Positioning mode changing method and apparatus, electronic device, and storage medium

Abstract

This application relates to the field of positioning technology, and provides a positioning mode changing method, device, electronic device, and storage medium. Multiple data acquisition elements are triggered to collect data according to a preset sampling period. At the end of each sampling period, the data from each data acquisition element are fused based on a positioning mode changing weight associated with the user-selected application scenario to obtain a decision value. Then, a target positioning mode is determined based on the decision value and a positioning mode changing strategy associated with the user-selected application scenario. Switching is performed when the target positioning mode differs from the current positioning mode, ensuring that the positioning mode can both match the user's current movement state in real time and match the user-selected application scenario. This avoids the positioning module being in a working state for extended periods while maintaining positioning accuracy, thus reducing power consumption.

Description

Technical Field

[0001] This application relates to the field of positioning technology, and more specifically, to a positioning mode changing method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the widespread use of location-based services in mobile terminals (such as smartphones and wearable devices), positioning functionality has become one of the core features of these devices. Currently, satellite positioning technology, represented by the Global Positioning System (GPS), is widely used in various mobile terminals due to its ability to provide high-precision outdoor positioning information.

[0003] However, the positioning module needs to continuously receive and process signals from multiple satellites when it is working, and its radio frequency front-end and baseband processing unit consume a lot of power, which seriously restricts the battery life of mobile terminals such as smartphones and wearable devices.

[0004] Therefore, how to reduce power consumption while meeting the accuracy requirements of positioning services has become a pressing technical challenge. Summary of the Invention

[0005] The purpose of this application is to provide a positioning mode changing method, apparatus, electronic device, and storage medium to improve the above-mentioned problems.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows: In a first aspect, embodiments of this application provide a method for changing a positioning mode, the method comprising: Obtain the location mode change weights and location mode change strategies associated with the user-selected application scenario; According to the preset sampling period, multiple data acquisition elements are triggered to collect data according to the current positioning mode; At the end of each sampling period, based on the positioning mode change weight, the original datasets collected by each data acquisition element in the current sampling period are fused to obtain a decision value; Based on the decision value and the positioning mode change strategy, the target positioning mode is determined; In the next sampling cycle, the multiple data acquisition elements are controlled to operate according to the target positioning mode.

[0007] Optionally, the positioning mode change weights include the weights of each of the data acquisition elements; The step of fusing the original datasets collected by each data acquisition element in the current sampling period based on the positioning mode change weights to obtain decision values ​​includes: For each data acquisition element, the raw dataset acquired by the data acquisition element within the current sampling period is subjected to preliminary calculation processing to generate a recorded dataset of the data acquisition element; the recorded dataset is subjected to feature-based statistical processing to obtain the data statistical value corresponding to the data acquisition element. Normalize each of the data statistics to obtain each normalized data statistics; Based on the weight of each data acquisition element, the normalized statistical values ​​of each data acquisition element are weighted and summed to obtain the decision value.

[0008] Optionally, when the data acquisition element is an accelerometer, the raw dataset includes multiple triaxial accelerations read continuously within a pre-configured single sampling duration; The steps of performing preliminary calculations on the raw dataset collected by the data acquisition element within the current sampling period to generate a recorded dataset of the data acquisition element, and performing feature-based statistical processing on the recorded dataset to obtain the data statistical values ​​corresponding to the data acquisition element, include: Calculate the vector sum of each of the three-axis accelerations to obtain the recorded dataset of the acceleration sensor; calculate the variance of all the vector sums in the recorded dataset to obtain the data statistics corresponding to the acceleration sensor. When the data acquisition element is a gyroscope, the raw dataset includes multiple triaxial angular velocities continuously read within a pre-configured single sampling duration; The steps of performing preliminary calculations on the raw dataset collected by the data acquisition element within the current sampling period to generate a recorded dataset of the data acquisition element, and performing feature-based statistical processing on the recorded dataset to obtain the data statistical values ​​corresponding to the data acquisition element, include: The original dataset is used as the recording dataset of the gyroscope; the range of each of the three-axis angular velocities in the recording dataset is calculated, and the maximum value among all the ranges is taken to obtain the data statistics value corresponding to the gyroscope; When the data acquisition element is a power monitoring element, the raw dataset includes the remaining power read when entering the current sampling period, and the recorded dataset includes the percentage of remaining power. The steps of performing preliminary calculations on the raw dataset collected by the data acquisition element within the current sampling period to generate a recorded dataset of the data acquisition element, and performing feature-based statistical processing on the recorded dataset to obtain the data statistical values ​​corresponding to the data acquisition element, include: Calculate the ratio of the power reserve to the power capacity to obtain the power reserve percentage; use the power reserve percentage as the data statistics value corresponding to the power monitoring element.

[0009] Optionally, when the data acquisition element is a positioning module, the original dataset includes the positioning activation status of the positioning module when entering the current sampling period, the number of satellites searched, latitude and longitude, and recording time; The steps of performing preliminary calculations on the raw dataset collected by the data acquisition element within the current sampling period to generate a recorded dataset of the data acquisition element, and performing feature-based statistical processing on the recorded dataset to obtain the data statistical values ​​corresponding to the data acquisition element, include: Obtain the original dataset of the previous sampling period to obtain the recording dataset of the positioning module. The recording dataset includes the original dataset of the current sampling period and the original dataset of the previous sampling period. The average speed of the device is calculated based on the latitude, longitude, and recording time corresponding to the current sampling period and the latitude, longitude, and recording time corresponding to the previous sampling period in the recorded dataset. The positioning reliability index is calculated based on the positioning activation status and the number of satellites corresponding to the current sampling period in the recorded dataset; Obtain the data statistics corresponding to the positioning module, including the average speed of the device and the positioning reliability index.

[0010] Optionally, when the data acquisition element is a network module, the raw dataset includes the network dialing status and network signal strength of the network module when entering the current sampling period; The steps of performing preliminary calculations on the raw dataset collected by the data acquisition element within the current sampling period to generate a recorded dataset of the data acquisition element, and performing feature-based statistical processing on the recorded dataset to obtain the data statistical values ​​corresponding to the data acquisition element, include: The original dataset is used as the record dataset of the network module; based on the network dialing status and the network signal strength, network availability data is generated, and the network availability data is used as the data statistics value corresponding to the network module.

[0011] Optionally, the positioning mode change strategy includes multiple positioning modes and a decision threshold range corresponding to each positioning mode; The step of determining the target positioning mode based on the decision value and the positioning mode change strategy includes: From all the aforementioned decision threshold intervals, determine the target decision threshold interval to which the decision value belongs; Obtain the positioning mode corresponding to the target decision threshold range to obtain the target positioning mode.

