environmental state prediction data as input data;

[0035] Set dehumidification control targets and equipment operating constraints;

[0036] Based on the input data, control objectives, and constraints, the operating parameters and operating sequence of each dehumidification execution unit within the future time window are calculated using a model predictive control algorithm.

[0037] The operating parameters and operating sequence are compiled to generate a dehumidification control strategy that can be directly executed;

[0038] The dehumidification control strategy is output to the dehumidification execution unit.

[0039] Furthermore, in S5, the step of controlling the operation of the dehumidification execution unit according to the dehumidification control strategy includes:

[0040] Receive the start-stop time, operating frequency, and valve opening parameters of each dehumidification execution unit in the dehumidification control strategy;

[0041] The dehumidification control strategy is converted into a sequence of control commands and sent to the corresponding dehumidification execution unit;

[0042] The dehumidification unit is started, adjusted, or stopped according to control commands to form a predetermined dehumidification airflow covering the main cable area.

[0043] Monitor the feedback status of each dehumidification unit, including operating power and output airflow parameters, and ensure that its execution is consistent with the dehumidification control strategy.

[0044] Furthermore, in S5, the step of continuously collecting and updating environmental parameter data during the dehumidification process includes:

[0045] Continuously collect humidity and temperature data inside the main cable;

[0046] Simultaneously collect relevant parameters of the dehumidification airflow, including air supply pressure and air volume;

[0047] Obtain external environmental parameters of the bridge site, including air humidity, temperature, and rainfall information;

[0048] The collected data is transmitted in real time to the data processing unit or controller for environmental status updates and subsequent control.

[0049] Furthermore, in S6, the step of switching between a preset centralized control mode and a local control mode according to preset mode switching rules includes:

[0050] The communication status and feedback signals from the dehumidification execution unit are detected in real time by a controller or programmable logic controller.

[0051] Determine whether the switching conditions are met based on the preset mode switching rules;

[0052] Before switching, key operating parameters in centralized control mode are stored in local cache or controller memory;

[0053] The controller or programmable logic controller switches to local control mode and activates the local automatic control program to independently control the operation of the dehumidification unit.

[0054] Update the internal mode identifier of the system, write the mode flag corresponding to the current control mode into the system status variable or control register, and send the mode switching identifier information to each submodule;

[0055] Once the communication or control anomaly is resolved, the centralized control mode is restored according to preset rules, and local operating data is synchronized to the centralized control system.

[0056] The present invention has the following beneficial effects:

[0057] 1. In this invention, by collecting humidity parameters, temperature parameters, dehumidification airflow operation parameters, and external environmental parameters of the bridge main cable, and by preprocessing, fusing, predicting, and regulating the collected environmental parameter data, continuous perception and closed-loop regulation of the internal environmental state of the main cable are achieved. This improves the problem that traditional bridge main cable dehumidification methods mostly rely on manual inspection or single-point monitoring, which lack unified perception and dynamic prediction of multi-source environmental parameters, resulting in delayed response and discontinuous regulation of humidity changes inside the main cable.

[0058] 2. In this invention, environmental parameter data collected from the inside of the main cable, the dehumidifying airflow, and the external environment are denoised, completed, time-synchronized, and multi-source fused to form unified environmental status data. This improves the problem that traditional main cable environmental monitoring mostly uses raw data directly for analysis, which leads to unstable environmental status judgment and large deviations in analysis results due to the dispersed data sources and inconsistent time bases.

[0059] 3. In this invention, by using environmental state data and combining a preset environmental state prediction model and prediction time window, the changes in the internal environmental state of the main cable are predicted, generating environmental state prediction data. This improves the problem that traditional dehumidification control mostly relies on current or historical states for judgment, and lacks the ability to predict future environmental change trends, resulting in delayed dehumidification control decisions and insufficient targeted regulation.

[0060] 4. In this invention, a dehumidification control strategy containing the operating parameters and operating sequence of the dehumidification execution unit is generated based on environmental state prediction data and according to preset dehumidification control rules. The dehumidification control strategy is then converted into control commands to drive the dehumidification execution unit to operate. This improves the problem that traditional main cable dehumidification systems mostly use fixed parameters or manually set operating modes, which lack coordinated planning of operating parameters and timing, resulting in unstable dehumidification efficiency and insufficient equipment operation coordination.

