Internet of Things equipment intelligent dormancy control system based on environment self-sensing

Abstract

The invention relates to the technical field of intelligent dormancy control of Internet of Things equipment, and discloses an intelligent dormancy control system of the Internet of Things equipment based on environment self-sensing, which comprises an environment feature extraction layer module, an environment dynamic model construction module, a self-adaptive dormancy decision engine module, a lightweight state cache module and a power supply cooperative control module. By acquiring the physical layer signal characteristics and the network layer connection state information of the NB-IoT protocol stack in real time, a dynamic model can be constructed based on the environmental signal fluctuation, the stability of the environment where the equipment is located can be accurately evaluated, the dormancy triggering condition can be dynamically adjusted, false wake-up caused by a fixed threshold value is avoided, dependence on a traditional sensor is reduced, and the reliability of the equipment is improved. The energy efficiency and adaptability of the equipment are improved, the anti-interference capability in an interference environment is improved, and the long-term stable operation of the equipment in a complex industrial environment is ensured.

Description

Technical Field

[0001] The present invention relates to an intelligent sleep control system for Internet of Things devices based on environmental self-awareness, belonging to the technical field of intelligent sleep control for Internet of Things devices. Background Art

[0002] With the continuous development of Internet of Things (IoT) technology, Internet of Things devices are widely used in multiple scenarios such as smart home, intelligent warehousing, and industrial monitoring. To ensure the stable operation of devices in the face of environmental changes, these devices need to have environmental perception capabilities. Traditional environmental perception methods rely on a variety of sensors, such as temperature and humidity sensors, light sensors, motion sensors, etc. These sensors can capture environmental changes in real time and provide necessary data support. However, this method has significant energy consumption problems in practical applications.

[0003] Existing Internet of Things devices usually adopt a continuous power supply method to keep the sensors in an operating state, which results in a significant shortening of the device battery life and an increase in maintenance costs. For example, many sensors need to work continuously to obtain environmental data, preventing the device from effectively entering a low-power sleep mode. To reduce energy consumption, some measures have been taken in the industry, such as introducing a timed sleep mechanism, partial shutdown of sensors, etc. However, these methods have not completely solved the energy consumption problem and often introduce new challenges.

[0004] While traditional Internet of Things devices maintain environmental perception, they need to collect data from a variety of sensors in real time. However, continuous operation of the sensors causes the device to consume a large amount of electrical energy in the normal operating state, and the sleep cycle is fragmented, making it difficult to ensure long-term use of the battery. This not only increases the energy consumption burden of the device but also causes the device to be unable to effectively enter the sleep state, thus affecting the stability and lifespan of the device.

[0005] Most existing Internet of Things devices are designed with fixed sleep threshold conditions, such as triggering sleep or wake-up based on a fixed amount of environmental change; however, this fixed threshold cannot cope with the changes in the dynamic environment. Especially in some complex industrial scenarios, sudden environmental disturbances can cause the device to be frequently mis-awakened or have a response delay, resulting in data loss or device damage. This method cannot effectively guarantee the operation stability of the device in some high-demand industrial applications; and when existing Internet of Things devices handle power management, they usually decouple the state of the protocol stack from power management. When the device enters the sleep state, the protocol stack often interrupts the connection and needs to re-establish the network connection; this process not only increases the communication delay of the system but also consumes a large amount of electrical energy, especially when the device is frequently awakened, the energy consumption problem is more prominent.

[0006] To avoid these problems, various solutions have been adopted in the industry. For example, some devices have introduced low-power communication protocols or mechanisms for dynamically adjusting the sleep duration, attempting to balance the requirements of energy efficiency and environmental perception. Although these methods can reduce energy consumption to a certain extent, they still cannot fundamentally solve the stability problem of devices in a changing environment, and at the same time cannot effectively reduce the energy waste caused by frequent device wake-up; therefore, how to reduce energy consumption while ensuring environmental perception and improve the adaptability of devices in a dynamic environment has become a difficult problem in the current technological development. Summary of the Invention

[0007] The present invention provides an intelligent sleep control system for Internet of Things devices based on environmental self-perception, and its main purpose is to solve the problems of excessive energy consumption, poor environmental adaptability, and fragmentation between the protocol stack and power management.

