Low-power multi-positioning intelligent terminal positioning trajectory correction method, system and medium

Abstract

This application relates to a method, system and medium for correcting the positioning trajectory of a low-power multi-location intelligent terminal. The method includes monitoring the acceleration data of the user. When it exceeds the preset acceleration threshold, it is determined that the user is in a moving state. At this time, it is the T0 moment, and the data packet at the T0 moment is obtained; preliminary positioning is performed to obtain a preliminary positioning result, GIS data is obtained, and the initial positioning information at the T0 moment is calculated based on the preliminary positioning result and the GIS data. The position at this time is recorded as the positioning point at the T0 moment, and the motion characteristic data of the user at this time is obtained; the acceleration data at the T n moment is obtained, the positioning point at the T n moment is selected, and the motion characteristic data at this time is obtained; when the user ends the moving state, all the positioning points are read and corrected and simulated to obtain a simulated trajectory P; the similarity between the simulated trajectory P and the real map route is matched, and the actual movement path of the user is determined according to the matching result. This application has the effect of reducing the power consumption of the positioning trajectory of the intelligent terminal and improving the accuracy.

Description

Technical Field

[0001] The present application relates to the technical field of smart terminal positioning and correction, and in particular to a low-power multi-positioning smart terminal positioning trajectory correction method, system and medium. Background Art

[0002] At present, in the field of IoT smart wearable devices, such as smart watches and smart glasses, most products have positioning functions and the function of providing positioning trajectories.

[0003] Existing smart terminals generally use integrated GPS positioning and 4G network connection functions to achieve positioning and generate positioning trajectories, but they often bring high power consumption, forcing users to rely on large-capacity batteries or frequent charging to meet daily usage needs. In addition, relying solely on GPS positioning is easily affected by external environmental factors such as urban canyon effects and weather changes, thereby reducing positioning accuracy.

[0004] With respect to the above-mentioned related technologies, the inventors believe that there are defects in that the positioning trajectory of the smart terminal has high power consumption and low accuracy. Summary of the invention

[0005] In order to reduce the power consumption of smart terminal positioning trajectory and improve the accuracy, the present application provides a low-power multi-positioning smart terminal positioning trajectory correction method, system and medium.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions:

[0007] A low-power multi-positioning intelligent terminal positioning trajectory correction method includes:

[0008] S10: Continuously monitor the acceleration data of the user. When the acceleration data exceeds a preset acceleration threshold, determine that the user is in motion, define the time at this time as time T0, obtain the broadcast signal data at time T0 and package it into a data packet at time T0, wherein the broadcast signal data at time T0 includes the T0 Bluetooth RSSI, T0 WiFi RSSI and T0 4G base station information at time T0;

[0009] S20: Perform preliminary positioning according to the data packet at time T0 to obtain a preliminary positioning result, obtain GIS data, perform calculations according to the preliminary positioning result and the GIS data to obtain the initialization positioning information at time T0, record the corresponding position of the user as the positioning point at time T0, and obtain the motion feature data of the user at the positioning point at time T0, wherein the motion feature data includes the motion speed, acceleration and direction change rate of the user;

[0010] S30: Obtain the user's T nAcceleration data at each moment, according to the T n The acceleration data at time T is selected n The time positioning point is at and obtains the T n The motion characteristic data of the user at the time of the positioning point;

[0011] S40: When the acceleration data is lower than the preset acceleration threshold, determine that the user ends the motion state, read all the positioning points in the motion state, and the positioning points include the T0 positioning point and the T n Anchor point;

[0012] S50: calibrating and simulating all the positioning points to obtain a simulated trajectory P;

[0013] S60: performing similarity matching between the simulated trajectory P and the real map route, and determining the actual movement path of the user according to the matching result.

[0014] By adopting the above technical solution, by continuously monitoring the acceleration data of the user, when the acceleration data exceeds the preset threshold, it is judged that the user is in motion and defined as the T0 moment. It can accurately identify the moment when the user starts to exercise, thereby ensuring that the positioning system can be started in time and accurately track the trajectory, avoiding misjudgment due to stillness or other interference factors, thereby improving the accuracy of positioning. At the same time, the acceleration monitoring of the acceleration sensor replaces the positioning mode of the traditional smart terminal to start GPS positioning in real time, greatly reducing the power consumption of the positioning trajectory generation; by obtaining the broadcast signal data at the T0 moment (including Bluetooth RSSI, WiFi RSSI and 4G base station information), and pack them into data packets for preliminary positioning, which can achieve high-precision positioning with the help of multiple signal sources, enhance the robustness of positioning, and ensure high positioning accuracy, especially in the case of weak signals or complex environments; by performing subsequent positioning based on the motion feature data of the positioning point at time T0 (such as motion speed, acceleration and direction change rate), the user's positioning trajectory can be tracked in real time, ensuring continuous monitoring of the user's motion state during the movement process, providing dynamic positioning feedback, avoiding errors caused by path changes, and only turning on GPS positioning once at the positioning point, replacing the trajectory generation mode of turning on GPS positioning in real time in traditional smart terminals, greatly reducing the power consumption of positioning trajectory generation; by reading all positioning points and correcting and simulating them, a simulated trajectory is obtained, which can remove the positioning deviation caused by external environment changes or sensor fluctuations, ensure that the simulated trajectory is more accurate, and thus provide a reliable basis for subsequent path matching; by matching the simulated trajectory with the real map route for similarity, the accuracy of the trajectory can be evaluated in a quantitative way, ensuring that the actual movement path of the user is highly consistent with the real path, thereby improving the credibility of the positioning result and providing users with more accurate and real positioning services.

[0015] In a preferred example, the present application can be further configured as follows: performing preliminary positioning according to the data packet at time T0, obtaining preliminary positioning results, acquiring GIS data, and performing calculations according to the preliminary positioning results and GIS data to obtain initialization positioning information at time T0, specifically including:

[0016] S21: Acquire a wireless signal fingerprint identification library, match the data packet at time T0 with the wireless signal fingerprint identification library, and obtain a matching result, wherein the wireless signal fingerprint identification library includes historical Bluetooth RSSI, historical WiFi RSSI, and historical 4G base station information;

[0017] S22: Calculate and obtain a preliminary positioning result of the user according to the matching result;

[0018] S23: Acquire a building boundary, match the preliminary positioning result with the building boundary, and determine whether the user is inside the building;

[0019] S24: when the user is inside the building, query the shortest distance data between the user and the building boundary to obtain the building boundary distance, and output the initial positioning position according to the building boundary distance and the preliminary positioning result;

[0020] S25: When the user is outside the building, a GPS positioning auxiliary file is generated according to the preliminary positioning result, and GPS positioning is performed according to the GPS positioning auxiliary file to obtain an initialized positioning position.