[0061] 5. In this invention, when a communication or control anomaly is detected, the system switches between a centralized control mode and a local control mode according to a preset mode switching rule, and performs local storage and synchronization processing on key operating parameters and operating data. This improves the problem that traditional bridge dehumidification systems mostly rely on a single centralized control method, which lacks an effective operation takeover mechanism in the event of a communication or control anomaly, resulting in system operation interruption or loss of operating data. Attached Figure Description

[0062] Figure 1 This is a flowchart of the operation method of the intelligent bridge dehumidification system based on environmental parameter sensing proposed in this invention. Detailed Implementation

[0063] 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.

[0064] In embodiments of the present invention, a method for operating a bridge intelligent dehumidification system based on environmental parameter sensing is provided, such as... Figure 1 As shown, the process includes the following steps: S1, collecting humidity and temperature parameters inside the main cable of the bridge, collecting the operating parameters of the dehumidifying airflow, and collecting external environmental parameters of the bridge site to form corresponding environmental parameter collection data.

[0065] Furthermore, in S1, the steps of collecting operating parameters of the dehumidified airflow and collecting external environmental parameters of the bridge site to form corresponding environmental parameter acquisition data include:

[0066] Data acquisition units are installed in the supply air duct and return air duct of the dehumidification execution unit to acquire operating parameters that characterize the operating status of the dehumidification airflow.

[0067] The operating parameters are correlated with the corresponding time information to generate dehumidification airflow operating parameter data;

[0068] Obtain the external environmental parameters of the bridge site area and collect the data of the external environmental parameters;

[0069] The dehumidification airflow operation parameters data are collected together with external environmental parameters and humidity and temperature parameters inside the main cable of the bridge to form environmental parameter acquisition data.

[0070] Specifically, the environmental parameter data is generated through the synchronous sensing and data integration of the internal environment of the main cable, the operating status of the dehumidification airflow, and the external environment of the bridge site. This is achieved through multi-source sensor acquisition and time-aligned data aggregation and processing. Humidity and temperature sensors are deployed axially or radially inside the main cable to collect real-time humidity and temperature parameters of the air inside the main cable. These parameters reflect the microenvironmental state inside the main cable and serve as the basic input data for subsequent environmental state analysis. Several humidity and temperature sensors are deployed axially inside the main cable, with one sensor at regular intervals, and multiple sensors are deployed radially to ensure comprehensive monitoring of the internal environment of the main cable. At least one airflow parameter acquisition unit is installed in both the supply and return air channels of the dehumidification execution unit to comprehensively collect supply and return air pressures. Simultaneously, airflow parameter acquisition units are installed in the supply and return air channels of the dehumidification execution unit to collect the operating parameters of the dehumidification airflow. These operating parameters include at least one of the following: supply air pressure, return air pressure, airflow volume, or airflow velocity. Multiple data collection methods are used to characterize the actual operating state of the dehumidifying airflow within the main cable. The collected dehumidifying airflow operating parameters are assigned corresponding time stamps during acquisition, forming a time-varying data sequence to ensure the alignment of data from different sources across time dimensions. Furthermore, external environment acquisition units are deployed in the bridge site area to acquire environmental parameters outside the bridge site, including air humidity, ambient temperature, and rainfall information. These external environmental parameters are acquired through meteorological sensors or meteorological data interfaces and then processed. Subsequently, the dehumidifying airflow operating parameter data with unified time stamps, the external environmental parameter data, and the humidity and temperature parameters collected inside the main cable are uniformly aggregated to form a structured environmental parameter acquisition data set. This environmental parameter acquisition data serves as a comprehensive characterization of the internal and external environment of the main cable and the dehumidification operating state, and is used as input data for subsequent environmental parameter preprocessing, environmental state data fusion, and environmental state prediction, thus providing a data foundation for generating dehumidification system operation strategies.

[0071] S2. Preprocess the environmental parameter acquisition data and fuse the environmental parameter acquisition data from different sources to generate environmental status data that characterizes the internal environmental state of the main cable.

[0072] Furthermore, in S2, the preprocessing steps for the collected environmental parameter data include:

[0073] The collected raw environmental parameter data is denoised to remove transient outliers and measurement errors.

[0074] Missing data is filled by linear interpolation or nearest neighbor substitution.

