Low-power-consumption remote monitoring method and system for vehicle

Abstract

The invention relates to the field of vehicle monitoring, in particular to a low-power-consumption remote monitoring method and system for a vehicle. The method comprises the following steps: acquiring acquired vehicle multi-axis acceleration, voltage and temperature data; in response to the wake-up trigger signal, constructing a multi-axis acceleration sequence in a preset time window, and calculating an environmental interference suppression factor representing a continuous fluctuation degree of external mechanical vibration according to fluctuation characteristics of the multi-axis acceleration sequence; calculating a vehicle vulnerability degree representing a nonlinear degradation risk of the vehicle-mounted battery based on the voltage variation and temperature data of the battery between two adjacent wake-up states; and acquiring the previous actual sleep time, calculating the periodic scaling according to the environmental interference suppression factor and the vehicle vulnerability degree, taking the product of the periodic scaling and the previous actual sleep time as the latter sleep time, and configuring the latter sleep time to a comparison register of the clock peripheral to control the sleep duration. The method and the device have the effect of realizing self-adaptive low-power-consumption control.

Description

Technical Field

[0001] This application relates to the field of vehicle monitoring, and more particularly to a low-power remote monitoring method and system for vehicles. Background Technology

[0002] With the development of vehicle intelligence and connectivity, vehicles heavily rely on onboard remote monitoring terminals for status tracking and safety warnings during long-term parking or long-distance transport. Current technologies typically employ fixed-time wake-up or sensor interruption wake-up mechanisms based on static physical thresholds. In long-term parking scenarios, high-frequency fixed wake-ups can lead to a rapid depletion of the vehicle's low-voltage battery or power battery (discharge). Conversely, fixed-threshold sensor wake-up mechanisms are prone to false wake-ups under complex environmental interference such as wind loads or large vehicles passing by, resulting in significant redundant data transmission overhead and severely impacting vehicle standby power consumption. Furthermore, rigid timing strategies ignore the non-linear degradation of onboard batteries caused by sudden temperature drops or the passage of time, easily leading to severe battery depletion and vehicle disconnection. Therefore, a remote monitoring method that can overcome the inherent technical contradiction between "high-frequency reliable monitoring" and "extremely low standby power consumption" is urgently needed. Summary of the Invention

[0003] To address the aforementioned technical problems, this application provides a low-power remote monitoring method and system for vehicles.

[0004] Firstly, this application provides a low-power remote monitoring method for vehicles, employing the following technical solution: A low-power remote monitoring method for a vehicle includes the following steps: acquiring collected multi-axis acceleration, voltage, and temperature data of the vehicle; in response to a wake-up trigger signal, constructing a multi-axis acceleration sequence within a preset time window, and calculating an environmental interference suppression factor representing the degree of continuous fluctuation of external mechanical vibration based on the fluctuation characteristics of the multi-axis acceleration sequence; calculating the vehicle vulnerability level representing the nonlinear degradation risk of the on-board battery based on the voltage change and temperature data of the battery between two adjacent wake-up states; acquiring the previous actual sleep time, calculating a period scaling ratio based on the environmental interference suppression factor and the vehicle vulnerability level, multiplying the period scaling ratio by the previous actual sleep time as the next sleep time, and configuring the next sleep time into the comparison register of the clock peripheral to control the sleep duration.

[0005] Optionally, the method for calculating the environmental interference suppression factor is as follows: calculate the local average fluctuation intensity within any time window; calculate the instantaneous fluctuation intensity within the time window; perform negative correlation normalization on the product of the ratio of the local average fluctuation intensity to the instantaneous fluctuation intensity and the preset first hyperparameter to obtain a normalized value, and use the difference between 1 and the normalized value as the environmental interference suppression factor.

[0006] Optionally, the method for calculating the local average fluctuation intensity is as follows: obtain the instantaneous acceleration sequence within the time window, calculate the mean of all acceleration samples within the time window, and then calculate the average of the sum of squares of the deviations of each instantaneous acceleration from the mean, as the local average fluctuation intensity.

[0007] Optionally, the method for calculating the vehicle vulnerability is as follows: in the current wake-up state, calculate the severity of leakage current or power consumption leakage; obtain the temperature influence factor of the battery in the wake-up state, and use the product of the severity and the temperature influence factor as the vehicle vulnerability.