[0008] To achieve the above object, an intelligent sleep control system for Internet of Things devices based on environmental self-perception provided by the present invention includes: An environmental feature extraction layer module, configured to obtain physical layer signal features and network layer connection status information in real time through the NB-IoT protocol stack of the Internet of Things device. The physical layer signal features at least include signal strength RSSI and bit error rate BER, and the network layer connection status information at least includes an attachment retention status and a channel resource reservation status; An environmental dynamic model construction module, communicatively connected to the environmental feature extraction layer module, configured to construct a dynamic model reflecting the stability of the physical environment where the device is located based on the obtained physical layer signal features and network layer connection status information. Among them, the signal strength fluctuation is mapped to the environmental interference degree, and the change in the bit error rate is mapped to the channel quality. The two are combined to infer the stability of the physical environment; the environmental stability is quantified by calculating the signal fluctuation entropy value The calculation formula is: , where represents the probability that the signal strength or bit error rate is in the th state within a preset time window; An adaptive sleep decision engine module, communicatively connected to the environmental dynamic model construction module, configured to adaptively adjust the sleep trigger condition based on the environmental dynamic model by using a dynamic threshold algorithm. The dynamic threshold algorithm dynamically adjusts the sleep trigger condition based on historical environmental data to avoid over-wake-up caused by a fixed threshold. And, before determining that sleep is allowed, the adaptive sleep decision engine module sends a sleep request carrying a proposed sleep status code to the core network through the NB-IoT protocol stack to maintain the attachment relationship and reserve channel resources; The lightweight state caching module is communicatively connected to the adaptive sleep decision engine module, and is configured to save the key protocol stack parameters in a non-volatile memory before the IoT device enters the sleep state, and directly restore the key protocol stack parameters from the non-volatile memory when the IoT device wakes up, skipping the system information broadcast reception process; The power cooperation control module is communicatively connected to the adaptive sleep decision engine module, and is configured to control the power management unit of the IoT device to enter or exit the sleep state according to the decision result of the adaptive sleep decision engine module, and dynamically adjust the sleep duration according to the evaluation result of the environmental dynamic model.

[0009] Preferably, the environmental dynamic model construction module further includes: a timing feature analysis unit, configured to analyze the timing fluctuation pattern of the physical layer signal characteristics, and distinguish instantaneous interference and persistent environmental changes by matching with a predefined signal mutation feature library.

[0010] Preferably, the environmental dynamic model construction module further includes: a dynamic weight regulator, configured to automatically reduce the weight of the instantaneous signal characteristics in the current environmental stability evaluation when the timing feature analysis unit detects a burst interference feature that conforms to the signal mutation feature library and increase the weight of the historical environmental data for making a sleep decision, wherein, and when a burst interference is detected, the value of is dynamically adjusted to be less than 0.5.

[0011] Preferably, it further includes: an interference immune wake-up mechanism unit, configured to prevent the wake-up operation triggered by the instantaneous signal fluctuation within a preset time window period after identifying a burst interference, and maintain the current sleep state.

[0012] Preferably, the historical environmental data includes signal stability data within the past twenty-four hours.

[0013] Preferably, the key protocol stack parameters at least include the discontinuous reception (DRX) period and the channel configuration information.

[0014] Preferably, the signal mutation feature library includes signal fluctuation patterns characterizing motor startup, electromagnetic noise, or metal shielding interference sources.

[0015] Preferably, the matching degree threshold of the preset interference template is greater than or equal to eighty percent.

[0016] Preferably, the preset time window period is ten seconds.

[0017] Preferably, when the environmental feature extraction layer module fails to effectively obtain the physical layer signal features, the adaptive sleep decision engine module switches to a sleep control strategy based on a preset timer.

[0018] Compared with the problems in the background art, the beneficial effects of the present invention are as follows: 1. By utilizing the physical layer signal features (such as signal strength and bit error rate) of the NB-IoT protocol stack and the network layer connection status information, the system can realize the reconstruction of the environmental perception function without relying on traditional sensors. This mechanism not only effectively reduces the number of hardware components, lowers the complexity of the system, but also improves the energy efficiency. Especially when the device is in the sleep state, the power consumption of the hardware is significantly controlled, thereby extending the service life of the device and avoiding redundant hardware consumption and power waste.

