Riding fall early warning method and apparatus, and device and storage medium

Abstract

The present application relates to the technical field of early warning monitoring. Disclosed are a riding fall early warning method and apparatus, and a device and a storage medium. The method comprises: performing riding monitoring on a target rider by means of a GNSS positioning module, so as to obtain a first riding speed, and performing riding monitoring on the target rider, so as to obtain a second riding speed and acceleration data; when the communication state of the GNSS positioning module is normal, monitoring the first riding speed in real time; when the acceleration data is within a first threshold range, performing threshold analysis on the first riding speed, and performing riding posture analysis on the target rider to obtain riding posture data; when the first riding speed is within a preset second threshold range and the riding posture data indicates an abnormal riding posture, playing an alarm prompt tone to the target rider by means of a loudspeaker module, and collecting operation data of the target rider in real time; and when the operation data of the target rider is not collected, sending help seeking data to a remote server, and sending the help seeking data to a plurality of riders in a target area.

Description

Method, device and equipment for early warning of falling during cycling and storage medium TECHNICAL FIELD

[0001] The present application relates to the field of early warning, in particular to a method, device and equipment for early warning of falling during cycling and storage medium. BACKGROUND

[0002] In recent years, cycling has gradually become popular, and the number of accidents caused by falling has also increased year by year. After falling, the body movement of the rider may be hindered or even lost consciousness, and the rider cannot contact the rescue by himself, which may result in missing the best rescue time. Some cycling equipment (motorcycle, helmet, etc.) can monitor the impact through an acceleration sensor to determine whether the rider has fallen, and send a help-seeking message with location information through a mobile terminal.

[0003] In the prior art, only an acceleration sensor is used to determine whether the rider has fallen, and the judgment condition is single. When a rider falls during team cycling, the other riders in the team cannot be timely warned, and the help-seeking message can only be sent through a mobile terminal. If the mobile terminal is disconnected, the help-seeking message cannot be normally sent. SUMMARY

[0004] The present application provides a method, device and equipment for early warning of falling during cycling and storage medium, which can improve the timeliness and accuracy of early warning of falling during cycling.

[0005] In a first aspect, the present application provides a method for early warning of falling during cycling, which comprises: S1, monitoring the cycling of a target rider at a preset frequency through a GNSS positioning module to obtain a first cycling speed, and monitoring the cycling of the target rider at the preset frequency through a six-axis inertial measurement unit module to obtain a second cycling speed and acceleration data;

[0006] S2, monitoring the communication state of the GNSS positioning module in real time, and monitoring the first cycling speed in real time when the communication state of the GNSS positioning module is normal;

[0007] S3, analyzing the acceleration data, extracting the acceleration data corresponding to the first cycling speed, and performing step S4 when the acceleration data meets a preset first threshold interval, and repeating steps S1-S3 when the acceleration data does not meet the preset first threshold interval;

[0008] S4, performing threshold analysis on the first riding speed in a preset time zone, performing riding posture analysis on the target rider to obtain riding posture data, playing an alarm prompt sound to the target rider through a speaker module when the first riding speed meets a preset second threshold interval and the riding posture data is an abnormal riding posture, and collecting operation data of the target rider in real time; and repeating steps S1 to S4 when the first riding speed does not meet the preset second threshold interval or the riding posture data is normal.

[0009] S5, when the operation data of the target rider is not collected, sending a help-seeking data to a preset remote server and sending the help-seeking data to a plurality of riders in a target area through a Mesh network, wherein the help-seeking data comprises position information of the target rider.

[0010] With reference to the first aspect, in a first implementation manner of the first aspect of the present application, further comprising:

[0011] When the communication state of the GNSS positioning module is abnormal, the second riding speed is monitored in real time.

[0012] The acceleration data is analyzed, and when the acceleration data meets a preset first threshold interval, step S4 is performed; and when the acceleration data does not meet the preset first threshold interval, steps S1 to S3 are repeatedly performed.

[0013] In a preset time zone, threshold analysis is performed on the second riding speed, and riding posture analysis is performed on the target rider to obtain riding posture data; an alarm prompt sound is played to the target rider through a speaker module when the second riding speed meets a preset second threshold interval and the riding posture data is an abnormal riding posture; and operation data of the target rider is collected in real time; and steps S1 to S4 are repeatedly performed when the first riding speed does not meet the preset second threshold interval or the riding posture data is normal.