[0021] By adopting the above technical solution, by matching the T0 time data packet with the wireless signal fingerprint recognition library, the positioning accuracy can be effectively improved, and the deviation caused by signal interference or positioning error can be avoided, thereby ensuring that the initialization positioning information is more accurate; by obtaining the building boundary and combining it with the positioning result for further matching, it can intelligently identify whether the user is inside the building, thereby effectively improving the indoor and outdoor positioning accuracy and adapting to the positioning needs in different environments.

[0022] In a preferred example, the present application can be further configured as follows: n The acceleration data at time T is selected n Moment positioning points include:

[0023] S31: According to the T n The acceleration data at the moment is calculated to get T n-1 Time to the T n The moving distance of the user at the time is recorded as T n Moving distance, and according to the T nThe acceleration data at the moment is matched to obtain T n Moving distance threshold;

[0024] S32: When the T n Move the distance to reach the T n When the moving distance threshold is reached, the user is located and the user's position is recorded as T n Moment positioning point.

[0025] By adopting the above technical solution, according to T n The acceleration data at each moment calculates the moving distance and selects the positioning point, which can improve the positioning accuracy through precise motion data extraction, thereby ensuring that subsequent positioning results are more reliable; by setting the moving distance threshold and performing positioning, recording when the moving distance reaches the set value, it can avoid too frequent positioning point updates, reduce unnecessary calculation and processing burdens, and thus improve the overall positioning efficiency.

[0026] In a preferred example, the present application can be further configured as follows: n The acceleration data at the moment is calculated to obtain the T n-1 Time to the T n The user's moving distance at the moment is recorded as T n Moving distance, and according to the T n The acceleration data at the moment is matched to obtain T n Moving distance threshold, including:

[0027] S311: According to the T n The momentary acceleration data is converted into T through the step counting algorithm n-1 Time to T n The number of user steps at the moment, and according to the T n The instantaneous acceleration data determines the user's T n Always in motion mode;

[0028] S312: According to the T n-1 Time to T n The number of user steps at time T is calculated by the step length estimation model n-1 Time to T n The user's movement distance at the moment;

[0029] S313: According to the T n The motion mode at the moment matches the preset moving distance threshold of the corresponding motion mode, denoted as T n Moving distance threshold.

[0030] By adopting the above technical solution, by nConverting the acceleration data at each moment into the number of steps and estimating the step length can more accurately calculate the user's movement distance and avoid the errors caused by relying solely on simple acceleration data, thereby improving the accuracy of positioning point selection; by matching the user's motion pattern and setting the corresponding movement distance threshold, more accurate positioning judgment can be made according to different motion states, thereby improving the adaptability and flexibility of dynamic trajectory correction.

[0031] In a preferred example, the present application can be further configured as follows: n Move the distance to reach the T n When the moving distance threshold is reached, the user is located and the user's position is recorded as T n Moment positioning points include:

[0032] S321: In the T n Moving distance exceeds the T n When the moving distance threshold is reached, collect T n Broadcast signal data at all times and package it into T n Time packet, the T n The broadcast signal data includes T n Time T n Bluetooth RSSI, T n WiFi RSSI and T n 4G base station information;

[0033] S322: The T n The T in the time packet n Bluetooth RSSI and the T n The WiFi RSSI is distinguished and compared with the T0 Bluetooth RSSI and the T0 WiFi RSSI in the T0 time packet to obtain a broadcast signal change value;

[0034] S323: Obtain a preset broadcast signal change threshold, and obtain T n GIS data at the moment;

[0035] S324: If the broadcast signal change value does not exceed the broadcast signal change threshold, then the T n The time positioning point is equal to the T0 time positioning point;

[0036] S325: When the broadcast signal change value exceeds the broadcast signal change threshold and the user is inside a building, n Time data packet, the T n The GIS data and triangulation method at the time are used to calculate T n The location information of the user at the time is recorded as Tn Moment positioning point;

[0037] S326: When the broadcast signal change value exceeds the broadcast signal change threshold and the user is outside the building, perform GPS positioning according to the GPS positioning auxiliary file to obtain T n The location information of the user at the time is recorded as T n Moment positioning point.

[0038] By adopting the above technical solution, by collecting T n The signal data is broadcast at all times and compared with the data packet at T0, which can effectively identify signal changes and locate them through comparative analysis, ensuring that the selected T n The momentary positioning point reflects the actual positioning trajectory; by combining the broadcast signal change value and GIS data, the triangulation method is used for precise positioning, which can effectively improve the positioning accuracy in complex environments.

[0039] In a preferred example, the present application can be further configured as follows: the calibration and simulation of all the positioning points to obtain the simulated trajectory P specifically includes:

[0040] S51: performing data cleaning on the positioning information corresponding to all the positioning points to obtain positioning information correction data corresponding to each positioning point;

[0041] S52: matching the user motion pattern corresponding to each positioning point according to the acceleration data corresponding to each positioning point, and setting the corresponding positioning trajectory offset parameter according to the user motion pattern;

[0042] S53: Obtaining the positioning point GIS data corresponding to each positioning point;

[0043] S54: smoothing all the positioning points according to the motion feature data corresponding to each of the positioning points, and simulating and generating an original positioning trajectory;

[0044] S55: Correcting the original positioning trajectory according to the positioning point GIS data and the positioning trajectory offset parameter to obtain a first simulated positioning trajectory;

[0045] S56: comparing the first simulated positioning trajectory with the positioning point, and calculating the overlap rate between the two;

[0046] S57: Outputting a simulation trajectory P according to the overlap ratio.

[0047] By adopting the above technical solution, through data cleaning and correction of all positioning points, positioning errors or outliers can be removed to ensure that the final generated simulation trajectory is more consistent with the actual motion path, thereby improving the accuracy and reliability of trajectory correction; through smoothing processing and the application of positioning trajectory offset parameters, the simulation trajectory can be made smoother, with fewer jumps and incoherent trajectory segments, thereby improving the naturalness and usability of the final trajectory.

[0048] In a preferred example, the present application may be further configured as follows: outputting the simulated trajectory P according to the overlap rate specifically includes:

[0049] S571: when the overlap rate exceeds 80%, outputting the first simulated positioning trajectory as a simulated trajectory P;

[0050] S572: When the overlap rate does not exceed 80%, repeat step S55 and step S56 N times until the overlap rate exceeds 80%, and then output the Nth simulated positioning trajectory as simulated trajectory P.

