IMU and navigation collaborative energy-saving method, device, vehicle-mounted navigation equipment and medium

Abstract

The present invention discloses a collaborative energy-saving method, device, vehicle-mounted navigation equipment and storage medium for IMU and navigation, including: obtaining raw data, collecting data on the raw data, and obtaining an initial sampling sequence; segmenting the initial sampling sequence, and statistically analyzing the energy consumption distribution characteristics under different motion states; dividing the working state of the system into different energy consumption areas based on the energy consumption distribution characteristics, and formulating a hierarchical sampling configuration scheme for IMU sensors and navigation modules for different energy consumption areas; according to the hierarchical sampling configuration scheme, calculating the energy consumption parameter combination of the IMU sensor and the navigation module under each working state, and obtaining the energy consumption control parameters of the system; based on the real-time working state detection result of the system, adjusting the IMU sensor and the navigation module to the corresponding working frequency according to the energy consumption control parameters. The technical solution of the present invention reduces the power consumption of the vehicle-mounted navigation equipment and improves the energy utilization rate of the automobile under the premise of ensuring the positioning accuracy.

Description

Technical Field

[0001] The present invention relates to the field of vehicle-mounted navigation technology, and in particular to an IMU and navigation collaborative energy-saving method, device, vehicle-mounted navigation equipment and medium. Background Art

[0002] With the development of intelligent driving technology, the requirements for the accuracy and reliability of vehicle navigation and positioning systems are getting higher and higher. At present, mainstream vehicle navigation systems usually use a combination of IMU (Inertial Measurement Unit) and satellite navigation receivers. Among them, IMU measures the motion state of the carrier in real time through sensors such as accelerometers, gyroscopes and magnetometers, and provides attitude and motion parameters for the navigation system; satellite navigation receivers obtain high-precision absolute position information by receiving and solving satellite signals.

[0003] In the prior art, IMU sensors and navigation receivers usually work with fixed sampling frequencies and update rates; for example, IMU sensors generally use a sampling frequency of 200Hz, and navigation receivers use an update rate of 10Hz. Although this fixed parameter working mode ensures the positioning performance of the vehicle navigation system under various working conditions, the IMU sensor and navigation receiver always maintain a high power consumption state, and the energy consumption is very large, especially in simple motion states such as the vehicle is stationary or driving at a constant speed. High-frequency sampling and high-rate updates cause unnecessary energy waste, resulting in low energy utilization of electric system vehicles. Summary of the invention

[0004] The present invention provides an IMU and navigation collaborative energy-saving method, which reduces the power consumption of a vehicle-mounted navigation device and improves the energy utilization rate of a vehicle while ensuring positioning accuracy.

[0005] To achieve the above object, the IMU and navigation collaborative energy saving method proposed in the present invention is applied to a vehicle-mounted navigation device, including:

[0006] Acquire raw data from an IMU sensor and a navigation module, and collect data on the raw data according to the sampling frequency of the IMU sensor and the update rate of the navigation module configured by the system to obtain an initial sampling sequence;

[0007] The initial sampling sequence is processed in segments, and the energy consumption distribution characteristics of the IMU sensor and the navigation module under different motion states are statistically analyzed;

[0008] Based on the energy consumption distribution characteristics, the working state of the system is divided into a low energy consumption area, a medium energy consumption area and a high energy consumption area, and a hierarchical sampling configuration scheme of the IMU sensor and the navigation module is formulated for different energy consumption areas, and the hierarchical sampling configuration scheme includes a sampling frequency and an update rate corresponding to each energy consumption area;

[0009] According to the hierarchical sampling configuration scheme, the energy consumption parameter combination of the IMU sensor and the navigation module under each working state is calculated, a mapping relationship between state-frequency-energy consumption is established, and the energy consumption control parameter of the system is obtained;

[0010] Based on the real-time working status detection result of the system, the IMU sensor and the navigation module are adjusted to the corresponding working frequency according to the energy consumption control parameter.

[0011] In some embodiments, after the step of adjusting the IMU sensor and the navigation module to corresponding operating frequencies according to the energy consumption control parameter, the method further includes:

[0012] The actual operating power consumption of the monitoring system is compared and analyzed with the target power consumption corresponding to each working state, and the energy consumption deviation value is calculated;

[0013] According to the energy consumption deviation analysis result, the hierarchical sampling configuration scheme is optimized and adjusted to obtain an updated hierarchical sampling configuration scheme;

[0014] Performing system performance monitoring on the updated hierarchical sampling configuration scheme, collecting and recording key performance indicator parameters, wherein the key performance indicator parameters include at least positioning accuracy, system power consumption and data latency;

[0015] By combining the performance test index with the original energy consumption configuration scheme, the optimal sampling frequency combination of the IMU sensor and the navigation module under each working state is determined to obtain the optimal sampling configuration scheme.

[0016] The present invention also proposes an IMU and navigation collaborative energy-saving device, which is applied to a vehicle-mounted navigation device, comprising:

[0017] An acquisition module is used to acquire raw data from the IMU sensor and the navigation module, and collect the raw data according to the sampling frequency of the IMU sensor and the update rate of the navigation module configured by the system to obtain an initial sampling sequence;

[0018] An analysis module, used for segmenting the initial sampling sequence and statistically analyzing the energy consumption distribution characteristics of the IMU sensor and the navigation module under different motion states;

[0019] A formulation module, used to divide the working state of the system into a low energy consumption area, a medium energy consumption area and a high energy consumption area based on the energy consumption distribution characteristics, and formulate a hierarchical sampling configuration scheme of the IMU sensor and the navigation module for different energy consumption areas, wherein the hierarchical sampling configuration scheme includes a sampling frequency and an update rate corresponding to each energy consumption area;

[0020] A calculation module, used to calculate the energy consumption parameter combination of the IMU sensor and the navigation module under each working state according to the hierarchical sampling configuration scheme, establish a mapping relationship between state-frequency-energy consumption, and obtain the energy consumption control parameters of the system;

[0021] The adjustment module is used to adjust the IMU sensor and the navigation module to the corresponding working frequency according to the energy consumption control parameter based on the real-time working status detection result of the system.

[0022] The present invention also provides a vehicle-mounted navigation device, including an IMU sensor and a navigation module. The vehicle-mounted navigation device also includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the above-mentioned IMU and navigation collaborative energy-saving method are implemented.

[0023] The present invention also proposes a medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned IMU and navigation collaborative energy-saving method are implemented.

[0024] The technical solution of the IMU and navigation collaborative energy-saving method of the present invention establishes a hierarchical sampling configuration scheme based on the motion state, and through real-time analysis of the vehicle motion state data, the driving state can be intelligently classified into three working states: static state, uniform motion state (or uniform driving state) and violent motion state. For different working states, the system adopts corresponding sampling configuration schemes, which can greatly reduce the sampling frequency of the IMU sensor and the update rate of the navigation module when static to save energy, maintain a moderate sampling level of the IMU sensor and the navigation module when driving at a uniform speed, and increase the sampling frequency of the IMU sensor and the update rate of the navigation module during key actions such as turning, high acceleration or deceleration to ensure accuracy; in this way, a solution for dynamically adjusting the sampling frequency of the IMU sensor and the update rate of the navigation module based on the motion state is realized, which achieves the optimal balance between performance and energy consumption compared with the fixed sampling frequency of the traditional navigation system, reduces the power consumption of the vehicle navigation system while ensuring the positioning accuracy, and improves the energy utilization rate of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a flowchart of the first embodiment of the collaborative energy-saving method of IMU and navigation of the present invention;

[0026] Figure 2 for Figure 1 A schematic diagram of a flow chart of an embodiment of step S20 in the embodiment;

[0027] Figure 3 for Figure 1 A schematic diagram of a flow chart of an embodiment of step S30 in the embodiment;

[0028] Figure 4 for Figure 1 A schematic diagram of a flow chart of an embodiment of step S40 in the embodiment;

[0029] Figure 5 It is a flow chart of the second embodiment of the IMU and navigation collaborative energy saving method of the present invention;

[0030] Figure 6 for Figure 5 A schematic diagram of a flow chart of an embodiment of step S70 in the embodiment;

[0031] Figure 7 for Figure 5 A schematic diagram of a flow chart of an embodiment of step S90 in the embodiment;

[0032] Figure 8 A schematic diagram of the module structure of an embodiment of the IMU and navigation collaborative energy-saving device of the present invention;

[0033] Fig. 9 A schematic diagram of the module structure of another embodiment of the IMU and navigation collaborative energy-saving device of the present invention;

[0034] Fig.10 It is a schematic diagram of the structure of an electronic device in a hardware operating environment involved in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] The present invention proposes a collaborative energy-saving method of IMU and navigation, which is applied to a vehicle-mounted navigation device, wherein IMU is the abbreviation of Inertial Measurement Unit. The implementation terminal of the collaborative energy-saving method of IMU and navigation of the present invention can be a vehicle-mounted navigation device or an electronic device, and the electronic device can be a controller of the vehicle-mounted navigation device, or a computing device such as a single-chip microcomputer, a desktop computer, a notebook, a handheld computer, and a server.

[0037] like Figure 1 As shown, Figure 1 It is a flow chart of the first embodiment of the IMU and navigation collaborative energy saving method of the present invention.

