Container battery environment monitoring system based on edge computing

Abstract

The application discloses a container type battery environment monitoring system based on edge computing and relates to the technical field of battery environment monitoring.The system is arranged with sensors and edge nodes in each cabin of a battery energy storage device, collects internal operating environment data of the container type battery energy storage device, forms a set of pneumatic environment data and a set of water vapor critical data after data processing, constructs a cross-cabin air exchange efficiency Eex to evaluate the air exchange qualification, constructs a condensation nucleation potential index Cnp when the air exchange is abnormal, generates a comprehensive condensation risk index Rall in combination with the cross-cabin air exchange efficiency Eex to evaluate the condensation risk, issues instructions such as fan power regulation, dehumidification device start-stop and power limitation according to the risk grade, adds information tags to the evaluation data set and uploads the evaluation data set to the cloud in real time.The application realizes real-time monitoring of the container type battery environment, dynamic prediction of the condensation risk and automatic control, and improves the safety and reliability of the energy storage system operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery environment monitoring, in particular to a container type battery environment monitoring system based on edge computing. BACKGROUND

[0002] With the rapid development of new energy storage industry, container type battery energy storage devices are widely used due to their high energy density, modular installation and convenient transportation. However, the battery energy storage device operates in a closed cabin structure, which is limited by space, ventilation and temperature and humidity conditions, and is prone to form a complex air flow and humidity accumulation environment. Traditional environment monitoring relies on centralized collection and backend processing, which has the problems of response lag and untimely control. In recent years, the introduction of edge computing enables environment monitoring to directly complete preprocessing and real-time analysis at the cabin node, improving the immediacy of data and the active prevention and control ability of the system. Under this background, air exchange efficiency and condensation risk have become the most critical safety risk sources in the battery cabin: once air exchange is insufficient or water vapor condensation is intensified, short circuit, corrosion and thermal runaway accidents are easily induced.

[0003] At present, most container type battery systems still use fixed threshold or simple sensor monitoring means to evaluate air exchange and humidity level, lacking dynamic diagnosis and comprehensive risk analysis for complex environmental changes. This kind of method often only focuses on a single parameter and fails to consider multi-dimensional factors such as inter-cabin air exchange efficiency, humidity gradient and dew point temperature, resulting in incomplete risk identification. When air exchange is slightly blocked or humidity fluctuates rapidly, existing monitoring means are difficult to capture and give effective control instructions in time, so that the condensation risk is not discovered in the incubation stage. In addition, some systems rely only on manual maintenance or backend cloud analysis, resulting in condensation risk often being identified after it has caused an impact, which has obvious timeliness defects. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a container type battery environment monitoring system based on edge computing, which solves the problems in the background art.

[0005] To achieve the above purpose, the present application is realized by the following technical scheme: a container type battery environment monitoring system based on edge computing, comprising a data acquisition module, an air diagnosis module, a comprehensive risk analysis module, an execution control module and an archive feedback module;

[0006] The data acquisition module is used to embed an edge node in the container type battery energy storage device, install a sensor group in the container type battery energy storage device, and acquire real-time operation environment data, to obtain a set of aerodynamic environment data and a set of water vapor critical data after edge node data processing;

[0007] The air diagnosis module is configured to construct a cross-cabin air exchange efficiency Eex according to a set of pneumatic environment data, perform air exchange eligibility evaluation with a cross-cabin air exchange critical threshold Te, and execute condensation risk analysis when cabin air exchange is abnormal;

[0008] The comprehensive risk analysis module is configured to execute condensation risk analysis, construct a condensation nucleation potential index Cnp according to a set of water vapor critical data, perform comprehensive calculation of a comprehensive condensation risk index Rall with the cross-cabin air exchange efficiency Eex, and perform condensation risk evaluation with a condensation nucleation trigger threshold Ra and a condensation risk judgment threshold Rl;

[0009] The execution control module is configured to transmit evaluation information generated by the condensation risk evaluation to a central controller, and execute corresponding control instructions;

[0010] The archive feedback module is configured to add information labels to the set of condensation risk evaluations by a data archiving unit at the edge node, and transmit the labeled data to cloud storage in real time by a remote uploading unit.

[0011] Preferably, the data acquisition module includes a node arrangement unit, a data acquisition unit, and a data processing unit;

[0012] The node arrangement unit is configured to embed a set of edge nodes in an auxiliary control cabin of the container-type battery energy storage device;

[0013] The container-type battery energy storage device is internally divided into a battery cabin, an auxiliary control cabin, and an air exchange passage cabin, and air circulation between the cabins is achieved by means of fans and ventilation openings;

[0014] The data acquisition unit is configured to install a set of sensors at various positions of the container-type battery energy storage device to acquire real-time operation environment data inside the container-type battery energy storage device;

[0015] The set of sensors includes an anemometer, a differential pressure sensor, a rotational speed sensor, a cold mirror dew point meter, and a temperature and humidity sensor;

[0016] The anemometer and the differential pressure sensor are configured to be installed at the ventilation hole positions of the battery cabin and the air exchange passage cabin to respectively acquire ventilation opening airflow speed va and cabin air pressure difference Δpa;

[0017] The rotational speed sensor is configured to be arranged at the shaft end of the fan in the air exchange passage cabin to acquire real-time fan rotational speed fs;

[0018] The cold mirror dew point meter is configured to be installed at a height of 1.5 meters above the battery cluster in the battery cabin to acquire real-time dew point temperature Td;

[0019] The temperature and humidity sensor is arranged in the main battery cabin, the auxiliary control cabin and the air exchange passage cabin respectively to collect the cabin wall surface temperature Ts, the cabin temperature wd and the cabin humidity sd in real time.

