Moistureproof control method, system and equipment for cable tunnel

Abstract

The invention discloses a moisture-proof control method, system and equipment for a cable tunnel, and relates to the technical field of tunnel moisture-proof, and the method comprises the steps: collecting the environment data of the cable tunnel in real time through a plurality of temperature and humidity sensors, transmitting the first environment data to a central controller for comparison, judgment and analysis, and generating a first judgment result; activating a moisture-proof execution mechanism according to the first judgment result, continuously monitoring the cable tunnel, and obtaining environment change feedback data; and according to the environment change feedback data, backtracking to a moisture-proof execution mechanism for dynamic adjustment, generating second environment data, transmitting the second environment data to the central controller for closed-loop determination, and generating a second determination result for moisture-proof iterative control of the cable tunnel. According to the invention, the technical problems of poor moisture-proof effect, high energy consumption and easy generation of cable potential safety hazards caused by lack of pertinence and dynamic adjustment capability in the prior art are solved, and the technical effects of reducing cable insulation fault risks, reducing energy consumption and guaranteeing long-term stable operation of the cable tunnel are achieved.

Description

Technical Field

[0001] This invention relates to the field of tunnel moisture-proofing technology, and in particular to a moisture-proofing control method, system and equipment for cable tunnels. Background Technology

[0002] In the field of cable tunnel moisture control, cables are located in underground enclosed or semi-enclosed environments for extended periods, making them susceptible to factors such as soil moisture infiltration, poor air circulation, and diurnal temperature variations, leading to increased humidity and even condensation within the tunnel. Traditional cable tunnel moisture control solutions often employ a sparse, fixed-point deployment of temperature and humidity sensors, which is ill-suited to the environmental differences in various structural areas within the tunnel, such as low-lying areas prone to water accumulation and areas with dense cable joints. Furthermore, these solutions fail to dynamically adjust operating parameters based on the risk levels of different areas within the tunnel. This not only wastes energy but may also lead to condensation in high-risk areas, causing a decline in cable insulation performance, joint corrosion, and even short-circuit faults, seriously threatening the stable operation of the power transmission system.

[0003] In summary, existing technologies lack specificity and dynamic adjustment capabilities, resulting in poor moisture protection, high energy consumption, and potential safety hazards for cables. Summary of the Invention

[0004] This application provides a method, system, and equipment for moisture control in cable tunnels, which addresses the technical problems of existing technologies lacking specificity and dynamic adjustment capabilities, resulting in poor moisture control, high energy consumption, and potential safety hazards to cables.

[0005] The first aspect of this application provides a method for moisture control in cable tunnels, the method comprising: The environmental data of the cable tunnel is collected in real time by multiple temperature and humidity sensors. The first environmental data is transmitted to the central controller for comparison and analysis to generate a first judgment result. Based on the first judgment result, the moisture-proof actuator is activated, and the cable tunnel is continuously monitored by multiple temperature and humidity sensors to obtain environmental change feedback data. Based on the environmental change feedback data, the moisture-proof actuator is dynamically adjusted to generate a second environmental data, which is transmitted to the central controller for closed-loop judgment. The second judgment result is then generated to iteratively control the moisture-proofing of the cable tunnel.

[0006] A second aspect of this application provides a moisture-proof control system for cable tunnels, the system comprising: The data acquisition module collects environmental data of the cable tunnel in real time through multiple temperature and humidity sensors, transmits the first environmental data to the central controller for comparison and analysis, and generates a first judgment result. The monitoring module activates the moisture-proof actuator based on the first judgment result, continuously monitors the cable tunnel through multiple temperature and humidity sensors, and obtains environmental change feedback data. The adjustment module dynamically adjusts the moisture-proof actuator based on the environmental change feedback data, generates a second environmental data, transmits it to the central controller for closed-loop judgment, and generates a second judgment result to iteratively control the moisture-proofing of the cable tunnel.

[0007] A third aspect of this application provides an electronic device comprising: a memory for storing executable instructions; and a processor for implementing a moisture-proof control method for a cable tunnel when executing the executable instructions stored in the memory.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application achieves accurate real-time monitoring of the entire tunnel environment by deploying multiple temperature and humidity sensors and collecting data in a cyclical manner, combining the structural and environmental characteristics of the cable tunnel, thus avoiding monitoring blind spots. Differentiated humidity safety thresholds and condensation temperature thresholds are set based on cable type and operational requirements. A judgment matrix is ​​constructed using humidity deviation values ​​and condensation risk coefficients to generate a first judgment result that includes regional risk levels and locations, providing a precise basis for moisture-proofing measures. Multi-level moisture-proofing actuators are then activated according to the risk level. Environmental response parameters are constructed using multi-source regression analysis and time decay factors, combined with environmental change feedback data, enabling dynamic adjustment of the moisture-proofing actuators. Finally, a closed-loop judgment is used to compare environmental data before and after moisture-proofing, classifying moisture-proofing levels and identifying key areas for iterative control. This improves the targeting and accuracy of moisture-proofing measures, effectively suppresses condensation formation, reduces the risk of cable insulation failure, reduces energy consumption, and ensures the long-term stable operation of the cable tunnel. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic flowchart of a moisture control method for cable tunnels provided in an embodiment of this application.

[0011] Figure 2 This is a schematic diagram of the structure of a moisture-proof control system for a cable tunnel provided in an embodiment of this application.

[0012] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0013] Explanation of reference numerals in the attached drawings: Acquisition module 11, Monitoring module 12, Adjustment module 13, Input device 301, Processor 302, Memory 303, Output device 304. Detailed Implementation

[0014] This application provides a method, system, and equipment for moisture control in cable tunnels, which addresses the technical problems of existing technologies lacking specificity and dynamic adjustment capabilities, resulting in poor moisture control, high energy consumption, and potential safety hazards to cables.

[0015] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0016] It should be noted that the terms "first," "second," etc., used in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices.

[0017] Example 1, as Figure 1 As shown, a moisture control method for cable tunnels includes: Step S100: Collect environmental data of the cable tunnel in real time through multiple temperature and humidity sensors, transmit the first environmental data to the central controller for comparison and analysis, and generate the first judgment result.

