Moisture-proof control method and device, equipment and medium

Abstract

The embodiment of the application provides a kind of damp-proof control method, device, equipment and medium.The method comprises the following steps: collecting multi-modal environmental parameters, the multi-modal environmental parameters include humidity in the cabinet, humidity outside the cabinet and temperature difference between inside and outside the cabinet;Multi-modal environmental parameters are fused and processed to generate an environmental state representation;According to the environmental state representation, determine the condensation risk index;According to the condensation risk index, trigger the dual-mode dehumidification operation.The method is used to improve the effect of moisture-proof.

Description

Technical Field

[0001] This application relates to the field of power equipment maintenance technology, and in particular to a moisture control method, device, equipment and medium. Background Technology

[0002] In fields such as electronic equipment, precision instruments, and warehousing systems, moisture-proof technology is a key element in ensuring the long-term stable operation of equipment and product quality. As application scenarios become increasingly complex, the requirements for moisture-proof performance are constantly increasing, especially in environments with high humidity, frequent temperature changes, or limited sealing. Single moisture-proof methods are no longer sufficient to meet practical needs.

[0003] Currently, mainstream moisture-proofing solutions fall into two main categories: one uses a physical sealing structure to prevent external moisture intrusion; the other relies on active dehumidification devices (such as desiccants, electric heating dehumidifiers, or semiconductor condensers) to reduce internal humidity and inhibit condensation. These methods are typically deployed independently, each targeting a specific type of moisture source.

[0004] However, the sealed structure cannot effectively deal with the condensation problem caused by temperature changes inside the equipment, and the active dehumidification system is difficult to prevent the continuous infiltration of external moisture. As a result, the existing cabinet has limited effectiveness in dealing with both external water seepage and internal condensation at the same time, thus affecting the moisture protection effect of electronic equipment. Summary of the Invention

[0005] The moisture-proof control method, device, equipment, and medium provided in the embodiments of this application are used to improve the moisture-proof effect.

[0006] In a first aspect, embodiments of this application provide a moisture control method, including:

[0007] Collect multimodal environmental parameters, including humidity inside the cabinet, humidity outside the cabinet, and temperature difference between inside and outside the cabinet;

[0008] Multimodal environmental parameters are fused to generate an environmental state characterization.

[0009] The condensation risk index is determined based on the environmental condition characterization.

[0010] Based on the condensation risk index, a dual-mode dehumidification operation is triggered.

[0011] In one possible implementation, the multimodal environmental parameters are fused to generate an environmental state characterization, including:

[0012] The weighting coefficients of each multimodal environmental parameter are adjusted based on the health status of the sensors used to collect multimodal environmental parameters.

[0013] An environmental state representation is generated based on multimodal environmental parameters and corresponding weighting coefficients.

[0014] In one possible implementation, the weighting coefficients of each multimodal environmental parameter are adjusted based on the health status of the sensors used to collect the multimodal environmental parameters, including:

[0015] Based on the historical data acquired by the sensor, the consistency variance of the historical data is determined; the consistency variance of the historical data is used to characterize the health of the sensor.

[0016] When the consistency variance of historical data is higher than the preset variance, the weighting coefficient of the sensor is reduced.

[0017] In one possible implementation, the environmental state characterization incorporates humidity values;

[0018] Based on environmental condition characterization, the condensation risk index is determined, including:

[0019] Determine the dew point humidity based on the environmental condition characterization and the temperature inside the cabinet;

[0020] The condensation risk index is determined based on the material sensitivity coefficient, dew point humidity, cabinet humidity, and cabinet temperature; the material sensitivity coefficient is determined based on the cabinet material.

[0021] In one possible implementation, a condensation risk index is determined based on the material sensitivity coefficient, dew point humidity, cabinet humidity, and cabinet temperature, including:

[0022] The corrected humidity difference is determined based on the material sensitivity coefficient and the humidity difference between the humidity inside the cabinet and the dew point humidity;

[0023] The temperature sensitivity is determined based on the internal temperature of the cabinet and the preset temperature threshold.

[0024] The condensation risk index is determined based on the adjusted humidity difference and temperature sensitivity.

[0025] In one possible implementation, a dual-mode dehumidification operation is triggered based on a condensation risk index, including:

[0026] When the condensation risk index is greater than or equal to the first preset threshold, the operation of the first dehumidification mode is triggered.

[0027] When the condensation risk index is less than the first preset threshold and greater than or equal to the second preset threshold, the operation of the second dehumidification mode is triggered.

[0028] When the condensation risk index is less than the second preset threshold, the system enters monitoring mode.

[0029] In one possible implementation, after triggering the first dehumidification mode, the method further includes:

[0030] Detect whether the condensation risk index remains below the second preset threshold for a preset time.

[0031] If the condensation risk index remains below the second preset threshold for a preset period of time, the first dehumidification mode will be exited.

[0032] If the condensation risk index does not remain below the second preset threshold within a preset time, the first dehumidification mode will continue to be triggered.

[0033] Secondly, embodiments of this application provide a moisture control device, comprising:

[0034] The data acquisition module is used to collect multimodal environmental parameters, including the humidity inside the cabinet, the humidity outside the cabinet, and the temperature difference between inside and outside the cabinet.

[0035] The generation module is used to fuse multimodal environmental parameters and generate environmental state representations.

[0036] The determination module is used to determine the condensation risk index based on the environmental condition characterization;

[0037] The trigger module is used to trigger dual-mode dehumidification operation based on the condensation risk index.

[0038] In one possible implementation, the generation module can also be used for:

[0039] The weighting coefficients of each multimodal environmental parameter are adjusted based on the health status of the sensors used to collect multimodal environmental parameters.

[0040] An environmental state representation is generated based on multimodal environmental parameters and corresponding weighting coefficients.

[0041] In one possible implementation, the generation module can also be used for:

[0042] Based on the historical data acquired by the sensor, the consistency variance of the historical data is determined; the consistency variance of the historical data is used to characterize the health of the sensor.

[0043] When the consistency variance of historical data is higher than the preset variance, the weighting coefficient of the sensor is reduced.

[0044] In one possible implementation, the environmental state characterization incorporates humidity values; the determination module can also be used for:

[0045] Determine the dew point humidity based on the environmental condition characterization and the temperature inside the cabinet;

[0046] The condensation risk index is determined based on the material sensitivity coefficient, dew point humidity, cabinet humidity, and cabinet temperature; the material sensitivity coefficient is determined based on the cabinet material.

[0047] In one possible implementation, the determining module can also be used for:

[0048] The corrected humidity difference is determined based on the material sensitivity coefficient and the humidity difference between the humidity inside the cabinet and the dew point humidity;

[0049] The temperature sensitivity is determined based on the internal temperature of the cabinet and the preset temperature threshold.

[0050] The condensation risk index is determined based on the adjusted humidity difference and temperature sensitivity.

[0051] In one possible implementation, the trigger module can also be used for:

[0052] When the condensation risk index is greater than or equal to the first preset threshold, the operation of the first dehumidification mode is triggered.