[0051] By adopting the above technical solution and outputting the simulated trajectory according to the overlap rate, it is possible to ensure that the final trajectory is highly consistent with the actual path, thereby improving the accuracy of positioning correction; through continuous iteration and comparison, the simulated trajectory can be optimized so that the final output positioning trajectory can better conform to the actual movement situation, thereby improving the accuracy and practicality of the system.

[0052] By outputting the simulated trajectory according to the overlap rate, it can be ensured that the final trajectory is highly consistent with the actual path, thereby improving the accuracy of positioning correction; through continuous iteration and comparison, the simulated trajectory can be optimized so that the final output positioning trajectory can better conform to the actual movement situation, thereby improving the accuracy of positioning trajectory correction.

[0053] In a preferred example, the present application may be further configured as follows: performing similarity matching between the simulated trajectory P and the real map route, and determining the actual motion path of the user according to the matching result, specifically including:

[0054] S61: Acquire a real map route and a road constraint, and evaluate the similarity between the simulated trajectory P and the real map route according to the road constraint;

[0055] S62: when the similarity exceeds 95%, determining the simulated trajectory P as the actual motion path of the user;

[0056] S63: When the similarity does not exceed 95%, repeat step S55, step S56 and step S57 M times until the similarity exceeds 95%, and then determine that the Mth simulated positioning trajectory is the actual movement path of the user.

[0057] By adopting the above technical solution, by matching the simulated trajectory with the real map route for similarity, the accuracy of the trajectory can be quantified to ensure that the user's actual movement path is highly consistent with the actual situation; by determining the final path based on road constraints and similarity evaluation, the accuracy of the trajectory can be effectively optimized, adapting to different environments and complex terrains, and providing more accurate positioning results.

[0058] The second object of the invention is achieved by the following technical solutions:

[0059] A low-power multi-positioning intelligent terminal positioning trajectory correction system comprises:

[0060] The system integrates a 4G network module, a GPS module, a WiFi module, a Bluetooth beacon module, an acceleration sensor, a processor, a small-capacity battery, and a remote location resolution server;

[0061] The 4G network module, the GPS module, the WiFi module, the Bluetooth beacon module, the acceleration sensor, the processor and the small-capacity battery are integrated on a main board through an integrated design, and the main board can be directly applied to smart wearable products;

[0062] The output ends of the GPS module, the WiFi module, the Bluetooth beacon module, the acceleration sensor, and the small-capacity battery are electrically connected to the input end of the processor, and the output end of the processor is electrically connected to the input ends of the 4G module and the Bluetooth beacon module.

[0063] By adopting the above technical solution, by integrating 4G network module, GPS module, WiFi module, Bluetooth beacon module, acceleration sensor, processor, small capacity battery, remote location resolution server and other components, a variety of positioning and sensing technologies can be closely combined to achieve low-power, high-efficiency multiple positioning solutions, meet the positioning needs in various usage scenarios, and improve the flexibility and adaptability of the system; by integrating all modules on a main board through integrated design, the hardware footprint can be reduced, the device size and weight can be reduced, so that the system can be embedded in smart wearable products to improve the convenience and comfort of users; by electrically connecting the GPS module, WiFi module, Bluetooth beacon module, acceleration sensor and processor, the sensor data from each module can be acquired and processed in real time, providing accurate information support for the correction of the positioning trajectory, ensuring real-time response to the user's movement changes; by electrically connecting the output end of the processor with the input end of the 4G module and the Bluetooth beacon module, remote transmission and real-time update of data can be achieved, ensuring that the user's positioning information can be quickly transmitted to the remote server, providing cloud analysis and feedback, and enhancing the real-time and accuracy of the system.

[0064] The third objective of the present application is achieved through the following technical solutions:

[0065] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned low-power multi-positioning intelligent terminal positioning trajectory correction method are implemented.

[0066] In summary, the present application includes at least one of the following beneficial technical effects:

[0067] 1. By continuously monitoring the user's acceleration data, when the acceleration data exceeds the preset threshold, it is judged that the user is in motion and defined as the T0 moment. It can accurately identify the moment when the user starts to exercise. The acceleration monitoring of the acceleration sensor replaces the real-time GPS positioning mode of the traditional smart terminal. By performing subsequent positioning based on the motion feature data of the positioning point at the T0 moment (such as motion speed, acceleration and direction change rate), it can track the user's positioning trajectory in real time. Only one GPS positioning is started at the positioning point, which replaces the real-time GPS positioning trajectory generation mode of the traditional smart terminal, thereby reducing the power consumption of the smart terminal positioning trajectory correction;

[0068] 2. By matching the T0 data packet with the wireless signal fingerprint recognition library, the positioning accuracy can be effectively improved, avoiding deviations caused by signal interference or positioning errors, thereby ensuring that the initial positioning information is more accurate; by obtaining the building boundary and combining it with the positioning results for further matching, it can intelligently identify whether the user is inside the building, thereby effectively improving the indoor and outdoor positioning accuracy. n The acceleration data at each moment is converted into the number of steps and the step length is estimated, which can more accurately calculate the user's moving distance and avoid the error caused by relying solely on simple acceleration data, thereby improving the accuracy of positioning point selection; by matching the user's motion mode and setting the corresponding moving distance threshold, more accurate positioning judgment can be made according to different motion states, thereby improving the adaptability and flexibility of dynamic trajectory correction. n The signal data is broadcast at all times and compared with the data packet at T0, which can effectively identify signal changes and locate them through comparative analysis, ensuring that the selected T nThe moment positioning point reflects the real positioning trajectory; by combining the broadcast signal change value and GIS data, the triangulation method is used for precise positioning, which further improves the positioning accuracy in complex environments. By cleaning and correcting the data of all positioning points, the positioning error or outlier can be removed to ensure that the final generated simulation trajectory is more consistent with the actual motion path, thereby improving the accuracy and reliability of the trajectory correction; by smoothing and applying the positioning trajectory offset parameters, the simulation trajectory can be made smoother, and the jumps and incoherent trajectory segments can be reduced, thereby improving the accuracy of the final trajectory. By outputting the simulation trajectory according to the overlap rate, it can be ensured that the final trajectory is highly consistent with the real path, thereby improving the accuracy of positioning correction; through continuous iteration and comparison, the simulation trajectory can be optimized so that the final output positioning trajectory can better meet the actual movement situation, thereby improving the accuracy of positioning trajectory correction; by matching the simulation trajectory with the real map route for similarity, the accuracy of the trajectory can be quantified to ensure that the actual movement path of the final user is highly consistent with the actual situation; by determining the final path based on road constraints and similarity evaluation, the accuracy of the trajectory can be effectively optimized, adapting to different environments and complex terrains, and providing more accurate positioning results, thereby improving the accuracy of the positioning trajectory correction of the smart terminal;

[0069] 3. According to T n The acceleration data at each moment calculates the moving distance and selects the positioning point, which can improve the positioning accuracy through precise motion data extraction, thereby ensuring that subsequent positioning results are more reliable; by setting the moving distance threshold and performing positioning, recording when the moving distance reaches the set value, it can avoid too frequent positioning point updates, reduce unnecessary calculation and processing burdens, and thus improve the efficiency of smart terminal positioning trajectory correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 It is a flow chart of a method for correcting the positioning trajectory of a low-power multi-positioning intelligent terminal in one embodiment of the present application.