[0019] 2. Based on the signal strength fluctuation and bit error rate change, the environmental dynamic model constructed by the system can efficiently and accurately evaluate the environmental stability, and adopts an adaptive dynamic threshold algorithm to adjust the sleep trigger condition, enabling flexible response under different environmental changes and avoiding frequent false awakenings caused by fixed thresholds in traditional methods; by sending a quasi-sleep status code before going to sleep and maintaining the attachment relationship with the core network, the occurrence of false awakenings is further reduced. When sudden interferences such as electromagnetic interference occur, through the matching mechanism of the timing feature analysis unit and the sudden interference feature library, the instantaneous fluctuation of the signal and the real environmental change can be quickly distinguished; the dynamic weight regulator of the system automatically reduces the weight of the instantaneous signal features and preferentially uses historical environmental data, thereby avoiding false awakenings in the case of severe interference, effectively enhancing the interference immunity of the system, avoiding unnecessary awakenings caused by electromagnetic noise in traditional solutions, and significantly reducing the invalid awakening events of the device.

[0020] 3. The lightweight state cache module in the system saves the necessary protocol stack parameters before the device enters the sleep state, so that the core data can be directly restored during wake-up, skipping the redundant process of re-receiving the system information broadcast in traditional systems. This not only greatly reduces the energy consumption during wake-up, but also improves the system response speed, ensuring that the device can quickly resume the working state. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of the core module processing of the present invention.

[0022] Figure 2 It is a flowchart of the system operation state of the present invention.

[0023] Figure 3 It is a flowchart of the environmental dynamic model construction module of the present invention.

[0024] The realization of the object, functional features and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0025] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0026] The embodiment of the present application provides an intelligent sleep control system for Internet of Things devices based on environmental self-awareness, including: an environmental feature extraction layer module, which is used to obtain physical layer signal features and network layer connection status information in real time through the NB-IoT protocol stack of the Internet of Things device. The physical layer signal features at least include signal strength RSSI and bit error rate BER, and the network layer connection status information at least includes attachment retention status and channel resource reservation status; An environmental dynamic model construction module, which is communicatively connected to the environmental feature extraction layer module, and is used to construct a dynamic model reflecting the stability of the physical environment where the device is located based on the obtained physical layer signal features and network layer connection status information. Among them, the signal strength fluctuation is mapped to the environmental interference degree, and the change of the bit error rate is mapped to the channel quality. The two are combined to infer the stability of the physical environment; the environmental stability is quantified by calculating the signal fluctuation entropy value The calculation formula is: , where, represents the probability that the signal strength or bit error rate is in the th state within a preset time window; An adaptive sleep decision engine module, which is communicatively connected to the environmental dynamic model construction module, and is used to adaptively adjust the sleep trigger condition based on the environmental dynamic model by using a dynamic threshold algorithm. The dynamic threshold algorithm dynamically adjusts the sleep trigger condition based on historical environmental data to avoid over-wake caused by a fixed threshold. Moreover, before judging that sleep is allowed, the adaptive sleep decision engine module sends a sleep request carrying a proposed sleep status code to the core network through the NB-IoT protocol stack to maintain the attachment relationship and reserve channel resources; A lightweight state cache module, which is communicatively connected to the adaptive sleep decision engine module, and is used to save key protocol stack parameters in a non-volatile memory before the Internet of Things device enters the sleep state, and directly restore the key protocol stack parameters from the non-volatile memory when the Internet of Things device wakes up, skipping the system information broadcast reception process; A power supply cooperative control module, which is communicatively connected to the adaptive sleep decision engine module, and is used to control the power management unit of the Internet of Things device to enter or exit the sleep state according to the decision result of the adaptive sleep decision engine module, and dynamically adjust the sleep duration according to the evaluation result of the environmental dynamic model.

[0027] Preferably, the environmental dynamic model construction module further includes: a timing feature analysis unit, configured to analyze the timing fluctuation pattern of the physical layer signal features, and distinguish instantaneous interference from continuous environmental changes by matching with a predefined signal mutation feature library.

