Adaptive control method and system for container monitoring terminal

Abstract

The invention discloses a self-adaptive control method and system for a container monitoring terminal. The method comprises the following steps: collecting pulse current response characteristics and environment temperature of a super capacitor; calculating a temperature indication factor based on the pulse current response characteristics of the super capacitor, and compensating the environment temperature by using the temperature indication factor to obtain a corrected effective temperature; correcting the effective capacity of the lithium ion battery based on the effective temperature, and collecting the current of the lithium ion battery to execute coulombmeter integration to update the state of charge of the battery; performing joint estimation on the state of charge and the internal resistance of the battery based on the corrected effective capacity by using an extended Kalman filtering algorithm; performing trend analysis on the environment temperature and humidity time sequence, predicting future environment change and calculating a risk level; the robustness and cruising ability of the container monitoring terminal in complex scenes such as cold-chain transportation can be greatly enhanced, the maintenance requirement is reduced, and the overall energy efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of transportation equipment, and in particular to an adaptive control method and system for a container monitoring terminal. Background Technology

[0002] In existing container monitoring terminal technologies, the equipment mainly relies on a single lithium battery for power. However, under long-term operation or high-load scenarios, the battery power prediction error increases significantly, especially when the temperature fluctuates drastically, the change in the internal resistance of the lithium battery leads to a serious decrease in estimation accuracy. Although some solutions introduce supercapacitors as auxiliary power to alleviate short-term load pressure, there are still problems such as inaccurate temperature compensation and large power prediction deviation. Moreover, the existing systems lack dynamic prediction and adaptive response mechanisms for external factors such as ambient temperature and humidity, and cannot proactively optimize energy consumption strategies according to future environmental changes. This results in insufficient stability, limited endurance, and high maintenance costs for the equipment in complex transportation or warehousing environments. There is an urgent need for an efficient control method that can integrate temperature correction, accurate power estimation, and intelligent power consumption scheduling. Summary of the Invention

[0003] This invention proposes an adaptive control method for a container monitoring terminal, comprising: S1. Collect the pulse current response characteristics of the supercapacitor and the ambient temperature.

[0004] S2. Calculate the temperature indication factor based on the pulse current response characteristics of the supercapacitor, and use the temperature indication factor to compensate for the ambient temperature to obtain the corrected effective temperature.

[0005] S3. Based on the effective temperature, correct the effective capacity of the lithium-ion battery, and collect the lithium-ion battery current to perform coulomb integration to update the battery state of charge.

[0006] S4. Using the extended Kalman filter algorithm, the battery state of charge and battery internal resistance are jointly estimated based on the corrected effective capacity.

[0007] S5. Perform trend analysis on the time series of environmental temperature and humidity to predict future environmental changes and calculate the risk level.

[0008] S6. Based on the risk level and battery state of charge, dynamically adjust the sleep cycle, load scheduling strategy and communication strategy of the terminal device, and dynamically adjust the power distribution ratio between the supercapacitor and the lithium-ion battery based on the load current.

[0009] Further, S2 specifically includes: extracting the current response characteristics of the supercapacitor under pulsed load, including the current rise rate and peak current within a specific time window; calculating a multidimensional temperature indicator factor based on the current response characteristics; converting the multidimensional temperature indicator factor into a temperature compensation value using a preset temperature correction function; and combining the temperature compensation value with the ambient temperature measurement value to calculate the corrected effective temperature.

[0010] Furthermore, S4 specifically includes: establishing a system state equation that includes the battery state of charge and battery internal resistance; establishing a measurement equation with the battery terminal voltage as the measured value; updating the system state equation using the corrected effective capacity; updating the system state vector by combining the gain of the extended Kalman filter algorithm with the deviation between the actual observed value of the battery terminal voltage and the voltage estimate predicted based on the state vector; and smoothing the battery state of charge residual after each extended Kalman filter algorithm update, and realizing online self-calibration of model parameters based on the residual change trend.

