Container type battery environment monitoring system based on edge calculation

Abstract

The invention discloses a container type battery environment monitoring system based on edge calculation, which relates to the technical field of battery environment monitoring, and is characterized in that sensors and edge nodes are arranged in cabins of a battery energy storage device to acquire internal operation environment data of the container type battery energy storage device; a pneumatic environment data set and a water vapor critical data set are formed after data processing, cross-cabin air exchange efficiency Eex is constructed for air exchange qualification evaluation, a condensation nucleation potential index Cnp is constructed when air exchange is abnormal, and a comprehensive condensation risk index Rall is generated in combination with the cross-cabin air exchange efficiency Eex for condensation risk evaluation. Instructions such as fan power adjustment, dehumidification device start and stop and power limitation are issued according to the risk level, and information labels are added to the evaluation data set and uploaded to the cloud in real time. According to the invention, the real-time monitoring of the container type battery environment, the dynamic prediction of the condensation risk and the automatic control are realized, and the safety and reliability of the operation of the energy storage system are improved.

Description

Technical Field

[0001] This invention relates to the field of battery environmental monitoring technology, specifically to a containerized battery environmental monitoring system based on edge computing. Background Technology

[0002] With the rapid development of the new energy storage industry, containerized battery energy storage devices are widely used due to their high energy density, modular installation, and ease of transportation. However, operating within a closed compartment structure, these devices are constrained by space, ventilation, and temperature and humidity conditions, making them prone to complex airflow and humidity accumulation environments. Traditional environmental monitoring relies on centralized data collection and back-end processing, which suffers from response lag and untimely control. In recent years, the introduction of edge computing has enabled environmental monitoring to perform preprocessing and real-time analysis directly at nodes within the compartment, improving data immediacy and the system's proactive prevention and control capabilities. Against this backdrop, air exchange efficiency and condensation risk have become the most critical safety risks within the battery compartment: insufficient air exchange or intensified water vapor condensation can easily induce accidents such as short circuits, corrosion, and thermal runaway.

[0003] Currently, most containerized battery systems still rely on fixed thresholds or simple sensor monitoring to assess air exchange and humidity levels, lacking dynamic diagnostics and comprehensive risk analysis for complex environmental changes. These methods often focus on a single parameter without considering multi-dimensional factors such as cross-compartment air exchange efficiency, humidity gradients, and dew point temperature, leading to incomplete risk identification. When slight obstruction occurs in air exchange or rapid humidity fluctuations, existing monitoring methods struggle to detect and provide effective control commands in a timely manner, thus failing to detect condensation risks during their latent stages. Furthermore, some systems rely solely on manual maintenance or backend cloud analysis, resulting in condensation risks often being identified only after they have already caused impact, exhibiting a significant timeliness deficiency. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a containerized battery environmental monitoring system based on edge computing, which solves the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: a containerized battery environmental monitoring system based on edge computing, including a data acquisition module, an air diagnostic module, a comprehensive risk analysis module, an execution control module, and an archive feedback module; The data acquisition module is used to embed edge nodes in the containerized battery energy storage device and install sensor groups in the containerized battery energy storage device to collect operating environment data in real time. After processing the edge node data, aerodynamic environment data groups and water vapor critical data groups are obtained. The air diagnostic module is used to construct the cross-compartment air exchange efficiency Eex based on the aerodynamic environment data set, and to conduct an air exchange qualification assessment with the cross-compartment air exchange critical threshold Te, and to perform condensation risk analysis when the air exchange in the compartment is abnormal. The comprehensive risk analysis module is used to perform condensation risk analysis. It constructs a condensation nucleation potential index Cnp based on the water vapor critical data set, and calculates a comprehensive condensation risk index Rall by combining it with the cross-chamber air exchange efficiency Eex. Then, it assesses the condensation risk by combining it with the condensation nucleation trigger threshold Ra and the condensation risk judgment threshold Rl. The execution control module is used to transmit the assessment information generated by the condensation risk assessment to the central controller and execute the corresponding control commands; The archive feedback module is used to add information tags to the condensation risk assessment set at the edge node through the data archive unit, and transmit the tagged data to the cloud storage in real time through the remote upload unit.

[0006] Preferably, the data acquisition module includes a node arrangement unit, a data acquisition unit, and a data processing unit; The node arrangement unit is used to embed a set of edge nodes in the auxiliary control compartment of the containerized battery energy storage device. The containerized battery energy storage device is internally divided into a battery compartment, an auxiliary control compartment, and a ventilation channel compartment. Air circulation between the compartments is achieved by fans and ventilation openings. The data acquisition unit is used to install sensor groups at various locations of the containerized battery energy storage device to collect real-time operating environment data inside the containerized battery energy storage device. The sensor group includes an anemometer, differential pressure sensor, speed sensor, cold mirror dew point meter, and temperature and humidity sensor; The anemometer and differential pressure sensor are installed at the ventilation openings of the battery compartment and the ventilation channel compartment to collect the airflow velocity va and the air pressure difference Δpa of the compartment, respectively. The speed sensor is installed at the fan shaft end of the ventilation duct compartment to collect the fan speed fs in real time. The cold mirror dew point meter is installed 1.5 meters above the battery cluster inside the battery compartment to collect the dew point temperature Td in real time. The temperature and humidity sensors are respectively installed inside the main battery compartment, auxiliary control compartment and ventilation channel compartment to collect the surface temperature Ts of the compartment wall, the compartment temperature wd and the compartment humidity sd of each compartment in real time.

[0007] Preferably, the data processing unit is used to establish a transmission channel between the edge node and the sensor group through a wireless network, transmit the operating environment data to the edge node in real time for data processing, and obtain the aerodynamic environment data group and the water vapor critical data group. The data processing is used to preprocess the operating environment data before performing data analysis; The preprocessing includes noise reduction, timestamp alignment, and dimensionless processing; The denoising process removes noise from the operating environment data through bandpass filtering; timestamp alignment projects operating environment data with different sampling frequencies onto the same time axis, and fills in missing points and uneven operating environment data with linear interpolation to form a data sequence with a unified time step; dimensionless processing removes the dimensional influence of state data through the Max-Min method. The data analysis includes humidity gradient analysis and humidity change rate analysis; The humidity gradient analysis is used to obtain the vertical humidity gradient Hg by dividing the humidity difference collected by the humidity sensors at the top and bottom of the cabin by the height difference of the sensors. Humidity change rate analysis is used to perform time-domain first-order derivative calculation on cabin humidity sd to obtain the humidity change rate sb. The aerodynamic environment data set includes vent airflow velocity va, fan speed fs, cabin air pressure difference Δpa, cabin temperature wd, and cabin humidity sd; The critical water vapor data set includes the vertical humidity gradient Hg, the humidity change rate sb, the bulkhead surface temperature Ts, and the dew point temperature Td.