[0075] Data from different acquisition frequencies are sampled synchronously over time to unify them to the same time step.

[0076] The preprocessed data is categorized and stored according to parameter type to generate a standardized environmental parameter dataset.

[0077] Specifically, environmental status data is generated through unified preprocessing and fusion of multi-source environmental parameter acquisition data. This includes data cleaning, missing data completion, time synchronization, and structured processing. The environmental parameter acquisition data serves as input, originating from humidity and temperature parameters collected within the main cable, dehumidification airflow parameters, and external environmental parameters at the bridge site. During acquisition, this data may contain abnormal fluctuations due to sensor noise, transient interference, or communication jitter. Therefore, the raw environmental parameter data is first denoised. Transient outliers and measurement errors significantly deviating from normal trends are filtered out by smoothing or thresholding the time series data, resulting in a noise-suppressed environmental parameter data sequence. Furthermore, for data gaps caused by short-term sensor failures or communication interruptions, missing data points are completed using a linear data completion method. Interpolation methods are used to estimate missing values ​​based on valid data from adjacent time points, or to employ nearest-neighbor substitution methods to ensure the continuity of the data sequence. Subsequently, due to differences in the acquisition frequency of different types of environmental parameters, time-synchronized sampling is performed on various types of environmental parameter data after denoising and completion processing. Through resampling, each data sequence is unified to the same time step, forming a time-aligned data set. Finally, the time-synchronized environmental parameter data is classified and stored according to parameter type, generating a standardized environmental parameter dataset. This standardized environmental parameter dataset serves as the output result for subsequent data fusion processing between environmental parameters from different sources. Further fusion generates environmental state data that comprehensively reflects the internal environmental state of the main cable, serving as the basic input data for environmental state prediction and dehumidification control strategy generation, thereby supporting state judgment and control decisions during the operation of the dehumidification system.

[0078] Furthermore, in S2, the step of fusing environmental parameter acquisition data from different sources includes:

[0079] The preprocessed data from sensors inside the main cable, dehumidification airflow sensors, and external meteorological data are uniformly identified and time-aligned.

[0080] Perform weighted averaging or filtering fusion on parameters of the same type;

[0081] Different types of parameters are combined and processed according to logical relationships, such as the relationship between temperature and humidity, and the relationship between air supply parameters and environmental parameters.

[0082] Output a set of environmental status data to characterize the internal environment of the main cable, forming a unified data interface.

[0083] Specifically, the internal environmental status data of the main cable is generated by structured fusion of environmental parameter data from different sources, based on unified identification, time alignment, and parameter-level fusion processing. The input data for the fusion processing is pre-processed environmental parameter data, which comes from humidity and temperature parameters collected by sensors inside the main cable, air pressure and air volume collected by dehumidifying airflow sensors, and external environmental parameters such as humidity and temperature obtained by external meteorological acquisition units. These data have undergone noise reduction, completion, and time synchronization during the preprocessing stage. In the fusion stage, data from different sources are first assigned a unified data identifier, and various environmental parameter data are time-aligned based on timestamps to form multi-source parameter data groups corresponding to the same time step. Subsequently, for parameters from different sources but with the same physical meaning, such as humidity parameters collected by multiple humidity sensors, weighted averaging or filtering fusion methods are used. The fusion result can be expressed as follows: ;in Indicates the first Similar environmental parameter data collected by multiple sensors This represents the corresponding weighting coefficient, with the weights set based on the sensor placement or historical stability. The results of the fused parameters are represented by the weighted average or filtered fusion. The weights of each sensor can be set according to its placement, historical measurement stability, or measurement accuracy to ensure that the fusion results are more accurate and reliable. Based on this, parameters of different physical types are combined according to preset logical relationships. The internal temperature and humidity parameters of the main cable are correlated, and the dehumidification air supply parameters are correlated with external environmental parameters to form a parameter set that can reflect the comprehensive state of the internal environment of the main cable. Finally, an environmental state data set is output. This environmental state data set includes the fused internal temperature and humidity of the main cable, as well as the state parameters correlated with the dehumidification airflow and the external environment. It is provided as input data to the subsequent environmental state prediction step through a unified data interface to support the prediction and calculation of the internal environmental change trend of the main cable and the generation of subsequent dehumidification control strategies.