[0071] Figure 2 It is a flowchart for implementing step S20 in the low-power multi-positioning intelligent terminal positioning trajectory correction method in one embodiment of the present application.

[0072] Figure 3 It is a flowchart for implementing step S30 in the low-power multi-positioning intelligent terminal positioning trajectory correction method in one embodiment of the present application.

[0073] Figure 4 It is a flowchart for implementing step S31 in the low-power multi-positioning intelligent terminal positioning trajectory correction method in one embodiment of the present application.

[0074] Figure 5It is a flowchart for implementing step S32 in the low-power multi-positioning intelligent terminal positioning trajectory correction method in one embodiment of the present application.

[0075] Figure 6 It is a flowchart for implementing step S50 in the low-power multi-positioning intelligent terminal positioning trajectory correction method in one embodiment of the present application.

[0076] Figure 7 It is a flowchart for implementing step S57 in the low-power multi-positioning intelligent terminal positioning trajectory correction method in one embodiment of the present application.

[0077] Figure 8 It is a flowchart for implementing step S60 in the low-power multi-positioning intelligent terminal positioning trajectory correction method in one embodiment of the present application.

[0078] Fig. 9 It is a principle block diagram of a low-power multi-positioning intelligent terminal positioning trajectory correction system in one embodiment of the present application. DETAILED DESCRIPTION

[0079] The present application is further described in detail below in conjunction with the accompanying drawings.

[0080] In one embodiment, if Figure 1 As shown, the present application discloses a low-power multi-positioning intelligent terminal positioning trajectory correction method, which specifically includes the following steps:

[0081] S10: Continuously monitor the acceleration data of the user. When the acceleration data exceeds the preset acceleration threshold, it is determined that the user is in motion, and the moment at this moment is defined as moment T0. The broadcast signal data at moment T0 is obtained and packaged into a moment T0 data packet. The broadcast signal data at moment T0 includes the T0 Bluetooth RSSI, T0 WiFi RSSI, and T0 4G base station information at moment T0.

[0082] In this embodiment, the Bluetooth RSSI refers to the broadcast signal strength indicator of the Bluetooth beacon, and the WiFi RSSI refers to the broadcast signal strength indicator of the WiFi access point.

[0083] Specifically, the smart terminal uses a built-in acceleration sensor (G-sensor nsor) continuously monitors the acceleration data of the device. When the acceleration data exceeds the preset acceleration threshold for the first time, it is determined that the user wearing the smart terminal is in motion. This moment is defined as T0, which triggers the system to start the WiFi scanning module and the Bluetooth receiver, collect the broadcast signal strength indications of the WiFi access points and Bluetooth beacons in the surrounding environment, and activate a 4G network connection to obtain the 4G base station information in the current area. The smart terminal packages the collected WiFi APs RSSI data, Bluetooth beacon RSSI data and 4G base station information, and sends them to the remote server through the established 4G connection.

[0084] S20: Perform preliminary positioning according to the data packet at time T0, obtain preliminary positioning results, acquire GIS data, perform calculations based on the preliminary positioning results and GIS data, obtain initialization positioning information at time T0, record the position of the corresponding user as the positioning point at time T0, and obtain the user's motion feature data when at the positioning point at time T0.

[0085] In this embodiment, GIS data refers to data of a geographic information system, and motion feature data refers to the speed, acceleration, and direction change rate of a user's motion analyzed according to a motion feature extraction algorithm.

[0086] Specifically, after the remote server receives the data packet, it preliminarily locates the user's position. After the preliminary positioning is completed, the server further obtains data from the geographic information system (GIS), matches the preliminary positioning result with the building boundary, obtains the user's current position, and defines the user's positioning point at this time as the positioning point at time T0. The smart terminal analyzes the user's movement speed, acceleration and direction change rate at time T0 according to the motion feature extraction algorithm to form the user's motion feature data at time T0.

[0087] S30: Get the user's T n Acceleration data at each moment, according to T n The acceleration data at time T is selected n Locate the point at the moment and obtain the n The user's motion feature data at the time of positioning.

[0088] Specifically, the smart terminal uses the built-in acceleration sensor to continuously monitor the acceleration data of the user, thereby calculating the user's movement distance. Whenever the movement distance reaches a certain movement distance threshold, the time at that time is selected as T n moment, where the motion distance threshold can be adaptively adjusted according to the pace of different users, and the smart terminal analyzes T according to the motion feature extraction algorithm n The speed, acceleration and direction change rate of the user's movement at any moment form T n User motion feature data at each moment.

[0089] S40: When the acceleration data is lower than the preset acceleration threshold, it is determined that the user has ended the motion state, and all positioning points in the motion state are read, including the T0 positioning point and the T n Anchor point.

[0090] Specifically, when the acceleration data is lower than a preset acceleration threshold, it is determined that the user has ended the motion state, and the local device uploads the data of all historical positioning points to the remote server via the 4G network.

[0091] S50: Correct and simulate all positioning points to obtain a simulated trajectory P.

[0092] Specifically, the remote server uses a path reconstruction algorithm (such as least squares fitting and spline interpolation) to smooth all positioning points, simulate and generate a continuous and reasonable positioning trajectory, and then correct it with GIS data to obtain the simulated trajectory P.

[0093] S60: Perform similarity matching between the simulated trajectory P and the real map route, and determine the actual movement path of the user according to the matching result.

[0094] In this embodiment, the real map route refers to the route of the actual high-precision map (HD Maps) and transportation network.

[0095] Specifically, the similarity between the simulated route P and the real map route is evaluated. When the similarity reaches a certain threshold, the user's actual movement path is determined. The server stores these data in the database and can generate a visual interface to allow the user to intuitively understand his or her movement path.