[0038] In this embodiment, the IMU and navigation collaborative energy saving method includes:

[0039] Step S10, acquiring the raw data of the IMU sensor and the navigation module, collecting the raw data according to the sampling frequency of the IMU sensor configured by the system and the update rate of the navigation module, and obtaining an initial sampling sequence;

[0040] Among them, the raw data acquisition of the IMU sensor and the navigation module starts from the start of the sensor array, and the three core components of the accelerometer, gyroscope and magnetometer start working at the same time. The accelerometer continuously outputs the linear acceleration changes in the three axes of X, Y and Z. The measurement range meets the monitoring needs of various motion states such as normal driving and emergency maneuvers of the vehicle. The capacitive micromechanical structure is used to realize acceleration detection. The built-in temperature compensation circuit eliminates the influence of temperature drift. The signal conditioning circuit converts the weak capacitance change into a standard voltage output. The gyroscope measures the angular velocity changes around three orthogonal axes in real time, and uses a MEMS vibration structure to detect the angular velocity. The Coriolis effect of the vibration arm produces a displacement proportional to the angular velocity, which is converted into an electrical signal output. The magnetometer realizes three-axis magnetic field detection through the anisotropic magnetoresistance effect. The integrated drive circuit generates a periodic excitation signal, and the sampling and holding circuit locks the peak output. The analog signals of the three types of sensors are respectively sent to the high-precision analog-to-digital converter. The conversion rate fully meets the sampling requirements to ensure that the signal distortion is minimized during the sampling process. The navigation system uses a multi-mode combination receiver to realize satellite signal reception and resolution. The RF front end processes GPS and Beidou dual-frequency signals simultaneously. An adaptive antenna array is used to suppress multipath interference. The digital signal processor performs real-time carrier phase tracking and ambiguity fixation, and outputs high-precision position resolution results.

[0041] The sampling parameters configured in the system set the working mode for different sensors. For example, the IMU sensor uses a unified sampling frequency of 200Hz, and the sampling clock signal generated by the timer synchronously triggers the data acquisition of the three sensors. The accelerometer is configured with an appropriate range and low-pass filtering, and the internal digital filter performs oversampling to suppress high-frequency noise. The gyroscope enables the built-in self-test function to periodically verify the zero-bias stability. The magnetometer adopts a continuous measurement mode and realizes fast sampling of the magnetic field through a suitable excitation signal frequency. The ADC conversion channel polls and samples the outputs of three groups of sensors, and the conversion results are directly transferred to the data buffer through the DMA channel. For example, the receiver of the navigation module is configured with 10Hz, and the tracking loop bandwidth is adaptively adjusted to dynamically optimize the tracking performance according to the carrier's motion state. The system clock uses the GPS atomic clock signal as a unified benchmark, and the sampling control signal generated by the timer is phase-locked to the GPS second pulse, eliminating the time synchronization error between different data sources.

[0042] The initial sampling sequence records the sensor output data in a fixed format. The raw data of the accelerometer, gyroscope and magnetometer are calibrated and converted to obtain physical quantities in standard units. The navigation data contains the position coordinates in the WGS84 coordinate system, and the latitude and longitude values ​​and the ellipsoid height values ​​are stored in a standard format. The velocity value is expressed in the local northeast celestial coordinate system, and the velocity components in three directions are recorded. The time information includes the GPS week number and the number of seconds in the week, accurate to microseconds. The timestamp of each record in the sampling sequence is calculated by interpolation to obtain the offset relative to the whole second, ensuring that the data points have a strict time sequence relationship. The accuracy indicators of the position solution output include the geometric precision factor, the positioning status flag and the number of available satellites, which are used to evaluate the reliability of the positioning results. The data records are stored in a compact binary format, which reduces the space occupied by data storage and provides efficient random access capabilities.

[0043] The final complete sampling sequence records all sensor outputs during the system's motion. The data points of the IMU sensor in the sequence form a fixed interval based on the sampling frequency. Each data point contains nine dimensions of motion parameters, which fully describe the instantaneous motion state of the carrier. The data of the navigation module is updated regularly according to the update rate, providing a high-precision absolute position and velocity reference.

[0044] Step S20, segmenting the initial sampling sequence, and statistically analyzing the energy consumption distribution characteristics of the IMU sensor and the navigation module under different motion states;

[0045] Combination Figure 2 In some embodiments, step S20 may include: step S21, step S22 and step S23.

[0046] Step S21, moving segment by segment from the starting position of the initial sampling sequence according to a preset step length, segmenting the initial sampling sequence to obtain a number of non-overlapping time windows;

[0047] The segmentation of the initial data sequence is achieved by setting a fixed time window. For example, the window size T is selected as 10 seconds as the basic analysis unit. The data segmentation starts from the start position of the sequence and moves segment by segment with the window size as the step size, so that several time windows are obtained, and it can be ensured that the analysis range covers the entire data sequence and there is no overlap between adjacent windows (i.e., time windows). Each time window [t, t+T] contains a complete set of IMU data (i.e., IMU sensor data) and navigation data (i.e., navigation module data). The IMU sensor data is recorded at a sampling frequency of 200Hz, including three sets of sensor data of accelerometer, gyroscope and magnetometer, and each set of data records the measurement values ​​of three axes respectively; the navigation module data is output at an update rate of 10Hz, including position coordinates, velocity vector and signal quality parameters. In this way, a single window contains 2000 IMU data points and 100 navigation data points respectively. The system organizes these data according to a unified time base to provide a basic processing unit for energy consumption analysis.

[0048] Step S22, calculating the average power consumption of the IMU sensor and the navigation module in each time window, and performing statistical analysis using a sliding average method to obtain energy consumption distribution characteristics;

[0049] After determining the window division scheme, carry out targeted energy consumption distribution feature analysis. The power consumption calculation of the IMU sensor is based on a single data point. By collecting the voltage and current parameters of the chip power supply pin, the instantaneous power changes during the operation of the sensor are recorded. Let P_IMU represent the average power consumption of the IMU module within the window, and its calculation method is the arithmetic average of the power consumption values ​​of all sampling points in the window. For example, in actual analysis, the typical power consumption of the accelerometer at a sampling rate of 200Hz is 50mW, of which the signal conditioning circuit and the data conversion circuit are the main sources of energy consumption. At the same time, the power consumption analysis of the navigation module focuses on the working status of the receiver, and records the energy consumption contributions of the RF front end, baseband processor and solution unit respectively, and calculates the average power consumption P_GNSS of the navigation module within the window period. For another example, the average power consumption of the navigation receiver at a 10Hz update rate is 200mW, of which the signal amplification and down-conversion process of the RF front end consumes most of the energy.

[0050] For the power consumption data obtained, the system uses the sliding average method for statistical analysis, which can smooth the fluctuations of instantaneous power consumption and reflect the energy consumption level of the system under stable working conditions. This statistical method intuitively reflects the energy consumption change law of the system under different working conditions, and forms a complete energy consumption distribution feature by recording the mean, maximum and minimum power consumption of the IMU and navigation modules. The power consumption fluctuations within the window are quantified by calculating the standard deviation. A large standard deviation indicates that the system power consumption is unstable, which may be related to the violent motion state. The power consumption fluctuation of the system is small in the static state, and the power consumption of the IMU and navigation module is basically maintained at a fixed level; in the moving state, due to the change of sensor load, the power consumption fluctuates significantly, especially the energy consumption change of the gyroscope during the steering process is the most significant.

[0051] Step S23, using the sensor data in each time window to carry out motion state recognition, combining the motion state recognition result with the energy consumption distribution characteristics, and establishing a mapping relationship between the motion state and the energy consumption level.

[0052] On the basis of completing the energy consumption characteristic analysis, the system uses the sensor data in the time window to carry out motion state recognition. According to the sampling frequency and update rate configured by the system, the IMU sensor outputs the three-axis measurement data of the accelerometer, gyroscope and magnetometer, and the navigation system outputs the position and speed information. The state recognition process first calculates the composite value of the three-axis acceleration and its rate of change to detect the acceleration and deceleration process. For example, when the composite acceleration suddenly changes from 0.02g to 0.3g and lasts for more than 2s, it can be determined as an acceleration process; when the composite acceleration value is stable below 0.01g and the speed is close to 0, it can be determined as a stationary state. At the same time, the system analyzes the amplitude and duration of the three-axis angular velocity to identify the severity of the turn. For example, when the Y-axis angular velocity is detected to suddenly change to 30° / s and lasts for more than 0.5s, it can be determined as a turning process; when the three-axis angular velocities are all lower than 1° / s and the composite acceleration is stable below 0.05g, it can be determined as a uniform linear motion state.

[0053] The system calculates the mean and variance of these motion parameters within the time window and establishes basic characteristic indicators. For example, calculating the root mean square value of acceleration and angular velocity within a 10s window can effectively reflect the intensity of the movement; by calculating the variance, the fluctuation of the parameters can be evaluated, and a larger variance usually corresponds to the state switching process. In addition, the speed information output by the navigation system is an important auxiliary criterion, which can not only verify the acceleration integration result, but also distinguish some special states. For example, when the speed is close to 0 but the engine speed is not 0, it can be judged as idling waiting; when the speed is 0 and the duration exceeds 30s, it is likely to be a parking state. Through the comprehensive analysis of these characteristic indicators, the system has established a reliable motion state judgment mechanism.

[0054] Finally, the system combines the motion state recognition results with the energy consumption distribution characteristics to establish a mapping relationship between the state and the energy consumption level. This mapping reflects the energy consumption distribution law under different motion modes: in the static state, the IMU mainly consumes basic working power consumption, the internal clock circuit and data interface circuit continue to work, but the energy consumption of the core part of the sensor is low; the navigation module still needs to keep tracking satellite signals at this time and maintain the standard receiving power consumption level. When the vehicle enters a normal uniform driving state (including urban roads and highways), the sensor's measurement circuit is fully activated, the power consumption is increased compared to the static state, and the power consumption of the navigation module remains basically stable. Strenuous movements such as steering cause a significant increase in the power consumption of the IMU, especially the mechanical resonance structure of the gyroscope requires a larger drive power, and the load of the data acquisition and processing circuits is also increased accordingly.