[0008] Optionally, the method for calculating the severity includes: calculating the voltage difference of the battery between the current wake-up state and the previous wake-up state; calculating the time difference between the start time of the current wake-up state and the previous wake-up state; and normalizing the absolute ratio of the voltage difference to the time difference as the severity.

[0009] Optionally, the absolute difference between the average battery temperature in the current wake-up state and the average temperature in all historical wake-up states can be calculated, and the normalized result of the absolute difference can be used as the temperature influence factor.

[0010] Optionally, the period scaling ratio is calculated based on the vehicle's vulnerability and the environmental interference suppression factor.

[0011] Secondly, this application provides a low-power remote monitoring system for vehicles, employing the following technical solution: A low-power remote monitoring system for a vehicle includes a processor and a memory, wherein the memory stores computer program instructions that, when executed by the processor, implement the low-power remote monitoring method for a vehicle as described above.

[0012] This application has the following technical effects: 1. To address the issue of false wake-ups caused by fixed thresholds, this application constructs an environmental interference suppression factor. Utilizing the ratio of local average fluctuation intensity to instantaneous fluctuation intensity, it accurately distinguishes between "persistent environmental interference" and "instantaneous collision events." This algorithm eliminates invalid wake-ups caused by environmental interference present in existing technologies, significantly reducing standby power consumption and communication overhead under complex operating conditions. It effectively suppresses false wake-ups due to environmental interference and significantly reduces standby power consumption.

[0013] 2. By integrating the voltage change rate and temperature deviation between adjacent wake-up cycles, the vehicle vulnerability assessment can identify risks of internal resistance mutations, low-temperature activity decline, or nonlinear degradation caused by aging before the battery voltage reaches the hardware protection threshold. This provides an early warning window for the remote operation and maintenance platform, reducing the probability of vehicle-wide disconnection caused by "cliff-like power loss" and improving fleet management security.

[0014] 3. The boundedness of the hyperbolic tangent function constrains the scaling ratio within a safe range, preventing drastic oscillations in sleep time due to parameter mutations. Simultaneously, its smoothing properties during gradual input changes enable the system to dynamically adjust the duty cycle based on vehicle status (vulnerability) and environmental conditions (interference factors). This adaptive mechanism avoids power waste caused by fixed cycles and prevents monitoring blind spots due to excessive sleep, achieving an optimal balance between power consumption and reliability. Attached Figure Description

[0015] Figure 1 This is a flowchart of a low-power remote monitoring method for a vehicle according to an embodiment of this application.

[0016] Figure 2 This is a flowchart illustrating the method for calculating the environmental interference suppression factor in a low-power remote monitoring method for vehicles according to an embodiment of this application.

[0017] Figure 3 This is a flowchart of a method for calculating the vulnerability of a vehicle in a low-power remote monitoring method according to an embodiment of this application. Detailed Implementation

[0018] This application discloses a low-power remote monitoring method for vehicles, applicable to remote status monitoring and low-power power management of logistics vehicles / new energy vehicles during long-term parking, pausing, or long-distance transportation. (Refer to...) Figure 1 The process includes steps S1-S4, as detailed below: S1: Acquire the collected vehicle multi-axis acceleration, voltage, and temperature data.

[0019] In logistics vehicles and new energy vehicles that are parked, stationary, or on long-distance transport routes for extended periods, the vehicles are in a complex physical state. The external environment presents highly uncontrollable mechanical interferences (such as port gusts or ground shock waves generated by passing heavy trucks), which continuously inject mechanical waves into the vehicle body, attempting to reactivate the system. Furthermore, the core energy reserves inside the vehicle (low-voltage batteries or high-voltage power batteries) undergo non-linear degradation of their electrochemical activity under low-temperature or aging conditions, posing a constant risk of sudden and precipitous power loss.

[0020] Therefore, this invention collects vehicle monitoring data from multiple dimensions to facilitate subsequent analysis and further achieve low-power vehicle monitoring.