[0018] Specifically, firstly, considering the structural characteristics and environmental features of the cable tunnel, multiple temperature and humidity sensors are rationally deployed. These sensors are used to detect temperature and humidity information within the tunnel in real time. After deployment, the system will cycle through all temperature and humidity sensors according to a preset acquisition frequency to achieve real-time data acquisition. This means continuously and uninterruptedly acquiring environmental parameters from each monitoring point at a fixed frequency, ensuring that the data reflects the current environmental status of the tunnel in real time. The collection of raw temperature and humidity data from all sensors, covering different areas of the tunnel, constitutes the primary environmental data. This data is transmitted error-free to the central controller via a stable transmission link, such as a wired or wireless communication module. The central controller is the brain of the entire moisture-proof control system. As the core processing unit, it is responsible for receiving and parsing the primary environmental data, initiating comparative analysis, and generating the primary judgment result, which includes a comprehensive conclusion regarding the risk level and risk area.

[0019] Step S200: Activate the moisture-proof actuator according to the first determination result, and continuously monitor the cable tunnel through multiple temperature and humidity sensors to obtain environmental change feedback data.

[0020] Specifically, the first step is to activate the moisture-proof execution mechanism based on the risk level (e.g., low, medium, and high) and risk area information, such as high-risk areas with dense cable joints and medium-risk areas prone to water accumulation in low-lying tunnels. This mechanism translates moisture-proof commands into actual moisture-proof operations. Specifically, it includes a far-infrared heating module that releases far-infrared rays to raise the local ambient temperature and reduce relative humidity to suppress condensation, and a ventilation system that forces airflow to expel humid air from the tunnel and introduce dry external air. Subsequently, multiple temperature and humidity sensors deployed throughout the tunnel are used for continuous monitoring at a specific sampling frequency, i.e., collecting temperature and humidity data from each monitoring point uninterruptedly and at high frequency, with a particular focus on high-risk areas. During continuous monitoring, the sensors will transmit multiple real-time environmental monitoring data in real time. The system will compare and analyze these real-time data with the first environmental data collected in the first step, namely the original environmental baseline data before the implementation of moisture-proof measures, and calculate key indicators such as the difference in humidity change, the difference in temperature change, and the reduction in the condensation risk coefficient, thereby obtaining environmental change feedback data. The environmental change feedback data is a comprehensive data set that can intuitively reflect the impact of moisture-proof measures on the environment and can quantitatively present the actual effect of the current moisture-proof actions.

[0021] Step S300: Based on the environmental change feedback data, the moisture-proof actuator is dynamically adjusted, a second environmental data is generated and transmitted to the central controller for closed-loop determination, and a second determination result is generated to iteratively control the moisture-proofing of the cable tunnel.

[0022] Specifically, the first step is to conduct a retrospective analysis based on the acquired environmental change feedback data, such as humidity decreases, temperature increases, and changes in condensation risk coefficients. This retrospective analysis involves correlating environmental data reflecting the effectiveness of moisture-proofing measures with the current operating parameters of the moisture-proofing actuators, such as the real-time power of the far-infrared heating module, the fan speed setting of the ventilation system, and operating time. Quantitative analysis clarifies the relationship between the two. Specifically, the system first breaks down the operating status of the moisture-proofing actuators, extracting core operating parameters such as heating power and ventilation intensity. Then, it matches each indicator in the environmental change feedback data with these parameters one by one, calculating multiple correlation coefficients to determine the actual contribution of different operating parameters to environmental improvement.

[0023] After the backtracking is complete, the system will initiate dynamic adjustment based on correlation coefficients. Dynamic adjustment means abandoning fixed execution parameters and adjusting the operating status of the moisture-proof actuator in real time according to the environmental response pattern. The system will perform multivariate stepwise regression analysis on the first environmental data according to multiple correlation coefficients to construct a quantitative model of each operating parameter and environmental changes. At the same time, considering the lag in environmental changes, a time decay factor will be incorporated into the model to form environmental response parameters that include the time dimension. Subsequently, control targets will be set based on the environmental response parameters, and the moisture-proof actuator will be adjusted accordingly: if the humidity reduction in a certain area does not reach the target, the power of the far-infrared heating module can be increased, or the ventilation system speed can be adjusted from medium speed to high speed, generating specific moisture-proof adjustment data.

[0024] Subsequently, the system activates multiple temperature and humidity sensors and resamples the entire cable tunnel area according to a preset sampling frequency, collecting real-time temperature and humidity data for each area after adjustment. This new set of data, which covers the adjusted environmental conditions, is the second environmental data.

[0025] Next, the second environmental data is transmitted to the central controller, which then initiates a closed-loop judgment. Closed-loop judgment involves comparing environmental data before and after adjustment to quantitatively evaluate the moisture-proofing effect. Specifically: the central controller first scores the moisture-proofing based on the first and second environmental data, generating a first moisture-proofing adjustment score and a second moisture-proofing adjustment score. If the second moisture-proofing adjustment score is greater than the first moisture-proofing adjustment score, it indicates that the current adjustment is effective, and environmental moisture-proofing improvement information is generated. Subsequently, the moisture-proofing effect is analyzed in conjunction with the improvement information, and moisture-proofing levels are classified as excellent, good, and requiring improvement based on indicators such as humidity reduction and risk area reduction ratio. Furthermore, key moisture-proofing areas are re-determined based on the current state of each area. The comprehensive conclusion generated by the closed-loop judgment, including moisture-proofing effect evaluation, new moisture-proofing level, and key moisture-proofing areas, is the second judgment result.

[0026] Finally, the system will conduct iterative control based on the second judgment result. Iterative control means that if the second judgment result shows that there are still risk areas that do not meet the standards, the system will repeat the process of backtracking, dynamic adjustment, generating second environmental data, and closed-loop judgment based on the second judgment result, and readjust the key moisture-proof areas again. If the environment of all areas meets the standards, the sampling frequency of the temperature and humidity sensors will be reduced, and the moisture-proof actuator will be put into standby mode. The next round of iteration will be started when new risks are detected in the future.

