Low-power-consumption optimization method, device and equipment for MEMS sensor and storage medium

Abstract

The invention relates to a low-power-consumption optimization method and device for an MEMS sensor, equipment and a storage medium, and the method comprises the following steps: carrying out the adaptive threshold compression of an output signal of the MEMS sensor, obtaining a compressed data stream, carrying out the time domain feature extraction of the compressed data stream, and obtaining a signal activity feature set; performing energy consumption point analysis on the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map; performing hierarchical energy efficiency configuration mapping on the MEMS sensor based on the power consumption distribution map to obtain an energy efficiency configuration matrix; performing nonlinear optimization solution on the energy efficiency configuration matrix to obtain an optimal power consumption control sequence; and performing dynamic power consumption adjustment on the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor, thereby solving the technical problem that the energy-saving effect is limited in a specific use scene due to the fact that some existing power consumption management schemes are too simplified and cannot fully consider dynamic change factors in actual operation.

Description

Technical Field

[0001] The present invention relates to the technical field of MEMS sensors, and in particular to a low power consumption optimization method, device, equipment and storage medium for a MEMS sensor. Background Art

[0002] In the context of the rapid development of the Internet of Things and smart devices, MEMS (micro-electromechanical system) sensors, as a bridge connecting the physical world and the digital world, are expanding their application scope, from smartphones and wearable devices to smart homes and industrial automation. However, with the increasing complexity of the functions of these devices and the increasing requirements for portability and continuous operation time, how to effectively reduce the energy consumption of sensors has become an urgent problem to be solved. Traditional optimization methods often focus on the hardware level, such as improving the manufacturing process or selecting low-power materials, but this method faces problems such as high cost and difficulty in technical implementation.

[0003] On the other hand, software-level optimization strategies, such as the application of data compression and signal processing algorithms, can reduce the energy consumption of sensors to a certain extent, but most existing methods lack flexibility and adaptability, and are difficult to effectively adjust for different application scenarios and working conditions. In addition, some existing power management solutions are too simplified and fail to fully consider the dynamic changes in actual operations, resulting in limited energy saving effects in specific usage scenarios. Therefore, a more intelligent and sophisticated method is needed to optimize the energy consumption performance of MEMS sensors.

[0004] Based on the above challenges, researchers have begun to explore new methods to improve the energy efficiency of MEMS sensors, especially to maintain efficient operation under complex environmental conditions while minimizing energy consumption. This new method not only needs to consider the coordinated optimization of hardware and software, but also needs to introduce advanced algorithms and technologies, such as adaptive threshold compression, time domain feature extraction and nonlinear optimization, so as to achieve precise control and dynamic adjustment of sensor energy consumption. This provides a theoretical basis and technical support for the development of smarter and more efficient MEMS sensors, and also promotes the development of related research in a deeper and broader direction. Summary of the invention

[0005] The main purpose of the present invention is to provide a low-power consumption optimization method, device, equipment and storage medium for a MEMS sensor, which solves the technical problem that some existing power consumption management schemes are overly simplified and fail to fully consider the dynamic change factors in actual operation, resulting in limited energy-saving effects in specific usage scenarios.

[0006] To achieve the above object, the present invention provides a low power consumption optimization method for a MEMS sensor, comprising the following steps: Performing adaptive threshold compression on the output signal of the MEMS sensor to obtain a compressed data stream, and performing time domain feature extraction on the compressed data stream to obtain a signal activity feature set; Performing energy consumption point analysis on the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map; Based on the power consumption distribution map, the MEMS sensor is mapped in a hierarchical energy efficiency configuration to obtain an energy efficiency configuration matrix; Performing nonlinear optimization on the energy efficiency configuration matrix to obtain an optimal power consumption control sequence; Dynamically adjust the power consumption of the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor.

[0007] Furthermore, the step of performing adaptive threshold compression on the output signal of the MEMS sensor to obtain a compressed data stream includes: Performing wavelet packet decomposition on the output signal of the MEMS sensor to obtain multi-level wavelet coefficients, and performing energy entropy calculation based on the multi-level wavelet coefficients to obtain a signal energy entropy feature vector; The signal energy entropy feature vector is threshold segmented by a dynamic threshold adjustment algorithm to obtain a binary feature sequence, and run-length encoding is performed based on the binary feature sequence to obtain a compressed data stream.

[0008] Furthermore, the energy consumption point analysis of the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map includes: Dividing the signal activity feature set into time segments to obtain an activity time segment group, and calculating energy consumption features of the activity time segment group to obtain an energy consumption feature point set; Performing energy efficiency correlation analysis on the energy consumption feature point set through a preset voltage-frequency mapping table to obtain an energy efficiency correlation matrix, and classifying energy consumption characteristics of the MEMS sensor based on the energy efficiency correlation matrix to obtain an energy consumption type sequence; Performing spatiotemporal correlation analysis on the energy consumption type sequence to obtain an energy consumption correlation graph, performing spatial distribution statistics on the energy consumption correlation graph through energy consumption density calculation to obtain an energy consumption density graph, and locating energy consumption hot spots based on the energy consumption density graph to obtain an energy consumption hot spot set; Perform time domain evolution analysis on the energy consumption hotspot set to obtain an energy consumption evolution sequence, perform energy consumption trend prediction based on the energy consumption evolution sequence to obtain an energy consumption trend graph, and generate a power consumption spectrum based on the energy consumption trend graph to obtain a power consumption distribution spectrum.

[0009] Furthermore, the performing hierarchical energy efficiency configuration mapping on the MEMS sensor based on the power consumption distribution map to obtain an energy efficiency configuration matrix includes: Based on the power consumption distribution map, feature hierarchical division is performed to obtain a hierarchical feature table, and based on the hierarchical feature table, power consumption level classification is performed to obtain a power consumption level sequence; Mapping and converting the power consumption level sequence through a preset voltage and frequency configuration table to obtain a configuration parameter set, and performing working mode matching based on the configuration parameter set to obtain a working mode group; Performing state transition analysis on the working mode group to obtain a state transition diagram, and performing transition cost calculation based on the state transition diagram to obtain a transition cost matrix; Performing cost balancing processing on the conversion cost matrix through a preset multi-objective weight table to obtain a balance coefficient set, and performing parameter optimization configuration on the balance coefficient set to obtain an optimized configuration group; Verifying the constraints of the optimized configuration group to obtain a valid configuration sequence, and mapping configuration items based on the valid configuration sequence to obtain a mapping configuration table; An energy efficiency matrix is ​​generated based on the mapping configuration table to obtain an energy efficiency configuration matrix; wherein the energy efficiency configuration matrix includes static energy efficiency configuration, dynamic energy efficiency configuration and transient energy efficiency configuration.

[0010] Furthermore, performing nonlinear optimization on the energy efficiency configuration matrix to obtain an optimal power consumption control sequence includes: Performing constraint analysis on the energy efficiency configuration matrix to obtain a constraint set, and constructing a feasible domain based on the constraint set to obtain a feasible solution space; Constructing an objective function for the feasible solution space by using the Lagrange multiplier method to obtain an optimized objective function, and performing gradient calculation on the optimized objective function to obtain a gradient vector group; Determining a search direction for the gradient vector group to obtain a search direction sequence, and calculating a step length for the search direction sequence to obtain an iterative step length table; Iteratively optimizing the parameters of the iterative step table through nonlinear programming to obtain an iterative solution sequence, and verifying the convergence of the iterative solution sequence to obtain a converged solution set; Performing a multi-objective trade-off analysis on the MEMS sensor based on the converged solution set to obtain a trade-off coefficient matrix, and reconstructing a solution space based on the trade-off coefficient matrix to obtain a reconstructed solution sequence; The reconstructed solution sequence is mapped to control instructions by a preset control sequence generator to obtain a control instruction group, and the MEMS sensor is time-scheduled based on the control instruction group to obtain an optimal power consumption control sequence, wherein the optimal power consumption control sequence includes a voltage control sequence, a frequency control sequence and a mode control sequence.