[0008] Preferably, the air diagnostic module includes an exchange rate diagnostic unit and an exchange rate assessment unit; The exchange rate diagnostic unit is used to fit the aerodynamic environment data set based on the edge nodes. It multiplies the airflow velocity va at the vent and the fan speed fs, takes the square root, and then multiplies it with the air pressure difference Δpa in the compartment to construct the denominator. Then, it performs logarithmic processing on the square of the temperature difference Δwd between adjacent compartments and adds the humidity difference Δsd between adjacent compartments after transforming it through the arctangent function to construct the numerator. By fitting the numerator and denominator, it constructs the cross-compartment air exchange efficiency Eex, analyzes the smoothness of airflow between compartments, and measures the air exchange efficiency between different compartments.

[0009] Preferably, the exchange evaluation unit is used to collect the cross-cabin air exchange efficiency Eex when the cabin air exchange is normal within a historical month, calculate the mean according to the statistical method, and preset the mean as the cross-cabin air exchange critical threshold Te, and then conduct an air exchange qualification evaluation with the cross-cabin air exchange efficiency Eex obtained in real time. The specific evaluation scheme is as follows. When the cross-compartment air exchange efficiency Eex < the critical threshold Te for cross-compartment air exchange, it indicates that the air exchange between compartments is abnormal and there is cross-compartment air blockage. At this time, a condensation risk analysis should be performed. When the inter-cabin air exchange efficiency Eex ≥ the inter-cabin air exchange critical threshold Te, it indicates that the cabin air exchange is normal and there is no inter-cabin air blockage. At this time, normal operation is maintained.

[0010] Preferably, the comprehensive risk analysis module is used to perform condensation risk analysis when the air exchange qualification assessment indicates abnormal cabin air exchange, specifically including a condensation potential analysis unit and a comprehensive risk analysis unit; The condensation potential analysis unit is used to fit the critical water vapor data set based on the edge nodes. The rate of moisture accumulation is represented by the product of the humidity gradient (vertical humidity gradient Hg) and the logarithm of the humidity change rate (sb). The absolute value of the difference between the bulkhead surface temperature (Ts) and the dew point temperature (Td) is then taken to reflect the influence of the temperature difference on condensation. Finally, a condensation nucleation potential index (Cnp) is constructed by fitting the data. This index is used to analyze the risk of condensation on the inner wall of the container and predict the trend of condensation formation.

[0011] Preferably, the comprehensive risk analysis unit includes a fusion analysis unit and a comprehensive risk assessment unit; The fusion analysis unit is used to fit data on the cross-compartment air exchange efficiency Eex and the condensation nucleation potential index Cnp based on the edge nodes. The ratio of the condensation nucleation potential index Cnp to the cross-compartment air exchange efficiency Eex reflects the degree of influence of air exchange on condensation risk. The ratio is then substituted into the exponential function, and 1 is subtracted from the exponential result to construct the comprehensive condensation risk index Rall, which represents the degree of danger of condensation formation in the containerized battery compartment.

[0012] Preferably, the comprehensive risk assessment unit is used to collect all comprehensive condensation risk indices Rall within a historical month, sort them from smallest to largest, and use the percentile method to preset the 50th percentile as the condensation nucleation trigger threshold Ra and the 90th percentile as the condensation risk judgment threshold Rl. Then, it is used to perform condensation risk assessment with the real-time acquired comprehensive condensation risk index Rall. The specific assessment scheme is as follows. When the comprehensive condensation risk index Rall < condensation nucleation trigger threshold Ra, it indicates that the air exchange efficiency of the compartment is insufficient to cause the battery compartment to have condensation nucleation conditions, and ventilation information is generated at this time. When the condensation nucleation trigger threshold Ra ≤ the comprehensive condensation risk index Rall ≤ the condensation risk judgment threshold Rl, it indicates that the air exchange efficiency of the cabin is insufficient, resulting in the battery compartment having the conditions for condensation nucleation. At this time, dehumidification information is generated. When the comprehensive condensation risk index Rall > the condensation risk judgment threshold Rl, it indicates that condensation has entered an uncontrollable risk state, and risk information is generated at this time.

[0013] Preferably, the execution control module is used to transmit the assessment information generated by the condensation risk assessment to the central controller in real time via the wireless network. The central controller executes corresponding control commands on each device of the containerized battery energy storage device based on the assessment information, as follows; Ventilation information: Increase the fan operating power by 30% and mark it as a potential condensation condition, then conduct iterative evaluation through the air diagnostic module; Dehumidification information: Increase the fan operating power by 50%, issue a dehumidification device start command, turn on the drying device and cooling dehumidification function in the battery compartment, and then conduct iterative evaluation through the air diagnostic module; Risk Information: Limit battery discharge power to 50%, start all backup fans and increase their power to the maximum design value, and simultaneously turn on the dehumidifier and air conditioning dehumidification mode. Then, notify on-site maintenance personnel to immediately carry out maintenance.

[0014] Preferably, the archive feedback module includes a data archiving unit and a remote upload unit; The data archiving unit is used to add information tags to each condensation risk assessment set in the edge nodes based on the assessment information generated by the condensation risk assessment. The condensation risk assessment set includes the aforementioned aerodynamic environment data set, water vapor critical data set, cross-chamber air exchange efficiency Eex, condensation nucleation potential index Cnp, and comprehensive condensation risk index Rall. The information tag includes a timestamp and the corresponding evaluation status and control instructions; The remote upload unit is used to transmit data with added information tags to the cloud for storage in real time via the wireless network.