[0053] When the condensation risk index is less than the first preset threshold and greater than or equal to the second preset threshold, the operation of the second dehumidification mode is triggered.

[0054] When the condensation risk index is less than the second preset threshold, the system enters monitoring mode.

[0055] In one possible implementation, the trigger module can also be used for:

[0056] Detect whether the condensation risk index remains below the second preset threshold for a preset time.

[0057] If the condensation risk index remains below the second preset threshold for a preset period of time, the first dehumidification mode will be exited.

[0058] If the condensation risk index does not remain below the second preset threshold within a preset time, the first dehumidification mode will continue to be triggered.

[0059] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0060] The memory stores the instructions that the computer executes;

[0061] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0062] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0063] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0064] The moisture control method, device, equipment, and medium provided in this application collect multimodal environmental parameters, including humidity inside the cabinet, humidity outside the cabinet, and temperature difference between inside and outside the cabinet. Based on the multimodal environmental parameters, an environmental state characterization is generated to eliminate single sensor errors through fusion processing, ensuring the robustness of environmental perception. Based on the environmental state characterization, a condensation risk index is determined, thereby avoiding misjudgments caused by fixed thresholds, accurately triggering the corresponding dual-mode dehumidification operation, and thus improving the reliability and adaptability of the moisture control effect. Attached Figure Description

[0065] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0066] Figure 1 A schematic diagram illustrating a scenario for the moisture control method provided in this application;

[0067] Figure 2 Flowchart of the moisture control method provided in this application Figure 1 ;

[0068] Figure 3 Flowchart of the moisture control method provided in this application Figure 2 ;

[0069] Figure 4 A schematic diagram of the moisture control device provided in this application;

[0070] Figure 5 A schematic diagram of the electronic structure provided in this application.

[0071] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0072] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0073] Outdoor power distribution cabinets, when operating in humid and rainy environments for extended periods, are prone to moisture accumulation and condensation inside, leading to corrosion, aging, decreased insulation performance, and even short circuits. Existing technologies primarily address this problem through three approaches:

[0074] 1. Mechanical hydrophobic structure:

[0075] Top drainage device: A drainage plate and drainage holes are installed on the top of the distribution cabinet. For example, the drainage plate is used to guide rainwater into the drainage pipe for discharge. Although this solution can divert external rainwater, it cannot solve the problem of condensation inside the cabinet, and the drainage holes are easily blocked by foreign objects.

[0076] Sealed and leak-proof design: A seal is achieved by setting grooves and sealing strips on the edge of the cabinet door, with the baffle embedded in the groove. However, after long-term use, the sealing strip ages and fails, and excessive sealing actually hinders the dissipation of internal moisture.

[0077] 2. Active dehumidification system:

[0078] Heating and drying technology: This method uses an air pump and heating module to introduce hot air into the cabinet for drying. However, this method has high energy consumption (continuous power > 90W) and may exacerbate condensation in high temperature and high humidity environments.

[0079] 3. Intelligent Control System:

[0080] Deployable rainproof tarpaulin: The rainproof tarpaulin deployment and retraction device is triggered by a rain sensor. Although it can adjust the protective orientation according to wind direction, the mechanical structure is complex and has a high failure rate.

[0081] Therefore, existing moisture-proof technologies suffer from several problems: passive waterproofing (such as sealing strips and hydrophobic devices) cannot handle internal condensation and is prone to failure due to aging; active dehumidification (such as heating or semiconductor solutions) has high energy consumption, performance drops sharply in low-temperature environments, and relies on drainage for maintenance; and existing intelligent control strategies are mostly limited to circulating dehumidification, still requiring external power support. The core issue is that various solutions only target a single source of moisture (external water ingress or internal condensation), lacking a collaborative sensing and adaptive control mechanism for multi-source moisture, resulting in limited overall moisture-proof performance and difficulty in optimizing energy efficiency.

[0082] The existing methods have the following technical problems:

[0083] 1. Traditional heating dehumidification or semiconductor dehumidification is only suitable for specific scenarios (such as high humidity or critical condensation) and cannot adapt to the complex humidity fluctuations caused by diurnal temperature variations.

[0084] 2. Relying on a single sensor or static threshold to determine humidity status without combining temperature and humidity gradients with material characteristics for dynamic risk assessment leads to delayed or misjudged dehumidification strategies.

[0085] 3. The existing sealing strip and ventilation duct design are not linked with the dehumidification system. Overly tight sealing will hinder the discharge of moisture, while excessive ventilation will introduce external moisture, creating a conflict.

[0086] 4. The lack of a fault early warning mechanism and reliance on manual inspection can easily lead to maintenance delays, increasing operation and maintenance costs and safety risks.

[0087] The moisture control method provided in this application addresses the dependence of existing systems on a single environmental parameter (such as humidity) by proposing a sensing scheme that integrates multi-source data such as internal / external temperature and humidity, and pressure difference. It improves measurement accuracy through a dynamic weighting algorithm. Furthermore, based on the humidity sensitivity of metallic materials (such as the exponential relationship between dew point humidity and humidity), a condensation risk index formula is designed, binding risk classification with dehumidification mode switching logic. To address the issues of high energy consumption and low efficiency, a coupling scheme of semiconductor condensation (low power consumption) and pulsed hot air (high efficiency) is proposed, and a hysteresis switching mechanism avoids frequent start-stop cycles, thereby improving the reliability and adaptability of the moisture control effect.

[0088] Figure 1 A schematic diagram of a scenario for the moisture control method provided in this application, such as... Figure 1 As shown, the specific application scenario of this application is a moisture-proof control system. This system can achieve moisture-proof protection for outdoor metering cabinets through closed-loop control of environmental perception, mode decision-making, dynamic execution, and feedback optimization.

[0089] In some embodiments, the moisture control system may include a multi-sensor data acquisition module, a data fusion module and a condensation risk assessment algorithm module, an intelligent decision control module and various actuators. By monitoring the ambient temperature and humidity in real time, dynamically calculating the condensation risk index, and automatically switching between hot air circulation, semiconductor cooling or low power consumption mode according to the risk level, it can achieve proactive prevention and efficient management of internal condensation.

[0090] This application does not restrict the implementing entity, as long as it can collect multimodal environmental parameters, including humidity inside the cabinet, humidity outside the cabinet, and temperature difference between inside and outside the cabinet; perform fusion processing on the multimodal environmental parameters to generate an environmental state characterization; determine the condensation risk index based on the environmental state characterization; and trigger dual-mode dehumidification operation based on the condensation risk index.

[0091] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0092] Figure 2 Flowchart of the moisture control method provided in this application Figure 1 ,like Figure 2 As shown, the method includes:

[0093] S201. Collect multimodal environmental parameters, including humidity inside the cabinet, humidity outside the cabinet, and temperature difference between inside and outside the cabinet.