[0096] In one embodiment, if Figure 2 As shown, in step S20, preliminary positioning is performed according to the data packet at time T0, a preliminary positioning result is obtained, GIS data is acquired, and calculation is performed according to the preliminary positioning result and the GIS data to obtain the initialization positioning information at time T0, which specifically includes:

[0097] S21: Acquire a wireless signal fingerprint recognition library, match the data packet at time T0 with the wireless signal fingerprint recognition library, and obtain a matching result. The wireless signal fingerprint recognition library includes historical Bluetooth RSSI, historical WiFi RSSI, and historical 4G base station information.

[0098] In this embodiment, the wireless signal fingerprint identification library refers to a database including data such as broadcast signal strength indicators of preset WiFi access points, broadcast signal strength indicators of preset Bluetooth beacons, and preset 4G base station locations.

[0099] Specifically, after receiving the data packet, the remote server uses database query technology based on the stored wireless signal fingerprint recognition library to quickly match the data packet at time T0, that is, the Bluetooth RSSI, WiFi RSSI, and 4G base station information at time T0 are matched with the pre-set Bluetooth RSSI, WiFi RSSI, and 4G base station information.

[0100] S22: Calculate the preliminary positioning result of the user based on the matching result.

[0101] Specifically, according to the matching results of the position coordinates of multiple WiFi access points and Bluetooth beacons, the user's position is preliminarily calculated using triangulation, that is, the preliminary positioning result.

[0102] S23: Obtain the building boundary, match the preliminary positioning result with the building boundary, and determine whether the user is inside the building.

[0103] Specifically, after the initial positioning is completed, the server further uses the building outline data in the Geographic Information System (GIS) to match the initial positioning results with the building boundaries to determine whether the device is located inside the building.

[0104] S24: When the user is inside the building, query the shortest distance data between the user and the building boundary to obtain the building boundary distance, and output the initial positioning position according to the building boundary distance and the preliminary positioning result.

[0105] In this embodiment, the building boundary distance is the shortest vertical distance between the user's current position and the boundary of the building.

[0106] Specifically, if the user is inside a building, the shortest distance data of the building boundary relative to the current user position will be queried, and the obtained initial positioning information and building boundary distance will be sent to the smart terminal to provide accurate location data display.

[0107] S25: When the user is outside the building, a GPS positioning auxiliary file is generated according to the preliminary positioning result, and GPS positioning is performed according to the GPS positioning auxiliary file to obtain an initial positioning position.

[0108] Specifically, if the user is not inside a building, the server will use the high-precision map (HD Maps), the latest ephemeris data and almanac (Alma nac Data), generates a GPS positioning auxiliary file for the user's area. The smart terminal receives the preliminary positioning result, building boundary distance and GPS positioning auxiliary file from the remote server. After receiving the GPS positioning auxiliary file, it is immediately passed to the GPS module. After receiving the GPS positioning auxiliary file, the GPS module can lock the satellite signal more quickly and shorten the first positioning time, so that the smart terminal can quickly obtain accurate geographic location information and achieve high-precision positioning.

[0109] In one embodiment, if Figure 3 As shown, in step S30, that is, according to T n The acceleration data at time T is selected n Moment positioning points include:

[0110] S31: According to T n The acceleration data at the moment is calculated to get T n-1 Time to T n The user's moving distance at the moment is recorded as T n Moving distance, and according to T n The acceleration data at the moment is matched to obtain T n Moving distance threshold.

[0111] Specifically, according to T n The acceleration data at each moment is used to calculate the user's moving distance from the last positioning point to the current position, that is, T n-1 Time to T n The moving distance at time is recorded as T n The moving distance is matched according to the acceleration data at the current moment to obtain T n Moving distance threshold.

[0112] S32: When T n Move distance to T n When the moving distance threshold is reached, the user is located and the user's position is recorded as T n Moment positioning point.

[0113] Specifically, when the user moves the distance from the previous positioning point to the current position, that is, T n Move distance to T n When the moving distance threshold is reached, the current time is determined to be T n At this moment, the current position is T n Moment positioning point.

[0114] In one embodiment, if Figure 4 As shown, in step S31, according to T n The acceleration data at the moment is calculated to get T n-1 Time to T nThe user's moving distance at the moment is recorded as T n Moving distance, and according to T n The acceleration data at the moment is matched to obtain T n Moving distance threshold, including:

[0115] S311: According to T n The momentary acceleration data is converted into T through the step counting algorithm n-1 Time to T n The number of user steps at the moment, and according to T n The user's T is determined by the acceleration data at the moment n Always in sports mode.

[0116] Specifically, the smart terminal uses the built-in acceleration sensor to continuously monitor the user's acceleration data, and uses the step counting algorithm (gait analysis based on machine learning) to calculate the user's gait data. n The acceleration data at each moment is converted into an accurate number of steps. The remote server analyzes the acceleration data in real time to identify the T n The user's exercise mode at all times (such as walking, running, etc.).

[0117] S312: According to T n-1 Time to T n The number of user steps at time T is calculated by the step length estimation model n-1 Time to T n The user's movement distance at a certain moment.

[0118] Specifically, combined with the step length estimation model, Kalman filtering is used to convert the previous positioning point T n-1 The number of steps after that is converted to T n-1 Time to T n The user's movement distance at the moment.

[0119] S313: According to T n The motion mode at the moment matches the preset moving distance threshold of the corresponding motion mode, denoted as T n Moving distance threshold.

[0120] Specifically, according to the difference in pace of different user motion modes, the remote server n Momentary sports mode, adaptive adjustment from T n-1 Time to T n The moving distance threshold at the moment is set to ensure that it can accurately determine when to record as a positioning point in various situations.

[0121] In one embodiment, if Figure 5 As shown, in step S32, when T n Move distance to T nWhen the moving distance threshold is reached, the user is located and the user's position is recorded as T n Moment positioning points include:

[0122] S321: In T n Moving distance exceeds T n When the moving distance threshold is reached, collect T n Broadcast signal data at all times and package it into T n Time packet, T n The broadcast signal data includes T n Time T n Bluetooth RSSI, T n WiFi RSSI and T n 4G base station information.

[0123] Specifically, when it is confirmed that the user's moving distance exceeds the corresponding moving distance threshold, it is determined that the current position should be set as the positioning point, and the current time is T n At this moment, the smart terminal starts the WiFi scanning module and Bluetooth receiver to collect the broadcast signal strength indication (RSSI) of WiFi access points and Bluetooth beacons in the surrounding environment, as well as 4G base station information, and transmits these T n The Bluetooth RSSI, WiFi RSSI and 4G base station information at the moment are packaged into T n The time data packet is sent to the remote server.

