Positioning method for mobile device, electronic device and storage medium

Abstract

The embodiment of the present application provides a positioning method for a mobile device. The method comprises the following steps: obtaining a historical positioning point and sensor data of the mobile device at a previous moment; and performing pre-positioning on the mobile device based on the historical positioning point and the sensor data. The embodiment of the present application stores the historical positioning point when the mobile device is in sleep mode or is powered off, and automatically and quickly performs pre-positioning on the mobile device by using the stored historical positioning point and the sensor data obtained during the period from the sleep moment to the wake-up moment when the mobile device is woken up, so that the clean unmanned equipment has low requirements on hardware, does not need strong computing and communication capabilities, the accuracy and efficiency of positioning of the mobile device when the mobile device is woken up are improved, and the waiting time of a user is reduced.

Description

Technical Field

[0001] This invention relates to the field of positioning information technology, and more particularly to a positioning method, electronic device, and storage medium for mobile devices. Background Technology

[0002] After startup, autonomous driving equipment requires manual loading of map data by the user. Following map loading, the equipment needs to locate itself using sensor data. For example, an autonomous cleaning vehicle needs a map of that area to clean it and can then follow the designated route. When cleaning in a shopping mall, a map of the mall is required; similarly, a map of the corresponding area is needed when cleaning in an office park. After map loading, the autonomous vehicle's location is determined, and it then executes the task based on the pre-defined path.

[0003] In the process of realizing this invention, the inventors discovered at least the following problems in the related technology:

[0004] Because clean-type autonomous driving devices do not have high hardware requirements and do not need strong computing and communication capabilities, if the scenes in the maps stored by the autonomous driving device are relatively simple or have many repetitive scenes, when matching and locating the image data collected by the autonomous driving device with the map of the simple or repetitive scenes, multiple areas in the map may match the collected image data at the same time (for example, in some unmarked underground parking lots, the image data collected by the autonomous driving device only shows pillars and parking spaces, but multiple pillars and parking spaces in the same location can be matched in the map of the underground parking lot, but it is still impossible to determine the location of the autonomous driving device in the underground parking lot). Matching multiple identical / similar scenes will also consume a lot of the autonomous driving device's computing power, which may lead to very long positioning time or even positioning failure, thus making it impossible to perform the corresponding tasks. Summary of the Invention

[0005] To at least address the problems of slow mapping and positioning in existing technologies for autonomous driving devices, and positioning failures when the autonomous driving device is in a simple environment or has many repetitive scenes, the present invention provides a positioning method for a mobile device, comprising:

[0006] Obtain the historical location points and sensor data of the mobile device at the previous moment;

[0007] Based on the historical location points and the sensor data, the mobile device is pre-positioned.

[0008] In a second aspect, embodiments of the present invention provide a positioning execution device for a mobile device, comprising:

[0009] The positioning point acquisition module is used to acquire the historical positioning point and sensor data of the mobile device at the previous moment;

[0010] The positioning module is used to pre-position the mobile device based on the historical positioning points and the sensor data.

[0011] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of a positioning method for a mobile device according to any embodiment of the present invention.

[0012] Fourthly, embodiments of the present invention provide a mobile device, including a main body and an electronic device according to any embodiment of the present invention mounted on the main body.

[0013] Fifthly, embodiments of the present invention provide a storage medium storing a computer program thereon, characterized in that, when the program is executed by a processor, it implements the steps of a positioning method for a mobile device according to any embodiment of the present invention.

[0014] Sixthly, embodiments of the present invention also provide a computer program product that, when run on a computer, causes the computer to execute the positioning method for a mobile device as described in any one of the embodiments of the present invention.