[0021] In one embodiment, vehicle triaxial acceleration data can be collected by installing triaxial accelerometers on the rigid load-bearing structure of the vehicle chassis (such as the frame longitudinal beams), with each sampling point containing data for the XYZ axes; voltage data can be collected by using a voltage acquisition circuit connected in parallel to the positive and negative terminals of the vehicle's main control system battery; and temperature data can be collected by installing a temperature sensor on the battery. The acquisition frequency can be set to 50Hz. At this point, vehicle monitoring data acquisition is complete.

[0022] It should be noted that the low-power remote monitoring system of this application supports a multi-source wake-up mechanism in its main control microcontroller. In one embodiment, the wake-up trigger signal includes, but is not limited to, a sensor interrupt signal, and may also include a timer matching signal. The system is configured with dual wake-up sources: one is a sensor interrupt wake-up source, used to respond to external mechanical vibration events to ensure that the system can promptly capture collisions or abnormal movements (corresponding to vibration analysis in step S2); the other is a timer wake-up source, used to provide the system's basic heartbeat cycle, ensuring that the system can still periodically wake up even when the vehicle is stationary for a long time without vibration. Regardless of which wake-up source triggers the system, after entering the wake-up state, the complete process of steps S1 to S4 will be executed, thereby ensuring the real-time and continuous calculation of battery vulnerability and avoiding the loss of battery status monitoring due to long-term lack of vibration.

[0023] S2: In response to the wake-up trigger signal, construct a multi-axis acceleration sequence within a preset time window, and calculate the environmental disturbance suppression factor representing the degree of continuous fluctuation of external mechanical vibration based on the fluctuation characteristics of the multi-axis acceleration sequence.

[0024] After the sensor is triggered, a time window of size N is divided to continuously acquire N multi-axis acceleration data, i.e., continuously acquire the instantaneous acceleration sequence of the multi-axis accelerometer; for example, N=200. Specifically, the sensor is triggered when the multi-axis accelerometer, in a low-power listening state, detects an external mechanical vibration signal transmitted by the vehicle body through its underlying hardware circuitry. When the transient analog quantity of this mechanical vibration signal exceeds the hardware-level wake-up baseline preset in the sensor's internal register (i.e., the sensor's built-in low-power motion interruption threshold), the sensor's interrupt pin level physically flips (e.g., from a low level to a high level pulse). At this time, the sensor outputs the asynchronous interrupt signal to the main control microcontroller. Upon receiving this signal, the system recognizes that it is in a "triggered state" and wakes up the analog-to-digital converter to enter the high-speed data acquisition mode.

[0025] In one embodiment, reference Figure 2 The calculation method for the environmental disturbance inhibition factor includes steps S20-S22, specifically: S20: Calculate the local average fluctuation intensity within any time window.

[0026] In one embodiment, the method for calculating the local average fluctuation intensity is as follows: obtain the instantaneous acceleration sequence within the time window, calculate the mean of all acceleration samples within the time window, and then calculate the average of the sum of squares of the deviations of each instantaneous acceleration from the mean, which is used as the local average fluctuation intensity.

[0027] Local average fluctuation intensity The mathematical expression can be: ;in, Indicates the size of the time window, and the time for data collection during each wake-up. No more data will be collected after the first sampling point; Indicates the first Within the time window of the next wake-up state The sampling point of the first sampling point Instantaneous acceleration data in each dimension; Indicates the first time within this time window The mean of all acceleration samples in each dimension; It represents any one of the three dimensions X, Y, and Z. Indicates the total number of dimensions.

[0028] S21: Calculate the instantaneous fluctuation intensity within this time window.

[0029] Specifically, the mathematical expression for the instantaneous fluctuation intensity can be: ;in, Indicates the first Within the time window of the next wake-up state The sampling point of the first sampling point Instantaneous acceleration data in each dimension. Indicates the first Within the time window of the next wake-up state The sum acceleration of each sampling point This represents the maximum value function. It represents any one of the three dimensions X, Y, and Z. Indicates the total number of dimensions.

[0030] S22: The product of the ratio of local average fluctuation intensity to instantaneous fluctuation intensity and the preset first hyperparameter is negatively correlated and normalized to obtain a normalized value. The difference between 1 and the normalized value is used as the environmental disturbance suppression factor.

[0031] Specifically, the environmental disturbance inhibition factor can be expressed as: , This represents an exponential function with base e. This represents the first hyperparameter, preset. ,avoid Too large This is the second hyperparameter. To prevent the denominator from being 0, it can be set to 0.01, for example.