[0014] When the operation data of the target rider is not collected, a help-seeking data is sent to a preset remote server, wherein the help-seeking data comprises position information of the target rider, and the help-seeking data is broadcasted to a plurality of riders in a target area through a preset Mesh network.

[0015] With reference to the first aspect, in a second implementation manner of the first aspect of the present application, step S4 comprises:

[0016] performing threshold analysis on the first riding speed in a preset time zone, and performing riding posture analysis on the target rider to obtain riding posture data, when the first riding speed meets a preset second threshold interval, extracting forward tilt angle data, rear tilt angle data and left-right tilt angle data of the target rider collected by the six-axis inertial measurement unit module;

[0017] performing riding posture analysis on the target rider according to the forward tilt angle data, the rear tilt angle data and the left-right tilt angle data to obtain riding posture data, when the riding posture data is an abnormal riding posture, playing an alarm prompt sound to the target rider through a loudspeaker module, and collecting operation data of the target rider in real time, when the first riding speed does not meet the preset second threshold interval or the riding posture data is normal, repeating steps S1 to S4.

[0018] In combination with the first aspect, in a third implementation manner of the first aspect of the present application, the riding posture analysis on the target rider according to the forward tilt angle data, the rear tilt angle data and the left-right tilt angle data to obtain riding posture data includes:

[0019] Meanwhile, the left-right tilt angle data, the rear tilt angle data and the forward tilt angle data are analyzed to determine whether the riding posture data is an abnormal riding posture, including one or more of the following: the left-right tilt angle data exceeds a preset first angle threshold, the forward tilt angle data exceeds a preset second angle threshold, and the rear tilt angle data exceeds a preset third angle threshold.

[0020] In combination with the first aspect, in a fourth implementation manner of the first aspect of the present application, further comprising: when the riding posture data is a normal riding posture and the first riding speed meets the preset second threshold interval, repeating steps S1 to S4.

[0021] In combination with the first aspect, in a fifth implementation manner of the first aspect of the present application, further comprising:

[0022] When the operation data of the target rider is collected, steps S1 to S5 are repeated.

[0023] In combination with the first aspect, in a sixth implementation manner of the first aspect of the present application, the step S5 includes:

[0024] When the operation data of the target rider is not collected, sending a help-seeking data to a preset remote server, wherein the help-seeking data is sent to the preset remote server in the following manner:

[0025] connecting a remote server through a WiFi hotspot by a preset WiFi module, and sending a help-seeking data to the remote server, or

[0026] connecting a remote server through a WiFi hotspot by a preset WiFi module, and sending a help-seeking data to the remote server, or

[0027] In a second aspect, the application provides a falling early warning device for cycling, which comprises:

[0028] The acquisition module is configured to monitor the target cyclist for cycling at a preset frequency by a GNSS positioning module to obtain a first cycling speed, and monitor the target cyclist for cycling at the preset frequency by a six-axis inertial measurement unit module to obtain a second cycling speed and acceleration data.

[0029] The monitoring module is configured to monitor the communication state of the GNSS positioning module in real time, and monitor the first cycling speed in real time when the communication state of the GNSS positioning module is normal.

[0030] The solving module is configured to analyze the acceleration data, execute step S4 when the acceleration data meets a preset first threshold interval, and repeat steps S1-S3 when the acceleration data does not meet the preset first threshold interval.

[0031] The playing module is configured to analyze the first cycling speed by a threshold value within a preset time interval, analyze the cycling posture of the target cyclist to obtain cycling posture data, play an alarm prompt sound to the target cyclist through a speaker module when the first cycling speed meets a preset second threshold interval and the cycling posture data is an abnormal cycling posture, and collect operation data of the target cyclist in real time.

[0032] The early warning module is configured to send a help-seeking data to a preset remote server and a plurality of cyclists in a target area through a Mesh network when no operation data of the target cyclist is collected, wherein the help-seeking data comprises location information of the target cyclist.

[0033] The third aspect of the present application provides a falling warning device for cycling, comprising: a memory and at least one processor, the memory storing instructions; the at least one processor calling the instructions in the memory to make the falling warning device for cycling execute the falling warning method for cycling described above.

[0034] The fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium storing instructions, when running on a computer, making the computer execute the falling warning method for cycling described above.