[0012] Optionally, before the step of obtaining the location mode change weights and location mode change strategies associated with the user-selected application scenario, the location mode change method further includes: Configure the location mode change weight and location mode change strategy associated with each set application scenario.

[0013] Secondly, embodiments of this application provide a positioning mode changing device, the positioning mode changing device comprising: The acquisition module is used to acquire the location mode change weights and location mode change strategies associated with the application scenario selected by the user. The execution module is used to: trigger multiple data acquisition elements to collect data according to the current positioning mode according to a preset sampling period; at the end of each sampling period, based on the positioning mode change weight, fuse the original datasets collected by each data acquisition element in the current sampling period to obtain a decision value; determine the target positioning mode based on the decision value and the positioning mode change strategy; and control the multiple data acquisition elements to work according to the target positioning mode in the next sampling period.

[0014] Thirdly, embodiments of this application provide an electronic device, including a processor and a memory, wherein the memory is used to store a program, and the processor is used to implement the positioning mode changing method in the first aspect above when executing the program.

[0015] Fourthly, embodiments of this application also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the positioning mode changing method described in the first aspect above.

[0016] Compared to existing technologies, the positioning mode changing method, apparatus, electronic device, and storage medium provided in this application embodiment trigger multiple data acquisition elements to collect data according to a preset sampling period. At the end of each sampling period, the data collected by each data acquisition element in the current sampling period is fused based on a positioning mode changing weight associated with the user-selected application scenario to obtain a decision value. Then, a target positioning mode is determined based on the decision value and the positioning mode changing strategy associated with the user-selected application scenario, and switching occurs when the target positioning mode differs from the current positioning mode. Thus, on the one hand, by periodically fusing multi-source data to determine whether to switch positioning modes, it ensures that the positioning mode can match the user's current movement state in real time, avoiding the positioning module from being in a working state for a long time and reducing power consumption; on the other hand, by introducing a positioning mode changing weight and positioning mode changing strategy associated with the user-selected application scenario, it ensures that the positioning mode matches the user-selected application scenario, guaranteeing positioning accuracy. Attached Figure Description

[0017] Figure 1 The diagram shows a hardware architecture of a positioning mode changing method provided in an embodiment of this application.

[0018] Figure 2 This document illustrates a flowchart of a positioning mode changing method provided in an embodiment of this application. Figure 1 .

[0019] Figure 3 An example diagram of user operation provided in an embodiment of this application is shown.

[0020] Figure 4 The diagram illustrates parameter configuration examples under different application scenarios provided in the embodiments of this application.

[0021] Figure 5 This document illustrates a flowchart of a positioning mode changing method provided in an embodiment of this application. Figure 2 .

[0022] Figure 6 A block diagram of a positioning mode changing device provided in an embodiment of this application is shown.

[0023] Figure 7 A block diagram of an electronic device provided in an embodiment of this application is shown.

[0024] Icons: 100-Positioning mode change device; 101-Configuration module; 102-Acquisition module; 103-Execution module; 10-Electronic device; 11-Processor; 12-Memory; 13-Bus. Detailed Implementation

[0025] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0026] The positioning mode changing method provided in this application is applied to electronic devices with satellite positioning capabilities, such as smartphones, wearable devices, and IoT terminals. Figure 1 As shown, the electronic device may include a processor and n data acquisition elements, with the processor communicating with each data acquisition element.

[0027] Optionally, a data acquisition element refers to hardware capable of acquiring certain parameter data, such as sensors, components, or modules. Data acquisition elements can be, but are not limited to, accelerometers, gyroscopes, power monitoring elements, positioning modules, network modules, etc., used to collect various raw data from electronic devices.

[0028] The processor is used to trigger multiple data acquisition elements to collect raw data according to a preset sampling period. At the end of each sampling period, the processor fuses the data collected by each data acquisition element in the current sampling period based on the positioning mode change weight associated with the application scenario selected by the user to obtain a decision value. Based on the decision value and the positioning mode change strategy associated with the application scenario selected by the user, the processor determines the target positioning mode and switches when the target positioning mode is different from the current positioning mode.

[0029] In existing technologies, to reduce the power consumption of positioning modules, accelerometer data is often used to determine the user's motion state, and then the positioning sampling frequency is adjusted according to the user's motion state. However, in practical applications, relying solely on data from a single sensor as an evaluation standard makes it difficult to accurately determine the current motion state. For example, the acceleration is almost the same when stationary and in uniform motion, and the accelerometer itself cannot distinguish between these two motion states. In other words, single sensor data cannot accurately depict the actual motion state of the device and environmental constraints, resulting in insufficient robustness of positioning mode switching decisions and limited scene adaptability.

[0030] The positioning mode changing method provided in this application uses periodic multi-source data fusion and introduces positioning mode changing weights and strategies associated with the user-selected application scenario to determine whether to switch positioning modes. This ensures that the positioning mode can match the user's current motion state in real time and match the user-selected application scenario. As a result, while ensuring positioning accuracy, the positioning module can avoid being in a working state for a long time, thus reducing power consumption.

[0031] The following is based on Figure 1 The hardware architecture shown in this application provides a detailed description of the positioning mode change method provided in the embodiments of this application.

[0032] Please refer to Figure 2 , Figure 2 This illustration shows a flowchart of a positioning mode changing method provided in an embodiment of this application, which may include the following steps: S101, Obtain the location mode change weight and location mode change strategy associated with the application scenario selected by the user; S102, according to the preset sampling period, trigger multiple data acquisition elements to collect data according to the current positioning mode; S103, at the end of each sampling period, based on the positioning mode change weight, the original datasets collected by each data acquisition element in the current sampling period are fused to obtain the decision value; S104, Determine the target positioning mode based on the decision value and positioning mode change strategy; S105 controls multiple data acquisition elements to work according to the target positioning mode in the next sampling cycle.

[0033] In step S101, several application scenarios with different requirements are pre-defined, such as scenarios where recording trajectories or single-point monitoring is the primary requirement (e.g., inspection), scenarios where high-precision positioning is the primary requirement, scenarios where saving equipment energy consumption is the primary requirement, scenarios where fixed-location monitoring is the primary requirement, and scenarios where other conditions are the primary requirements. Simultaneously, each application scenario has associated positioning mode change weights and positioning mode change strategies.

[0034] When the device starts up or the scene changes, it can obtain the positioning mode change weights and positioning mode change strategies that match the application scenario indicated by the current user operation. The positioning mode change weights can include the data weights corresponding to multiple data acquisition elements, with the sum of all weights being 1. Each weight is set based on the varying degrees of dependence of the application scenario on dimensions such as device posture, power status, positioning module operating status, and network connectivity. The positioning mode change strategy defines the mapping relationship between different decision value ranges and specific positioning modes. Each positioning mode can include independently configurable adjustable parameters such as positioning switch status, positioning update frequency, positioning data source, and coordinate system type.

