Monitoring and early warning method and system based on satellite communication

Abstract

The invention discloses a monitoring and early warning method and system based on satellite communication. The method comprises the following steps: deploying a lightweight AI model at an edge node, and receiving and preprocessing multi-source sensor data such as videos, radars, hydrology, meteorology and the like in real time; performing triple judgment of range compliance, behavior compliance and time compliance in sequence, and adding corresponding marks for non-compliance data; performing composite judgment according to the mark combination, and determining first-level to third-level alarm types; key information is extracted to form an alarm abstract, the alarm abstract is transmitted to a cloud end through a satellite link, and original data remains edges; and the cloud end issues a processing instruction after manual rechecking. According to the invention, through a collaborative architecture of edge intelligence, satellite return and cloud recheck, high-efficiency, accurate and graded early warning of high-risk events in a wide-area scene is realized while low time delay and low bandwidth occupation are ensured.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring technology, and in particular relates to a monitoring and early warning method and system based on satellite communication. Background Technology

[0002] While the city's "Skynet" system has made some progress, some problems and shortcomings still exist in communication blind spots, as follows: Data transmission bottleneck: Remote areas rely on satellite communication, but the large volume of raw video / radar data and insufficient transmission bandwidth result in high latency.

[0003] High false alarm rate: Single sensor data analysis is easily affected by environmental interference and lacks a multi-source data collaborative verification mechanism.

[0004] Low processing efficiency: Centralized cloud processing of massive amounts of data results in slow response times, making it difficult to meet the needs of real-time early warning.

[0005] Compliance assessment is simplistic: Existing technologies only trigger alarms based on a single dimension, without combining complex logic such as behavior and time. Summary of the Invention

[0006] To address these issues, this invention provides a satellite communication-based monitoring and early warning method to solve the aforementioned problems.

[0007] In a first aspect, the present invention provides a monitoring and early warning method based on satellite communication, comprising: A lightweight AI model is deployed at the edge node to receive multi-source sensor data from the monitoring area in real time. The multi-source sensor data is then preprocessed, including video data, radar data, hydrological data, meteorological data, and geographic coordinates. The multi-source sensor data is subjected to range compliance judgment. If the range of the multi-source sensor data is non-compliant, a range non-compliance mark is added to the multi-source sensor data, and behavioral compliance judgment is performed on the multi-source sensor data; if the range of the multi-source sensor data is compliant, behavioral compliance judgment is performed on the multi-source sensor data. If the behavior of the multi-source sensor data is non-compliant, a non-compliant behavior flag is added to the multi-source sensor data, and the time compliance of the multi-source sensor data is judged; if the behavior of the multi-source sensor data is compliant, the time compliance of the multi-source sensor data is judged. If the time of the multi-source sensor data is non-compliant, a time non-compliance mark is added to the multi-source sensor data, and a composite judgment is made on the added mark to the multi-source sensor data to determine the specific alarm type; if the time of the multi-source sensor data is compliant, a composite judgment is made on the added mark to the multi-source sensor data to determine the specific alarm type. Key information from the multi-source sensor data is extracted and integrated to form an alarm summary, which is then transmitted to the cloud via satellite to trigger an alarm. The original data is stored at the edge node. Upon receiving an alarm, a manual review is conducted. Once the review is successful, a processing instruction is sent.

[0008] Secondly, the present invention provides a monitoring and early warning system based on satellite communication, comprising: The preprocessing module is configured to receive multi-source sensor data from the monitoring area in real time, and then preprocess the multi-source sensor data. The first judgment module is configured to perform a range compliance judgment on the multi-source sensor data; The second judgment module is configured to perform behavioral compliance judgment on the multi-source sensor data; The third judgment module is configured to perform time compliance judgment on the multi-source sensor data; The determination module is configured to perform a composite judgment on the tags added to the multi-source sensor data to determine the specific alarm type.

[0009] 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 the satellite communication-based monitoring and early warning method according to any embodiment of the present invention.

[0010] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the satellite communication-based monitoring and early warning method according to any embodiment of the present invention.