[0055] Step S30, dividing the working state of the system into a low energy consumption area, a medium energy consumption area and a high energy consumption area based on the energy consumption distribution characteristics, and formulating a hierarchical sampling configuration scheme of the IMU sensor and the navigation module for different energy consumption areas, wherein the hierarchical sampling configuration scheme includes a sampling frequency and an update rate corresponding to each energy consumption area;

[0056] Combination Figure 3 In some embodiments, step S30 may include: step S31 and step S32.

[0057] Step S31, classifying the working state of the system based on the acquired energy consumption distribution characteristics to obtain a low energy consumption area, a medium energy consumption area and a high energy consumption area;

[0058] Based on the energy consumption distribution characteristics obtained in the early stage, the system working state (for example, it may include stationary state, uniform speed driving state, and intense movement state) is classified. The analysis shows that when the vehicle is stationary (such as waiting for a red light, parking, etc.), the total power consumption is maintained at a low level; when the vehicle enters a normal uniform speed driving state (including urban roads and highways), the power consumption increases; and the system power consumption reaches the highest during intense movements such as turning, changing lanes, high acceleration or deceleration. According to this energy consumption distribution law, the system working state is divided into three energy consumption areas: low, medium, and high. For example, when the total power consumption is below 300mW, it is classified as a low energy consumption area, between 300-400mW, it is classified as a medium energy consumption area, and more than 400mW is defined as a high energy consumption area.

[0059] Step S32, according to the characteristics of the working states corresponding to different energy consumption areas, the sampling frequencies and update rates of different energy consumption areas are set respectively, and the switching between different energy consumption areas adopts progressive frequency adjustment to formulate a hierarchical sampling configuration scheme for different energy consumption areas.

[0060] After determining the zoning standards, formulate a corresponding sampling configuration plan for the low-energy consumption area. Since this area mainly corresponds to the static state, the energy consumption of the core part of the sensor is low, so the sampling frequency can be appropriately reduced. For example, the sampling frequency of the IMU sensor is reduced from 200Hz to about 50Hz, and the navigation module is reduced from 10Hz to about 1Hz. This configuration scheme is aimed at scenarios such as vehicles waiting at traffic lights or parked in parking lots. At this time, the vehicle is in a completely static state, and the requirements for sampling accuracy are relatively low. The total power consumption of the system can be reduced to about 30% of the original level.

[0061] Based on the configuration idea of ​​the low-energy consumption area, a sampling scheme for the medium-energy consumption area is further formulated. This area mainly corresponds to the uniform motion state, and it is necessary to find a balance between positioning accuracy and power consumption. By setting the IMU sampling frequency (that is, the sampling frequency of the IMU sensor) to 100Hz and the update rate of the navigation module to 5Hz, the system's ability to track the uniform motion of the vehicle is guaranteed, and reasonable control of energy consumption is achieved. For example, in a normal uniform driving scenario, the vehicle maintains a stable speed and the motion state changes slowly. This sampling configuration can meet the positioning requirements while controlling the power consumption to about 50% of the original level.

[0062] For high-energy consumption areas, considering the corresponding violent motion characteristics such as turning, a higher sampling accuracy is required to capture transient motion parameters. Therefore, the original sampling frequency configuration of the IMU and navigation module remains unchanged, such as the IMU maintains 200Hz sampling and the navigation module maintains 10Hz update. In scenarios such as changing lanes, turning or emergency obstacle avoidance, the vehicle's motion state changes rapidly, and a higher sampling frequency is required to capture these dynamic characteristics. Although the power consumption reaches the peak level at this time, since this type of motion state usually lasts no more than a few seconds, the impact on the overall energy consumption of the system is limited.

[0063] In order to ensure that the system switches smoothly between different energy consumption areas, a gradual frequency adjustment strategy is used to handle the state transition process. When it is detected that the state is about to change, the sampling frequency will be adjusted step by step according to the preset step size to avoid data jumps caused by sudden changes in parameters. For example, when the vehicle starts from a stationary state, the IMU sampling frequency will gradually increase in steps of 20Hz, and the navigation update rate (that is, the update rate of the navigation module) will also increase accordingly; when the vehicle slows down and stops, the sampling frequency will be gradually reduced in similar steps. This gradual adjustment mechanism not only ensures the continuity of the data, but also avoids the system burden caused by frequent fluctuations in sampling parameters.

[0064] The final hierarchical sampling configuration scheme established three levels: the low-energy consumption area adopted a lower frequency sampling combination, mainly for static conditions; the medium-energy consumption area used a compromise configuration scheme for uniform motion conditions; the high-energy consumption area maintained the original sampling frequency to cope with intense motion conditions such as turning.

[0065] Step S40, according to the hierarchical sampling configuration scheme, calculate the energy consumption parameter combination of the IMU sensor and the navigation module under each working state, establish a mapping relationship between state-frequency-energy consumption, and obtain the energy consumption control parameter of the system;

[0066] Combination Figure 4 In some embodiments, step S40 may include: step S41 and step S42.

[0067] Step S41, based on the hierarchical sampling configuration scheme, calculating the energy consumption parameter combination under each working state, the energy consumption parameter combination including the power consumption of the IMU sensor and the power consumption of the navigation module, and constructing a mapping relationship between the working state and the total power consumption;

[0068] According to the hierarchical sampling configuration scheme, the energy consumption parameter combination under each working state is first calculated. The power consumption of the IMU sensor includes the power consumption generated by the signal acquisition, conditioning and data transmission process. The power consumption of each sensor of the accelerometer, gyroscope and magnetometer consists of the basic circuit power consumption and frequency-related power consumption, which can be expressed as: P_IMU = k·f + P0 Where: P_IMU is the total power consumption of the IMU, k is the frequency coefficient, which represents the sensitivity of the power consumption to the sampling frequency, f is the sampling frequency, and P0 is the basic power consumption, which represents the energy consumption required to maintain the basic operation of the IMU. For example, the basic power consumption of the accelerometer includes the static power consumption of the clock circuit and the temperature compensation circuit, and the frequency-related power consumption mainly comes from the signal conditioning and data conversion process.

[0069] The power consumption of the navigation module comes from the RF front-end signal amplification and baseband processor signal resolution process, which is mainly determined by the signal processing load and has a relationship with the update rate R: P_GNSS = a·R + b Where: P_GNSS is the total power consumption of the navigation module, a is the update rate coefficient, which characterizes the sensitivity of power consumption to the update rate, and b is the basic load coefficient, which reflects the basic circuit power consumption of the navigation module.

[0070] Based on the power consumption characteristics of the above-mentioned individual modules, the mapping relationship between the working state and the total power consumption is constructed: P(S) = P_IMU(f) + P_GNSS(R) = (k·f + P0) + (a·R + b) In the formula: P is the total power consumption of the system, S is the working state, and the corresponding sampling frequency f and update rate R jointly determine the system power consumption level, k is the IMU frequency coefficient, reflecting the sensitivity of IMU power consumption to changes in sampling frequency, P0 is the basic power consumption of IMU, a is the navigation update rate coefficient, characterizing the sensitivity of power consumption to changes in update rate, and b is the basic power consumption of the navigation module. For example, in a stationary state, due to the slow change of motion parameters, a lower sampling frequency can be used; while in a violent motion state, a higher sampling frequency is required to capture motion characteristics.

[0071] Substituting the sampling frequency and update rate corresponding to different working states into the above mapping relationship, the target power consumption value that the system should achieve in each state can be obtained. If the stationary state adopts an IMU sampling frequency of 50Hz and a navigation update rate of 1Hz, the target power consumption is P_target1 = k·50 + P0 + a·1 + b, where k·50 + P0 represents the power consumption of the IMU at a sampling frequency of 50Hz, and a·1 + b represents the power consumption of the navigation module at an update rate of 1Hz; if the normal driving state adopts a sampling frequency of 100Hz and an update rate of 5Hz, the target power consumption is P_target2 = k·100 + P0 + a·5 + b; if the intense exercise state adopts a sampling frequency of 200Hz and an update rate of 10Hz, the target power consumption is P_target3 = k·200+ P0 + a·10 + b. These target power consumption values ​​serve as reference benchmarks for subsequent energy consumption optimization control.

[0072] Step S42, by adding positioning accuracy constraints, sampling frequency range constraints, update rate range constraints and power consumption change rate constraints during state switching, the mapping relationship is optimized and solved to obtain energy consumption control parameters, which include target power consumption values ​​under different working states, the mapping relationship between working states and total power consumption, and power consumption gradient parameters during state switching.

[0073] Since the establishment of the mapping relationship needs to consider the positioning accuracy constraint, the optimization problem under different working conditions is solved: min P(S) = k·f + P0 + a·R + b; the constraints include: f_min≤f≤f_max, that is, the sampling frequency range constraint; R_min≤R≤ R_max, that is, the update rate range constraint, ε(f, R)≤ε_max, that is, the positioning accuracy constraint, in which: f_min, f_max are the minimum sampling frequency and maximum sampling frequency, R_min, R_max are the minimum update rate and maximum update rate, ε(f, R) is the positioning error function, and ε_max is the maximum allowable positioning error. For example, the positioning error is required to be no more than 1 meter in a stationary state, no more than 0.5 meters in a normal driving state, and no more than 0.3 meters in a violent motion state.