[0035] In practice, after receiving a user's location request for a specific application scenario, such as Figure 3 As shown, assuming the user selects the "inspection" application scenario, the electronic device obtains the positioning mode change weight and positioning mode change strategy associated with "inspection" through the parameter decision mapping function module preset by various scenarios, and then adaptively changes the positioning mode according to the process of steps S102 to S105.

[0036] In this embodiment, positioning mode refers to a positioning method with different positioning parameters. The positioning parameters in the positioning mode include, but are not limited to, positioning switch, positioning update frequency, positioning data source, and coordinate system type. Specifically, positioning switch refers to whether different positioning data sources are enabled; positioning update frequency refers to the interval between the acquisition of positioning data by the positioning data source; positioning data source refers to the different ways of acquiring positioning data when the device has different positioning hardware or software, positioning methods, etc., such as multiple positioning hardware of the same or different types in the device, network positioning interfaces from different servers, etc.; coordinate system type refers to, but is not limited to, different coordinate system types such as BD09, GCJ02, and WGS84.

[0037] In step S102, according to a preset sampling period and the currently active positioning mode, acquisition commands are synchronously sent to data acquisition components such as the accelerometer, gyroscope, power monitoring element, positioning module, and network module. Within this sampling period, each of these data acquisition components outputs raw datasets. For example, the accelerometer and gyroscope continuously acquire data to form multi-point time-series data, the power monitoring element performs a single instantaneous read, the positioning module records its activation status, the number of cached satellites, latitude and longitude, and timestamp, and the network module reports dialing status and signal strength, etc.

[0038] In step S103, at the end of each sampling period, statistical processing adapted to the physical characteristics and application scenario is performed on each data acquisition element to obtain corresponding statistical values. For example, the variance of the raw dataset from the accelerometer is calculated after vector synthesis; the maximum value of the three-axis angular velocity range is extracted from the raw dataset from the gyroscope; the raw data from the power monitoring element is directly converted into a power percentage; the average speed of the raw dataset from the positioning module is calculated using two consecutive latitude, longitude, and timestamps, and the satellite acquisition capability is evaluated; and the signal strength and dialing success status are extracted from the raw dataset from the network module. Subsequently, the statistical values ​​of the data obtained from each data acquisition element are weighted and summed with their corresponding positioning mode change weights to generate a decision value.

[0039] In step S104, the positioning mode change strategy may include multiple positioning modes and a decision threshold range corresponding to each positioning mode. After obtaining the decision value in step S103, the target decision threshold range to which the decision value belongs can be determined from all decision threshold ranges, and then the positioning mode corresponding to the target decision threshold range can be obtained as the target positioning mode. Optionally, the positioning mode may include a set of structured parameters, including whether the positioning switch is enabled, the specific value of the positioning update frequency, the hardware or service interface identifier of the positioning data source, and the encoding identifier of the coordinate system type, etc.

[0040] In step S105, multiple data acquisition elements are controlled to operate according to the determined target positioning mode in the next sampling period. That is, if the target positioning mode is different from the current positioning mode, i.e., when the decision value triggers a positioning mode change, new operating parameter instructions are issued to each data acquisition element. If the target positioning mode is the same as the current positioning mode, no new operating parameter instructions need to be issued. For example, if the target positioning mode requires turning off the positioning module and enabling WiFi-assisted positioning, which is different from the current positioning mode, a deactivation signal is sent to the positioning module before the start of the next sampling period. At the same time, the WiFi scanning function of the network module is activated, and its signal acquisition frequency and parsing logic are adjusted. From the next sampling period onwards, all data acquisition elements operate according to the newly allocated parameters, thereby completing the switching of the positioning mode.

[0041] In practice, for each specific application scenario, such as Figure 3 For applications such as inspection, high precision, energy saving, and fixed monitoring, positioning mode change weights and strategies associated with each application scenario need to be pre-configured. The device can adaptively change its positioning mode simply by selecting the desired scenario mode. Therefore, before step S101, the positioning mode change method provided in this embodiment further includes step S10A.

[0042] S10A configures the location mode change weights and location mode change strategies associated with each set application scenario.

[0043] In this embodiment, the positioning mode change weight and positioning mode change strategy can be configured according to different application scenarios and the actual situation of the device. The positioning mode change weight refers to the weight assigned to sampled data from multiple data acquisition components during fusion, such as the weights of device attitude data, positioning module data, power status data, and other data. Device attitude data refers to data related to device attitude from accelerometers, gyroscopes, etc.; positioning module data refers to data from the positioning module; power status data refers to power-related data from power monitoring components; and other data refers to data from other data acquisition components (such as network modules).

[0044] Optionally, the positioning mode change strategy refers to the conditions for changing different positioning modes. When the conditions for changing a certain positioning mode are met, the system switches to that positioning mode. For example, the positioning mode change strategy may include multiple positioning modes and a decision threshold range corresponding to each positioning mode. In practice, the positioning mode corresponding to the decision value can be determined by fusing the calculated decision value with the decision threshold range corresponding to each positioning mode.

[0045] The following section explains the configuration process of location mode change weights and location mode change strategies in the context of practical application scenarios.

[0046] In applications primarily focused on recording trajectories or single-point monitoring, the weighting of positioning mode changes leans towards data collected by data acquisition elements capable of calculating the device's motion posture. Specifically, in trajectory recording applications, where a continuous supply of positioning data is needed to construct a clear trajectory, the positioning mode change direction is to increase the update frequency when the device is moving, and to decrease the update frequency or disable positioning when the device is stationary. In single-point monitoring applications, where more accurate positioning data is required for fixed-point data acquisition or imaging, the positioning mode change direction is to decrease the update frequency or disable positioning when the device is moving, and to increase the update frequency when the device is stationary.

[0047] like Figure 4 As shown, in application scenarios where the primary requirement is to record trajectories or monitor single points, the positioning mode change weight can be configured as follows: device attitude data weight is 60%, positioning module data weight is 2%, power status data weight is 10%, and other data accounts for the remaining 28%. The positioning mode change strategy can be configured as follows: Mode 1 {Positioning module: On, update frequency: 10 minutes / time, data source: positioning module, ...}, Mode 2 {Positioning module: On, update frequency: 30 minutes / time, data source: positioning module, ...}, ... When the decision value is within the numerical range [0, a1), Mode 1 is selected; when the decision value is within the numerical range [a1, a2), Mode 2 is selected, and so on.

