Method and system for monitoring power consumption of smart warning device based on internet of things

Abstract

The present application relates to the technical field of data processing, and especially relates to a power consumption monitoring method and system of an intelligent warning device based on the Internet of Things, wherein the method obtains a static basic reporting period according to signal quality of a region where a target vehicle monitored by the intelligent warning device is located at a current time and a minimum average reporting period allowed by the intelligent warning device; the static basic reporting period and a preset retransmission number are adaptively adjusted according to signal quality of a communication link of the intelligent warning device, network congestion, a time when the target vehicle fails, acceleration of the target vehicle, and data of various state monitoring indexes of the intelligent warning device at the current time, so as to obtain an adaptive reporting period and an adaptive retransmission number, and then complete next power consumption data reporting, thereby improving accuracy of power consumption monitoring of the intelligent warning device while ensuring working power and data timeliness of the intelligent warning device.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for monitoring the power consumption of intelligent warning devices based on the Internet of Things. Background Technology

[0002] With the deep application of IoT technology in road construction and traffic safety, vehicle-mounted intelligent warning devices have become indispensable on-site protection terminals in scenarios such as nighttime construction and accident towing. However, these devices are typically battery-powered, installed on the exterior of vehicles, and exposed to harsh environments such as high temperatures, vibrations, and rain for extended periods. Furthermore, once a vehicle is towed to a remote location or enters a weak signal area such as an underground parking garage, replacing and recharging the intelligent warning device becomes extremely difficult. If the intelligent warning device runs out of power prematurely due to improper power management, a safety blind spot will immediately form on-site, easily triggering a chain reaction of accidents during high-risk periods such as nighttime construction, heavy rain, and fog. Simultaneously, if the power consumption status and remaining battery level of the intelligent warning device cannot be transmitted back to the management platform in a timely and accurate manner, maintenance personnel will find it difficult to predict the risk of running out of power in advance, often only discovering the problem passively after the device has gone offline, missing the optimal window for maintenance and recovery. Therefore, it is necessary to minimize device communication power consumption, extend battery life, and reliably transmit power status information while ensuring timely warning response.

[0003] In the traditional approach, a fixed time interval reporting strategy is used. Designers set a constant reporting cycle based on the typical working scenario of the device, such as once every 60 seconds. After the device is powered on, the NB-IoT module wakes up at regular intervals according to this fixed cycle, packages and sends data such as GPS location, device status of smart alarm devices, instantaneous power consumption and historical average power consumption to the cloud platform, and immediately enters sleep mode after sending, waiting for the next cycle. While a fixed interval ensures the regularity and stability of data updates, in areas with weak NB-IoT signal coverage (such as mountainous areas, underground parking garages, and remote roads), smart warning devices attempt to report at fixed intervals. Due to the poor signal, each report often requires multiple retransmissions to succeed, causing the wireless communication module to repeatedly enter a high-power transmission state, resulting in a significant increase in power consumption. In severe cases, the battery may be depleted within hours. In areas with good signal, fixed-interval reporting may be too frequent (e.g., reporting every 60 seconds when the location has hardly changed), generating a large amount of redundant data and consuming unnecessary power. When the vehicle is moving quickly, the fixed interval may be too sparse, causing a lag in location updates and affecting the rescue team's ability to track the vehicle's movement. At the same time, maintaining high-frequency reporting when the battery is low can easily lead to premature power failure when a long-term warning is needed.

[0004] Therefore, how to adaptively set the reporting cycle to improve the accuracy of power consumption monitoring of intelligent warning devices while ensuring the working power and data timeliness of the devices has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a power consumption monitoring method and system for intelligent warning devices based on the Internet of Things, in order to solve the problem of how to adaptively set the reporting cycle, so as to improve the accuracy of power consumption monitoring of intelligent warning devices while ensuring the working power consumption and data timeliness of intelligent warning devices.

[0006] In a first aspect, embodiments of the present invention provide a method for monitoring the power consumption of an intelligent warning device based on the Internet of Things, the method comprising the following steps: Based on the signal quality of the area where the target vehicle is located at the current moment, as monitored by the intelligent warning device, and the minimum average reporting cycle allowed by the intelligent warning device, the static basic reporting cycle of the intelligent warning device at the current moment is obtained. Based on the signal quality and network congestion of the communication link of the intelligent warning device at the current moment, as well as the retransmission ratio in the preset number of reported data closest to the current moment, the channel quality coefficient at the current moment is obtained. Based on the time when the target vehicle most recently experienced a fault, the acceleration of the target vehicle at the time of the most recently reported data (a preset number of times), and the data of various status monitoring indicators of the intelligent warning device at the current time, the data timeliness requirement coefficient at the current time is obtained. Based on the remaining power of the intelligent warning device at the current moment, as well as the channel quality coefficient and the data timeliness requirement coefficient, the static reporting cycle and preset retransmission number are adaptively adjusted to obtain the adaptive reporting cycle and adaptive retransmission number. The next power consumption data report is completed according to the adaptive reporting cycle and adaptive retransmission number, thereby realizing the power consumption monitoring of the intelligent warning device.

[0007] Preferably, the step of obtaining the static basic reporting cycle of the intelligent warning device at the current moment based on the signal quality of the area where the target vehicle is located, as monitored by the intelligent warning device, and the minimum allowable average reporting cycle of the intelligent warning device includes: The total battery capacity, expected working time, and average energy consumption per data report of the intelligent warning device are obtained. The total battery capacity is used as the denominator, and the product of the expected working time and the average energy consumption is used as the numerator to obtain the minimum average reporting cycle allowed by the intelligent warning device. If there is a regional signal prior table for the area where the target vehicle is located at the current moment, as monitored by the intelligent warning device, the signal level of the area where the target vehicle is located at the current moment is obtained according to the regional signal prior table. The signal levels are good and poor. If the signal level of the area where the target vehicle is located at the current moment is good, the recommended benchmark period for regional historical signal quality is set to be equal to the minimum average reporting period. If the signal level of the area where the target vehicle is located at the current moment is poor, the minimum average reporting period is taken as the recommended benchmark period for regional historical signal quality by a preset multiple, where the preset multiple is greater than 1. If there is no signal strength distribution map of the area where the target vehicle is located at the current moment, as monitored by the intelligent warning device, then the recommended benchmark period for historical signal quality in the area is set to be equal to the minimum average reporting period. Based on the type of target vehicle, application scenario, performance of intelligent warning device, and regulatory requirements, the theoretical minimum reporting cycle of intelligent warning device is obtained. The median of the theoretical minimum reporting cycle, the minimum average reporting cycle, and the recommended baseline cycle of regional historical signal quality is obtained to obtain the static basic reporting cycle of intelligent warning device at the current moment.