[0032] When the external environment is subjected to continuous wind load or engine resonance, the acceleration fluctuates significantly around the mean, causing the local average fluctuation intensity to increase. The magnitude is relatively large, but lacks a prominent peak, resulting in a smaller instantaneous fluctuation intensity. The smaller value makes the environmental disturbance suppression factor approach 1; when a single real collision occurs, the instantaneous fluctuation intensity The acceleration increases rapidly, while the other accelerations remain relatively stable, i.e., the local average fluctuation intensity. The smaller the value, the closer the environmental interference inhibition factor is to 0.

[0033] This completes the calculation of the environmental interference suppression factor in any wake-up state.

[0034] S3: Based on the voltage change and temperature data of the battery between two adjacent wake-up states, calculate the vehicle vulnerability, which represents the non-linear degradation risk of the on-board battery.

[0035] Specifically, the execution of step S3 is independent of any specific wake-up source type. When the system is woken up by a timer, the primary focus is on executing step S3 to assess battery health and the risk of non-linear degradation. When the system is woken up by a sensor interruption, steps S2 and S3 are executed in parallel to assess both environmental interference and battery vulnerability. Preferably, step S3 is executed in every wake-up state, using the battery voltage change and temperature data between two adjacent wake-up states (regardless of whether the interval is controlled by a timer or triggered by a sensor) to calculate the vehicle's vulnerability. This mechanism ensures that even during periods of inactivity without vibration, the system can be woken up by a timer to force the execution of S3, promptly detecting battery leakage or aging risks.

[0036] During long-term dormant monitoring, the health of the battery (low-voltage or high-voltage power battery) changes as a non-linear electrochemical degradation process. Existing technologies typically determine whether the system needs to report an emergency by comparing the current voltage with a manually set "minimum protection voltage threshold." However, in low-temperature environments or under severe battery aging conditions, the battery's internal resistance undergoes a drastic non-linear abrupt change, causing the battery to fail completely due to a sudden voltage drop (voltage cliff) before reaching the "safe voltage threshold," resulting in power loss for the monitoring terminal and loss of connection for the entire vehicle. Furthermore, assessing only the internal state without considering the external environment can easily miss extremely subtle signs of internal anomalies when external interference is strong. Therefore, this application captures the dynamic changes in battery terminal voltage over time to calculate the vehicle's vulnerability, enabling the system to perceive its own physical trend towards power loss or thermal runaway in real time.

[0037] Reference Figure 3 The method for calculating the vulnerability of a vehicle is as follows: S30: In the current wake-up state, calculate the severity of leakage current or power consumption leakage.

[0038] The severity calculation method includes: calculating the voltage difference of the battery between the current wake-up state and the previous wake-up state; calculating the time difference between the start time of the current wake-up state and the previous wake-up state; and normalizing the absolute ratio of the voltage difference to the time difference as the severity.

[0039] The mathematical expression for severity can be: , Indicates the first Voltage during the second wake-up state Indicates the first Voltage during the second wake-up state Table 1 The start time of the next wake-up state; Indicates the first The start time of the next wake-up state.

[0040] The standard normalization function can be Min-Max normalization, and the specific formula is as follows: ;in, and These are theoretical boundary values ​​preset based on the vehicle's physical characteristics. For example, for the rate of voltage change, It can be set to 0. This can be set to the voltage change value corresponding to the battery's maximum allowable discharge rate; for the temperature difference value calculated subsequently, It can be set to 0. This can be set as the maximum permissible temperature difference threshold for vehicle operation. If the calculated result exceeds... The range is then truncated to 0 or 1.

[0041] S31: Obtain the temperature impact factor of the battery in the wake-up state, and use the product of the severity and the temperature impact factor as the vehicle vulnerability level.

[0042] Calculate the absolute difference between the average battery temperature in the current wake-up state and the average temperature in all historical wake-up states. Use the normalized result of this absolute difference as the temperature influence factor. The mathematical expression for the temperature influence factor can be: , Represents the standard normalized function. Indicates the first Average battery temperature during the first wake-up state This represents the average battery temperature across all historical wake-up states. The greater the temperature difference, the further the current wake-up deviates from the normal state, meaning the more fragile the system is.