[0011] Furthermore, the preset temperature correction function is obtained through the following methods: collecting supercapacitor pulse current response characteristic data and corresponding real ambient temperature data on a cloud server; calculating the actual temperature difference between the supercapacitor current response characteristic data and the real ambient temperature; training a model on the relationship between the current response characteristic data and the actual temperature difference using a machine learning algorithm; sending the trained simplified model parameters to the container monitoring terminal; and calculating the temperature compensation value on the container monitoring terminal side using the simplified model parameters and the real-time collected supercapacitor pulse current response characteristic data.

[0012] Furthermore, S5 specifically includes: real-time acquisition of temperature and humidity data inside the container to form a time series; using an exponentially weighted moving average algorithm to calculate the short-term trend of the temperature and humidity time series; analyzing the rate and direction of change of the short-term trend to predict the changes in environmental temperature and humidity within a specific future time period; setting a risk level based on the predicted magnitude of environmental temperature and humidity changes and whether they exceed a predetermined threshold, combined with the current state of battery charge; and forcibly entering the highest risk level when an abnormal signal of water immersion or smoke is detected.

[0013] Furthermore, the power consumption control strategy of the terminal device also includes: dynamically configuring the data upload cycle of the communication and positioning module according to the risk level and battery charge state; prioritizing the protection of core communication functions and reducing the upload volume of non-critical data when the risk level increases; and using supercapacitors to provide transient high current power supply to the communication and positioning module to ensure the emergency information reporting capability when the battery charge state is extremely low.

[0014] Furthermore, in S6, the dynamic adjustment of the sleep cycle, load scheduling strategy, and communication strategy of the terminal device is implemented in the following ways: setting the sleep cycle duration according to the risk level; adjusting the sampling frequency of non-critical sensor modules; reducing the duty cycle of the communication and positioning module; and activating the supercapacitor enhanced power supply mode when the battery state of charge is lower than the predetermined value or the risk level is high.

[0015] Furthermore, the dynamic adjustment of the power supply allocation ratio based on the load current in S6 specifically includes: real-time monitoring of the total load current of the terminal equipment; calculating the current ratio that the supercapacitor should bear when the total load current exceeds a predetermined threshold; calculating the actual output current of the supercapacitor and the actual output current of the lithium-ion battery according to the current ratio and the total load current; controlling the supercapacitor and the lithium-ion battery to supply power according to the actual output current; and shutting down all non-critical functional modules and maintaining power supply only to the communication and positioning module when an abnormal signal of water immersion or smoke is detected and the risk level is at its highest.

[0016] Furthermore, the present invention also proposes a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the adaptive control method of the container monitoring terminal.

[0017] Furthermore, this invention proposes a power prediction and environmental condition adaptive control system for a container monitoring terminal, characterized by comprising: a data acquisition module for acquiring the pulse current response characteristics of a supercapacitor and the ambient temperature; a correction module for calculating a temperature indicator factor based on the pulse current response characteristics of the supercapacitor and compensating for the ambient temperature to obtain an effective temperature; a calculation module for correcting the effective capacity of the lithium-ion battery based on the effective temperature and acquiring the lithium-ion battery current to perform coulomb integration to update the battery state of charge; an estimation module for using an extended Kalman filter algorithm to jointly estimate the battery state of charge and battery internal resistance based on the corrected effective capacity; a prediction module for performing trend analysis on the environmental temperature and humidity time series to predict future environmental changes and calculate the risk level; and a control module for dynamically adjusting the terminal device's sleep cycle, load scheduling strategy, communication strategy, and power supply allocation ratio according to the risk level and battery state of charge.