[0094] The multimodal environmental parameters can include the humidity inside the cabinet, the humidity outside the cabinet, and the temperature difference between the inside and outside of the cabinet. The humidity inside the cabinet can refer to the water vapor content in the air inside a closed or semi-closed equipment cabinet, which can be expressed as relative humidity (%RH). This parameter directly reflects the humidity level of the environment inside the cabinet, in order to determine whether condensation, corrosion, or a decrease in insulation performance may occur.

[0095] External humidity refers to the humidity level of the air in the external environment where the equipment cabinet is located, and it can be measured as relative humidity (%RH). External humidity not only affects the amount of water vapor that penetrates into the cabinet through sealed gaps or materials, but also increases the risk of condensation when temperature differences exist.

[0096] The temperature difference between the inside and outside of the cabinet refers to the temperature difference between the inside of the equipment cabinet and the external environment, which can be expressed in degrees Celsius (°C). When the temperature inside the cabinet is lower than that of the external environment and the external humidity is high, condensation is likely to form on the cabinet walls or the cold surfaces inside. Conversely, if the heating elements inside the cabinet cause the internal temperature to be significantly higher than that outside, instantaneous moisture condensation may also occur when the cabinet door is opened or ventilation is present.

[0097] In this embodiment, integrated temperature and humidity sensors can be deployed inside and outside the cabinet to collect the humidity inside and outside the cabinet and their corresponding temperature values ​​in real time. The data processing unit can then simultaneously calculate the temperature difference between the inside and outside of the cabinet to obtain complete multimodal environmental parameters.

[0098] To accurately acquire multimodal environmental parameters, temperature and humidity sensors need to be strategically deployed both inside and outside the equipment cabinet. Sensors inside the cabinet should avoid heat-generating components, areas directly exposed to fan airflow, and the bottom where water may accumulate. They are typically installed on the upper side wall or in the center of the back panel of the cabinet to ensure that the measured data accurately reflects the overall environmental conditions inside. For larger cabinets, multiple sensors can be deployed, and the average value can be used to improve data representativeness.

[0099] For the installation of external sensors, it is crucial to mitigate external interference. The installation location should be a cool, well-ventilated area outside the cabinet, protected from direct sunlight and rain. For example, a sunshade can be installed above the cabinet door, or the sensor can be placed inside a protective grille. In outdoor environments, it is recommended to use a Stevenson screen to shield the sensor from radiation, preventing artificially inflated temperatures caused by sunlight, which could affect the accuracy of temperature difference and condensation detection.

[0100] The integrated temperature and humidity sensor can be an industrial-grade sensor, such as SHT35, BME280, or a transmitter supporting the Modbus protocol. It requires humidity accuracy within ±2%RH, temperature error not exceeding ±0.3°C, and good long-term stability and anti-interference capabilities. Data is synchronously collected by the main control unit (such as an embedded controller or PLC) from the temperature and humidity values ​​inside and outside the cabinet, real-time calculation of the temperature difference, and dew point humidity estimation based on external humidity. When the temperature inside the cabinet approaches or falls below the dew point of the outside air, the system can determine that there is a risk of condensation.

[0101] In some embodiments, the sampling frequency of the temperature and humidity sensor can be flexibly set according to different scenarios. Under stable external environmental conditions, sampling once every 30 seconds to 1 minute is sufficient to meet monitoring requirements; if in an environment with drastic temperature and humidity changes (such as tropical outdoor or laboratory aging tests), the sampling frequency can be increased to once every 5 to 10 seconds; while at remote sites that rely on battery power, the sampling frequency can be reduced to once every 5 to 10 minutes to save energy.

[0102] After data acquisition, error packets can be eliminated using a verification mechanism (such as CRC), and random noise can be removed using moving average or median filtering. Subsequently, the main control unit synchronously extracts the temperature and humidity values ​​inside and outside the cabinet and calculates the temperature difference (…). The system estimates the dew point humidity based on external humidity. When the temperature inside the cabinet is close to or below the dew point, the system determines that there is a risk of condensation.

[0103] Finally, the processed multimodal parameters (including internal humidity, external humidity, temperature difference, dew point, and risk status) are timestamped, stored locally on an SD card or Flash memory, and uploaded to the monitoring platform via wired (e.g., RS485, Ethernet) or wireless (e.g., LoRa, NB-IoT) methods. This data is used not only for real-time alarms but also for long-term trend analysis to help identify potential problems such as seal aging and dehumidification failure, thereby improving equipment operational reliability.

[0104] S202. Perform fusion processing on multimodal environmental parameters to generate an environmental state characterization.

[0105] Fusion processing refers to the weighted fusion of multimodal environmental parameters using a pre-defined algorithm to generate an index that comprehensively represents the environmental state. For example, an adaptive weighting algorithm can be used to dynamically adjust the weights of each parameter based on the sensor's health status.

[0106] Environmental condition characterization can refer to a comprehensive index generated through fusion processing to describe the environmental conditions inside and outside the cabinet. For example, environmental condition characterization can be a fused humidity value, which can satisfy the following:

[0107] .

[0108] The humidity inside the cabinet, the humidity outside the cabinet, and the temperature difference between inside and outside the cabinet are normalized data. Specifically, the normalized humidity inside the cabinet satisfies the following condition: Normalized humidity inside the cabinet = Humidity inside the cabinet / 100.

[0109] Regarding the external humidity, the following conditions must be met:

[0110] Normalized external humidity = external humidity / 100.

[0111] For example, when the external humidity is 50%, the normalized external humidity is 0.5.

[0112] Regarding the temperature difference between the inside and outside of the cabinet, the following conditions must be met:

[0113] .

[0114] For example, when the temperature difference between the inside and outside of the cabinet is 15 and the threshold for the temperature difference between the inside and outside of the cabinet is 10, the normalized temperature difference between the inside and outside of the cabinet is: .

[0115] a+b+c=1. This formula assigns a high weight to the humidity inside the cabinet because it is directly related to whether the equipment surface is damp; at the same time, it introduces the humidity and temperature difference outside the cabinet to reflect the potential for external moisture penetration and the driving force of condensation.

[0116] In some embodiments, the temperature difference between the inside and outside of the cabinet can also correspond to a positional temperature difference influence coefficient, which is determined by the temperature difference between the inside and outside of the cabinet and the maximum allowable temperature difference threshold.

[0117] In practical applications, this fused humidity value can effectively improve early warning sensitivity. For example, during monitoring, the humidity inside the cabinet is 65%RH, the humidity outside the cabinet reaches 80%RH, and the temperature inside the cabinet is 6℃ lower than the outside temperature. The normalized baseline for both the internal and external humidity is set to 100%, and the normalized baseline for the temperature difference (maximum allowable temperature difference threshold) is set to 10℃. If the weighting coefficients are set a=0.4, b=0.3, and c=0.3, then the normalized data are: internal humidity 0.65, external humidity 0.80, and internal / external temperature difference 0.60. Substituting these values ​​into the formula yields:

[0118] .