[0074] Solve this optimization problem for the stationary state. At this time, the vehicle is completely stationary, the motion parameters change slowly, the positioning accuracy constraints are relatively loose, and there is a large optimization space. According to the mapping relationship calculation, it is found that reducing the sampling frequency has little effect on the positioning performance, but can significantly reduce power consumption. For example, if the IMU sampling frequency is reduced to 1 / 4 of the original frequency, the positioning error will not increase by more than 5%, but the power consumption can be reduced by about 70%.

[0075] Under normal driving conditions, the weight of the positioning accuracy constraint increases. Since the vehicle is in motion, it is necessary to capture changes in speed and posture in a timely manner, which requires that the sampling frequency must meet dynamic response requirements. For example, to detect speed changes within 0.1 seconds, the sampling frequency of the IMU sensor cannot be lower than 50Hz. The optimization solution needs to find the optimal sampling parameter combination while ensuring tracking performance.

[0076] In the case of intense motion, positioning accuracy constraints play a dominant role. When turning or emergency braking, motion parameters change significantly, requiring high sampling accuracy. For example, during a sharp turn, the angular velocity may reach 100° / s, and a sampling frequency of at least 200Hz is required to accurately capture this change.

[0077] In addition, a power consumption change rate constraint is added during the state switching process: |ΔP / Δt| ≤λ, where ΔP is the power consumption change, Δt is the time change, and λ is the allowed power consumption change rate threshold. This makes the adjustment of the sampling parameters progressive, avoiding power consumption jumps and data discontinuity caused by parameter mutations. For example, when switching from a stationary state to a moving state, the sampling frequency of the IMU sensor is gradually increased in steps of 20Hz, and the update rate of the navigation module is gradually increased in steps of 1Hz.

[0078] By solving the above optimization problem, we finally get a complete set of energy consumption control parameters, including: the target power consumption value of the system under different working states, that is, the comprehensive power consumption index of the IMU and navigation module in static state, normal driving and intense exercise; the mapping relationship between state and power consumption, reflecting the impact of changes in sampling frequency and update rate on the total power consumption of the system; and the power consumption gradient parameter during state switching, which is used to control the smooth transition of power consumption changes. This set of energy consumption control parameters comprehensively describes the power consumption characteristics of the system under various working states, and realizes precise control of power consumption by dynamically adjusting the sampling frequency.

[0079] Step S50, based on the real-time working status detection result of the system, the IMU sensor and the navigation module are adjusted to the corresponding working frequency according to the energy consumption control parameter.

[0080] First, a real-time state monitoring model is established. Based on the motion state recognition method established in step S20, the system monitors the change characteristics of IMU sensor data and navigation data in real time, which can be expressed as: S(t) = F[a(t), ω(t), v(t)]. Where: S(t) is the system state at time t, F is the state mapping function, a(t) is the three-axis acceleration vector {ax, ay, az}, ω(t) is the three-axis angular velocity vector {ωx, ωy, ωz}, and v(t) is the three-dimensional velocity vector {vx, vy, vz}. Different from the offline analysis in step 2, a 1s sliding window is used here for real-time feature calculation, and the state judgment result is updated every 100ms. The system focuses on the changing trend of the characteristic values. For example, when the vehicle turns from a sharp turn to a straight road, the angular velocity variance Vω will drop rapidly from a level greater than 100 (° / s)²; when the vehicle stops, the acceleration variance Va drops below 0.1g², and the speed root mean square value drops below 1km / h; when the vehicle is driving at a constant speed, these characteristic values ​​will remain in a stable range. This real-time monitoring mechanism ensures that the system can detect changes in the working status in a timely manner.

[0081] After detecting that the state has changed, the system immediately calls the energy consumption control parameters obtained in step S40. The state parameter mapping uses two mapping functions: f_new = G(S_new), R_new = H(S_new). Where: f_new is the IMU target sampling frequency in the new state, G is the mapping function from the state to the IMU sampling frequency, R_new is the navigation target update rate in the new state, H is the mapping function from the state to the navigation update rate, and S_new is the new working state detected by the system, which may be stationary, normal driving, or violent motion. When it is detected that the vehicle enters a stationary state, the low-power configuration of the IMU sampling frequency of 50Hz and the navigation update rate of 1Hz will be called; when the normal driving state is detected, the medium-frequency configuration of the IMU sampling frequency of 100Hz and the navigation update rate of 5Hz will be called; when a violent motion state is detected, the high-frequency configuration of the IMU sampling frequency of 200Hz and the navigation update rate of 10Hz will be called.

[0082] In order to avoid data jumps caused by sudden changes in sampling parameters, the frequency adjustment is performed in a gradual manner: f(t) =f_old +Δf·t / T, R(t) = R_old +ΔR·t / T Where: f(t) and R(t) are the instantaneous values ​​of the IMU sampling frequency and navigation update rate, respectively, f_old and R_old are the initial frequencies before adjustment, Δf and ΔR are the total adjustments of the IMU frequency and navigation rate, respectively, t is the current adjustment time, and T is the preset total adjustment duration. For example, when adjusting from high frequency to low frequency, the total adjustment of the IMU frequency Δf is -150Hz (from 200Hz to 50Hz), and the total adjustment of the navigation rate ΔR is -9Hz (from 10Hz to 1Hz). The frequency adjustment process is precisely controlled by the timer. The IMU sampling frequency is adjusted every 100ms, and the adjustment step is set to 20Hz, which can achieve a faster response speed while ensuring data continuity. For example, it takes 8 adjustments to reduce from 200Hz to 50Hz, and the total time is about 800ms. The navigation update rate takes into account the characteristics of the solution cycle and adopts a slower adjustment strategy, adjusting 1Hz every 500ms. It takes about 4.5 seconds to reduce from 10Hz to 1Hz. This differentiated adjustment strategy ensures a smooth transition between IMU data and navigation data.

[0083] The dynamic adjustment process forms a complete closed-loop control: the system continuously monitors the characteristics of sensor data and makes a state judgment every 100ms; after detecting a state change, it immediately searches for the corresponding energy consumption control parameters; then performs a gradual frequency adjustment to gradually adjust the sampling parameters to the target value. For example, when a vehicle turns from a high-speed turn into a straight road and gradually stops, the system first detects that the angular velocity variance decreases from more than 100 (° / s)² and determines that the turn is over; then it detects that the speed gradually decreases until it is close to a standstill, and finally confirms that it has entered a stationary state. The entire state recognition and frequency adjustment process is carried out automatically, realizing real-time optimization of sampling parameters.

[0084] This dynamic adjustment mechanism based on real-time state detection ensures the reliability and real-time performance of system state switching by reasonably setting the monitoring cycle and adjustment step size. The selection of the length of the sliding window and the sliding interval must ensure the accuracy of feature extraction and meet the needs of rapid response. The differentiated adjustment strategies of the IMU sensor and the navigation module fully consider the differences in the working mechanism and performance requirements of the two sensors. Through this coordination and cooperation, the smooth switching and dynamic optimization control of the system sampling frequency are finally achieved. In addition, unlike the traditional sampling frequency mutation adjustment method, this embodiment scheme innovatively proposes a progressive adjustment strategy. When the system detects that the state is about to change, it will plan a complete frequency adjustment sequence in advance, slowly adjust the sampling parameters according to the preset step size, and set an appropriate stable waiting period after each adjustment. This smooth transition adjustment method not only ensures the continuity and reliability of the data, but also avoids the system burden caused by frequent adjustments, making the entire navigation system run more stable and efficient.

[0085] The technical solution of the IMU and navigation collaborative energy-saving method of this embodiment establishes a hierarchical sampling configuration scheme based on the motion state, and through real-time analysis of the vehicle motion state data, the driving state can be intelligently classified into three working states: static state, uniform motion state (or uniform driving state) and violent motion state. For different working states, the system adopts corresponding sampling configuration schemes, which can greatly reduce the sampling frequency of the IMU sensor and the update rate of the navigation module when static to save energy, maintain a moderate sampling level of the IMU sensor and the navigation module when driving at a uniform speed, and increase the sampling frequency of the IMU sensor and the update rate of the navigation module during key actions such as turning, high acceleration or deceleration to ensure accuracy; in this way, a solution for dynamically adjusting the sampling frequency of the IMU sensor and the update rate of the navigation module based on the motion state is implemented, which achieves the optimal balance between performance and energy consumption compared to the fixed sampling frequency of the traditional navigation system, reduces the power consumption of the vehicle navigation system while ensuring positioning accuracy, and improves the energy utilization rate of the vehicle.

[0086] like Figure 5 As shown, Figure 5 It is a flow chart of the second embodiment of the IMU and navigation collaborative energy saving method of the present invention.

[0087] In this embodiment, after step S50, the IMU and navigation collaborative energy saving method further includes:

[0088] Step S60, monitoring the actual operating power consumption of the system, comparing and analyzing the actual operating power consumption of each working state with the target power consumption corresponding to each working state, and calculating the energy consumption deviation value;

[0089] The actual operating power consumption of the monitoring system is firstly monitored by using a current detection circuit to collect the working current of the IMU sensor and the navigation module in real time. The power consumption is calculated by using the moving average method, and the average power consumption value is calculated every 100ms: P_real = V·Iavg Where: P_real is the actual operating power consumption, V is the operating voltage, and Iavg is the average operating current. In order to accurately obtain power consumption data, the system sets up independent current detection channels to monitor the working current of the IMU and navigation modules respectively. The IMU part includes the power supply current of the three sensors of the accelerometer, gyroscope and magnetometer. The working current of each sensor is collected in real time through a high-precision current sampling circuit; the navigation part includes the working current of the RF front end and the baseband processor, which are monitored by the same sampling circuit. For example, when the vehicle turns, the system can accurately capture the rapid rise of the gyroscope current; when the vehicle is driving at a constant speed, the stable working current of all sensors is recorded; when the vehicle is stationary, the minimum power consumption level under the basic working state is monitored.