[0008] Preferably, obtaining the channel quality coefficient at the current moment based on the signal quality and network congestion of the communication link of the intelligent warning device at the current moment, and the retransmission ratio in the preset number of reported data closest to the current moment, includes: Obtain a preset weak coverage threshold and the received signal strength of the communication link of the intelligent warning device at the current moment. If the preset weak coverage threshold is less than the received signal strength at the current moment, then a constant 0 is used as the signal gap at the current moment. If the preset weak coverage threshold is greater than or equal to the received signal strength at the current moment, then the value of the preset weak coverage threshold minus the received signal strength at the current moment is normalized to... The interval is used to obtain the signal gap at the current moment; Obtain the signal-to-noise ratio (SNR) of the communication link of the intelligent warning device at the current moment, and normalize the SNR to... The signal-to-noise ratio (SNR) normalized value is obtained by subtracting the SNR normalized value from the constant 1, and the SNR degradation at the current time is obtained. Obtain the actual number of retransmissions and the preset maximum number of retransmissions for each data report submitted by the intelligent warning device in the preset number of data reports most recently. Calculate the sum of all actual retransmissions and its proportion in the sum of all preset maximum retransmissions to obtain the retransmission ratio at the current time. Obtain the network load status of the communication link of the intelligent alarm device at the current moment. If the network load status at the current moment is busy, set the network load factor at the current moment to a constant 1. If the network load status at the current moment is idle, set the network load factor at the current moment to a constant 0. The sum of the signal gap, the signal-to-noise ratio degradation, the retransmission ratio, and the network load factor is calculated to obtain the channel degradation degree of the communication link of the intelligent warning device at the current moment. Obtain a preset value range and the remaining power of the intelligent warning device at the current moment. The minimum value of the preset value range is 1. Calculate the reciprocal of the sum of the remaining power and a preset constant. Normalize the product of the reciprocal and the channel degradation degree to the preset value range to obtain the channel quality coefficient at the current moment.

[0009] Preferably, obtaining the data timeliness requirement coefficient at the current moment includes: The time when the target vehicle most recently experienced a fault is obtained and recorded as the most recent fault time. A preset value range is obtained, which is the same as the value range of the channel quality coefficient. If the time interval between the most recent fault time and the current time is less than or equal to a preset fault interval threshold, the maximum value in the preset value range is used as the data timeliness requirement coefficient at the current time. If the time interval between the most recent fault time and the current time is greater than the preset fault interval threshold, then the data timeliness requirement coefficient at the current time is obtained based on the acceleration of the intelligent warning device at the preset number of data reporting times closest to the current time, the data of various status monitoring indicators of the intelligent warning device at the current time, and the time corresponding to the oldest data in the cache of the intelligent warning device.

[0010] Preferably, the status monitoring indicators include the battery voltage, LED driving current, and temperature of the intelligent warning device. Then, obtaining the data timeliness requirement coefficient for the current moment based on the acceleration of the intelligent warning device at the most recent preset number of data reporting times, the data of various status monitoring indicators of the intelligent warning device at the current moment, and the time corresponding to the oldest data in the intelligent warning device's cache, includes: Obtain the acceleration at the corresponding moment when the intelligent warning device reports a preset number of data points most recently, calculate the mean absolute deviation of all accelerations, and normalize the mean absolute deviation to... The interval is used to obtain the degree of acceleration dispersion at the current moment; The instantaneous rate of change of battery voltage, LED drive current, and temperature of the intelligent warning device at the current moment are obtained respectively, and the instantaneous rate of change of battery voltage, LED drive current, and temperature are normalized respectively. Within the range, we obtain the normalized values ​​of the instantaneous rate of change of battery voltage, the instantaneous rate of change of LED driving current, and the instantaneous rate of change of temperature. Obtain the time corresponding to the oldest data in the cache of the smart alarm device, and normalize the time interval between that time and the current time. The interval is used to determine the current cache retention level. The acceleration dispersion, the normalized instantaneous change rate of battery voltage, the normalized instantaneous change rate of LED driving current, the normalized instantaneous change rate of temperature, and the buffer retention are accumulated and normalized to the preset value range to obtain the data timeliness requirement coefficient at the current moment.

[0011] Preferably, obtaining the adaptive reporting period includes: Obtain the remaining battery power of the intelligent warning device at the current moment, as well as a preset battery shortage threshold, calculate the proportion of the remaining battery power in the preset battery shortage threshold, and obtain the energy sensing weight factor at the current moment. Using the energy-sensing weight factor as the denominator and the difference between the channel quality coefficient and constant 1 as the numerator, the target value is obtained. The sum between constant 1 and the target value is calculated to obtain the first adjustment coefficient. Obtain the value range of the data timeliness demand coefficient, take the difference between the maximum value in the value range and the data timeliness demand coefficient as the base, and take the energy perception weight factor as the exponent to obtain the second adjustment coefficient; The cumulative product of the static basic reporting period, the first adjustment coefficient, and the second adjustment coefficient is calculated to obtain the cumulative product result. Based on the type of the target vehicle, the application scenario, and the performance of the intelligent warning device, the theoretical minimum reporting period and the theoretical maximum reporting period of the intelligent warning device are obtained. The cumulative product result is then limited to between the theoretical minimum reporting period and the theoretical maximum reporting period using the clamp function to obtain the adaptive reporting period.

[0012] Preferably, obtaining the adaptive retransmission count includes: Calculate the product of the preset retransmission count, the reciprocal of the channel quality coefficient, the data timeliness requirement coefficient, and the energy-aware weight factor. Round the product to the nearest integer to obtain the theoretical minimum retransmission count and the theoretical maximum retransmission count. Use the clamp function to limit the rounded result between the theoretical minimum retransmission count and the theoretical maximum retransmission count to obtain the adaptive retransmission count.

[0013] Secondly, embodiments of the present invention also provide a power consumption monitoring system for an IoT-based intelligent warning device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the power consumption monitoring method for an IoT-based intelligent warning device as described in the first aspect.