[0119] It is important to note that the weighting coefficients for internal humidity, external humidity, and temperature difference between the inside and outside of the cabinet can be dynamically adjusted according to the specific scenario. In coastal areas with high salt spray, the weight of external humidity can be increased (e.g., 0.4) to emphasize the impact of corrosive external moisture; while in well-sealed data center power distribution cabinets, the weight of internal humidity can be increased to 0.6 to highlight the dominant role of internal moisture accumulation. This configurable fusion strategy allows environmental condition characterization to adapt to different equipment types, improving the effectiveness of use.

[0120] In this embodiment of the application, the multimodal environmental parameters are fused to generate an environmental state characterization, including:

[0121] The weighting coefficients of each multimodal environmental parameter are adjusted based on the health status of the sensors used to collect multimodal environmental parameters.

[0122] An environmental state representation is generated based on multimodal environmental parameters and corresponding weighting coefficients.

[0123] Among them, the health of the sensor used to collect multimodal environmental parameters can characterize the reliability, accuracy and stability of the sensor's output data under the current working state. It can be quantitatively evaluated through self-diagnostic information (such as signal drift, response delay, calibration deviation, communication anomaly or lifespan loss, etc.). The higher the health, the more reliable the sensor data is, and it should be given a higher weight coefficient in the fusion calculation. Conversely, its influence should be reduced to improve the overall robustness and authenticity of the environmental state parameters.

[0124] For example, the temperature and humidity sensor installed inside the cabinet experienced slight zero-point drift after two years of continuous operation. Its self-test module detected that the temperature reading was 0.8°C higher than the factory calibration value. Intermittent communication timeouts occurred. Therefore, its health score was adjusted from the initial 1.0 to 0.75. In subsequent humidity value calculations, the 0.4 weight originally assigned to this sensor was dynamically adjusted to... This avoids misjudging environmental conditions due to the performance degradation of a single sensor.

[0125] For example, external temperature and humidity sensors, due to prolonged exposure to high salt spray environments, exhibit significantly slower response times. Self-diagnosis indicates that their rise-edge response time exceeds the threshold (>30 seconds), and the cumulative calibration deviation over the past month has reached [amount missing]. The system automatically lowered its health level to 0.6 and triggered a low-confidence flag.

[0126] For example, taking the aforementioned formula for the fused humidity value as an example, the algorithm first multiplies the original weight of the external humidity (0.3) by its health score, thus adjusting it to... .

[0127] Therefore, by introducing a health-driven weighting mechanism, multi-source sensing systems can continuously output highly reliable environmental status representations under complex conditions such as equipment aging, environmental corrosion, or sudden failures, thereby improving the reliability of intelligent operation and maintenance and proactive protection. In some embodiments, the confidence level representing health and the initial weights of each multimodal environmental parameter can be normalized and redistributed to obtain the weight coefficients of the multimodal environmental parameters.

[0128] For example, the initial weights for the three environmental parameters—indoor humidity, outdoor humidity, and temperature difference—are set to 0.4, 0.3, and 0.3, respectively. During operation, the system monitors the health status of each sensor in real time: the indoor humidity sensor experiences slight signal drift due to prolonged exposure to high humidity, causing its health score to drop from 1.0 to 0.7; the outdoor humidity sensor functions normally with a health score of 0.95; and both temperature sensors used for temperature difference calculation operate stably with a health score of 0.98. Therefore, the overall confidence score for the temperature difference term is set to 0.98. Subsequently, the system multiplies the initial weights of each parameter by their corresponding health scores to obtain the weighted confidence values.

[0129] ;

[0130] Then normalize these values ​​by dividing them by the sum of the three:

[0131] 0.28 + 0.285 + 0.294 = 0.859;

[0132] Therefore, the dynamically adjusted fusion weights are obtained as follows:

[0133] The new weighting for humidity inside the cabinet is 0.28 / 0.859 ≈ 0.326;

[0134] The new weight for external humidity is 0.285 / 0.859 ≈ 0.332.

[0135] The new weighting for temperature difference is 0.294 = 0.294 / 0.859 ≈ 0.342;

[0136] Thus, while preserving the original weight design intent, the impact of performance degradation sensors is effectively reduced, making the fusion results closer to the real environment and improving the accuracy of multimodal perception.

[0137] In some embodiments, the dynamically adjusted fusion weights can also be determined based on the weights of the humidity inside the cabinet, the humidity outside the cabinet, and the temperature difference.

[0138] For example, when the weights of the humidity inside the cabinet, the humidity outside the cabinet, and the temperature difference are 0.4, 0.3, and 0.3 respectively, that is, the weight of the humidity outside the cabinet or the weight of the temperature difference is less than the weight of the humidity inside the cabinet.

[0139] The new weighting for humidity inside the cabinet is 0.28 / 0.859 ≈ 0.326;

[0140] The new weight for external humidity is 0.285 / 0.859 ≈ 0.332.

[0141] The new weighting for temperature difference is 1 - 0.326 - 0.332 = 0.342.

[0142] Alternatively, the new weighting for humidity inside the cabinet is approximately 0.28 / 0.859 ≈ 0.326.

[0143] The new weighting for temperature difference is 0.294 = 0.294 / 0.859 ≈ 0.342;

[0144] The new weight of temperature difference = 1 - 0.326 - 0.342 = 0.332.

[0145] After determining the weighting coefficients, the environmental state characterization is obtained by multiplying each weighting coefficient with each multimodal environmental parameter and summing the results.

[0146] Furthermore, in this embodiment, the weighting coefficients of each multimodal environmental parameter are adjusted based on the health status of the sensors used to collect multimodal environmental parameters, including:

[0147] Based on the historical data acquired by the sensor, the consistency variance of the historical data is determined; the consistency variance of the historical data is used to characterize the health of the sensor.

[0148] When the consistency variance of historical data is higher than the preset variance, the weighting coefficient of the sensor is reduced.

[0149] The historical data acquired by the sensor can refer to the raw measurement values ​​or the effective output values ​​after preliminary processing that the sensor continuously collects over a period of time (such as the past 24 hours, 7 days, or a calibration cycle). These data reflect the sensor's output behavior in the actual operating environment.

[0150] For example, the humidity sensor inside the cabinet records the relative humidity value every 5 minutes, and the resulting time series is its historical data. This data not only includes the numerical value itself, but may also include auxiliary information such as timestamps and environmental conditions (such as temperature and power supply voltage) for subsequent health status analysis.

[0151] Historical data consistency variance refers to the degree of dispersion of the sensor's values ​​around a local mean, calculated based on the raw output sequence recorded by the sensor within a continuous time window (such as the most recent 6 hours or 24 hours). This indicator quantifies the volatility of sensor readings through sample variance or standard deviation. If the output is smooth and repeatable, the variance is small, indicating high consistency and good health. If the readings fluctuate frequently and irregularly, the variance is large, reflecting potential problems such as noise interference, hardware aging, poor contact, or internal faults.