[0020] Preferably, the data processing unit is configured 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 for data processing in real time, and obtain the pneumatic environment data group and the water vapor critical data group.

[0021] The data processing is used for pre-processing the operating environment data and then performing data analysis.

[0022] The preprocessing includes denoising, timestamp alignment and dimensionless processing.

[0023] The denoising removes the noise in the operating environment data through band-pass filtering; the timestamp alignment is used to project the operating environment data with different sampling frequencies onto the same time axis, and linear interpolation is used to fill in the missing points and uneven operating environment data to form a data sequence with uniform time steps; and the dimensionless processing removes the dimension influence of the state data by the Max-Min maximum and minimum method.

[0024] The data analysis includes humidity gradient analysis and humidity change rate analysis.

[0025] The humidity gradient analysis is used to obtain the humidity vertical gradient Hg by dividing the humidity difference collected by the cabin top and bottom humidity sensors by the sensor height difference.

[0026] The humidity change rate analysis is used to perform time domain first-order derivative operation on the cabin humidity sd to obtain the humidity change rate sb.

[0027] The pneumatic environment data group includes the air flow velocity va of the ventilation port, the fan speed fs, the cabin pressure difference Δpa, the cabin temperature wd and the cabin humidity sd.

[0028] The water vapor critical data group includes the humidity vertical gradient Hg, the humidity change rate sb, the cabin wall surface temperature Ts and the dew point temperature Td.

[0029] Preferably, the air diagnosis module includes an exchange rate diagnosis unit and an exchange evaluation unit.

[0030] The exchange rate diagnosis unit is used for data fitting of the aerodynamic environment data set by the edge node, multiplying the blast opening airflow velocity va and the fan rotating speed fs, taking square root, and then multiplying the cabin air pressure difference Δpa to construct the denominator part; then, the square of the temperature difference Δwd between the adjacent cabins is logarithmically processed, and the humidity difference Δsd between the adjacent cabins is converted by the inverse tangent function and then added to construct the numerator part, the cross-cabin air exchange efficiency Eex is constructed through the fitting of the numerator and the denominator, the smoothness of the air flow between the cabins is analyzed, and the air exchange efficiency between different cabins is measured.

[0031] Preferably, the exchange evaluation unit is used for collecting the cross-cabin air exchange efficiency Eex when the cabin air exchange is normal in the past month, calculating the mean value according to the statistical method, and presetting the mean value as the cross-cabin air exchange critical threshold Te, and then performing air exchange qualification evaluation on the real-time acquired cross-cabin air exchange efficiency Eex, and the specific evaluation scheme is as follows:

[0032] When the cross-cabin air exchange efficiency Eex is less than the cross-cabin air exchange critical threshold Te, it indicates that the cabin air exchange is abnormal, and there is a cross-cabin air resistance phenomenon, and at this time, the condensation risk analysis is performed.

[0033] When the cross-cabin air exchange efficiency Eex is greater than or equal to the cross-cabin air exchange critical threshold Te, it indicates that the cabin air exchange is normal, and there is no cross-cabin air resistance phenomenon, and at this time, the normal operation is maintained.

[0034] Preferably, the comprehensive risk analysis module is used for performing the condensation risk analysis when the air exchange qualification evaluation is cabin air exchange abnormality, and specifically includes a condensation potential analysis unit and a comprehensive risk analysis unit.

[0035] The condensation potential analysis unit is used for data fitting of the water vapor critical data set by the edge node, representing the rate of moisture accumulation by the product of the logarithmic values of the humidity gradient Hg and the humidity change rate sb, taking the absolute value of the difference between the cabin wall surface temperature Ts and the dew point temperature Td to reflect the influence of the temperature difference on condensation, and finally fitting to construct the condensation nucleation potential index Cnp, which is used for analyzing the risk of condensation water on the inner wall of the container and predicting the formation trend of the condensation water.

[0036] Preferably, the comprehensive risk analysis unit includes a fusion analysis unit and a comprehensive risk evaluation unit.

[0037] The fusion analysis unit is used for data fitting of the cross-cabin air exchange efficiency Eex and the condensation nucleation potential index Cnp according to the edge node, reflecting the influence degree of air exchange on the condensation risk by ratio of the condensation nucleation potential index Cnp to the cross-cabin air exchange efficiency Eex, and constructing a comprehensive condensation risk index Rall by substituting the ratio into an exponential function, subtracting 1 from the exponential result, and representing the danger degree of condensation water generation in the container battery cabin.