[0124] S322: T n T in the time packet n Bluetooth RSSI and T n The WiFi RSSI is distinguished and compared with the T0 Bluetooth RSSI and T0 WiFi RSSI in the data packet at time T0 to obtain the broadcast signal change value.

[0125] Specifically, comparing T n The broadcast signal strength indication (RSSI) at time T1 and time T0 is used to evaluate whether there is a significant change in the external broadcast signal and record the changed value.

[0126] S323: Obtain a preset broadcast signal change threshold, and obtain T n GIS data at the moment.

[0127] Specifically, a preset broadcast signal change threshold and T n Geographic Information System data (GIS data) at the moment.

[0128] S324: If the broadcast signal change value does not exceed the broadcast signal change threshold, T n The time positioning point is equal to the T0 time positioning point.

[0129] Specifically, if the broadcast signal change value does not exceed the broadcast signal change threshold, it proves that the detected WiFi and Bluetooth broadcast signals have no obvious changes, indicating that the user is still in the same relative position. At this time, no additional positioning operation is performed to save power consumption. n The time positioning point is equal to the T0 time positioning point.

[0130] S325: When the broadcast signal change value exceeds the broadcast signal change threshold and the user is inside a building, n Time data packet, T n The GIS data and triangulation method at the time are used to calculate T n The location information at the time, the corresponding user's location is recorded as T n Moment positioning point.

[0131] Specifically, if the broadcast signal change value exceeds the broadcast signal change threshold, it proves that a significant change in the broadcast signal is detected. At this time, if the preliminary positioning result shows that the user is inside a building, accurate location information can be quickly obtained only through triangulation.

[0132] S326: When the broadcast signal change value exceeds the broadcast signal change threshold and the user is outside the building, GPS positioning is performed according to the GPS positioning auxiliary file to obtain T n The location information at the time, the corresponding user's location is recorded as T n Moment positioning point.

[0133] Specifically, if the broadcast signal change value exceeds the broadcast signal change threshold, indicating that a significant change in the broadcast signal is detected, and the user is not in the building, the system will activate the GPS module and combine it with the GPS positioning auxiliary file (A-GPS) previously obtained from the server to quickly achieve high-precision positioning. The new location result will be stored as T n Moment positioning point.

[0134] In one embodiment, if Figure 6 As shown, in step S50, all positioning points are simulated and corrected according to the motion feature data to obtain a simulated trajectory P, which specifically includes:

[0135] S51: performing data cleaning on the positioning information corresponding to all positioning points to obtain positioning information correction data corresponding to each positioning point.

[0136] Specifically, when the user stops the activity, the smart terminal uploads all historical positioning data, that is, the coordinate data of all positioning points starting from time T0, to the remote server through the 4G network. The server uses a smoothing algorithm to clean the uploaded data, remove outliers and noise points, and obtain the positioning information correction data corresponding to each positioning point.

[0137] S52: matching the user motion patterns corresponding to the respective positioning points according to the acceleration data corresponding to the respective positioning points, and setting the corresponding positioning trajectory offset parameters according to the user motion patterns.

[0138] In this embodiment, the positioning trajectory offset parameters refer to parameters that may cause positioning offsets due to different movements that are pre-stored in the database.

[0139] Specifically, according to the acceleration data collected by the acceleration sensor at each positioning point, the user's motion mode (such as walking, running, cycling, etc.) at each positioning point is judged by matching it with the preset value, and the positioning trajectory offset parameters stored in the database are matched according to the motion mode.

[0140] S53: Obtain the positioning point GIS data corresponding to each positioning point.

[0141] Specifically, the geographic information system data within a preset range around each positioning point is obtained.

[0142] S54: Smoothing all positioning points according to the motion feature data to simulate and generate an original positioning trajectory.

[0143] Specifically, according to the motion characteristic data of each positioning point, that is, the speed, acceleration and direction change rate of each positioning point, the corresponding path reconstruction algorithm (such as least squares fitting, spline interpolation) is adopted to smooth the original positioning points and simulate and generate a continuous and reasonable original positioning trajectory.

[0144] S55: Correcting the original positioning trajectory according to the positioning point GIS data and the positioning trajectory offset parameter to obtain a first simulated positioning trajectory.

[0145] Specifically, the original positioning trajectory is corrected in combination with the building outline data, terrain information and other environmental features in the geographic information system (GIS) to eliminate the position deviation caused by GPS signal drift and multipath effects, and all trajectories within the building range and in areas where normal movement is impossible are corrected along the building outline or terrain. The positioning trajectory offset parameter is combined during the correction, wherein the corresponding motion speed threshold is matched from a pre-set comparison library according to the motion mode type. For example, the motion mode between two adjacent positioning points is running, and the running motion speed threshold is 5-15km / h. The median of the motion speed threshold is taken as the trajectory offset parameter by default, and the trajectory offset parameter of the simulated running trajectory is 10km / h by default. The allowable offset range between the two positioning points is calculated. If the trajectory exceeds the range, the corresponding correction is performed to finally obtain the first simulated positioning trajectory.

[0146] S56: Compare the first simulated positioning trajectory with the positioning point, and calculate the overlap rate between the two.

[0147] Specifically, the first simulated positioning trajectory generated by simulation is compared with the positioning points uploaded by the intelligent terminal, and a spatial geometric matching algorithm (such as Hausdorff distance) is used to quantitatively calculate the coincidence rate between the connection line of the positioning points and the first simulated positioning trajectory.

[0148] S57: Output the simulation trajectory P according to the overlap ratio.

[0149] Specifically, a corresponding output is performed according to the overlap rate to obtain a simulation trajectory P.

[0150] In one embodiment, if Figure 7 As shown, in step S57, the simulation trajectory P is output according to the overlap rate, which specifically includes:

[0151] S571: When the overlap rate exceeds 80%, output the first simulated positioning trajectory as the simulated trajectory P.

[0152] Specifically, if the overlap rate between the first simulated positioning trajectory and the trajectory of the positioning point is above 80%, the simulation result is considered to be relatively credible, and the next step of verification can be entered, and the first simulated positioning trajectory is output as the simulated trajectory P.

[0153] S572: When the overlap rate does not exceed 80%, repeat steps S55 and S56 N times until the overlap rate exceeds 80%, and then output the Nth simulated positioning trajectory as simulated trajectory P.

[0154] In this embodiment, the Nth simulated positioning trajectory refers to a simulated positioning trajectory generated after the Nth cycle.

