A low-power consumption control method of a GPS module

Abstract

The application provides a low-power consumption control method of a GPS module. Through data fusion of a gyroscope and GPS, precise perception of the motion state of the device is realized, and in combination with dynamic frequency modulation technology and hardware-level power control, the system power consumption is significantly reduced under the premise of ensuring positioning accuracy and real-time performance. The application detects the angular velocity change of the device in real time through the gyroscope, determines the device in a motion, low-speed movement or static state in combination with GPS position information. The GPS sampling period is dynamically adjusted according to the state division result. The sliding window algorithm is adopted to smooth the rate fluctuation, the multi-threshold hysteresis mechanism is combined to avoid state misjudgment, and a mathematical optimization model is constructed to iteratively optimize the control parameters, so that the balance between power consumption and performance is realized. Through hardware-level power control, zero power consumption in the static state is realized. In the long-endurance demand scenarios such as Internet of Things devices, wearable devices, field animal research, etc., the average power consumption of the GPS module can be greatly reduced while maintaining high positioning accuracy.

Description

Technical Field

[0001] This invention relates to the field of GPS module control, and more specifically to a low-power control method for a GPS module. Background Technology

[0002] With the rapid development of the Internet of Things (IoT), wearable devices, asset tracking, and portable navigation devices, the power consumption of the Global Positioning System (GPS) module, as a core positioning component, is increasingly becoming a key bottleneck restricting device performance. Traditional GPS modules consume a lot of power during continuous operation, which directly leads to a significant reduction in battery life for battery-powered devices. For example, in applications such as field environmental monitoring, logistics asset tracking, and smartwatches, devices often need to operate continuously for extended periods. Frequent charging or battery replacement not only severely impacts user experience but also reduces the overall reliability and stability of the system.

[0003] While existing low-power solutions attempt to reduce power consumption through intermittent operating modes and assisted positioning technologies, they still have significant limitations, including:

[0004] (1) When using periodic switching control, frequent sleep and wake-up operations can cause the location information to be updated late, which cannot meet the needs of scenarios with high real-time requirements such as motion tracking and emergency rescue.

[0005] (2) Relying solely on software-level sleep commands (such as sending control commands via serial port) makes it difficult to minimize the power consumption of the GPS module. Static power consumption remains at a high level, which cannot fundamentally solve the battery life problem.

[0006] (3) Traditional solutions rely on a single GPS sensor to determine the motion status of the device. This is prone to false motion detection due to signal drift and other issues, which leads to the mis-triggering of power consumption control strategies and further exacerbates the problem 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 real-time positioning and accuracy. Summary of the Invention

[0008] To address the aforementioned shortcomings of existing technologies, this invention proposes a low-power control method for a GPS module, comprising the following steps:

[0009] S1. Set control parameters, including rate threshold, sampling period, state switching hysteresis time and gyroscope angular velocity threshold.

[0010] 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.

[0011] 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.

[0012] S4. Continuously optimize control parameters based on actual operating data to achieve a dynamic balance between GPS module power consumption and positioning performance.

[0013] Preferably, the rate threshold includes:

[0014] (1) Motion rate threshold: used to determine whether the device is in a dynamic movement state;

[0015] (2) Stationary rate threshold: used to determine whether the device is stationary;

[0016] The sampling period includes:

[0017] (1) Motion sampling period: applicable when the device is in motion;

[0018] (2) Low-speed sampling period: applicable when the device is in a low-speed state;

[0019] The states include motion, low speed, or stillness.

[0020] Preferably, step S2 includes:

[0021] S21. The average speed of the equipment movement is calculated using the sliding window averaging method.

[0022] S22. Determine the equipment status based on the average rate;

[0023] S23. Match the sampling period according to the device status.

[0024] Preferably, S21 includes:

[0025] (1) Define a sliding window containing location data from the most recent multiple valid sampling periods, and update the window dynamically over time;

[0026] (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.

[0027] (3) Calculate the average speed, which is equal to the total displacement divided by the total time.

[0028] Preferably, S22 includes:

[0029] (1) When the average speed is greater than the motion speed threshold, the device is in motion state;

[0030] (2) When the average speed is less than the stationary speed threshold, the equipment is in a stationary state;

[0031] (3) The average speed is between the moving speed threshold and the stationary speed threshold, and the equipment is in a low-speed state.

[0032] Preferably, S23 includes:

[0033] (1) When the device is in motion, the motion sampling period is used;

[0034] (2) When the device is stationary, the GPS module is powered off and does not sample;

[0035] (3) When the equipment is in a low-speed state, a low-speed sampling period is used.

[0036] Preferably, step S3 includes:

[0037] (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.

[0038] (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.

[0039] (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;

[0040] (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.

[0041] Preferably, 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.