[0035] In the technical solution provided by the present application, whether the rider falls is comprehensively judged by monitoring the change of the riding speed of the rider and the riding posture, thereby improving the detection accuracy. Before sending the help-seeking information, the current rider is informed through the broadcast prompt sound, and other riders in the group are warned, and the current rider can actively cancel the broadcast and warning. The help-seeking information is sent through multiple channels such as mobile phones, WIFI and Mesh, the data of GNSS and six-axis inertial measurement unit module are comprehensively monitored to monitor the riding condition of the rider, and whether the rider falls is more efficiently and accurately identified. When it is monitored that the rider falls, the rider is timely informed and other members in the team are warned, if the warning is not cancelled, the help-seeking information is sent through mobile phones, WIFI and Mesh, thereby improving the timeliness and accuracy of the falling warning for cycling. BRIEF DESCRIPTION OF DRAWINGS

[0036] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor based on these drawings.

[0037] FIG. 1 is a schematic diagram of one embodiment of the falling warning method for cycling in the present application;

[0038] FIG. 2 is a schematic diagram of one embodiment of the falling warning device for cycling in the present application. DETAILED DESCRIPTION

[0039] The embodiments of the present application provide a falling early warning method, device and equipment for cycling and a storage medium. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0040] For ease of understanding, the specific process of the embodiments of the present application is described below. Referring to FIG. 1, one embodiment of the falling early warning method for cycling in the embodiments of the present application includes the following steps.

[0041] S1, monitoring the target cyclist according to a preset frequency through a GNSS positioning module to obtain a first cycling speed, and monitoring the target cyclist according to a preset frequency through a six-axis inertial measurement unit module to obtain a second cycling speed and acceleration data;

[0042] S2, monitoring the communication state of the GNSS positioning module in real time, and monitoring the first cycling speed in real time when the communication state of the GNSS positioning module is normal;

[0043] S3, analyzing the acceleration data, and performing step S4 when the acceleration data meets a preset first threshold interval, and repeating steps S1 to S3 when the acceleration data does not meet the preset first threshold interval;

[0044] In this step, the first threshold interval is 1.3g~2g, wherein g is the unit of acceleration, and the specific value is 。

[0045] S4, performing threshold analysis on the first cycling speed within a preset time interval, and performing cycling posture analysis on the target cyclist to obtain cycling posture data, playing an alarm prompt sound to the target cyclist through a loudspeaker module when the first cycling speed meets a preset second threshold interval and the cycling posture data is an abnormal cycling posture, and collecting operation data of the target cyclist in real time, and repeating steps S1 to S4 when the first cycling speed does not meet the preset second threshold interval or the cycling posture data is normal;

[0046] In this step, the preset time interval is 2s~6s.

[0047] S5, when the operation data of the target rider is not collected, sending a distress data to a preset remote server, and sending the distress data to a plurality of riders in the target area through a mesh network, wherein the distress data comprises position information of the target rider.

[0048] It can be understood that the execution subject of the present application can be a falling warning device for cycling, and can also be a terminal or a server, which is not limited here. The server is taken as an example for description in the embodiments of the present application.

[0049] It should be noted that the target rider is monitored at a preset frequency to obtain the first cycling speed in the embodiment. The first step is to monitor the first cycling speed of the target rider by using the GNSS positioning module, and at the same time, another set of cycling data is obtained by the six-axis inertial measurement unit module at the same preset frequency, which is the second cycling speed and acceleration data. The two sets of speed measurement systems serve as backup for each other, ensuring the continuity and accuracy of the rider speed monitoring. Assuming that a rider is training in an open environment, the GNSS module tracks and measures the speed of the rider's cycling path, and the six-axis inertial measurement unit module provides speed and attitude data as an auxiliary, improving the robustness of the system.

[0050] The communication state of the GNSS positioning module is monitored in real time. In the case of normal communication, the first cycling speed is tracked in real time to confirm whether the motion state of the rider is stable. If the GNSS communication state is abnormal, the second cycling speed data is automatically used as the main source of speed monitoring. For example, when the weather is fine and the communication condition is good, the GNSS data will be the main force of speed monitoring, and the second cycling speed will be the auxiliary verification. Further, when the speed of the rider enters a preset speed interval, the role of the six-axis inertial measurement unit module becomes crucial, which needs to solve the current attitude to obtain important attitude data such as pitch angle, roll angle and yaw angle. Suppose the rider speeds up during downhill, the six-axis inertial measurement unit module will closely monitor the small changes in the rider's attitude to determine whether it enters a dangerous attitude. When the attitude data of the rider shows abnormality, such as sudden pitch or roll angle increase, it will be determined that a falling accident has occurred or is likely to occur, and an alarm prompt sound will be played through the loudspeaker module to alert the rider to take immediate action.