[0038] Preferably, the comprehensive risk assessment unit is used for collecting all comprehensive condensation risk indexes Rall in the past month, sorting them from small to large, and presetting the 50% percentile as a condensation nucleation trigger threshold Ra and the 90% percentile as a condensation risk judgment threshold Rl by the percentile method, and then performing condensation risk assessment on the comprehensive condensation risk index Rall obtained in real time, and the specific assessment scheme is as follows.

[0039] When the comprehensive condensation risk index Rall is less than the condensation nucleation trigger threshold Ra, it indicates that the cabin air exchange efficiency is insufficient to cause the battery cabin to have condensation nucleation conditions, and ventilation information is generated at this time.

[0040] When the condensation nucleation trigger threshold Ra is less than or equal to the comprehensive condensation risk index Rall and is less than or equal to the condensation risk judgment threshold Rl, it indicates that the cabin air exchange efficiency is insufficient to cause the battery cabin to have condensation nucleation conditions, and dehumidification information is generated at this time.

[0041] When the comprehensive condensation risk index Rall is greater than the condensation risk judgment threshold Rl, it indicates that the condensation enters an uncontrollable risk state, and risk information is generated at this time.

[0042] Preferably, the execution control module is used for transmitting the evaluation information generated by the condensation risk assessment to the central controller in real time according to the wireless network, and the central controller executes corresponding control instructions on each device of the container battery energy storage device according to the evaluation information, and the specific control instructions are as follows.

[0043] The ventilation information: increase the fan operating power by 30%, and mark it as a potential condensation state, and then perform iterative evaluation through the air diagnosis module.

[0044] The dehumidification information: increase the fan operating power by 50%, and issue a dehumidification device start instruction, start the drying device and refrigeration dehumidification function in the battery cabin, and then perform iterative evaluation through the air diagnosis module.

[0045] The risk information: limit the battery discharge power to 50%, start all standby fans and increase the power to the maximum design value, start the dehumidification device and air conditioning dehumidification mode, and prompt the on-site operation and maintenance personnel to immediately perform maintenance.

[0046] Preferably, the archive feedback module comprises a data archiving unit and a remote uploading unit.

[0047] The data archiving unit is used to add information tags to each set of condensation risk assessment sets according to the evaluation information generated by the condensation risk assessment in the edge node;

[0048] The condensation risk assessment set includes the above-mentioned pneumatic environment data group, the water vapor critical data group, the cross-cabin air exchange efficiency Eex, the condensation nucleation potential index Cnp and the comprehensive condensation risk index Rall;

[0049] The information tag contains a time stamp and corresponding evaluation state and control instructions;

[0050] The remote uploading unit is used to transmit the data with added information tags to the cloud for storage in real time according to a wireless network.

[0051] The application provides a container battery environment monitoring system based on edge computing.

[0052] (1) The system data acquisition module forms a complete operating environment perception network through multiple types of sensors arranged in the battery cabin, auxiliary control cabin and air exchange channel cabin. The edge node performs denoising, time alignment and dimensionless processing locally, unifies data of different sampling frequencies and noise interference to a standard time axis and a unified dimension, and ensures 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 original humidity data. This layer provides a solid and reliable foundation for subsequent diagnosis and risk analysis, so that the monitoring data truly reflects the dynamic changes of air and water vapor in the container battery cabin.

[0053] (2) The air diagnosis module of the system constructs the cross-cabin air exchange efficiency Eex according to the pneumatic environment data group, and performs air exchange qualification evaluation with the cross-cabin air exchange critical threshold Te, realizes real-time diagnosis of the cabin air exchange state, and can accurately identify potential air blockage problems. When the cabin air exchange is abnormal, the system will automatically enter a deeper condensation potential analysis stage, construct the condensation nucleation potential index Cnp according to the water vapor critical data group, and further calculate the comprehensive condensation risk index Rall in combination with the cross-cabin air exchange efficiency Eex, and then perform condensation risk evaluation with the condensation nucleation trigger threshold Ra and the condensation risk judgment threshold Rl, realizing the hierarchical judgment of the condensation risk from "potential nucleation" to "uncontrollable risk". The beneficial effect of this layer is to establish a hierarchical and nonlinear risk warning system, which can more accurately identify the trend of condensation water generation, thereby avoiding the battery environment from entering an uncontrollable state.

[0054] (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

[0055] 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;

[0056] 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

[0057] 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

[0058] Please see Figure 1 This 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;

[0059] 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.

[0060] 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.

[0061] The comprehensive risk analysis module is configured to perform a condensation risk analysis, construct a condensation nucleation potential index Cnp according to water vapor critical data, and perform a comprehensive calculation of a comprehensive condensation risk index Rall with a cross-cabin air exchange efficiency Eex, and perform a condensation risk evaluation with a condensation nucleation trigger threshold Ra and a condensation risk judgment threshold Rl;

[0062] The execution control module is configured to execute a corresponding control instruction according to evaluation information generated by the condensation risk evaluation;

[0063] The archive feedback module is configured to add information tags to a set of condensation risk evaluations by a data archiving unit at an edge node, and transmit the tagged data to a cloud storage in real time by a remote uploading unit.

