Low-power-consumption control method of GPS module

Abstract

The invention provides a low-power-consumption control method of a GPS module. Through data fusion of the gyroscope and the GPS, accurate perception of the motion state of the equipment is realized, and in combination with a dynamic frequency modulation technology and hardware-level power supply control, the power consumption of the system is remarkably reduced on the premise of ensuring the positioning accuracy and the real-time performance. The angular velocity change of the equipment is detected in real time through the gyroscope, and in combination with GPS position information, the equipment is judged to be in a moving, low-speed moving or static state. And dynamically adjusting the GPS sampling period according to the state division result. A sliding window algorithm is adopted to smooth rate fluctuation, a multi-threshold hysteresis mechanism is combined to avoid state misjudgment, a mathematical optimization model is constructed to carry out iterative optimization on control parameters, and balance between power consumption and performance is achieved. Through hardware-level power supply control, zero power consumption in a static state is realized, the average power consumption of the GPS module can be greatly reduced in long-endurance demand scenes such as Internet of Things equipment, wearable equipment, field animal research and the like, and meanwhile, relatively high positioning precision is maintained.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of GPS module control, and particularly relates to a low-power consumption control method of a GPS module. BACKGROUND

[0002] With the rapid development of Internet of Things (IoT), wearable devices, asset tracking and portable navigation devices, the power consumption problem of Global Positioning System (GPS) modules as core positioning components has increasingly become a key bottleneck restricting device performance. The power consumption of traditional GPS modules is high in the continuous working state, which directly leads to a significant reduction in the endurance time for devices relying on battery power. For example, in application scenarios such as field environment monitoring, logistics asset tracking, smart watches and the like, the device often needs to run continuously for a long time, and frequent charging or battery replacement operations not only seriously affect the user experience, but also reduce the overall reliability and stability of the system.

[0003] Although the existing low-power consumption scheme attempts to reduce power consumption through intermittent working modes, auxiliary positioning technologies and the like, there are still obvious limitations, including:

[0004] (1) When using periodic on-off control, frequent sleep and wake-up operations are easy to cause positioning information update lag, which cannot meet the needs of scenarios with high real-time requirements such as motion tracking and emergency rescue.

[0005] (2) Simply relying on sleep instructions at the software level (such as sending control commands through a serial port) cannot reduce the power consumption of the GPS module to the minimum, and the static power consumption is still maintained at a high level, which cannot fundamentally solve the endurance problem.

[0006] (3) Traditional schemes mostly rely on a single GPS sensor to determine the motion state of the device, which is easy to produce false movement detection due to signal drift and the like, leading to mis-triggering of the power consumption regulation strategy, further exacerbating the problems of power consumption waste and positioning accuracy imbalance.

[0007] Therefore, an innovative control method is needed that can significantly reduce the power consumption of the GPS module while ensuring the real-time and accuracy of positioning. SUMMARY

[0008] In view of the above defects of the prior art, the present application provides a low-power consumption control method of a GPS module, comprising the following steps:

[0009] S1, setting control parameters, the control parameters including a speed threshold, a sampling period, a state switching hysteresis time and a gyroscope angular velocity threshold;

[0010] S2, calculating the average speed of the device motion through the position information obtained by the GPS module, determining the current state of the device in combination with the speed threshold, and matching the sampling period corresponding to the state;

[0011] S3, based on the current state of the device, in combination with the real-time monitoring of the average rate of change, the orderly switching between states is realized, and the sampling period is updated synchronously;

[0012] S4, based on the actual operation data, the control parameters are continuously optimized, and the dynamic balance of the GPS module power consumption and positioning performance is realized.

[0013] Preferably, the rate threshold includes:

[0014] (1) motion rate threshold: used to define whether the device is in a dynamic moving state;

[0015] (2) static rate threshold: used to define whether the device is in a static state;

[0016] The sampling period includes:

[0017] (1) motion sampling period: suitable for the device in motion state;

[0018] (2) low-speed sampling period: suitable for the device in low-speed state;

[0019] The state includes motion, low speed or static.

[0020] Preferably, the step S2 includes:

[0021] S21, using sliding window average method to calculate the average motion rate of the device;

[0022] S22, according to the average rate to determine the state of the device;

[0023] S23, according to the device state to match the sampling period.