[0015] The beneficial effects of this invention are as follows: when the mobile device is in sleep mode, it stores historical location points. When the mobile device is woken up, it automatically and quickly pre-positions the mobile device using the stored historical location points and sensor data acquired between the sleep time and the wake-up time. This improves the accuracy and efficiency of positioning when the mobile device is woken up, which is a type of mobile device that does not have high hardware requirements and does not require strong computing and communication capabilities, and reduces user waiting time. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart of a positioning method for a mobile device provided in an embodiment of the present invention;

[0018] Figure 2This is an overall positioning flowchart of a positioning method for a mobile device provided in an embodiment of the present invention;

[0019] Figure 3 This is a flowchart of a power-on / wake-up map loading method for a positioning method of a mobile device provided in an embodiment of the present invention;

[0020] Figure 4 This is a schematic diagram of the structure of an execution device for blind zone memory provided in an embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of an embodiment of an electronic device for positioning a mobile device, provided by an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Those skilled in the art will recognize that embodiments of this application can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0024] For ease of understanding, the technical terms used in this application are explained below:

[0025] The term "mobile device" as used in this application includes, but is not limited to, vehicles with the six levels of autonomous driving technology (L0-L5) as defined by the Society of Automotive Engineers International (SAE International) or the Chinese national standard "Classification of Driving Automation for Automobiles".

[0026] In some embodiments, the mobile device may be a vehicle or robot having various functions, such as:

[0027] (1) Passenger transport function, such as family cars, buses, etc.;

[0028] (2) Cargo carrying function, such as ordinary trucks, box trucks, trailers, enclosed trucks, tank trucks, flatbed trucks, container trucks, dump trucks, special structure trucks, etc.

[0029] (3) Tool functions, such as logistics delivery vehicles, automated guided vehicles (AGVs), patrol vehicles, cranes, hoists, excavators, bulldozers, loaders, road rollers, loaders, off-road engineering vehicles, armored engineering vehicles, sewage treatment vehicles, sanitation vehicles, vacuum trucks, floor scrubbers, water sprinkler trucks, sweeping robots, food delivery robots, shopping guide robots, lawnmowers, golf carts, etc.

[0030] (4) Entertainment functions, such as recreational vehicles, amusement park automatic driving devices, balance bikes, etc.;

[0031] (5) Special rescue functions, such as fire trucks, ambulances, power repair vehicles, and engineering emergency rescue vehicles.

[0032] like Figure 1 The diagram shows a flowchart of a positioning method for a mobile device according to an embodiment of the present invention, which includes the following steps:

[0033] S11: Obtain the historical location point and sensor data of the mobile device at the previous moment;

[0034] S12: Based on the historical positioning points and the sensor data, pre-position the mobile device.

[0035] In this embodiment, the inventors proposed a positioning method for mobile devices to overcome the discovered technical problems, which can be applied to various mobile devices.

[0036] The mobile device can be an unmanned vehicle, such as an unmanned sweeper, unmanned floor scrubber, unmanned logistics vehicle, or unmanned taxi, or even a robot, etc., and this invention is not limited thereto. The following explanation uses an unmanned floor scrubber as an example. In a shopping mall, a certain number of unmanned floor scrubbers can be deployed to clean the mall (or other areas). The unmanned floor scrubbers will clean according to a preset path. After completing the task, the unmanned floor scrubber will return to its standby location to await the next task. When not performing a task and in sleep mode, only the main unit is in sleep mode; other sensors are not in deep sleep mode. Users can also move the unmanned floor scrubber to other locations as needed. Before the unmanned floor scrubber goes into sleep mode or is powered off, its location on the current map is recorded. This location can be stored on local storage or in the cloud for future use.

[0037] For step S11, during positioning, the historical positioning and sensor data of the unmanned floor scrubber at the previous moment are obtained. Specifically, for example, the previous moment could be when the unmanned floor scrubber was in sleep mode or when it was turned off.

[0038] Correspondingly, if the unmanned floor scrubber is woken up from its sleep state, the historical location of the unmanned floor scrubber during its sleep state, as well as the sensor data of the unmanned floor scrubber during the process from sleep to wake-up, are obtained; if the unmanned floor scrubber is turned on from its off state, the historical location of the unmanned floor scrubber during its off state, as well as the sensor data of the unmanned floor scrubber during the process from off to on, are obtained.