[0018] This invention proposes an adaptive control method and system for a container monitoring terminal. By coordinating the work of hybrid power supply units and combining temperature-compensated extended Kalman filtering with supercapacitor pulse current characteristics for joint estimation, the accuracy and adaptability of battery power prediction are significantly improved. Furthermore, environmental trend prediction is integrated to dynamically adjust the sleep cycle, load scheduling, and communication strategy, ensuring reliable operation under drastic temperature changes or emergency conditions, while optimizing the power supply allocation ratio to extend battery life. This method significantly enhances the robustness and endurance of the container monitoring terminal in complex scenarios such as cold chain transportation, reduces maintenance requirements, and improves overall energy efficiency. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating an adaptive control method for a container monitoring terminal proposed in this invention. Detailed Implementation

[0020] refer to Figure 1 This invention proposes an adaptive control method for a container monitoring terminal, comprising: S1. Collect the pulse current response characteristics of the supercapacitor and the ambient temperature.

[0021] S2. Calculate the temperature indication factor based on the pulse current response characteristics of the supercapacitor, and use the temperature indication factor to compensate for the ambient temperature to obtain the corrected effective temperature.

[0022] S3. Based on the effective temperature, correct the effective capacity of the lithium-ion battery, and collect the lithium-ion battery current to perform coulomb integration to update the battery state of charge.

[0023] S4. Using the extended Kalman filter algorithm, the battery state of charge and battery internal resistance are jointly estimated based on the corrected effective capacity.

[0024] S5. Perform trend analysis on the time series of environmental temperature and humidity to predict future environmental changes and calculate the risk level.

[0025] S6. Based on the risk level and battery state of charge, dynamically adjust the sleep cycle, load scheduling strategy and communication strategy of the terminal device, and dynamically adjust the power distribution ratio between the supercapacitor and the lithium-ion battery based on the load current.

[0026] Specifically, the supercapacitor pulse current response characteristics refer to the dynamic characteristics of current change over time when the supercapacitor undergoes a sudden load change; the ambient temperature refers to the original value of the container's internal temperature directly measured by a temperature sensor; the temperature indicator factor refers to the characteristic parameters extracted from the supercapacitor pulse current response to characterize the actual operating temperature; the effective temperature refers to the true operating temperature of the lithium battery after compensation by the temperature indicator factor; the effective capacity refers to the actual usable charge storage of the lithium battery at the current effective temperature; the battery state of charge refers to the percentage of remaining charge in the lithium battery relative to its total capacity; the battery internal resistance refers to the equivalent impedance within the lithium battery that impedes current flow; and the coulomb integral refers to the... The lithium battery current is accumulated over time to calculate the change in charge; the extended Kalman filter algorithm is a dynamic system state estimation method that combines the system state equation and noise statistics; the environmental temperature and humidity time series is a set of temperature and humidity data collected in chronological order; the risk level is the degree of system operation risk classified according to environmental trends and charge status; the sleep cycle is the time interval between devices entering low-power mode; the load scheduling strategy is the control rules for the working priority and working duration of each functional module of the terminal; the communication strategy is the control mechanism for data upload frequency and communication module start / stop; the power supply allocation ratio is the share of load current borne by the supercapacitor and the lithium battery respectively. Specifically, this method first simultaneously acquires the transient current change process of the supercapacitor under sudden load changes and temperature sensor readings; it then forms a temperature indicator factor by analyzing the rate of change and peak characteristics of the supercapacitor current waveform, and uses this factor to dynamically correct the original ambient temperature measurement value, eliminating temperature measurement lag and bias; based on the corrected actual battery operating temperature, it adjusts the effective capacity parameters of the lithium battery, and simultaneously updates the initial value of the battery's state of charge using the Coulomb integral method combined with the real-time acquired lithium battery current data; it uses an extended Kalman filter algorithm to integrate the temperature-calibrated effective capacity into the battery state model, and simultaneously estimates the battery's state of charge and internal resistance parameters by iteratively correcting the difference between the observed and predicted terminal voltage values; it continuously analyzes the changing patterns of historical temperature and humidity data, uses a sliding weighted average method to predict future environmental evolution trends, and sets graded risk indicators based on the current remaining battery power, immediately initiating the highest risk response when sudden events such as water immersion or smoke are detected; finally, based on the risk level and battery state of charge, it adaptively extends the device's sleep time, prioritizes the load of sensors and communication modules, and allocates the current output ratio of the supercapacitor and lithium battery in real time, maintaining only the core communication function in extreme cases.