[0027] Furthermore, environmental data of the cable tunnel is collected in real time using multiple temperature and humidity sensors. The first environmental data is transmitted to the central controller for comparison and analysis to generate a first judgment result. The method includes: Based on the structural characteristics and environmental features of the cable tunnel, multiple temperature and humidity sensors are deployed to cyclically read data at a preset acquisition frequency to obtain first environmental data. This first environmental data is then transmitted to a central controller for comparison and analysis: S1: The cable type and operating requirements of the cable tunnel are retrieved, and humidity safety thresholds and dew point temperature thresholds for different areas within the cable tunnel are set; S2: The first environmental data is parsed to obtain environmental humidity data and environmental temperature data. The environmental humidity data is compared with the humidity safety thresholds to obtain a humidity deviation value; S3: Based on the humidity deviation value, the environmental temperature data is compared with the dew point temperature thresholds, and a condensation risk coefficient is calculated based on the comparison results. Based on the condensation risk coefficient and the humidity deviation value, a regional risk assessment of the cable tunnel is performed, generating a first assessment result.

[0028] Specifically, the first step is to deploy multiple temperature and humidity sensors based on the structural characteristics and environmental features of the cable tunnel. Structural characteristics refer to the tunnel's physical parameters, including its total length, the location of bends, ventilation openings, and areas with dense cable joints. These parameters determine the key monitoring points that the sensors must cover; for example, joint areas require denser sensor deployment to prevent condensation from corroding the insulation layer. Environmental characteristics encompass areas prone to humidity anomalies, such as areas with a history of frequent dampness and areas with significant temperature differences between the inside and outside of the tunnel. During deployment, sensor density needs to be increased to avoid blind spots. After sensor deployment, the system will continuously acquire raw temperature and humidity parameters from all monitoring points throughout the tunnel, following a preset acquisition frequency—a fixed data acquisition interval pre-set based on tunnel environmental stability. This raw, unprocessed environmental parameter set, covering all monitoring points, constitutes the primary environmental data, which is the foundational data source for all subsequent analysis and judgments. This data must be transmitted completely to the central controller via a stable communication link.

[0029] After receiving the initial environmental data, the central controller initiates a phased comparison and analysis. It first enters the S1 process to retrieve the type of cable laid in the cable tunnel, such as high-voltage cross-linked polyethylene cable or low-voltage polyvinyl chloride cable. Different types of cables have different upper limits for tolerance to environmental humidity due to differences in insulation material composition. The cable operation requirements, i.e., the environmental parameter standards during normal cable operation, are specified by the cable manufacturer or industry standards. For example, a certain type of cable requires an operating environment humidity not exceeding 70%RH. Combining the functional attributes of different areas of the tunnel, such as cable joint areas and straight sections of cable laying, humidity safety thresholds are set for each area. These thresholds refer to the maximum allowable humidity value and the dew point temperature threshold, i.e., the critical temperature at which water vapor in the air begins to condense into liquid water. These thresholds need to be calculated based on historical temperature and humidity data of the tunnel and local climate characteristics. For example, the dew point temperature threshold for a certain tunnel is set at 12℃. When the ambient temperature is below this value and the humidity is high, condensation is likely to occur.

[0030] The process then proceeds to S2: The central controller parses the first environmental data, performing preprocessing such as format conversion and outlier removal to convert the data into standard format temperature and humidity values. It then extracts the environmental humidity data (actual humidity value) and environmental temperature data (actual temperature value) for each monitoring point. Next, it compares the environmental humidity data of each area with the corresponding humidity safety threshold, calculating the difference between the actual humidity value and the safety threshold to obtain the humidity deviation value. If the actual humidity is higher than the safety threshold, the deviation value is positive; the larger the positive value, the more severe the humidity exceedance. If the actual humidity is lower than the threshold, the deviation value is negative, indicating that the current humidity is within the safe range.

[0031] The S3 process is then initiated: Based on the humidity deviation values ​​of each region, the ambient temperature data of that region is compared with the dew point temperature threshold. According to the comparison results, a preset algorithm, such as the weighted summation method, is used to calculate the condensation risk coefficient, with the humidity deviation value accounting for 60% and the difference between the temperature and the dew point accounting for 40%. This coefficient usually ranges from 0 to 1. The closer the coefficient is to 1, the higher the probability of condensation in that region. Finally, based on the condensation risk coefficient of each region, combined with the corresponding humidity deviation value, a regional risk assessment is carried out. That is, the degree of humidity exceeding the standard and the condensation risk of each region are comprehensively evaluated, and the regional risk is divided into three levels: low, medium and high. The specific regional locations corresponding to each risk level are marked. These comprehensive assessment conclusions, which include the risk level and risk location of each region, are the first judgment results.

[0032] Furthermore, based on the condensation risk coefficient and the humidity deviation value, a regional risk assessment is performed on the cable tunnel to generate a first assessment result. The method includes: Based on the condensation risk coefficient, multiple areas of the cable tunnel are matched to generate multiple regional condensation risk coefficients; the multiple regional condensation risk coefficients are combined with the humidity deviation value to construct a judgment matrix; a multi-dimensional risk assessment is performed based on the judgment matrix to generate a regional risk distribution map; the regional risk distribution map is traversed to identify regional risks in the cable tunnel, generating risk level and risk area information; the risk level and risk area information are added to the first judgment result.

[0033] Specifically, the first step is to iterate through the cable tunnel based on the calculated condensation risk coefficient. This means the system covers all sub-regions of the cable tunnel according to a pre-defined tunnel area division rule, ensuring no area is missed. Then, multiple regions are matched, correlating the overall condensation risk coefficient with the actual environmental characteristics of each specific sub-region. This avoids the bias caused by applying a single coefficient to all regions, ultimately generating multiple regional condensation risk coefficients—a quantified condensation risk value specific to each sub-region. This coefficient directly reflects the likelihood of water vapor condensation in the corresponding region.

[0034] Next, a combined assessment is performed, integrating the condensation risk coefficients of multiple areas within each sub-region with the previously calculated humidity deviation values ​​for that region. This is because some areas may have high condensation risk coefficients but small humidity deviations (e.g., temperatures close to the condensation point but humidity not severely exceeding the standard), or large humidity deviations but low condensation risk coefficients (e.g., humidity exceeding the standard but temperatures far above the condensation point). A single indicator cannot accurately determine the actual risk. Based on this combined assessment, a judgment matrix is ​​further constructed: the matrix's row dimension represents all sub-regions of the cable tunnel, such as region 1, ..., region n; the column dimension is divided into regional condensation risk coefficient levels and humidity deviation value levels. Each cell in the matrix is ​​filled with the specific status of the corresponding sub-region in both dimensions. This structured integration of dual-dimensional data provides a clear basis for subsequent assessments.