[0024] Preferably, the S21 includes:

[0025] (1) define sliding window, containing the position data of the recent multiple effective sampling periods, the window is dynamically updated with time;

[0026] (2) in each sampling period, the GPS module obtains the current latitude and longitude coordinates, calculates the straight line distance with the last period coordinates to obtain the displacement in the period; accumulate the total displacement and total time of multiple periods in the window;

[0027] (3) calculate the average rate, the average rate is equal to the total displacement divided by the total time.

[0028] Preferably, the S22 includes:

[0029] (1) when the average rate is greater than the motion rate threshold, the device state is motion state;

[0030] (2) when the average speed is less than the static speed threshold, the device state is static state;

[0031] (3) when the average speed is between the motion speed threshold and the static speed threshold, the device state is low speed state.

[0032] Preferably, the S23 comprises:

[0033] (1) when the device is in motion state, a motion sampling period is adopted;

[0034] (2) when the device is in static state, the GPS module is powered off and no sampling is performed;

[0035] (3) when the device is in low speed state, a low speed sampling period is adopted.

[0036] Preferably, the step S3 comprises:

[0037] (1) switching from motion state to low speed state: when the device is in motion state, if the average speed continuously is lower than the motion speed threshold, it is determined that the device enters low speed state, and the sampling period is switched to low speed sampling period;

[0038] (2) switching from low speed state to motion state or static state: when the device is in low speed state, if the average speed continuously is higher than the motion speed threshold, it is switched to motion state, and the sampling period is restored to motion sampling period; if the average speed continuously is lower than the static speed threshold, it is switched to static state, and the GPS module is powered off;

[0039] (3) switching from static state to low speed state: the GPS is powered on by the gyroscope trigger or the timer trigger, and is switched to low speed state;

[0040] (4) the power supply of the GPS is controlled by a single GPIO, and the power off and power on of the GPS module are operated.

[0041] Preferably, the gyroscope trigger is triggered when the calculated cumulative angle change amount exceeds the gyroscope angular velocity threshold; the calculation of the cumulative angle change amount comprises: (1) acquiring the instantaneous angular velocity value of the gyroscope; (2) performing calibration and filtering processing on the angular velocity value; (3) integrating the processed angular velocity value in the time dimension; (4) smoothing the angular velocity integration by a sliding window.

[0042] Preferably, in the step S4, the collection of historical data for system optimization specifically comprises:

[0043] S41, historical data collection and storage: collecting device state data, GPS sampling and power consumption data, positioning and motion data, and environment and scene data, and storing them;

[0044] S42, data preprocessing and feature extraction: clean and extract features from the collected data;

[0045] S43, multi-objective optimization model construction: clear optimization target, decision variable and constraint condition, establish mathematical model;

[0046] S44, parameter optimization based on intelligent algorithm: solve the optimal parameter combination through optimization algorithm;

[0047] S45, optimization result verification and dynamic iteration: verify the actual effect of the optimization parameter, and continuously iterate the scene change.

[0048] Preferably, the optimization target includes minimizing the total power consumption of GPS, reducing the number of GPS power-off and reducing the number of state switching; the decision variable includes the motion rate threshold, the static rate threshold, the motion sampling period, the low-speed sampling period, the state switching hysteresis time; the constraint condition includes the positioning accuracy constraint and the real-time constraint.

[0049] The present application realizes the following significant improvements through multi-sensor cooperation and dynamic power management technology:

[0050] (1) The average power consumption of GPS is greatly reduced. Through the dynamic frequency adjustment strategy, the GPS endurance time is significantly improved. Combined with the sleep control circuit, the GPS module realizes zero power consumption or extremely low power consumption operation in deep static state.

[0051] (2) Positioning accuracy and real-time performance are guaranteed. The GPS positioning error is less than or equal to 5 meters, and the sliding window algorithm and multi-threshold hysteresis mechanism are used to avoid positioning delay or data loss caused by state misjudgment.

[0052] (3) The scene adaptability is enhanced. When the Internet of Things terminal is deployed in the wild for a long time, the control parameters are optimized through historical data to balance power consumption and positioning demand. When the user is static, the GPS is in sleep state, and only the gyroscope monitors the basic motion. When moving, it wakes up quickly.