[0011] The satellite communication-based monitoring and early warning method and system of this application have the following specific advantages: 1) Bandwidth optimization: Only alarm summaries are transmitted, reducing satellite bandwidth usage; raw data is stored locally, supporting post-event retrieval and review. 2) Precise alerts: Three-level compliance judgment combined with labeling logic reduces false alarm rate; lightweight AI model verifies high-risk behaviors with 85% confidence, reducing false alarms; 3) High real-time performance: edge node preprocessing and judgment shorten response time; 4) System robustness: Automatic alignment of timestamps from multiple data sources ensures consistency in analysis; manual review mechanism further guarantees reliability. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. 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.

[0013] Figure 1 A flowchart illustrating a satellite communication-based monitoring and early warning method according to an embodiment of the present invention; Figure 2 The following is a structural block diagram of a satellite communication-based monitoring and early warning system according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0014] 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.

[0015] Please see Figure 1 The diagram shows a flowchart of a satellite communication-based monitoring and early warning method according to this application.

[0016] Step S1: Deploy a lightweight AI model on the edge node to receive multi-source sensor data from the monitoring area in real time, and then preprocess the multi-source sensor data, which includes video data, radar data, hydrological data, meteorological data, and geographic coordinates. In this step, deploying lightweight AI models on edge nodes enables resource-constrained edge devices (such as field monitoring stations) to run efficient inference models, while reducing computational load, power consumption, and adapting to areas without power grids; the preferred lightweight AI model is the pruned and optimized YOLOv7-tiny model. Multi-source sensor data includes infrared high-definition cameras, millimeter-wave radar, hydrological sensors, meteorological sensors, and a positioning module; video data is acquired through infrared high-definition cameras, including video streams and target thermal images; radar data is acquired through millimeter-wave radar, including moving target trajectories and target speeds; hydrological data is acquired through hydrological sensors, including water level, flow velocity, and water quality; meteorological data is acquired through meteorological sensors, including temperature, humidity, wind speed, and rainfall; and geographic coordinates are obtained through the positioning module. Specifically, the preprocessing of the multi-source sensor data includes: Using the edge node's own time as a reference, the multi-source sensor data are re-aligned so that the timestamps of the video data, radar data, hydrological data, meteorological data, and geographic data are on the same time axis.

[0017] Step S2: Perform range compliance judgment on the multi-source sensor data. If the range of the multi-source sensor data is not compliant, add a range non-compliance mark to the multi-source sensor data and perform behavior compliance judgment on the multi-source sensor data; if the range of the multi-source sensor data is compliant, perform behavior compliance judgment on the multi-source sensor data. In this step, the compliance of hydrological and meteorological data is assessed to check whether water levels, wind speeds, and temperatures exceed safety thresholds; and the compliance of video and radar data is assessed to check for any intrusion attempts. Specifically, the range compliance determination of the multi-source sensor data includes: The range of multi-source sensor data is compared with a preset threshold range to determine the size between the range of multi-source sensor data and the preset threshold range. If the range of multi-source sensor data is included by the preset threshold range, the range of multi-source sensor data is compliant; otherwise, the range of multi-source sensor data is non-compliant.

[0018] In one embodiment of the present invention, the present invention is used for hydrological monitoring. In the specific use process, the acquired hydrological data is preprocessed and compared with a preset hydrological threshold range. If the acquired hydrological data is not within the preset hydrological threshold range, the acquired hydrological data is non-compliant.

[0019] Specifically, the behavioral compliance assessment of the multi-source sensor data includes: The multi-source sensor data is used to infer the presence of high-risk behaviors through the lightweight AI model. If no high-risk behavior exists, the behavior of the multi-source sensor data is compliant; if high-risk behavior exists, a confidence score is calculated to determine whether the high-risk behavior detected by the lightweight AI model is credible. If the confidence level is greater than or equal to 85%, the high-risk behavior detected by the lightweight AI model is credible, and the multi-source sensor data does indeed contain high-risk behavior; if the confidence level is less than 85%, the high-risk behavior detected by the lightweight AI model is unreliable, and the multi-source sensor data is stored in the observation queue.

[0020] In one embodiment of the present invention, the present invention is used for intrusion detection. In the specific use process, the acquired video data and radar data are preprocessed and then sent to the YOLOv7-tiny model, which infers and judges whether there is high-risk behavior. If there is no high-risk behavior, then the video data and radar data behavior is compliant; If high-risk behavior is detected, a confidence level is calculated. If the confidence level is greater than or equal to 85%, the existence of high-risk behavior inferred by the YOLOv7-tiny model is credible, and the video data and radar data do indeed contain high-risk behavior. If the confidence level is less than 85%, the existence of high-risk behavior inferred by the YOLOv7-tiny model is unreliable, and the video data and radar data are stored in the observation queue.