[0011] Furthermore, the performing timing scheduling planning on the MEMS sensor based on the control instruction group to obtain an optimal power consumption control sequence includes: Performing instruction dependency analysis on the control instruction group to obtain an instruction dependency graph, and performing key path extraction based on the instruction dependency graph to obtain a key instruction linked list; Performing execution timing analysis on the key instruction chain list through timing constraint analysis to obtain an execution timing diagram, and detecting whether there is resource competition in the control instruction group based on the execution timing diagram; If so, performing conflict resolution processing on the control instruction group to obtain a conflict-free instruction group, and performing parallelism analysis on the conflict-free instruction group to obtain a parallel scheduling table; The parallel scheduling table is allocated with time slices to obtain a time slice allocation scheme, and the MEMS sensor is scheduled with timing based on the time slice allocation scheme to obtain an optimal power consumption control sequence.

[0012] Furthermore, dynamically adjusting the power consumption of the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor includes: Performing timing analysis on the optimal power consumption control sequence to obtain a control timing table, and performing state migration analysis based on the control timing table to obtain a migration path set; Performing voltage regulation parameter configuration on the migration path set through a preset voltage regulator to obtain a voltage regulation sequence, and performing frequency dynamic compensation based on the voltage regulation sequence to obtain a frequency compensation vector; Decoding the frequency compensation vector according to the working mode to obtain a mode switching instruction set, and performing clock gating configuration on the mode switching instruction set to obtain a gating configuration table; By means of a power consumption monitoring unit, real-time power consumption sampling is performed on the MEMS sensor based on the gating configuration table to obtain a power consumption sampling sequence, and deviation analysis is performed on the power consumption sampling sequence to obtain deviation characteristic data; Calculating compensation parameters for the deviation characteristic data to obtain a compensation vector group, and reconstructing adjustment instructions based on the compensation vector group to obtain a reconstructed instruction sequence; Through a dynamic feedback mechanism, the power consumption state of the data is adjusted based on the reconstruction instruction sequence to obtain a target power consumption state of the MEMS sensor.

[0013] The present invention also provides a low power consumption optimization device for a MEMS sensor, comprising: An extraction module, used to perform adaptive threshold compression on the output signal of the MEMS sensor to obtain a compressed data stream, and perform time domain feature extraction on the compressed data stream to obtain a signal activity feature set; An analysis module, configured to perform energy consumption point analysis on the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map; A mapping module, used to perform hierarchical energy efficiency configuration mapping on the MEMS sensor based on the power consumption distribution map to obtain an energy efficiency configuration matrix; A solution module, used for performing nonlinear optimization on the energy efficiency configuration matrix to obtain an optimal power consumption control sequence; The adjustment module is used to dynamically adjust the power consumption of the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0016] The present invention provides a low power consumption optimization method for a MEMS sensor, comprising the following steps: adaptively compressing the output signal of the MEMS sensor with a threshold to obtain a compressed data stream, and extracting time domain features from the compressed data stream to obtain a signal activity feature set; performing energy consumption point analysis on the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map; performing hierarchical energy efficiency configuration mapping on the MEMS sensor based on the power consumption distribution map to obtain an energy efficiency configuration matrix; performing nonlinear optimization and solving the energy efficiency configuration matrix to obtain an optimal power consumption control sequence; dynamically adjusting the power consumption of the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor, solving the technical problem that some existing power consumption management schemes are too simplified and fail to fully consider the dynamic change factors in actual operation, resulting in limited energy saving effects in specific usage scenarios, and realizing solving the energy efficiency configuration matrix through a nonlinear optimization algorithm to obtain the optimal power consumption control sequence. This method takes into account the complex interactions and dynamic change factors within the system, ensuring that the proposed control strategy can achieve the best energy saving effect under various working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the steps of a low power consumption optimization method for a MEMS sensor in one embodiment of the present invention; Figure 2 is a structural block diagram of a low power consumption optimization device for a MEMS sensor in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a low power consumption optimization method of a MEMS sensor in one embodiment of the present invention; In one embodiment of the present invention, a low power consumption optimization method for a MEMS sensor is provided, comprising the following steps: Step S1, performing adaptive threshold compression on the output signal of the MEMS sensor to obtain a compressed data stream, and performing time domain feature extraction on the compressed data stream to obtain a signal activity feature set.

[0021] Specifically, in the low power optimization method of MEMS sensors, it is first necessary to perform adaptive threshold compression on the sensor output signal to obtain a compressed data stream, and further extract time domain features from the compressed data stream to obtain a signal activity feature set. The core of this process is to automatically adjust the threshold value through an intelligent algorithm to achieve data compression while ensuring that key information is not lost. Specifically, the adaptive threshold compression technology adjusts the compression threshold in real time according to the dynamic range and characteristics of the input signal, so that only when the signal exceeds a certain threshold will it be recorded, and the signal below the threshold will be regarded as background noise or redundant information and ignored. The advantage of this is that it can significantly reduce the amount of data and reduce the burden of subsequent processing and transmission. Once the adaptive threshold compression is completed, the next step is to extract time domain features from the compressed data stream. The so-called time domain features here refer to the characteristics of the signal changing over time, such as peak value, frequency, amplitude change, etc. By extracting these features, important activity patterns in the signal can be identified, such as periodic fluctuations, burst pulses, etc., which may be associated with the target event monitored by the sensor. For example, in a smart home environment, a MEMS accelerometer is used to detect whether someone is moving in the room. In this case, the sensor continuously collects vibration data in the environment, but not all data is helpful to determine whether there is a person. By applying the above method, the corresponding data will only be recorded and analyzed when the vibration intensity exceeds the set adaptive threshold, which helps to filter out the interference signals caused by daily environmental noise and focus on capturing truly meaningful human activity signals. Then, using the time domain feature extraction technology, a series of parameters describing the signal behavior characteristics can be extracted from the compressed data stream to form a signal activity feature set. These feature sets not only contain important information about the signal itself, but also provide basic data for subsequent energy consumption point analysis. For example, in the above smart home scenario, if the system detects a series of short and high-frequency vibration signals, it may be that someone is walking through the room quickly; while slow and continuous vibrations may mean that someone is walking in the room. In this way, the system can understand the environmental status more accurately and make more reasonable decisions. The whole process not only improves the data processing efficiency, but also enhances the response speed and accuracy of the system, and finally realizes the efficient operation and low power consumption of MEMS sensors in complex application scenarios.

[0022] Step S2: performing energy consumption point analysis on the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map.

[0023] Specifically, the process of analyzing the energy consumption points of MEMS sensors based on the signal activity feature set and obtaining the power consumption distribution map aims to identify the energy consumption mode of the sensor under different working conditions by deeply analyzing the dynamic characteristics of the signal. First, the signal activity feature set extracted in the previous step, including but not limited to peak value, frequency change, amplitude fluctuation, etc., can comprehensively describe the behavior mode of the sensor in actual operation. Next, by analyzing the relationship between these features and the energy consumption of the sensor, it is possible to determine which specific signal activities will lead to higher energy consumption, and clarify the conditions and environment in which these high-energy activities occur. For example, when a MEMS accelerometer is used to monitor the movement of people in a room in a smart home environment, the system will perform a detailed analysis based on the different types of vibration signals detected (such as short and high-frequency pulses or slow and continuous fluctuations) and their corresponding energy consumption. Specifically, when the sensor captures a short and high-frequency vibration signal generated by someone walking quickly through the room, the process may result in higher energy consumption because a higher sampling rate and more complex signal processing algorithms are required to accurately identify these signals. On the contrary, for slow and continuous vibration signals, although they also need to be processed, because they are relatively simple and require less computing resources, the energy consumption is lower. Through such analysis, a detailed power consumption distribution map can be generated, which not only shows the energy consumption of the sensor during the entire operation cycle, but also reveals the specific impact of different signal activities on energy consumption. This provides an important basis for further optimizing the energy efficiency configuration of the sensor, allowing designers to adjust the operating parameters of the sensor according to actual needs to achieve the best energy consumption performance. For example, reducing the sensitivity of the sensor or reducing the frequency of data collection during low activity periods at night can achieve energy saving effects. This not only improves the overall efficiency of the system, but also extends the service life of the equipment.