[0028] Preferably, the environmental dynamic model construction module further includes: a dynamic weight regulator, configured to automatically reduce the weight of the instantaneous signal features in the current environmental stability assessment when the timing feature analysis unit detects a burst interference feature that conforms to the signal mutation feature library , and increase the weight of the historical environmental data , for making a sleep decision, where , and when a burst interference is detected, the value of is dynamically adjusted to be less than 0.5.

[0029] Preferably, it further includes: an interference immune wake-up mechanism unit, configured to prevent the wake-up operation triggered by the instantaneous signal fluctuation within a preset time window period after identifying a burst interference, and maintain the current sleep state.

[0030] Preferably, the historical environmental data includes signal stability data within the past twenty-four hours.

[0031] Preferably, the key protocol stack parameters at least include the discontinuous reception (DRX) period and channel configuration information.

[0032] Preferably, the signal mutation feature library includes signal fluctuation patterns characterizing motor startup, electromagnetic noise, or metal shielding interference sources.

[0033] Preferably, the matching degree threshold of the preset interference template is greater than or equal to eighty percent.

[0034] Preferably, the preset time window period is ten seconds.

[0035] Preferably, when the environmental feature extraction layer module fails to effectively obtain the physical layer signal features, the adaptive sleep decision engine module switches to a sleep control strategy based on a preset timer.

[0036] Figure 1This is the processing flow chart of the core module of the present invention, which is mainly divided into two parts: the core processing flow and the status and power management. In the core processing flow, first, the environmental feature extraction layer module obtains the physical layer signal features and network layer connection status information in real time, and transmits this information to the environmental dynamic model construction module; the environmental dynamic model construction module performs environmental stability evaluation based on the received information, generates dynamic model parameters, and sends these parameters to the adaptive sleep decision engine module; the adaptive sleep decision engine module makes a sleep decision according to the received dynamic model parameters, combined with historical data, and outputs a sleep control instruction to the status and power management part. In the status and power management part, the adaptive sleep decision engine module also sends a protocol stack parameter recovery instruction to the lightweight status cache module for quickly recovering protocol stack parameters when the device wakes up; at the same time, the adaptive sleep decision engine module sends a power control instruction to the power cooperation control module to control the power state of the device; the power cooperation control module feeds back the power state feedback to the adaptive sleep decision engine module. Figure 2 This is the system operation status flow chart of the present invention, showing the overall operation status of the system. Starting from the initial running state, the device will be triggered regularly (every 5 minutes) to enter the environmental monitoring state; in the environmental monitoring state, if a signal mutation is detected, the system will enter the anti-interference evaluation state to determine whether it matches the preset interference template ≥ 80%; if the matching degree reaches, it will enter the interference immunity state. First, enter this state, and then start a time window (10 seconds), during which the wake-up signal is blocked and the sleep state is maintained; if the window period ends (10 seconds), it will return to the environmental monitoring state; in the environmental monitoring state, if the stability meets the standard (entropy value < threshold), it will enter the sleep decision state, confirm the channel reservation with the core network, then enter the sleep preparation state, cache the protocol stack parameters, and finally enter the deep sleep state; in the deep sleep state, it can enter the wake-up recovery state through timer triggering or event triggering, directly resume the DRX cycle, and then return to the running state; if no signal mutation is detected in the environmental monitoring state and the stability does not meet the standard (entropy value ≥ threshold), it will continue to stay in the environmental monitoring state. Figure 3 This is the flow chart of the environmental dynamic model construction module of the present invention, showing in detail the working process of this module. First, real-time signal collection is carried out to obtain the physical layer signal features and network layer connection status information; then signal fluctuation analysis is carried out to analyze the fluctuations of signal strength and bit error rate; then the fluctuation entropy value H is calculated, and the entropy value is used to quantify the uncertainty of the environment and evaluate the stability of the environment; subsequently, environmental stability judgment is carried out. If the environment is stable, the current weight is reduced; if the environment is unstable, the historical weight is increased; finally, dynamic model parameters are generated based on the data after weight adjustment and output to the decision engine for subsequent sleep decision.