[0090] In the actual power consumption collection process, the system adopts different recording strategies for IMU and navigation modules. The power consumption data of the IMU sensor is synchronized with the sampling process, and current sampling and recording are performed every 5ms to ensure that the power consumption changes of the sensor under high dynamic working conditions can be captured; the power consumption collection of the navigation module is synchronized with the position solution process, and is recorded every 100ms. This synchronization strategy ensures the correspondence between the power consumption data and the working state of the system. The original current data is averaged using a sliding window of 1s in length, and the window moves 100ms each time. In this way, the dynamic characteristics of power consumption changes are retained and the influence of sampling noise can be effectively suppressed.

[0091] Based on the actual power consumption data collected continuously, the system compares and analyzes the target power consumption P_target1, P_target2 and P_target3 corresponding to each state. In the stationary state, the basic power consumption of the three sensors of the IMU and the maintenance power consumption of the navigation module are recorded; in the normal driving state, the power consumption levels of the two modules in stable operation are counted respectively; in the state of intense movement, the peak power consumption under high dynamic conditions is recorded. This hierarchical comparison method enables the system to clearly distinguish the power consumption differences under different working conditions. For example, the power consumption changes during a turn are fully recorded, including the rapid increase in power consumption at the beginning of the turn, the continuous high power consumption during the turn, and the gradual decrease in power consumption after the turn.

[0092] The results of the power consumption comparison analysis are used to calculate the relative deviation: ΔP = (P_real - P_target) / P_target× 100% Where: ΔP is the relative deviation of power consumption, P_real is the actual power consumption, and P_target is the target power consumption corresponding to the current state. The system calculates the power consumption deviation for the IMU and navigation module separately to form a complete deviation record sequence. This calculation method not only reflects the degree of difference between the actual power consumption and the target value, but also reflects the dynamic change characteristics of the power consumption deviation. For example, record the change in power consumption deviation during the vehicle's acceleration from rest to start, or record the fluctuation of the IMU power consumption deviation during continuous steering operations.

[0093] Through the above power consumption monitoring and deviation analysis process, the system finally obtains the power consumption deviation value that reflects the actual working status of the IMU and navigation module. This deviation value quantifies the difference between the actual power consumption and the target power consumption. The system can adjust the sampling frequency configuration in a targeted manner and further optimize the energy consumption control parameters. For example, when a continuous positive deviation is detected in a stationary state, it means that the current low-frequency sampling configuration still has room for optimization; if a large negative deviation occurs in a violent motion state, it indicates that the sampling frequency may need to be appropriately increased to meet the positioning accuracy requirements.

[0094] Step S70, optimizing and adjusting the hierarchical sampling configuration scheme according to the energy consumption deviation analysis result to obtain an updated hierarchical sampling configuration scheme;

[0095] Combination Figure 6 As shown, step S70 may include: step S71, step S72, step S73, step S74 and step S75. Among them:

[0096] Step S71, establishing a mapping relationship between the energy consumption deviation value and the sampling frequency of the IMU sensor and the update rate of the navigation module;

[0097] The system first establishes a correlation mapping between the power consumption deviation and the sampling frequency, and uses the power consumption deviation value ΔP obtained in step S60 as a core reference to formulate a corresponding frequency adjustment plan.

[0098] Step S72, when the energy consumption deviation value is a positive power consumption deviation, reducing the sampling frequency of the IMU sensor and / or the update rate of the navigation module according to the adjustment strategy of the corresponding energy consumption area;

[0099] Step S73, when the energy consumption deviation value is a negative power consumption deviation, increasing the sampling frequency of the IMU sensor and / or the update rate of the navigation module according to the adjustment strategy of the corresponding energy consumption area;

[0100] The basic adjustment idea is: when a positive power consumption deviation occurs, it means that the actual power consumption is higher than the target value, and the sampling frequency needs to be appropriately reduced (including reducing the sampling frequency of the IMU sensor and / or the update frequency of the navigation module); when a negative deviation occurs, the sampling frequency needs to be increased to meet the system performance requirements. Each energy consumption area (low energy consumption area, medium energy consumption area, high energy consumption area) has a preset corresponding adjustment strategy. First, determine the energy consumption area corresponding to the energy consumption deviation value, that is, determine which energy consumption area generates the energy consumption deviation value, and then adjust the IMU sampling frequency and navigation update frequency according to the adjustment strategy of the determined energy consumption area, that is, when the energy consumption deviation value is a positive power consumption deviation, reduce the sampling frequency of the IMU sensor and / or the update rate of the navigation module according to the adjustment strategy of the determined energy consumption area, and when the energy consumption deviation value is a negative power consumption deviation, increase the sampling frequency of the IMU sensor and / or the update rate of the navigation module according to the adjustment strategy of the determined energy consumption area.

[0101] In some embodiments, the adjustment process can also consider two key indicators: the size and duration of the deviation. For example, when it is detected that the IMU power consumption in a stationary state is 10% higher than the target value for 5 minutes, the system will reduce the current 50Hz sampling frequency to 40Hz; when it is found that the navigation module power consumption is continuously lower than the target value by 15% within 3 minutes, it will be increased from 1Hz to 2Hz accordingly to ensure that the positioning accuracy meets the requirements. At the same time, the system will also weigh the changing trend of the deviation and adopt a more conservative adjustment strategy for the deviation value with larger fluctuations.

[0102] Among them, the system adopts differentiated optimization and adjustment strategies for different energy consumption areas. For example, in low-energy consumption areas, the optimization is mainly for the stationary state; because the vehicle is completely stationary at this time, the motion parameters change very little, and the optimization focus of the system is to reduce the sampling frequency as much as possible while ensuring the basic positioning accuracy. The adjustment process will monitor the change amplitude of the acceleration and angular velocity data in real time, and the frequency adjustment will only be performed when it is confirmed that the motion parameters are continuously stable. For example, if it is found that the fluctuation of the three-axis acceleration value is always kept within 0.01g and the positioning error is kept within 1 meter at the current 40Hz IMU sampling frequency and 1Hz navigation update rate, and the power consumption has a positive deviation of 5%, then the system will try to further reduce the IMU frequency to 30Hz. After each adjustment, it will be observed for at least 1 minute to confirm that the positioning performance has not been significantly degraded before determining the new sampling parameters.

[0103] For another example, the medium energy consumption area corresponds to the normal driving state, and it is necessary to find the best balance between positioning accuracy and power consumption control. The system will adjust the sampling strategy according to the size and duration of the power consumption deviation, and combine the speed and acceleration information to evaluate the feasibility of the adjustment. For example, when the vehicle is cruising at a constant speed on the highway, if the IMU power consumption is continuously monitored to be 8% higher than the target value within 15 minutes, the vehicle speed fluctuation is less than 2km / h, and the positioning accuracy is much better than the required 0.5-meter threshold, the system will reduce the IMU sampling frequency from 100Hz to 80Hz. Especially in the stable high-speed cruising scenario, the system found through data analysis of similar sections within a week that when the vehicle maintains a stable speed and the motion parameters remain stable, the current sampling frequency has a certain redundancy. Based on this, the system will gradually adjust the sampling parameter configuration scheme, reduce the sampling frequency while ensuring that the positioning accuracy meets the requirements, and further optimize the energy consumption. For another example, the high energy consumption area is mainly for violent motion states such as turning, acceleration and deceleration. At this time, the system pays more attention to ensuring positioning accuracy. Even if a positive deviation in power consumption is detected, the system will carefully adjust the frequency, and multiple conditions must be met at the same time before the frequency reduction operation is performed. For example, during continuous steering, even if the gyroscope power consumption is found to exceed the target value by 20%, the sampling frequency will only be slightly reduced from 200Hz to 180Hz to ensure that critical motion information is not lost. Before adjustment, the angular velocity change trend will be evaluated first, and the frequency adjustment will only be performed when the maximum value of the steering angular velocity begins to decrease and no new steering intention is detected within 10 consecutive sampling cycles. Similarly, the sampling frequency adjustment during acceleration and deceleration must be performed only after the acceleration value has significantly decreased.

[0104] Step S74, in the process of reducing or increasing the sampling frequency of the IMU sensor, if abnormal data fluctuation occurs, the sampling frequency of the IMU sensor and / or the navigation module is called back;

[0105] In this embodiment, during the process of adjusting the sampling frequency of the IMU sensor, the data quality of three types of sensors, namely the accelerometer, gyroscope and magnetometer, will be monitored simultaneously. When any type of data shows abnormal fluctuations, the sampling frequency callback mechanism will be triggered to ensure the data quality of the IMU sensor.

[0106] In some embodiments, the adjustment strategy is to make incremental adjustments according to a preset adjustment step size, and a preset time interval is set between two adjacent adjustments to confirm whether the adjusted data is collected normally to determine whether to continue the next adjustment. The adjustment process of the sampling frequency of the IMU sensor and the update frequency of the navigation module adopts a progressive strategy to avoid data jumps caused by parameter mutations; the design of the adjustment step size fully considers the characteristics and data processing capabilities of different sensors. For example, when the IMU frequency needs to be reduced from 200Hz to 50Hz, the system will gradually adjust in steps of 20Hz per second, and the whole process will last for 7.5 seconds. After each frequency reduction, a stabilization period of more than 50ms will be waited, and the next adjustment will be performed only after confirming that the data collection is normal. The update rate adjustment of the navigation module adopts a similar strategy, but considering its particularity, the adjustment step size is set smaller, usually changing at a rate of 1Hz per second, and the stable waiting period after each change is extended to more than 200ms.