[0014] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: This invention first obtains the static basic reporting cycle of the intelligent warning device at the current moment based on the signal quality of the area where the target vehicle monitored by the intelligent warning device is located, and the minimum allowable average reporting cycle of the intelligent warning device. This provides a starting point for subsequent adaptive adjustments that balance safety and energy saving. Then, based on the signal quality of the communication link and network congestion of the intelligent warning device, the channel quality coefficient at the current moment is obtained. This coefficient is used to adaptively adjust the static basic reporting cycle, lengthening the reporting cycle when the signal is poor to reduce invalid retransmissions, and appropriately shortening the reporting cycle when the signal is good. Furthermore, by analyzing the time when the target vehicle monitored by the intelligent warning device most recently experienced a fault, and the data of various status monitoring indicators of the intelligent warning device at the current moment, the safety environment of the target vehicle at the current moment is reflected, and data is obtained. The timeliness requirement coefficient, together with the channel quality coefficient, adjusts the static reporting cycle. The two are mutually restrictive, avoiding the one-sidedness of single-dimensional decision-making, and outputting an optimal reporting cycle that balances channel reliability and service urgency. This allows the intelligent warning device to be flexible and efficient when the signal is good, rationally hibernate when the signal is poor, spare no effort when there is a security threat, and reduce power consumption when there is no security threat. Finally, the preset retransmission number of the static reporting cycle is adaptively adjusted using the channel quality coefficient and the data timeliness requirement coefficient to obtain an adaptive reporting cycle and adaptive retransmission number, which is used to complete the next power consumption data report. This enables the intelligent warning device to reduce invalid retransmissions in weak signal areas, shorten the reporting cycle in emergency windows, and prioritize energy conservation when the battery is low. This allows the intelligent warning device to improve the accuracy of power consumption monitoring while ensuring working power and data timeliness. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for monitoring the power consumption of an intelligent warning device based on the Internet of Things, provided in Embodiment 1 of the present invention. Detailed Implementation

[0017] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0018] It should be noted that the terms "first," "second," etc., used in this disclosure and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure.

[0019] To illustrate the technical solution of the present invention, specific embodiments are described below.

[0020] See Figure 1 This is a flowchart of a method for monitoring the power consumption of an IoT-based intelligent warning device, as provided in Embodiment 1 of the present invention. Figure 1 As shown, the method may include: Step S101: Based on the signal quality of the area where the target vehicle is located at the current moment, as monitored by the intelligent warning device, and the minimum average reporting cycle allowed by the intelligent warning device, obtain the static basic reporting cycle of the intelligent warning device at the current moment.

[0021] Traditionally, intelligent warning devices upload their power consumption status data at a fixed reporting cycle to indirectly reflect the vehicle's health status. However, the fixed reporting cycle mode may suffer from problems such as power consumption surges in weak signal areas, redundant reporting in strong signal areas wasting energy, and insufficient response during critical periods. Therefore, in this embodiment of the invention, the reporting cycle is adaptively obtained based on signal quality and vehicle rescue tracking needs, in order to improve the accuracy of power consumption monitoring of intelligent warning devices while ensuring the operating power consumption and data timeliness of the devices.

[0022] First, by using the theoretical minimum reporting cycle of the intelligent warning device, the minimum allowable average reporting cycle of the intelligent warning device, and the signal quality of the area where the target vehicle monitored by the intelligent warning device is located at the current moment, the static basic reporting cycle of the intelligent warning device at the current moment is obtained. This allows for the determination of a static benchmark from three dimensions: safety baseline, energy consumption upper limit, and environmental conditions, providing a starting point for subsequent adaptive adjustments that balances safety and energy saving.

[0023] The static reporting period is obtained as follows: Obtain the total battery capacity (mAh), expected operating time (hours), and average energy consumption per data report (mAs) of the intelligent alert device. Using the total battery capacity as the denominator and the product of the expected operating time and the average energy consumption as the numerator, calculate the minimum allowable average reporting cycle (seconds) for the intelligent alert device, denoted as . ; When the intelligent warning device is powered on for the first time, it downloads a regional signal prior table of the target vehicle's location from the cloud. This table contains typical RSRP statistics for different geographical areas (cities, suburbs, mountains, underground parking garages, etc.). If a regional signal prior table exists for the target vehicle's location at the current moment, the device obtains the signal level of the target vehicle's location based on this table. These signal levels are categorized as "good" and "poor." For example, an RSRP greater than -100 is considered "good," and an RSRP less than or equal to -100 is considered "poor." This is not a limitation and can be set by the implementer according to the target vehicle's application scenario. If the signal level of the target vehicle's location at the current moment is "good," then... Okay, then the recommended baseline period for historical signal quality in the region is set to be equal to the minimum average reporting period. If the signal level of the target vehicle in the region at the current moment is poor, then the minimum average reporting period of the preset multiple is used as the recommended baseline period for historical signal quality in the region. The preset multiple is greater than 1. In this embodiment of the invention, the preset multiple is set to 1.5. This setting is based on the measured data that the number of NB-IoT communication retransmissions generally increases by about 50% in typical weak signal areas (such as mountainous areas and underground garages). The aim is to reserve sufficient buffer intervals for areas with poor signal to avoid a surge in power consumption due to frequent retransmissions, while not excessively extending the reporting period and affecting the safety response. The preset multiple can be dynamically adjusted according to the actual scenario. If there is no reliable data, i.e., no signal strength distribution map of the area where the target vehicle monitored by the intelligent warning device is located at the current moment, then the recommended baseline period for regional historical signal quality is set equal to the minimum average reporting period, and the recommended baseline period for regional historical signal quality is denoted as... ; Based on the type of target vehicle, application scenario, performance of intelligent warning devices, and regulatory requirements, the theoretical minimum reporting cycle of intelligent warning devices is obtained, denoted as [missing information]. For example, the theoretical minimum reporting cycle of the Spanish V16 smart warning device is 30 seconds. However, within Spain, the technical specifications of the V16 device clearly stipulate that information should be sent once every 100 seconds. Since the sources of the three data points—the theoretical minimum reporting cycle, the minimum average reporting cycle, and the regional historical signal quality recommended baseline cycle—are different and have varying reliability, the median of the theoretical minimum reporting cycle, the minimum average reporting cycle, and the regional historical signal quality recommended baseline cycle is obtained to get the static basic reporting cycle of the smart warning device at the current moment.