[0015] This invention provides a containerized battery environmental monitoring system based on edge computing. It has the following beneficial effects: (1) The system's data acquisition module forms a complete operating environment perception network through multiple types of sensors deployed in the battery compartment, auxiliary control compartment, and ventilation channel compartment. Edge nodes perform denoising, time alignment, and dimensionless processing locally, unifying data with different sampling frequencies and noise interference to a standard time axis and unified dimensions, ensuring data continuity and comparability. At the same time, by calculating the humidity gradient and humidity change rate, more indicative critical parameters can be extracted from the raw humidity data. This layer serves to provide a solid and reliable foundation for subsequent diagnosis and risk analysis, enabling the monitoring data to truly reflect the dynamic changes in air and water vapor in the container battery compartment.

[0016] (2) The air diagnostic module of this system constructs the cross-compartment air exchange efficiency Eex based on the aerodynamic environment data set, and performs air exchange qualification assessment with the cross-compartment air exchange critical threshold Te, realizing real-time diagnosis of the air exchange status of the compartment and accurately identifying potential air blockage problems. When the air exchange of the compartment is abnormal, the system will automatically enter a more in-depth condensation potential analysis stage, construct the condensation nucleation potential index Cnp based on the water vapor critical data set, and further calculate the comprehensive condensation risk index Rall in combination with the cross-compartment air exchange efficiency Eex, and then perform condensation risk assessment with the condensation nucleation trigger threshold Ra and the condensation risk judgment threshold Rl, realizing the graded judgment of condensation risk from "potential nucleation" to "uncontrollable risk". The beneficial effect of this layer is that it establishes a hierarchical and nonlinear risk early warning system, which can identify the trend of condensate generation earlier and more accurately, thereby avoiding the battery environment from entering a runaway state.

[0017] (3) The system's execution control module automatically triggers control commands of varying intensities based on the risk level. It increases fan power during potential risks, activates dehumidification devices under nucleation conditions, and simultaneously performs power-limited operation, maximizes ventilation and dehumidification during uncontrollable risks, while also issuing maintenance reminders to maintenance personnel. The archive feedback module timestamps and labels all risk assessment results and control measures and uploads them to the cloud for storage, forming a historical data archive. Cloud data not only provides decision-making support for subsequent operation and maintenance management but can also be used for feedback learning and dynamic threshold optimization, enabling the system to gradually improve risk identification accuracy and environmental adaptability over long-term operation. The beneficial effect of this layer is the formation of a complete "monitoring-diagnosis-control-feedback" closed loop, achieving not only immediate prevention and control but also enabling the system to continuously evolve, significantly improving the safety and reliability of battery energy storage devices. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the process of the containerized battery environmental monitoring system based on edge computing of the present invention; Figure 2 This is a schematic diagram illustrating the operating principle of the containerized battery environmental monitoring system based on edge computing according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example

[0020] Please see Figure 1This invention provides a containerized battery environmental monitoring system based on edge computing. To achieve the above objectives, this invention is implemented through the following technical solutions: including a data acquisition module, an air diagnostic module, a comprehensive risk analysis module, an execution control module, and an archive feedback module; The data acquisition module is used to embed edge nodes in the containerized battery energy storage device and install sensor groups in the containerized battery energy storage device to collect operating environment data in real time. After processing the edge node data, aerodynamic environment data groups and water vapor critical data groups are obtained. The air diagnostic module is used to construct the cross-compartment air exchange efficiency Eex based on the aerodynamic environment data set, and to conduct an air exchange qualification assessment with the cross-compartment air exchange critical threshold Te, and to perform condensation risk analysis when the air exchange in the compartment is abnormal. The comprehensive risk analysis module is used to perform condensation risk analysis. It constructs a condensation nucleation potential index Cnp based on the water vapor critical data set, and calculates a comprehensive condensation risk index Rall by combining it with the cross-chamber air exchange efficiency Eex. Then, it assesses the condensation risk by combining it with the condensation nucleation trigger threshold Ra and the condensation risk judgment threshold Rl. The execution control module is used to execute corresponding control commands based on the assessment information generated from the condensation risk assessment. The archive feedback module is used to add information tags to the condensation risk assessment set at the edge node through the data archive unit, and transmit the tagged data to the cloud storage in real time through the remote upload unit.

[0021] In this embodiment, the data acquisition module deploys edge nodes and multiple types of sensor groups within the containerized battery energy storage device. The system can continuously and in real time acquire the operating environment data inside the containerized battery energy storage device. After denoising, time alignment, and dimensionless processing at the edge side, humidity gradient analysis and humidity change rate analysis are performed to extract aerodynamic environment data groups and water vapor critical data groups. This design effectively solves the problems of relying on a single sensor, isolated data, and uncorrected data in existing technologies, enabling input data to have higher integrity and consistency. This provides a high-quality data foundation for subsequent diagnosis and analysis, avoiding risk identification delays caused by data distortion in traditional monitoring. The air diagnostic module constructs the cross-compartment air exchange efficiency Eex based on the aerodynamic environment data groups and evaluates the air exchange qualification against the cross-compartment air exchange critical threshold Te, realizing real-time diagnosis of the air exchange status of the compartments and accurately identifying potential air obstruction problems. When abnormal air exchange occurs within the compartment, the system automatically enters a more in-depth condensation potential analysis phase. Based on the critical water vapor data set, a condensation nucleation potential index Cnp is constructed, and combined with the cross-compartment air exchange efficiency Eex, a comprehensive condensation risk index Rall is further calculated. This index is then compared with the condensation nucleation trigger threshold Ra and the condensation risk judgment threshold Rl to conduct a condensation risk assessment, achieving a graded assessment of condensation risk. Compared to the static judgment method in existing technologies that solely rely on dew point or humidity values, this solution establishes a dynamic, multi-level diagnostic logic, upgrading risk identification from single-indicator judgment to a nonlinear, multi-factor fusion predictive model. This improvement significantly enhances the sensitivity and foresight of risk identification, enabling early detection of potential condensation trends and reducing the probability of uncontrollable condensation within the energy storage battery compartment. The execution control module automatically issues differentiated commands such as fan adjustment, dehumidification device activation, and power-limited operation based on the risk level, ensuring targeted control of condensation risk at different stages. Simultaneously, the archiving feedback module adds information tags to the condensation risk assessment set at edge nodes and achieves cloud storage through remote upload, forming a traceable risk database. Compared to existing technologies that only provide manual alarms and lack closed-loop processing and historical learning, this system establishes a complete closed loop of "acquisition-diagnosis-analysis-control-feedback," which not only enables immediate suppression of condensation risks but also allows for adaptive threshold optimization based on historical data during long-term operation. As a result, the overall safety, reliability, and self-evolution capabilities of the system are significantly improved. Example