[0035] Based on the constructed judgment matrix, a multi-dimensional risk assessment is conducted: each sub-region is comprehensively scored from two dimensions: the probability of condensation and the degree of humidity exceeding the standard. For example, high condensation risk is scored as 3 points, medium risk as 2 points, and low risk as 1 point; severe exceeding the standard is scored as 3 points, slight exceeding the standard as 2 points, and safe as 1 point. The scores of the two dimensions are added together, and the risk level is determined according to the total score. For example, 5-6 points is high risk, 3-4 points is medium risk, and 2 points is low risk, ensuring that the assessment results take into account both types of risk factors. After the assessment is completed, a regional risk distribution map is generated. Based on the physical layout map of the cable tunnel, the risk status of each sub-region is marked with different colors, such as red for high risk, yellow for medium risk, and green for low risk. The start and end positions of each sub-region are also marked on the map, transforming the abstract assessment results into an intuitive visual chart for easy and rapid location of risk areas.

[0036] Next, regional risk identification is performed: the generated regional risk distribution map is traversed, and detailed information is marked for each sub-region marked with color to clarify the risk level, that is, the high, medium or low risk category to which the sub-region belongs, providing a standard for judging the strength of subsequent moisture prevention measures; on the other hand, information on risk areas is supplemented, including the specific physical range of the sub-region, the type of cable in the area, and the historical moisture records of the area, making the risk information more complete and traceable.

[0037] Finally, the risk levels of all sub-regions and their corresponding risk area information are integrated and added to the first judgment result, upgrading the first judgment result from simple coefficient and deviation data to an executable decision-making basis that includes specific risk locations, risk levels, and regional details.

[0038] Furthermore, based on the first determination result, the moisture-proof actuator is activated, and multiple temperature and humidity sensors are used to continuously monitor the cable tunnel to obtain environmental change feedback data. The method includes: Based on the risk level and risk area information, a moisture-proof analysis is performed to generate multi-level moisture-proof instructions. These instructions are then sent to the moisture-proof execution mechanism for activation. The mechanism updates the sampling frequency to generate a sampling duration frequency. Multiple temperature and humidity sensors monitor the cable tunnel in real time according to the sampling duration frequency, obtaining multiple real-time environmental monitoring data. Change analysis is performed between these real-time environmental monitoring data and the first environmental data to generate environmental change feedback data.

[0039] Specifically, the first step is to conduct a moisture-proofing analysis based on the risk level and risk area information identified in the initial assessment, such as tunnels. This involves combining the environmental characteristics of different risk areas with the intervention requirements corresponding to the risk level to determine the type and intensity of moisture-proofing measures to be activated in each area. For example, high-risk areas require simultaneous activation of far-infrared heating modules and ventilation systems for rapid dehumidification and condensation prevention; medium-risk areas only require activation of the ventilation system for routine dehumidification; and low-risk areas do not require activation of main moisture-proofing equipment, only basic monitoring. This differentiated analysis ensures that moisture-proofing measures are accurately adapted to the risk situation. Based on this analysis, multi-level moisture-proofing instructions are generated. These instructions are a set of operational commands categorized by region and intensity, enabling differentiated control of different risk areas.

[0040] Subsequently, the system sends the generated multi-level moisture-proof commands to the corresponding moisture-proof actuators, such as far-infrared heating modules and ventilation systems, via a stable communication link, such as a wired industrial bus or a wireless IoT module. This triggers the actuators to start operation according to the command parameters. Simultaneously, to track the effectiveness of moisture-proof measures in real time, the sampling frequency is updated through the control module associated with the moisture-proof actuators. This means discarding the initial sampling frequency before moisture-proofing and increasing the sampling frequency according to the risk level. The sampling frequency is increased more significantly in high-risk areas where close monitoring of environmental changes is necessary, followed by medium-risk areas. This ensures timely capture of the environmental impact of moisture-proof measures and avoids misjudgments due to data lag. The updated fixed sampling frequency used for continuous monitoring is the sampling continuous frequency.

[0041] Next, multiple temperature and humidity sensors deployed throughout the cable tunnel conduct real-time environmental monitoring at a set sampling frequency. This means continuously collecting temperature and humidity data from each area at a fixed high frequency, with a focus on high-risk areas to ensure that no monitoring points in each high-risk area are missed or delayed. The collection of dynamic environmental parameters covering the implementation of moisture-proofing measures in each area constitutes the real-time environmental monitoring data.

[0042] Finally, the system performs change analysis on multiple real-time environmental monitoring data sets and the initial environmental data collected before the implementation of moisture-proofing measures. This involves calculating the differences between the two sets of data on key indicators such as humidity, temperature, and condensation risk coefficients to quantitatively analyze the improvement in environmental parameters after the implementation of moisture-proofing measures, and determining whether the current measures have effectively suppressed excessive humidity or the risk of condensation. The comprehensive data set obtained from all change analyses, including the magnitude of changes and improvement trends of environmental parameters in each area, constitutes the environmental change feedback data. This data clearly presents the operational effectiveness of the moisture-proofing implementation agency.

[0043] Furthermore, based on the environmental change feedback data, the moisture-proof actuator is dynamically adjusted to generate second environmental data. The method includes: Based on the moisture-proof actuator, moisture-proof operation analysis is performed to obtain moisture-proof operation parameters; the environmental change feedback data is traced back to the moisture-proof actuator to perform correlation analysis on the moisture-proof operation parameters, generating multiple correlation coefficients; multi-source regression analysis is performed according to the multiple correlation coefficients to construct environmental response parameters; the moisture-proof actuator is dynamically adjusted based on the environmental response parameters to generate moisture-proof adjustment data; based on the moisture-proof adjustment data, multiple temperature and humidity sensors are activated to perform environmental resampling to generate the second environmental data.

[0044] Specifically, the first step is to conduct a moisture-proof operation analysis based on the current working status of the moisture-proof actuator. This involves a comprehensive breakdown of the operation process of the activated moisture-proof actuator, focusing on the operation status and parameters of its core components. Ultimately, moisture-proof operation parameters that directly reflect the moisture-proof capability are extracted. These parameters specifically include key indicators such as the real-time power of the far-infrared heating module, the wind speed setting of the ventilation system, and the cumulative running time of the actuator.