[0039] Corresponding to step S12, positioning is performed based on the data determined in step S11. If only historical positioning points are used, it is impossible to determine the specific location of the unmanned floor cleaning vehicle after it is activated (for example, in a shopping mall environment, even if GPS real-time positioning is used, it is difficult to determine which floor the unmanned floor cleaning vehicle is on after powering on and performing positioning). The unmanned floor cleaning vehicle needs to be able to intelligently move between floors to clean the floor, and being able to identify the floor is also very important.

[0040] As one implementation, the pre-positioning of the mobile device based on the historical location points and the sensor data includes:

[0041] When the previous moment is the sleep time of the mobile device, pre-positioning is performed based on the historical positioning point at the sleep time, the motion data acquired by the first sensor during the period from the sleep time to the wake-up time, and the external environment data sensed by the second sensor at the wake-up time;

[0042] When the previous time is the time when the mobile device is powered off, pre-positioning is performed based on the historical positioning point of the mobile device at the time of power-off and the external environment data sensed by the second sensor when the mobile device is powered on.

[0043] In this embodiment, when the unmanned floor scrubber is awakened from its sleep state, in response to its own awakening, the unmanned floor scrubber will automatically retrieve the specific coordinates of its last historical location from local storage or the cloud. During the sleep process, only the main unit of the unmanned floor scrubber is in sleep mode, while other sensors associated with the unmanned floor scrubber will not be in deep sleep and will continue to record data in real time. For example, the sensors on the wheels (i.e., the first sensors) will record the rolling data of the wheels; the camera (the second sensor) can collect images of the surrounding environment.

[0044] If the unmanned floor scrubber is powered on by the user from a powered-off state, its historical location points before being powered off are automatically acquired. Since it is powered off, only the historical location points at the time of power-off and the external environment data sensed by the camera (second sensor) at the time of power-on are available. The pre-positioning of the unmanned floor scrubber is determined by using these historical location points and the external environment data sensed at power-on. The pre-positioning steps for using historical location points and external environment data are not detailed here; the overall steps described above are as follows: Figure 2 As shown.

[0045] As one implementation, the travel trajectory of the mobile device during the period from the sleep time to the wake-up time is estimated based on the moving speed in the motion data, the number of turns during the period from the sleep time to the wake-up time, and the turning angle corresponding to the number of turns.

[0046] The current location of the mobile device is obtained based on the historical location points and the travel trajectory of the mobile device during the period from the sleep time to the wake-up time.

[0047] Pre-positioning is performed based on the current location and the external environment data. For example, when the unmanned floor cleaning vehicle is performing a task, it stores a map of the shopping mall. This map helps determine the vehicle's location based on historical location points and sensor data acquired by the vehicle's sensors between sleep and wake-up times. This allows the pre-positioning of the unmanned floor cleaning vehicle to be determined solely through sensor data and historical location points, without using real-time GPS positioning. This solves the problems of slow positioning and positioning failures after the unmanned floor cleaning vehicle has been powered on, gone from sleep, and woken up.

[0048] Specifically, sensor data includes motion data and perceived external environmental data. For example, when an unmanned floor scrubber is pushed away by a worker while in a dormant state, its position changes. During this process, wheel speed sensors can determine its motion data, such as speed or a combination of speed and steering angle, to calculate the trajectory of the mobile device from the dormant state to the awakening state. Thus, the current location of the unmanned floor scrubber after being awakened can be estimated using historical location points recorded during dormancy and the trajectory collected during the dormant-to-awakening period. (Of course, if the unmanned floor scrubber was not moved during dormancy, there is no motion data, and the historical location point is temporarily estimated as the current location point).

[0049] The current location of the unmanned floor scrubber has been estimated. To determine a more specific and accurate location, the external environment data perceived by the unmanned floor scrubber at the moment of wake-up is needed. For example, an existing shopping mall map can already indicate the approximate location of the unmanned floor scrubber. However, to determine the specific floor or, more precisely, the exact coordinates, further sensor data is required. Shopping mall environments often contain a large number of different stores. The external environment data perceived at the moment of wake-up, such as image data (which may contain mall landmarks, store signs, and floor information), and the shopping mall map storing the specific locations of these landmarks and store signs, can be used to determine the location.