[0107] Special attention should be paid to the frequency adjustment during the state switching process. When the system detects that the working state is about to change, it will plan a complete frequency adjustment sequence in advance. For example, when the vehicle is turning from a high-speed turn to a straight road, the system will monitor the changing trend of the steering angular velocity in real time. For example, when the angular velocity begins to decrease, the IMU sampling frequency is gradually reduced from 200Hz to 100Hz according to the preset adjustment step; when it is confirmed that it has completely entered the straight road uniform speed driving state, it will decide whether to further reduce the frequency based on the power consumption deviation.

[0108] In some embodiments, step S70 may further include: during the process of reducing or increasing the sampling frequency of the IMU sensor and / or the update rate of the navigation module, if the positioning accuracy exceeds a preset threshold, suspending the adjustment. During the entire adjustment process, the change of the positioning error is monitored in real time. Once it is found that the accuracy index exceeds the preset threshold, the frequency adjustment is immediately suspended to ensure the positioning accuracy.

[0109] Step S75, generating an updated hierarchical sampling configuration scheme according to the optimized and adjusted sampling frequency of the IMU sensor and the update rate of the navigation module, as well as the working states corresponding to the adjusted sampling frequency and update rate;

[0110] Finally, the system generates an updated hierarchical sampling configuration scheme based on the results of the optimization adjustment. The hierarchical sampling configuration scheme establishes a complete hierarchical control strategy by differentially treating each energy consumption area. In the low-energy consumption area, the sampling frequency is strictly controlled, based on the positioning requirements of the static state; in the medium-energy consumption area, balanced control is achieved to balance the positioning accuracy and power consumption requirements; in the high-energy consumption area, sensitive dynamic response capabilities are maintained to ensure that the system can accurately capture the characteristics of intense motion. Through this progressive, multi-level control mechanism, the system achieves precise adjustment of sampling parameters, which not only solves the problem of power consumption deviation, but also establishes a set of dynamic optimization strategies that adapt to different working states.

[0111] Step S80, performing system performance monitoring on the updated hierarchical sampling configuration scheme, collecting and recording key performance indicator parameters, where the key performance indicator parameters at least include positioning accuracy, system power consumption, and data latency;

[0112] System performance monitoring first establishes a complete monitoring framework. In typical scenarios such as urban roads, highways, and parking lots, the system deploys high-precision RTK positioning equipment as a reference benchmark, and is equipped with professional power analyzers and data acquisition equipment to achieve real-time monitoring of the system's operating status. The RTK base station uses a dual-frequency GPS / Beidou receiver with an operating frequency of 1Hz and a positioning accuracy of better than 2cm, which can accurately record the system's positioning data. At the signal receiving end, an adaptive antenna array is configured to suppress multipath effects; at the data processing end, real-time carrier phase tracking and ambiguity fixation technology are used to ensure the high accuracy of the reference data. For example, during the repeated start-stop process at a traffic light intersection, the RTK reference data accurately records the static position and slow-moving trajectory of the vehicle, including the initial braking position, small displacement during the waiting process, and the trajectory of starting and accelerating; during the continuous turning process of the high-speed ramp, the complete driving trajectory data including entering, turning, and exiting the corner is recorded; when performing repeated parking tests in the parking lot, the position data of the vehicle at low speed is recorded, including the complete trajectory of complex parking actions such as forward, backward, and turning. The monitoring process uses a sliding window recording method, with the window length set to 1 second and the sliding interval set to 100ms, to ensure that the system performance change data can be continuously collected. When encountering sudden state changes such as sharp turns or emergency braking, the system automatically shortens the window interval to 20ms to collect these key process data with higher time resolution.

[0113] In the parameter collection phase, the system focuses on recording three types of key performance indicators. The first is the positioning accuracy indicator, which is the most direct basis for evaluating system performance. The system records the position data every 100ms and records it with the RTK reference position. In the horizontal direction, the position data of the east and north directions are collected respectively, and the accuracy requirement is better than the centimeter level; in the vertical direction, the elevation data is collected, and the accuracy is required to be at the decimeter level. The system not only collects the final positioning results, but also records the key parameters in the positioning solution process. For example, under the condition of uniform speed driving on the highway, the system continuously records the positioning data for 30 minutes, collecting the original IMU measurement value, attitude solution result and the final combined positioning result at a sampling frequency of 100Hz. In specific sections, such as under viaducts and areas with dense buildings where the GPS signal quality is poor, the system additionally records the auxiliary parameters of the navigation solution, including the carrier-to-noise ratio, multipath effect intensity, geometric precision factor, etc. of each visible satellite. In the case that the satellite is blocked under the viaduct, the system records the change process of the number of available satellites in detail and collects the positioning accuracy change data under different satellite combinations.

[0114] The collection process of system power consumption indicators is more detailed. The power analyzer is used to monitor the working voltage and current of the IMU sensor group and the navigation module respectively, and collect real-time power consumption data. The sampling frequency of the power analyzer is set to 1MHz to ensure that tiny current changes can be captured. The IMU part records the power supply parameters of the three sensors, the accelerometer, the gyroscope and the magnetometer, including the working voltage fluctuation range, the static working current and the dynamic peak current; the navigation module collects the energy consumption data of the RF front end and the baseband processor respectively, and records the power consumption changes in each link of signal reception, baseband processing and position solution. In order to accurately collect transient power consumption, the system uses a high-speed data acquisition card, sets the sampling resolution to 16 bits, and the full scale to 100mA to ensure that the power consumption changes at the mW level can be accurately recorded. For example, in a complete turn process, the system records the complete data of the gyroscope power consumption rising from 15mW in the static state to a peak of 45mW, and falling back after the turn. The sampling interval is 1 microsecond, which fully records the dynamic process of power consumption changes. For different energy consumption areas, the system adopts different collection strategies. In low-speed areas such as parking lots, the focus is on collecting the static power consumption data of the sensor, and the recording interval can be appropriately relaxed to 1ms; when driving on urban roads, more attention is paid to the dynamic power consumption data during acceleration and deceleration, maintaining a sampling accuracy of microseconds.

[0115] The monitoring of data delay indicators runs through the entire data processing process. The system adds precise timestamps to each link of the data flow, uses atomic clock signals as a unified time reference, and the timestamp accuracy is better than 100ns. During the processing of IMU data, the data sampling time, the time when the data enters the processor, the time when the attitude solution is completed, and the final output time are collected respectively. For IMU sampling data, the sensor output delay, AD conversion delay, data transmission delay and processing delay are recorded; for navigation data, the signal capture delay, tracking loop delay and positioning solution delay are collected. The system records the processing link data when switching from 200Hz to 50Hz sampling frequency, and collects complete delay data including interrupt response time, data cache delay, algorithm processing time and output waiting time.

[0116] Through this complete performance monitoring system, the system finally collected three types of key performance indicator parameters: positioning accuracy parameters, including position error, speed accuracy and attitude data; system power consumption parameters, recording the energy consumption data of IMU and navigation modules in different states; data delay parameters, collecting and processing delay data of each link in the chain. These key performance indicator parameters provide necessary data support for subsequent system optimization.

[0117] Step S90, combining the performance test index with the original energy consumption configuration scheme, determining the optimal sampling frequency combination of the IMU sensor and the navigation module under each working state, and obtaining the optimal sampling configuration scheme.

[0118] Combination Figure 7 As shown, in some embodiments, step S90 may include: step S91 and step S92. Among them:

[0119] Step S91, constructing an evaluation function of comprehensive performance;

[0120] Based on the performance test index of step S80, the optimal sampling frequency combination is calculated by constructing a comprehensive performance evaluation function. The evaluation function is defined as: E_total = w1·(E / E_max) + w2·(D / D_max) + w3·(P / P_max), where E is the positioning error, E_max is the maximum allowable error (e.g. 1 meter), D is the data delay, D_max is the maximum allowable delay (e.g. 20ms), P is the system power consumption, and P_max is the maximum power consumption (e.g. 250mW). The weight coefficients w1, w2, and w3 reflect the importance of different performance indicators, and satisfy w1 + w2 + w3 = 1. For example, in a stationary state, w1=0.3, w2=0.2, and w3=0.5 are set to focus more on power consumption control; in a normal driving state, w1=0.4, w2=0.3, and w3=0.3 are set to balance various performances; in a violent motion state, w1=0.5, w2=0.3, and w3=0.2 are set to prioritize positioning accuracy. This weight configuration fully considers the focus of system performance under different working conditions.

[0121] Step S92, based on the preset constraints corresponding to each working state, the optimal sampling frequency combination of each working state is calculated according to the evaluation function to obtain the optimal sampling configuration scheme.

[0122] For example, for the stationary state, constraints are set based on the performance test indicators: the positioning accuracy constraint E does not exceed 1 meter, the data delay D does not exceed 20ms, and the system power consumption P does not exceed 30% of the maximum power consumption (about 75mW). Under these constraints, the IMU sampling frequency f is in the range of 20Hz to 200Hz, and the navigation update rate R is in the range of 1Hz to 10Hz for traversal calculation. The system traverses the IMU sampling frequency once every 10Hz and the navigation update rate once every 1Hz to ensure that the optimal solution is not missed. For example, when parking or waiting for a red light, when the IMU uses 30Hz sampling and the navigation module uses 1Hz update, the performance test obtains the results of E=0.85m, D=15ms, and P=65mW. Substituting into the performance evaluation function, we can get E_total = 0.3×(0.85 / 1) + 0.2×(15 / 20) + 0.5×(65 / 250) = 0.4375. In this way, the E_total values ​​of all possible combinations are calculated, and finally the parameter combination with the smallest E_total and satisfying all constraints is selected as the optimal configuration in this state.

