Battery changing cabinet battery charging control method based on Internet of Things

Abstract

The invention discloses a battery charging control method for a battery replacement cabinet based on the Internet of Things, relates to the technical field of charging control, and has higher adaptability in the aspects of battery replacement demand prediction, charging mode switching and charging safety management compared with a traditional fixed charging mode. The problems of sudden increase of battery replacement demands, changes of environmental factors and the like can be effectively solved, and the operation stability of the battery replacement cabinet is improved; the method comprises the following steps: acquiring a battery state, a battery replacement demand and meteorological data, predicting a future battery replacement demand curve in combination with a long-short term memory (LSTM) network and a multiple linear regression model, introducing a meteorological influence factor, adjusting a charging plan of a battery replacement cabinet 24 hours before severe weather early warning, and matching the battery stock with a peak demand; the charging modes comprise a fast charging mode, a general charging mode and a slow charging mode, dynamic switching is carried out according to the battery replacement demand peak value and the daily average battery replacement demand, the fast charging mode is preferentially adopted in the battery replacement peak period, the battery supply rate is increased, and meanwhile part of charging resources are reserved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging control, and in particular to a method for controlling battery charging of a battery swap cabinet based on the Internet of Things. Background Art

[0002] With the rapid development of the food delivery and express delivery industries, battery swap cabinets have become the core battery supply facilities for short-distance travel of electric vehicles. Current battery swap cabinets generally adopt a fixed charging strategy, that is, centralized charging during the low period of the power grid at night, and providing battery swap services during the day.

[0003] However, the demand for battery swapping is not constant, and the traditional fixed charging control mode cannot quickly respond to fluctuations in the demand for battery swapping. When the demand for battery swapping increases sharply, the batteries in the battery swapping cabinet are taken out in a short period of time, and the charging system is difficult to replenish in time, causing users to queue for a long time and even affecting the rider's journey. On the contrary, when the demand drops in a short period of time, the battery swapping cabinet is still charged according to the original plan, causing the battery to be in a high SOC state for a long time, accelerating aging and reducing operational efficiency; in addition, in bad weather, such as strong winds and heavy rains, users may replace batteries in advance, causing a shortage of batteries in the battery swapping cabinet in a short period of time. Heavy rain or other conditions may also cause riders to reduce travel, and the demand for battery swapping drops sharply. Some batteries are not used after being fully charged. Long-term high SOC further accelerates battery aging. In addition, high humidity may affect the battery contact points, increase the risk of short circuits, and easily cause safety hazards.

[0004] It can be seen that the current battery charging control method of the battery swap cabinet cannot adapt to the battery swap demand in severe weather or emergency situations, the charging management lacks predictive ability, and the regulation method is lagging behind. Therefore, a battery charging control method for the battery swap cabinet based on the Internet of Things is urgently needed to solve such problems. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides a method for controlling battery charging of a battery swap cabinet based on the Internet of Things, which solves the problems that traditional battery swap cabinets have fixed charging strategies, lack of prediction capabilities, environmental perception and dynamic regulation, and are unable to cope with severe weather and sudden demand fluctuations.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: The embodiment of the present invention provides a method for controlling battery charging of a battery swap cabinet based on the Internet of Things, which includes: Step S1, collecting battery status data and battery replacement demand data, and monitoring meteorological data; Step S2, predicting the future peak demand for battery replacement based on meteorological data and battery replacement demand data; Step S3, adjusting the charging plan and switching the charging mode according to the peak value of battery replacement demand predicted in step S2; Step S4: The battery swap cabinet has a built-in sensor, and the sensor monitors environmental data during the execution of the charging plan of step S3; Step S5, compare the environmental data collected in step S4 with the safety threshold. If the humidity exceeds the threshold, adjust the charging power and the fast charging mode; if the temperature is lower than the threshold, enable the fast charging mode.

[0008] As a preferred solution of the method for controlling battery charging of a battery swap cabinet based on the Internet of Things described in the present invention, the battery status data and battery swap demand data include battery power, battery inventory, battery swap frequency and historical battery swap records.