[0045] Subsequently, the system traces the previously acquired environmental change feedback data back to the moisture-proof actuator, initiating a correlation analysis. This analysis uses algorithms to establish a correspondence between the environmental change feedback data and the moisture-proof operating parameters, quantifying the actual impact of different operating parameters on environmental improvement, and generating correlation coefficients. The correlation coefficient is a quantitative value reflecting the strength of the correlation between the two. For example, the positive correlation coefficient between heating power and humidity reduction is 0.8, indicating that increasing heating power has a strong promoting effect on humidity reduction; the correlation coefficient between ventilation level and temperature increase is 0.6, indicating that ventilation intensity has a moderate impact on temperature change. The magnitude of these values ​​directly determines the direction and magnitude of subsequent parameter adjustments.

[0046] Based on the generated correlation coefficients, the system further conducts multi-source regression analysis. This analysis integrates the correlation coefficients corresponding to multiple moisture-proof operating parameters such as heating power and ventilation level, constructing a mathematical model with operating parameters as independent variables and environmental changes as dependent variables. Through model fitting, the comprehensive contribution of each operating parameter to environmental changes is determined, thereby constructing environmental response parameters. These environmental response parameters not only include the quantitative relationship between each operating parameter and environmental changes but also incorporate a time decay factor to adapt to the lag characteristics of environmental changes, ensuring that parameter adjustments conform to actual environmental response patterns.

[0047] Based on the constructed environmental response parameters, the system initiates dynamic adjustment of the moisture-proof actuator. That is, it abandons the fixed operating parameter settings and adjusts the working status of the actuator in real time according to the environmental response pattern: if the environmental change feedback data shows that the humidity drop is not as expected, the heating power is increased or the ventilation speed is increased from medium speed to high speed in combination with the environmental response parameters; if the humidity drops too quickly and the energy consumption is too high, the operating parameters are appropriately reduced, and finally, moisture-proof adjustment data containing specific adjustment values ​​is generated.

[0048] Finally, based on the moisture-proofing adjustment data, the system activates multiple temperature and humidity sensors deployed throughout the cable tunnel, initiating environmental resampling. This means the sensors re-collect real-time temperature and humidity data for each area after adjustment, maintaining the same sampling frequency as when the moisture-proofing actuators were activated. This ensures the data collection covers all risk areas and key monitoring points, preventing data omissions. This set of temperature and humidity data, obtained through resampling and comprehensively reflecting the environmental state after moisture-proofing adjustment, constitutes the second environmental data.

[0049] Furthermore, multi-source regression analysis is performed based on the aforementioned multiple correlation coefficients to construct environmental response parameters. The moisture-proof actuator is then dynamically adjusted based on these environmental response parameters to generate moisture-proof adjustment data. The method includes: Multiple stepwise regression is performed on the first environmental data according to the multiple correlation coefficients to obtain regression analysis results; environmental time-varying analysis is performed based on the regression analysis results to construct environmental response parameters, which include a time decay factor; the range of environmental change parameters is set based on the time decay factor, and the range of environmental change parameters is used as a control target to dynamically adjust the moisture-proof actuator to generate the moisture-proof adjustment data.

[0050] Specifically, the process begins by using previously generated correlation coefficients—quantitative values ​​reflecting the strength of the correlation between moisture-proof operating parameters and environmental changes—as a basis. These coefficients include the correlation coefficient between heating power and humidity reduction, and the correlation coefficient between ventilation level and temperature increase. A multivariate stepwise regression is then performed on the initial environmental data. This involves progressively filtering independent variables that significantly affect the dependent variable (i.e., changes in environmental temperature and humidity), specifically the moisture-proof operating parameters corresponding to the correlation coefficients (such as heating power and ventilation speed). The goal is to construct the optimal regression model using statistical analysis methods that eliminate redundant parameters with low correlation, focus on core influencing factors, and avoid irrelevant variables interfering with the analysis results. In practice, the system uses each moisture-proof operating parameter as an independent variable, and the baseline values ​​and potential trends of humidity and temperature in the initial environmental data as dependent variables. Variables are progressively introduced or removed according to a preset significance level, retaining only those parameters that significantly affect environmental changes. The final regression analysis results are typically presented as regression equations, such as humidity reduction = 0.03 × heating power increase + 0.02 × ventilation level increase - 0.1, thus quantifying the mathematical relationship between the adjustment of moisture-proof operating parameters and environmental changes.

[0051] Based on the regression analysis results, the system further conducts environmental time-varying analysis. This analysis focuses on the time lag characteristics of the measures and their effects after the implementation of moisture-proofing measures. Specifically, after the moisture-proofing actuator adjusts its parameters, the ambient temperature and humidity do not change immediately. This lag effect is quantified by retrieving the correspondence between parameter adjustment time and environmental change time from historical monitoring data. Combining this analysis, the system constructs environmental response parameters. This parameter system integrates moisture-proofing operation parameters, environmental change patterns, and time lag characteristics. Its key component is the time decay factor, which characterizes the degree of decay or delay in the effectiveness of moisture-proofing measures over time. This factor can correct the bias of the immediacy assumptions in the regression analysis results, making the environmental response parameters more closely reflect the actual changing patterns of the tunnel environment.

[0052] Subsequently, based on the time decay factor, the range of environmental change parameters is set. This means that, considering the time lag between measures and their effects, the range of environmental temperature and humidity changes that should be achieved within a reasonable time window after adjusting specific moisture-proof operating parameters is determined. This range avoids both under-adjustment due to not considering time lag (e.g., misjudging the measures as ineffective and excessively increasing power) and over-adjustment (e.g., frequently adjusting parameters before the effects are apparent). The system defines this range of environmental change parameters as the control target, i.e., the environmental improvement target that the moisture-proof actuator must achieve after adjustment, providing a clear and quantifiable standard for subsequent adjustment actions.

[0053] Based on the control objectives, the system dynamically adjusts the moisture-proof actuators by comparing the current environmental trends with the control objectives in real time. If the humidity decreases slower than the lower limit of the control objective, the heating power or ventilation level is increased; if the humidity decreases faster than the upper limit of the control objective, the operating parameters are appropriately reduced to avoid energy waste; if the temperature rises beyond the control objective range, the ventilation intensity is also fine-tuned to balance temperature and humidity. Finally, the system integrates these specific parameter adjustments into moisture-proof adjustment data, which directly guides the next steps of the moisture-proof actuators.