[0022] Please refer to Figure 1 and Figure 2 Specifically: the data acquisition module includes a node arrangement unit, a data acquisition unit, and a data processing unit; The node arrangement unit is used to embed a set of edge nodes in the auxiliary control compartment of the containerized battery energy storage device. The containerized battery energy storage device is internally divided into a battery compartment, an auxiliary control compartment, and a ventilation channel compartment. Air circulation between the compartments is achieved by fans and ventilation openings. The data acquisition unit is used to install sensor groups at various locations of the containerized battery energy storage device to collect real-time operating environment data inside the containerized battery energy storage device. The sensor group includes an anemometer, differential pressure sensor, speed sensor, cold mirror dew point meter, and temperature and humidity sensor; The anemometer and differential pressure sensor are installed at the ventilation openings of the battery compartment and the ventilation channel compartment to collect the airflow velocity va and the air pressure difference Δpa of the compartment, respectively. The airflow velocity va at the vent represents the speed of gas flow through the vent, reflecting the driving force of the airflow. The cabin pressure difference Δpa represents the pressure difference between adjacent cabins. The greater the pressure difference, the stronger the driving force for air exchange, and the easier it is for air to flow and for heat and humidity to be transferred. The speed sensor is installed on the fan shaft end of the ventilation duct chamber to collect the fan speed fs in real time, which represents the operating speed of the fan. The faster the speed, the greater the airflow driving force. The cold mirror dew point meter is installed 1.5 meters above the battery cluster inside the battery compartment to collect the dew point temperature Td in real time, which represents the temperature at which water vapor begins to condense. The temperature and humidity sensors are respectively installed inside the main battery compartment, auxiliary control compartment and ventilation channel compartment to collect the surface temperature Ts of the compartment wall, the compartment temperature wd and the compartment humidity sd of each compartment in real time.

[0023] The data processing unit is used to establish a transmission channel between the edge node and the sensor group through a wireless network, transmit the operating environment data to the edge node in real time for data processing, and obtain the aerodynamic environment data group and the water vapor critical data group. The data processing is used to preprocess the operating environment data before performing data analysis; The preprocessing includes noise reduction, timestamp alignment, and dimensionless processing; The denoising process removes noise from the operating environment data through bandpass filtering; timestamp alignment projects operating environment data with different sampling frequencies onto the same time axis, and fills in missing points and uneven operating environment data with linear interpolation to form a data sequence with a unified time step; dimensionless processing removes the dimensional influence of state data through the Max-Min method. The data analysis includes humidity gradient analysis and humidity change rate analysis; The humidity gradient analysis is used to obtain the vertical humidity gradient Hg by dividing the humidity difference collected by the humidity sensors at the top and bottom of the cabin by the sensor height difference. This gradient represents the rate of humidity change between different altitudes within the cabin. Specifically: , where, sd s and sd x These represent the humidity values ​​collected by the humidity sensors at the top and bottom of the cabin, respectively, and ∆gd represents the height difference between the humidity sensors at the top and bottom of the cabin. Humidity change rate analysis is used to perform time-domain first-order derivative calculation on the cabin humidity sd to obtain the humidity change rate sb, which represents the rate of change of humidity. Specifically: In the formula, dt represents the time differential element; The aerodynamic environment data set includes vent airflow velocity va, fan speed fs, cabin air pressure difference Δpa, cabin temperature wd, and cabin humidity sd; The critical water vapor data set includes the vertical humidity gradient Hg, the humidity change rate sb, the bulkhead surface temperature Ts, and the dew point temperature Td.

[0024] In this embodiment, the data acquisition module embeds edge nodes in the auxiliary control compartment through a node arrangement unit. Combined with a sensor group consisting of an anemometer, differential pressure sensor, speed sensor, cold mirror dew point meter, and temperature and humidity sensors, it can collect real-time operating environment data within the battery compartment, auxiliary control compartment, and ventilation passage compartment. The data processing unit transmits the operating data to the edge nodes via a wireless network, performs noise reduction, timestamp alignment, and dimensionless processing, and further conducts humidity gradient and humidity change rate analysis to obtain aerodynamic environment data sets and water vapor critical data sets. This implementation achieves simultaneous monitoring and unified analysis of multi-dimensional parameters such as airflow driving force, compartment air pressure difference, temperature and humidity distribution, and dew point temperature. Compared to the single sensor and single parameter judgment methods in existing technologies, it can accurately reflect the dynamic process of air circulation and water vapor accumulation within the compartment, thereby improving the completeness and accuracy of the data from the source. This not only provides high-quality data support for subsequent air exchange diagnosis and condensation risk analysis but also enables proactive risk prediction and more precise judgment, effectively improving the safety and operational reliability of containerized battery energy storage devices in complex environments. Example

[0025] Please refer to Figure 1 and Figure 2 Specifically: the air diagnostic module includes an exchange rate diagnostic unit and an exchange rate assessment unit; The exchange rate diagnostic unit is used to fit the aerodynamic environment data set based on the edge nodes. It multiplies the airflow velocity va at the vent by the fan speed fs, takes the square root, and then multiplies it by the cabin pressure difference Δpa to construct the denominator. Next, it logarithmically processes the square of the temperature difference Δwd between adjacent cabins and converts the humidity difference Δsd between adjacent cabins using the arctangent function before summing the results to construct the numerator. By fitting the numerator and denominator, it constructs the inter-cabin air exchange efficiency Eex, analyzes the smoothness of airflow between cabins, and measures the air exchange efficiency between different cabins. The specific formula is as follows: In the formula, ln represents the logarithmic function, arctan represents the hyperbolic sine function, and ∆wd and ∆sd represent the temperature difference and humidity difference between adjacent compartments, respectively. It indicates the velocity of gas flowing through the vent, reflecting the driving force of the airflow. This represents the temperature difference between two compartments. The square of the temperature difference is processed to make the effect more significant when the temperature difference is large. A logarithmic function is used to prevent the temperature difference from excessively amplifying its impact on airflow. It represents the humidity difference between different compartments. The arctangent function is used to suppress the excessive influence of humidity on air exchange, making the effect of humidity difference more gradual.