[0024] In one implementation, the formula for calculating the reporting period based on static conditions is: In the formula, This indicates the reporting period based on a static basis. This represents the theoretical minimum reporting period. Here, E represents the minimum average reporting cycle, H represents the average energy consumption of the smart alert device during a single data report, and Q represents the expected operating time of the smart alert device. This indicates the recommended baseline period for historical signal quality in the region. This indicates taking the median.

[0025] The median is insensitive to outliers, which can prevent a single parameter that is too large or too small from dominating the benchmark. This makes the static benchmark reporting cycle more robust in taking into account the safety lower limit, energy consumption constraints and environmental priors. It ensures that the frequency does not fall below the safety response frequency under any circumstances, avoids excessive power consumption under typical operating conditions, and reserves a buffer margin for areas with poor signal. This lays the foundation for a static benchmark that takes into account both safety and energy saving for subsequent adaptive adjustments.

[0026] Thus, the reporting cycle based on static data was obtained.

[0027] Step S102: Based on the signal quality and network congestion of the communication link of the intelligent warning device at the current moment, and the retransmission ratio in the preset number of reported data closest to the current moment, obtain the channel quality coefficient at the current moment.

[0028] Channel quality determines whether data can be successfully uploaded. Therefore, by analyzing the signal quality and network congestion of the communication link of the intelligent warning device at the current moment, as well as the retransmission ratio in the preset number of reported data closest to the current moment, the channel quality coefficient at the current moment is obtained. This coefficient is then used to adaptively adjust the static reporting cycle, so that when the signal is poor, the reporting cycle is lengthened to reduce invalid retransmissions, and when the signal is good, the reporting frequency can be appropriately increased (i.e., the reporting cycle is reduced).

[0029] The channel quality coefficient is obtained as follows: Obtain the preset weak coverage threshold, denoted as For example, in routine engineering optimization, an RSRP above -105dBm is considered a well-covered area, where services can connect and transmit normally, resulting in a smooth experience. However, an RSRP below -105dBm indicates a weak coverage area. While communication may still be possible, this will lead to increased retransmissions, higher power consumption, and greater latency. Therefore, a preset weak coverage threshold of -105dBm can be set. This is not a strict limitation; implementers can set it according to specific scenarios. The received signal strength of the communication link of the intelligent warning device at the current moment is recorded as... If the preset weak coverage threshold is less than the received signal strength at the current time, that is, if This indicates that data can be transmitted normally and smoothly at the current moment, so the constant 0 is taken as the signal gap at the current moment, denoted as . If the preset weak coverage threshold is greater than or equal to the received signal strength at the current time, that is, if This indicates that the channel quality is poor at the current moment, so we use max-min normalization to... Values ​​normalized to The interval is used to obtain the signal gap at the current moment, denoted as . ; The signal-to-noise ratio of the communication link of the intelligent warning device at the current moment is denoted as . The signal-to-noise ratio is normalized to The signal-to-noise ratio (SNR) normalized value is obtained by subtracting the SNR normalized value from the constant 1, and the SNR degradation at the current time is obtained. The system obtains the actual number of retransmissions and the preset maximum number of retransmissions (i.e., if the data still fails to be reported within the preset maximum number of retransmissions, it is considered that the data report has failed. Usually, the preset maximum number of retransmissions is set to 3) in the 10 most recent data reports from the intelligent warning device (no limit is set here; the implementer can set it according to the historical reporting cycle before the current time. If the historical reporting cycle is small, the number of retransmissions can be increased appropriately). The system calculates the proportion of the sum of all actual retransmissions in the sum of all preset maximum retransmissions (in this embodiment of the invention, the preset maximum number of retransmissions for each data report is 3, and there are a total of 10 data reports, so the sum of all preset maximum retransmissions is 30) to obtain the retransmission ratio at the current time. To obtain the network load status of the communication link of the intelligent warning device at the current moment: the network load status is determined by reading the system information block (SIB) of the base station. If the base station is found to have access denial (ACB), the network load status is considered to be busy; otherwise, it is considered to be idle. Alternatively, the network load status can be determined based on the cell's wireless resource utilization and service request rejection rate. For example, if the wireless resource utilization exceeds 70% or the service request rejection rate exceeds 2%, the network load status is considered to be busy; otherwise, it is considered to be idle. There are no restrictions here, and the implementer can set it according to the specific scenario. If the network load status at the current moment is busy, the network load factor at the current moment is set to a constant 1; if the network load status at the current moment is idle, the network load factor at the current moment is set to a constant 0. The sum of the signal gap, the signal-to-noise ratio degradation, the retransmission ratio, and the network load factor is calculated to obtain the channel degradation degree of the communication link of the intelligent warning device at the current moment. Obtain a preset value range and the remaining battery power (in percentage form) of the smart warning device at the current moment, wherein the minimum value of the preset value range is 1. In this embodiment of the invention, the preset value range is set to... This is used to ensure the adjustment range of the static base reporting period. There are no restrictions here; implementers can set it according to specific scenarios. The remaining battery power can be used to enhance the impact of signal quality on the static base reporting period; that is, the less remaining battery power, the worse the signal quality, and the greater the extension of the static base reporting period. The reciprocal of the sum of the remaining battery power and a preset constant is calculated, and the product of this reciprocal and the channel degradation level is normalized to a preset range. The interval is used to obtain the channel quality coefficient at the current time.

[0030] In one embodiment, the formula for calculating the channel quality coefficient at the current moment is: In the formula, This represents the channel quality coefficient at the current time (assuming the current time is t). This indicates the remaining battery power of the smart alarm device at the current moment. Indicates the signal gap amount. This indicates the signal-to-noise ratio of the communication link of the intelligent warning device at the current moment. Indicates will Normalization to The value after the interval, This represents the sum of the actual retransmission counts for each of the 10 most recent data reports submitted by the intelligent warning device. This represents the sum of all preset maximum retransmission counts. This represents the network load factor at the current moment. , This means normalizing the value to... The interval is represented by c, which is a preset constant used to ensure that the fraction is meaningful. In this embodiment of the invention, c is set to 0.01. There is no restriction here, and the implementer can set it according to the specific scenario.