[0064] In this embodiment, the data acquisition module is arranged in the container type battery energy storage device to arrange edge nodes and multiple types of sensor groups. The system can continuously and real-timely acquire the operation environment data inside the container type battery energy storage device, and after the denoising, time alignment and dimensionless processing are completed at the edge side, the humidity gradient analysis and humidity change rate analysis are performed, and the aerodynamic environment data group and the water vapor critical data group are extracted. This design effectively solves the problem of relying on a single sensor, data isolation and no correction in the prior art, so that the input data has higher integrity and consistency, thereby providing a high-quality data basis for subsequent diagnosis and analysis, and avoiding the risk identification delay caused by data distortion in traditional monitoring. The air diagnosis module constructs the cross-cabin air exchange efficiency Eex according to the aerodynamic environment data group, and performs air exchange qualification evaluation with the cross-cabin air exchange critical threshold Te, realizes real-time diagnosis of the cabin air exchange state, and can accurately identify potential air blockage problems. When the cabin air exchange is abnormal, the system will automatically enter a deeper condensation potential analysis stage, construct the condensation nucleation potential index Cnp according to the water vapor critical data group, and further calculate the comprehensive condensation risk index Rall in combination with the cross-cabin air exchange efficiency Eex, and then perform condensation risk evaluation with the condensation nucleation trigger threshold Ra and the condensation risk judgment threshold Rl, to realize the grading evaluation of the condensation risk. Compared with the static judgment mode of simply relying on the dew point or humidity value in the prior art, the present scheme establishes a dynamic and multi-level diagnosis logic, so that the risk identification is upgraded from a single index judgment to a nonlinear and multi-factor fusion prediction model. This improvement significantly improves the sensitivity and foresight of risk identification, can discover potential condensation trends in advance, and reduces the probability of uncontrollable condensation in the energy storage battery cabin. The execution control module, the system can automatically issue differentiated instructions such as fan adjustment, dehumidification device start, power limiting operation according to the risk level, to ensure that the condensation risk can be controlled in different stages. At the same time, the archive feedback module adds an information label to the condensation risk evaluation set at the edge node, and realizes cloud storage through remote uploading to form a traceable risk database. Compared with the prior art which only provides manual alarm and lacks closed-loop disposal and historical learning, the present system establishes a complete closed loop of "collection-diagnosis-analysis-control-feedback", which not only realizes the immediate suppression of condensation risk, but also realizes the threshold self-adaptive optimization relying on historical data in long-term operation. Therefore, the safety, reliability and self-evolution ability of the overall operation of the system are significantly improved. Embodiment

[0065] 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;

[0066] The node arrangement unit is used for embedding a set of edge nodes in the auxiliary control cabin of the container type battery energy storage device.

[0067] The container type battery energy storage device is internally divided into a battery cabin, an auxiliary control cabin and a ventilation passage cabin, and air circulation is achieved between the cabins by means of a fan and a ventilation opening;

[0068] The data acquisition unit is used for installing a sensor group at each position of the container type battery energy storage device to acquire the operation environment data of the container type battery energy storage device in real time;

[0069] The sensor group comprises an anemometer, a differential pressure sensor, a rotational speed sensor, a cold mirror dew point meter and a temperature and humidity sensor;

[0070] The anemometer and the differential pressure sensor are used for being installed at the ventilation hole positions of the battery cabin and the ventilation passage cabin to acquire the ventilation opening airflow speed va and the cabin air pressure difference Δpa respectively;

[0071] The ventilation opening airflow speed va represents the gas flow speed through the ventilation opening and reflects the driving force of air flow;

[0072] The cabin air pressure difference Δpa represents the air pressure difference between adjacent cabins, and the greater the air pressure difference, the stronger the driving force of air exchange, and the easier the air flow and the transfer of heat and humidity;

[0073] The rotational speed sensor is used for being arranged at the shaft end of the fan in the ventilation passage cabin to acquire the fan rotational speed fs in real time, which represents the running speed of the fan, and the faster the rotational speed, the greater the airflow driving force;

[0074] The cold mirror dew point meter is used for being installed at a height of 1.5 meters above the battery cluster in the battery cabin to acquire the dew point temperature Td in real time, which represents the temperature at which water vapor begins to condense;

[0075] The temperature and humidity sensor is used for being arranged in the main battery cabin, the auxiliary control cabin and the ventilation passage cabin respectively to acquire the cabin wall surface temperature Ts, the cabin temperature wd and the cabin humidity sd of each cabin in real time.