[0048] In application scenarios where high-precision positioning is the primary requirement, the weighting of positioning mode changes leans more towards the statistical data of the positioning module used to describe its positioning accuracy and reliability. Specifically, when the positioning module's accuracy decreases or there is no satellite signal, such as when entering indoors, the direction of change is to switch to network positioning (including but not limited to base station, WiFi, and Bluetooth positioning); when the positioning module's accuracy improves or the satellite signal is good, such as when leaving indoors, the direction of change is to switch to satellite positioning.

[0049] like Figure 4 As shown, in application scenarios where high-precision positioning is the primary requirement, the positioning mode change weights can be configured as follows: device attitude data weight is 6%, positioning module data weight is 50%, power status data weight is 30%, and other data account for the remaining 14%. The positioning mode change strategy can be configured as follows: Mode 1 {Positioning module: On, Update frequency: Continuous positioning, Data source: Positioning module, ...}, Mode 2 {Positioning module: Off, Update frequency: Continuous positioning, Data source: Network positioning, ...}, ... When the decision value is within the numerical range [0, b1), Mode 1 is selected; when the decision value is within the numerical range [b1, b2), Mode 2 is selected, and so on.

[0050] In application scenarios where energy conservation is the primary requirement, the weighting of positioning mode changes is more biased towards the data collected by power status monitoring modules. Specifically, when the battery is low, the change direction is to reduce the update frequency and disable positioning; when the battery is sufficient, the change direction is to increase the update frequency.

[0051] like Figure 4 As shown, in application scenarios where energy saving is the primary requirement, the positioning mode change weights can be configured as follows: device attitude data weight is 8%, positioning module data weight is 12%, power status data weight is 70%, and other data account for the remaining 10%. The positioning mode change strategy can be configured as follows: Mode 1 {Positioning module: On, update frequency: 20 minutes / time, data source: positioning module, ...}, Mode 2 {Positioning module: Off, update frequency: no update, data source: none, ...}, ... When the decision value is within the numerical range [0, c1), Mode 1 is selected; when the decision value is within the numerical range [c1, c2), Mode 2 is selected, and so on.

[0052] In application scenarios where fixed-location monitoring is the primary requirement, the positioning mode change weights do not consider device posture, but rather focus on the data collected by power status monitoring modules. Specifically, when the battery is low, the change direction is to reduce the update frequency and disable positioning; when the battery is sufficient, the positioning mode change direction is to increase the update frequency.

[0053] like Figure 4 As shown, in application scenarios where fixed-location monitoring is the primary requirement, the positioning mode change weight can be configured as follows: device attitude data weight is 0%, positioning module data weight is 10%, power status data weight is 80%, and other data accounts for the remaining 10%. The positioning mode change strategy can be configured as follows: Mode 1 {Positioning module: On, update frequency: 60 minutes / time, data source: positioning module, ...}, Mode 2 {Positioning module: Off, update frequency: no update, data source: none, ...}, ... When the decision value is within the numerical range [0, e1), Mode 1 is selected; when the decision value is within the numerical range [e1, e2), Mode 2 is selected, and so on.

[0054] The following is a detailed description of step S103.

[0055] In one possible implementation, step S103, which involves fusing the raw datasets collected by each data acquisition element in the current sampling period based on the positioning mode change weights to obtain decision values, may include: S1031, For each data acquisition element, perform preliminary calculation processing on the raw dataset acquired by the data acquisition element within the current sampling period to generate a record dataset of the data acquisition element; perform feature statistical processing on the record dataset to obtain the data statistical values ​​corresponding to the data acquisition element; S1032, normalize each data statistical value to obtain each normalized data statistical value; S1033, based on the weight of each data acquisition element, the normalized statistical values ​​of each data are weighted and summed to obtain the decision value.

[0056] In sub-step S1031, because the original dataset is characterized by large data volume and poor readability, therefore, as Figure 5 As shown, after obtaining raw data from each data acquisition element in a single reading or in a continuously read raw dataset, the corresponding record dataset can be obtained after preliminary calculation and processing. The record dataset is then recorded in the local storage module, thereby reducing the amount of data stored.

[0057] Then, at the end of each sampling period, the recorded dataset is read from the storage module into the processing module for statistical calculations to obtain data statistical values. A single data acquisition element may calculate multiple data statistical values. Optionally, the data statistical methods of the data acquisition element include, but are not limited to, different statistical calculation methods such as calculating the mean, variance, range, taking the last recorded value, or multi-parameter and multi-condition conversion of the recorded dataset.

[0058] Optionally, different data acquisition components may use different methods to calculate their corresponding statistical values. For example, the statistical value for an accelerometer may be the variance calculated after vector synthesis of each three-axis acceleration to characterize the fluctuation of the device's motion state; the statistical value for a gyroscope may be the maximum value of the range of three-axis angular velocities to reflect the device's rotation amplitude; the statistical value for a power monitoring component may be a percentage of power consumption; the statistical value for a positioning module may be the average speed calculated from two consecutive latitude, longitude, and timestamp measurements, combined with the signal availability assessed by the number of cached satellites; and the statistical value for a network module may be the signal strength and a Boolean value indicating successful dialing.

[0059] In sub-step S1032, each data statistical value is normalized, that is, the data statistical values ​​corresponding to each data acquisition element are mapped to a unified numerical range to eliminate weight interference caused by differences in the original dimensions and numerical scales, thus obtaining each normalized data statistical value. Optionally, the normalization method can be linear scaling, which compresses each data statistical value proportionally according to the historical typical value range or preset reasonable boundary of its data acquisition element, so that all normalized data statistical values ​​fall within the [0,1] interval. For example, the variance value of the accelerometer is represented as the relative motion activity after normalization, and the percentage of power of the power monitoring element is itself in a normalized form and can be directly used in subsequent calculations.

[0060] In sub-step S1033, the normalized statistical values ​​of each data acquisition element are multiplied by their associated positioning mode change weights in the current application scenario, and then summed to generate a decision value. The decision value is a dimensionless numerical value, and its magnitude reflects the comprehensive tendency of the current device's multidimensional state characteristics under the guidance of a given scenario.

[0061] In other words, based on the calculated statistical values, the statistical value of the i-th data obtained through the data acquisition element is... The weight is The decision value S, obtained from the statistical values ​​of m data points calculated by n data sampling elements, is calculated as follows:

[0062] The weights are in percentage form, the sum of the weights is 100%, and m is greater than n.