[0050] By estimating the current location of the unmanned floor scrubbing vehicle on the mall map, nearby visible landmarks or signs can be identified. This information, combined with external environmental data collected at the wake-up time, allows for a rough and accurate determination of the vehicle's orientation and distance from these landmarks or signs. If the external environmental data collected at the wake-up time matches any of the nearby visible landmarks or signs, this further confirms the accuracy of the pre-positioning. For example, by estimating the current location and combining it with the orientation and distance determined from external environmental data, the precise location of the unmanned floor scrubbing vehicle can be determined.

[0051] As can be seen from this implementation, the mobile device stores historical location points when it is in sleep or powered off state. When the mobile device is woken up, it automatically and quickly pre-positions the mobile device using the stored historical location points and sensor data acquired between the sleep time and the wake-up time. This improves the accuracy and efficiency of positioning when the mobile device is woken up, which is a type of mobile device that does not have high hardware requirements and does not require strong computing and communication capabilities, and reduces user waiting time.

[0052] In one implementation, when the mobile device is woken up from sleep mode or powered on, it checks whether there is an available map. If there is a usable map file, it selects the most recently used map file.

[0053] The current location point is projected onto the map file for use in locating the mobile device.

[0054] In this embodiment, such as Figure 3 As shown, when the vehicle is powered on / wakes from sleep, it checks whether there is a map for cleaning tasks inside the vehicle. If so, it automatically loads the most recently used map. This allows for efficient use of the current location point to be projected onto the map file during positioning, thus completing the positioning of the mobile device.

[0055] As can be seen from this implementation method, during the vehicle startup process, the vehicle can preload the default map or the last used map, saving the user the process of manually loading the map and reducing the time spent on preparation work before startup.

[0056] Furthermore, the pre-positioning based on the current location point and the external environment data includes:

[0057] The reference image data projected from the current positioning point onto the map file is matched with the actual image data of the external environment data sensed by the previous positioning point. If they match, the pre-positioning is successful; otherwise, the pre-positioning fails.

[0058] When the pre-positioning is successful, the positioning point obtained after the pre-positioning is used as the current positioning point of the mobile device;

[0059] When pre-positioning fails, the current laser point cloud data is further obtained as auxiliary data based on the auxiliary positioning command input by the user, and the current positioning point of the mobile device is finally determined.

[0060] In this embodiment, the reference image data projected from the current positioning point onto the map file is matched with the actual image data of the external environment data sensed by the previous positioning point. If they match, the pre-positioning is successful; otherwise, the pre-positioning fails.

[0061] When pre-positioning fails, the current laser point cloud data is further obtained as auxiliary data based on the auxiliary positioning command input by the user, and the current positioning point of the mobile device is finally determined.

[0062] In this implementation, it's possible that the unmanned floor scrubber might be moved after being turned off, resulting in the loss of operational data. In such cases, due to missing or inaccurate data, it's impossible to successfully determine the unmanned floor scrubber's location (e.g., the external environment data collected at the time of wake-up doesn't match visible landmarks or signs in the vicinity, or historical location points are lacking). This necessitates providing users with an input method for the external environment, specifically, laser point cloud data. This allows users to manually locate themselves, utilizing more accurate and perceptive information about the surrounding objects, and employing high-precision laser point cloud data to assist in determining the unmanned floor scrubber's current location.

[0063] As can be seen from this implementation method, when unexpected situations prevent successful automatic positioning, a manual positioning method is provided for the user, thus ensuring the stability of mobile device positioning.

[0064] like Figure 4 The diagram shows a structural schematic of a positioning execution device for a mobile device according to an embodiment of the present invention. The execution device can execute the positioning method for a mobile device described in any of the above embodiments and is configured in a terminal.

[0065] This embodiment provides a positioning execution device 10 for a mobile device, which includes a positioning point acquisition module 11 and a positioning module 12.

[0066] The positioning point acquisition module 11 is used to acquire the historical positioning point and sensor data of the mobile device at the previous moment; the positioning module 12 is used to pre-position the mobile device based on the historical positioning point and the sensor data.