[0076] The data processing unit is used for establishing a transmission channel between the edge node and the sensor group through a wireless network, transmitting the operation environment data to the edge node for data processing in real time, and obtaining the aerodynamic environment data group and the water vapor critical data group;

[0077] The data processing is used for pre-processing the operation environment data and then performing data analysis;

[0078] The preprocessing comprises denoising, timestamp alignment and dimensionless processing;

[0079] The denoising removes the noise influence in the operation environment data through band-pass filtering; the timestamp alignment is used to project the operation environment data with different sampling frequencies to the same time axis, and linear interpolation is used to fill the missing points and uneven operation environment data to form a data sequence with uniform time step; the dimensionless processing removes the dimension influence of the state data through the Max-Min minimization method;

[0080] The data analysis includes humidity gradient analysis and humidity change rate analysis;

[0081] The humidity gradient analysis is used to obtain a humidity vertical gradient Hg by dividing the humidity difference collected by the cabin top and bottom humidity sensors by the sensor height difference, which represents the humidity change rate between different heights in the cabin, and is specifically: , wherein sd s and sd x respectively represent the humidity values collected by the cabin top and bottom humidity sensors, and ∆gd represents the height difference between the cabin top and bottom humidity sensors;

[0082] The humidity change rate analysis is used to perform a time domain first-order derivative operation on the cabin humidity sd to obtain a humidity change rate sb, which represents the humidity change speed, and is specifically: , wherein dt represents a time differential element;

[0083] The aerodynamic environment data set includes a ventilation port air flow velocity va, a fan speed fs, a cabin pressure difference Δpa, a cabin temperature wd, and a cabin humidity sd;

[0084] The water vapor critical data set includes a humidity vertical gradient Hg, a humidity change rate sb, a cabin wall surface temperature Ts, and a dew point temperature Td.

[0085] In this embodiment, the data acquisition module embeds edge nodes in the auxiliary control cabin through the node arrangement unit, and in combination with the sensor group composed of an anemometer, a differential pressure sensor, a rotating speed sensor, a cold mirror dew point instrument and a temperature and humidity sensor, can collect real-time operation environment data in the battery cabin, the auxiliary control cabin and the air exchange channel cabin. The data processing unit transmits the operation data to the edge nodes through a wireless network, performs denoising, timestamp alignment and dimensionless processing, and further carries out humidity gradient and humidity change rate analysis to obtain the aerodynamic environment data group and the water vapor critical data group. This implementation realizes the synchronous monitoring and unified analysis of multi-dimensional parameters such as air flow driving force, cabin pressure difference, temperature and humidity distribution and dew point temperature, compared with the single sensor and single parameter determination mode in the prior art, can accurately reflect the dynamic process of air circulation and water vapor accumulation in the cabin, thereby improving the integrity and accuracy of the data at the source. Thus, not only does it provide high-quality data support for subsequent air exchange diagnosis and condensation risk analysis, but also realizes the advance of risk prediction and the precision of judgment, effectively improving the safety and operation reliability of the container type battery energy storage device in complex environments. Embodiment

[0086] Please refer to Figure 1 and Figure 2 , specifically: the air diagnosis module includes an exchange rate diagnosis unit and an exchange evaluation unit;

[0087] The exchange rate diagnosis unit is used to perform data fitting on the aerodynamic environment data group according to the edge node, multiply the air inlet gas flow speed va and the fan rotating speed fs, and then take the square root, and then multiply it with the cabin pressure difference Δpa to construct the denominator part; then, the square of the temperature difference Δwd between adjacent cabins is logarithmically processed, and the humidity difference Δsd between adjacent cabins is converted by the inverse tangent function and then added to construct the numerator part, through the fitting of the numerator and the denominator, the cross-cabin air exchange efficiency Eex is constructed, the smoothness of air flow between cabins is analyzed, and the air exchange efficiency between different cabins is measured, the specific formula is: , in the formula, ln represents the logarithmic function, arctan represents the hyperbolic sine function, Δwd and Δsd represent the temperature difference and humidity difference between adjacent cabins respectively, represents the gas flow speed through the air vent, reflecting the driving force of air flow, represents the temperature difference between two cabins, the square of the temperature difference is processed to make the influence more significant when the temperature difference is large, and the logarithmic function is taken to prevent the temperature difference from being amplified too much, represents the humidity difference between different cabins, and the inverse tangent function is used to suppress the excessive influence of humidity on air exchange, so that the influence of humidity difference is more gentle.

[0088] The exchange evaluation unit is used to collect the cross-cabin air exchange efficiency Eex in the normal air exchange of the cabin in the past month, calculate the mean value according to the statistical method, and preset the mean value as the cross-cabin air exchange critical threshold Te, and then perform air exchange qualification evaluation on the real-time acquired cross-cabin air exchange efficiency Eex. The specific evaluation scheme is as follows:

[0089] When the cross-cabin air exchange efficiency Eex is less than the cross-cabin air exchange critical threshold Te, it indicates that the cabin air exchange is abnormal, and there is a cross-cabin air resistance phenomenon. At this time, the condensation risk analysis is performed.

[0090] When the cross-cabin air exchange efficiency Eex is greater than or equal to the cross-cabin air exchange critical threshold Te, it indicates that the cabin air exchange is normal, and there is no cross-cabin air resistance phenomenon. At this time, the normal operation is maintained.

[0091] In this embodiment, the air diagnosis module performs data fitting on the aerodynamic environment data set through the exchange rate diagnosis unit, constructs the blast opening air flow velocity va, the fan speed fs and the cabin pressure difference Δpa as the denominator part of the air flow driving force, and combines the cabin temperature difference Δwd and the humidity difference Δsd after logarithmic and inverse tangent function processing to construct the numerator part, calculates the cross-cabin air exchange efficiency Eex, and realizes the quantitative representation of the smoothness of the air flow between the cabins.