[0053] The present application realizes the following significant improvements through multi-sensor cooperation and dynamic power management technology: BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 The step graph of the control method of the embodiment of the present application. DETAILED DESCRIPTION

[0055] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work belong to the protection scope of the present application.

[0056] The low-power consumption control method of the GPS module in the embodiment of the present application comprises the following steps as shown in the figure: Figure 1

[0057] S1, setting control parameters, including rate threshold, sampling period, state switching hysteresis time and gyroscope angular velocity threshold, etc.

[0058] In the embodiment of the present application, the following key parameters need to be pre-configured to provide quantitative basis for device state judgment and GPS power consumption regulation, including the following control parameters:

[0059] (1) Motion rate threshold: the default value can be set to 1 m / s, which is used to define whether the device is in dynamic moving state. When the device motion rate exceeds this value, it is determined to be in motion state.

[0060] (2) Static rate threshold: the default value is set to 0.1 m / s, which is used to define whether the device is in static state. When the device motion rate is lower than this value, it is determined to be in static state.

[0061] (3) Motion sampling period: the default value can be set to 1 second, which is suitable for the device in motion state to ensure the positioning real-time performance with high frequency sampling, such as logistics tracking and field animal monitoring scenarios.

[0062] (4) Low-speed sampling period: the default value can be set to 5 seconds, which is suitable for the device in low-speed state to reduce the sampling frequency under the premise of balancing positioning accuracy and power consumption.

[0063] (5) State switching hysteresis time: the default value can be set to 2 seconds, which is used to reduce the state ping-pong switching of the device near the critical rate, such as frequent alternation between motion and low-speed state, to improve system stability.

[0064] (6) Gyroscope angular velocity threshold: the default value can be set to 5° / s, which is used for wake-up judgment in static state. When the integral of the angular velocity detected by the gyroscope exceeds this threshold, the device is triggered to switch from static state to low-speed state.

[0065] The above parameters can be initially configured according to the actual application scenarios, such as field monitoring, logistics tracking, wearable devices, etc. Subsequently, they will be dynamically adjusted by optimizing the historical data to adapt to the power consumption and performance requirements in different scenarios.

[0066] ​S2, device state determination based on GPS data and dynamic configuration of sampling period.

[0067] The position information obtained by the GPS module is used to calculate the device motion rate, and the device current state is determined by combining a preset threshold. The state can be motion, low speed, or static, and the corresponding GPS sampling period is matched. The specific process is as follows:

[0068] S21, calculate the GPS motion rate using the sliding window average method.

[0069] The sliding window algorithm is used to process continuous sampling data to reduce the interference of instantaneous errors on rate determination. The specific steps are as follows:

[0070] (1) Window setting: define a sliding window containing the position data of the last N valid sampling periods (N is a preset value, such as 5 periods). The window is dynamically updated over time, always retaining the latest data.

[0071] (2) Displacement and time calculation: in each sampling period, the GPS module obtains the current latitude and longitude coordinates, calculates the straight-line distance from the last period coordinates, and obtains the displacement amount in that period. At the same time, the total displacement amount and total time (total time = N x single sampling period duration) of the N periods in the window are accumulated.

[0072] (3) Average rate calculation: GPS calculation rate = total displacement amount in window ÷ total time.

[0073] S22, device state determination rule.

[0074] Based on the comparison of the GPS calculation rate with the preset threshold (motion rate threshold, static rate threshold), the device state is determined:

[0075] (1) Motion state: GPS calculation rate > motion rate threshold;

[0076] (2) Static state: GPS calculation rate < static rate threshold;

[0077] (3) Low speed state: GPS calculation rate is between the above two thresholds.

[0078] S23, GPS sampling period matching strategy

[0079] According to the device state, the sampling period is automatically configured to balance the power consumption and real-time performance:

[0080] (1) Motion state: use motion sampling period (default value 1 second) to ensure real-time positioning (suitable for logistics tracking, motion monitoring, etc.);

[0081] (2) Static state: GPS module is powered off, no sampling, i.e. the sampling period is infinite, and only low-power sensors are used to maintain state monitoring;

[0082] (3) Low-speed state: a low-speed sampling period (default value 5 seconds) is used, which reduces the sampling frequency while ensuring positioning accuracy.