[0027] Further, S2 specifically includes: extracting the current response characteristics of the supercapacitor under pulsed load, including the current rise rate and peak current within a specific time window; calculating a multi-dimensional temperature indicator factor based on the current response characteristics; converting the multi-dimensional temperature indicator factor into a temperature compensation value using a preset temperature correction function; and combining the temperature compensation value with the ambient temperature measurement value to calculate the corrected effective temperature. Specifically, the pulsed load refers to a sudden current demand scenario for the equipment in a short period; the specific time window refers to a pre-set current observation period; the current rise rate refers to the rate at which the current increases per unit time; the peak current refers to the maximum instantaneous current value under the pulsed load; the multi-dimensional temperature indicator factor refers to a feature set of associated temperatures extracted from current curves across multiple time windows; the temperature compensation value refers to the adjustment amount used to correct the ambient temperature measurement deviation; and the effective temperature refers to the actual battery operating ambient temperature obtained by superimposing the temperature compensation value. Specifically, when the load changes abruptly, the rising trajectory and the highest current point of the supercapacitor current in the preset time interval are recorded. The rising slope and peak value of different time spans are extracted from the trajectory to form multidimensional temperature feature parameters. The preset temperature correlation function is called to map the multidimensional feature parameters into temperature compensation values. The temperature compensation values ​​are superimposed with the original ambient temperature measurement values ​​to generate the final effective temperature value, which reflects the actual thermal environment of the lithium battery.

[0028] Further, S4 specifically includes: establishing a system state equation that includes the battery state of charge and battery internal resistance; establishing a measurement equation with the battery terminal voltage as the measured value; updating the system state equation using the corrected effective capacity; updating the system state vector using the deviation between the actual observed value of the battery terminal voltage and the voltage estimate based on the state vector prediction, combined with the gain of the extended Kalman filter algorithm; and smoothing the battery state of charge residual after each extended Kalman filter algorithm update, and realizing online self-calibration of model parameters based on the residual change trend. Specifically, the system state equation is a mathematical expression describing the dynamic changes of the battery state of charge and internal resistance; the measurement equation is a state verification model constructed based on battery voltage observations; the battery terminal voltage is the measured voltage value at the lithium battery output port; the state vector is a set of variables to be estimated, including the battery state of charge and internal resistance; the voltage estimate is the theoretical value of the terminal voltage calculated through the state equation; the battery state of charge residual is the difference in state variables before and after the extended Kalman filter update; and the online self-calibration is the process of dynamically adjusting model parameters based on operating data. Specifically, a battery state-of-charge (POC) and internal resistance evolution equation, including the effective capacity after temperature correction, is constructed, and a nonlinear observation model of terminal voltage and state vector is established. The environmentally calibrated effective capacity parameters are input into the state equation, and the theoretical value of the battery terminal voltage is calculated using the extended Kalman filter algorithm. The state vector estimation result is iteratively optimized by measuring the difference between the voltage and the theoretical value in real time and by using the Kalman gain matrix. After each state update, the trend analysis of the POC estimation residual is performed, and the internal resistance parameter is dynamically adjusted according to the residual change to achieve real-time model calibration.

[0029] Furthermore, the preset temperature correction function is obtained through the following methods: collecting supercapacitor pulse current response characteristic data and corresponding real ambient temperature data on a cloud server; calculating the actual temperature difference between the supercapacitor current response characteristic data and the real ambient temperature; training a model on the relationship between the current response characteristic data and the actual temperature difference using a machine learning algorithm; sending the trained simplified model parameters to the container monitoring terminal; and calculating the temperature compensation value on the container monitoring terminal side using the simplified model parameters and the real-time collected supercapacitor pulse current response characteristic data. Specifically, multiple sets of supercapacitor pulse current response characteristics at different temperatures and corresponding high-precision thermometer measurements are collected on the cloud platform, and the correlation offset value between the capacitor current characteristics and the real temperature is calculated; a machine learning model is used to train the mapping relationship between the current characteristics and the temperature offset to generate a temperature compensation function; the key parameters of the function are sent to the terminal device, and the device calculates the temperature compensation amount based on the real-time current characteristics using these parameters during operation.