[0051] If no operation data of the rider is collected after the emergency warning is triggered, it may mean that the rider has lost control of the situation, possibly due to falling unconscious or being unable to take action. In this case, the pre-installed WIFI module will immediately send a distress signal to the remote server and send a distress voice to other riders in the target area, which initiates rescue operations immediately. At the same time, the distress data is broadcast in real time through the built-in Mesh network module, which not only covers areas that WIFI may not cover, but also ensures that rescue information can be accurately delivered even in unstable network environments.

[0052] For example, if a rider is training on a mountain bike path, and the loudspeaker detects a sudden change in speed and an abnormal posture when the rider is steeply downhill, it will immediately issue an alarm prompt sound. If the rider fails to respond in time due to panic, technical error or any other reason, the distress program is triggered quickly without detecting any operation data, asking nearby riders and remote rescue units to intervene. The rescue signal is broadcast through the Mesh network, ensuring that even in possible WIFI blind areas such as valleys, the rescue request can be received by nearby riders or search and rescue teams, and they can quickly gather at the accident site to provide assistance.

[0053] In the embodiments of the present application, the change in the riding speed of the rider and the riding posture are monitored to determine whether the rider has fallen, improving the detection accuracy. Before sending the distress information, a prompt sound is broadcast to inform the current rider and alert other riders in the group, and the current rider can actively cancel the broadcast and alert. The distress information is sent through multiple channels such as mobile phones, WIFI and Mesh, which can monitor the riding situation of the rider by combining the data of GNSS and six-axis inertial measurement unit modules, and more efficiently and accurately identify whether the rider has fallen. When the rider is detected to have fallen, the rider is promptly informed and other members in the team are alerted, and if the alert is not cancelled, the distress information is sent through mobile phones, WIFI and Mesh, which can improve the timeliness and accuracy of the pre-warning of the riding fall.

[0054] In a specific embodiment, the pre-warning method for riding fall can further include the following steps:

[0055] (1) When the communication state of the GNSS positioning module is abnormal, the second riding speed is monitored in real time;

[0056] (2) The acceleration data is analyzed, and when the acceleration data meets the preset first threshold interval, step S4 is performed, and when the acceleration data does not meet the preset first threshold interval, steps S1 to S3 are repeatedly performed;

[0057] (3) In the preset time zone, the second riding speed is subjected to threshold analysis, and the target rider is subjected to riding posture analysis to obtain riding posture data, when the second riding speed meets the preset second threshold interval and the riding posture data is an abnormal riding posture, an alarm prompt sound is played to the target rider through the loudspeaker module, and operation data of the target rider is collected in real time, when the first riding speed does not meet the preset second threshold interval or the riding posture data is normal, steps S1 to S4 are repeatedly executed.

[0058] (4) When the operation data of the target rider is not collected, rescue data including the position information of the target rider is sent to the preset remote server, and the rescue data is broadcasted to a plurality of riders in the target area through the preset Mesh network.

[0059] In a specific embodiment, the process of executing step S4 can specifically include the following steps:

[0060] (1) In the preset time zone, the first riding speed is subjected to threshold analysis, and the target rider is subjected to riding posture analysis to obtain riding posture data, when the first riding speed meets the preset second threshold interval, the forward tilt angle data, the rear tilt angle data and the left-right tilt angle data of the target rider collected by the six-axis inertial measurement unit module are extracted;

[0061] (2) The target rider is subjected to riding posture analysis according to the forward tilt angle data, the rear tilt angle data and the left-right tilt angle data to obtain riding posture data, when the riding posture data is an abnormal riding posture, an alarm prompt sound is played to the target rider through the loudspeaker module, and operation data of the target rider is collected in real time, when the first riding speed does not meet the preset second threshold interval or the riding posture data is normal, steps S1 to S4 are repeatedly executed.

[0062] Specifically, when the first riding speed changes to the preset speed interval, the six-axis inertial measurement unit module is used to collect the three-axis angular velocity data of the target rider to obtain the first angular velocity data, the second angular velocity data and the third angular velocity data in the riding falling early warning method in the present application.