[0083] This step smoothes the rate fluctuations through a sliding window algorithm, and divides the device state by multiple thresholds, providing accurate basis for subsequent dynamic power regulation, avoiding misjudgment caused by single sampling error, and achieving power optimization through differentiated sampling period.

[0084] S3, device state dynamic switching and GPS sampling period linkage adjustment.

[0085] Based on the determination of the current state of the device (motion, low speed, static) in S2, combined with the real-time monitoring of the motion parameter changes, the ordered switching between states is realized, and the GPS sampling period is updated synchronously, ensuring the dynamic balance of power consumption and positioning performance.

[0086] The embodiment of the application adopts a minimalist state and switching design. Only three states can be set, only the minimum switching requirement is supported, and the design complexity is reduced. The specific rules of device state dynamic switching are as follows:

[0087] (1) Switching from motion state to low-speed state

[0088] When the device is in motion state, the sampling period is the motion sampling period. If the GPS rate calculated by the sliding window algorithm is continuously lower than the motion rate threshold and the duration reaches the state switching hysteresis time, it is determined that the device enters the low-speed state. At this time, the GPS sampling period is automatically switched from the motion sampling period to the low-speed sampling period to reduce the power consumption caused by high-frequency sampling.

[0089] (2) Switching from low-speed state to motion state / static state

[0090] When the device is in low-speed state, the sampling period is the low-speed sampling period, and the state is switched bidirectionally according to the GPS rate change:

[0091] (a) If the GPS calculated rate is continuously higher than the motion rate threshold and the duration reaches the hysteresis time, it is switched to the motion state, and the sampling period is restored to the motion sampling period to ensure the real-time positioning in the motion scene;

[0092] (b) If the GPS calculated rate is continuously lower than the static rate threshold and the duration reaches the hysteresis time, it is switched to the static state, at which time the GPS module is powered off and sampling is stopped, and the sampling period is set to infinity, only the low-power gyroscope is used for state monitoring.

[0093] (3) Switching from static state to low-speed state

[0094] When the device is in the static state, the GPS is powered off, and the low-power monitoring of the gyroscope can trigger the wake-up, or the timing trigger wake-up, and the specific trigger conditions are:

[0095] (a) Gyroscope trigger: The gyroscope detects the angular velocity of the device in real time, and after pre-processing such as zero drift calibration and noise filtering, the integral operation is performed. If the cumulative angular change exceeds the gyroscope angular velocity threshold, it is determined that the device has moved, and the module is immediately powered on, switched to the low-speed state, and the sampling period is set to 5 seconds.

[0096] (b) Timing wake-up: If the gyroscope does not detect effective movement, i.e. the angular velocity integral does not reach the threshold, the static wake-up timer can also be started, with a default value of 30 minutes. After the timer expires, the module is automatically powered on, switched to the low-speed state for a sampling; if the sampling result is still static (speed < 0.01 m / s), the GPS is powered off again, and the static state is maintained and the timing is restarted.

[0097] During the state switching process, the hysteresis time is set to avoid "ping-pong switching" in the critical state (such as repeated state switching caused by frequent fluctuations of the speed near the threshold), and to improve the stability of the system. At the same time, the gyroscope can be relied on for monitoring in the static state, and the static power consumption is minimized, and the low-speed sampling period is used to quickly verify the device state after wake-up, balancing energy saving and positioning continuity.

[0098] The GPS power-off control circuit logic is added in the embodiment of the application. The traditional GPS sleep mode can send control commands to the GPS serial port to achieve the sleep strategy, but the GPS still cannot reduce the power consumption of the GPS to the minimum. The embodiment of the application introduces a separate GPIO (General-Purpose Input Output, general-purpose input output port) to control the power supply of the GPS, and performs power-off and power-on operations on the GPS, so that the GPS has no power consumption in the static state of the device.

[0099] The power of the gyroscope is generally one order of magnitude lower than that of the GPS. When the GPS is powered off, the gyroscope can make a rough judgment on the motion state of the device. The embodiment of the application calculates the angular velocity integral by the gyroscope, and the specific steps are as follows:

[0100] (1) Obtain the original output data of the gyroscope. The gyroscope detects the rotational motion of the device around the three coordinate axes (X, Y, Z axes) and outputs the instantaneous angular velocity value of each axis (usually in ° / s or rad / s). The data reflects the speed and direction of the device at each sampling time.