[0024] Step S3, performing hierarchical energy efficiency configuration mapping on the MEMS sensor based on the power consumption distribution map to obtain an energy efficiency configuration matrix.

[0025] Specifically, the process of mapping the MEMS sensor's hierarchical energy efficiency configuration based on the power consumption distribution map to obtain the energy efficiency configuration matrix is ​​to allocate the optimal energy management strategy for each working state by analyzing the energy consumption characteristics under different working modes. First, using the power consumption distribution map generated in the previous step, this map shows in detail the energy consumption of the sensor under various signal activities. Next, based on this information, it is possible to identify which operating modes or signal processing tasks require higher energy support and which can be operated at lower energy consumption. For example, in an application scenario where a MEMS accelerometer is used to monitor the movement of people in a room in a smart home environment, the system can understand from the power consumption distribution map that the energy required when detecting fast movement is higher, while the energy consumption is lower in a stationary state. Then, by hierarchically classifying these different energy consumption modes, the working state of the sensor is divided into multiple levels, each of which corresponds to a specific energy requirement and performance target. For example, the operation of the sensor can be divided into several levels such as high sensitivity mode, standard mode and low power mode. For each level, the corresponding energy efficiency configuration parameters are further determined, such as sampling rate, data transmission frequency and complexity of the signal processing algorithm. In this way, an energy efficiency configuration matrix can be constructed, which not only clarifies the specific configuration requirements of each level, but also provides guidelines on how to optimize energy usage in different application scenarios. For example, at night or when no one is around, the system can automatically switch to low-power mode to reduce unnecessary data collection and processing, thereby significantly reducing overall energy consumption; during the day or when activity is detected, it switches back to high-sensitivity mode to ensure that all important signal changes are accurately captured. This hierarchical energy efficiency configuration method enables MEMS sensors to achieve optimal energy management while meeting performance requirements, which not only improves the efficiency of the system, but also extends the service life of the equipment.

[0026] Step S4, performing nonlinear optimization on the energy efficiency configuration matrix to obtain an optimal power consumption control sequence.

[0027] Specifically, the process of solving the energy efficiency configuration matrix through nonlinear optimization to obtain the optimal power consumption control sequence aims to find the control strategy that minimizes the overall energy consumption of the sensor under different working modes through mathematical optimization methods. First, based on the energy efficiency configuration matrix constructed in the previous step, the matrix lists in detail the energy requirements and performance goals at each level, including parameters such as sampling rate, data transmission frequency, and signal processing algorithm complexity. Next, nonlinear optimization algorithms are used, which can handle complex multivariable problems and find global or local optimal solutions. Specifically, the nonlinear optimization solution process takes into account the dynamic changes of sensors in different application scenarios. For example, when using MEMS accelerometers to monitor the movement of people in a room in a smart home environment, the system needs to adjust its working mode according to real-time activity to achieve the best energy consumption performance. In this process, the nonlinear optimization algorithm simulates the operating state of the sensor under different configurations and calculates the energy consumption corresponding to each configuration. By continuously iterating and adjusting parameter settings, the algorithm can gradually approach a solution that minimizes the total energy consumption. For example, during low activity periods at night, the system can save energy by reducing the sampling rate and reducing the frequency of data transmission; while during high activity periods during the day, it is necessary to increase sensitivity and data processing capabilities to ensure that all important signals are accurately captured. In addition, the nonlinear optimization algorithm can also take into account changes in external environmental factors, such as the impact of temperature fluctuations on sensor performance, and dynamically adjust the optimization scheme accordingly. Finally, after a series of complex calculations and optimizations, the algorithm will generate an optimal power consumption control sequence, which includes specific control measures to be taken in different time periods and conditions, such as when to switch to energy-saving mode and when to restore to high-sensitivity mode. For example, in a smart home scenario, the system can automatically adjust the working mode of the accelerometer according to different times of the day: high-sensitivity mode is used when activities are frequent in the early morning and evening, and low-power mode is switched to late at night to extend battery life. This method based on nonlinear optimization not only improves the energy efficiency of the system, but also enhances the flexibility and adaptability of the system, ensuring the best energy management effect in various complex application scenarios. In this way, MEMS sensors can minimize energy consumption while meeting functional requirements, thereby improving the reliability and sustainability of the overall system.

[0028] Step S5: dynamically adjust the power consumption of the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor.

[0029] Specifically, the process of dynamically adjusting the power consumption of MEMS sensors based on the optimal power consumption control sequence to achieve the target power consumption state aims to achieve optimal energy management by adjusting the operating parameters of the sensor in real time. First, the optimal power consumption control sequence generated in the previous step is used, which specifies in detail the specific control measures to be taken under different time periods and conditions, including parameter settings such as sampling rate, data transmission frequency, and signal processing algorithm complexity. Next, the system gradually implements the corresponding adjustment strategy according to this sequence to ensure that the sensor can meet performance requirements while minimizing energy consumption. For example, in the application scenario of using MEMS accelerometers to monitor the movement of people in a room in a smart home environment, the system can automatically adjust the working mode of the accelerometer according to different times of the day. Specifically, when the system detects that the current time period is a low activity period (such as late at night), the sampling rate and data transmission frequency of the accelerometer are reduced according to the instructions in the optimal power consumption control sequence, and the signal processing algorithm is simplified to reduce energy consumption. On the contrary, during high activity periods (such as daytime or evening), the system will increase the sampling rate and data processing capabilities to ensure that all important signals can be accurately captured. This dynamic adjustment mechanism not only takes into account the changes in daily activity patterns, but also responds to emergencies, such as temporary increases in activity events. For example, if an abnormal vibration signal is suddenly detected late at night, the system can quickly switch to high-sensitivity mode, increase the sampling rate and enable more complex signal processing algorithms to identify potential security threats in a timely and accurate manner. In addition, the dynamic power adjustment process also incorporates the impact of external environmental factors, such as the impact of temperature changes on sensor performance. By monitoring these factors in real time, the system is able to further optimize its power control strategy to ensure that optimal performance is maintained under various operating conditions. Ultimately, after a series of fine dynamic adjustments, the MEMS sensor will reach its target power consumption state, which not only improves the energy efficiency of the system, but also extends the service life of the device. For example, in smart home applications, this intelligent power management method can not only ensure that the accelerometer provides high-precision data when needed, but also significantly reduce overall energy consumption, thereby improving user experience and system sustainability. In this way, MEMS sensors achieve a perfect balance between high efficiency and low power consumption in complex and changing application scenarios.

[0030] In a specific embodiment, the step of performing adaptive threshold compression on the output signal of the MEMS sensor to obtain a compressed data stream includes: Performing wavelet packet decomposition on the output signal of the MEMS sensor to obtain multi-level wavelet coefficients, and performing energy entropy calculation based on the multi-level wavelet coefficients to obtain a signal energy entropy feature vector; The signal energy entropy feature vector is threshold segmented by a dynamic threshold adjustment algorithm to obtain a binary feature sequence, and run-length encoding is performed based on the binary feature sequence to obtain a compressed data stream.