[0031] It should be noted that, The larger the value, the worse the current channel quality. In this case, the reporting cycle should be appropriately increased to reduce the power consumption of the intelligent warning device. The larger; The smaller the value, the stronger the noise relative to the signal at the current moment, and the worse the signal quality. In this case, the reporting cycle should be appropriately increased to reduce the power consumption waste of the intelligent warning device. The larger; The larger the value, the more retransmissions are needed, and the less reliable the current channel is. In this case, the reporting cycle should be extended to reduce the power consumption waste of the intelligent warning device. The larger; A value of 1 indicates that the network is currently congested. In this case, the reporting cycle should be extended to avoid time windows with high retransmission probability, thereby reducing power consumption waste of the intelligent warning device. The larger; The smaller the value, the less power the intelligent warning device has at the current moment. This means the signal quality has a greater impact on the reporting cycle, and the reporting cycle should be significantly extended to reduce the power consumption of the intelligent warning device. The larger.

[0032] Thus, the channel quality coefficient at the current moment is obtained.

[0033] Step S103: Based on the time when the target vehicle most recently experienced a fault, the acceleration of the target vehicle at the time of the most recently reported data (a preset number of times), and the data of various status monitoring indicators of the intelligent warning device at the current time, obtain the data timeliness requirement coefficient for the current time.

[0034] If the static reporting cycle is adjusted solely based on channel quality, data update delays may occur, affecting the tracking of vehicle movement paths. Therefore, in this embodiment of the invention, by analyzing the target vehicle monitored by the intelligent warning device at the moment closest to the current time when it experienced a fault, the target vehicle's acceleration at the moment closest to the current time when it reported data a preset number of times, and the data of various status monitoring indicators of the intelligent warning device at the current time, the safety environment of the target vehicle at the current time is reflected, and the data timeliness requirement coefficient at the current time is obtained. This coefficient, together with the channel quality coefficient, adjusts the static reporting cycle. The two are mutually restrictive, avoiding the one-sidedness of single-dimensional decision-making, and outputting an optimal reporting cycle that takes into account both channel reliability and service urgency. This allows the intelligent warning device to be flexible and efficient when the signal is good, to rationally hibernate when the signal is poor, to spare no effort when there is a security threat, and to reduce power consumption when there is no security threat.

[0035] The method for obtaining the data timeliness requirement coefficient at the current moment is as follows: Obtain the time when the target vehicle most recently experienced a fault, denoted as the most recent fault time. Obtain a preset value range, which is the same as the value range of the channel quality coefficient. If the time interval between the most recent fault time and the current time is less than or equal to the preset fault interval threshold, it indicates that the urgency of the rescue is high. At this time, the data timeliness requirement is the highest, and the reporting cycle should be shortened to the maximum extent to ensure that the rescue personnel obtain the target vehicle's path trajectory at the highest frequency. Therefore, the maximum value in the preset value range (i.e., 5) is taken as the data timeliness requirement coefficient at the current time. Considering that accelerometers can reflect whether a target vehicle has experienced abnormal movement or a collision, if the time interval between the most recent fault time and the current time is greater than a preset fault interval threshold, the acceleration at the corresponding time of the 10 most recent reported data from the intelligent warning device (physically measuring the acceleration of the intelligent warning device, but logically representing the acceleration of the target vehicle) is obtained. The greater the dispersion of the acceleration, the greater the possibility that the target vehicle has suffered a collision or danger. In this case, the timeliness requirement is high, so the average absolute deviation of all accelerations is calculated, and the average absolute deviation is normalized to... The interval is used to obtain the degree of acceleration dispersion at the current moment; Simultaneously, the rate of change of the intelligent warning device's own state must also be considered. If the state monitored internally by the intelligent warning device fluctuates drastically, it indicates a possible malfunction or a sudden change in the environment, requiring high data timeliness. In this embodiment of the invention, the battery voltage, LED drive current, and temperature of the intelligent warning device are used as state monitoring indicators to reflect the state of the intelligent warning device itself. This is not limited here; implementers can set the parameters according to specific scenarios, obtaining the instantaneous rate of change of the battery voltage, the instantaneous rate of change of the LED drive current, and the instantaneous rate of change of the temperature of the intelligent warning device at the current moment, and normalizing these parameters to... Within the range, we obtain the normalized values ​​of the instantaneous rate of change of battery voltage, the instantaneous rate of change of LED driving current, and the instantaneous rate of change of temperature. Furthermore, considering that if historical signal strength is poor or network congestion occurs, some data may remain in the local cache. The longer the cached data remains in the cache, i.e., the greater the time difference between the current moment and the moment corresponding to the oldest data in the cache, the greater the possibility of data invalidation, and the more urgently it needs to be sent. Therefore, the moment corresponding to the oldest data in the cache of the intelligent alarm device is obtained, and the time interval between that moment and the current moment is normalized to... The interval is used to obtain the cache retention level at the current time. It is worth noting that if there is no data in the cache, the cache retention level is 0. The acceleration dispersion, the normalized instantaneous rate of change of battery voltage, the normalized instantaneous rate of change of LED driving current, the normalized instantaneous rate of change of temperature, and the buffer retention are accumulated and normalized to a preset value range. The interval is used to obtain the data timeliness requirement coefficient at the current moment.

[0036] In one implementation, the formula for calculating the data timeliness requirement coefficient at the current moment is: In the formula, This represents the data timeliness requirement coefficient at the current moment. This represents the time interval between the most recent failure time and the current time. This indicates the preset fault interval threshold. This represents the acceleration of the target vehicle at the time corresponding to the j-th reported data out of the 10 most recent reported data points. In this embodiment of the invention... , This represents the average acceleration of the target vehicle at the corresponding time point across all reported data points in the 10 most recent reported data points. This represents the average absolute deviation of the target vehicle's acceleration at the corresponding time point across all reported data points in the 10 most recent reported data points. Indicates will Normalization to Data after the interval, This represents the normalized value of the instantaneous rate of change of battery voltage. This represents the normalized value of the instantaneous rate of change of the LED driving current. This represents the normalized value of the instantaneous rate of change of temperature. Indicates the degree of cache retention. Represents the absolute value symbol. This means normalizing the value to... Interval.