[0063] It's important to note that the positioning mode change weights are not fixed global parameters, but rather a dynamic configuration set associated with the user-selected application scenario. The weight of each data acquisition element varies across different application scenarios. For example, in applications where high-precision positioning is the primary requirement, the weight of the positioning module is set significantly higher than that of other data acquisition elements. Conversely, in applications where energy conservation is the primary requirement, the weight of the power monitoring element dominates. This non-uniform weight distribution design allows the contribution of the same data acquisition element to the final decision value to change substantially in different scenarios, thus supporting the adaptive shift of the positioning mode change logic according to the task objective.

[0064] Next, for each data acquisition element, the process of performing preliminary calculations on the raw dataset collected by the data acquisition element within the current sampling period in sub-step S1031 to generate a record dataset of the data acquisition element, and performing feature-based statistical processing on the record dataset to obtain the data statistical values ​​corresponding to the data acquisition element will be introduced.

[0065] When the data acquisition element is an accelerometer, the raw dataset includes multiple triaxial accelerations continuously read within a pre-configured single sampling duration. Sub-step S1031 performs preliminary calculations on the raw dataset acquired by the data acquisition element within the current sampling period to generate a recorded dataset for the data acquisition element. The process of performing characteristic statistical processing on the recorded dataset to obtain the data statistics corresponding to the data acquisition element may include: calculating the vector sum of each triaxial acceleration to obtain the recorded dataset of the accelerometer; and calculating the variance of all vector sums in the recorded dataset to obtain the data statistics corresponding to the accelerometer.

[0066] In this embodiment, the raw dataset collected by the accelerometer during the current sampling period consists of multiple triaxial accelerations continuously read within a pre-configured single sampling duration. Each set of triaxial accelerations includes instantaneous acceleration components along the X, Y, and Z axes, which are used to characterize the overall force change trend of the device during that time period, rather than an isolated static attitude.

[0067] Optionally, to transform the original multidimensional time series into scalar features that can participate in fusion operations, a vector synthesis operation needs to be performed on each group of three-axis accelerations to obtain the acceleration vector sum at the corresponding time. The calculation method for this vector sum can be the square root of the sum of squares of the acceleration components in the three orthogonal directions at the same time, thereby eliminating the statistical interference caused by directional uncertainty and allowing subsequent processing to focus on the motion intensity itself. After obtaining the acceleration vector sums corresponding to all times, the variance of the series is further calculated. This variance reflects the degree of acceleration fluctuation of the device within a single sampling duration. The larger the value, the more unstable the motion state, and the more likely it is to be in an acceleration, deceleration, or complex turning process, thus providing a quantitative basis for the dynamic adjustment of the positioning update frequency.

[0068] In other words, the three axial accelerations acquired by the accelerometer are its raw data, denoted as... The system begins continuous reading upon entering the sampling period and stops reading when the duration of a single sampling is reached, resulting in n triaxial accelerations. Given the original dataset, after the sampling period ends, calculate the vector sum of the triaxial accelerations. The sum of the n vectors obtained by calculation This is the dataset recorded by the accelerometer.

[0069] Then, calculate the variance of the recorded dataset; that is, assuming the i-th data in the recorded dataset is... Then record the variance of the sum of n vectors in the dataset. The calculation method is as follows:

[0070] When the data acquisition element is a gyroscope, the original dataset includes multiple triaxial angular velocities continuously read within a pre-configured single sampling duration. Sub-step S1031 performs preliminary calculations on the original dataset acquired by the data acquisition element within the current sampling period to generate a recording dataset for the data acquisition element. The process of performing feature-based statistical processing on the recording dataset to obtain the data statistics corresponding to the data acquisition element may include: using the original dataset as the recording dataset of the gyroscope; calculating the range of each triaxial angular velocity in the recording dataset and taking the maximum value among all ranges to obtain the data statistics corresponding to the gyroscope.

[0071] In this embodiment, the raw dataset collected by the gyroscope in the current sampling period consists of multiple three-axis angular velocities continuously read within a pre-configured single sampling duration. Each set of three-axis angular velocities includes instantaneous angular velocity components rotating around the X-axis, Y-axis, and Z-axis. This raw dataset directly characterizes the spatial rotation behavior of the device and is a key input for identifying stationary, uniform rotation, or sudden attitude shifts.

[0072] Optionally, to extract discriminative state features, the range of the angular velocity sequences in the X, Y, and Z axes needs to be calculated separately. This range represents the difference between the maximum and minimum angular velocities in each axis, indicating the span of angular velocity variation along that axis within the sampling period and reflecting the activity level of the device's rotation around that axis. Subsequently, the maximum value from the ranges obtained in the X, Y, and Z axes is selected as the corresponding statistical value of the gyroscope data, i.e., the maximum range. This avoids feature bias caused by coordinate system selection, ensuring that regardless of which principal axis the device rotates significantly with, the most sensitive axis can capture and amplify this information as a decision-making basis, thereby enhancing the device's responsiveness to sudden attitude changes.

[0073] In other words, the angular velocities along the three axes obtained by the gyroscope are its raw data, denoted as . The system begins continuous reading upon entering the sampling period and stops reading when the duration of a single sampling is reached, resulting in n triaxial angular velocities. The original dataset is used as the recording dataset after the sampling period ends.

[0074] Then, after calculating the range of the three axial angular velocities in the recorded dataset, the maximum range is output to obtain the corresponding data statistics value of the gyroscope. For example, suppose... and This refers to calculating the maximum and range (the difference between the maximum and minimum values) of the dataset within the parentheses, which represents the statistical values ​​of the n triaxial angular velocities recorded by the gyroscope. The calculation method is as follows:

[0075] Clearly, the statistical values ​​of the accelerometer and gyroscope reflect the attitude of the device, that is, whether the device is stationary, accelerating, moving at a constant speed, or rotating in the current acquisition cycle. It is mainly used to determine whether to change the positioning update frequency.

[0076] When the data acquisition element is a power monitoring element, the raw dataset includes the power reserve read when entering the current sampling period, and the recorded dataset includes the percentage of power reserve. Sub-step S1031 performs preliminary calculation processing on the raw dataset collected by the data acquisition element within the current sampling period to generate the recorded dataset of the data acquisition element. The process of performing feature-based statistical processing on the recorded dataset to obtain the data statistical value corresponding to the data acquisition element may include: calculating the ratio of power reserve to power capacity to obtain the percentage of power reserve; and using the percentage of power reserve as the data statistical value corresponding to the power monitoring element.

[0077] In this embodiment, the raw dataset collected by the power monitoring element during the current sampling period only contains the power reserve read once at the beginning of the sampling period, in milliampere-hours (mAh). In order to make it compatible with other multidimensional dynamic data, it needs to be converted into a dimensionless relative power index. For example, by calculating the ratio of the power reserve to the nominal power capacity of the device, the power reserve percentage is obtained, which is the data statistics value corresponding to the power monitoring element.