[0043] In one embodiment, to avoid memory overflow caused by storing all historical data, The recursive moving average method is used for updating. Historical average temperature after the second wake-up The calculation formula is: ;in, For the first The historical average temperature stored after each wake-up. For the first The average battery temperature collected during the second wake-up. The system only needs to maintain one variable in memory. and counting This allows for infinitely long-term mean updates.

[0044] S4: Obtain the previous actual sleep time, calculate the cycle scaling ratio based on the environmental interference suppression factor and the vehicle's vulnerability, multiply the cycle scaling ratio by the previous actual sleep time as the next sleep time, and configure the next sleep time into the clock peripheral's compare register to control the sleep duration.

[0045] Specifically, the clock peripheral's compare register is used not only to control the sleep duration, but the compare-match event it generates also serves as the timer wake-up source signal. When the sleep duration countdown reaches zero, the clock peripheral generates an interrupt signal to wake up the main microcontroller. This is considered a wake-up state triggered by the timer. Subsequently, the system resets the timer and enters the configuration for the next sleep cycle. By dynamically adjusting the value of the compare register in step S4, the system can adaptively change the timer wake-up interval, achieving a balance between "high-frequency reliable monitoring" and "high-frequency reliable monitoring".

[0046] Existing technologies typically follow a preset static baseline sleep cycle when handling the sleep scheduling of remote terminals. However, during long-term vehicle parking and logistics transportation, the physical degradation of on-board batteries exhibits a non-linear accelerating trend. No static time baseline can adapt to the extremely deep boundary conditions where the overall battery power tends to be depleted in the later stages of vehicle parking. This reliance on a static baseline inevitably leads to system overload during frequent external interference, resulting in unnecessary power consumption, or into monitoring blind spots due to excessively long sleep cycles when the system is on the verge of power failure.

[0047] It should be noted that the low-power remote monitoring system of the present invention operates on an alternating "wake-up (processing) - sleep" duty cycle. The microcontroller's timeline is formed by splicing the start and end of the wake-up state time window and the sleep state time window (wake-up and sleep occur in pairs; in this application, ...). This indicates the ordinal number of the wake-up state, or the ordinal number of the sleep state, i.e., the [number of wake-up]. Group alternation, including the first Secondary hibernation and the first (Second wake-up).

[0048] The mathematical expression for the next sleep time can be: ; Indicates the next sleep time (i.e., the first sleep time) (duration of hibernation period) This indicates the time elapsed since the previous sleep session (i.e., the time elapsed since the last sleep session). (duration of each sleep period); Indicates the first Cycle scaling ratio in the next wake-up state.

[0049] Specifically, the calculation involves the period scaling ratio. The mathematical expression can be: ,in, Indicates the first The vulnerability of the vehicle in the second wake-up state; Indicates the first Environmental interference suppression factor in the second wake-up state; This represents the third hyperparameter, which can be 0.5. This represents the hyperbolic tangent function.

[0050] Vehicle vulnerability The lower the value, the more normal the vehicle condition, and the longer the expected sleep time, corresponding to the cycle scaling ratio. The larger (because in the formula) The preceding sign indicates a monotonically decreasing relationship; hyperparameters A value of 0.5 is used as the center offset reference to ensure that, under ideal operating conditions where the vehicle is in normal condition and the environment is stable, Within a moderate amplification range, it avoids both excessively short sleep cycles leading to frequent wake-ups and energy consumption, and excessively long cycles causing the omission of critical state changes; and the introduction of a hyperbolic tangent function The core purpose is to utilize the boundedness of its output. Constrain the scaling ratio to Within a safe range, its smooth nonlinearity and saturation characteristics enable the cycle adjustment to respond gently and continuously when the vehicle state or environment changes gradually, and automatically limit the amplitude under extreme inputs, ensuring the robustness and safety of the system's sleep strategy.

[0051] At the beginning of step S1, system initialization is required, and the default sleep time is set. For example, That is, 30 minutes to 2 hours, in actual deployment. It can be configured as a writable parameter, dynamically adjusted via remote commands or local calibration, without needing to be fixed in the code.