[0084] S3. Based on environmental status data, predict the changes in the internal environmental status of the main cable according to the preset environmental status prediction model and prediction time window, and generate environmental status prediction data.

[0085] Furthermore, in S3, the steps for predicting changes in the internal environmental state of the main cable according to a preset environmental state prediction model and prediction time window include:

[0086] Extract the internal temperature, humidity, and air supply parameters of the main cable from the environmental condition data, and organize them into an input data sequence according to the time series.

[0087] Based on the preset prediction time window, the input data sequence is segmented to form sliding time window data;

[0088] Input the sliding time window data into the preset environmental state prediction model to calculate the change value of the internal environmental state of the main cable within the prediction time window.

[0089] The calculated future internal temperature, humidity and air supply parameters of the main cable are combined according to time series to form an environmental state prediction data set, which is then output for the generation of subsequent dehumidification control strategies.

[0090] Specifically, the prediction of the internal environmental state of the main cable is based on the generated environmental state data combined with time series prediction methods. The input data comes from the environmental state data set obtained in the aforementioned steps. This set includes at least the internal temperature parameter, internal humidity parameter, and dehumidification and air supply related operating parameters of the main cable. All of these parameters are collected in real time by the corresponding sensors and obtained through preprocessing and fusion. First, the environmental state data is sorted according to the timestamp to construct an input data sequence arranged in ascending order of time. ;in Indicates at time The environmental state vector includes state components such as temperature, humidity, and air supply parameters; then, based on the preset prediction time window length... The input data sequence is segmented using a sliding window approach to form several consecutive window data segments. Each window corresponds to an input sample for a single prediction calculation. Based on this, the sliding time window data is input into a pre-defined environmental state prediction model for computation. This prediction model is established based on the relationship between historical environmental states and time evolution. The environmental state prediction model can employ linear regression, recurrent neural networks, or other time series models, established according to the relationship between historical environmental states and time evolution. No specific parameters need to be limited; this is provided for technical personnel to reference and implement. The output of the prediction model is the change in the internal environmental state of the main cable within the prediction time window, which can be expressed as:

[0091] ;

[0092] in This represents an environmental state prediction model. This represents the predicted environmental state value at each future moment within the prediction time window. Indicates from time up to the current moment A sequence of historical environmental state data is used as input for the prediction model;

[0093] The prediction model outputs predicted values ​​for the internal temperature and humidity of the main cable at future times, as well as the corresponding predicted values ​​for the air supply parameters. These prediction results are then combined in chronological order to form an environmental state prediction dataset. This environmental state prediction dataset is used as input data for the subsequent dehumidification control strategy generation step to support the calculation of the operating parameters and operating sequence of the dehumidification execution unit, thereby achieving forward-looking control based on the evolution trend of the environmental state.

[0094] S4. Based on environmental condition prediction data, generate a dehumidification control strategy including the operating parameters and operating sequence of the dehumidification execution unit according to the preset dehumidification control rules.

[0095] Furthermore, in S4, the step of generating a dehumidification control strategy, including the operating parameters and operating sequence of the dehumidification execution unit, according to the preset dehumidification control rules, includes:

[0096] Use environmental state prediction data as input data;

[0097] Set dehumidification control targets and equipment operating constraints;

[0098] Based on the input data, control objectives and constraints, the operating parameters and operating sequence of each dehumidification unit within the future time window are calculated using the model predictive control algorithm.

[0099] Organize operating parameters and operating sequences to generate dehumidification control strategies that can be executed directly;

[0100] The dehumidification control strategy is output to the dehumidification execution unit.