[0009] As a preferred solution of the method for controlling battery charging of a battery swap cabinet based on the Internet of Things described in the present invention, in step S1, the step of collecting battery status data and battery swap demand data and monitoring meteorological data is as follows: The battery management system obtains the battery power and remaining capacity, and collects single battery parameters, including: battery voltage, battery current, battery temperature, and remaining available power. Collect battery replacement demand data and record the frequency of battery replacement, including battery replacement time, user identification and battery replacement mode data. Statistics of historical battery replacement records, including: average battery replacement cycle, battery replacement time interval, battery replacement mode selection of each user, calculation of the number of battery replacements per unit time, and formation of a battery replacement demand forecast curve. The battery swap cabinet is deployed with temperature, humidity, wind speed, and precipitation sensors to monitor the meteorological parameters of the location of the battery swap cabinet. At the same time, it is connected to the third-party meteorological data API to obtain weather warning information, combine historical weather data, analyze the impact of meteorological changes on battery swap demand, and dynamically adjust data weights.

[0010] As a preferred solution of the method for controlling battery charging of a battery swap cabinet based on the Internet of Things described in the present invention, in step S2, the step of predicting the future peak value of battery swap demand based on meteorological data and battery swap demand data is as follows: A battery swap demand prediction model is established, and the long short-term memory network LSTM is used for time series prediction to establish a battery swap demand curve, which is expressed as: , in, represents the battery replacement demand at time t, represents the battery replacement demand data at the past n moments, Represents the mapping function of the LSTM prediction model; Introducing the influence of meteorological factors, a multivariate linear regression model is used to calculate the impact of meteorological factors on the demand for battery replacement. The formula is: , in, represents the revised forecast value of battery replacement demand, represents the temperature at time t, represents the humidity at time t, represents the precipitation at time t, Represents the meteorological impact weight coefficient, which is determined through historical data regression analysis. Set weather severity factors based on weather warning data : , in, represents the final predicted value of battery replacement demand, Indicates the weather severity weight, and its value range is defined as follows: Normal Weather: ; Mild severe weather (light rain, light wind): ; Moderately severe weather (heavy rain, strong winds): ; Extreme weather (typhoon, blizzard): ; Set the forecast time window to the next 24 hours and calculate the peak demand. The calculation formula is: , in, Indicates the peak demand for battery replacement in the next 24 hours.

[0011] As a preferred solution of the method for controlling battery charging of a battery swap cabinet based on the Internet of Things described in the present invention, the charging plan includes checking the battery reserve, adopting the fast charging mode, and adjusting the battery reserve within 24 hours before the severe weather warning.

[0012] As a preferred solution of the method for controlling battery charging of a battery swap cabinet based on the Internet of Things described in the present invention, the charging mode includes a fast charging mode, a general charging mode and a slow charging mode.

[0013] As a preferred solution of the method for controlling battery charging of a battery swap cabinet based on the Internet of Things described in the present invention, in step S3, the step of adjusting the charging plan and switching the charging mode according to the peak value of the battery swap demand predicted in step S2 is as follows: Calculate the current battery reserve using the following formula: , in, Indicates the current available battery reserve. Indicates the current available battery quantity. Indicates the minimum inventory battery threshold, like , then execute the fast charge mode, Peak demand for battery swapping based on prediction , select charging mode: like , use slow charging mode, like , use the normal charging mode, like , switch to fast charge mode, in, represents the average daily demand for battery replacement, 24 hours before a severe weather warning: Check the battery inventory and require that the inventory of batteries is higher than the peak demand. Start fast charging mode to increase the charging speed. Reserve some charging resources to meet sudden battery replacement needs.

[0014] As a preferred solution of the method for controlling battery charging of a battery swap cabinet based on the Internet of Things described in the present invention, the environmental data includes the humidity and temperature of the battery swap cabinet.

[0015] As a preferred solution of the method for controlling battery charging of a battery swap cabinet based on the Internet of Things described in the present invention, in step S5, the charging power and fast charging mode are adaptively adjusted based on the monitored environmental data, specifically: Assume that the real-time data monitoring process of the built-in sensor of the power exchange cabinet is as follows: , in, Indicates the current humidity. Indicates the current temperature. Represents the sensor data reading function, If detected , reduce the charging power, and adjust the formula to: , in, Indicates the original charging power, Indicates humidity influence coefficient, value range .