[0063] In the embodiment of the present disclosure, the second threshold interval is the judgment standard of the first riding speed, and the value is 0m / s~3m / s. At the same time, whether the rider is riding abnormally is judged, if the front and rear tilt angles and the left and right tilt angles exceed the normal range, it is judged that there is a risk of falling. For example, in the above mountain riding example, if the roll angle of the rider exceeds the normal value, it is judged that the rider may have fallen, the early warning is started immediately, the early warning is sent to the rider, and timely measures are taken to reduce the possible harm.

[0064] In a specific embodiment, the process of performing riding posture analysis on the target rider according to the forward tilt angle data, the backward tilt angle data, and the left-right tilt angle data to obtain the riding posture data can specifically include the following steps:

[0065] Meanwhile, analyzing the left-right tilt angle data, the backward tilt angle data, and the forward tilt angle data to determine whether the riding posture data is an abnormal riding posture includes one or more of the following: the left-right tilt angle data exceeds a preset first angle threshold, the backward tilt angle data exceeds a preset second angle threshold, and the backward tilt angle data exceeds a preset third angle threshold.

[0066] It should be noted that in one possible embodiment of the present disclosure, the first angle threshold is 20°-45°, the second angle threshold is 10°-20°, and the third angle threshold is 40°-90°.

[0067] In a specific embodiment, the method for early warning of falling while riding can further specifically include the following steps: when the riding posture data is a normal riding posture and the first riding speed meets a preset second threshold interval, repeating steps S1-S4.

[0068] In this embodiment, when the riding speed does not meet the threshold 0m / s-3m / s or the riding posture is normal within the preset time interval, steps S1-S5 are repeatedly executed.

[0069] In a specific embodiment, the method for early warning of falling while riding can further specifically include the following steps: when the operation data of the target rider is collected, steps S1-S5 are repeatedly executed.

[0070] In a specific embodiment, the process of performing step S5 can specifically include the following steps:

[0071] (1) When the operation data of the target rider is not collected, sending a distress data to a preset remote server, wherein the distress data is sent to the preset remote server by the following method:

[0072] (2) Connecting the remote server directly through a WiFi hotspot through a preset WiFi module and sending the distress data to the remote server, or

[0073] (3) Connecting the remote server through a mobile phone APP through a preset Bluetooth module, sending the distress data to the remote server through the mobile phone APP, wherein the distress data includes the location information of the target rider, and broadcasting the distress data to multiple riders in the target area through a preset Mesh network.

[0074] Specifically, the emergency response mechanism starts sending the help data when the target rider cannot operate normally due to uncontrollable factors. First, the WIFI module pre-installed on the rider device will automatically send help data to the pre-configured remote server without collecting rider operation data. After receiving this signal, the server will immediately start the rescue program and notify the rescue team members. Such a design is very suitable for the actual needs of mountain biking, because in areas far from urban areas with poor signal coverage, timely rescue request is crucial. Identify the target area riders. This is done by analyzing the devices identified in the WIFI network, which can detect and confirm the presence of other rider devices in the area. When the riders are confirmed, the device information of each rider is obtained, and the specific signal transmission channel of each device is matched. Then the voice help information is sent to these riders through the corresponding signal transmission channel. In order to ensure that the help data can be delivered in the case where WIFI signal cannot be covered, the help data is also broadcast in real time through the pre-installed Mesh network module. Mesh network is particularly suitable for complex terrain and wide coverage due to its multi-hop transmission characteristics, even in areas without direct WIFI connection, the help data can be transmitted through the network constructed between devices, matched with the signal transmission channel of each rider, and sent to multiple riders in the target area through the WIFI module based on the signal transmission channel of each rider. At the same time, the help data is broadcast in real time through the pre-installed Mesh network module.

[0075] The above describes the method for warning of falling while riding in the embodiments of the present application. The warning device for falling while riding in the embodiments of the present application is described below. Referring to FIG. 2, one embodiment of the warning device for falling while riding in the embodiments of the present application includes:

[0076] The acquisition module 201 is configured to monitor the target rider's riding according to a preset frequency through the GNSS positioning module to obtain a first riding speed, and monitor the target rider's riding according to a preset frequency through the six-axis inertial measurement unit module to obtain a second riding speed and acceleration data.

[0077] The monitoring module 202 is configured to monitor the communication state of the GNSS positioning module in real time, and monitor the first riding speed in real time when the communication state of the GNSS positioning module is normal.