[0031] Specifically, the process of adaptive threshold compression of the output signal of the MEMS sensor to obtain a compressed data stream first involves wavelet packet decomposition of the sensor output signal. Specifically, wavelet packet decomposition is a multi-resolution analysis method that can decompose the original signal into multiple frequency bands to obtain wavelet coefficients in different frequency bands. These wavelet coefficients contain rich signal information and can reflect the energy distribution of the signal in each frequency band. Next, energy entropy is calculated based on these multi-level wavelet coefficients to obtain the signal energy entropy feature vector. Energy entropy is an important indicator to measure signal complexity. It evaluates the randomness and irregularity of the signal by quantifying the energy distribution of wavelet coefficients in each frequency band. For example, when a MEMS accelerometer is used to monitor the movement of people in a room in a smart home environment, the signal energy entropy feature vector can help distinguish different types of activity patterns, such as fast movement, slow walking or stationary state. Once the signal energy entropy feature vector is obtained, the next step is to perform threshold segmentation on it through a dynamic threshold adjustment algorithm. The key to this process is to dynamically adjust the threshold according to the current signal characteristics and system requirements, so that only those features exceeding a specific threshold are retained, and the rest are regarded as redundant information and ignored. In this way, the amount of data can be effectively reduced while retaining key information. In the specific implementation, an initial threshold is first set according to the signal energy entropy feature vector, and then the threshold is adjusted through iterative optimization until the optimal segmentation point is found, thereby generating a binary feature sequence. This binary feature sequence not only simplifies the original signal representation, but also provides a basis for further data compression. Subsequently, run-length encoding (RLE) is performed based on the generated binary feature sequence to obtain the final compressed data stream. Run-length encoding is a simple but effective lossless data compression technology that reduces storage space by recording the length of consecutive identical symbols. In this step, the system scans the binary feature sequence, identifies continuous runs of "1" or "0", and uses a pair of numerical values ​​to represent the starting position and length of each run. For example, in the above-mentioned smart home application scenario, when rapid movement is detected in the room, due to the drastic signal changes at this time, more "1" may appear in the binary feature sequence, while in the static state it is mainly composed of "0". Through RLE compression, the system can significantly reduce the amount of data that needs to be transmitted and processed, and improve overall efficiency. The whole process not only achieves effective compression of the MEMS sensor output signal, but also greatly reduces the burden of subsequent processing and transmission while ensuring that key information is not lost. For example, in a smart home system, by performing the above processing on the accelerometer signal, it is not only possible to more efficiently identify the user's activity pattern, but also to extend the device battery life and improve the user experience. In addition, this adaptive threshold compression method has good flexibility and adaptability, and can dynamically adjust parameter settings according to the needs of actual application scenarios to ensure that the best compression effect and energy consumption performance can be achieved under various working conditions.Therefore, this method provides a practical solution for achieving a balance between high efficiency and low power consumption. Through this series of steps, MEMS sensors can significantly reduce energy consumption while maintaining high accuracy in complex and changing application scenarios, further promoting the development and application of smart sensing technology.

[0032] In a specific embodiment, the energy consumption point analysis of the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map includes: Dividing the signal activity feature set into time segments to obtain an activity time segment group, and calculating energy consumption features of the activity time segment group to obtain an energy consumption feature point set; Performing energy efficiency correlation analysis on the energy consumption feature point set through a preset voltage-frequency mapping table to obtain an energy efficiency correlation matrix, and classifying energy consumption characteristics of the MEMS sensor based on the energy efficiency correlation matrix to obtain an energy consumption type sequence; Performing spatiotemporal correlation analysis on the energy consumption type sequence to obtain an energy consumption correlation graph, performing spatial distribution statistics on the energy consumption correlation graph through energy consumption density calculation to obtain an energy consumption density graph, and locating energy consumption hot spots based on the energy consumption density graph to obtain an energy consumption hot spot set; Perform time domain evolution analysis on the energy consumption hotspot set to obtain an energy consumption evolution sequence, perform energy consumption trend prediction based on the energy consumption evolution sequence to obtain an energy consumption trend graph, and generate a power consumption spectrum based on the energy consumption trend graph to obtain a power consumption distribution spectrum.

[0033] Specifically, the process of analyzing the energy consumption points of MEMS sensors based on the signal activity feature set to obtain the power consumption distribution map first requires the signal activity feature set to be divided into time segments to obtain the activity time segment group. This process is achieved by dividing the continuous signal data into several segments with specific time intervals, each of which represents the sensor activity pattern within a period of time. For example, when using a MEMS accelerometer to monitor the movement of people in a room in a smart home environment, the data of 24 hours a day can be divided into 96 15-minute time periods, each of which represents an activity time segment. By calculating the signal activity features in these time periods, such as peak values, frequency changes, and amplitude fluctuations, a set of energy consumption feature point sets can be obtained. Assume that during the daytime period (8:00-20:00), due to frequent activities, there may be 3-5 significant energy consumption feature points per hour; while during the nighttime period (20:00-8:00), due to reduced activities, there may be only 1-2 energy consumption feature points per hour. Next, the energy efficiency correlation analysis of the energy consumption feature point set is performed using the preset voltage-frequency mapping table to generate an energy efficiency correlation matrix. The core of this step is to evaluate the energy consumption of the sensor in different working states according to different voltage and frequency combinations. For example, when the sensor is in high-sensitivity mode, its operating voltage is 3.3V and the frequency is 1MHz, while in low-power mode, the operating voltage drops to 1.8V and the frequency drops to 250kHz. Through this mapping relationship, the system can identify which operating modes or signal processing tasks require higher energy support and which can be operated at lower energy consumption. Based on this information, the system can further classify the energy consumption feature points and generate an energy consumption type sequence. Assume that in a typical home environment, the sensor is in high-sensitivity mode for about 70% of the daytime and in low-power mode for about 90% of the nighttime. Then, the energy consumption type sequence is analyzed for spatiotemporal correlation to obtain an energy consumption correlation graph, and the energy consumption correlation graph is spatially distributed and statistically analyzed through energy consumption density calculation to generate an energy consumption density graph. In this process, the system not only considers the energy consumption changes in the time dimension, but also combines the influencing factors of spatial location. For example, in a smart home, if a room is close to the entrance, the sensors in that room may record more activity data, resulting in higher energy consumption density. By calculating the average energy consumption density of each room, it can be found that the average energy consumption per square meter per day in rooms close to the entrance is about 0.5 watt-hours, while in rooms far from the entrance it is only 0.2 watt-hours. Based on this data, the system can further locate energy consumption hotspots, that is, those areas or time periods where energy consumption is significantly higher than the average level. Next, the energy consumption hotspots are analyzed in the time domain to obtain an energy consumption evolution sequence, and energy consumption trends are predicted based on the sequence to generate an energy consumption trend graph. This process helps predict future energy consumption patterns by analyzing the changing trends of energy consumption hotspots over time.For example, in the smart home scenario above, the system can analyze the data of the past month and find that the energy consumption hotspots on Friday nights have increased significantly, which may be due to family members gathering on weekends. Through this long-term trend analysis, the system can make adjustments in advance and optimize the working mode of the sensor to cope with the upcoming high energy consumption period. Assuming that in the past month, the energy consumption on Friday nights is about 40% higher than usual, the system can predict the energy consumption growth in similar time periods in the future based on this trend and take corresponding energy-saving measures. Finally, a power consumption distribution map is generated based on the energy consumption trend chart. This map not only shows the energy consumption of the sensor throughout the operation cycle, but also reveals the specific impact of different signal activities on energy consumption. For example, in smart home applications, the system can generate a detailed power consumption distribution map showing the energy consumption of each room and each time period. Assuming that the total energy consumption of the living room is 10 watt-hours, the bedroom is 5 watt-hours, and the kitchen is 8 watt-hours in a week, through this map, users can clearly see which areas and time periods have high energy consumption, and adjust their living habits or device settings accordingly to achieve energy saving goals. In addition, this power consumption distribution map can also help designers and users better understand the operating efficiency of the system and provide a basis for further optimization, so that MEMS sensors can achieve the best energy management effect while meeting performance requirements, which not only improves the overall efficiency of the system but also extends the service life of the equipment. Through this series of steps, MEMS sensors have achieved a balance between high efficiency and low power consumption in complex and changing application scenarios, promoting the development and application of intelligent sensing technology.

[0034] In a specific embodiment, the step of performing hierarchical energy efficiency configuration mapping on the MEMS sensor based on the power consumption distribution map to obtain an energy efficiency configuration matrix includes: Based on the power consumption distribution map, feature hierarchical division is performed to obtain a hierarchical feature table, and based on the hierarchical feature table, power consumption level classification is performed to obtain a power consumption level sequence; Mapping and converting the power consumption level sequence through a preset voltage and frequency configuration table to obtain a configuration parameter set, and performing working mode matching based on the configuration parameter set to obtain a working mode group; Performing state transition analysis on the working mode group to obtain a state transition diagram, and performing transition cost calculation based on the state transition diagram to obtain a transition cost matrix; Performing cost balancing processing on the conversion cost matrix through a preset multi-objective weight table to obtain a balance coefficient set, and performing parameter optimization configuration on the balance coefficient set to obtain an optimized configuration group; Verifying the constraints of the optimized configuration group to obtain a valid configuration sequence, and mapping configuration items based on the valid configuration sequence to obtain a mapping configuration table; An energy efficiency matrix is ​​generated based on the mapping configuration table to obtain an energy efficiency configuration matrix; wherein the energy efficiency configuration matrix includes static energy efficiency configuration, dynamic energy efficiency configuration and transient energy efficiency configuration.