[0016] As a preferred solution of the method for controlling battery charging of a battery swap cabinet based on the Internet of Things described in the present invention, the step of adaptively adjusting the charging power and fast charging mode also includes: like , increase the charging power, and adjust the formula to: , in, Indicates the temperature compensation coefficient, the value range is , Adjust charging power in real time: , in, Indicates the adjusted charging power.

[0017] The beneficial effects of the present invention are as follows: compared with the traditional fixed charging mode, the present invention has stronger adaptability in battery replacement demand prediction, charging mode switching and charging safety management, can effectively cope with problems such as sudden increase in battery replacement demand and changes in environmental factors, and improve the operating stability of the battery replacement cabinet.

[0018] The present invention collects battery status, battery replacement demand and meteorological data, combines the long short-term memory network LSTM with the multivariate linear regression model, predicts the future battery replacement demand curve, introduces meteorological influencing factors, and adjusts the charging plan of the battery replacement cabinet 24 hours before the severe weather warning to match the battery stock with peak demand.

[0019] In the present invention, three charging modes are set, namely, fast charging, normal charging and slow charging, and dynamic switching is performed according to the peak value of battery replacement demand and the average daily battery replacement demand. The fast charging mode is preferentially adopted during the peak period of battery replacement to increase the battery supply rate. At the same time, some charging resources are reserved to cope with sudden battery replacement demand and avoid the problem of insufficient battery supply or waste of battery stock.

[0020] In the present invention, the battery swap cabinet has a built-in environmental sensor to monitor environmental parameters such as temperature and humidity in real time, and dynamically adjusts the charging power in combination with the safety threshold. When the humidity exceeds the threshold, the charging power is reduced and fast charging is delayed to prevent the risk of short circuit. When the temperature is lower than the threshold, the fast charging mode is enabled to improve the charging efficiency in low temperature environment. The battery swap cabinet can maintain stable operation under different environmental conditions.