[0092] The formula logic and physical meaning, the core idea is to take the air flow driving force as the numerator, and take the environmental restriction factor as the denominator, to construct the ratio structure similar to the "transfer coefficient" in heat and mass transfer, and the derivation basis of this formula is derived from fluid mechanics and heat and moisture transfer theory. The numerator part The blast opening air flow velocity va, the fan speed fs and the cabin pressure difference Δpa are combined, which reflects the dynamic pressure and mechanical driving effect of air exchange, and the square root method is used to introduce non-linear balance, which avoids the distortion of the numerical value caused by the large product of wind speed and speed, and conforms to the actual air flow law of the cabin. This part is derived from the fluid kinetic energy expression, which is a quantitative description of the air flow driving force. The denominator part combines the resistance effects of temperature difference and humidity difference, wherein represents the amplification effect of the temperature difference between the cabins, the square ensures that the influence is doubled when the temperature difference is large, and the logarithmic transformation suppresses the extreme value, which reflects the influence of temperature difference on air stratification, The resistance of the humidity difference of the expression cabin to the air flow, the asymptotic property of the arctangent function avoids the infinite amplification of the humidity difference when it is too large, and the non-linear suppression function from the signal processing field makes the weakening effect of the overall efficiency more smooth. The overall formula follows the operation logic of "driving force / resistance", that is, when the air flow velocity va of the air port, the fan speed fs and the cabin pressure difference Δpa rise, the numerator increases, the cross-cabin air exchange efficiency Eex improves, indicating that the air exchange is smooth; when the temperature difference Δwd between adjacent cabins or the humidity difference Δsd between adjacent cabins increases, the denominator rises, the cross-cabin air exchange efficiency Eex decreases, indicating that the air exchange is blocked. In a physical sense, the cross-cabin air exchange efficiency Eex reflects the smoothness of air circulation between different cabins, and can be used as a pre-diagnostic indicator for condensation risk analysis.

[0093] The exchange evaluation unit sets the cross-cabin air exchange critical threshold Te based on the average cross-cabin air exchange efficiency Eex of the historical one-month air exchange under normal conditions, and compares it with the real-time cross-cabin air exchange efficiency Eex. When the cross-cabin air exchange efficiency Eex is lower than the cross-cabin air exchange critical threshold Te, the condensation risk analysis is triggered, and when the cross-cabin air exchange efficiency Eex is higher than the cross-cabin air exchange critical threshold Te, the normal operation is maintained. Through this embodiment, the system realizes real-time diagnosis and dynamic threshold comparison of air exchange state, overcoming the shortcomings of fixed setting and response lag in the prior art, and can more accurately and timely identify cabin air resistance and potential condensation risk. Its beneficial effects lie in significantly improving the accuracy and foresight of air diagnosis, enabling condensation risk to be discovered and controlled at an early stage, thereby improving the safety and environmental adaptability of battery energy storage device operation as a whole. Embodiment

[0094] Please refer to Figure 1 and Figure 2 , in particular: the comprehensive risk analysis module is used to perform condensation risk analysis when the air exchange eligibility evaluation is abnormal cabin air exchange, specifically including a condensation potential analysis unit and a comprehensive risk analysis unit;

[0095] The condensation potential analysis unit is used to fit the water vapor critical data set according to the edge node, to express the rate of moisture accumulation through the product of the humidity gradient Hg and the logarithm of the humidity change rate sb, and to reflect the influence of temperature difference on condensation by taking the absolute value of the difference between the cabin wall surface temperature Ts and the dew point temperature Td. Finally, the condensation nucleation potential index Cnp is fitted and constructed, which is used to analyze the risk of condensation water on the inner wall of the container and predict the formation trend of condensation water. The specific formula is: , where ln represents the logarithmic function, The change rate of humidity, and the logarithm of the humidity change rate is taken to make the influence of the humidity change rate more gentle, and to avoid over-amplification of the condensation potential caused by a too high humidity change rate, The square root of the difference between the bulkhead surface temperature Ts and the dew point temperature Td, which represents the temperature difference between the wall surface and the dew point, and the smaller the temperature difference, the greater the potential for condensation water generation.

[0096] The comprehensive risk analysis unit includes a fusion analysis unit and a comprehensive risk assessment unit.

[0097] The fusion analysis unit is used to perform data fitting on the cross-cabin air exchange efficiency Eex and the condensation nucleation potential index Cnp according to the edge node, to reflect the influence degree of air exchange on condensation risk by taking the ratio of the condensation nucleation potential index Cnp to the cross-cabin air exchange efficiency Eex, to quickly amplify the influence of the condensation potential by substituting the ratio into an exponential function, to reflect the trend of a sharp rise in condensation risk when air exchange is not smooth, and to subtract 1 from the exponential result to construct a comprehensive condensation risk index Rall, which represents the degree of danger of condensation water generation in the container battery cabin. The specific formula is: , wherein exp represents an exponential decay function, represents a small constant to prevent the denominator from being zero, and the value is 0.001, The exponential function is used to express the nonlinear growth relationship of the condensation risk, and the subtraction of 1 is used to adjust the range of the result to ensure that the comprehensive condensation risk index Rall starts from 0.