[0021] In calculating confidence levels, the confidence threshold for high-risk behaviors is dynamically adjusted based on weather conditions: 85% for good weather (e.g., sunny days); 80% for moderate weather (e.g., cloudy days, light rain); 75% for poor weather (e.g., moderate rain, heavy rain, light smog); and 70% for severe weather (e.g., thunderstorms, heavy smog).

[0022] Step S3: If the behavior of the multi-source sensor data is non-compliant, add a non-compliant behavior mark to the multi-source sensor data and perform a time compliance judgment on the multi-source sensor data; if the behavior of the multi-source sensor data is compliant, perform a time compliance judgment on the multi-source sensor data. In this step, the time data is combined with video data to determine the time compliance and check whether the operation time is compliant. Specifically, the time compliance determination of the multi-source sensor data includes: The timestamps in the multi-source sensor data are compared with the time window of the operation work order to determine whether the timestamps are included in the time window of the operation work order. If the timestamps are included in the time window of the operation work order, the time of the multi-source sensor data is compliant; otherwise, the time of the multi-source sensor data is non-compliant.

[0023] In one embodiment of the present invention, the present invention is used for work supervision. In specific use, the acquired video data and timestamps are preprocessed and then sent to the YOLOv7-tiny model. The YOLOv7-tiny model infers the time when the person performs the task operation and compares it with the time window of the operation order. If both the operation time and the timestamp are within the time window of the operation order, the data time is compliant; if neither the operation time nor the timestamp is within the time window of the operation order, the data time is non-compliant.

[0024] Step S4: If the time of the multi-source sensor data is non-compliant, add a time non-compliance mark to the multi-source sensor data, and perform a composite judgment on the added mark to the multi-source sensor data to determine the specific alarm type; if the time of the multi-source sensor data is compliant, perform a composite judgment on the added mark to the multi-source sensor data to determine the specific alarm type. In this step, the type of alarm to be triggered is further determined based on the tags added to the multi-source sensor data; Specifically, if only one tag is added to the multi-source sensor data, the alarm type is a level one alarm; If only two tags are added to the multi-source sensor data, the alarm type is a level two alarm; If three tags are added to the multi-source sensor data simultaneously, the alarm type is a level three alarm.

[0025] Step S5: Extract and integrate key information from the multi-source sensor data to form an alarm summary, and transmit the alarm summary to the cloud via satellite to issue an alarm, while the original data is stored at the edge node; In this step, the alarm-related information needs to be simplified and integrated to form an alarm summary, which is then transmitted to the cloud via satellite to trigger the alarm. The original alarm-related information is stored on the edge node. In one embodiment of the present invention, the present invention is used for hydrological monitoring. When an area with an extremely high water level is detected, a non-compliant range marker is added after the range compliance is determined. The alarm type is a level one alarm, and key information is extracted to form an alarm summary. The alarm summary includes: Alarm type: Level 1 alarm; Alarm details: time, area, non-compliant markings, water level exceeding the limit, and the geographical location and coordinates of the area with the excessively high water level; Attachments: Images or video screenshots of areas with excessively high water levels, and links to the original data.

[0026] Step S6: After receiving the alarm, perform a manual review. Once the review is successful, send a processing instruction.

[0027] In this step, upon receiving an alarm, view the alarm summary, determine the accuracy of the alarm based on the alarm summary information, and send a processing command after confirming accuracy.

[0028] In summary, the method of this application relates to a monitoring and early warning method and system based on satellite communication. The method includes: deploying a lightweight AI model at an edge node to receive and preprocess multi-source sensor data such as video, radar, hydrology, and meteorology in real time; performing a triple judgment on range compliance, behavior compliance, and time compliance in sequence, adding corresponding tags to non-compliant data; performing a composite judgment based on the tag combination to determine the alarm type from level one to level three; extracting key information to form an alarm summary, transmitting it to the cloud via a satellite link, while the original data is retained at the edge; and issuing processing instructions after manual review at the cloud. This invention, through a collaborative architecture of "edge intelligence + satellite backhaul + cloud review," achieves efficient, accurate, and tiered early warning of high-risk events in wide-area scenarios while ensuring low latency and low bandwidth consumption.