[0037] Example 1: In this example, the role of the environmental feature extraction layer module is to obtain physical layer signal features and network layer connection status information in real time through the NB-IoT protocol stack. The signal features include, but are not limited to, signal strength RSSI and bit error rate BER, while the network layer information includes attachment retention status and channel resource reservation status. To ensure that the dynamic changes in signal strength RSSI and bit error rate BER reflect the stability of the physical environment, we define: RSSI is the signal strength between the device and the base station, with the unit of dBm. Its change can be used to infer the interference situation of the environment where the device is located; BER is the bit error rate, which reflects the channel quality, with the unit of bit error rate. Through the fluctuation of BER, the quality and stability of the channel can be evaluated. These physical layer signal features and network layer information will be used for dynamic modeling in subsequent steps to further determine the device's sleep decision.

[0038] The core task of this module is to construct a dynamic environment model based on the collected signal strength and bit error rate data. The environmental stability is quantified by calculating the entropy value H of the signal fluctuation. The calculation formula of the entropy value H is as follows: , where, represents the probability that the signal strength or bit error rate is in the th state within a preset time window. This formula is used to describe the uncertainty in the environment and then evaluate the environmental stability. For the changes in signal strength RSSI and bit error rate BER, the model will comprehensively consider their fluctuation situations to distinguish between sudden interference and long-term stable changes in the environment; represents the state probability of the signal strength or bit error rate at a certain moment; H is the entropy value, which reflects the unpredictability of the signal fluctuation. The higher the entropy value, the more unstable the environment.

[0039] In this example, the calculation of the entropy value will dynamically reflect the intensity of environmental interference and provide a basis for subsequent sleep decisions. Based on the environmental stability assessment provided by the environmental dynamic model construction module, the sleep decision engine module adaptively adjusts the sleep trigger condition through a dynamic threshold algorithm. The role of this engine is to postpone the sleep trigger when the signal fluctuation is large or the environment is unstable, to avoid the device being frequently awakened due to environmental interference; in this module, the decision engine combines historical data with real-time signal features and dynamically adjusts the sleep threshold according to the changes in the environment. Specifically, when the device detects a large environmental fluctuation, it reduces the weight of the current signal features and increases the weight of the historical data, so as to avoid false awakenings caused by short-term sudden interference. This process is achieved through the following formula: , where, and represent the weights of the current signal features and historical environmental data respectively. According to the interference situation of the environment, The value will be automatically adjusted to be less than 0.5 to prioritize historical data.

[0040] Before the device enters the sleep state, the lightweight state cache module saves the key parameters of the protocol stack (such as the discontinuous reception DRX cycle and channel configuration information) in the non-volatile memory. When waking up, the device can quickly restore these key parameters, thus skipping the redundant broadcast reception steps in the traditional system, reducing energy consumption, and improving the response speed of the device after waking up; Discontinuous reception cycle: The periodic reception ability of the device, used to adjust the time interval between sleep and wake-up; Channel configuration information: The communication channel status information between the device and the base station, ensuring that the device can quickly restore to the previous communication state when waking up. At the same time, the power cooperation control module works closely with the adaptive sleep decision engine to dynamically adjust the power management strategy of the device according to the evaluation results of the environmental dynamic model. Specifically, this module adjusts the sleep duration under different environmental conditions according to the environmental state of the device, thereby optimizing the use of power. The system can flexibly control the device to enter and exit the sleep mode to ensure the maximization of battery life.