[0030] Furthermore, S5 specifically includes: real-time acquisition of temperature and humidity data inside the container to form a time series; calculation of short-term trends in the temperature and humidity time series using an exponentially weighted moving average algorithm; analysis of the rate and direction of change of the short-term trends to predict changes in environmental temperature and humidity within a specific future time period; setting a risk level based on the predicted magnitude of environmental temperature and humidity changes and whether they exceed a predetermined threshold, combined with the current battery charge state; and forcibly entering the highest risk level when an abnormal signal of water immersion or smoke is detected. Specifically, temperature and humidity data inside the container are continuously acquired to form a time series record, and an exponentially weighted moving average algorithm is used to predict the direction and magnitude of recent temperature and humidity changes; the degree of deviation of the prediction results and whether they exceed a preset safety range are analyzed, and the system risk level is divided based on the current battery charge state; when a smoke sensor or liquid sensor triggers an alarm, the system directly switches to the highest risk level and activates the emergency plan.

[0031] Furthermore, the power consumption control strategy for the terminal device also includes: dynamically configuring the data upload cycle of the communication and positioning module according to the risk level and battery charge state; prioritizing core communication functions and reducing the upload volume of non-critical data when the risk level increases; and using a supercapacitor to provide transient high-current power supply to the communication and positioning module to ensure emergency information reporting capabilities when the battery charge state is extremely low. Specifically, the data transmission interval of the communication and positioning module is dynamically extended or shortened according to the risk level and battery charge state; the core communication link is prioritized and the upload frequency of non-critical sensor data is reduced when the risk escalates; and the transient power supply function of the supercapacitor is activated when the battery power is extremely low to ensure the forced transmission capability of emergency alarm information.

[0032] Furthermore, in S6, the dynamic adjustment of the terminal device's sleep cycle, load scheduling strategy, and communication strategy is implemented in the following ways: setting the sleep cycle duration according to the risk level; adjusting the sampling frequency of non-critical sensor modules; reducing the duty cycle of the communication and positioning modules; and activating the supercapacitor-enhanced power supply mode when the battery state of charge is below a predetermined value or the risk level is high. Specifically, the device sleep duration is set according to the risk level; the sampling density of non-core sensors is reduced hierarchically; the operating time ratio of the secondary communication modules is reduced; and the supercapacitor-enhanced power supply strategy is activated to share the load of critical modules when the risk increases or the power is insufficient.

[0033] Furthermore, the dynamic adjustment of the power supply allocation ratio based on load current in S6 specifically includes: real-time monitoring of the total load current of the terminal device; calculating the current ratio that the supercapacitor should bear when the total load current exceeds a predetermined threshold; calculating the actual output current of the supercapacitor and the actual output current of the lithium-ion battery according to the current ratio and the total load current; controlling the supercapacitor and lithium-ion battery to supply power according to the actual output current; and shutting down all non-critical functional modules and maintaining power supply only to the communication and positioning module when an abnormal signal of water immersion or smoke is detected and the risk level is at its highest. Specifically, the total operating current of the terminal is monitored in real time; when the total current exceeds a specific limit, the output current quota of the supercapacitor and lithium battery is allocated according to a preset ratio; the physical hardware is driven to output the corresponding current according to the quota; and in the highest risk mode, only the communication and positioning power supply is maintained and other modules are forcibly shut down.

[0034] Furthermore, the present invention also proposes a computer-readable storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps of the adaptive control method of the container monitoring terminal.