[0021] In summary, the present invention reduces the waste of resources caused by fixed modes through intelligent charging control, improves the responsiveness of the charging system to changes in demand, and enhances safety protection in harsh environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0023] Figure 1It is a flow chart of the method for controlling battery charging of a battery swap cabinet based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0025] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0026] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0027] Example 1, reference Figure 1 This embodiment provides a method for controlling battery charging of a battery swap cabinet based on the Internet of Things, comprising the following steps: Step S1, collecting battery status data and battery replacement demand data, and monitoring meteorological data; Battery status data and battery replacement demand data include battery power, battery inventory, battery replacement frequency and historical battery replacement records; In step S1, the steps of collecting battery status data and battery replacement demand data and monitoring meteorological data are as follows: The battery management system obtains the battery power and remaining capacity, and collects single battery parameters, including: battery voltage, battery current, battery temperature, and remaining available power. Collect battery replacement demand data and record the frequency of battery replacement, including battery replacement time, user identification and battery replacement mode data. Statistics of historical battery replacement records, including: average battery replacement cycle, battery replacement time interval, battery replacement mode selection of each user, calculation of the number of battery replacements per unit time, and formation of a battery replacement demand forecast curve. The battery swap cabinet is equipped with temperature, humidity, wind speed, and precipitation sensors to monitor the meteorological parameters of the location of the battery swap cabinet. At the same time, it is connected to the third-party meteorological data API to obtain weather warning information. Combined with historical weather data, it analyzes the impact of meteorological changes on battery swap demand and dynamically adjusts data weights. Specifically, the battery power, inventory and battery cell parameters are monitored through the BMS, and historical data is recorded with the help of the database to provide support for subsequent analysis; the battery replacement demand data is collected based on the battery replacement frequency, historical records and user behavior analysis, which can effectively reflect the battery replacement demand trend in different time periods; In addition, the use of environmental sensors combined with third-party data APIs to monitor meteorological data not only ensures real-time performance but also enhances data reliability. Meteorological data is crucial for subsequent battery swap demand forecasts and can improve forecast accuracy, thereby optimizing charging strategies, reducing energy waste, and improving the operational efficiency of battery swap cabinets. Step S2, predicting the future peak demand for battery replacement based on meteorological data and battery replacement demand data; In step S2, based on the meteorological data and the battery swap demand data, the steps of predicting the future battery swap demand peak are: A battery swap demand prediction model is established, and the long short-term memory network LSTM is used for time series prediction to establish a battery swap demand curve, which is expressed as: , in, represents the battery replacement demand at time t, represents the battery replacement demand data at the past n moments, Represents the mapping function of the LSTM prediction model; Introducing the influence of meteorological factors, a multivariate linear regression model is used to calculate the impact of meteorological factors on the demand for battery replacement. The formula is: , in, represents the revised forecast value of battery replacement demand, represents the temperature at time t, represents the humidity at time t, represents the precipitation at time t, Represents the meteorological impact weight coefficient, which is determined through historical data regression analysis. Set weather severity factors based on weather warning data : , in, represents the final predicted value of battery replacement demand, Indicates the weather severity weight, and its value range is defined as follows: Normal Weather: ; Mild severe weather (light rain, light wind): ; Moderately severe weather (heavy rain, strong winds): ; Extreme weather (typhoon, blizzard): ; Set the forecast time window to the next 24 hours and calculate the peak demand. The calculation formula is: , in, Indicates the peak demand for battery replacement in the next 24 hours; Specifically, the core here is to establish a battery swap demand prediction model to improve prediction accuracy; LSTM is used for time series prediction to dynamically adjust the changing trend of battery swapping demand, and the multivariate linear regression method is combined to quantify the impact of meteorological factors on battery swapping demand, thereby improving the adaptability of the prediction model. In addition, the weather severity factor is determined based on historical data analysis and meteorological warning signals to adjust the battery swapping demand, so that the model can adapt to sudden weather changes, and finally calculate the peak value of battery swapping demand as the basis for subsequent charging plan adjustments; it can not only optimize battery resource allocation, but also improve the response speed of battery swapping cabinets; Step S3, adjusting the charging plan and switching the charging mode according to the peak value of battery replacement demand predicted in step S2; The charging plan includes checking the battery reserve, using fast charging mode, and adjusting the battery reserve within 24 hours before the severe weather warning; Charging modes include fast charging mode, normal charging mode and slow charging mode; In step S3, the charging plan is adjusted according to the peak value of the battery replacement demand predicted in step S2, and the steps of switching the charging mode are as follows: Calculate the current battery reserve using the following formula: , in, Indicates the current available battery reserve. Indicates the current available battery quantity. Indicates the minimum inventory battery threshold, like , then execute the fast charge mode, Peak demand for battery swapping based on prediction , select charging mode: like , use slow charging mode, like , use the normal charging mode, like , switch to fast charging mode, in, represents the average daily demand for battery replacement, 24 hours before a severe weather warning: Check the battery inventory and require that the inventory of batteries is higher than the peak demand. Start fast charging mode to increase the charging speed. Reserve some charging resources to cope with sudden battery replacement needs; Specifically, the current battery inventory is calculated here to determine whether the fast charging mode needs to be executed in advance to avoid battery shortages in the battery swap cabinet during peak demand periods; Secondly, the charging mode is switched in a demand-driven manner, and the charging strategy is determined based on the peak and daily average demand for battery swapping, to achieve intelligent dynamic adjustment. In addition, in response to severe weather warnings, the charging plan is adjusted 24 hours in advance to minimize the impact of weather factors on battery swapping services, effectively improve the operating efficiency of battery swapping cabinets, avoid unnecessary energy waste, and improve the stability of battery supply. Step S4: The battery swap cabinet has a built-in sensor, and the sensor monitors environmental data during the execution of the charging plan of step S3; Environmental data includes humidity and temperature of the battery swap cabinet; Step S5, comparing the environmental data collected in step S4 with the safety threshold, if the humidity exceeds the threshold, adjusting the charging power and the fast charging mode; if the temperature is lower than the threshold, enabling the fast charging mode; In step S5, the charging power and fast charging mode are adaptively adjusted based on the monitored environmental data. Specifically: Assume that the real-time data monitoring process of the built-in sensor of the power exchange cabinet is as follows: , in, Indicates the current humidity. Indicates the current temperature. Represents the sensor data reading function, If detected , reduce the charging power, and adjust the formula to: , in, Indicates the original charging power, Indicates humidity influence coefficient, value range , like , increase the charging power, and adjust the formula to: , in, Indicates the temperature compensation coefficient, the value range is , Adjust charging power in real time: , in, Indicates the adjusted charging power; Specifically, in step S5, environmental parameters are dynamically monitored during the charging process and adaptively adjusted to improve charging safety and efficiency. The temperature and humidity are monitored in real time through the built-in sensors in the battery swap cabinet. Based on the safety threshold, the charging power can be reduced when the humidity exceeds the standard to reduce safety hazards. At the same time, the charging power is automatically increased in low temperature environments to avoid the impact of sudden environmental changes on the charging system. The battery swap cabinet can operate efficiently under various environmental conditions, improve battery life, and reduce instability caused by environmental factors.