[0101] Specifically, the dehumidification control strategy is generated based on the environmental state prediction data obtained in the aforementioned steps and combined with preset dehumidification control rules. The environmental state prediction data serves as the input for control calculations and includes at least the predicted values ​​of the main cable's internal temperature, humidity, and dehumidification-related air supply operating parameters at each moment within the prediction time window. This data is output from the environmental state prediction model and organized according to a time series. First, dehumidification control objectives are set based on engineering design requirements. These objectives describe the expected trend of environmental changes within the main cable's internal environment within the prediction time range. Dehumidification control objectives may include maintaining the humidity within the main cable within a set range, ensuring continuous air supply, and minimizing energy consumption. Constraints may include the maximum power of the dehumidification fan, valve opening range, and start-stop sequence constraints. For example, controlling the humidity within the main cable within a predetermined range while maintaining continuous air supply operation. Simultaneously, equipment operation constraints for the dehumidification execution unit are set. These constraints are determined by the structural parameters and operating specifications of the dehumidification execution unit and are used to limit operating boundaries such as air supply frequency, airflow adjustment range, and start-stop sequence. Based on this, a state vector is constructed using the environmental state prediction data. As the input to the model predictive control algorithm, it is combined with the control objective function. Given the operating constraints, the optimal control sequence within the future time window is calculated using a rolling optimization method, where the control quantity can be expressed as: The control sequence corresponds to the operating parameters and operating sequence of each dehumidification execution unit at different prediction times. The model predictive control algorithm obtains the control result that meets the control objective by solving the constraint optimization problem in each prediction cycle. Then, the operating parameters and operating sequence in the control sequence are sorted and encapsulated to form a dehumidification control strategy that can be directly used for equipment control. This dehumidification control strategy clearly gives the start-stop sequence and corresponding operating parameter settings of each dehumidification execution unit in the future time window, and sends it to the dehumidification execution unit as a control output to guide the specific operation and adjustment in the subsequent dehumidification process.

[0102] S5. Control the operation of the dehumidification execution unit according to the dehumidification control strategy, and continuously collect and update environmental parameter data during the dehumidification execution process;

[0103] Furthermore, in S5, the steps for controlling the operation of the dehumidification execution unit according to the dehumidification control strategy include:

[0104] Receive the start-stop time, operating frequency, and valve opening parameters of each dehumidification execution unit in the dehumidification control strategy;

[0105] The dehumidification control strategy is converted into a sequence of control commands and sent to the corresponding dehumidification execution unit;

[0106] The dehumidification unit is started, adjusted, or stopped according to control commands to form a predetermined dehumidification airflow covering the main cable area.

[0107] Monitor the feedback status of each dehumidification unit, including operating power and output airflow parameters, and ensure that its execution is consistent with the dehumidification control strategy.

[0108] Specifically, the operation control of the dehumidification execution unit is based directly on the generated dehumidification control strategy. This strategy, as control input data, includes at least the start / stop time sequence, operating frequency parameters, and valve opening parameters corresponding to each dehumidification execution unit. These parameters are output from the aforementioned dehumidification control strategy generation step and organized in chronological order. The system first parses the dehumidification control strategy, mapping the described operating parameters into a sequence of control commands that can be recognized by the control system. This sequence of control commands can be represented as... ,in Corresponding time The control quantities for each dehumidification actuator are composed of the operating frequency setpoint, valve opening setpoint, and start / stop status. Subsequently, a sequence of control commands is sent to the corresponding dehumidification actuator control port via a communication interface, causing the dehumidification actuators to start, adjust, or stop sequentially according to their start / stop times and operating parameters, thereby creating a dehumidification airflow distribution within the main cable that matches the control strategy. During dehumidification, feedback data is acquired by collecting operational feedback from the dehumidification actuators. This feedback data includes at least operating power data and output airflow parameter data, and can be represented as follows: The system is used to characterize the actual operating status of the dehumidification unit at the current moment. The system compares the feedback data with the control commands at the corresponding moment to confirm the consistency between the actual operating status of the dehumidification unit and the dehumidification control strategy. The relevant data generated during the operation is used as the basic input for the continuous collection and updating of environmental parameters, providing data support for the next round of environmental status assessment and control strategy generation.

[0109] Furthermore, in S5, the steps for continuously collecting and updating environmental parameter data during the dehumidification process include:

[0110] Continuously collect humidity and temperature data inside the main cable;

[0111] Simultaneously collect relevant parameters of the dehumidification airflow, including air supply pressure and air volume;

[0112] Obtain external environmental parameters of the bridge site, including air humidity, temperature, and rainfall information;

[0113] The collected data is transmitted to the data processing unit or controller in real time for environmental status updates and subsequent control. Environmental parameter data can be transmitted to the data processing unit or controller in real time via wired or wireless network. The data update frequency can be set to the second or minute level to ensure the real-time performance of the dehumidification control closed loop.