[0152] In calculating the consistency variance of historical data, the most recent value for that sensor can be taken. A time series is constructed by taking 72 valid sampling points (e.g., once every 5 minutes, for a total of 72 points over 6 hours), and then calculating the sample variance of this data set:

[0153]

[0154] in, For the effective sampling points of the time series, This represents the average value of each sampling point in the time series.

[0155] Preset variance refers to a quantitative benchmark for the consistency level that the sensor's output data should possess under healthy and normal operating conditions. It represents the reasonable upper limit of historical data fluctuations under ideal or typical operating conditions. This value is not arbitrarily set, but is based on prior knowledge of the sensor's performance and is used to compare it with the real-time calculated consistency variance to determine whether its current state is abnormal.

[0156] In some embodiments, the preset variance can typically be obtained through several methods. One approach is to refer to technical parameters provided by the equipment manufacturer, such as repeatability error, short-term stability, or noise density, and convert them into corresponding variance values ​​based on the actual sampling frequency and data processing method. Another method, which is closer to actual operating conditions, involves selecting a historical data segment during the initial operation phase of the system in a stable environment without fault alarms, calculating the statistical characteristics of its consistency variance (such as mean, median, or 95th percentile), and using this statistical result as the preset variance.

[0157] When the consistency variance of historical data exceeds the preset variance, it indicates a decrease in output stability, potentially indicating drift, interference, or hardware degradation. In this case, the system should dynamically reduce the weight coefficient of this sensor in multi-source information fusion to minimize its negative impact on the overall judgment.

[0158] For example, by continuously collecting data from the humidity sensor inside the cabinet over the past 24 hours (i.e., historical data), the local variance within its sliding window (e.g., 10 sampling points per hour) is calculated, and the mean of these local variances is used as the consistency variance. If the consistency variance is 0.8, while the preset normal threshold is 1.5, it indicates that the sensor output is stable, with small fluctuations, high data consistency, and good health, and its weight can be maintained or appropriately increased. Conversely, if the consistency variance of another humidity sensor outside the cabinet reaches 3.2, which is higher than the preset threshold of 1.5, it indicates that its historical data fluctuations are abnormal and there may be drift or interference. Based on this, it can be determined that its health has declined, and its corresponding weight coefficient is automatically reduced when integrating and calculating environmental state parameters (e.g., from the default 0.3 to 0.15), thereby reducing the impact of unreliable data on the overall judgment.

[0159] In this embodiment, a normal consistency variance threshold (i.e., preset variance) can be set for each type of sensor. By calculating the consistency variance of its historical data in real time (e.g., the most recent hour), if the consistency variance of the historical data is higher than the preset variance, the sensor's health is determined to have declined, and it can be adjusted according to the following formula:

[0160]

[0161] in, These are the adjusted weighting coefficients. These are the initial weighting coefficients; The attenuation coefficient; The consistency variance of historical data, The variance is the preset value.

[0162] For example, the fusion humidity value satisfies:

[0163] hour,

[0164] The humidity sensor outside the cabinet is currently showing a slight abnormality:

[0165] Preset variance ;

[0166] Consistency variance of historical data ;

[0167] The set attenuation coefficient ;

[0168] Substitute into the dynamic weight formula:

[0169]

[0170] As a result, the weight of external humidity decreased from 0.3 to 0.234, a reduction of approximately 23.4%. Normalization was then performed (keeping the sum of all values ​​equal to 1):

[0171] The original sum of the weights of the other two items was 0.4 + 0.3 = 0.7, and the new total weight was 0.4 + 0.234 + 0.3 = 0.934.

[0172] Dividing all three terms by 0.934 yields the normalized weights:

[0173] The new weighting for humidity inside the cabinet: 0.4 / 0.934≈0.428;

[0174] The new weighting for external humidity: 0.234 / 0.934≈0.251;

[0175] The new weighting for temperature difference: 0.3 / 0.934 ≈ 0.321;

[0176] The formula for merging humidity values ​​becomes:

[0177] .

[0178] S203. Determine the condensation risk index based on the environmental condition characterization.

[0179] The condensation risk index is a quantitative indicator that reflects the likelihood of condensation occurring inside the equipment cabinet. Its value depends mainly on how close the humidity inside the cabinet is to the dew point humidity at the current temperature, and takes into account the influence of cabinet material properties and cabinet temperature on condensation sensitivity. The index increases as the humidity inside the cabinet approaches or exceeds the dew point humidity, indicating that water vapor is more likely to condense into water droplets on the cabinet walls, components, and other surfaces.

[0180] In some embodiments, after calculating the environmental state characterization (i.e., the fused humidity value) that reflects the microenvironment characteristics of the equipment based on multimodal environmental parameters, the fused humidity value can be combined with the current ambient temperature using thermodynamic empirical formulas (such as the Magnus formula) to obtain the current dew point humidity (i.e., the critical temperature at which water vapor begins to undergo a phase change). Finally, by comparing the lowest temperature of key surfaces inside the equipment (such as metal cabinet walls and cold-end components) with this dew point humidity, the safe temperature difference is obtained. The condensation risk index is a quantitative indicator constructed based on this safe temperature difference: when the surface temperature is much higher than the dew point humidity, the index approaches 0, representing a dry and safe environment; as the surface temperature gradually approaches the dew point humidity, the safe temperature difference decreases, and the index rises sharply and non-linearly, intuitively reflecting the rapid accumulation of the risk of water vapor condensing into water droplets on the surface of objects inside the cabinet.

[0181] For example, the current multimodal environmental parameters inside and outside the equipment cabinet are: humidity inside the cabinet 65%, humidity outside the cabinet 90%, and temperature difference between inside and outside the cabinet 4℃.

[0182] Substitute the humidity inside the cabinet, the humidity outside the cabinet, and the temperature difference between the inside and outside of the cabinet into the " Calculations were performed, and the results were:

[0183] .

[0184] Next, if the actual ambient temperature inside the cabinet is 25℃, combined with a combined humidity value of 54.2%, the dew point humidity of the current air can be calculated using the thermodynamic empirical formula (Magnus formula) to be approximately 16.7℃. At this point, if the system monitors a minimum surface temperature of 19.3℃ on the metal cabinet walls or precision components inside the cabinet, then the current environment has a safe temperature margin of 2.6℃ (19.3℃-16.7℃).

[0185] Based on a safe temperature difference of 2.6℃, the corresponding risk index (e.g., 0.3) is obtained through methods such as looking up tables. This indicates that although there is some humidity, there is still a safe distance before condensation, and the system can maintain normal ventilation. Conversely, if the humidity outside the cabinet continues to rise, causing the condensation humidity value to increase, or if the cabinet wall temperature drops to 16℃ due to nighttime cooling, compressing the safe temperature difference to less than 1℃, the condensation risk index will rapidly approach 1.0, thereby triggering an emergency heating or dehumidification mechanism to prevent short circuit damage to the equipment.