[0035] Furthermore, this invention proposes a power prediction and environmental condition adaptive control system for a container monitoring terminal, comprising: a data acquisition module, a correction module, a calculation module, an estimation module, a prediction module, and a control module. The data acquisition module is used to acquire the pulse current response characteristics of a supercapacitor and the ambient temperature; the correction module is used to calculate a temperature indicator factor based on the pulse current response characteristics of the supercapacitor and compensate for the ambient temperature to obtain an effective temperature; the calculation module is used to correct the effective capacity of the lithium-ion battery based on the effective temperature and acquire the lithium-ion battery current to perform coulomb integration to update the battery state of charge; the estimation module is used to use an extended Kalman filter algorithm to jointly estimate the battery state of charge and battery internal resistance based on the corrected effective capacity; the prediction module is used to perform trend analysis on the time series of ambient temperature and humidity to predict future environmental changes and calculate the risk level; the control module is used to dynamically adjust the terminal equipment's sleep cycle, load scheduling strategy, communication strategy, and power supply allocation ratio according to the risk level and battery state of charge. The system comprises the following modules: an acquisition module (hardware circuitry for acquiring supercapacitor current pulse characteristics and ambient temperature signals); a correction module (processor unit for calculating temperature indicator factors and compensating for ambient temperature); a calculation module (computation unit for updating battery effective capacity and state of charge values); an estimation module (state estimation coprocessor running an extended Kalman filter algorithm); a prediction module (analysis unit for analyzing environmental trends and assessing risk levels); and a control module (decision-making unit for scheduling equipment hibernation, load allocation, and communication strategies). Specifically, the acquisition module continuously acquires the dynamic current characteristics of the supercapacitor and the original ambient temperature value; the correction module corrects the original temperature by calculating current pulse characteristic parameters to generate an effective temperature; the calculation module updates the battery capacity model based on the effective temperature and simultaneously refreshes the state of charge by integrating the lithium battery current; the estimation module uses an extended Kalman filter algorithm to jointly optimize the state of charge and internal resistance estimation based on the temperature-corrected system parameters; the prediction module analyzes historical temperature and humidity data to predict environmental evolution trends and sets risk levels; and the control module dynamically adjusts hibernation time, load task allocation ratio, communication data transmission rules, and the collaborative power supply intensity of the supercapacitor and lithium battery based on the risk level and battery state of charge.

[0036] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An adaptive control method for a container monitoring terminal, characterized in that, include: S1. Collect the pulse current response characteristics of the supercapacitor and the ambient temperature; S2. Calculate the temperature indication factor based on the pulse current response characteristics of the supercapacitor, and use the temperature indication factor to compensate for the ambient temperature to obtain the corrected effective temperature. S3. Based on the effective temperature, correct the effective capacity of the lithium-ion battery, and collect the lithium-ion battery current to perform coulomb integration to update the battery state of charge. S4. Using the extended Kalman filter algorithm, the battery state of charge and battery internal resistance are jointly estimated based on the corrected effective capacity. S5. Perform trend analysis on the time series of ambient temperature and humidity, predict future environmental changes and calculate risk levels; S6. Based on the risk level and battery state of charge, dynamically adjust the sleep cycle, load scheduling strategy and communication strategy of the terminal device, and dynamically adjust the power distribution ratio between the supercapacitor and the lithium-ion battery based on the load current.

2. The adaptive control method for the container monitoring terminal as described in claim 1, characterized in that, S2 specifically includes: Extract the current response characteristics of supercapacitors under pulsed load, including the current rise rate and peak current within a specific time window; Calculate the multidimensional temperature indication factor based on the aforementioned current response characteristics; The multidimensional temperature indicator factor is converted into a temperature compensation value using a preset temperature correction function. The corrected effective temperature is calculated by combining the temperature compensation value with the ambient temperature measurement value.

3. The adaptive control method for the container monitoring terminal as described in claim 2, characterized in that, S4 specifically includes: Establish the system state equations that include the battery state of charge and battery internal resistance; Establish a measurement equation with the battery terminal voltage as the measured value; Update the system state equations using the corrected effective capacity; The system state vector is updated by utilizing the deviation between the actual observed value of the battery terminal voltage and the voltage estimate based on the state vector prediction, combined with the gain of the extended Kalman filter algorithm. After each update of the extended Kalman filter algorithm, the residual of the battery state of charge is smoothed, and the online self-calibration of the model parameters is achieved based on the trend of residual change.