[0037] It should be noted that in this embodiment of the invention, the preset fault interval threshold is set to 5 minutes, but this is not a limitation and can be set by the implementer according to the specific scenario. This indicates that the urgency of the rescue is high at this time, and the reporting cycle should be shortened as much as possible to ensure that rescuers obtain the target vehicle's route trajectory at the highest frequency. The data timeliness requirement is the highest, and the reporting cycle should be shortened to the maximum extent. Therefore, the maximum value in the preset value range (i.e., 5) is taken as the data timeliness requirement coefficient at the current moment. At this point, it is necessary to determine the data timeliness requirements based on the status of the intelligent warning device itself: The larger the value, the greater the likelihood of the target vehicle being involved in a collision or danger. In this case, the timeliness of the data is crucial. The larger the value, the greater the reduction in the reporting cycle based on the static basis; , , The larger the value, the greater the likelihood of the intelligent warning device malfunctioning at that moment, and the higher the requirement for data timeliness. The larger the value, the greater the reduction in the reporting cycle based on the static basis; The larger the value, the longer the cached data remains, making it more prone to being discarded and causing data loss. Therefore, the requirement for data timeliness is higher. The larger the value, the greater the reduction in the reporting cycle on a static basis, preventing data from being discarded.

[0038] Thus, the data timeliness requirement coefficient at the current moment has been obtained.

[0039] Step S104: Based on the remaining power of the intelligent warning device at the current moment, as well as the channel quality coefficient and the data timeliness requirement coefficient, the static basic reporting period and preset retransmission number are adaptively adjusted to obtain the adaptive reporting period and adaptive retransmission number. The next power consumption data report is completed according to the adaptive reporting period and adaptive retransmission number to realize the power consumption monitoring of the intelligent warning device.

[0040] After obtaining the channel quality coefficient and data timeliness requirement coefficient through the above steps, the static reporting cycle is adaptively adjusted, and the number of retransmissions is also adjusted to obtain the adaptive reporting cycle and adaptive retransmission count, which are used to complete the next power consumption data reporting. This achieves a closed-loop adaptive system of "less reporting for weak signals, faster reporting in emergencies, and extremely low power consumption in idle times," thus completing the power consumption monitoring of the intelligent warning device.

[0041] The adaptive reporting period is obtained as follows: The remaining battery power of the intelligent warning device at the current moment and a preset battery shortage threshold are obtained. The proportion of the remaining battery power in the preset battery shortage threshold is calculated to obtain the energy sensing weight factor at the current moment, denoted as . ,Right now In the formula, This indicates the remaining battery power of the smart alarm device at the current moment. In this embodiment of the invention, a preset power shortage threshold is set. , There are no restrictions here; implementers can set them according to the specific scenario. Using the energy-sensing weight factor as the denominator and the difference between the channel quality coefficient and constant 1 as the numerator, the target value is obtained. The sum between constant 1 and the target value is calculated to obtain the first adjustment coefficient. The value range of the data timeliness requirement coefficient is obtained, in this embodiment of the invention, as follows: The difference between the maximum value in the range (i.e., 5) and the data timeliness demand coefficient is used as the base, and the energy perception weight factor is used as the exponent to obtain the second adjustment coefficient. The cumulative product of the static base reporting cycle, the first adjustment coefficient, and the second adjustment coefficient is calculated to obtain the cumulative product result. Based on the type of target vehicle, application scenario, and performance of the intelligent warning device, the theoretical minimum reporting cycle of the intelligent warning device is obtained. and the theoretical maximum reporting period For example, the theoretical minimum reporting period of the Spanish V16 intelligent warning device is 30s, and the theoretical maximum reporting period is 600s. The theoretical minimum and maximum reporting periods can be adjusted according to industry standards or actual working conditions. Using the clamp function, the cumulative result is limited between the theoretical minimum and the theoretical maximum reporting periods to obtain an adaptive reporting period.

[0042] In one implementation, the formula for calculating the adaptive reporting period is: In the formula, Indicates the adaptive reporting period. This indicates the reporting period based on a static basis. This represents the channel quality coefficient at the current moment. This represents the data timeliness requirement coefficient at the current moment. This represents the energy perception weighting factor at the current moment. This indicates the remaining battery power of the smart alarm device at the current moment. This indicates the preset battery shortage threshold. This represents the theoretical minimum reporting period. This indicates the theoretical maximum reporting period. Used to clamp elements in a tensor, restricting them to a specific size. Within the range.

[0043] It should be noted that, The larger the value, the higher the timeliness requirement for the data at the current moment, necessitating a shorter reporting cycle to ensure data integrity and accurate tracking of the target vehicle's movement path. The smaller; The larger the value, the worse the signal quality and the lower the reliability at the current moment. In this case, the reporting cycle should be increased to reduce the probability of invalid retransmissions and prevent the communication module of the intelligent warning device from repeatedly entering a high-power transmission state, thus wasting energy. The larger. If , This indicates that the smart alarm devices are currently experiencing low battery levels, and there should be no excessive focus on urgent reporting; that is, the timeliness of data reporting should be prioritized. The impact should be appropriately reduced to conserve energy; if , This indicates that the battery is currently sufficient. At this point, data timeliness should be the primary consideration, prioritizing data integrity and the accuracy of tracking the target vehicle's movement path. Therefore, in this embodiment of the invention, [the following is omitted as it is not directly related to the preceding text]. As The index will As The denominator is used for adjustment. and The degree to which adaptive reporting cycle is dominated under different power conditions: when hour, To reduce the data timeliness requirement coefficient right Influence, while increasing right The influence, that is In this case, Depend on Dominant; when hour, Increase the data timeliness requirement coefficient right The influence, while reducing right The influence, that is In this case, Depend on leading.

[0044] Thus, the adaptive reporting period is obtained. Further, the adaptive retransmission count is obtained, specifically: Calculate the product of the preset maximum retransmission count (i.e., the preset maximum retransmission count in step S102, typically 3 times), the reciprocal of the channel quality coefficient, the data timeliness requirement coefficient, and the energy-sensing weight factor. Round the product to the nearest integer to obtain the theoretical minimum retransmission count. (Usually 1 time) and theoretical maximum number of retransmissions (Usually 5 times, but no limit is set here; implementers can set it according to specific scenarios). Using the clamp function, the rounding result is limited to between the theoretical minimum number of retransmissions and the theoretical maximum number of retransmissions to obtain the adaptive number of retransmissions.