[0026] The exchange evaluation unit is used to collect the cross-cabin air exchange efficiency Eex when the cabin air exchange is normal within a month in history, calculate the mean according to the statistical method, and preset the mean as the cross-cabin air exchange critical threshold Te. Then, it is compared with the real-time cross-cabin air exchange efficiency Eex to evaluate the air exchange qualification. The specific evaluation scheme is as follows. When the cross-compartment air exchange efficiency Eex < the critical threshold Te for cross-compartment air exchange, it indicates that the air exchange between compartments is abnormal and there is cross-compartment air blockage. At this time, a condensation risk analysis should be performed. When the inter-cabin air exchange efficiency Eex ≥ the inter-cabin air exchange critical threshold Te, it indicates that the cabin air exchange is normal and there is no inter-cabin air blockage. At this time, normal operation is maintained.

[0027] In this embodiment, the air diagnostic module uses the exchange rate diagnostic unit to fit the aerodynamic environment data set based on the edge nodes. The airflow velocity va at the vent, the fan speed fs, and the cabin air pressure difference Δpa are constructed as the denominator of the airflow driving force. Then, the numerator is constructed by processing the cabin temperature difference Δwd and humidity difference Δsd with logarithmic and arctangent functions. The cross-cabin air exchange efficiency Eex is calculated to achieve a quantitative characterization of the smoothness of airflow between cabins.

[0028] The core idea behind the formula's logic and physical meaning is to construct a ratio structure similar to the "transfer coefficient" in heat and mass transfer by using the airflow driving force as the numerator and environmental constraints as the denominator. The derivation of this formula is based on fluid mechanics and the theory of heat and moisture transport. (Molecular part) Combining the airflow velocity *va* at the vent, the fan speed *fs*, and the cabin air pressure difference *Δpa*, this reflects the dynamic pressure and mechanical driving effects of air exchange. Taking the square root introduces a nonlinear balance, avoiding distortion caused by an excessively large product of wind speed and fan speed, thus conforming to the actual airflow patterns within the cabin. This part is derived from the fluid kinetic energy expression, providing a quantitative description of the driving force of airflow. The denominator incorporates the hindering effects of temperature and humidity differences. This represents the amplification effect of temperature differences between compartments. The square ensures that the effect is magnified when the temperature difference is large, while the logarithmic transformation suppresses extreme values, reflecting the influence of temperature differences on air stratification. The formula expresses the obstructive effect of humidity difference between compartments on airflow. The asymptotic properties of the arctangent function prevent the infinite amplification of excessive humidity differences, thus smoothing out the weakening effect on overall efficiency. This is derived from the nonlinear suppression function in signal processing. The formula follows the "driving force / resistance" calculation logic: when the airflow velocity va, fan speed fs, and compartment air pressure difference Δpa increase, the numerator increases, and the inter-compartment air exchange efficiency Eex improves, indicating smooth air exchange. Conversely, when the temperature difference Δwd between adjacent compartments is too large or the humidity difference Δsd between adjacent compartments increases, the denominator increases, and the inter-compartment air exchange efficiency Eex decreases, indicating obstructed air exchange. Physically, the inter-compartment air exchange efficiency Eex reflects the smoothness of airflow between different compartments and can serve as a preliminary diagnostic indicator for condensation risk analysis.

[0029] The air exchange assessment unit sets a critical threshold Te for inter-compartment air exchange based on the average inter-compartment air exchange efficiency Eex during a month of normal air exchange, and compares it with the real-time inter-compartment air exchange efficiency Eex. When the inter-compartment air exchange efficiency Eex is lower than the critical threshold Te, condensation risk analysis is triggered; when the inter-compartment air exchange efficiency Eex is higher than the critical threshold Te, normal operation is maintained. Through this implementation method, the system achieves real-time diagnosis and dynamic threshold comparison of air exchange status, overcoming the shortcomings of existing technologies that rely on fixed settings and have delayed response. It can more accurately and timely identify compartment air obstruction and potential condensation risks. Its beneficial effects are that it significantly improves the accuracy and foresight of air diagnosis, enabling condensation risks to be detected and controlled at an early stage, thereby improving the overall safety and environmental adaptability of battery energy storage devices. Example

[0030] Please refer to Figure 1 and Figure 2Specifically: the comprehensive risk analysis module is used to perform condensation risk analysis when the air exchange qualification assessment indicates abnormal cabin air exchange, and specifically includes a condensation potential analysis unit and a comprehensive risk analysis unit; The condensation potential analysis unit is used to fit the critical water vapor data set based on edge nodes. The rate of moisture accumulation is represented by the product of the humidity gradient (vertical humidity gradient Hg) and the logarithm of the humidity change rate (sb). The absolute value of the difference between the bulkhead surface temperature (Ts) and the dew point temperature (Td) is then used to reflect the influence of the temperature difference on condensation. Finally, a condensation nucleation potential index (Cnp) is constructed to analyze the risk of condensation on the inner wall of the container and predict the trend of condensation formation. The specific formula is as follows: In the formula, ln represents the logarithmic function. This represents the rate of change in humidity. Taking the logarithm makes the effect of the humidity change rate more gradual, avoiding excessive amplification of the condensation potential when the humidity change rate is too high. The square root of the difference between the bulkhead surface temperature Ts and the dew point temperature Td represents the temperature difference between the bulkhead and the dew point. The smaller the temperature difference, the greater the potential for condensation formation.

[0031] The comprehensive risk analysis unit includes a fusion analysis unit and a comprehensive risk assessment unit; The fusion analysis unit is used to fit data on the cross-compartment air exchange efficiency Eex and the condensation nucleation potential index Cnp based on edge nodes. The ratio of the condensation nucleation potential index Cnp to the cross-compartment air exchange efficiency Eex reflects the impact of air exchange on condensation risk. This ratio is then substituted into an exponential function to rapidly amplify the impact of condensation potential, demonstrating the trend of a sharp increase in condensation risk when air exchange is inefficient. Subtracting 1 from the exponential result constructs a comprehensive condensation risk index Rall, representing the degree of danger of condensation formation in the containerized battery compartment. The specific formula is as follows: In the formula, exp represents the exponential decay function. This represents a small constant to prevent the denominator from being zero, and its value is 0.001. The non-linear growth relationship of condensation risk is expressed using an exponential function, with 1 deducted to adjust the range of the results, ensuring that the comprehensive condensation risk index Rall starts from 0.