[0067] Furthermore, the positioning module is used for:

[0068] When the previous moment is the sleep time of the mobile device, pre-positioning is performed based on the historical positioning point at the sleep time, the motion data acquired by the first sensor during the period from the sleep time to the wake-up time, and the external environment data sensed by the second sensor at the wake-up time;

[0069] When the previous time is the time when the mobile device is powered off, pre-positioning is performed based on the historical positioning point of the mobile device at the time of power-off and the external environment data sensed by the second sensor when the mobile device is powered on.

[0070] It should be noted that those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention. In the above embodiments, the descriptions of each embodiment have different focuses, and for parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0071] As one implementation method, this invention also provides a mobile device, including a main body and an electronic device according to any of the foregoing embodiments mounted on the main body. The mobile device can be an unmanned vehicle, such as an unmanned sweeper, unmanned floor scrubber, unmanned logistics vehicle, unmanned passenger car, unmanned sanitation vehicle, unmanned minibus / bus, truck, mining truck, etc., or it can be a robot, etc.

[0072] In some embodiments, the present invention also provides a computer program product that, when run on a computer, causes the computer to execute the positioning method for a mobile device as described in any one of the embodiments of the present invention.

[0073] This invention also provides a non-volatile computer storage medium storing computer-executable instructions that can execute the positioning method for a mobile device in any of the above method embodiments.

[0074] In one embodiment, the non-volatile computer storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0075] Obtain the historical location points and sensor data of the mobile device at the previous moment;

[0076] Based on the historical location points and the sensor data, the mobile device is pre-positioned.

[0077] As a non-volatile computer-readable storage medium, it can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as program instructions / modules corresponding to the methods in the embodiments of the present invention. One or more program instructions are stored in the non-volatile computer-readable storage medium, and when executed by a processor, perform the positioning method for a mobile device in any of the above method embodiments.

[0078] Figure 5 This is a schematic diagram of the hardware structure of an electronic device for a positioning method for a mobile device provided in another embodiment of this application, as shown below. Figure 5 As shown, the device includes:

[0079] One or more processors 510 and memory 520, Figure 5 Taking a processor 510 as an example, the device for the positioning method of a mobile device may further include an input device 530 and an output device 540.

[0080] The processor 510, memory 520, input device 530, and output device 540 can be connected via a bus or other means. Figure 5 Taking the example of a connection between China and Israel via a bus.

[0081] The memory 520, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the positioning method for a mobile device in the embodiments of this application. The processor 510 executes various functional applications and data processing of the server by running the non-volatile software programs, instructions, and modules stored in the memory 520, thereby implementing the positioning method for a mobile device described in the above embodiments.

[0082] The memory 520 may include a program storage area and a data storage area, wherein the program storage area may store the operating system and applications required for at least one function; the data storage area may store data, etc. Furthermore, the memory 520 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 520 may optionally include memory remotely located relative to the processor 510, and these remote memories may be connected to the mobile device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0083] Input device 530 can receive input numerical or character information. Output device 540 may include display devices such as a display screen.

[0084] The one or more modules are stored in the memory 520, and when executed by the one or more processors 510, they perform the positioning method for a mobile device in any of the above method embodiments.

[0085] The above-described product can perform the methods provided in the embodiments of this application, and has the corresponding functional modules and beneficial effects for performing the methods. Technical details not described in detail in this embodiment can be found in the methods provided in the embodiments of this application.

[0086] Non-volatile computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the device, etc. Furthermore, the non-volatile computer-readable storage medium may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the non-volatile computer-readable storage medium may optionally include memory remotely located relative to the processor, and these remote memories may be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0087] This invention also provides an electronic device comprising: at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the positioning method for a mobile device according to any embodiment of this invention.

[0088] The electronic devices described in this application exist in various forms, including but not limited to:

[0089] (1) Mobile communication devices: These devices are characterized by their mobile communication capabilities and primarily aim to provide voice and data communication. These terminals include smartphones, multimedia phones, feature phones, and low-end phones.