[0186] In this embodiment of the application, the environmental state characterization incorporates the humidity value;

[0187] Based on environmental condition characterization, the condensation risk index is determined, including:

[0188] Determine the dew point humidity based on the environmental condition characterization and the temperature inside the cabinet;

[0189] The condensation risk index is determined based on the material sensitivity coefficient, dew point humidity, cabinet humidity, and cabinet temperature; the material sensitivity coefficient is determined based on the cabinet material.

[0190] Specifically, the environmental condition and air temperature are substituted into a pre-set model to calculate the current actual water vapor pressure. Among them, the current actual water vapor pressure satisfy:

[0191]

[0192] in, As a characterization of environmental conditions, This refers to the air temperature.

[0193] Then, based on the current actual water vapor pressure Determine the dew point humidity Among them, dew point humidity satisfy:

[0194]

[0195] Obtain the dew point humidity Then, the condensation risk index can be determined based on the material sensitivity coefficient, dew point humidity, cabinet humidity, and cabinet temperature. .

[0196] In this embodiment of the application, determining the condensation risk index based on environmental condition characterization may include, and may further include:

[0197] Determine the dew point humidity based on the environmental condition characterization and the temperature inside the cabinet;

[0198] The corrected humidity difference is determined based on the material sensitivity coefficient and the humidity difference between the humidity inside the cabinet and the dew point humidity;

[0199] The temperature sensitivity is determined based on the internal temperature of the cabinet and the preset temperature threshold.

[0200] The condensation risk index is determined based on the adjusted humidity difference and temperature sensitivity.

[0201] Among them, the condensation risk index satisfy:

[0202]

[0203] in, The material sensitivity coefficient is a correction parameter reflecting the degree of sensitivity of different cabinet materials to condensation. Its value is determined based on the physical properties of the material, such as thermal conductivity, surface roughness, hydrophilicity, and heat capacity. For example, metal cabinets (such as steel plates and aluminum alloys) conduct heat quickly and their surface temperature changes rapidly with the environment, making them more prone to condensation under the same temperature and humidity conditions. Therefore, they are given a higher sensitivity coefficient (e.g., 0.08). In contrast, plastic or composite material cabinets conduct heat slowly and their surfaces are less prone to condensation, resulting in a lower sensitivity coefficient (e.g., 0.03–0.05).

[0204] Humidity inside the cabinet;

[0205] This refers to the temperature inside the cabinet.

[0206] For example, if the current air temperature inside the equipment cabinet Furthermore, the environmental state characterization obtained after weighted calculation of multi-source data .

[0207] Therefore: Current actual water vapor pressure:

[0208]

[0209] Then, based on the current actual water vapor pressure Determine the dew point humidity ,Right now:

[0210]

[0211] The condensation risk index can be determined based on the material sensitivity coefficient, dew point humidity, cabinet humidity, and cabinet temperature. .

[0212] If the core equipment inside the cabinet is installed in a steel plate or aluminum alloy cabinet, these materials conduct heat quickly and their surfaces are easily affected by sudden environmental changes. In this case, the material sensitivity coefficient can be obtained by referring to a table. ;

[0213]

[0214] S204. Based on the condensation risk index, trigger dual-mode dehumidification operation.

[0215] After obtaining the condensation risk index, the condensation risk index can be normalized. By comparing the normalized condensation risk index with a preset threshold, it can be determined whether to trigger the dual-mode dehumidification operation.

[0216] For example, the original score of the current condensation risk index calculated above is 42.94. In practical engineering applications, the system maps this original score to a condensation risk percentage, meaning the current environmental condensation risk is 42.94%. This percentage represents the relative danger of the current environmental state from triggering the condensation threshold. Therefore, a comparison can be made with a preset threshold.

[0217] For example, when the condensation risk index after normalization is less than the first preset threshold, the system starts the semiconductor condensation mode, which uses the Peltier effect to cool the humid air in the cabinet to below the dew point, so that water vapor condenses and is discharged at the cold end, achieving efficient dehumidification; when the condensation risk index is greater than or equal to the first preset threshold, the hot air circulation mode is activated simultaneously, which forms forced convection through heating elements and fans, uniformly increases the temperature in the cabinet, eliminates temperature dead zones, suppresses condensation formation and accelerates moisture diffusion. The two modes can be operated alone or in combination, taking into account both precise dehumidification and overall environmental control.

[0218] In this embodiment of the application, the dual-mode dehumidification operation is triggered based on the condensation risk index, including:

[0219] When the condensation risk index is greater than or equal to the first preset threshold, the operation of the first dehumidification mode is triggered.

[0220] When the condensation risk index is less than the first preset threshold and greater than or equal to the second preset threshold, the operation of the second dehumidification mode is triggered.

[0221] When the condensation risk index is less than the second preset threshold, the system enters monitoring mode.

[0222] The first preset threshold can be 0.3, and the second preset threshold can be 0.1.

[0223] when Switching to hot air circulation mode (first dehumidification mode), power consumption 90W;

[0224] when : Activate semiconductor condensation mode (second dehumidification mode), power consumption 55W;

[0225] when : Enter monitoring mode, only collect environmental parameters and calculate risk index, without starting any dehumidification operation, to achieve energy-saving standby.

[0226] In this embodiment of the application, after triggering the first dehumidification mode, the method further includes:

[0227] Detect whether the condensation risk index remains below the second preset threshold for a preset time.

[0228] If the condensation risk index remains below the second preset threshold for a preset period of time, the first dehumidification mode will be exited.

[0229] If the condensation risk index does not remain below the second preset threshold within a preset time, the first dehumidification mode will continue to be triggered.

[0230] To avoid frequent switching of dehumidification modes or premature exit leading to repeated condensation, a delayed judgment mechanism is activated when preparing to exit the first dehumidification mode: only when the condensation risk index remains below the second preset threshold (e.g., 0.1) for a continuous preset time (e.g., 10 minutes) is the environment confirmed to be stable and safe, and the high-power hot air circulation mode is officially exited; if the risk index rises to 0.1 or above during this period, it is determined that the risk has not been eliminated, and the first dehumidification mode continues to operate.

[0231] A server rack was damaged by a sudden downpour. The temperature rises to 0.35, and the system activates the 90W hot air circulation mode; after 20 minutes, If the value drops to 0.12, the system will begin timing and monitoring; if it remains below 0.12 for the next 10 minutes... If the humidity remains ≤0.14, the hot air mode can be safely exited; however, if the humidity suddenly increases due to the cabinet door being opened at the 8th minute, If the rebound reaches 0.18, the timer is reset and the hot air circulation continues to run until the sustained low-risk condition is met again.

[0232] The moisture control method provided in this application eliminates errors from a single data source through multi-sensor fusion and adapts to changes in sensor performance by combining dynamic weight adjustment, ensuring the accuracy of risk assessment. At the same time, the dual-mode dehumidification system switches operating modes according to the risk level. Under normal conditions, it uses low-power semiconductor condensation and only starts high-efficiency hot air dehumidification when there is a high risk, avoiding continuous high energy consumption. The hysteresis switching mechanism reduces frequent start-stop of equipment and extends its service life. Thus, it achieves all-weather, low-energy-consumption, and high-reliability moisture protection, reducing equipment failure rate and maintenance costs.