[0101] (2) Preprocess the gyroscope data. Since the gyroscope has zero drift, it outputs non-zero values when stationary, and may have noise interference, it is necessary to first eliminate the zero drift error through calibration algorithm such as mean filter, and then use low-pass filter such as Kalman filter or moving average filter to filter high-frequency noise and obtain stable angular velocity signal.

[0102] (3) Convert angular velocity to angular change through integral operation. Integrate the preprocessed angular velocity data in time dimension (Δ angle = angular velocity × sampling time interval) to obtain the rotation angle of the device around each axis in each sampling period, and further obtain the real-time attitude of the device such as pitch angle, roll angle and heading angle.

[0103] (4) Optimize the results through sliding window smoothing. Since the integral operation is prone to accumulate errors, use sliding window to average the angular velocity of continuous multiple sampling periods to reduce error fluctuations and obtain stable angular velocity integral.

[0104] S4, collect historical data and optimize the system.

[0105] In the application of GPS, a large amount of historical data can usually be obtained. For example, in the field of animal monitoring and research, animals wearing GPS move for a long time, and it is necessary to reduce the power consumption of GPS. In order to achieve dynamic balance between GPS module power consumption and positioning performance, it is necessary to continuously optimize control parameters based on actual operation data, which can be divided into the following sub-steps:

[0106] S41, historical data acquisition and storage.

[0107] Historical data acquisition can obtain full-quantity operation data covering multiple scenes and states, providing a basis for parameter optimization. In different optimization scenarios, the content and method of collecting data can be selected. The collected data content can include:

[0108] (1) Device state data: including the duration of the device in motion, low speed and stationary states (such as motion state cumulative T1, low speed state cumulative T2), state switching time and triggering reason (such as switching due to speed change, switching due to timer timeout).

[0109] (2) GPS sampling and power consumption data: sampling period corresponding to each state (such as motion sampling period 1 second, low speed sampling period 5 seconds, etc.), real-time power consumption of GPS module under different periods (motion sampling period power consumption W1, low speed sampling period W2), gyroscope working power consumption, GPS power-off times and duration of each power-off, etc.

[0110] (3) Positioning and motion data: GPS calculation rate (sliding window average result), gyroscope angular velocity integration, positioning coordinates (latitude and longitude), positioning error (deviation from the reference position), actual motion trajectory of the device (such as the migration path of wild animals, the transportation route of logistics vehicles).

[0111] (4) Environment and scene data: scene label of the device (such as indoor stationary, outdoor motion, high-speed movement), GPS signal strength, gyroscope zero drift and noise characteristics, etc.

[0112] The collection method can be local storage regular access or real-time storage of the above data to a cloud database.

[0113] S42, data preprocessing and feature extraction.

[0114] Clean up noise data and extract key features to provide high-quality input for optimization model, including the following operations:

[0115] (1) Data cleaning, including:

[0116] (a) Remove outliers: remove abnormal power consumption caused by sensor failure, such as instantaneous power consumption exceeding 10 times the normal value; and incorrect positioning coordinates, such as latitude and longitude exceeding the reasonable range.

[0117] (b) Complete missing values: for short-time data missing, such as 1-2 missing sampling points caused by GPS signal interruption, use linear interpolation method to complete; for long-time missing, such as device offline, mark as "invalid period" and exclude from analysis range.

[0118] (c) Time alignment: align GPS data, gyroscope data and state switching records according to timestamp, ensure that multi-source data at the same time can be correlated and analyzed, such as matching the speed value at a certain time with the corresponding state and power consumption value.

[0119] (2) Feature extraction.

[0120] State features: calculate the proportion of motion / low speed / static state in each scene (such as 40% of wild animal monitoring in static state), state switching frequency (such as switching times per hour).

[0121] Power consumption features: calculate the average power consumption per unit time (total power consumption / running time), power consumption proportion in different states (such as 40% of power consumption in motion state).

[0122] Performance features: calculate the average positioning error, positioning delay when switching states (such as the time taken for the first valid positioning when switching from stationary to motion).