[0098] The comprehensive risk assessment unit is used to collect all comprehensive condensation risk indexes Rall in the past month, sort them from small to large, and preset the 50% percentile as a condensation nucleation trigger threshold Ra and the 90% percentile as a condensation risk judgment threshold Rl by using the percentile method, and then perform condensation risk assessment on the comprehensive condensation risk index Rall obtained in real time. The specific assessment scheme is as follows:

[0099] When the comprehensive condensation risk index Rall is less than the condensation nucleation trigger threshold Ra, it indicates that the cabin air exchange efficiency is insufficient to cause the battery cabin to have condensation nucleation conditions, and ventilation information is generated at this time.

[0100] When the condensation nucleation trigger threshold Ra is less than or equal to the comprehensive condensation risk index Rall and is less than or equal to the condensation risk judgment threshold Rl, it indicates that the cabin air exchange efficiency is insufficient to cause the battery cabin to have condensation nucleation conditions, and dehumidification information is generated at this time.

[0101] When the comprehensive condensation risk index Rall is greater than the condensation risk judgment threshold Rl, it indicates that the condensation enters an uncontrollable risk state, and risk information is generated at this time.

[0102] In this embodiment, the integrated risk analysis module automatically triggers the condensation risk analysis when the cabin air exchange is abnormal. The condensation potential analysis unit constructs a 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, so as to accurately depict the influence of the cabin humidity accumulation rate and the temperature difference on the condensation nucleation.

[0103] The formula logic and physical meaning, the construction of the condensation nucleation potential index Cnp is based on the classical principle of air humidity transmission and condensation physical process, and its derivation logic comes from the “driving force-resistance” relationship in heat and mass transfer. The humidity vertical gradient Hg reflects the spatial distribution of water vapor in the cabin, which is the spatial driving force of water vapor accumulation. The humidity change rate sb represents the dynamic growth trend of humidity with time. In order to avoid the non-physical amplification of the index caused by too high change rate, the logarithmic function is introduced to perform nonlinear compression, so that the influence of humidity change on condensation potential is more gentle. The multiplication of the two forms the numerator part, which represents the accumulation rate of cabin humidity. The denominator part takes the absolute value of the difference between the cabin wall surface temperature Ts and the dew point temperature Td, and introduces it in the form of square root to reflect the temperature difference constraint of the wall and the dew point. The larger the temperature difference, the less likely the condensation occurs. When the temperature difference decreases, the condensation potential increases sharply. The numerator corresponds to the strength of humidity accumulation, and the denominator represents the resistance of temperature difference inhibition. The ratio of the two truly depicts the possibility of condensation formation. Its operation logic conforms to the “driving force / resistance” mode of thermodynamics, that is, the balance relationship between humidity accumulation and temperature difference constraint. At the same time, combined with the logarithmic function and square root function in mathematics, the index has nonlinear harmonic characteristics in numerical value, which is closer to the actual law of condensation critical burst.

[0104] The fusion analysis unit performs ratio operation on the condensation nucleation potential index Cnp and the cabin air exchange efficiency Eex, and amplifies the condensation trend when air exchange is insufficient through the exponential function to form the integrated condensation risk index Rall, which effectively reflects the nonlinear growth characteristics of the condensation risk.

[0105] The formula logic and physical meaning, the calculation formula of the comprehensive condensation risk index Rall is proposed on the basis of the mutual restriction theory of driving force and resistance in heat and mass transfer. The occurrence of condensation depends on the accumulation trend of cabin humidity and the dilution effect of cabin air exchange. By constructing the ratio relationship of driving force and resistance, and combining the nonlinear amplification characteristics of the exponential function, the comprehensive condensation risk index Rall calculation formula is formed, which can describe the suddenness of condensation risk. The numerator part condensation nucleation potential index Cnp represents the “driving force” of condensation, and the denominator part trans-cabin air exchange efficiency Eex+ε represents the “resistance” of air exchange. The ratio of the two constitutes the relative strength between condensation potential and air dilution. The exponential function further amplifies the sensitivity of the ratio in the critical state, ensuring that the risk index increases sharply when air exchange is insufficient, which is consistent with the explosive process of actual condensate generation.