[0054] Furthermore, the generated second environmental data is transmitted to the central controller for closed-loop determination. The generated second determination result is used for iterative control of moisture protection in the cable tunnel. The method includes: The second environmental data is transmitted to the central controller and compared with the first environmental data: a moisture-proof score is generated based on the second environmental data, and a second moisture-proof adjustment score is generated; a moisture-proof score is generated based on the first environmental data, and a first moisture-proof adjustment score is generated; it is determined whether the second moisture-proof adjustment score is greater than the first moisture-proof adjustment score. When the second moisture-proof adjustment score is greater than the first moisture-proof adjustment score, environmental moisture-proof improvement information is generated; closed-loop analysis is performed based on the environmental moisture-proof improvement information to obtain the moisture-proof effect; multiple moisture-proof levels are divided based on the moisture-proof effect; moisture-proof identification of cable tunnels is performed according to the multiple moisture-proof levels to determine key moisture-proof areas; iterative control of moisture-proofing of cable tunnels is performed according to the key moisture-proof areas.

[0055] Specifically, the first step is to transmit the second environmental data, reflecting the environmental state after moisture control adjustment, to the central controller via a stable communication link. The central controller will immediately retrieve the previously stored first environmental data, i.e., the initial environmental data before moisture control adjustment, and initiate the comparative analysis process between the two. First, a moisture control score is calculated. The central controller uses a preset quantitative scoring system, with scoring dimensions covering humidity compliance rate, condensation risk coefficient, and risk area coverage. Each dimension is weighted and assigned a score, such as humidity compliance rate accounting for 40%, condensation risk coefficient for 35%, and risk area for 25%, with a total score of 0-100. A higher score indicates a better moisture control environment. The two types of environmental data are scored separately: the score calculated based on the second environmental data is the second moisture control adjustment score; for example, if the humidity compliance rate improves and the condensation risk decreases after adjustment, the score is 85. The score calculated based on the first environmental data is the first moisture control adjustment score.

[0056] Subsequently, the central controller automatically determines whether the second moisture control score is greater than the first moisture control score. If the second score is higher, it indicates that the current moisture control measures have effectively improved the tunnel environment. The system will then generate environmental moisture control improvement information, which is a quantitative summary of the moisture control effect. This information includes specific improvement indicators and corresponding explanations of the control measures, clearly presenting the causal relationship between control and improvement.

[0057] Next, the system initiates closed-loop analysis based on environmental moisture control improvement information. This involves integrating the previous multi-level moisture control instructions, the operating parameters of the moisture control actuators, and the environmental improvement results, and evaluating them from both effectiveness and cost dimensions. On the one hand, it determines whether the moisture control effect has reached the preset target, such as whether the humidity has dropped to the safe threshold. On the other hand, it analyzes whether there is energy waste in the current operating parameters, such as excessive power consumption even though the humidity has reached the standard. Finally, a comprehensive evaluation conclusion is formed, which is the moisture control effect. For example, if the moisture control effect is good, the humidity meets the standard and the energy consumption is within a reasonable range, or if the moisture control effect needs to be optimized, the humidity does not meet the standard and the ventilation intensity needs to be further increased.

[0058] Based on the moisture-proof effect, the system classifies multiple moisture-proof levels. The classification criteria are based on the degree of improvement and the achievement of target standards, and are generally divided into three levels: Excellent, Good, and Needs Optimization. Excellent means that humidity and condensation risk meet the standards and energy consumption is low; Good means that the indicators meet the standards but some areas still need fine-tuning; Needs Optimization means that the core indicators do not meet the standards and stronger adjustment measures are required. Subsequently, the system identifies the moisture-proof performance of the entire cable tunnel according to these multiple moisture-proof levels, that is, it matches the environmental data of each area with the moisture-proof level standards one by one, and filters out the areas that are still in the Needs Optimization level. These areas that do not meet the standards are the key moisture-proof areas and are the targets of subsequent adjustments.

[0059] Finally, the system initiates iterative control based on key moisture-proof areas. Iterative control refers to using the current moisture-proof effect assessment and key moisture-proof areas as a new starting point, repeating the previous process of retrospectively adjusting the moisture-proof actuators, generating new second environmental data, and making a closed-loop judgment. If key moisture-proof areas exist, the moisture-proof instructions are optimized for those areas, such as increasing the ventilation system level in key areas, restarting the moisture-proof actuators and collecting new environmental data, and then entering the closed-loop judgment again. If all areas reach the excellent or good level and there are no key moisture-proof areas, the sampling frequency of the temperature and humidity sensors is reduced, the moisture-proof actuators are put into standby mode, and only basic monitoring is maintained. The next round of iteration is started when new risks are detected, ensuring the long-term stability of the tunnel environment.

[0060] In summary, the moisture control method for cable tunnels provided in this application has the following technical effects: This application achieves accurate real-time monitoring of the entire tunnel environment by deploying multiple temperature and humidity sensors and collecting data in a cyclical manner, combining the structural and environmental characteristics of the cable tunnel, thus avoiding monitoring blind spots. Differentiated humidity safety thresholds and condensation temperature thresholds are set based on cable type and operational requirements. A judgment matrix is ​​constructed using humidity deviation values ​​and condensation risk coefficients to generate a first judgment result that includes regional risk levels and locations, providing a precise basis for moisture-proofing measures. Multi-level moisture-proofing actuators are then activated according to the risk level. Environmental response parameters are constructed using multi-source regression analysis and time decay factors, combined with environmental change feedback data, enabling dynamic adjustment of the moisture-proofing actuators. Finally, a closed-loop judgment is used to compare environmental data before and after moisture-proofing, classifying moisture-proofing levels and identifying key areas for iterative control. This improves the targeting and accuracy of moisture-proofing measures, effectively suppresses condensation formation, reduces the risk of cable insulation failure, reduces energy consumption, and ensures the long-term stable operation of the cable tunnel.