[0090] (2) Ultra-mobile personal computer devices: These devices fall under the category of personal computers, possessing computing and processing capabilities, and generally also have mobile internet access features. These terminals include PDAs, MIDs, and UMPCs, such as tablet computers.

[0091] (3) Portable entertainment devices: These devices can display and play multimedia content. This category includes audio and video players, handheld game consoles, e-book readers, as well as smart toys and portable car navigation devices.

[0092] (4) Other airborne electronic devices with data interaction capabilities, such as vehicle-mounted systems installed on vehicles.

[0093] In this document, the terms "comprising" or "including" include not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0094] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0095] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A positioning method for a mobile device, comprising: Before the mobile device goes into sleep mode or is powered off, the location point on the current map is recorded. When the host device is in sleep mode and not performing any tasks, the sensors do not go into sleep mode. When the mobile device is woken up from sleep mode or powered on, it checks whether there is an available map. If there is a usable map file, it selects the most recently used map file, projects the positioning point onto the map file, and obtains the historical positioning point and sensor data of the mobile device at the previous moment. Based on the historical location points and the sensor data, the mobile device is pre-positioned. When the previous time is the time when the mobile device is in sleep mode, the pre-positioning is performed based on the historical location points at the time of sleep mode, the motion data acquired by the non-sleep first sensor during the period from the time of sleep mode to the time of wake-up, and the external environment data sensed by the second sensor when the device is woken up. When the previous time is the time when the mobile device is powered off, the pre-positioning is performed based on the historical location points at the time when the mobile device is powered off and the external environment data sensed by the second sensor when the mobile device is powered on, so as to obtain the current location point of the mobile device. The reference image data projected onto the map file from the current location point obtained after pre-positioning is matched with the actual image data of the external environment data perceived by the current location point. If a match is found, the pre-positioning is successful. When the pre-positioning is successful, the location point obtained after pre-positioning is used as the location point of the mobile device.

2. The method according to claim 1, characterized in that, The current location point of the mobile device is obtained; Based on the movement speed in the motion data and the number of turns and the turning angles corresponding to the number of turns during the period from the dormant time to the awake time, the travel trajectory of the mobile device during the period from the dormant time to the awake time is estimated; The current location of the mobile device is obtained based on the historical location points and the travel trajectory of the mobile device during the period from the sleep time to the wake-up time.

3. The method according to claim 1, characterized in that, The method includes: If there is a mismatch, the pre-positioning fails. When the pre-positioning fails, the current laser point cloud data is further obtained as auxiliary data according to the auxiliary positioning command input by the user, and the positioning point of the mobile device is finally determined.

4. A positioning execution device for a mobile device, comprising: The location point acquisition module records the location points on the current map before the mobile device goes into sleep mode or is powered off. When the device is in sleep mode without performing any tasks, the host sleep sensor does not go into sleep mode. When the mobile device is woken up from sleep mode or powered on, it checks if there is an available map. If there is an available map file, it selects the most recently used map file and projects the location points onto the map file. It acquires the historical location points and sensor data of the mobile device at the previous moment. Based on the historical location points and sensor data, it performs pre-positioning of the mobile device. When the previous moment is the time when the mobile device is in sleep mode, it performs pre-positioning based on the historical location points at the time of sleep mode, the motion data acquired by the first sensor that is not in sleep mode during the period from the time of sleep mode to the time of wake-up, and the external environment data sensed by the second sensor when the device is woken up. When the previous moment is the time when the mobile device is powered off, it performs pre-positioning based on the historical location points at the time when the mobile device is powered off and the external environment data sensed by the second sensor when the mobile device is powered on, and obtains the current location point of the mobile device. The positioning module is used to match the reference image data projected onto the map file by the current positioning point obtained after pre-positioning with the actual image data of the external environment data perceived by the current positioning point. If a match is found, the pre-positioning is successful. When the pre-positioning is successful, the positioning point obtained after pre-positioning is used as the positioning point of the mobile device.

5. An electronic device, comprising: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the method according to any one of claims 1-3.

6. A mobile device comprising a body and an electronic device according to claim 5 mounted on the body.

7. A storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method described in any one of claims 1-3.

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