[0041] Embodiment 2: In this embodiment, the environmental feature extraction module obtains the physical layer signal features (such as signal strength RSSI and bit error rate BER) and network layer connection status information in real time through the NB-IoT protocol stack. Signal strength RSSI is used to evaluate the signal quality between the device and the base station, and the bit error rate BER is used to measure the channel quality. When the device collects environmental data through the physical layer signal and network layer connection status information, the data will be transmitted to the environmental dynamic model construction module; According to the received environmental features, the environmental dynamic model construction module will evaluate the environmental stability based on the following two types of features: Signal strength fluctuation (the change of RSSI), used to infer the interference situation in the environment; Bit error rate change (the fluctuation of BER), reflecting the quality of the channel, helping to evaluate the stability of the communication channel. The uncertainty of the environment is quantified by calculating the entropy value H of the signal fluctuation. The formula is as follows: , where, represents that within the preset time window, the signal strength or bit error rate is at the The probability of a state. This entropy value is used to measure the stability of the environment. The higher the entropy value, the more unstable the environment. In this way, the model can effectively distinguish whether the environment where the device is located is stable or has sudden interference. When the signal fluctuation entropy value is high, it indicates that there is a high degree of uncertainty in the environment, and the system will postpone entering the sleep state to avoid false wake-up. At the same time, the adaptive sleep decision engine module, based on the dynamic model evaluation results, uses a dynamic threshold algorithm to determine whether the device enters the sleep state. To avoid the false wake-up problem caused by the traditional fixed threshold method, the system will dynamically adjust the sleep trigger condition according to the fluctuations of the signal strength and the bit error rate. For example, when a sudden interference is detected, the dynamic threshold algorithm automatically reduces the weight of the current signal fluctuation characteristics , while increasing the weight of the historical environment data , the formula is: , where and represent the weights of the current signal characteristics and historical data respectively. When the environmental stability is poor (the entropy value is high), the system will give priority to historical data to avoid frequent false wake-up caused by short-term interference. Before the device enters the sleep state, the lightweight state cache module will save the key parameters in the protocol stack (such as the discontinuous reception cycle DRX cycle and channel configuration information) in the non-volatile memory. In this way, when the device wakes up, these key parameters can be directly restored from the non-volatile memory without having to receive the system information broadcast again, thus saving energy and improving the response speed.

[0042] The power co-control module works in cooperation with the adaptive sleep decision engine to dynamically adjust the power management strategy of the device according to the results of the environmental model evaluation. By dynamically adjusting the sleep duration and the power consumption during wake-up, it ensures that the device always maintains the lowest energy consumption under environmental changes and optimizes the battery life.

[0043] The signal fluctuation entropy value is a key indicator for evaluating environmental stability. By calculating the fluctuations of RSSI and BER, the system can identify the changing trend of the environment and then decide whether to enter the sleep state. In practical applications, when the device is in a strong interference environment, the system will postpone sleep to avoid false wake-up caused by environmental interference. Through the mechanism based on dynamic weight adjustment, the adaptive sleep decision engine can flexibly adjust the sleep trigger condition according to the combination of historical data and real-time environmental data. This mechanism can effectively reduce the invalid wake-up caused by sudden environmental interference and ensure the long-term stable operation of the device.

[0044] Example 3: In this example, when constructing the environmental dynamic model, by collecting the physical layer signal characteristics (such as signal strength RSSI and bit error rate BER) and the network layer connection status information, the evaluation method of environmental stability is further optimized. Specifically, the fluctuations of signal strength RSSI and bit error rate BER are used to construct the environmental stability model, and the fluctuation entropy value H of the environment is calculated by the following formula: , where, represents the probability that the signal strength or bit error rate is in the i-th state within a preset time window, and n is the total number of signal states. This formula quantifies the uncertainty of the environment by calculating the entropy value H of the signal fluctuation. The higher the entropy value, the greater the interference and instability of the environment, thus affecting the sleep decision of the device. Based on the above environmental stability evaluation, the adaptive sleep decision engine of the system can dynamically adjust the sleep trigger conditions to avoid excessive wake-up caused by sudden interference. In specific implementation, through the dynamic threshold algorithm, we combine historical environmental data with real-time signal characteristics to adjust the sleep threshold. To further optimize the decision-making mechanism, we define the weights of the current signal characteristics and historical environmental data: , where, represents the weight of the current signal characteristics, represents the weight of the historical environmental data. When the device detects a large environmental fluctuation, the weight of the current signal characteristics will be dynamically adjusted to be less than 0.5 to give priority to historical data, which can effectively avoid false wake-up caused by short-term interference. Before entering the sleep state, the lightweight state cache module saves the key parameters of the protocol stack (such as the discontinuous reception (DRX) period and channel configuration information) in the non-volatile memory. When the device wakes up, the system directly restores these parameters from the non-volatile memory, thus skipping the redundant process of re-receiving the system information broadcast in the traditional system. This mechanism can effectively reduce the energy consumption during wake-up and improve the wake-up response speed of the device. At the same time, the power coordination control module closely cooperates with the adaptive sleep decision engine and dynamically adjusts the power management strategy of the device according to the evaluation results of the environmental dynamic model. Specifically, the power management unit will adjust the timing of the device entering or exiting the sleep state according to the signal strength and environmental stability to further optimize the battery life.