[0061] Example 2, as Figure 2 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides a moisture-proof control system for cable tunnels, the system comprising: The data acquisition module 11 collects environmental data of the cable tunnel in real time through multiple temperature and humidity sensors, transmits the first environmental data to the central controller for comparison and analysis, and generates a first judgment result; the monitoring module 12 activates the moisture-proof actuator based on the first judgment result, continuously monitors the cable tunnel through multiple temperature and humidity sensors, and obtains environmental change feedback data; the adjustment module 13 dynamically adjusts the moisture-proof actuator based on the environmental change feedback data, generates a second environmental data, transmits it to the central controller for closed-loop judgment, and generates a second judgment result to iteratively control the moisture-proofing of the cable tunnel.

[0062] Furthermore, the acquisition module 11 in the moisture-proof control system for cable tunnels is used for: Based on the structural characteristics and environmental features of the cable tunnel, multiple temperature and humidity sensors are deployed to cyclically read data at a preset acquisition frequency to obtain first environmental data. This first environmental data is then transmitted to a central controller for comparison and analysis: S1: The cable type and operating requirements of the cable tunnel are retrieved, and humidity safety thresholds and dew point temperature thresholds for different areas within the cable tunnel are set; S2: The first environmental data is parsed to obtain environmental humidity data and environmental temperature data. The environmental humidity data is compared with the humidity safety thresholds to obtain a humidity deviation value; S3: Based on the humidity deviation value, the environmental temperature data is compared with the dew point temperature thresholds, and a condensation risk coefficient is calculated based on the comparison results. Based on the condensation risk coefficient and the humidity deviation value, a regional risk assessment of the cable tunnel is performed, generating a first assessment result.

[0063] The acquisition module 11 is also used for: Based on the condensation risk coefficient, the device matches multiple areas of the cable tunnel to generate multiple area condensation risk coefficients; the device combines the multiple area condensation risk coefficients with the humidity deviation value to make a judgment matrix; the device performs multi-dimensional risk assessment based on the judgment matrix to generate an area risk distribution map; the device traverses the area risk distribution map to identify the area risks of the cable tunnel and generates risk level and risk area information; the device adds the risk level and risk area information to the first judgment result.

[0064] Furthermore, the monitoring module 12 in the moisture-proof control system for cable tunnels is used for: Based on the risk level and risk area information, a moisture-proof analysis is performed to generate multi-level moisture-proof instructions. The device sends the multi-level moisture-proof instructions to the moisture-proof actuator for activation. The moisture-proof actuator updates the sampling frequency to generate a sampling duration frequency. Multiple temperature and humidity sensors perform real-time environmental monitoring of the cable tunnel according to the sampling duration frequency to obtain multiple real-time environmental monitoring data. Based on the multiple real-time environmental monitoring data and the first environmental data, a change analysis is performed to generate the environmental change feedback data.

[0065] Furthermore, the adjustment module 13 in the moisture-proof control system for cable tunnels is used for: Based on the moisture-proof actuator, moisture-proof operation analysis is performed to obtain moisture-proof operation parameters; the device traces the environmental change feedback data back to the moisture-proof actuator to perform correlation analysis on the moisture-proof operation parameters, generating multiple correlation coefficients; the device performs multi-source regression analysis according to the multiple correlation coefficients to construct environmental response parameters, and dynamically adjusts the moisture-proof actuator based on the environmental response parameters to generate moisture-proof adjustment data; the device activates multiple temperature and humidity sensors based on the moisture-proof adjustment data to perform environmental resampling, generating the second environmental data.

[0066] The adjustment module 13 is also used for: The first environmental data is subjected to multiple stepwise regression based on the multiple correlation coefficients to obtain regression analysis results; the device performs environmental time-varying analysis based on the regression analysis results to construct environmental response parameters, the environmental response parameters including a time decay factor; the device sets the range of environmental change parameters based on the time decay factor, and uses the range of environmental change parameters as a control target to dynamically adjust the moisture-proof actuator to generate the moisture-proof adjustment data.

[0067] The adjustment module 13 is also used for: The second environmental data is transmitted to the central controller for comparison with the first environmental data: the device performs a moisture-proof score based on the second environmental data, generating a second moisture-proof adjustment score; the device performs a moisture-proof score based on the first environmental data, generating a first moisture-proof adjustment score; the device determines whether the second moisture-proof adjustment score is greater than the first moisture-proof adjustment score, and when the second moisture-proof adjustment score is greater than the first moisture-proof adjustment score, environmental moisture-proof improvement information is generated; the device performs closed-loop analysis based on the environmental moisture-proof improvement information to obtain the moisture-proof effect, divides the moisture-proof effect into multiple moisture-proof levels, identifies the cable tunnel for moisture-proofing according to the multiple moisture-proof levels, and determines key moisture-proof areas; the device iteratively controls the moisture-proofing of the cable tunnel according to the key moisture-proof areas.

[0068] Example 3, as Figure 3 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides an electronic device, the electronic device comprising: The memory 303 is used to store executable instructions; the processor 302 is used to implement a moisture-proof control method for cable tunnels when executing the executable instructions stored in the memory 303.

[0069] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 3The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention. This electronic device is in the form of a general-purpose computing device, and its components may include, but are not limited to, an input device 301, a processor 302, a memory 303, and an output device 304. The processor 302 may be one or more; the memory 303 may include a computer-readable medium and at least one program product having a set (at least one) of program modules configured to perform the functions of the embodiments of this application.

[0070] The memory 303 shown in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable storage media can be, but is not limited to, infrared, semiconductor systems, devices or components, or any combination thereof, used to store software programs, computer-executable programs and modules, such as the program instructions / modules corresponding to the optimized manufacturing method of a composite copper-aluminum block in this embodiment of the invention. The processor 302 executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory 303, thereby realizing the above-mentioned moisture-proof control method for cable tunnels.

[0071] The moisture-proof control system for cable tunnels provided in this embodiment of the invention can execute the moisture-proof control method for cable tunnels provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0072] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0073] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method of moisture control for a cable tunnel, characterized by, The method comprises: Real-time collection of environmental data of the cable tunnel by multiple temperature and humidity sensors, transmission of first environmental data to a central controller for comparison and determination analysis to generate a first determination result; Activation of a moisture-proof execution mechanism according to the first determination result, continuous monitoring of the cable tunnel by multiple temperature and humidity sensors to obtain environmental change feedback data; According to the environmental change feedback data, the moisture-proof execution mechanism is adjusted dynamically, and second environmental data is generated and transmitted to the central controller for closed-loop determination to generate a second determination result for iterative control of the moisture-proof of the cable tunnel.