[0114] Specifically, continuous acquisition of environmental parameters is achieved by deploying sensors inside the main cable, in the dehumidification unit, and outside the bridge site. The acquired data includes humidity data inside the main cable. Temperature data Dehumidification airflow related supply air pressure With air volume and the air humidity of the external environment of the bridge site. Temperature Rainfall information Data is acquired and collected in real time according to time series; various environmental parameter data are collected. As input, the data is sent in real time to the data processing unit or controller via a data transmission channel to ensure data integrity and time sequence; the system will process the raw data vector... Used to update the current environment state and form the latest environment state dataset. This dataset can be used for the calculation of subsequent environmental state prediction models and the generation of dehumidification control strategies. The real-time updated environmental state data ensures that the control unit can dynamically adjust the operating parameters of each dehumidification execution unit according to the latest main cable and environmental conditions when executing the dehumidification strategy, so as to realize the closed-loop control of the dehumidification process, thereby keeping the internal environment of the main cable within the set target range and providing data support for continuous strategy optimization.

[0115] S6. When a communication or control anomaly is detected, the system switches between a preset centralized control mode and a local control mode according to preset mode switching rules, and performs local storage or synchronization processing on the running data.

[0116] Furthermore, in S6, the steps for switching between the preset centralized control mode and the local control mode according to the preset mode switching rules include:

[0117] The communication status and feedback signals from the dehumidification execution unit are detected in real time by a controller or programmable logic controller.

[0118] Based on the preset mode switching rules, it is determined whether the switching conditions are met. When communication abnormality and dehumidification execution unit abnormality occur at the same time, the communication abnormality mode can be switched first according to the preset priority rules, and then the equipment abnormality control can be executed to ensure system safety and continuous operation.

[0119] Before switching, key operating parameters in centralized control mode are stored in local cache or controller memory;

[0120] The controller or programmable logic controller switches to local control mode and activates the local automatic control program to independently control the operation of the dehumidification unit.

[0121] Update the internal mode identifier of the system, write the mode flag corresponding to the current control mode into the system status variable or control register, and send the mode switching identifier information to each submodule;

[0122] Once the communication or control anomaly is resolved, the centralized control mode is restored according to preset rules, and local operating data is synchronized to the centralized control system.

[0123] Specifically, mode switching is achieved through a controller or programmable logic controller, with real-time monitoring of communication status. Feedback signal from dehumidification unit Among them, B t This indicates the feedback signal from the dehumidification actuator, representing the operating status of the equipment, and is used as input data. According to the preset mode switching rules Determine if the switching conditions are met. ,when Indicating that the switching conditions have been met, the controller will first switch the key operating parameters in centralized control mode. The data is stored in the local cache or controller memory to ensure no data loss during the switching process. Then, a mode switching operation is performed, changing the control mode from centralized control to local control, activating the local automatic control program to independently control the dehumidification unit, and simultaneously updating the system's internal mode flag. The mode switching flag is broadcast to each submodule to ensure that each submodule recognizes the current control mode. After the communication or control anomaly is cleared, the local control mode is switched back to the centralized control mode according to the rules, and the local operating data is synchronized. The data is transmitted to the centralized control system, forming continuous and complete operational data records; input data... and Key operating parameters are acquired through the controller's data acquisition interface. Recorded by the controller, pattern identifier Write the system status variables or control registers, and output the current control mode of the system and the mode identification information received by each submodule. This is used to ensure that the dehumidification system can continue to operate or shut down safely under abnormal conditions, and to provide data support for the subsequent recovery of centralized control strategies.

[0124] The main cable of a certain bridge is exposed to high humidity sea breezes and rain / fog for extended periods. Traditional dehumidification systems, lacking environmental prediction and intelligent control capabilities, are prone to excessive humidity in the main cable, increasing the risk of steel wire corrosion. Furthermore, these systems are energy-intensive and rely on manual operation for maintenance, making it difficult to ensure the long-term safe operation of the bridge. To address these issues, the intelligent bridge dehumidification system operation method based on environmental parameter sensing, as provided in this invention, is adopted. The process is as follows: Figure 1 As shown. The specific implementation process of this method is as follows:

[0125] First, humidity and temperature data inside the main cable of the bridge are collected, as well as the operating parameters of the dehumidifying airflow and the external environmental parameters of the bridge site. This forms a complete set of environmental parameter data. By sensing the bridge and environmental conditions in real time, a reliable basis is provided for subsequent prediction and control, enabling early understanding of humidity changes.