[0233] Figure 3 Flowchart of the moisture control method provided in this application Figure 2 ,like Figure 3 As shown, the method includes:

[0234] 1. After the system starts up, it begins multi-sensor data acquisition to obtain key parameters such as temperature and humidity, and performs data fusion calculations to generate a comprehensive condensation risk index. Then, the risk assessment of condensation begins: if... This indicates a high risk of condensation. The system will then activate the hot air circulation mode, which involves turning on the PTC heater, starting the centrifugal fan, and closing the air valve. Hot air circulation will be used to raise the surface temperature and prevent condensation. This mode will run for 5 minutes, then stop for 3 minutes to determine if the target humidity has been reached. If it has, the system will switch to standby mode.

[0235] 2. If This indicates a low risk of condensation, and the system will make further judgments. Is it true? If it is not true (i.e.) If the conditions are met, the system enters a low-power monitoring state; if so, it activates semiconductor condensation mode, using a Peltier module for cooling, collecting condensate at the cold end, and then draining it through a drainage pipe. Regardless of the path taken, the system will subsequently check if the humidity level is within the acceptable range; if so, it switches to standby mode; otherwise, it continues operating.

[0236] 3. Update the moisture control system (for simulating and optimizing future operating strategies), and return to the initial data acquisition phase after a 5-minute delay to achieve closed-loop control.

[0237] The execution flow of the hot air circulation mode can be as follows: after the system starts, data is collected by multiple sensors and the condensation risk index is calculated. If the risk is high (>0.37), PTC heating and hot air circulation mode are activated to increase the surface temperature. If the risk is medium (0.1~0.37), semiconductor cooling is used to collect and drain condensate. When the risk is low, low power consumption monitoring is activated.

[0238] The semiconductor mode execution flow can be as follows: After system startup, environmental parameters are collected in real time, the condensation risk index is calculated, and different response modes are selected according to its magnitude. In case of high risk, PTC heating and hot air circulation are activated to raise the surface temperature; in case of medium risk, semiconductor refrigeration is used to collect condensate; and in case of low risk, energy-saving monitoring mode is entered. The centrifuge starts, and the air valve closes.

[0239] The closed-loop control mechanism can be as follows: After system startup, environmental data is collected through multiple sensors and fused for calculation to assess condensation risk indicators. Different strategies are adopted according to the risk level: if... Activate the hot air circulation mode (PTC heating + fan operation) to prevent condensation; if If the humidity level is within the specified range, the semiconductor condensation mode will be activated to collect condensate; otherwise, it will enter a low-power monitoring state. After each mode has run, it will determine whether the humidity level meets the standard. If it does, it will switch to standby mode and update the system. After a 5-minute delay, the process will repeat.

[0240] In some embodiments, the dual-mode switching logic can be: hot air mode prioritizes handling critical condensation states; semiconductor mode handles normal high humidity environments.

[0241] The switching lag design can be: the exit mode must meet the following requirements. Lasts for 30 minutes.

[0242] Energy-saving control mechanisms can be as follows: hot air mode uses a 5-minute ON / 3-minute OFF pulse cycle; semiconductor mode automatically adjusts cooling power according to humidity gradient.

[0243] Fail-safe design can be:

[0244] Dual temperature monitoring: Heating will be forcibly shut off when the temperature inside the cabinet exceeds 60℃;

[0245] Drainage anomaly detection: Alarm triggered when water level in the collection tank exceeds the limit;

[0246] Communication interruption handling: Local storage of 72 hours of operational data.

[0247] In some embodiments, for the dual-mode dehumidification mechanism:

[0248] The semiconductor condensation dehumidification mode (suitable for normal high humidity) uses a counter-flow Peltier module to create a -10°C cold surface at the top of the cabinet; humid air condenses and drains after contacting the cold surface, while dry air returns from the bottom. The drainage design features an inclined water collection tank. Nano-hydrophobic coating pipes External evaporator box.

[0249] The activation conditions for pulsed hot air dehumidification mode (suitable for critical condensation state) are: temperature difference between inside and outside the cabinet > 5℃ and humidity > 80%.

[0250] In some embodiments, the cabinet is designed as follows:

[0251] 1. Biomimetic hydrophobic top cover: Surface layer: Fluorosilicone nano-coating (contact angle >150°); Middle layer: V-shaped guide groove (slope 15°); Base layer: Porous titanium alloy (porosity 40%); Rainwater flows autonomously after contacting the surface, preventing water accumulation and penetration.

[0252] 2. Intelligent ventilation system: The cabinet is equipped with a maze-style ventilation channel on the side and an activated carbon filter layer (20mm thick) at the entrance to absorb moisture; shape memory alloy air valve: automatically opens when the temperature is >25℃ to increase the ventilation volume; linked with the dehumidification system: the air valve closes during dehumidification to prevent external moisture from entering.

[0253] 3. Dynamic sealing of cabinet doors: The cabinet door adopts a pneumatic sealing strip design: When the cabinet door is closed, the first pull line is pulled; the force plate compresses the double-rod piston cylinder; gas is injected into the sealing air cushion through the air guide pipe; the air cushion expands and fills the gap of the cabinet door; when the door is opened, the pressure is automatically released to avoid wear of the sealing strip.

[0254] 4. Predictive Maintenance Platform: Lifetime prediction and fault early warning based on mechanism models.

[0255] By integrating multi-dimensional data such as historical temperature and humidity curves, dehumidification system operating time, and changes in sealing strip pressure, a physical degradation model of the sealing components is established to achieve prediction of remaining life and early warning of failures.

[0256] The remaining lifetime is determined using the following lifetime decay model for fault prediction. The lifetime decay model satisfies:

[0257]

[0258] For the remaining lifespan of the sealing strip, Due to work pressure, The aging factor is... This represents the remaining lifespan of the initial sealing strip.

[0259] for example, Set to 100%;

[0260] : Set as This reflects the compressive aging rate of the rubber material under specific temperature and humidity conditions.

[0261] In actual operation, the pressure on the cabinet door sealing strip is usually fluctuating (e.g., affected by wind load, thermal expansion and contraction, or mechanical vibration).

[0262] For example, when using this model to determine the remaining lifespan, it is assumed that the sealing strip is subjected to a constant equivalent average working pressure during the monitoring period, i.e. .

[0263] Therefore, the remaining lifespan of the sealing strip after 1000 hours of operation is:

[0264] The remaining lifespan of the sealing strip is 50%.

[0265] The remaining lifespan after 5000 hours of operation is:

[0266] This indicates that the sealing strip has been cut off.

[0267] The moisture control method provided in this application embodiment embodies an intelligent management strategy based on real-time perception, risk assessment and adaptive control, taking into account both energy saving and anti-condensation effects.