[0029] Please see Figure 2 The diagram shows a structural block diagram of a satellite communication-based monitoring and early warning system according to this application.

[0030] like Figure 2 As shown, the module comprises a preprocessing module 200, a first judgment module 201, a second judgment module 202, a third judgment module 203, and a determination module 204.

[0031] The preprocessing module 200 is configured to receive multi-source sensor data from the monitoring area in real time and then preprocess the multi-source sensor data; the first judgment module 201 is configured to perform range compliance judgment on the multi-source sensor data; the second judgment module 202 is configured to perform behavior compliance judgment on the multi-source sensor data; the third judgment module 203 is configured to perform time compliance judgment on the multi-source sensor data; and the determination module 204 is configured to perform a composite judgment on the tags added to the multi-source sensor data to determine the specific alarm type.

[0032] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0033] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the satellite communication-based monitoring and early warning method in any of the above method embodiments; In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows: A lightweight AI model is deployed at the edge node to receive multi-source sensor data from the monitoring area in real time. The multi-source sensor data is then preprocessed, including video data, radar data, hydrological data, meteorological data, and geographic coordinates. The multi-source sensor data is subjected to range compliance judgment. If the range of the multi-source sensor data is non-compliant, a range non-compliance mark is added to the multi-source sensor data, and behavioral compliance judgment is performed on the multi-source sensor data; if the range of the multi-source sensor data is compliant, behavioral compliance judgment is performed on the multi-source sensor data. If the behavior of the multi-source sensor data is non-compliant, a non-compliant behavior flag is added to the multi-source sensor data, and the time compliance of the multi-source sensor data is judged; if the behavior of the multi-source sensor data is compliant, the time compliance of the multi-source sensor data is judged. If the time of the multi-source sensor data is non-compliant, a time non-compliance mark is added to the multi-source sensor data, and a composite judgment is made on the added mark to the multi-source sensor data to determine the specific alarm type; if the time of the multi-source sensor data is compliant, a composite judgment is made on the added mark to the multi-source sensor data to determine the specific alarm type. Key information from the multi-source sensor data is extracted and integrated to form an alarm summary, which is then transmitted to the cloud via satellite to trigger an alarm. The original data is stored at the edge node. Upon receiving an alarm, a manual review is conducted. Once the review is successful, a processing instruction is sent.

[0034] Computer-readable storage media may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required for at least one function; the data storage area may store data created based on the use of the satellite communication-based monitoring and early warning system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely located relative to a processor, which can be connected to the satellite communication-based monitoring and early warning system 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.

[0035] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby realizing the satellite communication-based monitoring and early warning method described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the satellite communication-based monitoring and early warning system. The output device 340 may include a display screen or other display device.

[0036] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0037] In one implementation, the above-described electronic device is applied to a satellite communication-based monitoring and early warning system for a client, 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: A lightweight AI model is deployed at the edge node to receive multi-source sensor data from the monitoring area in real time. The multi-source sensor data is then preprocessed, including video data, radar data, hydrological data, meteorological data, and geographic coordinates. The multi-source sensor data is subjected to range compliance judgment. If the range of the multi-source sensor data is non-compliant, a range non-compliance mark is added to the multi-source sensor data, and behavioral compliance judgment is performed on the multi-source sensor data; if the range of the multi-source sensor data is compliant, behavioral compliance judgment is performed on the multi-source sensor data. If the behavior of the multi-source sensor data is non-compliant, a non-compliant behavior flag is added to the multi-source sensor data, and the time compliance of the multi-source sensor data is judged; if the behavior of the multi-source sensor data is compliant, the time compliance of the multi-source sensor data is judged. If the time of the multi-source sensor data is non-compliant, a time non-compliance mark is added to the multi-source sensor data, and a composite judgment is made on the added mark to the multi-source sensor data to determine the specific alarm type; if the time of the multi-source sensor data is compliant, a composite judgment is made on the added mark to the multi-source sensor data to determine the specific alarm type. Key information from the multi-source sensor data is extracted and integrated to form an alarm summary, which is then transmitted to the cloud via satellite to trigger an alarm. The original data is stored at the edge node. Upon receiving an alarm, a manual review is conducted. Once the review is successful, a processing instruction is sent.