[0155] Specifically, if the overlap rate between the first simulated positioning trajectory and the trajectory of the positioning point is below 80%, the simulation result is considered unreliable, and it is necessary to adjust the positioning trajectory offset parameters, regenerate the simulated positioning trajectory, and re-match until, after N cycles, the overlap rate between the simulated positioning trajectory generated in the Nth cycle and the trajectory of the positioning point is above 80%, and the Nth simulated positioning trajectory is output as the simulated trajectory P.

[0156] In one embodiment, if Figure 8 As shown, in step S60, the simulated trajectory P is matched with the real map route for similarity, and the actual movement path of the user is determined according to the matching result, which specifically includes:

[0157] S61: Obtain a real map route and road constraints, and evaluate the similarity between the simulated trajectory P and the real map route according to the road constraints.

[0158] In this embodiment, the road constraint refers to finding a range on each road segment that allows the user to perform normal movement according to the actual road conditions.

[0159] Specifically, obtain the real map route including high-precision maps (HD Maps) and traffic network data provided by the map supplier, set the road constraints of each real road section according to the real map route, evaluate whether the simulated trajectory P meets the road constraints, calibrate the positioning points in the simulated trajectory P that do not meet the road constraints, and then regenerate the simulated trajectory P according to the calibrated positioning points until the entire simulated trajectory completely meets the road constraints, and use the spatial geometric matching algorithm to calculate the similarity between the regenerated positioning points and the real map route.

[0160] S62: When the similarity exceeds 95%, the simulated trajectory P is determined to be the actual movement path of the user.

[0161] Specifically, only when the similarity between the simulated route P and the real map route reaches more than 95%, the server will recognize this simulated route P as the user's actual movement path.

[0162] S63: When the similarity does not exceed 95%, repeat step S55, step S56 and step S57 M times until the similarity exceeds 80%, and then determine that the Mth simulated positioning trajectory is the actual movement path of the user.

[0163] Specifically, if the similarity between the simulated route P and the real map route does not reach more than 95%, the server will readjust the positioning trajectory offset parameters, regenerate the simulated positioning trajectory, and re-compare it until M cycles have passed, and the similarity between the simulated positioning trajectory of the Mth cycle and the real map route reaches more than 95%, and the Mth simulated positioning trajectory is output as the simulated trajectory P.

[0164] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0165] In one embodiment, a low-power multi-positioning intelligent terminal positioning trajectory correction system is provided, and the framework diagram can be as follows: Fig. 9 The low-power multi-positioning intelligent terminal positioning trajectory correction system includes:

[0166] The system integrates 4G network module, GPS module, WiFi module, Bluetooth beacon module, acceleration sensor, processor, small capacity battery, and remote location resolution server;

[0167] 4G network module, GPS module, WiFi module, Bluetooth beacon module, acceleration sensor, processor and small-capacity battery are integrated on a motherboard through integrated design, and the motherboard can be directly applied to smart wearable products;

[0168] The output ends of the GPS module, WiFi module, Bluetooth beacon module, acceleration sensor, and small-capacity battery are electrically connected to the input end of the processor, and the output end of the processor is electrically connected to the input end of the 4G module and the Bluetooth beacon module.

[0169] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0170] S10: Continuously monitor the acceleration data of the user. When the acceleration data exceeds a preset acceleration threshold, determine that the user is in motion, define the time at this time as time T0, obtain the broadcast signal data at time T0 and package it into a data packet at time T0. The broadcast signal data at time T0 includes T0 Bluetooth RSSI, T0 WiFi RSSI and T0 4G base station information at time T0.

[0171] S20: Perform preliminary positioning according to the data packet at time T0, obtain preliminary positioning results, obtain GIS data, perform calculations according to the preliminary positioning results and the GIS data, obtain initial positioning information at time T0, record the position of the corresponding user as the positioning point at time T0, and obtain the motion feature data of the user at the positioning point at time T0, the motion feature data including the user's motion speed, acceleration and direction change rate;

[0172] S30: Get the user's T n Acceleration data at each moment, according to T n The acceleration data at time T is selected n At the time positioning point, at and obtain T nThe user's motion characteristic data at the time of positioning;

[0173] S40: When the acceleration data is lower than the preset acceleration threshold, it is determined that the user has ended the motion state, and all positioning points in the motion state are read, including the T0 positioning point and the T n Anchor point;

[0174] S50: Correct and simulate all positioning points to obtain a simulated trajectory P;

[0175] S60: Perform similarity matching between the simulated trajectory P and the real map route, and determine the actual movement path of the user according to the matching result.

[0176] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Sy n chli n k) DRAM (SLDRAM), RAMbus direct RAM (RDRAM), RAMbus direct dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0177] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0178] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A low-power multi-positioning intelligent terminal positioning trajectory correction method, characterized in that: The low-power multi-positioning intelligent terminal positioning trajectory correction method comprises: S10: Continuously monitor the acceleration data of the user. When the acceleration data exceeds a preset acceleration threshold, determine that the user is in motion, define the time at this time as time T0, obtain the broadcast signal data at time T0 and package it into a data packet at time T0, wherein the broadcast signal data at time T0 includes the T0 Bluetooth RSSI, T0 WiFi RSSI and T0 4G base station information at time T0; S20: Perform preliminary positioning according to the data packet at time T0 to obtain a preliminary positioning result, obtain GIS data, perform calculations according to the preliminary positioning result and the GIS data to obtain the initialization positioning information at time T0, record the corresponding position of the user as the positioning point at time T0, and obtain the motion feature data of the user at the positioning point at time T0, wherein the motion feature data includes the motion speed, acceleration and direction change rate of the user; S30: Acquire the acceleration data of the user at time Tn, select a positioning point at time Tn according to the acceleration data at time Tn, and acquire the motion feature data of the user at the positioning point at time Tn; S40: when the acceleration data is lower than the preset acceleration threshold, determine that the user ends the motion state, and read all positioning points in the motion state, wherein the positioning points include the T0 positioning point and the Tn positioning point; S50: calibrating and simulating all the positioning points to obtain a simulated trajectory P; S60: performing similarity matching between the simulated trajectory P and the real map route, and determining the actual movement path of the user according to the matching result; The performing preliminary positioning according to the data packet at time T0, obtaining a preliminary positioning result, acquiring GIS data, and performing calculation according to the preliminary positioning result and the GIS data to obtain the initialization positioning information at time T0 specifically includes: S21: Acquire a wireless signal fingerprint identification library, match the data packet at time T0 with the wireless signal fingerprint identification library, and obtain a matching result, wherein the wireless signal fingerprint identification library includes historical Bluetooth RSSI, historical WiFi RSSI, and historical 4G base station information; S22: Calculate and obtain a preliminary positioning result of the user according to the matching result; S23: Acquire a building boundary, match the preliminary positioning result with the building boundary, and determine whether the user is inside the building; S24: when the user is inside the building, query the shortest distance data between the user and the building boundary to obtain the building boundary distance, and output the initial positioning position according to the building boundary distance and the preliminary positioning result; S25: when the user is outside the building, generating a GPS positioning auxiliary file according to the preliminary positioning result, performing GPS positioning according to the GPS positioning auxiliary file, and obtaining an initial positioning position; The selecting the positioning point at time Tn according to the acceleration data at time Tn specifically includes: S31: Calculate the moving distance of the user from time Tn-1 to time Tn according to the acceleration data at time Tn, record it as Tn moving distance, and obtain Tn moving distance threshold according to the acceleration data at time Tn; S32: When the Tn moving distance reaches the Tn moving distance threshold, the user is positioned and the user's position is recorded as the positioning point at time Tn.