[0035] Specifically, the process of mapping the MEMS sensor's hierarchical energy efficiency configuration based on the power consumption distribution map to obtain the energy efficiency configuration matrix first needs to start from the power consumption distribution map and obtain a hierarchical feature table through feature hierarchical division. This step aims to divide the entire system into several hierarchies with similar energy consumption characteristics according to the characteristics of energy consumption. For example, in a smart home scenario, considering the differences in usage frequency and activity level in different areas such as the living room, bedroom, and kitchen, these areas can be divided into high energy consumption areas (such as the living room), medium energy consumption areas (such as the kitchen), and low energy consumption areas (such as the bedroom) according to their average daily energy consumption. Assuming that the living room consumes 0.5 watt-hours of energy per square meter per day, and the bedroom consumes 0.2 watt-hours, each area can be classified according to the actual energy consumption to form a hierarchical feature table. Next, the power consumption level is classified based on the above hierarchical feature table to generate a power consumption level sequence. This process involves determining the energy consumption range of each level and assigning the corresponding power consumption level accordingly. For example, it can be set as a high level if it is higher than 0.4 watt-hours / square meter day, a medium level if it is between 0.2 and 0.4 watt-hours, and a low level if it is lower than 0.2 watt-hours. In the smart home application example, the living room is classified as a high level, the kitchen is classified as a medium level, and the bedroom is classified as a low level. This step not only helps to clarify the energy consumption status of each area, but also provides basic data support for further optimization. Subsequently, the power consumption level sequence is mapped and converted using the preset voltage and frequency configuration table to obtain a configuration parameter set, and the working mode matching is performed based on the set to generate a working mode group. The key here is to select the appropriate voltage and frequency combination according to different power consumption levels to achieve the best balance between performance and energy consumption. For example, for the high-level living room area, a higher working voltage (such as 3.3V) and frequency (such as 1MHz) can be selected, while in the low-level bedroom, a lower working voltage (such as 1.8V) and frequency (such as 250kHz) are used. In this way, the working mode of the sensor can be adjusted according to the actual needs of each area to ensure that energy consumption is minimized while meeting functional requirements. The next step is to analyze the state transition of the working mode group, obtain the state transition diagram, calculate the transition cost, and generate the transition cost matrix. The core of this stage is to evaluate the resource and time costs required to switch between different working modes. For example, in a smart home environment, when the user leaves the room, the system may need to switch from high-sensitivity mode to low-power mode, and vice versa. Assume that each mode switch consumes approximately an additional 0.01 watt-hour of energy and causes a response delay of about 1 second. By analyzing all possible state transitions, the system can quantify the cost of each transition, thereby guiding subsequent optimization decisions. Then, the transition cost matrix is ​​cost-balanced through a preset multi-objective weight table to obtain a set of balanced coefficients, and these coefficients are parameter-optimized to generate an optimized configuration group.During this process, the system will consider multiple optimization goals, such as reducing total energy consumption and improving response speed, and assign different weights according to their respective importance. For example, if the target weight of reducing energy consumption is 0.7 and the weight of improving response speed is 0.3, the system will find an optimal solution between the two. In the case of smart home, this means ensuring sufficient sensitivity to capture user actions while minimizing unnecessary energy consumption. Finally, the constraints of the optimized configuration group are verified to obtain a valid configuration sequence, and the configuration items are mapped based on this sequence, and finally a mapping configuration table is generated; based on this table, an efficiency configuration matrix is ​​generated. During this period, the system must ensure that all configuration options meet the hardware and software constraints, such as maximum operating voltage, minimum operating frequency, etc. For example, in smart home devices, all configuration schemes must ensure that the maximum operating voltage of the sensor is not exceeded, 3.3V, and the minimum operating frequency is not exceeded, 250kHz. Once the verification is completed, the system can establish a detailed energy efficiency configuration matrix based on the effective configuration scheme, including static energy efficiency configuration (such as fixed working mode), dynamic energy efficiency configuration (such as working mode that automatically adjusts over time) and transient energy efficiency configuration (such as temporary adjustment to cope with emergencies). In summary, through this series of steps, MEMS sensors can efficiently manage energy usage during their life cycle, which not only improves the overall performance of the system, but also extends the service life of the equipment. For example, in the application of smart home, through the carefully designed hierarchical energy efficiency configuration mapping strategy, the sensor network in the entire home environment can be made more intelligent and energy-saving. For example, by reasonably arranging the working mode of sensors in different rooms, the monthly energy consumption of the entire family can be reduced by about 10% to 15%, which is of great significance for improving user experience and saving energy. At the same time, this strategy also provides valuable reference experience for the application of MEMS sensors in other fields, and promotes the development and progress of related technologies.

[0036] In a specific embodiment, performing nonlinear optimization on the energy efficiency configuration matrix to obtain an optimal power consumption control sequence includes: Performing constraint analysis on the energy efficiency configuration matrix to obtain a constraint set, and constructing a feasible domain based on the constraint set to obtain a feasible solution space; Constructing an objective function for the feasible solution space by using the Lagrange multiplier method to obtain an optimized objective function, and performing gradient calculation on the optimized objective function to obtain a gradient vector group; Determining a search direction for the gradient vector group to obtain a search direction sequence, and calculating a step length for the search direction sequence to obtain an iterative step length table; Iteratively optimizing the parameters of the iterative step table through nonlinear programming to obtain an iterative solution sequence, and verifying the convergence of the iterative solution sequence to obtain a converged solution set; Performing a multi-objective trade-off analysis on the MEMS sensor based on the converged solution set to obtain a trade-off coefficient matrix, and reconstructing a solution space based on the trade-off coefficient matrix to obtain a reconstructed solution sequence; The reconstructed solution sequence is mapped to control instructions by a preset control sequence generator to obtain a control instruction group, and the MEMS sensor is time-scheduled based on the control instruction group to obtain an optimal power consumption control sequence, wherein the optimal power consumption control sequence includes a voltage control sequence, a frequency control sequence and a mode control sequence.