[0126] Then, the collected data is preprocessed, including noise reduction, outlier filtering, missing data completion and time synchronization. Data from sensors inside the main cable, dehumidification airflow sensors and external meteorological data are fused to generate environmental status data. Through standardization and fusion processing, a unified data interface is formed to improve the reliability and usability of the data and provide high-quality input for accurate prediction.

[0127] Next, based on the environmental condition data, according to the preset environmental condition prediction model and prediction time window, the internal temperature, humidity and air supply parameters of the main cable are extracted to generate future environmental condition prediction data. By predicting future humidity changes, active dehumidification prediction is achieved, which suppresses the risk of corrosion of the main cable steel wire from the source.

[0128] Subsequently, based on the predicted data, combined with the dehumidification control target and equipment operation constraints, the model predictive control algorithm is used to calculate the operating parameters and operating sequence of each dehumidification execution unit, organize and generate a dehumidification control strategy that can be directly executed and distribute it to the dehumidification execution unit to achieve on-demand dehumidification, reduce peak energy consumption, reduce human intervention, and improve system operating efficiency.

[0129] Then, the dehumidification execution unit is started, adjusted or stopped according to the control strategy, and the humidity, temperature, air supply pressure and air volume of the main cable and external environment data are continuously collected during the execution process and transmitted to the controller in real time for status update, realizing closed-loop control and real-time feedback of environmental status, so that the system can dynamically respond to environmental changes and optimize the dehumidification effect.

[0130] Finally, when a communication or control anomaly is detected, the controller switches the system from centralized control to local control according to the preset mode switching rules, stores key operating parameters in the local cache, updates the internal mode identifier of the system and synchronizes the identification of each sub-module, so as to achieve safe and autonomous operation in abnormal situations. When the anomaly is eliminated, the centralized control mode is restored and the local data is synchronized to the centralized system to ensure the stability and reliability of the system, while providing complete operating data support for subsequent centralized control.

[0131] 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 method for operating an intelligent bridge dehumidification system based on environmental parameter sensing, characterized in that, Includes the following steps: S1. Collect humidity and temperature parameters inside the main cable of the bridge, collect the operating parameters of the dehumidifying airflow, and collect the external environmental parameters of the bridge site to form corresponding environmental parameter collection data. S2. The environmental parameter acquisition data is preprocessed, and the environmental parameter acquisition data from different sources are fused to generate environmental status data that characterizes the internal environmental state of the main cable. S3. Based on the environmental status data, predict the changes in the internal environmental status of the main cable according to the preset environmental status prediction model and prediction time window, and generate environmental status prediction data. S4. Based on the environmental state prediction data, generate a dehumidification control strategy including the operating parameters and operating sequence of the dehumidification execution unit according to the preset dehumidification control rules. S5. Control the operation of the dehumidification execution unit according to the dehumidification control strategy, and continuously collect and update environmental parameter data during the dehumidification execution process; S6. When a communication or control anomaly is detected, the system switches between a preset centralized control mode and a local control mode according to preset mode switching rules, and performs local storage or synchronization processing on the running data.

2. The operation method of the bridge intelligent dehumidification system based on environmental parameter sensing according to claim 1, characterized in that, In S1, the steps of collecting the operating parameters of the dehumidifying airflow and collecting the external environmental parameters of the bridge site to form corresponding environmental parameter collection data include: Data acquisition units are installed in the supply air duct and return air duct of the dehumidification execution unit to acquire operating parameters that characterize the operating status of the dehumidification airflow. The operating parameters are associated with the corresponding time information to generate dehumidification airflow operating parameter data; Obtain the external environmental parameters of the bridge site area and collect the data of the external environmental parameters; The dehumidification airflow operation parameter data, along with the external environmental parameters and the humidity and temperature parameters inside the main cable of the bridge, are uniformly collected to form environmental parameter acquisition data.

3. The operation method of the bridge intelligent dehumidification system based on environmental parameter sensing according to claim 1, characterized in that, In S2, the step of preprocessing the environmental parameter acquisition data includes: The collected raw environmental parameter data is denoised to remove transient outliers and measurement errors. Missing data is filled by linear interpolation or nearest neighbor substitution. Data from different acquisition frequencies are sampled synchronously over time to unify them to the same time step. The preprocessed data is categorized and stored according to parameter type to generate a standardized environmental parameter dataset.