4. The adaptive control method for the container monitoring terminal as described in claim 2, characterized in that, The preset temperature correction function is obtained in the following way: Collect supercapacitor pulse current response characteristic data and corresponding real ambient temperature data on a cloud server; Calculate the actual temperature difference between the supercapacitor current response characteristic data and the real ambient temperature; The relationship between the current response characteristic data and the actual temperature difference is modeled using machine learning algorithms. The simplified model parameters obtained from the training are sent to the container monitoring terminal; On the container monitoring terminal side, the temperature compensation value is calculated using the simplified model parameters and the real-time collected supercapacitor pulse current response characteristic data.

5. The adaptive control method for the container monitoring terminal as described in claim 1, characterized in that, S5 specifically includes: Real-time collection of temperature and humidity data inside the container to form a time series; The temperature and humidity time series were calculated using an exponentially weighted moving average algorithm. Analyze the rate and direction of the short-term trend changes to predict the changes in environmental temperature and humidity over a specific future time period; Based on the predicted changes in ambient temperature and humidity and whether they exceed predetermined thresholds, the risk level is set in conjunction with the current state of battery charge. When an abnormal signal of water immersion or smoke is detected, the system will be forced to enter the highest risk level.

6. The adaptive control method for the container monitoring terminal as described in claim 5, characterized in that, The power consumption control strategy for the terminal device also includes: The data upload cycle of the communication and positioning module is dynamically configured according to the risk level and battery charge status. When the risk level rises, prioritize ensuring core communication functions and reduce the amount of non-critical data being uploaded. When the battery is at a very low state of charge, a supercapacitor is used to provide transient high current power to the communication and positioning module in order to ensure the ability to report emergency information.

7. The adaptive control method for the container monitoring terminal as described in claim 1, characterized in that, In S6, the dynamic adjustment of the sleep cycle, load scheduling strategy, and communication strategy of terminal devices is implemented in the following ways: Set the dormancy period duration according to the risk level; Adjust the sampling frequency of non-critical sensor modules; Reduce the duty cycle of the communication positioning module; The supercapacitor-enhanced power supply mode is activated when the battery state of charge is below a predetermined value or the risk level is high.

8. The adaptive control method for the container monitoring terminal as described in claim 1, characterized in that, The dynamic adjustment of power distribution ratio based on load current in S6 specifically includes: Real-time monitoring of the total load current of terminal devices; When the total load current exceeds a predetermined threshold, calculate the proportion of current that the supercapacitor should bear; Calculate the actual output current of the supercapacitor and the actual output current of the lithium-ion battery based on the current ratio and total load current. Control the supercapacitor and lithium-ion battery to supply power according to the actual output current; When an abnormal signal of water immersion or smoke is detected and the risk level is at its highest, all non-critical functional modules are shut down, and only the communication and positioning modules are kept powered.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the adaptive control method for the container monitoring terminal as described in any one of claims 1 to 8.

10. A power prediction and environmental condition adaptive control system for a container monitoring terminal, characterized in that, include: The data acquisition module is used to collect the pulse current response characteristics of the supercapacitor and the ambient temperature. The correction module is used to calculate the temperature indication factor based on the pulse current response characteristics of the supercapacitor and compensate for the ambient temperature to obtain the effective temperature. The calculation module is used to correct the effective capacity of the lithium-ion battery based on the effective temperature and to collect the lithium-ion battery current to perform coulomb integration and update the battery state of charge. The estimation module is used to jointly estimate the battery state of charge and battery internal resistance based on the corrected effective capacity using the extended Kalman filter algorithm. The prediction module is used to perform trend analysis on the time series of environmental temperature and humidity to predict future environmental changes and calculate risk levels; The control module is used to dynamically adjust the terminal device's sleep cycle, load scheduling strategy, communication strategy, and power supply allocation ratio according to the risk level and battery state of charge.

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