[0037] Specifically, the process of performing nonlinear optimization on the energy efficiency configuration matrix to obtain the optimal power consumption control sequence first requires the constraint analysis of the energy efficiency configuration matrix to obtain the constraint set. This process aims to identify various limiting factors in the operation of the system, such as the maximum operating voltage, the minimum operating frequency, and the physical limits of the hardware. For example, when a MEMS accelerometer is used to monitor the movement of people in a room in a smart home environment, the operating voltage of the sensor cannot exceed 3.3V and the minimum operating frequency is 250kHz. These constraints not only ensure the stability and reliability of the system, but also provide boundary conditions for subsequent optimization. Based on these constraints, a feasible solution space can be constructed, which contains all possible solutions that meet these conditions. Next, the Lagrange multiplier method is used to construct the objective function of the feasible solution space, generate the optimization objective function, and calculate the gradient of the function to obtain a gradient vector group. The Lagrange multiplier method is a commonly used mathematical tool for finding the optimal solution under constraints. In this process, the system converts each constraint into a corresponding Lagrange multiplier and incorporates it into the objective function. For example, in smart home applications, assuming that the goal is to minimize total energy consumption while ensuring a certain response speed, the system will construct an objective function that comprehensively considers these two factors. By performing gradient calculations on this objective function, the direction of change of each parameter, that is, the gradient vector group, can be determined, which provides a basis for subsequent search and optimization. Then, the search direction of the gradient vector group is determined to obtain a search direction sequence, and the step length of the search direction sequence is calculated to generate an iterative step length table. The determination of the search direction is one of the key steps in the optimization process, which determines how to gradually approach the optimal solution. Specifically, the system will select a suitable search direction based on the gradient information in the current state so that the objective function value can decrease as quickly as possible. For example, in a smart home scenario, if the current working mode of the sensor leads to high energy consumption, the system will choose a direction to reduce energy consumption for adjustment. Once the search direction is determined, the next step is to calculate the step length of each step, that is, the specific amplitude of each adjustment. Assuming that the step length of each adjustment is 0.1V or 0.05MHz, in this way, the system can gradually approach the optimal solution in a limited time. Subsequently, the iterative step table is optimized through nonlinear programming to obtain an iterative solution sequence, and the convergence of the iterative solution sequence is verified to obtain a converged solution set. Nonlinear programming is a powerful optimization technique that can find global or local optimal solutions in complex multivariable problems. In this process, the system will continuously adjust various parameters and gradually narrow the search range until a set of optimal solutions is found. For example, in smart home applications, the system can adjust the operating voltage and frequency of the sensor through multiple iterations, and finally find a set of parameter combinations that can ensure sufficient sensitivity and minimize energy consumption.In order to ensure that the solution found is indeed the optimal solution, the system also needs to perform convergence verification, that is, check whether the solution after each iteration tends to be stable and has no significant changes. Next, based on the converged solution set, a multi-objective trade-off analysis is performed on the MEMS sensor to generate a trade-off coefficient matrix, and the solution space is reconstructed based on the matrix to obtain a reconstructed solution sequence. The core of multi-objective trade-off analysis is to balance the relationship between different optimization goals, such as energy consumption and response speed. For example, in the smart home scenario, the system must not only consider how to reduce energy consumption, but also ensure that the sensor can capture the user's activity signals in a timely and accurate manner. By assigning different weights to each goal, the system can find a compromise solution to achieve the best overall performance. Assuming that the weight of reducing energy consumption is 0.7 and the weight of improving response speed is 0.3, the system will find an optimal solution between the two and generate a trade-off coefficient matrix based on this. Based on this matrix, the system can further reconstruct the solution space and find a set of better solution sequences. Finally, the control instruction mapping of the reconstructed solution sequence is performed through the preset control sequence generator to generate a control instruction group, and the timing scheduling planning of the MEMS sensor is performed based on the group to obtain the optimal power consumption control sequence. The control sequence generator here is a software module that is responsible for converting the optimization results into specific control instructions. For example, in smart home applications, the system can generate a series of voltage control instructions (such as from 3.3V to 1.8V), frequency control instructions (such as from 1MHz to 250kHz), and mode control instructions (such as switching from high sensitivity mode to low power mode) based on the optimization results. Through these control instructions, the system can dynamically adjust the working mode of the sensor to achieve the best energy management effect. Assume that at different times of the day, the system automatically adjusts the working mode of the sensor according to the user's activities: high sensitivity mode is used during the day, and low power mode is switched to at night. This not only ensures the normal operation of the system, but also significantly reduces energy consumption. In summary, through this series of steps, MEMS sensors can achieve a balance between high efficiency and low power consumption in complex and changing application scenarios. For example, in a smart home environment, a well-designed nonlinear optimization method can make the sensor network in the entire home environment more intelligent and energy-saving. Assume that through this optimization strategy, the monthly household energy consumption is reduced by about 10% to 15%, which is of great significance for improving user experience and saving energy. In addition, this method also provides valuable reference experience for MEMS sensor applications in other fields, promoting the development and progress of related technologies. Through continuous iterative optimization and real-time adjustment, the system can meet functional requirements while minimizing energy consumption, extending the service life of the equipment, and further improving the reliability and sustainability of the system.

[0038] In a specific embodiment, the performing timing scheduling planning on the MEMS sensor based on the control instruction group to obtain an optimal power consumption control sequence includes: Performing instruction dependency analysis on the control instruction group to obtain an instruction dependency graph, and performing key path extraction based on the instruction dependency graph to obtain a key instruction linked list; Performing execution timing analysis on the key instruction chain list through timing constraint analysis to obtain an execution timing diagram, and detecting whether there is resource competition in the control instruction group based on the execution timing diagram; If so, performing conflict resolution processing on the control instruction group to obtain a conflict-free instruction group, and performing parallelism analysis on the conflict-free instruction group to obtain a parallel scheduling table; The parallel scheduling table is allocated with time slices to obtain a time slice allocation scheme, and the MEMS sensor is scheduled with timing based on the time slice allocation scheme to obtain an optimal power consumption control sequence.

[0039] Specifically, the process of scheduling the MEMS sensor based on the control instruction group to obtain the optimal power consumption control sequence first requires the instruction dependency analysis of the control instruction group to generate an instruction dependency graph, and extract the critical path based on the graph to form a critical instruction list. This process aims to identify the dependencies between the various control instructions to ensure the correctness of the execution order. For example, when using a MEMS accelerometer to monitor the movement of people in a room in a smart home environment, the system may contain multiple control instructions, such as adjusting the operating voltage, changing the sampling frequency, and switching the operating mode. By performing dependency analysis on these instructions, it is possible to determine which instructions must be executed before or after other instructions, thereby constructing a clear instruction dependency graph. Then, through the critical path extraction method, find out the instruction chains that have the greatest impact on the overall performance, that is, the critical instruction list. These critical instructions determine the response speed and energy consumption level of the system, so they need special attention. Next, the critical instruction list is executed through timing constraint analysis to generate an execution timing graph, and based on the graph, it is detected whether there is resource competition in the control instruction group. The core of this step is to evaluate the execution time of each instruction and its time relationship with each other to ensure that all instructions can be completed within the specified time window. For example, in a smart home application, assuming that the system needs to quickly adjust the working mode of the sensor to capture activity signals within a few seconds after the user enters the room, then it is necessary to ensure that the relevant instructions can be executed in time. At the same time, the system also needs to check whether there is a resource competition problem, that is, whether multiple instructions try to access the same hardware resources at the same time, resulting in conflicts. For example, if two instructions require the operating voltage of the sensor to be changed at the same time, resource competition may occur, affecting the normal operation of the system. If resource competition is detected, the control instruction group needs to be conflict resolved to generate a conflict-free instruction group, and the group is analyzed for parallelism to generate a parallel scheduling table. The goal of conflict resolution is to rearrange the execution order of instructions or allocate different resources to avoid conflicts. For example, in the above smart home scenario, the resource competition problem can be resolved by adjusting the priority of the instructions or allocating different time periods. Once the conflict is resolved, the system can further analyze the parallelism of the instructions to determine which instructions can be executed simultaneously without interfering with each other. In this way, a detailed parallel scheduling table can be generated to clarify the execution time and resource requirements of each instruction and improve the overall efficiency of the system. Subsequently, the parallel schedule is allocated with time slices, a time slice allocation scheme is generated, and the MEMS sensor is scheduled based on the scheme to finally obtain the optimal power consumption control sequence. The key to time slice allocation is to reasonably divide the execution time interval of each instruction to ensure that system resources are fully utilized while avoiding overload. For example, in a smart home environment, assuming that different time periods of the day have different activity patterns, the system can dynamically adjust the working mode of the sensor according to the user's activity patterns.During the daytime, due to frequent activities, the system can allocate more resources to the high-sensitivity mode; at night, when activities decrease, the system can switch to low-power mode to save energy. Through this fine time slice allocation, the system can not only meet functional requirements, but also significantly reduce energy consumption. Throughout the process, the system will continuously optimize the control instruction group and timing scheduling scheme according to the needs of the actual application scenario. For example, in smart home applications, by real-time monitoring of user activities and environmental changes, the system can dynamically adjust the working mode of the sensor to ensure the best performance and lowest energy consumption in any situation. Specifically, when the system detects that the user leaves the room, it can automatically reduce the sensitivity of the sensor or reduce the frequency of data collection to extend the battery life; and when the user returns to the room, the system can quickly return to the high-sensitivity mode to ensure that all important activity signals are accurately captured. This flexible timing scheduling strategy enables MEMS sensors to achieve a balance between high efficiency and low power consumption in complex and changing application scenarios. In summary, through this series of steps, MEMS sensors can efficiently manage energy use during their life cycle, which not only improves the overall performance of the system, but also extends the service life of the device. For example, in smart home applications, a well-designed timing scheduling planning strategy can make the sensor network in the entire home environment more intelligent and energy-efficient. By reasonably arranging the working modes of sensors in different rooms, the system can not only ensure sufficient sensitivity to capture the user's actions, but also minimize unnecessary energy consumption. In addition, this method also provides valuable reference experience for MEMS sensor applications in other fields, and promotes the development and progress of related technologies. Through continuous iterative optimization and real-time adjustment, the system can minimize energy consumption, improve user experience and save energy while meeting functional requirements. This intelligent scheduling mechanism not only improves the reliability and sustainability of the system, but also lays a solid foundation for future smart homes and other IoT applications.