[0032] The comprehensive risk assessment unit is used to collect all comprehensive condensation risk indices Rall within a historical month, sort them from smallest to largest, and use the percentile method to preset the 50th percentile as the condensation nucleation trigger threshold Ra and the 90th percentile as the condensation risk judgment threshold Rl. Then, it is compared with the real-time acquired comprehensive condensation risk index Rall to conduct a condensation risk assessment. The specific assessment scheme is as follows. When the comprehensive condensation risk index Rall < condensation nucleation trigger threshold Ra, it indicates that the air exchange efficiency of the compartment is insufficient to cause the battery compartment to have condensation nucleation conditions, and ventilation information is generated at this time. When the condensation nucleation trigger threshold Ra ≤ the comprehensive condensation risk index Rall ≤ the condensation risk judgment threshold Rl, it indicates that the air exchange efficiency of the cabin is insufficient, resulting in the battery compartment having the conditions for condensation nucleation. At this time, dehumidification information is generated. When the comprehensive condensation risk index Rall > the condensation risk judgment threshold Rl, it indicates that condensation has entered an uncontrollable risk state, and risk information is generated at this time.

[0033] In this embodiment, the comprehensive risk analysis module automatically triggers condensation risk analysis when the cabin air exchange is abnormal. The condensation potential analysis unit constructs the condensation nucleation potential index Cnp based on the logarithmic product of the humidity vertical gradient Hg and the humidity change rate sb, combined with the difference between the cabin wall surface temperature Ts and the dew point temperature Td. This can accurately characterize the influence of the cabin moisture accumulation rate and temperature difference on condensation nucleation.

[0034] The formula logic and physical meaning of the condensation nucleation potential index Cnp are based on the classical principles of air humidity transport and condensation physical processes. Its derivation logic comes from the "driving force-resistance" relationship in heat and mass transfer. The vertical humidity gradient Hg reflects the uneven spatial distribution of water vapor within the cabin and is the spatial driving force for water vapor accumulation. The humidity change rate sb represents the dynamic growth trend of humidity over time. To avoid excessively high change rates leading to non-physical amplification of the index, a logarithmic function is introduced. Nonlinear compression is applied to smooth out the effect of humidity changes on condensation potential; the product of the two is the numerator, representing the rate of moisture accumulation inside the cabin; the denominator is the absolute value of the difference between the cabin wall surface temperature Ts and the dew point temperature Td, introduced in the form of a square root. The formula reflects the temperature difference constraint between the wall surface and the dew point. The greater the temperature difference, the less likely condensation will occur; conversely, the smaller the temperature difference, the greater the condensation potential. The numerator corresponds to the intensity of moisture accumulation, while the denominator represents the resistance to temperature difference suppression. The ratio of these two values ​​accurately depicts the probability of condensation formation. Its calculation logic conforms to the thermodynamic "driving force / resistance" model, i.e., the balance between moisture accumulation and temperature difference constraint. Furthermore, by incorporating logarithmic and square root functions from mathematics, the exponent exhibits non-linear harmonic characteristics, more closely approximating the actual laws governing the critical eruption of condensation.

[0035] The fusion analysis unit calculates the ratio between the condensation nucleation potential index Cnp and the inter-chamber air exchange efficiency Eex, and amplifies the condensation trend when air exchange is insufficient through an exponential function to form a comprehensive condensation risk index Rall, which effectively reflects the nonlinear growth characteristics of condensation risk.

[0036] The formula for calculating the Rall comprehensive condensation risk index is based on the theory of mutual constraint between driving force and resistance in heat and mass transfer. Condensation occurs due to both the accumulation trend of moisture within the chamber and the dilution effect of air exchange. By constructing the ratio of driving force to resistance and combining it with the nonlinear amplification characteristics of the exponential function, a comprehensive condensation risk index Rall calculation formula is formed that can characterize the suddenness of condensation risk. The numerator, the condensation nucleation potential index Cnp, represents the "driving force" of condensation, while the denominator, the inter-chamber air exchange efficiency Eex+ε, represents the "resistance" to air exchange. The ratio of these two constitutes the relative strength between condensation potential and air dilution. The exponential function further amplifies the sensitivity of this ratio under critical conditions, ensuring that the risk index increases sharply when air exchange is insufficient, consistent with the explosive process of actual condensate formation.

[0037] The comprehensive risk assessment unit statistically analyzes the historical comprehensive condensation risk index Rall, uses the percentile method to set the condensation nucleation trigger threshold Ra and the condensation risk judgment threshold Rl, and then compares it with the real-time acquired comprehensive condensation risk index Rall to conduct a condensation risk assessment, achieving a step-by-step early warning from potential condensation and nucleation conditions to uncontrollable risks. Through this approach, the system achieves a shift from "single-indicator monitoring" to "multi-factor fusion and hierarchical judgment." Compared to existing technologies that rely on static judgment methods based on humidity or dew point, this not only improves the sensitivity and accuracy of condensation risk identification but also automatically generates ventilation, dehumidification, and risk control commands based on the risk level, thereby achieving proactive prevention and dynamic adjustment of the battery environment. This significantly improves the operational safety and long-term reliability of containerized battery energy storage devices under complex climatic conditions. Example

[0038] Please refer to Figure 1 and Figure 2 Specifically: the execution control module is used to transmit the assessment information generated by the condensation risk assessment to the central controller in real time according to the wireless network. The central controller executes corresponding control commands on each device of the containerized battery energy storage device according to the assessment information, as follows; Ventilation information: Increase the fan operating power by 30% and mark it as a potential condensation condition, then conduct iterative evaluation through the air diagnostic module; Dehumidification information: Increase the fan operating power by 50%, issue a dehumidification device start command, turn on the drying device and cooling dehumidification function in the battery compartment, and then conduct iterative evaluation through the air diagnostic module; Risk Information: Limit battery discharge power to 50%, start all backup fans and increase their power to the maximum design value, and simultaneously turn on the dehumidifier and air conditioning dehumidification mode. Then, notify on-site maintenance personnel to immediately carry out maintenance.