[0028] In summary, the present invention: Compared with the traditional fixed charging mode, the present invention has stronger adaptability in battery replacement demand prediction, charging mode switching and charging safety management. It can effectively cope with problems such as sudden increase in battery replacement demand and changes in environmental factors, and improve the operating stability of the battery replacement cabinet.

[0029] The present invention collects battery status, battery replacement demand and meteorological data, combines the long short-term memory network LSTM with the multivariate linear regression model, predicts the future battery replacement demand curve, introduces meteorological influencing factors, and adjusts the charging plan of the battery replacement cabinet 24 hours before the severe weather warning to match the battery stock with peak demand.

[0030] In the present invention, three charging modes are set, namely, fast charging, normal charging and slow charging, and dynamic switching is performed according to the peak value of battery replacement demand and the average daily battery replacement demand. The fast charging mode is preferentially adopted during the peak period of battery replacement to increase the battery supply rate. At the same time, some charging resources are reserved to cope with sudden battery replacement demand and avoid the problem of insufficient battery supply or waste of battery stock.

[0031] In the present invention, the battery swap cabinet has a built-in environmental sensor to monitor environmental parameters such as temperature and humidity in real time, and dynamically adjusts the charging power in combination with the safety threshold. When the humidity exceeds the threshold, the charging power is reduced and fast charging is delayed to prevent the risk of short circuit. When the temperature is lower than the threshold, the fast charging mode is enabled to improve the charging efficiency in low temperature environment. The battery swap cabinet can maintain stable operation under different environmental conditions.

[0032] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for controlling battery charging of a battery swap cabinet based on the Internet of Things, characterized in that: include, Step S1, collecting battery status data and battery replacement demand data, and monitoring meteorological data; Step S2, predicting the future peak demand for battery replacement based on meteorological data and battery replacement demand data; Step S3, adjusting the charging plan and switching the charging mode according to the peak value of battery replacement demand predicted in step S2; Step S4: The battery swap cabinet has a built-in sensor, and the sensor monitors environmental data during the execution of the charging plan of step S3; Step S5, compare the environmental data collected in step S4 with the safety threshold. If the humidity exceeds the threshold, adjust the charging power and the fast charging mode; if the temperature is lower than the threshold, enable the fast charging mode.

2. The method for controlling battery charging of a battery swap cabinet based on the Internet of Things as claimed in claim 1, characterized in that: The battery status data and battery replacement demand data include battery power, battery inventory, battery replacement frequency and historical battery replacement records.