[0052] For the first wake-up ( Since there is no previous wake-up state ( The system executes the following initialization logic: Voltage difference calculation: Set the virtual initial voltage. This ensures that the initial severity calculation result is 0 or skips the severity calculation, directly using the default vulnerability value. Time difference calculation: Sets the virtual initial time. Alternatively, a preset standard interval can be used. Historical temperature average: Set initial value. The vehicle's nominal operating temperature (e.g., 25°C).

[0053] Previous actual hibernation time: Retrieve default initial values Through the above initialization settings, ensure that the formula... exist It can operate normally immediately without waiting for a second wake-up. Upon initial power-on, it sleeps for one hour before the timer wakes it up to perform battery status assessment and environmental interference analysis. Subsequent operations are then performed according to the formula. Adaptive adjustment.

[0054] This application also discloses a low-power remote monitoring system for vehicles, including a processor and a memory. The memory stores computer program instructions, which, when executed by the processor, implement a low-power remote monitoring method for vehicles according to this application.

[0055] The system also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the settings and functions of which are known in the art and will not be described in detail here.

[0056] In this application, the aforementioned memory can be any tangible medium that contains or stores a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory, dynamic random access memory, static random access memory, etc., or any other medium that can be used to store required information and can be accessed by an application program, module, or both.

[0057] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A low-power remote monitoring method for vehicles, characterized in that, Including the following steps: Acquire multi-axis acceleration, voltage, and temperature data of the vehicle; In response to the wake-up trigger signal, a multi-axis acceleration sequence within a preset time window is constructed, and an environmental disturbance suppression factor representing the degree of continuous fluctuation of external mechanical vibration is calculated based on the fluctuation characteristics of the multi-axis acceleration sequence. Based on the voltage change and temperature data of the battery between two adjacent wake-up states, the vehicle vulnerability level, which represents the nonlinear degradation risk of the on-board battery, is calculated. Obtain the previous actual sleep time, calculate the cycle scaling ratio based on the environmental interference suppression factor and the vehicle's vulnerability level, multiply the cycle scaling ratio by the previous actual sleep time as the next sleep time, and configure the next sleep time into the clock peripheral's compare register to control the sleep duration.

2. The low-power remote monitoring method for vehicles according to claim 1, characterized in that, in, The method for calculating the environmental interference suppression factor is as follows: Calculate the local average fluctuation intensity within any time window; Calculate the instantaneous fluctuation intensity within this time window; The product of the ratio of local average fluctuation intensity to instantaneous fluctuation intensity and the preset first hyperparameter is negatively correlated and normalized to obtain a normalized value. The difference between 1 and the normalized value is used as the environmental disturbance suppression factor.

3. The low-power remote monitoring method for vehicles according to claim 2, characterized in that, The method for calculating the local average fluctuation intensity is as follows: obtain the instantaneous acceleration sequence within the time window, calculate the mean of all acceleration samples within the time window, and then calculate the average of the sum of squares of the deviations of each instantaneous acceleration from the mean, which is taken as the local average fluctuation intensity.

4. The low-power remote monitoring method for vehicles according to claim 1, characterized in that, The method for calculating the vehicle vulnerability level is as follows: in the current wake-up state, calculate the severity of leakage current or power consumption leakage; obtain the temperature influence factor of the battery in the wake-up state, and use the product of the severity level and the temperature influence factor as the vehicle vulnerability level.

5. The low-power remote monitoring method for vehicles according to claim 4, characterized in that, The method for calculating the severity includes: calculating the voltage difference between the battery in the current wake-up state and the previous wake-up state; calculating the time difference between the start time of the current wake-up state and the previous wake-up state; and normalizing the absolute ratio of the voltage difference to the time difference as the severity.

6. The low-power remote monitoring method for vehicles according to claim 4, characterized in that, Calculate the absolute difference between the average battery temperature in the current wake-up state and the average temperature in all historical wake-up states, and use the normalized result of the absolute difference as the temperature influence factor.

7. The low-power remote monitoring method for vehicles according to claim 1, characterized in that, The period scaling ratio is calculated based on the vehicle's vulnerability level and environmental interference suppression factor.

8. A low-power remote monitoring system for vehicles, characterized in that, include: A processor and a memory, the memory storing computer program instructions that, when executed by the processor, implement the low-power remote monitoring method for a vehicle according to any one of claims 1-7.

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