[0268] In summary, the application scenarios provided by the embodiments of this application can be:

[0269] This system addresses the moisture-proofing needs of outdoor power distribution cabinets in complex environments characterized by high humidity, heavy rainfall, and large diurnal temperature variations. It can be used in coastal city power distribution cabinets, industrial park power distribution cabinets, and substation equipment in mountainous areas. The system utilizes multimodal sensors (temperature, humidity, and dew point detection), an edge computing unit, and a dual-mode dehumidification device deployed on the top of the cabinet to monitor environmental conditions in real time and dynamically adjust dehumidification strategies.

[0270] Figure 4 A schematic diagram of the moisture control device provided in this application is shown below. Figure 4 As shown, the moisture control device 40 provided in this embodiment includes:

[0271] The acquisition module 401 is used to acquire multimodal environmental parameters, including the humidity inside the cabinet, the humidity outside the cabinet, and the temperature difference between inside and outside the cabinet.

[0272] The generation module 402 is used to fuse multimodal environmental parameters and generate an environmental state representation.

[0273] Module 403 is used to determine the condensation risk index based on the environmental condition characterization.

[0274] Trigger module 404 is used to trigger dual-mode dehumidification operation based on the condensation risk index.

[0275] In one possible implementation, the generation module 402 can also be specifically used for:

[0276] The weighting coefficients of each multimodal environmental parameter are adjusted based on the health status of the sensors used to collect multimodal environmental parameters.

[0277] An environmental state representation is generated based on multimodal environmental parameters and corresponding weighting coefficients.

[0278] In one possible implementation, the generation module 402 can also be specifically used for:

[0279] Based on the historical data acquired by the sensor, the consistency variance of the historical data is determined; the consistency variance of the historical data is used to characterize the health of the sensor.

[0280] When the consistency variance of historical data is higher than the preset variance, the weighting coefficient of the sensor is reduced.

[0281] In one possible implementation, the environmental state characterization is integrated with the humidity value; the determination module 403 can also be specifically used for:

[0282] Determine the dew point humidity based on the environmental condition characterization and the temperature inside the cabinet;

[0283] The condensation risk index is determined based on the material sensitivity coefficient, dew point humidity, cabinet humidity, and cabinet temperature; the material sensitivity coefficient is determined based on the cabinet material.

[0284] In one possible implementation, the determining module 403 can also be specifically used for:

[0285] The corrected humidity difference is determined based on the material sensitivity coefficient and the humidity difference between the humidity inside the cabinet and the dew point humidity;

[0286] The temperature sensitivity is determined based on the internal temperature of the cabinet and the preset temperature threshold.

[0287] The condensation risk index is determined based on the adjusted humidity difference and temperature sensitivity.

[0288] In one possible implementation, the trigger module 404 can also be specifically used for:

[0289] When the condensation risk index is greater than or equal to the first preset threshold, the operation of the first dehumidification mode is triggered.

[0290] When the condensation risk index is less than the first preset threshold and greater than or equal to the second preset threshold, the operation of the second dehumidification mode is triggered.

[0291] When the condensation risk index is less than the second preset threshold, the system enters monitoring mode.

[0292] In one possible implementation, the trigger module 404 can also be specifically used for:

[0293] Detect whether the condensation risk index remains below the second preset threshold for a preset time.

[0294] If the condensation risk index remains below the second preset threshold for a preset period of time, the first dehumidification mode will be exited.

[0295] If the condensation risk index does not remain below the second preset threshold within a preset time, the first dehumidification mode will continue to be triggered.

[0296] The moisture control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0297] Figure 5 A schematic diagram of the electronic structure provided in this application. (See attached diagram.) Figure 5As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0298] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0299] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0300] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0301] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0302] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0303] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0304] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0305] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0306] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0307] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0308] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0309] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0310] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0311] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0312] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A moisture control method, characterized in that, include: Collect multimodal environmental parameters, including indoor humidity, outdoor humidity, and temperature difference between indoor and outdoor environments. The multimodal environmental parameters are fused to generate an environmental state characterization; Based on the environmental condition characterization, the condensation risk index is determined; Based on the condensation risk index, a dual-mode dehumidification operation is triggered.

2. The method according to claim 1, characterized in that, The process of fusing the multimodal environmental parameters to generate an environmental state characterization includes: The weighting coefficients of each of the multimodal environmental parameters are adjusted based on the health status of the sensors used to collect the multimodal environmental parameters. An environmental state characterization is generated based on the multimodal environmental parameters and their corresponding weighting coefficients.

3. The method according to claim 2, characterized in that, The step of adjusting the weighting coefficients of each of the multimodal environmental parameters based on the health status of the sensors used to collect the multimodal environmental parameters includes: Based on the historical data acquired by the sensor, the consistency variance of the historical data is determined; the consistency variance of the historical data is used to characterize the health of the sensor. When the consistency variance of the historical data is higher than the preset variance, the weighting coefficient of the sensor is reduced.

4. The method according to claim 1, characterized in that, The environmental state characterization incorporates humidity values; The determination of the condensation risk index based on the environmental condition characterization includes: Determine the dew point humidity based on the environmental condition description and the temperature inside the cabinet; The condensation risk index is determined based on the material sensitivity coefficient, the dew point humidity, the humidity inside the cabinet, and the temperature inside the cabinet; the material sensitivity coefficient is determined based on the cabinet material.

5. The method according to claim 4, characterized in that, The determination of the condensation risk index based on the material sensitivity coefficient, the dew point humidity, the humidity inside the cabinet, and the temperature inside the cabinet includes: The corrected humidity difference is determined based on the material sensitivity coefficient and the humidity difference between the humidity inside the cabinet and the dew point humidity. The temperature sensitivity is determined based on the cabinet temperature and the preset temperature threshold. The condensation risk index is determined based on the corrected humidity difference and the temperature sensitivity.

6. The method according to claim 1, characterized in that, The step of triggering dual-mode dehumidification operation based on the condensation risk index includes: When the condensation risk index is greater than or equal to the first preset threshold, the operation of the first dehumidification mode is triggered. When the condensation risk index is less than the first preset threshold and greater than or equal to the second preset threshold, the operation of the second dehumidification mode is triggered. When the condensation risk index is less than the second preset threshold, the system enters monitoring mode.

7. The method according to claim 6, characterized in that, After triggering the first dehumidification mode, the method further includes: Detect whether the condensation risk index remains below the second preset threshold for a preset time period; If the condensation risk index remains below the second preset threshold for a preset time, the first dehumidification mode will be exited. If the condensation risk index does not remain below the second preset threshold within a preset time, the first dehumidification mode will continue to be triggered.

8. A moisture control device, characterized in that, include: The data acquisition module is used to acquire multimodal environmental parameters, including the humidity inside the cabinet, the humidity outside the cabinet, and the temperature difference between inside and outside the cabinet. The generation module is used to fuse the multimodal environmental parameters and generate an environmental state representation. The determination module is used to determine the condensation risk index based on the environmental state characterization. The triggering module is used to trigger dual-mode dehumidification operation based on the condensation risk index.

9. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-7.

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