[0123] For normal driving conditions, the constraints are adjusted accordingly: the positioning accuracy requirement is increased to within 0.5 meters, the data delay does not exceed 10ms, and the system power consumption is controlled within 60% of the maximum power consumption (about 150mW). These stricter constraints reflect the system's higher requirements for dynamic performance. At this time, parameter optimization calculations are performed. For example, when the IMU sampling frequency is 80Hz and the navigation update rate is 5Hz, the performance test results show that E=0.35m, D=8ms, and P=130mW. Substituting into the evaluation function, E_total= 0.4×(0.35 / 0.5) + 0.3×(8 / 10) + 0.3×(130 / 250) = 0.632. The system also calculates all possible parameter combinations, covering the range of IMU sampling frequency 50Hz to 150Hz and navigation update rate 3Hz to 8Hz. The selection of this range is based on the performance requirements of actual driving conditions to ensure that the system can accurately capture the vehicle's motion characteristics.

[0124] For intense motion, the constraints are further tightened: the positioning accuracy is required to be within 0.3 meters, and the data delay is reduced to less than 5ms. At this time, the power consumption constraint can be relaxed to the maximum power consumption. Higher performance requirements ensure that the system can accurately track rapid motion changes. For example, when the IMU uses 160Hz sampling and the navigation module uses 8Hz update, the performance test shows E=0.25m, D=4ms, and P=220mW. Substituting into the evaluation function, Etotal = 0.5×(0.25 / 0.3) + 0.3×(4 / 5)+ 0.2×(220 / 250) = 0.677 is calculated. The optimization process is mainly carried out in the range of IMU sampling frequency 120Hz to 200Hz and navigation update rate 6Hz to 10Hz. This range ensures the system's ability to track high dynamic motion.

[0125] Through the above optimization calculations, the system finally determines the optimal sampling parameter combination under three working conditions. In the static state, the combination of IMU sampling at 30Hz and navigation updating at 1Hz is used, at which time Etotal is the smallest and power consumption is reduced to the lowest level; in the normal driving state, the combination of IMU sampling at 80Hz and navigation updating at 5Hz is used, which better balances performance and power consumption; in the intense motion state, the combination of IMU sampling at 160Hz and navigation updating at 8Hz is used to ensure the dynamic tracking performance of the system.

[0126] For the optimal sampling frequency combination obtained by optimization calculation, the system outputs a complete hierarchical energy-saving control strategy. The strategy first establishes a state recognition mechanism, and continues to use the real-time state monitoring model S(t) = F[a(t), ω(t), v(t)] established in step S50 to calculate the motion eigenvalues ​​through a sliding window, and monitor the change trends of acceleration, angular velocity and speed in real time. The system updates the calculation results every 0.1 seconds, and identifies the current motion state by analyzing the changes in the eigenvalues. This real-time monitoring mechanism can capture changes in the vehicle's motion state in a timely manner, such as turning from a sharp turn to a straight road, and switching from high-speed driving to parking.

[0127] The second part of the strategy is the sampling parameter control mechanism. The system automatically selects the corresponding optimal sampling frequency combination based on the identified working state. For example, in a stationary state, a combination of IMU sampling at 30Hz and navigation updating at 1Hz is used, with the main goal of reducing power consumption; in a normal driving state, a combination of IMU sampling at 80Hz and navigation updating at 5Hz is used. This set of parameters shows a good balance in performance testing, and can accurately track the regular movement of the vehicle while maintaining a low energy consumption level; in a state of intense movement, the system uses a combination of IMU 160Hz sampling and navigation 8Hz updating. High-frequency sampling ensures accurate capture of rapid movement changes, and the increase in navigation update rate enhances the real-time nature of position solution. This hierarchical parameter configuration scheme makes full use of the performance test results and achieves the optimal match between performance and energy consumption.

[0128] Switching control is the third important component of the strategy. To avoid sudden changes in sampling parameters from impacting system performance, the strategy adopts a gradual adjustment mechanism. When switching from low frequency to high frequency, the IMU sampling frequency is adjusted in a linear growth manner, and the growth rate is set to 100Hz / s to make the frequency adjustment process smooth. The adjustment of the navigation update rate is relatively slow, with a change rate of 5Hz / s. This setting takes into account the sensitive characteristics of the navigation solution to changes in sampling parameters. When switching from high frequency to low frequency, the adjustment rate can be increased accordingly, but the continuity of parameter changes is still maintained. For example, when switching from intense exercise to normal driving, the IMU sampling frequency completes the frequency reduction process from 160Hz to 80Hz in 0.8 seconds, and the navigation update rate is reduced from 8Hz to 5Hz in 0.6 seconds. This switching control mechanism based on actual needs ensures the smooth operation of the system during state transition.

[0129] This hierarchical energy-saving control strategy achieves a dynamic balance between system performance and energy consumption through the organic coordination of three links: state recognition, parameter regulation, and switching control. This strategy converts the optimal sampling frequency combination into an executable control scheme, providing a systematic solution for the coordinated energy saving of IMU sensors and navigation modules.

[0130] In the technical solution of this embodiment, the system combines real-time status monitoring with power consumption prediction, establishes a complete energy consumption optimization control mechanism, and accurately identifies the current working state by continuously monitoring the vehicle motion characteristics and system energy consumption performance; at the same time, combined with the power consumption prediction model, the energy consumption impact of different sampling strategies is evaluated in advance. This collaborative optimization method enables the system to adjust the working mode of the IMU sensor and navigation module in real time while ensuring positioning accuracy, so as to achieve the optimal energy consumption control effect. Especially under complex road conditions, the system can respond quickly to state changes and adjust the sampling strategy in time to ensure the dynamic balance between navigation performance and energy efficiency; this innovative collaborative mechanism greatly improves the practicality and reliability of the navigation system in actual applications.

[0131] It should be noted that all the above-mentioned embodiments of the collaborative energy-saving method of IMU and navigation of the present invention can be arbitrarily combined or combined to form a new embodiment when there is no contradiction or conflict between them.

[0132] Reference Figure 8 The present invention also proposes an IMU and navigation collaborative energy-saving device 100, which is applied to a vehicle-mounted navigation device.

[0133] In this embodiment, the IMU and navigation collaborative energy-saving device 100 includes:

[0134] The acquisition module 101 is used to acquire the raw data of the IMU sensor and the navigation module, collect the raw data according to the sampling frequency of the IMU sensor and the update rate of the navigation module configured by the system, and obtain an initial sampling sequence;

[0135] An analysis module 102 is used to segment the initial sampling sequence and statistically analyze the energy consumption distribution characteristics of the IMU sensor and the navigation module under different motion states;

[0136] A formulation module 103 is used to divide the working state of the system into a low energy consumption area, a medium energy consumption area and a high energy consumption area based on the energy consumption distribution characteristics, and formulate a hierarchical sampling configuration scheme of the IMU sensor and the navigation module for different energy consumption areas, and the hierarchical sampling configuration scheme includes a sampling frequency and an update rate corresponding to each energy consumption area;

[0137] The calculation module 104 is used to calculate the energy consumption parameter combination of the IMU sensor and the navigation module under each working state according to the hierarchical sampling configuration scheme, establish a mapping relationship between state-frequency-energy consumption, and obtain the energy consumption control parameters of the system;

[0138] The adjustment module 105 is used to adjust the IMU sensor and the navigation module to the corresponding working frequency according to the energy consumption control parameters based on the real-time working status detection result of the system.

[0139] like Fig. 9 As shown, in some embodiments, the IMU and navigation collaborative energy-saving device 100 may also include:

[0140] The comparison module 106 is used to monitor the actual operating power consumption of the system, compare and analyze the actual operating power consumption of each working state with the target power consumption corresponding to each working state, and calculate the energy consumption deviation value;

[0141] The optimization module 107 is used to optimize and adjust the hierarchical sampling configuration scheme according to the energy consumption deviation analysis result to obtain an updated hierarchical sampling configuration scheme;

[0142] The acquisition module 108 is used to monitor the system performance of the updated hierarchical sampling configuration scheme, collect and record key performance indicator parameters, and the key performance indicator parameters include at least positioning accuracy, system power consumption and data delay;

[0143] The comprehensive analysis module 109 is used to combine the performance test index with the original energy consumption configuration scheme to determine the optimal sampling frequency combination of the IMU sensor and the navigation module under each working state, and obtain the optimal sampling configuration scheme.

[0144] The IMU and navigation collaborative energy-saving device 100 of the present invention can implement the steps of the above-mentioned IMU and navigation collaborative energy-saving method, and therefore at least has all the beneficial effects brought by the technical solution of the above-mentioned IMU and navigation collaborative energy-saving method embodiment, which will not be described one by one here.

[0145] The present invention also provides a vehicle-mounted navigation device, which includes an IMU sensor, a navigation module and an electronic device, referring to Fig.10 , Fig.10 It is a schematic diagram of the structure of an electronic device in a hardware operating environment involved in an embodiment of the present invention.

[0146] The electronic device of the embodiment of the present invention may be a controller of a vehicle-mounted navigation device, or a computing device such as a single-chip microcomputer, a notebook, a palm computer, and a server. Fig.10As shown, the electronic device may include: a processor 1001 (such as a CPU), a network interface 1004, a user interface 1003, a memory 1005 and a communication bus 1002. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen (Display), an input unit, such as a keyboard (Keyboard), and the user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface). The memory 1005 may be a high-speed RAM memory, or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0147] Those skilled in the art will understand that Fig.10 The electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0148] like Fig.10 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module and a computer program.