[0106] The comprehensive risk assessment unit sets the condensation nucleation trigger threshold Ra and the condensation risk judgment threshold Rl by statistically analyzing the historical comprehensive condensation risk index Rall using the percentile method, and then performs condensation risk assessment on the real-time comprehensive condensation risk index Rall, achieving step-by-step early warning from potential condensation, nucleation conditions to uncontrollable risks. Through this way, the system achieves the transition from “single index monitoring” to “multi-factor fusion and hierarchical judgment”. Compared with the existing technology which relies on static judgment methods such as humidity or dew point, not only the sensitivity and accuracy of condensation risk identification are improved, but also the ventilation, dehumidification and risk control instructions can be automatically generated according to the risk level, thereby realizing active prevention and dynamic adjustment of the battery environment, significantly improving the operation safety and long-term reliability of the container-type battery energy storage device under complex climate conditions. Embodiments

[0107] Please refer to Figure 1 and Figure 2 , specifically: the execution control module is used for transmitting the evaluation information generated by the condensation risk assessment to the central controller in real time according to the wireless network, and the central controller executes corresponding control instructions on each device of the container-type battery energy storage device according to the evaluation information, specifically as follows:

[0108] Ventilation information: increase the fan operating power by 30%, and mark it as a potential condensation state, then iterate the evaluation through the air diagnosis module;

[0109] Dehumidification information: increase the fan operating power by 50%, and issue a dehumidification device start instruction to turn on the drying device and refrigeration dehumidification function in the battery cabin, and then iterate the evaluation through the air diagnosis module;

[0110] Risk information: limit the battery discharge power to 50%, start all standby fans and increase the power to the maximum design value, turn on the dehumidification device and air conditioning dehumidification mode, and prompt the on-site operation and maintenance personnel to carry out maintenance immediately.

[0111] In this embodiment, the execution control module transmits the evaluation information generated by the condensation risk assessment to the central controller through a wireless network, and the central controller automatically triggers differentiated control instructions according to the evaluation information. In the potential condensation state, preventive ventilation is realized by increasing the fan power by 30%; when the condensation nucleation condition is confirmed, the fan power is further increased by 50% and the drying device and refrigeration dehumidification function are started for active dehumidification; when entering the uncontrollable risk state, the battery discharge power is limited to 50%, all standby fans are started to the maximum power, and the dehumidification and air conditioning dehumidification modes are run at the same time, and the operation and maintenance personnel are prompted to repair. Through this hierarchical control strategy, the system realizes closed-loop control from risk identification to dynamic response, which not only ensures the timeliness and pertinence of environmental regulation, but also avoids the disadvantages of relying on manual intervention and slow response of traditional technical means, thereby significantly improving the environmental stability and operation safety of the energy storage device, and realizing the advance of condensation risk prevention and control and the improvement of overall safety. Embodiment

[0112] Please refer to Figure 1 and Figure 2 , specifically: the archive feedback module includes a data archiving unit and a remote uploading unit;

[0113] The data archiving unit is used to add information tags to each set of condensation risk assessment sets in the edge node according to the evaluation information generated by the condensation risk assessment;

[0114] The condensation risk assessment set includes the above-mentioned pneumatic environment data group, water vapor critical data group, cross-cabin air exchange efficiency Eex, condensation nucleation potential index Cnp and comprehensive condensation risk index Rall

[0115] The information tag contains a time stamp and corresponding evaluation state and control instruction;

[0116] The remote uploading unit is used to transmit the data with information tags to the cloud for storage in real time according to the wireless network.

[0117] In this embodiment, the archive feedback module processes the condensation risk assessment results in the edge node through the data archiving unit, packages the pneumatic environment data set, the water vapor critical data set, the cross-cabin air exchange efficiency Eex, the condensation nucleation potential index Cnp, and the comprehensive condensation risk index Rall into a condensation risk assessment set, adds information tags such as time stamps, assessment states, and control instructions, and then transmits the tagged data to the cloud storage in real time through the wireless network by the remote uploading unit. This implementation not only realizes the synchronous retention and traceability of the risk assessment results and the original data, guarantees the integrity and traceability of the monitoring data, but also provides accurate data basis for subsequent operation and maintenance personnel and model optimization. Compared with the existing technology which only performs single alarm or local recording, the module forms a closed-loop mechanism of "local marking-remote archiving-cloud calling", so that the system has the ability of historical experience accumulation and long-term optimization, thereby significantly improving the condensation risk management level and the safety and reliability of operation of the battery energy storage device.

[0118] Although embodiments of the present application have been shown and described, it is to be understood that various modifications, substitutions, replacements and changes can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application 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 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. The air diagnostic module is used to construct the cross-chamber air exchange efficiency Eex based on the aerodynamic environment data set. In the formula, ln represents the logarithmic function, arctan represents the hyperbolic sine function, ∆wd and ∆sd represent the temperature difference and humidity difference between adjacent compartments, respectively, and are used to evaluate the air exchange qualification with the critical threshold Te for cross-compartment air exchange, and to perform condensation risk analysis when the air exchange between compartments is abnormal; The comprehensive risk analysis module is used to perform condensation risk analysis and construct the condensation nucleation potential index Cnp based on the water vapor critical data set. In the formula, ln represents the logarithmic function, and is combined with the cross-chamber air exchange efficiency Eex to calculate the comprehensive condensation risk index Rall, and then combined with the condensation nucleation trigger threshold Ra and the condensation risk judgment threshold Rl to assess the condensation risk. 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 derivative calculation on cabin humidity sd to obtain the humidity change rate sb.

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 critical threshold Te for inter-cabin air exchange, it indicates that the air exchange between cabins 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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