[0039] In this embodiment, the execution control module transmits the assessment information generated by the condensation risk assessment to the central controller via a wireless network. The central controller automatically triggers differentiated control commands based on the assessment information. In a potential condensation state, preventative ventilation is achieved by increasing fan power by 30%. When condensation nucleation conditions are confirmed, the fan power is further increased by 50%, and the drying device and refrigeration / dehumidification function are activated for active dehumidification. In an uncontrollable risk state, the battery discharge power is limited to 50%, all standby fans are activated to maximum power, and dehumidification and air conditioning dehumidification modes are run simultaneously. Maintenance personnel are notified to perform maintenance. Through this hierarchical control strategy, the system achieves closed-loop control from risk identification to dynamic response. This ensures the timeliness and targeted nature of environmental regulation while avoiding the drawbacks of traditional technologies that rely on manual intervention and have delayed responses. This significantly improves the environmental stability and operational safety of the energy storage device, and achieves proactive condensation risk prevention and control, as well as overall safety enhancement. Example

[0040] Please refer to Figure 1 and Figure 2 Specifically: the archive feedback module includes a data archiving unit and a remote upload unit; The data archiving unit is used to add information tags to each condensation risk assessment set in the edge nodes based on the assessment information generated by the condensation risk assessment. The condensation risk assessment set includes the aforementioned aerodynamic environment data set, water vapor criticality data set, inter-chamber air exchange efficiency Eex, condensation nucleation potential index Cnp, and comprehensive condensation risk index Rall. The information tag includes a timestamp and the corresponding evaluation status and control instructions; The remote upload unit is used to transmit data with added information tags to the cloud for storage in real time via the wireless network.

[0041] In this embodiment, the archive feedback module processes the condensation risk assessment results at the edge node through the data archiving unit. It packages the aerodynamic environment data set, water vapor critical data set, cross-chamber air exchange efficiency (Eex), condensation nucleation potential index (Cnp), and comprehensive condensation risk index (Rall) into a unified condensation risk assessment set, adding timestamps, assessment status, and control command information tags. The tagged data is then transmitted in real-time to cloud storage via a remote upload unit through a wireless network. This implementation not only achieves synchronous retention and traceability of risk assessment results and original data, ensuring the integrity and traceability of monitoring data, but also provides accurate data for subsequent maintenance personnel and model optimization. Compared to existing technologies that only perform single alarms or partial recordings, this module forms a closed-loop mechanism of "local tagging—remote archiving—cloud retrieval," enabling the system to accumulate historical experience and perform long-term optimization, thereby significantly improving the condensation risk management level and operational safety and reliability of the battery energy storage device.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A containerized battery environmental monitoring system based on edge computing, characterized in that: It includes a data acquisition module, an air diagnostics module, a comprehensive risk analysis module, an execution control module, and an archive feedback module; The data acquisition module is used to embed edge nodes in the containerized battery energy storage device and install sensor groups in the containerized battery energy storage device to collect operating environment data in real time. After processing the edge node data, aerodynamic environment data groups and water vapor critical data groups are obtained. The air diagnostic module is used to construct the cross-compartment air exchange efficiency Eex based on the aerodynamic environment data set, and to conduct an air exchange qualification assessment with the cross-compartment air exchange critical threshold Te, and to perform condensation risk analysis when the air exchange in the compartment is abnormal. The comprehensive risk analysis module is used to perform condensation risk analysis. It constructs a condensation nucleation potential index Cnp based on the water vapor critical data set, and calculates a comprehensive condensation risk index Rall by combining it with the cross-chamber air exchange efficiency Eex. Then, it assesses the condensation risk by combining it with the condensation nucleation trigger threshold Ra and the condensation risk judgment threshold Rl. The execution control module is used to transmit the assessment information generated by the condensation risk assessment to the central controller and execute the corresponding control commands; The archive feedback module is used to add information tags to the condensation risk assessment set at the edge node through the data archive unit, and transmit the tagged data to the cloud storage in real time through the remote upload unit.

2. The containerized battery environmental monitoring system based on edge computing according to claim 1, characterized in that: The data acquisition module includes a node arrangement unit, a data acquisition unit, and a data processing unit; The node arrangement unit is used to embed a set of edge nodes in the auxiliary control compartment of the containerized battery energy storage device. The containerized battery energy storage device is internally divided into a battery compartment, an auxiliary control compartment, and a ventilation channel compartment. Air circulation between the compartments is achieved by fans and ventilation openings. The data acquisition unit is used to install sensor groups at various locations of the containerized battery energy storage device to collect real-time operating environment data inside the containerized battery energy storage device. The sensor group includes an anemometer, differential pressure sensor, speed sensor, cold mirror dew point meter, and temperature and humidity sensor; The anemometer and differential pressure sensor are installed at the ventilation openings of the battery compartment and the ventilation channel compartment to collect the airflow velocity va and the air pressure difference Δpa of the compartment, respectively. The speed sensor is installed at the fan shaft end of the ventilation duct compartment to collect the fan speed fs in real time. The cold mirror dew point meter is installed 1.5 meters above the battery cluster inside the battery compartment to collect the dew point temperature Td in real time. The temperature and humidity sensors are respectively installed inside the main battery compartment, auxiliary control compartment and ventilation channel compartment to collect the surface temperature Ts of the compartment wall, the compartment temperature wd and the compartment humidity sd of each compartment in real time.

3. The containerized battery environmental monitoring system based on edge computing according to claim 2, characterized in that: The data processing unit is used to establish a transmission channel between the edge node and the sensor group through a wireless network, transmit the operating environment data to the edge node in real time for data processing, and obtain the aerodynamic environment data group and the water vapor critical data group. The data processing is used to preprocess the operating environment data before performing data analysis; The preprocessing includes noise reduction, timestamp alignment, and dimensionless processing; The denoising is achieved by removing noise from the operating environment data through bandpass filtering. Timestamp Alignment is used to project operating environment data with different sampling frequencies onto the same time axis, and to fill in missing points and uneven operating environment data with linear interpolation to form a data sequence with a unified time step; dimensionless processing is performed to remove the dimensional influence of state data through the Max-Min method; The data analysis includes humidity gradient analysis and humidity change rate analysis; The humidity gradient analysis is used to obtain the vertical humidity gradient Hg by dividing the humidity difference collected by the humidity sensors at the top and bottom of the cabin by the height difference of the sensors. Humidity change rate analysis is used to perform time-domain first-order derivative calculation on cabin humidity sd to obtain the humidity change rate sb. The aerodynamic environment data set includes vent airflow velocity va, fan speed fs, cabin air pressure difference Δpa, cabin temperature wd, and cabin humidity sd; The critical water vapor data set includes the vertical humidity gradient Hg, the humidity change rate sb, the bulkhead surface temperature Ts, and the dew point temperature Td.