2. The low-power multi-positioning intelligent terminal positioning trajectory correction method according to claim 1 is characterized in that: The calculation of the user's moving distance from the time Tn-1 to the time Tn according to the acceleration data at the time Tn, recorded as Tn moving distance, and the matching of the acceleration data at the time Tn to obtain the Tn moving distance threshold specifically include: S311: converting the acceleration data at time Tn into the number of steps of the user from time Tn-1 to time Tn by using a step counting algorithm, and determining the movement mode of the user at time Tn according to the acceleration data at time Tn; S312: Calculate the user's moving distance from time Tn-1 to time Tn using a step length estimation model according to the number of steps of the user from time Tn-1 to time Tn; S313: Matching a preset moving distance threshold of a corresponding motion mode according to the motion mode at time Tn, recorded as Tn moving distance threshold.

3. The low-power multi-positioning intelligent terminal positioning trajectory correction method according to claim 1 is characterized in that: When the Tn moving distance reaches the Tn moving distance threshold, positioning the user and recording the user's position as a positioning point at time Tn specifically includes: S321: when the Tn moving distance exceeds the Tn moving distance threshold, collect the broadcast signal data at Tn and pack it into a data packet at Tn, wherein the broadcast signal data at Tn includes Tn Bluetooth RSSI, Tn WiFi RSSI and Tn 4G base station information at Tn; S322: Distinguish and compare the Tn Bluetooth RSSI and the Tn WiFi RSSI in the Tn time data packet with the T0 Bluetooth RSSI and the T0 WiFi RSSI in the T0 time data packet to obtain a broadcast signal change value; S323: Obtain a preset broadcast signal change threshold and obtain GIS data at time Tn; S324: If the broadcast signal change value does not exceed the broadcast signal change threshold, the Tn time location point is equal to the T0 time location point; S325: When the broadcast signal change value exceeds the broadcast signal change threshold and the user is inside a building, the positioning information at time Tn is calculated according to the data packet at time Tn, the GIS data at time Tn and the triangulation method, and the corresponding position of the user is recorded as the positioning point at time Tn; S326: When the broadcast signal change value exceeds the broadcast signal change threshold and the user is outside the building, GPS positioning is performed according to the GPS positioning auxiliary file to obtain the positioning information at time Tn, and the corresponding position of the user is recorded as the positioning point at time Tn.

4. The low-power multi-positioning intelligent terminal positioning trajectory correction method according to claim 1 is characterized in that: The correction and simulation of all the positioning points to obtain the simulated trajectory P specifically includes: S51: performing data cleaning on the positioning information corresponding to all the positioning points to obtain positioning information correction data corresponding to each positioning point; S52: matching the user motion pattern corresponding to each positioning point according to the acceleration data corresponding to each positioning point, and setting the corresponding positioning trajectory offset parameter according to the user motion pattern; S53: Obtaining the GIS data of the positioning points corresponding to each of the positioning points; S54: smoothing all the positioning points according to the motion feature data corresponding to each of the positioning points, and simulating and generating an original positioning trajectory; S55: Correcting the original positioning trajectory according to the positioning point GIS data and the positioning trajectory offset parameter to obtain a first simulated positioning trajectory; S56: comparing the first simulated positioning trajectory with the positioning point, and calculating the coincidence rate between the two; S57: Outputting a simulation trajectory P according to the overlap ratio.

5. The low-power multi-positioning intelligent terminal positioning trajectory correction method according to claim 4 is characterized in that: Outputting the simulation trajectory P according to the overlap rate specifically includes: S571: when the overlap rate exceeds 80%, outputting the first simulated positioning trajectory as a simulated trajectory P; S572: When the overlap rate does not exceed 80%, repeat step S55 and step S56 N times until the overlap rate exceeds 80%, and then output the Nth simulated positioning trajectory as simulated trajectory P.

6. The low-power multi-positioning intelligent terminal positioning trajectory correction method according to claim 5 is characterized in that: The similarity matching of the simulated trajectory P with the real map route and determining the actual movement path of the user according to the matching result specifically includes: S61: Acquire a real map route and a road constraint, and evaluate the similarity between the simulated trajectory P and the real map route according to the road constraint; S62: when the similarity exceeds 95%, determining the simulated trajectory P as the actual motion path of the user; S63: When the similarity does not exceed 95%, repeat step S55, step S56 and step S57 M times until the similarity exceeds 95%, and then determine that the Mth simulated positioning trajectory is the actual movement path of the user.

7. A low-power multi-positioning intelligent terminal positioning trajectory correction system for executing the low-power multi-positioning intelligent terminal positioning trajectory correction method according to any one of claims 1 to 6, characterized in that: The low-power multi-positioning intelligent terminal positioning trajectory correction system comprises: The system integrates a 4G network module, a GPS module, a WiFi module, a Bluetooth beacon module, an acceleration sensor, a processor, a small-capacity battery, and a remote location resolution server; The 4G network module, the GPS module, the WiFi module, the Bluetooth beacon module, the acceleration sensor, the processor and the small-capacity battery are integrated on a main board through an integrated design, and the main board can be directly applied to smart wearable products; The output ends of the GPS module, the WiFi module, the Bluetooth beacon module, the acceleration sensor, and the small-capacity battery are electrically connected to the input end of the processor, and the output end of the processor is electrically connected to the input ends of the 4G module and the Bluetooth beacon module.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, the steps of the low-power multi-positioning intelligent terminal positioning trajectory correction method as described in any one of claims 1 to 6 are implemented.

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