[0040] In a specific embodiment, dynamically adjusting the power consumption of the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor includes: Performing timing analysis on the optimal power consumption control sequence to obtain a control timing table, and performing state migration analysis based on the control timing table to obtain a migration path set; Performing voltage regulation parameter configuration on the migration path set through a preset voltage regulator to obtain a voltage regulation sequence, and performing frequency dynamic compensation based on the voltage regulation sequence to obtain a frequency compensation vector; Decoding the frequency compensation vector according to the working mode to obtain a mode switching instruction set, and performing clock gating configuration on the mode switching instruction set to obtain a gating configuration table; By means of a power consumption monitoring unit, real-time power consumption sampling is performed on the MEMS sensor based on the gating configuration table to obtain a power consumption sampling sequence, and deviation analysis is performed on the power consumption sampling sequence to obtain deviation characteristic data; Calculating compensation parameters for the deviation characteristic data to obtain a compensation vector group, and reconstructing adjustment instructions based on the compensation vector group to obtain a reconstructed instruction sequence; Through a dynamic feedback mechanism, the power consumption state of the data is adjusted based on the reconstruction instruction sequence to obtain a target power consumption state of the MEMS sensor.

[0041] Specifically, the process of dynamically adjusting the power consumption of MEMS sensors based on the optimal power consumption control sequence to obtain the target power consumption state first requires timing analysis of the optimal power consumption control sequence to generate a control timing table, and then state migration analysis is performed based on the table to obtain a migration path set. This process aims to convert abstract control instructions into specific execution steps to ensure that each instruction can be executed at the right time. For example, when a MEMS accelerometer is used to monitor the movement of people in a room in a smart home environment, the system will dynamically adjust the working mode of the sensor according to the user's activity pattern and environmental changes. By timing analysis of the optimal power consumption control sequence, the specific execution time and sequence of each control instruction can be clarified to form a detailed control timing table. Then, through state migration analysis, the system can determine the path required to migrate from the current working state to the target state, that is, the migration path set. These paths not only clarify the conversion relationship between each state, but also provide a basis for subsequent voltage and frequency adjustment. Next, the migration path set is configured with voltage adjustment parameters through a preset voltage regulator to generate a voltage adjustment sequence, and frequency dynamic compensation is performed based on the sequence to obtain a frequency compensation vector. Voltage regulation is one of the key steps to achieve dynamic power regulation. By accurately controlling the operating voltage of the sensor, energy consumption can be minimized while meeting performance requirements. Specifically, the system configures the corresponding voltage regulation parameters and generates a voltage regulation sequence based on each state migration in the migration path set. For example, in smart home applications, when the user leaves the room, the system can save energy by reducing the operating voltage of the sensor; and when the user returns to the room, it quickly returns to the normal operating voltage to ensure sensitivity. At the same time, in order to maintain the stability and response speed of the system, the system also needs to dynamically compensate the frequency and generate a frequency compensation vector. In this way, even when the voltage changes, the sensor can still maintain a high working efficiency. Then, the frequency compensation vector is decoded for the working mode, a mode switching instruction set is generated, and the instruction set is clock-gated to generate a gating configuration table. The goal of working mode decoding is to convert the frequency compensation vector into a specific mode switching instruction to ensure that the system can dynamically adjust the working mode according to actual needs. For example, in a smart home scenario, the system can automatically switch the working mode of the sensor according to the user's activities, such as switching from a high-sensitivity mode to a low-power mode. In order to further optimize energy consumption management, the system also needs to clock-gate the mode switching instruction set and generate a gating configuration table. Clock gating is an effective energy-saving technology that reduces dynamic power consumption by shutting down unnecessary clock signals. In this way, the system can significantly reduce energy consumption without affecting performance. Subsequently, the power consumption of the MEMS sensor is sampled in real time based on the gating configuration table through the power monitoring unit, a power sampling sequence is generated, and the sequence is analyzed for deviation to obtain deviation characteristic data.The power consumption monitoring unit plays a vital role in the whole process. It can monitor the power consumption of the sensor in real time and ensure that the system is always in the best power consumption state. For example, in smart home applications, the system can regularly collect the power consumption data of the sensor through the power consumption monitoring unit to generate a power consumption sampling sequence. By performing deviation analysis on these data, the difference between the actual power consumption and the expected value can be identified and the deviation characteristic data can be generated. These data not only help to find potential problems, but also provide a basis for the subsequent compensation parameter calculation. Then, the compensation parameters are calculated for the deviation characteristic data to generate a compensation vector group, and the adjustment instructions are reconstructed based on the group to generate a reconstructed instruction sequence. The core of the compensation parameter calculation is to adjust the control strategy of the system according to the deviation characteristic data to ensure that it can maintain the best performance under various working conditions. For example, in the smart home scenario, if the system detects that the actual power consumption of the sensor is higher than the expected value, the power consumption can be reduced by adjusting the voltage and frequency parameters. Specifically, the system will calculate a series of compensation parameters based on the deviation characteristic data, generate a compensation vector group, and reconstruct the adjustment instruction sequence based on the group. These instructions not only include new voltage and frequency settings, but also may include other optimization measures, such as further refining the clock gating strategy. Finally, through the dynamic feedback mechanism, the power consumption state of the data is adjusted based on the reconstructed instruction sequence to obtain the target power consumption state of the MEMS sensor. The dynamic feedback mechanism is a closed-loop control system that ensures that the system is always kept in the optimal power consumption state by continuously monitoring and adjusting the operating state of the system. For example, in smart home applications, the system can dynamically adjust the working mode, voltage, frequency and other parameters of the sensor according to the real-time power consumption data and compensation parameters to finally achieve the target power consumption state. This dynamic adjustment mechanism not only improves the energy efficiency of the system, but also enhances its adaptability and flexibility, enabling it to achieve a balance between high efficiency and low power consumption in complex and changing application scenarios. In summary, through this series of steps, MEMS sensors can achieve efficient energy management in complex and changing application scenarios. For example, in a smart home environment, through a carefully designed dynamic power consumption adjustment strategy, the sensor network in the entire home environment can be made more intelligent and energy-efficient. The system can not only dynamically adjust the working mode of the sensor according to the user's activity pattern, but also monitor and optimize its power consumption state in real time to ensure that it can provide the best performance and the lowest energy consumption under any circumstances. In addition, this method also provides valuable reference experience for the application of MEMS sensors in other fields, and promotes the development and progress of related technologies. Through continuous iterative optimization and real-time adjustment, the system can meet functional requirements while minimizing energy consumption, improving user experience and saving energy. This intelligent scheduling mechanism not only improves the reliability and sustainability of the system, but also lays a solid foundation for future smart homes and other IoT applications.

[0042] The above describes the low power consumption optimization method of the MEMS sensor in the embodiment of the present invention. The following describes the low power consumption optimization device of the MEMS sensor in the embodiment of the present invention. Figure 2 , an embodiment of a low power consumption optimization device for a MEMS sensor in an embodiment of the present invention includes: An extraction module 21 is used to perform adaptive threshold compression on the output signal of the MEMS sensor to obtain a compressed data stream, and perform time domain feature extraction on the compressed data stream to obtain a signal activity feature set; An analysis module 22, configured to perform energy consumption point analysis on the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map; A mapping module 23, configured to perform hierarchical energy efficiency configuration mapping on the MEMS sensor based on the power consumption distribution map to obtain an energy efficiency configuration matrix; A solution module 24 is used to perform nonlinear optimization on the energy efficiency configuration matrix to obtain an optimal power consumption control sequence; The adjustment module 25 is used to dynamically adjust the power consumption of the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor.