[0123] S43, multi-objective optimization model construction.

[0124] Clearly define the optimization objectives, decision variables, and constraints, and establish a mathematical model to quantify the relationship between parameters and performance.

[0125] The optimization objectives can include:

[0126] (1) Minimize total GPS power consumption: Objective function: Total GPS power consumption = W1 × T1 + W2 × T2, where W1 is the power consumption in motion state, T1 is the total time in motion state; W2 is the power consumption in low-speed state, T2 is the total time in low-speed state.

[0127] (2) Reduce the number of GPS power-off times: Avoid frequent power-off and power-on causing hardware wear and tear and positioning delay, for example, the target is to reduce the number of power-off times to ≤20 times per day, the target can be adjusted according to the scene.

[0128] (3) Reduce the number of state switching times: Reduce the impact of ping-pong effect on positioning stability, for example, the target is to reduce the number of switching times to ≤20 times per hour.

[0129] Decision variables: The core parameters that need to be optimized include: motion speed threshold, static speed threshold, motion sampling period, low-speed sampling period, state switching hysteresis time, etc.

[0130] Constraints:

[0131] Positioning accuracy constraint: positioning error ≤5 meters in motion state, ≤10 meters in low-speed state.

[0132] Real-time constraint: motion state sampling period ≤2 seconds to ensure no obvious delay in motion tracking.

[0133] S44, parameter optimization based on intelligent algorithm.

[0134] Optimal parameter combination is solved by optimization algorithm to balance power consumption and performance. The algorithm can use multi-objective genetic algorithm (MOGA) or particle swarm optimization algorithm (PSO), which can find Pareto optimal solution in multi-objective conflict scenarios, i.e. the solution cannot improve one target without compromising other targets. The specific optimization process steps are as follows:

[0135] (1) Initialize parameter range: set the motion speed threshold value range to 0.5-2 m / s, and the static speed threshold to 0.05-0.2 m / s.

[0136] (2) Fitness function design: quantify total power consumption, power-off times, and switching times as fitness values, adjust weights such as power consumption weight 0.5, switching times weight 0.3, and power-off times weight 0.2, etc., to evaluate the pros and cons of parameter combination.

[0137] (3) Iterative optimization: Based on the pre-processed historical data, the algorithm iterates 100-200 times, gradually approaching the optimal parameter combination. For example, the optimized motion rate threshold is adjusted to 1.2 m / s, and the low-speed sampling period is adjusted to 6 seconds.

[0138] S45, verification of optimization results and dynamic iteration

[0139] Verify the actual effect of the optimized parameters and continuously iterate to adapt to changes in the scene. The following verification methods can be used:

[0140] (1) Offline verification: Simulate the running effect of new parameters on historical data sets, compare the total power consumption (e.g., reduce by 20%), positioning error (e.g., maintain within 5 meters), and switching times (e.g., reduce by 30%) before and after optimization, and select the optimal parameter combination.

[0141] (2) Online testing: Deploy optimized parameters to a small number of pilot devices, such as 5 wild animals wearing GPS, and continuously run in target scenarios, such as outdoor monitoring, for 1-2 weeks, collect actual running data, and verify whether the constraints are met, such as no significant positioning delay.

[0142] (3) Dynamic iteration: If the optimization effect is not good in some scenes, such as mountainous and canyon areas, return to S41 to supplement special data for that scene, re-execute S42-S44, and update the parameters; perform full optimization based on newly accumulated historical data every 3 months to ensure that the parameters adapt to changes in device performance over a long period of time.

[0143] Through the above steps, dynamic optimization of GPS module control parameters can be achieved, which maximizes power consumption and prolongs device battery life while ensuring positioning accuracy and real-time performance.

[0144] The above is only a preferred embodiment of the present application. It should be noted that for ordinary skilled persons in the art, without departing from the technical principles of the present application, several improvements and refinements can be made, which should also be considered within the scope of protection of the present application.