[0078] The solving module 203 is configured to analyze the acceleration data, execute step S4 when the acceleration data meets a preset first threshold interval, and repeat steps S1 to S3 when the acceleration data does not meet the preset first threshold interval.

[0079] The playing module 204 is used for performing threshold analysis on the first riding speed in a preset time zone, performing riding posture analysis on the target rider to obtain riding posture data, playing an alarm prompt sound to the target rider through a loudspeaker module when the first riding speed meets a preset second threshold interval and the riding posture data is an abnormal riding posture, and collecting operation data of the target rider in real time, and repeating steps S1 to S4 when the first riding speed does not meet the preset second threshold interval or the riding posture data is normal.

[0080] The early warning module 205 is used for sending help-seeking data to a preset remote server and sending the help-seeking data to a plurality of riders in a target area through a Mesh network when operation data of the target rider is not collected, wherein the help-seeking data includes position information of the target rider.

[0081] Through the cooperation of the above components, whether the rider falls down is comprehensively judged by monitoring the change of the riding speed and the riding posture, and the detection accuracy is improved. Before sending the help-seeking information, the current rider is informed through the broadcast prompt sound, and other riders in the group are warned, and the current rider can actively cancel the broadcast and warning. The help-seeking information is sent through multiple channels of mobile phones, WIFI and Mesh, the riding condition of the rider is comprehensively monitored by combining the data of GNSS and a six-axis inertial measurement unit module, and whether the rider falls down is more efficiently and accurately identified. When the rider falls down is monitored, the rider is timely informed and other members in the team are warned, if the warning is not cancelled, the help-seeking information is sent through mobile phones, WIFI and Mesh, the timeliness and accuracy of the early warning of the riding fall can be improved.

[0082] The application also provides a riding fall early warning device, which comprises a memory and a processor, the memory stores computer readable instructions, and the computer readable instructions are executed by the processor to make the processor execute the steps of the riding fall early warning method in each of the above embodiments.

[0083] The application also provides a computer readable storage medium, which can be a non-volatile computer readable storage medium or a volatile computer readable storage medium, and the computer readable storage medium stores instructions, and the instructions make the computer execute the steps of the riding fall early warning method when the instructions run on the computer.

[0084] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0085] The above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still make modifications to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features; and these modifications or replacements 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 application.

Claims

1. A method of warning of a cycling fall, characterized in that, The early warning method of the riding fall comprises: S1, through the GNSS positioning module, the target rider is monitored according to the preset frequency, the first riding speed is obtained, and at the same time, through the six-axis inertial measurement unit module, the target rider is monitored according to the preset frequency, the second riding speed and acceleration data are obtained; S2, the communication state of the GNSS positioning module is monitored in real time, when the communication state of the GNSS positioning module is normal, the first riding speed is monitored in real time; S3, the acceleration data is analyzed, when the acceleration data meets the preset first threshold interval, step S4 is executed, when the acceleration data does not meet the preset first threshold interval, steps S1-S3 are repeatedly executed; S4, within a preset time interval, the first riding speed is analyzed by threshold, at the same time, the target rider is analyzed by riding posture, riding posture data is obtained, when the first riding speed meets the preset second threshold interval and the riding posture data is an abnormal riding posture, an alarm prompt sound is played to the target rider through the loudspeaker module, at the same time, the operation data of the target rider is collected in real time, when the first riding speed does not meet the preset second threshold interval or the riding posture data is normal, steps S1-S4 are repeatedly executed; S5, when the operation data of the target rider is not collected, rescue data is sent to the preset remote server, and the rescue data is sent to a plurality of riders in the target area through the Mesh network, wherein the rescue data comprises the position information of the target rider.

2. The method of claim 1, wherein, Further comprising: When the communication state of the GNSS positioning module is abnormal, the second riding speed is monitored in real time; The acceleration data is analyzed, when the acceleration data meets the preset first threshold interval, step S4 is executed, when the acceleration data does not meet the preset first threshold interval, steps S1-S3 are repeatedly executed; Within a preset time interval, the second riding speed is analyzed by threshold, at the same time, the target rider is analyzed by riding posture, riding posture data is obtained, when the second riding speed meets the preset second threshold interval and the riding posture data is an abnormal riding posture, an alarm prompt sound is played to the target rider through the loudspeaker module, at the same time, the operation data of the target rider is collected in real time, when the first riding speed does not meet the preset second threshold interval or the riding posture data is normal, steps S1-S4 are repeatedly executed; When the operation data of the target rider is not collected, rescue data is sent to the preset remote server, wherein the rescue data comprises the position information of the target rider, and the rescue data is broadcasted to a plurality of riders in the target area through the preset Mesh network.