[0078] In other words, the power monitoring element uses the power reserve (unit: mAh, milliampere-hour) as its raw data, and only reads it once when entering the sampling period. The percentage of power reserve is obtained by calculating the ratio of the power reserve to the power capacity (unit: mAh, milliampere-hour), and this percentage is used as the recorded data of the power monitoring element. That is, the recorded data of the power monitoring element contains only one data.

[0079] Then, the data statistics corresponding to the power monitoring element directly adopt the percentage of remaining power in the recorded data. The percentage of remaining power reflects the energy storage status of the device, that is, it represents the current power status of the device and whether it has sufficient power to determine the switching of the corresponding positioning mode. It is mainly used to determine whether to change the positioning update frequency and turn the positioning module on or off.

[0080] When the data acquisition element is a positioning module, the original dataset includes the positioning activation status of the positioning module at the time of entering the current sampling period, the number of satellites searched, latitude and longitude, and recording time. Sub-step S1031 performs preliminary calculations on the original dataset acquired by the data acquisition element within the current sampling period to generate a recording dataset for the data acquisition element. The process of performing feature-based statistical processing on the recording dataset to obtain the data statistics corresponding to the data acquisition element may include: obtaining the original dataset from the previous sampling period to obtain the recording dataset of the positioning module, the recording dataset including the original dataset of the current sampling period and the original dataset of the previous sampling period; calculating the average speed of the device based on the latitude and longitude and recording time corresponding to the current sampling period and the latitude and longitude and recording time corresponding to the previous sampling period in the recording dataset; calculating the positioning reliability index based on the positioning activation status and the number of satellites corresponding to the current sampling period in the recording dataset; and obtaining the data statistics corresponding to the positioning module, the data statistics including the average speed of the device and the positioning reliability index.

[0081] In this embodiment, the original dataset of the positioning module is a set of discrete parameters with clear physical meaning and timestamp association. It may include the positioning activation status of the positioning module when entering the current sampling period, the number of satellites searched, latitude and longitude, and the corresponding recording time. The positioning activation status represents whether the positioning function is activated or not in binary logic form. The number of satellites reflects the geometric distribution basis of visible navigation satellites, while latitude and longitude and recording time constitute the basic unit of the position time series.

[0082] Optionally, based on the raw dataset collected by the positioning module in the current sampling period and the raw dataset collected in the previous sampling period, the Euclidean distance between the two latitude and longitude coordinates is calculated and divided by the difference in the corresponding recording time to obtain the average speed of the device, which is used to quantify the displacement trend of the device between two consecutive sampling periods. Simultaneously, based on the positioning activation status and the number of satellites collected in the current sampling period, a positioning reliability index is formed to reflect the stability of the current positioning data in the signal observability dimension.

[0083] In other words, the original dataset of the positioning module is the current status of whether the positioning module is enabled (0 for disabled, 1 for enabled) when entering the sampling period, the number of searched satellites, longitude, latitude and recording time cached when the positioning data was last updated, etc., and the recording dataset includes the original dataset of the two most recent consecutive sampling periods.

[0084] Then, the positioning module has two statistical values: one is the average speed of the device calculated by the two latitude and longitude coordinates and the recording time in the recorded dataset, which reflects the attitude of the device; the other is the positioning reliability index calculated by the positioning module's activation status and the number of satellites searched in the current sampling period of the recorded dataset, which characterizes the working status of the positioning module and is mainly used to determine whether the positioning data source and coordinate system type have changed. When the data acquisition element is a network module, the original dataset includes the network dialing status and network signal strength of the network module when entering the current sampling period; the process of performing preliminary calculation processing on the original dataset collected by the data acquisition element in the current sampling period in sub-step S1031 to generate the recorded dataset of the data acquisition element; and performing feature-based statistical processing on the recorded dataset to obtain the statistical values ​​of the data acquisition element, which may include: using the original dataset as the recorded dataset of the network module; generating network availability data based on the network dialing status and network signal strength, and using the network availability data as the statistical values ​​of the network module.

[0085] In this embodiment, the original dataset of the network module consists of the network dialing status and network signal strength of the network module when entering the current sampling period. The network dialing status indicates whether the network connection attempt was successful in binary logic form, while the network signal strength is used to quantify the quality of the wireless link.

[0086] Optionally, network availability data is generated by determining whether the network dialing status is successful and whether the network signal strength is higher than a preset minimum acceptable value. The network availability data is a Boolean or normalized numerical output used to characterize whether the current network resources have the basic communication capabilities to support network-assisted positioning (such as base station positioning, Wi-Fi fingerprint positioning, or Bluetooth beacon positioning).

[0087] In other words, the original dataset of the network module includes the network dialing status (0 for dialing failure, 1 for dialing success) and the network signal strength (unit: dBm, decibel milliwatt), and the original dataset is directly used as the record dataset of the network module.

[0088] Then, based on the network dialing status and network signal strength in the recorded dataset, network availability data is generated as the statistical value corresponding to the network module. Network availability data characterizes the network status of the device and is mainly used to determine whether to change the location data source to network location, which includes, but is not limited to, base station, WiFi, and Bluetooth location.

[0089] Compared with the prior art, the positioning mode changing method provided in this application has the following beneficial effects: First, by integrating data collected by multiple data acquisition components and based on the positioning mode change weights and strategies of each data acquisition component in different application scenarios, the technical limitation that a single acceleration signal cannot distinguish between stationary and uniform motion states is overcome. Especially in the ambiguous region of device attitude discrimination (such as low-acceleration steady-state motion), the misjudgment rate is significantly reduced by the joint participation of multi-source data in statistical decision-making, so that the positioning mode change action matches the real user state or environmental conditions. This can avoid the positioning module being in a working state for a long time while ensuring positioning accuracy, thus reducing power consumption.

[0090] Secondly, the weights of each data acquisition element are configured according to the application scenario (e.g., attitude data weights are emphasized in trajectory recording scenarios, signal-to-noise ratio indicators of positioning modules are emphasized in high-precision scenarios, and power reserve weights are emphasized in low-power scenarios). A unified decision value is generated based on the weighted fusion of multi-source statistical values. This logic can adapt to application requirements with significant differences (e.g., vehicle trajectory recording, indoor asset monitoring, and low-power field inspection). There is no need to modify the core algorithm logic. Policy migration can be achieved simply by configuration, which greatly reduces customization costs and deployment complexity.

[0091] In order to perform the corresponding steps in the above method embodiments and various possible implementations, an implementation of a positioning mode changing device is given below.

[0092] Please refer to Figure 6 , Figure 6 A block diagram of a positioning mode changing device 100 provided in an embodiment of this application is shown. The positioning mode changing device 100 is applied to an electronic device and includes: an acquisition module 102 and an execution module 103.