4. The operation method of the bridge intelligent dehumidification system based on environmental parameter sensing according to claim 1, characterized in that, In S2, the step of fusing environmental parameter acquisition data from different sources includes: The preprocessed data from sensors inside the main cable, dehumidification airflow sensors, and external meteorological data are uniformly identified and time-aligned. Perform weighted averaging or filtering fusion on parameters of the same type; Different types of parameters are combined and processed according to logical relationships, such as the relationship between temperature and humidity, and the relationship between air supply parameters and environmental parameters. Output a set of environmental status data to characterize the internal environment of the main cable, forming a unified data interface.

5. The operation method of the bridge intelligent dehumidification system based on environmental parameter sensing according to claim 1, characterized in that, In S3, the step of predicting the changes in the internal environmental state of the main cable according to the preset environmental state prediction model and prediction time window includes: The internal temperature, humidity, and air supply parameters of the main cable are extracted from the environmental condition data and organized into an input data sequence according to the time series. According to the preset prediction time window, the input data sequence is segmented to form sliding time window data; The sliding time window data is input into the preset environmental state prediction model to calculate the change value of the internal environmental state of the main cable within the prediction time window. The calculated future internal temperature, humidity, and air supply parameters of the main cable are combined according to time series to form the environmental state prediction data set, and output for subsequent dehumidification control strategy generation.

6. The operation method of the bridge intelligent dehumidification system based on environmental parameter sensing according to claim 1, characterized in that, In S4, the step of generating a dehumidification control strategy, including the operating parameters and operating sequence of the dehumidification execution unit, according to preset dehumidification control rules, includes: Use the environmental state prediction data as input data; Set dehumidification control targets and equipment operating constraints; Based on the input data, control objectives, and constraints, the operating parameters and operating sequence of each dehumidification execution unit within the future time window are calculated using a model predictive control algorithm. The operating parameters and operating sequence are compiled to generate a dehumidification control strategy that can be directly executed; The dehumidification control strategy is output to the dehumidification execution unit.

7. The operation method of the bridge intelligent dehumidification system based on environmental parameter sensing according to claim 1, characterized in that, In S5, the step of controlling the operation of the dehumidification execution unit according to the dehumidification control strategy includes: Receive the start-stop time, operating frequency, and valve opening parameters of each dehumidification execution unit in the dehumidification control strategy; The dehumidification control strategy is converted into a sequence of control commands and sent to the corresponding dehumidification execution unit; The dehumidification unit is started, adjusted, or stopped according to control commands to form a predetermined dehumidification airflow covering the main cable area. Monitor the feedback status of each dehumidification unit, including operating power and output airflow parameters, and ensure that its execution is consistent with the dehumidification control strategy.

8. The operation method of the bridge intelligent dehumidification system based on environmental parameter sensing according to claim 1, characterized in that, In S5, the step of continuously collecting and updating environmental parameter data during the dehumidification process includes: Continuously collect humidity and temperature data inside the main cable; Simultaneously collect relevant parameters of the dehumidification airflow, including air supply pressure and air volume; Obtain external environmental parameters of the bridge site, including air humidity, temperature, and rainfall information; The collected data is transmitted in real time to the data processing unit or controller for environmental status updates and subsequent control.

9. The operation method of the bridge intelligent dehumidification system based on environmental parameter sensing according to claim 1, characterized in that, In S6, the step of switching between a preset centralized control mode and a local control mode according to preset mode switching rules includes: The communication status and feedback signals from the dehumidification execution unit are detected in real time by a controller or programmable logic controller. Determine whether the switching conditions are met based on the preset mode switching rules; Before switching, key operating parameters in centralized control mode are stored in local cache or controller memory; The controller or programmable logic controller switches to local control mode and activates the local automatic control program to independently control the operation of the dehumidification unit. Update the internal mode identifier of the system, write the mode flag corresponding to the current control mode into the system status variable or control register, and send the mode switching identifier information to each submodule; Once the communication or control anomaly is resolved, the centralized control mode is restored according to preset rules, and local operating data is synchronized to the centralized control system.

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