2. A method of moisture control for a cable tunnel as claimed in claim 1, wherein, Real-time collection of environmental data of the cable tunnel by multiple temperature and humidity sensors, transmission of first environmental data to a central controller for comparison and determination analysis to generate a first determination result, the method comprising: Based on the structural characteristics and environmental characteristics of the cable tunnel, multiple temperature and humidity sensors are arranged to read data in a round-robin manner according to a preset collection frequency to obtain first environmental data; The first environmental data is transmitted to the central controller for comparison and determination analysis: S1: Retrieve the cable type and cable operation requirements of the cable tunnel, set the humidity safety threshold and condensation point temperature threshold of different areas in the cable tunnel; S2: Analyze the first environmental data to obtain environmental humidity data and environmental temperature data, compare the environmental humidity data with the humidity safety threshold to obtain a humidity deviation value; S3: Based on the humidity deviation value, compare the environmental temperature data with the condensation point temperature threshold, and calculate a condensation risk coefficient according to the comparison result; Based on the condensation risk coefficient and the humidity deviation value, the regional risk of the cable tunnel is determined to generate a first determination result.

3. A method of moisture control for a cable tunnel as claimed in claim 2, wherein, Based on the condensation risk coefficient and the humidity deviation value, the regional risk of the cable tunnel is determined to generate a first determination result, the method comprising: Based on the condensation risk coefficient, traverse multiple regions of the cable tunnel to generate multiple regional condensation risk coefficients; According to the multiple regional condensation risk coefficients and the humidity deviation value, combination determination is performed to construct a determination matrix; Based on the determination matrix, multi-dimensional risk assessment is performed to generate a regional risk distribution map; Traverse the regional risk distribution map to identify the regional risk of the cable tunnel to generate risk level and risk area information; Add the risk level and risk area information to the first determination result.

4. A method of moisture control for a cable tunnel as defined in claim 3, wherein According to the first determination result, activate the moisture-proof execution mechanism, continuously monitor the cable tunnel by multiple temperature and humidity sensors to obtain environmental change feedback data, the method comprising: According to the risk level and risk area information, moisture-proof analysis is performed to generate multi-level moisture-proof instructions; Send the multi-level moisture-proof instructions to the moisture-proof execution mechanism for starting, update the sampling frequency through the moisture-proof execution mechanism, generate a sampling continuous frequency, and monitor the cable tunnel in real time through the multiple temperature and humidity sensors according to the sampling continuous frequency to obtain multiple real-time environmental monitoring data; Based on the multiple real-time environmental monitoring data and the first environmental data, change analysis is performed to generate the environmental change feedback data.

5. A method of moisture control for a cable tunnel as defined in claim 1, wherein, According to the environmental change feedback data, the dynamic adjustment is performed on the moisture-proof execution mechanism, second environmental data is generated, and the method comprises: Based on the moisture-proof execution mechanism, moisture-proof operation analysis is performed to obtain moisture-proof operation parameters; The environmental change feedback data is backtracked to the moisture-proof execution mechanism for correlation analysis of the moisture-proof operation parameters, and a plurality of correlation coefficients are generated; According to the plurality of correlation coefficients, multi-source regression analysis is performed to construct an environmental response parameter, and the moisture-proof execution mechanism is dynamically adjusted based on the environmental response parameter to generate moisture-proof adjustment data; Based on the moisture-proof adjustment data, a plurality of temperature and humidity sensors are activated for environmental resampling to generate the second environmental data.

6. A method of moisture control for a cable tunnel as defined in claim 5, wherein, According to the plurality of correlation coefficients, multi-source regression analysis is performed to construct an environmental response parameter, and the moisture-proof execution mechanism is dynamically adjusted based on the environmental response parameter to generate moisture-proof adjustment data, and the method comprises: According to the plurality of correlation coefficients, multi-source regression analysis is performed to construct an environmental response parameter, and the moisture-proof execution mechanism is dynamically adjusted based on the environmental response parameter to generate moisture-proof adjustment data, and the method comprises: According to the plurality of correlation coefficients, multi-source regression analysis is performed to construct an environmental response parameter, and the moisture-proof execution mechanism is dynamically adjusted based on the environmental response parameter to generate moisture-proof adjustment data, and the method comprises: The second environmental data is transmitted to the central controller for closed-loop judgment to generate a second judgment result for iterative control of the moisture-proof of the cable tunnel, and the method comprises:

7. A method of moisture control for a cable tunnel as defined in claim 1, wherein, The second environmental data is transmitted to the central controller for comparison with the first environmental data: Based on the second environmental data, a second moisture-proof adjustment score is generated; Based on the first environmental data, a first moisture-proof adjustment score is generated; Determine whether the second moisture-proof adjustment score is greater than the first moisture-proof adjustment score, when the second moisture-proof adjustment score is greater than the first moisture-proof adjustment score, generate environmental moisture-proof improvement information; According to the environmental moisture-proof improvement information, closed-loop analysis is performed to obtain a moisture-proof effect, a plurality of moisture-proof levels are divided according to the moisture-proof effect, and the moisture-proof of the cable tunnel is identified according to the plurality of moisture-proof levels to determine a key moisture-proof area; According to the key moisture-proof area, the moisture-proof of the cable tunnel is iteratively controlled. A moisture-proof control method for a cable tunnel according to any one of claims 1-7, the system comprises:

8. A moisture control system for a cable tunnel, characterized in that The acquisition module acquires real-time environmental data of the cable tunnel through a plurality of temperature and humidity sensors, transmits first environmental data to the central controller for comparison and judgment analysis, and generates a first judgment result; The monitoring module activates the moisture-proof execution mechanism according to the first judgment result, continuously monitors the cable tunnel through a plurality of temperature and humidity sensors, and obtains environmental change feedback data; The adjustment module performs dynamic adjustment on the moisture-proof execution mechanism according to the environmental change feedback data, generates second environmental data, and transmits the second environmental data to the central controller for closed-loop judgment to generate a second judgment result for iterative control of the moisture-proof of the cable tunnel. The electronic device comprises:

9. An electronic device, comprising: ​ a memory for storing executable instructions; a processor for implementing the moisture-proof control method of the cable tunnel according to any one of claims 1 to 7 when executing the executable instructions stored in the memory.

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