[0043] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0044] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0045] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0046] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0047] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0048] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0049] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A low power consumption optimization method for a MEMS sensor, characterized in that: The following steps are involved: Performing adaptive threshold compression on the output signal of the MEMS sensor to obtain a compressed data stream, and performing time domain feature extraction on the compressed data stream to obtain a signal activity feature set; Performing energy consumption point analysis on the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map; Based on the power consumption distribution map, the MEMS sensor is mapped in a hierarchical energy efficiency configuration to obtain an energy efficiency configuration matrix; Performing nonlinear optimization on the energy efficiency configuration matrix to obtain an optimal power consumption control sequence; Dynamically adjust the power consumption of the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor.

2. The low power consumption optimization method of a MEMS sensor according to claim 1, characterized in that: The step of performing adaptive threshold compression on the output signal of the MEMS sensor to obtain a compressed data stream includes: Performing wavelet packet decomposition on the output signal of the MEMS sensor to obtain multi-level wavelet coefficients, and performing energy entropy calculation based on the multi-level wavelet coefficients to obtain a signal energy entropy feature vector; The signal energy entropy feature vector is threshold segmented by a dynamic threshold adjustment algorithm to obtain a binary feature sequence, and run-length encoding is performed based on the binary feature sequence to obtain a compressed data stream.

3. The low power consumption optimization method of a MEMS sensor according to claim 1, characterized in that: The performing energy consumption point analysis on the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map includes: Dividing the signal activity feature set into time segments to obtain an activity time segment group, and calculating energy consumption features of the activity time segment group to obtain an energy consumption feature point set; Performing energy efficiency correlation analysis on the energy consumption feature point set through a preset voltage-frequency mapping table to obtain an energy efficiency correlation matrix, and classifying energy consumption characteristics of the MEMS sensor based on the energy efficiency correlation matrix to obtain an energy consumption type sequence; Performing spatiotemporal correlation analysis on the energy consumption type sequence to obtain an energy consumption correlation graph, performing spatial distribution statistics on the energy consumption correlation graph by energy consumption density calculation to obtain an energy consumption density graph, and locating energy consumption hot spots based on the energy consumption density graph to obtain an energy consumption hot spot set; Perform time domain evolution analysis on the energy consumption hotspot set to obtain an energy consumption evolution sequence, perform energy consumption trend prediction based on the energy consumption evolution sequence to obtain an energy consumption trend graph, and generate a power consumption spectrum based on the energy consumption trend graph to obtain a power consumption distribution spectrum.

4. The low power consumption optimization method of a MEMS sensor according to claim 1, characterized in that: The step of performing hierarchical energy efficiency configuration mapping on the MEMS sensor based on the power consumption distribution map to obtain an energy efficiency configuration matrix includes: Based on the power consumption distribution map, feature hierarchical division is performed to obtain a hierarchical feature table, and based on the hierarchical feature table, power consumption level classification is performed to obtain a power consumption level sequence; Mapping and converting the power consumption level sequence through a preset voltage and frequency configuration table to obtain a configuration parameter set, and performing working mode matching based on the configuration parameter set to obtain a working mode group; Performing state transition analysis on the working mode group to obtain a state transition diagram, and performing transition cost calculation based on the state transition diagram to obtain a transition cost matrix; Performing cost balancing processing on the conversion cost matrix through a preset multi-objective weight table to obtain a balance coefficient set, and performing parameter optimization configuration on the balance coefficient set to obtain an optimized configuration group; Verifying the constraints of the optimized configuration group to obtain a valid configuration sequence, and mapping configuration items based on the valid configuration sequence to obtain a mapping configuration table; An energy efficiency matrix is ​​generated based on the mapping configuration table to obtain an energy efficiency configuration matrix; wherein the energy efficiency configuration matrix includes static energy efficiency configuration, dynamic energy efficiency configuration and transient energy efficiency configuration.

5. The low power consumption optimization method of MEMS sensor according to claim 1, characterized in that: The nonlinear optimization solution of the energy efficiency configuration matrix to obtain an optimal power consumption control sequence includes: Performing constraint analysis on the energy efficiency configuration matrix to obtain a constraint set, and constructing a feasible domain based on the constraint set to obtain a feasible solution space; Constructing an objective function for the feasible solution space by using the Lagrange multiplier method to obtain an optimized objective function, and performing gradient calculation on the optimized objective function to obtain a gradient vector group; Determining a search direction for the gradient vector group to obtain a search direction sequence, and calculating a step length for the search direction sequence to obtain an iterative step length table; Iteratively optimizing the parameters of the iterative step table through nonlinear programming to obtain an iterative solution sequence, and verifying the convergence of the iterative solution sequence to obtain a converged solution set; Performing a multi-objective trade-off analysis on the MEMS sensor based on the converged solution set to obtain a trade-off coefficient matrix, and reconstructing a solution space based on the trade-off coefficient matrix to obtain a reconstructed solution sequence; The reconstructed solution sequence is mapped to control instructions by a preset control sequence generator to obtain a control instruction group, and the MEMS sensor is time-scheduled based on the control instruction group to obtain an optimal power consumption control sequence; wherein the optimal power consumption control sequence includes a voltage control sequence, a frequency control sequence and a mode control sequence.

6. The low power consumption optimization method of a MEMS sensor according to claim 5, characterized in that: The performing timing scheduling planning on the MEMS sensor based on the control instruction group to obtain an optimal power consumption control sequence includes: Performing instruction dependency analysis on the control instruction group to obtain an instruction dependency graph, and performing key path extraction based on the instruction dependency graph to obtain a key instruction linked list; Performing execution timing analysis on the key instruction chain list through timing constraint analysis to obtain an execution timing diagram, and detecting whether there is resource competition in the control instruction group based on the execution timing diagram; If so, performing conflict resolution processing on the control instruction group to obtain a conflict-free instruction group, and performing parallelism analysis on the conflict-free instruction group to obtain a parallel scheduling table; The parallel scheduling table is allocated with time slices to obtain a time slice allocation scheme, and the MEMS sensor is scheduled with timing based on the time slice allocation scheme to obtain an optimal power consumption control sequence.

7. The low power consumption optimization method of a MEMS sensor according to claim 1, characterized in that: The dynamically adjusting the power consumption of the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor includes: Performing timing analysis on the optimal power consumption control sequence to obtain a control timing table, and performing state migration analysis based on the control timing table to obtain a migration path set; Performing voltage regulation parameter configuration on the migration path set through a preset voltage regulator to obtain a voltage regulation sequence, and performing frequency dynamic compensation based on the voltage regulation sequence to obtain a frequency compensation vector; Decoding the frequency compensation vector according to the working mode to obtain a mode switching instruction set, and performing clock gating configuration on the mode switching instruction set to obtain a gating configuration table; By means of a power consumption monitoring unit, real-time power consumption sampling is performed on the MEMS sensor based on the gating configuration table to obtain a power consumption sampling sequence, and deviation analysis is performed on the power consumption sampling sequence to obtain deviation characteristic data; Calculating compensation parameters for the deviation characteristic data to obtain a compensation vector group, and reconstructing adjustment instructions based on the compensation vector group to obtain a reconstructed instruction sequence; Through a dynamic feedback mechanism, the power consumption state of the data is adjusted based on the reconstruction instruction sequence to obtain a target power consumption state of the MEMS sensor.

8. A low power consumption optimization device for a MEMS sensor, characterized in that: include: An extraction module, used to perform adaptive threshold compression on the output signal of the MEMS sensor to obtain a compressed data stream, and perform time domain feature extraction on the compressed data stream to obtain a signal activity feature set; An analysis module, configured to perform energy consumption point analysis on the MEMS sensor based on the signal activity feature set to obtain a power consumption distribution map; A mapping module, used to perform hierarchical energy efficiency configuration mapping on the MEMS sensor based on the power consumption distribution map to obtain an energy efficiency configuration matrix; A solution module, used for performing nonlinear optimization on the energy efficiency configuration matrix to obtain an optimal power consumption control sequence; The adjustment module is used to dynamically adjust the power consumption of the MEMS sensor based on the optimal power consumption control sequence to obtain a target power consumption state of the MEMS sensor.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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