[0045] In one implementation, the formula for calculating the adaptive retransmission count is: In the formula, Indicates the number of adaptive retransmissions. This indicates the preset maximum number of retransmissions. This represents the channel quality coefficient at the current moment. This represents the data timeliness requirement coefficient at the current moment. This represents the energy perception weighting factor at the current moment. The symbol represents rounding to the nearest integer. Used to truncate elements in a tensor, restricting them to a specific size. Within the range, in this embodiment of the invention, it is .

[0046] It should be noted that, The larger the value, the worse the signal quality at the current moment. In this case, the number of retransmissions should be reduced to decrease the probability of invalid retransmissions, prevent the communication module of the intelligent warning device from repeatedly entering a high-power transmission state, and reduce power waste. The smaller; The larger the value, the greater the likelihood that the target vehicle has malfunctioned or is in a dangerous environment at the current moment. In this case, the number of retransmissions should be increased to ensure response speed in emergency scenarios. The larger; The larger the value, the more sufficient the target vehicle's battery power is at the current moment. In this case, the number of retransmissions should be increased appropriately to ensure the integrity of the reported data and the accuracy of the target vehicle's path tracking. The larger.

[0047] Thus, the adaptive retransmission count was obtained.

[0048] Furthermore, based on the adaptive reporting cycle and adaptive retransmission count, the next power consumption data report is completed, realizing power consumption monitoring of the intelligent warning device. The general process is as follows: The adaptive reporting cycle and adaptive retransmission count are written into a timer and the narrowband IoT protocol stack. Upon timer triggering, the smart alert device wakes up and collects data such as battery voltage, drive current, GPS location, and accelerometer readings, calculating instantaneous power consumption and the average power consumption over the past five minutes. Then, the narrowband IoT transmission process is initiated: if the first transmission is successful, the communication time is recorded in the history window to update the static base reporting cycle for the next period; if it fails, retransmissions are exponentially avoided, up to a maximum of the currently calculated adaptive retransmission count. Before each retransmission, the battery voltage is checked; if it falls below 3.0V, the transmission is immediately terminated and marked "Low Battery Termination." After data reporting is complete, the current average power consumption is compared with the historical average of the last 10 times: if the deviation exceeds 30% for three consecutive times, a "Power Consumption Abnormality" flag is set locally and pushed to the cloud with the next report. The cloud platform automatically generates low battery warnings and abnormal repair work orders based on the power consumption data, battery level, and location information reported by the smart alert device. Throughout the process, the reporting cycle and retransmission count are dynamically adjusted in a closed loop based on the channel quality coefficient, data timeliness requirement coefficient, and remaining power. This enables the intelligent warning device to reduce invalid retransmissions in weak signal areas, shorten reporting delays during emergency windows, and prioritize energy conservation when power is low, thus realizing an intelligent management system of "monitoring-driven decision-making and decision-making controlling power consumption." The existing technology of reporting power consumption data based on the adaptive reporting cycle and adaptive retransmission count, and monitoring the power consumption of the intelligent warning device, will not be elaborated upon here.

[0049] Based on the same inventive concept as the above method, this embodiment of the invention also provides a power consumption monitoring system for an IoT-based intelligent warning device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described IoT-based intelligent warning device power consumption monitoring methods.

[0050] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A power consumption monitoring method for intelligent warning devices based on the Internet of Things, characterized in that, The power consumption monitoring method for IoT-based smart warning devices includes: Based on the signal quality of the area where the target vehicle is located at the current moment, as monitored by the intelligent warning device, and the minimum average reporting cycle allowed by the intelligent warning device, the static basic reporting cycle of the intelligent warning device at the current moment is obtained. Based on the signal quality and network congestion of the communication link of the intelligent warning device at the current moment, as well as the retransmission ratio in the preset number of reported data closest to the current moment, the channel quality coefficient at the current moment is obtained. Based on the time when the target vehicle most recently experienced a fault, the acceleration of the target vehicle at the time of the most recently reported data (a preset number of times), and the data of various status monitoring indicators of the intelligent warning device at the current time, the data timeliness requirement coefficient at the current time is obtained. Based on the remaining power of the intelligent warning device at the current moment, as well as the channel quality coefficient and the data timeliness requirement coefficient, the static reporting cycle and preset retransmission number are adaptively adjusted to obtain the adaptive reporting cycle and adaptive retransmission number. The next power consumption data report is completed according to the adaptive reporting cycle and adaptive retransmission number, thereby realizing the power consumption monitoring of the intelligent warning device.

2. The power consumption monitoring method for intelligent warning devices based on the Internet of Things according to claim 1, characterized in that, The step of obtaining the static basic reporting cycle of the intelligent warning device at the current moment, based on the signal quality of the area where the target vehicle is located as monitored by the intelligent warning device at the current moment and the minimum allowable average reporting cycle of the intelligent warning device, includes: The total battery capacity, expected working time, and average energy consumption per data report of the intelligent warning device are obtained. The total battery capacity is used as the denominator, and the product of the expected working time and the average energy consumption is used as the numerator to obtain the minimum average reporting cycle allowed by the intelligent warning device. If there is a regional signal prior table for the area where the target vehicle is located at the current moment, as monitored by the intelligent warning device, the signal level of the area where the target vehicle is located at the current moment is obtained according to the regional signal prior table. The signal levels are good and poor. If the signal level of the area where the target vehicle is located at the current moment is good, the recommended benchmark period for regional historical signal quality is set to be equal to the minimum average reporting period. If the signal level of the area where the target vehicle is located at the current moment is poor, the minimum average reporting period is taken as the recommended benchmark period for regional historical signal quality by a preset multiple, where the preset multiple is greater than 1. If there is no signal strength distribution map of the area where the target vehicle is located at the current moment, as monitored by the intelligent warning device, then the recommended benchmark period for historical signal quality in the area is set to be equal to the minimum average reporting period. Based on the type of target vehicle, application scenario, performance of intelligent warning device, and regulatory requirements, the theoretical minimum reporting cycle of intelligent warning device is obtained. The median of the theoretical minimum reporting cycle, the minimum average reporting cycle, and the recommended baseline cycle of regional historical signal quality is obtained to obtain the static basic reporting cycle of intelligent warning device at the current moment.