[0042] Preferably, in step S4, collecting historical data for system optimization specifically includes:

[0043] 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;

[0044] S42. Data Preprocessing and Feature Extraction: Cleaning and feature extraction of the collected data;

[0045] S43. Construction of Multi-Objective Optimization Model: Define the optimization objective, decision variables and constraints, and establish a mathematical model;

[0046] S44. Parameter optimization based on intelligent algorithms: Solving for the optimal parameter combination through optimization algorithms;

[0047] 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.

[0048] Preferably, the optimization objectives include minimizing total GPS power consumption, 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.

[0049] This invention achieves the following significant improvements through multi-sensor collaboration and dynamic power management technology:

[0050] (1) The average power consumption of GPS is significantly reduced. The dynamic frequency modulation strategy significantly improves the GPS battery life. Combined with the sleep control circuit, the GPS module can operate with zero or extremely low power consumption in a deep static state.

[0051] (2) Positioning accuracy and real-time performance are guaranteed. The GPS positioning error is ≤5 meters. A sliding window algorithm and a multi-threshold hysteresis mechanism are adopted to avoid positioning delay or data loss caused by misjudgment of state.

[0052] (3) Enhanced scenario adaptability. When IoT terminals are deployed in the field for a long time, control parameters are optimized through historical data to balance power consumption and positioning requirements. GPS is in sleep mode when the user is stationary, and only the gyroscope monitors basic motion. It is quickly woken up when the user moves.

[0053] This invention, through hardware-level power control and algorithm optimization, significantly extends the device's battery life while ensuring the real-time performance of location services, making it suitable for power-sensitive mobile terminals and IoT scenarios. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the steps of the control method according to an embodiment of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] An embodiment of the present invention provides a low-power control method for a GPS module, such as... Figure 1 As shown, it includes the following steps:

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

[0058] In this embodiment of the invention, the following key parameters need to be pre-configured to provide a quantitative basis for device status judgment and GPS power consumption control, including the following control parameters:

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

[0060] (2) Stationary speed threshold: The default value is set to 0.1 m / s, which is used to determine whether the equipment is stationary. When the equipment's movement speed is lower than this value, it is determined to be stationary.

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

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

[0063] (5) State switching hysteresis time: The default value can be set to 2 seconds to reduce the state ping-pong switching of the device near the critical speed, such as the frequent alternation between the motion and low speed states, and improve the 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 the static state. When the integral of the angular velocity detected by the gyroscope exceeds this threshold, the device is triggered to switch from the static state to the low-speed state.

[0065] The above parameters can be initially configured according to actual application scenarios, such as field monitoring, logistics tracking, and wearable devices. They will be dynamically adjusted through historical data optimization to adapt to the power consumption and performance requirements of different scenarios.

[0066] S2. Dynamic configuration of device status determination and sampling period based on GPS data.

[0067] The device's movement speed is calculated using location information obtained from the GPS module, and the current state of the device is determined by combining this information with a preset threshold. The state can be moving, slow speed, or stationary, and the corresponding GPS sampling period is matched accordingly. The specific process is as follows:

[0068] S21. The GPS motion rate is calculated using the sliding window averaging method.

[0069] A sliding window algorithm is used to process continuously sampled data to reduce the interference of instantaneous errors on rate judgment. The specific steps are as follows:

[0070] (1) Window settings: Define a sliding window containing the location data of the most recent N valid sampling periods (N is a preset value, such as 5 periods). The window updates dynamically over time and always retains 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 with the coordinates of the previous period, and obtains the displacement within the period; at the same time, it accumulates the total displacement and total time of N periods within the window (total time = N × duration of a single sampling period).

[0072] (3) Average speed calculation: GPS calculation speed = total displacement within the window ÷ total time.

[0073] S22, Equipment Status Determination Rules.

[0074] The device status is determined by comparing the GPS calculation rate with preset thresholds (movement rate threshold and stationary rate threshold).

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

[0076] (2) Stationary state: GPS calculation rate < stationary rate threshold;

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

[0078] S23, GPS sampling period matching strategy

[0079] The sampling period is automatically configured based on the device status to achieve a balance between power consumption and real-time performance.

[0080] (1) Motion status: The motion sampling period (default value 1 second) is adopted to ensure real-time positioning with high-frequency sampling (suitable for scenarios such as logistics tracking and motion monitoring).

[0081] (2) Static state: The GPS module is powered off and does not sample, that is, the sampling period is infinite, and the state monitoring is maintained only by low power sensors;

[0082] (3) Low speed state: adopt a low speed sampling period (default value 5 seconds) to ensure positioning accuracy while reducing the sampling frequency.

[0083] This step uses a sliding window algorithm to smooth out rate fluctuations and combines multiple thresholds to classify device states, providing a precise basis for subsequent dynamic power consumption control. This avoids misjudgments caused by single sampling errors and enables power consumption optimization through differentiated sampling periods.