3. The method of claim 2, wherein, S4 step, comprising: In a preset time zone, threshold analysis is performed on the first riding speed, and riding posture analysis is performed on the target rider to obtain riding posture data. When the first riding speed meets a preset second threshold interval, the forward tilt angle data, the rear tilt angle data, and the left and right tilt angle data of the target rider collected by the six-axis inertial measurement unit module are extracted. According to the forward tilt angle data, the rear tilt angle data, and the left and right tilt angle data, the target rider is analyzed for riding posture to obtain riding posture data. When the riding posture data is an abnormal riding posture, an alarm prompt sound is played to the target rider through a speaker module, and the operation data of the target rider is collected in real time. When the first riding speed does not meet the preset second threshold interval or the riding posture data is normal, steps S1 to S4 are repeatedly executed.

4. The method of claim 3, wherein, The riding posture analysis of the target rider according to the forward tilt angle data, the rear tilt angle data, and the left and right tilt angle data to obtain riding posture data includes: At the same time, the left and right tilt angle data, the rear tilt angle data, and the forward tilt angle data are analyzed to determine whether the riding posture data is an abnormal riding posture, including one or more of the following: the left and right tilt angle data exceeds a preset first angle threshold, the forward tilt angle data exceeds a preset second angle threshold, and the rear tilt angle data exceeds a preset third angle threshold.

5. The method of claim 1, wherein, Also includes: When the riding posture data is a normal riding posture and the first riding speed meets the preset second threshold interval, steps S1 to S4 are repeatedly executed.

6. The method of claim 1, wherein, Also includes: When the operation data of the target rider is collected, steps S1 to S5 are repeatedly executed.

7. The method of claim 1, wherein, S5 step, including: When the operation data of the target rider is not collected, send a distress call data to a preset remote server, wherein the distress call data is sent to the preset remote server in the following way: Through the preset WiFi module, connect to the remote server through WiFi hot spot, and send the distress call data to the remote server, or Connect to the mobile phone APP through the preset Bluetooth module, connect to the remote server through the mobile phone APP, and send the distress call data to the remote server, wherein the distress call data includes the location information of the target rider, and the distress call data is broadcasted to multiple riders in the target area through the preset Mesh network.

8. A falling early warning device for cycling, characterized in that, The riding fall warning device includes: The acquisition module is used to monitor the target rider's riding according to a preset frequency through the GNSS positioning module to obtain a first riding speed, and to monitor the target rider's riding according to the preset frequency through the six-axis inertial measurement unit module to obtain a second riding speed and acceleration data; The monitoring module is used to monitor the communication state of the GNSS positioning module in real time, and when the communication state of the GNSS positioning module is normal, the first riding speed is monitored in real time. The solving module analyzes the acceleration data, and when the acceleration data meets a preset first threshold interval, step S4 is performed, and when the acceleration data does not meet the preset first threshold interval, steps S1 to S3 are repeatedly performed. The playing module is configured to perform threshold analysis on the first riding speed within a preset time interval, perform riding posture analysis on the target rider to obtain riding posture data, and play an alarm prompt sound to the target rider through a loudspeaker module when the first riding speed meets a preset second threshold interval and the riding posture data is an abnormal riding posture, and simultaneously collect operation data of the target rider in real time. When the first riding speed does not meet the preset second threshold interval or the riding posture data is normal, steps S1 to S4 are repeatedly performed. The early warning module is configured to send a help-seeking data to a preset remote server and send the help-seeking data to a plurality of riders in a target area through a Mesh network when the operation data of the target rider is not collected, wherein the help-seeking data includes position information of the target rider.

9. A falling early warning device for cycling, characterized in that The riding fall early warning device includes a memory and at least one processor, and the memory stores instructions. The at least one processor invokes the instructions in the memory to enable the riding fall early warning device to perform the riding fall early warning method of any one of claims 1-7.

10. A computer-readable storage medium having stored thereon instructions, the instructions comprising, The instructions are executed by the processor to implement the riding fall early warning method of any one of claims 1-7.

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