[0093] The acquisition module 102 is used to acquire the location mode change weight and location mode change strategy associated with the application scenario selected by the user.

[0094] The execution module 103 is used to fuse the original datasets collected by each data acquisition element in the current sampling period at the end of each sampling period based on the positioning mode change weight to obtain a decision value; determine the target positioning mode based on the decision value and the positioning mode change strategy; and control multiple data acquisition elements to work according to the target positioning mode in the next sampling period.

[0095] Optionally, the positioning mode change weights include the weights of each data acquisition element; the execution module 103 performs a method based on the positioning mode change weights to fuse the original datasets collected by each data acquisition element in the current sampling period to obtain a decision value, which may include: for each data acquisition element, performing preliminary calculation processing on the original datasets collected by the data acquisition element in the current sampling period to generate a record dataset of the data acquisition element; performing feature statistical processing on the record dataset to obtain the data statistical values ​​corresponding to the data acquisition element; normalizing each data statistical value to obtain each normalized data statistical value; and performing a weighted summation of each normalized data statistical value according to the weights of each data acquisition element to obtain a decision value.

[0096] Optionally, when the data acquisition element is an accelerometer, the raw dataset includes multiple triaxial accelerations continuously read within a pre-configured single sampling duration; the execution module 103 performs preliminary calculation processing on the raw dataset acquired by the data acquisition element within the current sampling period to generate a recorded dataset of the data acquisition element; the recorded dataset is subjected to characteristic statistical processing to obtain the data statistical values ​​corresponding to the data acquisition element, including: calculating the vector sum of each triaxial acceleration to obtain the recorded dataset of the accelerometer; calculating the variance of all vector sums in the recorded dataset to obtain the data statistical values ​​corresponding to the accelerometer.

[0097] Optionally, when the data acquisition element is a gyroscope, the original dataset includes multiple three-axis angular velocities continuously read within a pre-configured single sampling duration; the execution module 103 performs preliminary calculation processing on the original dataset acquired by the data acquisition element within the current sampling period to generate a recording dataset of the data acquisition element; the method of performing feature-based statistical processing on the recording dataset to obtain the data statistical values ​​corresponding to the data acquisition element includes: using the original dataset as the recording dataset of the gyroscope; calculating the range of each three-axis angular velocity in the recording dataset, and taking the maximum value among all ranges to obtain the data statistical values ​​corresponding to the gyroscope.

[0098] Optionally, when the data acquisition element is a power monitoring element, the raw dataset includes the power reserve read when entering the current sampling period, and the recorded dataset includes the power reserve percentage; the execution module 103 performs preliminary calculation processing on the raw dataset acquired by the data acquisition element within the current sampling period to generate the recorded dataset of the data acquisition element; the recorded dataset is subjected to feature-based statistical processing to obtain the data statistical value corresponding to the data acquisition element, including: calculating the ratio of power reserve to power capacity to obtain the power reserve percentage; and using the power reserve percentage as the data statistical value corresponding to the power monitoring element.

[0099] Optionally, when the data acquisition element is a positioning module, the original dataset includes the positioning activation status of the positioning module when entering the current sampling period, the number of satellites searched, latitude and longitude, and recording time; the execution module 103 performs preliminary calculation processing on the original dataset acquired by the data acquisition element in the current sampling period to generate a recording dataset of the data acquisition element; the method of performing feature-based statistical processing on the recording dataset to obtain the data statistical values ​​corresponding to the data acquisition element includes: obtaining the original dataset of the previous sampling period to obtain the recording dataset of the positioning module, the recording dataset including the original dataset of the current sampling period and the original dataset of the previous sampling period; calculating the average speed of the device based on the latitude and longitude and recording time corresponding to the current sampling period and the latitude and longitude and recording time corresponding to the previous sampling period in the recording dataset; calculating the positioning reliability index based on the positioning activation status and the number of satellites corresponding to the current sampling period in the recording dataset; and obtaining the data statistical values ​​corresponding to the positioning module, the data statistical values ​​including the average speed of the device and the positioning reliability index.

[0100] Optionally, when the data acquisition element is a network module, the original dataset includes the network dialing status and network signal strength of the network module when entering the current sampling period; the execution module 103 performs preliminary calculation processing on the original dataset acquired by the data acquisition element within the current sampling period to generate a record dataset of the data acquisition element; the method of performing feature-based statistical processing on the record dataset to obtain the data statistical value corresponding to the data acquisition element includes: using the original dataset as the record dataset of the network module; generating network availability data based on the network dialing status and network signal strength, and using the network availability data as the data statistical value corresponding to the network module.

[0101] Optionally, the positioning mode change strategy includes multiple positioning modes and decision threshold intervals corresponding to each positioning mode; the execution module 103 executes a method to determine the target positioning mode based on the decision value and the positioning mode change strategy, including: determining the target decision threshold interval to which the decision value belongs from all decision threshold intervals; obtaining the positioning mode corresponding to the target decision threshold interval to obtain the target positioning mode.

[0102] Optionally, the positioning mode changing device 100 provided in this application embodiment further includes a configuration module 101, which is used to: configure the positioning mode changing weight and positioning mode changing strategy associated with each set application scenario.

[0103] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the positioning mode changing device 100 described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0104] Please refer to Figure 7, Figure 7 The diagram shows a block diagram of an electronic device 10 provided in an embodiment of this application, including a processor 11, a memory 12 and a bus 13, wherein the processor 11 is connected to the memory 12 via the bus 13.

[0105] The memory 12 is used to store programs. After receiving an execution instruction, the processor 11 executes the programs to implement the positioning mode change method disclosed in the above embodiments.

[0106] The memory 12 may include high-speed random access memory (RAM) and may also include non-volatile memory (NVM).

[0107] Processor 11 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed through integrated logic circuits in the hardware of processor 11 or through software instructions. Processor 11 can be a general-purpose processor, including a Central Processing Unit (CPU), a Microcontroller Unit (MCU), a Complex Programmable Logic Device (CPLD), a Field Programmable Gate Array (FPGA), embedded ARM chips, etc.

[0108] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by the processor 11, implements the positioning mode changing method disclosed in the above embodiments.