[0149] exist Fig.10 In the electronic device shown, the network interface 1004 is mainly used to connect to the background server and communicate data with the background server; the user interface 1003 is mainly used to connect to the client (user end) and communicate data with the client; and the processor 1001 can be used to call the computer program stored in the memory 1005. When the computer program is called and executed by the processor 1001, the steps of the above-mentioned IMU and navigation collaborative energy-saving method are implemented.

[0150] Based on the computer program proposed in the aforementioned embodiment, the present invention further proposes a storage medium, which stores the computer program. When the computer program is executed by a controller, the collaborative energy-saving method of IMU and navigation recorded in the aforementioned embodiment is implemented.

[0151] Since the vehicle-mounted navigation device and storage medium of the present invention can implement the steps of the above-mentioned IMU and navigation collaborative energy-saving method, they at least have all the beneficial effects brought by the technical solution of the above-mentioned IMU and navigation collaborative energy-saving method embodiment, which will not be repeated here one by one.

[0152] In the several embodiments provided in the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0153] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of software functional modules.

[0155] If the integrated module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0156] The above description is only a partial or preferred embodiment of the present invention. Neither the text nor the drawings can limit the scope of protection of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the overall concept of the present invention, or direct / indirect application in other related technical fields are included in the scope of protection of the present invention.

Claims

1. A collaborative energy-saving method of IMU and navigation, applied to a vehicle-mounted navigation device, characterized in that: include: Acquire raw data from an IMU sensor and a navigation module, and collect data on the raw data according to the sampling frequency of the IMU sensor and the update rate of the navigation module configured by the system to obtain an initial sampling sequence; The initial sampling sequence is processed in segments, and the energy consumption distribution characteristics of the IMU sensor and the navigation module under different motion states are statistically analyzed; Based on the energy consumption distribution characteristics, the working state of the system is divided into a low energy consumption area, a medium energy consumption area and a high energy consumption area, and a hierarchical sampling configuration scheme of the IMU sensor and the navigation module is formulated for different energy consumption areas, and the hierarchical sampling configuration scheme includes a sampling frequency and an update rate corresponding to each energy consumption area; According to the hierarchical sampling configuration scheme, the energy consumption parameter combination of the IMU sensor and the navigation module under each working state is calculated, a mapping relationship between state-frequency-energy consumption is established, and the energy consumption control parameter of the system is obtained; Based on the real-time working state detection result of the system, the IMU sensor and the navigation module are adjusted to corresponding working frequencies according to the energy consumption control parameters; The step of calculating the energy consumption parameter combination of the IMU sensor and the navigation module under each working state according to the hierarchical sampling configuration scheme, establishing a mapping relationship between state-frequency-energy consumption, and obtaining the energy consumption control parameters of the system includes: Based on the hierarchical sampling configuration scheme, the energy consumption parameter combination under each working state is calculated, and the energy consumption parameter combination includes the power consumption of the IMU sensor and the power consumption of the navigation module, and a mapping relationship between the working state and the total power consumption is constructed; By adding positioning accuracy constraints, sampling frequency range constraints, update rate range constraints and power consumption change rate constraints during state switching, the mapping relationship is optimized and solved to obtain energy consumption control parameters, which include target power consumption values ​​under different working states, a mapping relationship between working states and total power consumption, and power consumption gradient parameters during state switching.

2. The IMU and navigation collaborative energy saving method according to claim 1, characterized in that: The segmented processing of the initial sampling sequence and statistical analysis of energy consumption distribution characteristics of the IMU sensor and the navigation module under different motion states include: According to a preset step length, the initial sampling sequence is segmented by moving from the starting position of the initial sampling sequence to obtain a plurality of non-overlapping time windows; The average power consumption of the IMU sensor and the navigation module in each time window is obtained by calculation, and a sliding average method is used for statistical analysis to obtain energy consumption distribution characteristics; The sensor data in each time window is used to carry out motion state recognition, and the motion state recognition results are combined with the energy consumption distribution characteristics to establish a mapping relationship between motion state and energy consumption level.

3. The IMU and navigation collaborative energy saving method according to claim 1, characterized in that: The method divides the working state of the system into a low energy consumption area, a medium energy consumption area and a high energy consumption area based on the energy consumption distribution characteristics, and formulates a hierarchical sampling configuration scheme of the IMU sensor and the navigation module for different energy consumption areas, including: Based on the obtained energy consumption distribution characteristics, the working state of the system is classified to obtain a low energy consumption area, a medium energy consumption area and a high energy consumption area; According to the characteristics of the working states corresponding to different energy consumption areas, the sampling frequencies and update rates of different energy consumption areas are set respectively, and the switching between different energy consumption areas adopts progressive frequency adjustment to formulate a hierarchical sampling configuration scheme for different energy consumption areas.

4. The IMU and navigation collaborative energy saving method according to any one of claims 1 to 3, characterized in that: After the step of adjusting the IMU sensor and the navigation module to corresponding operating frequencies according to the energy consumption control parameter, the method further includes: The actual operating power consumption of the monitoring system is compared and analyzed with the target power consumption corresponding to each working state, and the energy consumption deviation value is calculated; According to the energy consumption deviation analysis result, the hierarchical sampling configuration scheme is optimized and adjusted to obtain an updated hierarchical sampling configuration scheme; Performing system performance monitoring on the updated hierarchical sampling configuration scheme, collecting and recording key performance indicator parameters, wherein the key performance indicator parameters include at least positioning accuracy, system power consumption and data latency; By combining the performance test index with the original energy consumption configuration scheme, the optimal sampling frequency combination of the IMU sensor and the navigation module under each working state is determined to obtain the optimal sampling configuration scheme.

5. The IMU and navigation collaborative energy saving method according to claim 4, characterized in that: The step of optimizing and adjusting the hierarchical sampling configuration scheme according to the energy consumption deviation analysis result to obtain an updated hierarchical sampling configuration scheme includes: Establishing a mapping relationship between the energy consumption deviation value and the sampling frequency of the IMU sensor and the update rate of the navigation module; When the energy consumption deviation value is a positive power consumption deviation, reducing the sampling frequency of the IMU sensor and / or the update rate of the navigation module according to the adjustment strategy of the corresponding energy consumption area; When the energy consumption deviation value is a negative power consumption deviation, increasing the sampling frequency of the IMU sensor and / or the update rate of the navigation module according to the adjustment strategy of the corresponding energy consumption area; In the process of reducing or increasing the sampling frequency of the IMU sensor, if abnormal data fluctuation occurs, the sampling frequency of the IMU sensor and / or the navigation module are called back; An updated hierarchical sampling configuration scheme is generated according to the optimized and adjusted sampling frequency of the IMU sensor and the update rate of the navigation module, as well as the working states corresponding to the adjusted sampling frequency and update rate.

6. The IMU and navigation collaborative energy saving method according to claim 5, characterized in that: The adjustment strategy is to make gradual adjustments according to a preset adjustment step length, and a preset time interval is set between two consecutive adjustments to confirm whether the adjusted data is collected normally, so as to determine whether to continue with the next adjustment; And / or, optimizing and adjusting the hierarchical sampling configuration scheme according to the energy consumption deviation analysis result to obtain an updated hierarchical sampling configuration scheme, further comprising: In the process of reducing or increasing the sampling frequency of the IMU sensor and / or the update rate of the navigation module, if the positioning accuracy exceeds a preset threshold, the adjustment is suspended.

7. An IMU and navigation collaborative energy-saving device, applied to vehicle-mounted navigation equipment, characterized in that: include: An acquisition module is used to acquire raw data from the IMU sensor and the navigation module, and collect the raw data according to the sampling frequency of the IMU sensor and the update rate of the navigation module configured by the system to obtain an initial sampling sequence; An analysis module, used for segmenting the initial sampling sequence and statistically analyzing the energy consumption distribution characteristics of the IMU sensor and the navigation module under different motion states; A formulation module, used to divide the working state of the system into a low energy consumption area, a medium energy consumption area and a high energy consumption area based on the energy consumption distribution characteristics, and formulate a hierarchical sampling configuration scheme of the IMU sensor and the navigation module for different energy consumption areas, wherein the hierarchical sampling configuration scheme includes a sampling frequency and an update rate corresponding to each energy consumption area; A calculation module, used to calculate the energy consumption parameter combination of the IMU sensor and the navigation module under each working state according to the hierarchical sampling configuration scheme, establish a mapping relationship between state-frequency-energy consumption, and obtain the energy consumption control parameters of the system; An adjustment module, configured to adjust the IMU sensor and the navigation module to corresponding operating frequencies according to the energy consumption control parameters based on a real-time working status detection result of the system; The step of calculating the energy consumption parameter combination of the IMU sensor and the navigation module under each working state according to the hierarchical sampling configuration scheme, establishing a mapping relationship between state-frequency-energy consumption, and obtaining the energy consumption control parameters of the system includes: Based on the hierarchical sampling configuration scheme, the energy consumption parameter combination under each working state is calculated, and the energy consumption parameter combination includes the power consumption of the IMU sensor and the power consumption of the navigation module, and a mapping relationship between the working state and the total power consumption is constructed; By adding positioning accuracy constraints, sampling frequency range constraints, update rate range constraints and power consumption change rate constraints during state switching, the mapping relationship is optimized and solved to obtain energy consumption control parameters, which include target power consumption values ​​under different working states, a mapping relationship between working states and total power consumption, and power consumption gradient parameters during state switching.

8. A vehicle-mounted navigation device, comprising an IMU sensor and a navigation module, characterized in that: The vehicle-mounted navigation device also includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the IMU and navigation collaborative energy-saving method as described in any one of claims 1 to 6 are implemented.

9. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the IMU and navigation collaborative energy-saving method as described in any one of claims 1 to 6.

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