[0038] 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., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0039] 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 monitoring and early warning method based on satellite communication, characterized in that, include: A lightweight AI model is deployed at the edge node to receive multi-source sensor data from the monitoring area in real time. The multi-source sensor data is then preprocessed, including video data, radar data, hydrological data, meteorological data, and geographic coordinates. The multi-source sensor data is subjected to range compliance judgment. If the range of the multi-source sensor data is non-compliant, a range non-compliance mark is added to the multi-source sensor data, and behavioral compliance judgment is performed on the multi-source sensor data; if the range of the multi-source sensor data is compliant, behavioral compliance judgment is performed on the multi-source sensor data. If the behavior of the multi-source sensor data is non-compliant, a non-compliant behavior flag is added to the multi-source sensor data, and the time compliance of the multi-source sensor data is judged; if the behavior of the multi-source sensor data is compliant, the time compliance of the multi-source sensor data is judged. If the time of the multi-source sensor data is non-compliant, a time non-compliance mark is added to the multi-source sensor data, and a composite judgment is made on the added mark to the multi-source sensor data to determine the specific alarm type; if the time of the multi-source sensor data is compliant, a composite judgment is made on the added mark to the multi-source sensor data to determine the specific alarm type. Key information from the multi-source sensor data is extracted and integrated to form an alarm summary, which is then transmitted to the cloud via satellite to trigger an alarm. The original data is stored at the edge node. Upon receiving an alarm, a manual review is conducted. Once the review is successful, a processing instruction is sent.

2. The monitoring and early warning method and system based on satellite communication according to claim 1, characterized in that, Preprocessing the multi-source sensor data includes: Using the edge node's own time as a reference, the multi-source sensor data are re-aligned so that the timestamps of the video data, radar data, hydrological data, meteorological data, and geographic data are on the same time axis.

3. The monitoring and early warning method and system based on satellite communication according to claim 1, characterized in that, The range compliance determination of the multi-source sensor data includes: The range of multi-source sensor data is compared with a preset threshold range to determine the size between the range of multi-source sensor data and the preset threshold range. If the range of multi-source sensor data is included by the preset threshold range, the range of multi-source sensor data is compliant; otherwise, the range of multi-source sensor data is non-compliant.

4. The monitoring and early warning method and system based on satellite communication according to claim 1, characterized in that, The behavioral compliance assessment of the multi-source sensor data includes: The multi-source sensor data is used to infer the presence of high-risk behaviors through the lightweight AI model. If no high-risk behavior exists, the behavior of the multi-source sensor data is compliant; if high-risk behavior exists, a confidence score is calculated to determine whether the high-risk behavior detected by the lightweight AI model is credible. If the confidence level is greater than or equal to 85%, the high-risk behavior detected by the lightweight AI model is credible, and the multi-source sensor data does indeed contain high-risk behavior; if the confidence level is less than 85%, the high-risk behavior detected by the lightweight AI model is unreliable, and the multi-source sensor data is stored in the observation queue.

5. The monitoring and early warning method and system based on satellite communication according to claim 1, characterized in that, The time compliance determination of the multi-source sensor data includes: The timestamps in the multi-source sensor data are compared with the time window of the operation work order to determine whether the timestamps are included in the time window of the operation work order. If the timestamps are included in the time window of the operation work order, the time of the multi-source sensor data is compliant; otherwise, the time of the multi-source sensor data is non-compliant.

6. The monitoring and early warning method and system based on satellite communication according to claim 1, characterized in that, The composite judgment of the tags added to the multi-source sensor data includes: If only one tag is added to the multi-source sensor data, the alarm type is a level one alarm; If only two tags are added to the multi-source sensor data, the alarm type is a level two alarm; If three tags are added to the multi-source sensor data simultaneously, the alarm type is a level three alarm.

7. A monitoring and early warning system based on satellite communication, characterized in that, include: The preprocessing module is configured to receive multi-source sensor data from the monitoring area in real time, and then preprocess the multi-source sensor data. The first judgment module is configured to perform a range compliance judgment on the multi-source sensor data; The second judgment module is configured to perform behavioral compliance judgment on the multi-source sensor data; The third judgment module is configured to perform time compliance judgment on the multi-source sensor data; The determination module is configured to perform a composite judgment on the tags added to the multi-source sensor data to determine the specific alarm type.

8. An electronic device, characterized in that, include: 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 method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the method described in any one of claims 1 to 6.

Images