3. A method for controlling battery charging of a battery swap cabinet based on the Internet of Things as described in claim 2, characterized in that: In step S1, the steps of collecting battery status data and battery replacement demand data and monitoring meteorological data are as follows: The battery management system obtains the battery power and remaining capacity, and collects single battery parameters, including: battery voltage, battery current, battery temperature, and remaining available power. Collect battery replacement demand data and record the frequency of battery replacement, including battery replacement time, user identification and battery replacement mode data. Statistics of historical battery replacement records, including: average battery replacement cycle, battery replacement time interval, battery replacement mode selection of each user, calculation of the number of battery replacements per unit time, and formation of a battery replacement demand forecast curve. The battery swap cabinet is deployed with temperature, humidity, wind speed, and precipitation sensors to monitor the meteorological parameters of the location of the battery swap cabinet. At the same time, it is connected to the third-party meteorological data API to obtain weather warning information, combine historical weather data, analyze the impact of meteorological changes on battery swap demand, and dynamically adjust data weights.

4. The method for controlling battery charging of a battery swap cabinet based on the Internet of Things as claimed in claim 3 is characterized in that: In step S2, the step of predicting the future peak value of battery replacement demand based on meteorological data and battery replacement demand data is as follows: A battery swap demand prediction model is established, and the long short-term memory network LSTM is used for time series prediction to establish a battery swap demand curve, which is expressed as: ， in, represents the battery replacement demand at time t, represents the battery replacement demand data at the past n moments, Represents the mapping function of the LSTM prediction model; Introducing the influence of meteorological factors, a multivariate linear regression model is used to calculate the impact of meteorological factors on the demand for battery replacement. The formula is: ， in, represents the revised forecast value of battery replacement demand, represents the temperature at time t, represents the humidity at time t, represents the precipitation at time t, Represents the meteorological impact weight coefficient, which is determined through historical data regression analysis. Set weather severity factors based on weather warning data : ， in, represents the final predicted value of battery replacement demand, Indicates the weather severity weight; Set the forecast time window to the next 24 hours and calculate the peak demand. The calculation formula is: ， in, Indicates the peak demand for battery replacement in the next 24 hours.

5. The method for controlling battery charging of a battery swap cabinet based on the Internet of Things as claimed in claim 4 is characterized in that: The charging plan includes checking the battery level, using fast charging mode, and adjusting the battery level within 24 hours before a severe weather warning.

6. A method for controlling battery charging of a battery swap cabinet based on the Internet of Things as claimed in claim 5, characterized in that: The charging modes include a fast charging mode, a normal charging mode and a slow charging mode.

7. A method for controlling battery charging of a battery swap cabinet based on the Internet of Things as claimed in claim 6, characterized in that: In step S3, the step of adjusting the charging plan and switching the charging mode according to the peak value of the battery replacement demand predicted in step S2 is as follows: Calculate the current battery reserve using the following formula: ， in, Indicates the current available battery reserve. Indicates the current available battery quantity. Indicates the minimum inventory battery threshold, like , then execute the fast charge mode, Based on the predicted peak demand for battery replacement , select charging mode: like , use slow charging mode, like , use the normal charging mode, like , switch to fast charging mode, in, represents the average daily demand for battery replacement, 24 hours before a severe weather warning: Check the battery inventory and require that the inventory of batteries is higher than the peak demand. Start fast charging mode to increase the charging speed. Reserve some charging resources to meet sudden battery replacement needs.

8. A method for controlling battery charging of a battery swap cabinet based on the Internet of Things as claimed in claim 7, characterized in that: The environmental data includes the humidity and temperature of the power swap cabinet.

9. A method for controlling battery charging of a battery swap cabinet based on the Internet of Things as claimed in claim 8, characterized in that: In step S5, the charging power and fast charging mode are adaptively adjusted based on the monitored environmental data. Specifically: Assume that the real-time data monitoring process of the built-in sensor of the power exchange cabinet is as follows: ， in, Indicates the current humidity. Indicates the current temperature. Represents the sensor data reading function, If detected , reduce the charging power, and adjust the formula to: ， in, Indicates the original charging power, Indicates humidity influence coefficient, value range .

10. A method for controlling battery charging of a battery swap cabinet based on the Internet of Things as claimed in claim 9, characterized in that: The step of adaptively adjusting the charging power and the fast charging mode also includes: like , increase the charging power, and adjust the formula to: ， in, Indicates the temperature compensation coefficient, the value range is , Adjust charging power in real time: ， in, Indicates the adjusted charging power.

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