[0045] To cope with sudden interference, after detecting sudden interference, we introduced an interference immunity wake-up mechanism. Within a preset time window, when the device detects sudden interference, the system will prevent wake-up operations triggered by transient signal fluctuations, ensuring that the device maintains its current sleep state. This mechanism helps reduce unnecessary false wake-up events, ensures that the device can operate stably for a long time in an interference environment, enables the system to better cope with environmental changes, especially in complex and dynamic industrial environments. In the specific implementation process, the device first collects signal characteristics and connection status information of the physical layer and network layer in real time, and constructs a dynamic environmental stability evaluation model. Based on this model, the system can flexibly adjust the sleep trigger conditions and save key parameters before the device enters the sleep state, thereby improving the stability and energy efficiency of the device during long-term operation. All these belong to the extended implementation methods known to those of ordinary skill in the art.

[0046] Example 4: In this example, the environmental feature extraction layer module first obtains the physical layer signal characteristics and network layer connection status information through the NB-IoT protocol stack of the Internet of Things device. Specifically, the physical layer signal characteristics include signal strength (RSSI) and bit error rate (BER), while the network layer connection status information includes the attachment retention status and channel resource reservation status. These signal characteristics and status information provide reliable data support during real-time environmental monitoring.

[0047] For the acquisition of physical layer signals, RSSI is used to evaluate the signal strength between the device and the base station, and BER, as an indicator of bit error rate, reflects the channel quality. These data are quantified by the following formula: , where, represents the probability that the signal strength or bit error rate is in the th state within a preset time window, and n is the total number of states. The fluctuation entropy value H of the signal is used to represent the uncertainty of the environment, and the stability of the physical environment is inferred by calculating the entropy value.

[0048] To further improve the adaptability of the sleep control system, this example optimizes the weight adjustment mechanism in the environmental dynamic model construction module. When sudden interference appears in the environment, the system automatically adjusts the weights of the current signal characteristics and historical environmental data according to the dynamic weight adjustment strategy of real-time signal characteristics and historical data. Specifically, in the dynamic model construction module, when the time series feature analysis unit detects signal fluctuations that conform to the predefined sudden interference feature library, the system automatically reduces the weight of the current signal characteristics ( ), and increases the weight of the historical data ( ), and the formula is as follows: , Under this mechanism, The weight value is dynamically adjusted to be less than 0.5 in case of burst interference. This mechanism effectively avoids false awakenings caused by short-term environmental interference. In addition, the system introduces an interference-immune wake-up mechanism. When burst interference is detected, the device will block wake-up operations caused by instantaneous signal fluctuations within a preset time window, ensuring that the device remains in the sleep state and avoiding unnecessary wake-up power consumption.

[0049] In this embodiment, the lightweight state cache module further optimizes the caching method of protocol stack parameters. When the Internet of Things device enters the sleep state, key protocol stack parameters (such as the discontinuous reception cycle DRX and channel configuration information) are saved to the non-volatile memory. When waking up, the system directly restores these parameters, avoiding the process of re-receiving the system information broadcast in the traditional solution, thus saving a large amount of energy. The power co-control module further dynamically adjusts the power management strategy of the Internet of Things device according to the decision result of the adaptive sleep decision engine, which all belong to the extended implementation manners known to those of ordinary skill in the art.