4. The containerized battery environmental monitoring system based on edge computing according to claim 3, characterized in that: The air diagnostic module includes an exchange rate diagnostic unit and an exchange evaluation unit; The exchange rate diagnostic unit is used to fit the aerodynamic environment data set based on the edge nodes. It multiplies the airflow velocity va at the vent and the fan speed fs, takes the square root, and then multiplies it with the air pressure difference Δpa in the compartment to construct the denominator. Then, it performs logarithmic processing on the square of the temperature difference Δwd between adjacent compartments and adds the humidity difference Δsd between adjacent compartments after transforming it through the arctangent function to construct the numerator. By fitting the numerator and denominator, it constructs the cross-compartment air exchange efficiency Eex, analyzes the smoothness of airflow between compartments, and measures the air exchange efficiency between different compartments.

5. The containerized battery environmental monitoring system based on edge computing according to claim 4, characterized in that: The exchange evaluation unit is used to collect the cross-cabin air exchange efficiency Eex when the cabin air exchange is normal within a month in history, calculate the mean according to the statistical method, and preset the mean as the cross-cabin air exchange critical threshold Te. Then, it is compared with the real-time cross-cabin air exchange efficiency Eex to evaluate the air exchange qualification. The specific evaluation scheme is as follows. When the cross-compartment air exchange efficiency Eex < the critical threshold Te for cross-compartment air exchange, it indicates that the air exchange between compartments is abnormal and there is cross-compartment air blockage. At this time, a condensation risk analysis should be performed. When the inter-cabin air exchange efficiency Eex ≥ the inter-cabin air exchange critical threshold Te, it indicates that the cabin air exchange is normal and there is no inter-cabin air blockage. At this time, normal operation is maintained.

6. The containerized battery environmental monitoring system based on edge computing according to claim 5, characterized in that: The comprehensive risk analysis module is used to perform condensation risk analysis when the air exchange qualification assessment indicates that the cabin air exchange is abnormal. Specifically, it includes a condensation potential analysis unit and a comprehensive risk analysis unit. The condensation potential analysis unit is used to fit the critical water vapor data set based on the edge nodes. The rate of moisture accumulation is represented by the product of the humidity gradient (vertical humidity gradient Hg) and the logarithm of the humidity change rate (sb). The absolute value of the difference between the bulkhead surface temperature (Ts) and the dew point temperature (Td) is then taken to reflect the influence of the temperature difference on condensation. Finally, a condensation nucleation potential index (Cnp) is constructed by fitting the data. This index is used to analyze the risk of condensation on the inner wall of the container and predict the trend of condensation formation.

7. The containerized battery environmental monitoring system based on edge computing according to claim 6, characterized in that: The comprehensive risk analysis unit includes a fusion analysis unit and a comprehensive risk assessment unit; The fusion analysis unit is used to fit data on the cross-compartment air exchange efficiency Eex and the condensation nucleation potential index Cnp based on the edge nodes. The ratio of the condensation nucleation potential index Cnp to the cross-compartment air exchange efficiency Eex reflects the degree of influence of air exchange on condensation risk. The ratio is then substituted into the exponential function, and 1 is subtracted from the exponential result to construct the comprehensive condensation risk index Rall, which represents the degree of danger of condensation formation in the containerized battery compartment.

8. The containerized battery environmental monitoring system based on edge computing according to claim 7, characterized in that: The comprehensive risk assessment unit is used to collect all comprehensive condensation risk indices Rall within a historical month, sort them from smallest to largest, and use the percentile method to preset the 50th percentile as the condensation nucleation trigger threshold Ra and the 90th percentile as the condensation risk judgment threshold Rl. Then, it is compared with the real-time acquired comprehensive condensation risk index Rall to conduct a condensation risk assessment. The specific assessment scheme is as follows. When the comprehensive condensation risk index Rall < condensation nucleation trigger threshold Ra, it indicates that the air exchange efficiency of the compartment is insufficient to cause the battery compartment to have condensation nucleation conditions, and ventilation information is generated at this time. When the condensation nucleation trigger threshold Ra ≤ the comprehensive condensation risk index Rall ≤ the condensation risk judgment threshold Rl, it indicates that the air exchange efficiency of the cabin is insufficient, resulting in the battery compartment having the conditions for condensation nucleation. At this time, dehumidification information is generated. When the comprehensive condensation risk index Rall > the condensation risk judgment threshold Rl, it indicates that condensation has entered an uncontrollable risk state, and risk information is generated at this time.

9. The containerized battery environmental monitoring system based on edge computing according to claim 8, characterized in that: The execution control module is used to transmit the assessment information generated by the condensation risk assessment to the central controller in real time via the wireless network. The central controller executes corresponding control commands on each device of the containerized battery energy storage device based on the assessment information, as follows; Ventilation information: Increase the fan operating power by 30% and mark it as a potential condensation condition, then conduct iterative evaluation through the air diagnostic module; Dehumidification information: Increase the fan operating power by 50%, issue a dehumidification device start command, turn on the drying device and cooling dehumidification function in the battery compartment, and then conduct iterative evaluation through the air diagnostic module; Risk Information: Limit battery discharge power to 50%, start all backup fans and increase their power to the maximum design value, and simultaneously turn on the dehumidifier and air conditioning dehumidification mode. Then, notify on-site maintenance personnel to immediately carry out maintenance.

10. The containerized battery environmental monitoring system based on edge computing according to claim 9, characterized in that: The archive feedback module includes a data archiving unit and a remote upload unit; The data archiving unit is used to add information tags to each condensation risk assessment set in the edge nodes based on the assessment information generated by the condensation risk assessment. The condensation risk assessment set includes the aforementioned aerodynamic environment data set, water vapor criticality data set, inter-chamber air exchange efficiency Eex, condensation nucleation potential index Cnp, and comprehensive condensation risk index Rall. The information tag includes a timestamp and the corresponding evaluation status and control instructions; The remote upload unit is used to transmit data with added information tags to the cloud for storage in real time via the wireless network.

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