[0084] S3. Dynamic switching of equipment status and linkage adjustment of GPS sampling cycle.

[0085] Based on the current device state (moving, low speed, stationary) determined by S2, and combined with real-time monitoring of changes in motion parameters, orderly switching between states is achieved, and the GPS sampling period is updated synchronously to ensure a dynamic balance between power consumption and positioning performance.

[0086] This invention employs a simplified state and switching design. Only three states are allowed, supporting only the minimum switching requirements, thus reducing design complexity. The specific rules for dynamic device state switching are as follows:

[0087] (1) Switching between motion state and low speed state

[0088] When the device is in motion, the sampling period is the motion sampling period. If the GPS rate calculated by the sliding window algorithm remains below the motion rate threshold for an extended period, and this duration reaches the state transition hysteresis time, the device is determined to have entered a low-speed state. At this time, the GPS sampling period automatically switches from the motion sampling period to the low-speed sampling period to reduce the power consumption caused by high-frequency sampling.

[0089] (2) Switching between low speed state → moving state / stationary state

[0090] When the device is in low-speed mode, the sampling period is the low-speed sampling period, which switches bidirectionally according to changes in GPS speed.

[0091] (a) If the GPS calculation rate is continuously higher than the motion rate threshold and the duration reaches the hysteresis time, then switch to motion state and restore the sampling period to the motion sampling period to ensure the real-time positioning in motion scenarios.

[0092] (b) If the GPS calculation rate remains below the stationary rate threshold and the duration reaches the hysteresis time, the system switches to a stationary state. At this time, the GPS module is powered off, sampling stops, the sampling period is set to infinity, and only the low-power gyroscope is retained for status monitoring.

[0093] (3) Switching between stationary state and low-speed state

[0094] When the device is stationary and the GPS is powered off, it can be woken up by low-power monitoring of the gyroscope or by a timed activation. The specific triggering conditions are as follows:

[0095] (a) Gyroscope trigger: The gyroscope detects the angular velocity of the device in real time. After preprocessing such as zero drift calibration and noise filtering, it performs integration calculation. If the cumulative angle change exceeds the gyroscope angular velocity threshold, it is determined that the device has moved. The power-on module is immediately switched to low speed mode and the sampling period is set to 5 seconds.

[0096] (b) Timed wake-up: If the gyroscope does not detect effective movement, i.e. the integral of angular velocity does not reach the threshold, the static wake-up timer can also be started. The default value is 30 minutes. After the timer expires, the module is automatically powered on and switched to low speed to perform one sampling. If the sampling result is still static (speed < 0.01 m / s), the GPS is powered off again to continue to maintain the static state and restart the timer.

[0097] During state transitions, hysteresis time settings are used to avoid "ping-pong switching" in critical states (such as repeated state transitions caused by frequent fluctuations in rate near a threshold), thus improving system stability. Meanwhile, in the static state, monitoring can be performed using a gyroscope to minimize static power consumption, while after waking up, the device status is quickly verified through a low-speed sampling cycle, balancing energy saving and positioning continuity.

[0098] This invention adds a GPS power-off control circuit logic. Traditional GPS sleep mode can achieve a sleep strategy by sending control commands to the GPS serial port, but this method still cannot minimize the GPS's power consumption. This invention introduces a separate GPIO (General-Purpose Input Output) to control the GPS power supply, performing power-off and power-on operations, thereby ensuring that the GPS consumes no power when the device is stationary.

[0099] The power consumption of a gyroscope is generally an order of magnitude lower than that of a GPS device. When the GPS is powered off, a gyroscope can be used to make a rough judgment on the device's motion status. This embodiment of the invention calculates the angular velocity integral using a gyroscope, and the specific steps are as follows:

[0100] (1) Obtain the raw output data of the gyroscope. The gyroscope detects the rotational motion of the device around the three-dimensional coordinate axes (X, Y, Z axes) and outputs the instantaneous angular velocity values ​​of each axis (usually in ° / s or rad / s). This data reflects the speed and direction of the device's rotation at each sampling moment.

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

[0102] (3) Convert angular velocity into angular change through integration. Integrate the preprocessed angular velocity data in the 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 then obtain the real-time attitude of the device, such as pitch angle, roll angle and heading angle.

[0103] (4) Optimize the results by smoothing the process using a sliding window. Since integral operations are prone to accumulating errors, a sliding window is used to average the angular velocities of multiple consecutive sampling periods to reduce error fluctuations and obtain a stable angular velocity integral.

[0104] S4. Collect historical data and optimize the system.

[0105] In GPS applications, a large amount of historical data is typically available. For example, in wildlife monitoring research, animals wearing GPS devices move around for extended periods, necessitating reduced GPS power consumption. To achieve a dynamic balance between GPS module power consumption and positioning performance, control parameters need to be continuously optimized based on actual operational data, specifically through the following sub-steps:

[0106] S41. Historical data collection and storage.