[0109] In summary, the positioning mode changing method, apparatus, electronic device, and storage medium provided in this application trigger multiple data acquisition elements to collect data according to a preset sampling period. At the end of each sampling period, the data collected by each data acquisition element in the current sampling period is fused based on a positioning mode changing weight associated with the user-selected application scenario to obtain a decision value. Then, a target positioning mode is determined based on the decision value and the positioning mode changing strategy associated with the user-selected application scenario, and switching occurs when the target positioning mode differs from the current positioning mode. Thus, on the one hand, by periodically fusing multi-source data to determine whether to switch positioning modes, it ensures that the positioning mode can match the user's current movement state in real time, avoiding the positioning module being in a working state for a long time and reducing power consumption; on the other hand, by introducing a positioning mode changing weight and a positioning mode changing strategy associated with the user-selected application scenario, it ensures that the positioning mode matches the user-selected application scenario, guaranteeing positioning accuracy.

[0110] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for changing a positioning mode, characterized in that, The location mode change method includes: Obtain the location mode change weights and location mode change strategies associated with the user-selected application scenario; According to the preset sampling period, multiple data acquisition elements are triggered to collect data according to the current positioning mode; At the end of each sampling period, based on the positioning mode change weight, the original datasets collected by each data acquisition element in the current sampling period are fused to obtain a decision value; Based on the decision value and the positioning mode change strategy, the target positioning mode is determined; In the next sampling cycle, the multiple data acquisition elements are controlled to operate according to the target positioning mode.

2. The positioning mode change method as described in claim 1, characterized in that, The positioning mode change weights include the weights of each of the data acquisition elements; The step of fusing the raw datasets collected by each data acquisition element in the current sampling period based on the positioning mode change weight to obtain a decision value includes: For each data acquisition element, the raw dataset acquired by the data acquisition element within the current sampling period is subjected to preliminary calculation processing to generate a recorded dataset of the data acquisition element; the recorded dataset is subjected to feature-based statistical processing to obtain the data statistical value corresponding to the data acquisition element. Normalize each of the data statistics to obtain each normalized data statistics; The decision value is obtained by weighting and summing the normalized data statistics based on the weight of each data acquisition element.

3. The positioning mode change method as described in claim 2, characterized in that, When the data acquisition element is an accelerometer, the raw dataset includes multiple triaxial accelerations read continuously within a pre-configured single sampling duration; The data acquisition element performs preliminary calculations on the raw dataset collected during the current sampling period to generate a recorded dataset of the data acquisition element. The step of performing feature-based statistical processing on the recorded dataset to obtain the data statistical values ​​corresponding to the data acquisition element includes: Calculate the vector sum of each of the three-axis accelerations to obtain the recorded dataset of the acceleration sensor; calculate the variance of all the vector sums in the recorded dataset to obtain the data statistics corresponding to the acceleration sensor. When the data acquisition element is a gyroscope, the raw dataset includes multiple triaxial angular velocities continuously read within a pre-configured single sampling duration; The steps of performing preliminary calculations on the raw dataset collected by the data acquisition element within the current sampling period to generate a recorded dataset of the data acquisition element, and performing feature-based statistical processing on the recorded dataset to obtain the data statistical values ​​corresponding to the data acquisition element, include: The original dataset is used as the recording dataset of the gyroscope; the range of each of the three-axis angular velocities in the recording dataset is calculated, and the maximum value among all the ranges is taken to obtain the data statistics value corresponding to the gyroscope; When the data acquisition element is a power monitoring element, the raw dataset includes the remaining power read when entering the current sampling period, and the recorded dataset includes the percentage of remaining power. The steps of performing preliminary calculations on the raw dataset collected by the data acquisition element within the current sampling period to generate a recorded dataset of the data acquisition element, and performing feature-based statistical processing on the recorded dataset to obtain the data statistical values ​​corresponding to the data acquisition element, include: Calculate the ratio of the power reserve to the power capacity to obtain the power reserve percentage; use the power reserve percentage as the data statistics value corresponding to the power monitoring element.

4. The positioning mode change method as described in claim 2, characterized in that, When the data acquisition element is a positioning module, the raw dataset includes the positioning activation status of the positioning module when entering the current sampling period, the number of satellites searched, latitude and longitude, and recording time; The data acquisition element performs preliminary calculations on the raw dataset collected during the current sampling period to generate a recorded dataset of the data acquisition element. The step of performing feature-based statistical processing on the recorded dataset to obtain the data statistical values ​​corresponding to the data acquisition element includes: Obtain the original dataset of the previous sampling period to obtain the recording dataset of the positioning module. The recording dataset includes the original dataset of the current sampling period and the original dataset of the previous sampling period. The average speed of the device is calculated based on the latitude, longitude, and recording time corresponding to the current sampling period and the latitude, longitude, and recording time corresponding to the previous sampling period in the recorded dataset. Based on the positioning activation status and the number of satellites corresponding to the current sampling period in the recorded dataset, a positioning reliability index is calculated. Obtain the data statistics corresponding to the positioning module, including the average speed of the device and the positioning reliability index.

5. The positioning mode change method as described in claim 2, characterized in that, When the data acquisition element is a network module, the raw dataset includes the network dialing status and network signal strength of the network module when entering the current sampling period; The data acquisition element performs preliminary calculations on the raw dataset collected during the current sampling period to generate a recorded dataset of the data acquisition element. The step of performing feature-based statistical processing on the recorded dataset to obtain the data statistical values ​​corresponding to the data acquisition element includes: The original dataset is used as the record dataset of the network module; Based on the network dialing status and the network signal strength, network availability data is generated, and the network availability data is used as the data statistics value corresponding to the network module.

6. The positioning mode change method as described in claim 1, characterized in that, The positioning mode change strategy includes multiple positioning modes and a decision threshold range corresponding to each positioning mode. The step of determining the target positioning mode based on the decision value and the positioning mode change strategy includes: From all the aforementioned decision threshold intervals, determine the target decision threshold interval to which the decision value belongs; Obtain the positioning mode corresponding to the target decision threshold range to obtain the target positioning mode.

7. The positioning mode change method as described in claim 1, characterized in that, Before the step of obtaining the location mode change weights and location mode change strategies associated with the user-selected application scenario, the location mode change method further includes: Configure the location mode change weight and location mode change strategy associated with each set application scenario.

8. A positioning mode changing device, characterized in that, The positioning mode changing device includes: The acquisition module is used to acquire the location mode change weights and location mode change strategies associated with the application scenario selected by the user. The execution module is used to: trigger multiple data acquisition elements to collect data according to the current positioning mode according to a preset sampling period; at the end of each sampling period, based on the positioning mode change weight, fuse the original datasets collected by each data acquisition element in the current sampling period to obtain a decision value; determine the target positioning mode based on the decision value and the positioning mode change strategy; and control the multiple data acquisition elements to work according to the target positioning mode in the next sampling period.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory being used to store a program, and the processor being used to implement the positioning mode changing method according to any one of claims 1-7 when executing the program.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the positioning mode change method as described in any one of claims 1-7.

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