Claims

1. A low-power control method for a GPS module, characterized in that, Includes the following steps: S1. Set control parameters, including rate threshold, sampling period, state switching hysteresis time and gyroscope angular velocity threshold. S2. Calculate the average speed of the device using the location information obtained by the GPS module, determine the current state of the device by combining the speed threshold, and match the sampling period corresponding to the state. S3. Based on the current state of the device and combined with the real-time monitoring of the average rate change, orderly switching between states is achieved, and the sampling period is updated synchronously. S4. Continuously optimize control parameters based on actual operating data to achieve a dynamic balance between GPS module power consumption and positioning performance.

2. The method according to claim 1, characterized in that, The rate threshold includes: (1) Motion rate threshold: used to determine whether the device is in a dynamic movement state; (2) Stationary rate threshold: used to determine whether the device is stationary; The sampling period includes: (1) Motion sampling period: applicable when the device is in motion; (2) Low-speed sampling period: applicable when the device is in a low-speed state; The states include motion, low speed, or stillness.

3. The method according to claim 2, characterized in that, Step S2 includes: S21. The average speed of the equipment movement is calculated using the sliding window averaging method. S22. Determine the equipment status based on the average rate; S23. Match the sampling period according to the device status.

4. The method according to claim 3, characterized in that, S21 includes: (1) Define a sliding window containing location data from the most recent multiple valid sampling periods, and update the window dynamically over time; (2) In each sampling period, the GPS module obtains the current latitude and longitude coordinates, calculates the straight-line distance from the coordinates of the previous period to obtain the displacement within that period; and accumulates the total displacement and total time of multiple periods within the window. (3) Calculate the average speed, which is equal to the total displacement divided by the total time.

5. The method according to claim 3, characterized in that, S22 includes: (1) When the average speed is greater than the motion speed threshold, the device is in motion state; (2) When the average speed is less than the stationary speed threshold, the equipment is in a stationary state; (3) The average speed is between the moving speed threshold and the stationary speed threshold, and the equipment is in a low-speed state.

6. The method according to claim 3, characterized in that, S23 includes: (1) When the device is in motion, the motion sampling period is used; (2) When the device is stationary, the GPS module is powered off and does not sample; (3) When the equipment is in a low-speed state, a low-speed sampling period is used.

7. The method according to claim 2, characterized in that, Step S3 includes: (1) Switching from motion state to low speed state: When the device is in motion state, if the average speed is continuously lower than the motion speed threshold, the device is determined to enter the low speed state and the sampling period is switched to the low speed sampling period. (2) Switching from low speed state to motion state or stationary state: When the device is in low speed state, if the average speed is continuously higher than the motion rate threshold, it will switch to motion state and the sampling period will be restored to the motion sampling period; if the average speed is continuously lower than the stationary rate threshold, it will switch to stationary state and the GPS module will be powered off. (3) Switching from stationary state to low speed state: The GPS is powered on by triggering the gyroscope or timer to switch to low speed state; (4) Use a separate GPIO to control the power supply of the GPS and operate the power-off and power-on of the GPS module.

8. The method according to claim 7, characterized in that, The gyroscope trigger is triggered when the cumulative angle change exceeds the gyroscope angular velocity threshold. The calculation of the cumulative angle change includes: (1) obtaining the instantaneous angular velocity value of the gyroscope; (2) calibrating and filtering the angular velocity value; (3) integrating the processed angular velocity value in the time dimension; and (4) smoothing the angular velocity integral through a sliding window.

9. The method according to claim 2, characterized in that, In step S4, collecting historical data for system optimization specifically includes: S41. Historical data collection and storage: Collect and store device status data, GPS sampling and power consumption data, positioning and motion data, and environmental and scene data; S42. Data Preprocessing and Feature Extraction: Cleaning and feature extraction of the collected data; S43. Construction of Multi-Objective Optimization Model: Define the optimization objective, decision variables and constraints, and establish a mathematical model; S44. Parameter optimization based on intelligent algorithms: Solving for the optimal parameter combination through optimization algorithms; S45. Optimization Result Verification and Dynamic Iteration: Verify the actual effect of the optimized parameters and continuously iterate to adapt to changes in the scenario.

10. The method according to claim 9, characterized in that, The optimization objectives include minimizing the total power consumption of GPS, reducing the number of GPS power outages, and reducing the number of state transitions; the decision variables include motion rate threshold, stationary rate threshold, motion sampling period, low-speed sampling period, and state transition hysteresis time; the constraints include positioning accuracy constraints and real-time constraints.

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