3. The power consumption monitoring method for intelligent warning devices based on the Internet of Things according to claim 1, characterized in that, The step of obtaining the channel quality coefficient at the current moment based on the signal quality and network congestion status of the communication link of the intelligent warning device at the current moment, and the retransmission ratio in the preset number of reported data closest to the current moment, includes: Obtain a preset weak coverage threshold and the received signal strength of the communication link of the intelligent warning device at the current moment. If the preset weak coverage threshold is less than the received signal strength at the current moment, then a constant 0 is used as the signal gap at the current moment. If the preset weak coverage threshold is greater than or equal to the received signal strength at the current moment, then the value of the preset weak coverage threshold minus the received signal strength at the current moment is normalized to... The interval is used to obtain the signal gap at the current moment; Obtain the signal-to-noise ratio (SNR) of the communication link of the intelligent warning device at the current moment, and normalize the SNR to... The signal-to-noise ratio (SNR) normalized value is obtained by subtracting the SNR normalized value from the constant 1, and the SNR degradation at the current time is obtained. Obtain the actual number of retransmissions and the preset maximum number of retransmissions for each data report submitted by the intelligent warning device in the preset number of data reports most recently. Calculate the sum of all actual retransmissions and its proportion in the sum of all preset maximum retransmissions to obtain the retransmission ratio at the current time. Obtain the network load status of the communication link of the intelligent alarm device at the current moment. If the network load status at the current moment is busy, set the network load factor at the current moment to a constant 1. If the network load status at the current moment is idle, set the network load factor at the current moment to a constant 0. The sum of the signal gap, the signal-to-noise ratio degradation, the retransmission ratio, and the network load factor is calculated to obtain the channel degradation degree of the communication link of the intelligent warning device at the current moment. Obtain a preset value range and the remaining power of the intelligent warning device at the current moment. The minimum value of the preset value range is 1. Calculate the reciprocal of the sum of the remaining power and a preset constant. Normalize the product of the reciprocal and the channel degradation degree to the preset value range to obtain the channel quality coefficient at the current moment.

4. The power consumption monitoring method for intelligent warning devices based on the Internet of Things according to claim 1, characterized in that, The acquisition of the data timeliness requirement coefficient at the current moment includes: The time when the target vehicle most recently experienced a fault is obtained and recorded as the most recent fault time. A preset value range is obtained, which is the same as the value range of the channel quality coefficient. If the time interval between the most recent fault time and the current time is less than or equal to a preset fault interval threshold, the maximum value in the preset value range is used as the data timeliness requirement coefficient at the current time. If the time interval between the most recent fault time and the current time is greater than the preset fault interval threshold, then the data timeliness requirement coefficient at the current time is obtained based on the acceleration of the intelligent warning device at the preset number of data reporting times closest to the current time, the data of various status monitoring indicators of the intelligent warning device at the current time, and the time corresponding to the oldest data in the cache of the intelligent warning device.

5. The power consumption monitoring method for intelligent warning devices based on the Internet of Things according to claim 4, characterized in that, The status monitoring indicators include the battery voltage, LED driving current, and temperature of the intelligent warning device. The process of obtaining the data timeliness requirement coefficient for the current moment based on the acceleration of the intelligent warning device at the most recent preset number of data reports, the data of each status monitoring indicator of the intelligent warning device at the current moment, and the moment corresponding to the oldest data in the intelligent warning device's cache, includes: Obtain the acceleration at the corresponding moment when the intelligent warning device reports a preset number of data points most recently, calculate the mean absolute deviation of all accelerations, and normalize the mean absolute deviation to... The interval is used to obtain the degree of acceleration dispersion at the current moment; The instantaneous rate of change of battery voltage, LED drive current, and temperature of the intelligent warning device at the current moment are obtained respectively, and the instantaneous rate of change of battery voltage, LED drive current, and temperature are normalized respectively. Within the range, we obtain the normalized values ​​of the instantaneous rate of change of battery voltage, the instantaneous rate of change of LED driving current, and the instantaneous rate of change of temperature. Obtain the time corresponding to the oldest data in the cache of the smart alarm device, and normalize the time interval between that time and the current time. The interval is used to determine the current cache retention level. The acceleration dispersion, the normalized instantaneous change rate of battery voltage, the normalized instantaneous change rate of LED driving current, the normalized instantaneous change rate of temperature, and the buffer retention are accumulated and normalized to the preset value range to obtain the data timeliness requirement coefficient at the current moment.

6. The power consumption monitoring method for intelligent warning devices based on the Internet of Things according to claim 1, characterized in that, The acquisition of the adaptive reporting period includes: Obtain the remaining battery power of the intelligent warning device at the current moment, as well as a preset battery shortage threshold, calculate the proportion of the remaining battery power in the preset battery shortage threshold, and obtain the energy sensing weight factor at the current moment. Using the energy-sensing weight factor as the denominator and the difference between the channel quality coefficient and constant 1 as the numerator, the target value is obtained. The sum between constant 1 and the target value is calculated to obtain the first adjustment coefficient. Obtain the value range of the data timeliness demand coefficient, take the difference between the maximum value in the value range and the data timeliness demand coefficient as the base, and take the energy perception weight factor as the exponent to obtain the second adjustment coefficient; The cumulative product of the static basic reporting period, the first adjustment coefficient, and the second adjustment coefficient is calculated to obtain the cumulative product result. Based on the type of the target vehicle, the application scenario, and the performance of the intelligent warning device, the theoretical minimum reporting period and the theoretical maximum reporting period of the intelligent warning device are obtained. The cumulative product result is then limited to between the theoretical minimum reporting period and the theoretical maximum reporting period using the clamp function to obtain the adaptive reporting period.

7. The power consumption monitoring method for intelligent warning devices based on the Internet of Things according to claim 6, characterized in that, The acquisition of the adaptive retransmission count includes: Calculate the product of the preset retransmission count, the reciprocal of the channel quality coefficient, the data timeliness requirement coefficient, and the energy-aware weight factor. Round the product to the nearest integer to obtain the theoretical minimum retransmission count and the theoretical maximum retransmission count. Use the clamp function to limit the rounded result between the theoretical minimum retransmission count and the theoretical maximum retransmission count to obtain the adaptive retransmission count.

8. A power consumption monitoring system for intelligent warning devices based on the Internet of Things, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the power consumption monitoring method for IoT-based smart warning devices as described in any one of claims 1-7.

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