[0107] Historical data collection can acquire comprehensive operational data covering multiple scenarios and states, providing a foundation for parameter optimization. The content and method of data collection can be selected depending on the optimization scenario. The collected data content may include:

[0108] (1) Equipment status data: including the duration of the equipment in various states such as motion, low speed and stationary (e.g., cumulative T1 in motion state, cumulative T2 in low speed state), state switching time and triggering reason (e.g., switching due to speed change, switching triggered by timer timeout).

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

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

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

[0112] The data can be collected by storing it locally and retrieving it periodically, or by storing the data in real time to a cloud database.

[0113] S42. Data preprocessing and feature extraction.

[0114] Cleaning noisy data and extracting key features provides high-quality input for the optimization model. This includes the following steps:

[0115] (1) Data cleaning. This includes:

[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, by using the 3σ criterion or box plot method.

[0117] (b) Fill in missing values: For short-term missing data, such as 1-2 missing sampling points due to GPS signal interruption, linear interpolation is used to fill in the missing data; for long-term missing data, such as when the device is offline, the missing data is marked as "invalid period" and excluded from the analysis.

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

[0119] (2) Feature extraction.

[0120] State characteristics: Calculate the proportion of moving / low-speed / stationary states in each scenario (e.g., 40% of the states are stationary in wildlife monitoring) and the state switching frequency (e.g., the number of times the state switches per hour).

[0121] Power consumption characteristics: average power consumption per unit time (total power consumption / running time), and power consumption percentage under different states (e.g., power consumption percentage of motion state is 40%).

[0122] Performance characteristics: Calculate the average positioning error and the positioning delay during state transitions (such as the time required for the first effective positioning when switching from a stationary to a moving state).

[0123] S43. Construction of multi-objective optimization model.

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

[0125] Optimization objectives may include:

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

[0127] (2) Reduce the number of GPS power outages: Avoid hardware wear and positioning delays caused by frequent power outages and power-on. For example, the target is ≤ 20 power outages per day. The target can be adjusted according to the scenario.

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

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

[0130] Constraints:

[0131] Positioning accuracy constraints: Positioning error ≤ 5 meters in motion, ≤ 10 meters in low speed.

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

[0133] S44. Parameter optimization based on intelligent algorithms.

[0134] The optimal parameter combination is found through optimization algorithms to balance power consumption and performance. Algorithms such as Multi-Objective Genetic Algorithm (MOGA) or Particle Swarm Optimization (PSO) can be used because they can find Pareto optimal solutions in multi-objective conflict scenarios, i.e., solutions that improve one objective without harming others. The specific optimization process steps are as follows:

[0135] (1) Initialization parameter range: For example, the range of motion speed threshold is set to 0.5-2m / s, and the range of stationary speed threshold is set to 0.05-0.2m / s.

[0136] (2) Fitness function design: The total power consumption, number of power outages and number of switching are quantified into fitness values. The parameters are adjusted by weighting, such as power consumption weight 0.5, switching number weight 0.3, power outage number weight 0.2, etc., to comprehensively evaluate the merits of the parameter combination.

[0137] (3) Iterative optimization: Based on the preprocessed historical data, the algorithm iterates for 100-200 generations to gradually approach the optimal parameter combination. For example, the motion speed threshold is adjusted to 1.2m / s after optimization, and the low-speed sampling period is adjusted to 6 seconds.

[0138] S45. Optimization Result Verification and Dynamic Iteration

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

[0140] (1) Offline verification: Simulate the effect of the new parameters on historical datasets, compare the total power consumption (e.g., reduced by 20%), positioning error (e.g., kept within 5 meters), and number of switching times (e.g., reduced by 30%) before and after optimization, and select the parameter combination with the best performance.

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

[0142] (3) Dynamic iteration: If a certain scenario is found to be poorly optimized in mountainous canyon areas during testing, return to S41 to supplement the specific data for that scenario, re-execute S42-S44, and update the parameters; perform a full optimization every 3 months based on the newly accumulated historical data to ensure that the parameters adapt to the performance changes of the equipment during long-term operation.

[0143] By following the steps above, the control parameters of the GPS module can be dynamically optimized, maximizing power consumption reduction and extending the device's battery life while ensuring positioning accuracy and real-time performance.

[0144] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A low power consumption control method of a GPS module, characterized by, 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; 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; 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.

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 activated when the cumulative angle change exceeds the gyroscope angular velocity threshold; the calculation of the cumulative angle change includes: (1) Obtain the instantaneous angular velocity value of the gyroscope; (2) The angular rate values ​​are calibrated and filtered; (3) The processed angular velocity value is integrated over the time dimension; (4) Smooth the angular velocity integral by using a sliding window.

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