[0050] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and without departing from the spirit or basic characteristics of the present invention, the present invention can be implemented in other specific forms.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An intelligent sleep control system for Internet of Things devices based on environmental self-awareness, characterized in that, Including: An environmental feature extraction layer module, which is used to obtain physical layer signal features and network layer connection status information in real time through the NB-IoT protocol stack of the Internet of Things device. The physical layer signal features at least include signal strength RSSI and bit error rate BER, and the network layer connection status information at least includes attachment retention status and channel resource reservation status; The environmental dynamic model construction module, which is communicatively connected to the environmental feature extraction layer module, is used to construct a dynamic model reflecting the stability of the physical environment where the device is located based on the obtained physical layer signal features and network layer connection status information. Among them, the signal strength fluctuation is mapped to the environmental interference degree, and the change in bit error rate is mapped to the channel quality. The two are combined to infer the stability of the physical environment; the stability of the environment is quantified by calculating the signal fluctuation entropy value and its calculation formula is: ， Among them, represents the probability that the signal strength or bit error rate is in the th state within a preset time window; An adaptive sleep decision engine module, which is communicatively connected to the environmental dynamic model construction module, and is used to adaptively adjust the sleep trigger condition based on the environmental dynamic model by using a dynamic threshold algorithm. The dynamic threshold algorithm dynamically adjusts the sleep trigger condition based on historical environmental data to avoid over-wake-up caused by a fixed threshold. Moreover, before determining that sleep is allowed, the adaptive sleep decision engine module sends a sleep request carrying a proposed sleep status code to the core network through the NB-IoT protocol stack; A lightweight status cache module, which is communicatively connected to the adaptive sleep decision engine module, and is used to save key protocol stack parameters in a non-volatile memory before the Internet of Things device enters the sleep state, and directly restore the key protocol stack parameters from the non-volatile memory when the Internet of Things device wakes up, skipping the system information broadcast reception process; A power cooperation control module, which is communicatively connected to the adaptive sleep decision engine module, and is used to control the power management unit of the Internet of Things device to enter or exit the sleep state according to the decision result of the adaptive sleep decision engine module, and dynamically adjust the sleep duration according to the evaluation result of the environmental dynamic model.

2. The intelligent sleep control system for Internet of Things devices based on environmental self-awareness according to claim 1, characterized in that, The environmental dynamic model construction module further includes: a timing feature analysis unit, which is used to analyze the timing fluctuation pattern of the physical layer signal features, and distinguish instantaneous interference from continuous environmental changes by matching with a predefined signal mutation feature library.

3. The intelligent sleep control system for Internet of Things devices based on environmental self-awareness according to claim 2, characterized in that, The environmental dynamic model construction module further includes: a dynamic weight regulator, configured to automatically reduce the weight of the instantaneous signal features in the current environmental stability assessment when the time series feature analysis unit detects a burst interference feature that conforms to the signal mutation feature library , and increase the weight of the historical environmental data , so as to make a dormancy decision, where , and when a burst interference is detected The value of is dynamically adjusted to be less than 0.

5.

4. The intelligent sleep control system for Internet of Things devices based on environmental self-awareness according to claim 2 or 3, characterized in that, Also including: An interference immune wake-up mechanism unit, which is used to prevent wake-up operations triggered by instantaneous signal fluctuations and maintain the current sleep state within a preset time window period after identifying burst interference.

5. The intelligent sleep control system for Internet of Things devices based on environmental self-awareness according to claim 1, characterized in that, The historical environmental data includes signal stability data within the past twenty-four hours.

6. The intelligent sleep control system for Internet of Things devices based on environmental self-awareness according to claim 1, wherein The key protocol stack parameters at least include a discontinuous reception DRX period and channel configuration information.

7. The intelligent sleep control system for Internet of Things devices based on environmental self-awareness according to claim 2, characterized in that, The signal mutation feature library includes signal fluctuation patterns representing motor startup, electromagnetic noise, or metal shielding interference sources.

8. The intelligent sleep control system for Internet of Things devices based on environmental self-awareness according to claim 3, characterized in that, The matching degree threshold of the preset interference template is greater than or equal to eighty percent.

9. The intelligent sleep control system for Internet of Things devices based on environmental self-perception according to claim 4, wherein The preset time window period is ten seconds.

10. The intelligent sleep control system for Internet of Things devices based on environmental self-awareness according to claim 1, characterized in that, When the environmental feature extraction layer module cannot effectively obtain physical layer signal features, the adaptive sleep decision engine module switches to a sleep control strategy based on a preset timer.

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