Intelligent riding management method based on motorcycle recorder

Abstract

The invention discloses an intelligent riding management method based on a motorcycle recorder, and relates to the technical field of riding management, and the method comprises the steps: obtaining the video information and basic information of a target user in the riding process based on the motorcycle recorder in a monitoring period; the video information and the basic information of the motorcycle in the riding process are analyzed and processed, safety analysis is conducted on the riding process of the motorcycle according to the analysis and processing result, a riding safety evaluation coefficient is confirmed, and then an early warning strategy is made according to the riding safety evaluation coefficient; according to the method, the early warning strategy is executed, the riding process of the target user in the next monitoring period is monitored, the riding safety of the target user is managed based on the monitoring result, the riding process of the target user is effectively analyzed, and the utilization rate of the motorcycle recorder is effectively improved.

Description

Technical Field

[0001] The present application relates to the technical field of riding management, and in particular to an intelligent riding management method based on a motorcycle recorder. Background Art

[0002] As the "motorcycle ban" is gradually lifted in various cities, motorcycles, a convenient means of transportation, have once again become the preferred means of transportation for commuting. Compared with electric vehicles of similar shape, motorcycles are much faster and more dangerous to drive, so urban management rules and traffic regulations have issued regulations to limit the speed of motorcycles in cities.

[0003] In the related technologies, existing motorcycle recorders are mostly used for accident recording, with single functions and lack of in-depth analysis of riding behavior. In addition, traditional recorders have limited applications in data processing and behavior recognition, and fail to fully utilize the collected data for effective management, leaving room for improvement. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present application provides an intelligent riding management method based on a motorcycle recorder.

[0005] The intelligent riding management method based on a motorcycle recorder provided in this application comprises the following steps:

[0006] During the monitoring period, the video information and basic information of the target user during riding are obtained based on the motorcycle recorder;

[0007] Analyze and process the video information and basic information of the motorcycle during riding, conduct safety analysis on the motorcycle riding process according to the analysis and processing results, confirm the riding safety assessment coefficient, and then formulate early warning strategies according to the riding safety assessment coefficient;

[0008] The early warning strategy is executed, and the riding process of the target user in the next monitoring cycle is monitored, and the riding safety of the target user is managed based on the monitoring results.

[0009] Preferably, the basic information of the target user during riding includes speed information, acceleration information and tilt angle information.

[0010] Preferably, analyzing and processing the video information and basic information of the motorcycle during riding includes:

[0011] According to the video information of the target user during the riding process, the road section information corresponding to the target user during the riding process is confirmed, and the road section information corresponding to the target user during the riding process is marked as each riding section;

[0012] Obtain speed limit information corresponding to each riding section when the target user is riding, extract speed information corresponding to each riding section from the basic information corresponding to the target user during the riding process, and determine the maximum speed value corresponding to each riding section of the target user from the speed information;

[0013] By formula The speed safety assessment coefficient α corresponding to the target user during riding is determined, where i represents the number corresponding to each riding section, i=1, 2, 3...j, and Vi and Vi′ represent the maximum speed value and the maximum allowed speed value corresponding to the i-th riding section, respectively.

[0014] Preferably, analyzing and processing the video information and basic information of the motorcycle during riding also specifically includes:

[0015] Confirming the basic information corresponding to the target user during the riding process, and extracting the acceleration information corresponding to the target user during the riding process from the basic information corresponding to the target user during the riding process;

[0016] Based on the acceleration information corresponding to the target user during the riding process, the sudden acceleration information and sudden braking information corresponding to the target user are identified, and the positive acceleration value corresponding to the sudden acceleration of the target user and the negative acceleration value corresponding to the sudden braking of the target user are respectively obtained;

[0017] The positive acceleration value corresponding to the sudden acceleration of the target user is compared with the preset positive acceleration threshold. If the positive acceleration value corresponding to the sudden acceleration of the target user exceeds the preset positive acceleration threshold, it is marked as a sudden acceleration event, and the corresponding number of sudden acceleration events Jjs in the monitoring period is obtained;

[0018] The negative acceleration value corresponding to the target user's emergency braking is compared with the preset negative acceleration threshold. If the negative acceleration value corresponding to the target user's emergency braking is lower than the preset negative acceleration threshold, it is marked as an emergency braking event, and the corresponding emergency braking event number Jsc within the monitoring period is obtained.

[0019] Confirm the number of sudden acceleration events Jjs and sudden braking events Jsc during the monitoring period, and confirm the corresponding riding distance D of the target user during the riding process, perform normalization, and use the formula The acceleration safety assessment coefficient β corresponding to the target user during riding is determined, where ω1 and ω2 represent preset weight coefficients respectively.

[0020] Preferably, analyzing and processing the video information and basic information of the motorcycle during riding also specifically includes:

[0021] Confirming the basic information corresponding to the target user during the riding process, and extracting the inclination angle information corresponding to the target user during the riding process from the basic information corresponding to the target user during the riding process;

[0022] Based on the corresponding inclination angle information of the target user during riding, the maximum inclination angle Mmax and the average inclination angle Mavg corresponding to the target user are identified;

[0023] By formula The corresponding inclination angle safety assessment coefficient γ of the target user during riding is identified, where Tmax and Tsafe represent the preset maximum safe inclination angle and average safe inclination angle, respectively, and Tmax>Tsafe, and k1 and k2 represent the preset adjustment parameters, respectively.

[0024] Preferably, a safety analysis is performed on the motorcycle riding process according to the result of the analysis and processing, and a riding safety assessment coefficient is determined, and then a warning strategy is formulated according to the riding safety assessment coefficient, specifically including:

[0025] Substitute the speed safety assessment coefficient α corresponding to the target user during riding, the acceleration safety assessment coefficient β corresponding to the target user during riding, and the tilt angle safety assessment coefficient γ corresponding to the target user during riding into the formula Calculate the riding safety assessment coefficient ψ corresponding to the target user during riding, where a1, a2, and a3 represent the weight coefficients of the preset speed safety assessment coefficient, acceleration safety assessment coefficient, and tilt angle safety assessment coefficient, respectively;

[0026] Compare the riding safety assessment coefficient ψ corresponding to the target user during riding with the preset riding safety assessment threshold ψ′;

[0027] If the target user's corresponding riding safety assessment coefficient ψ≥ψ′ during riding, there is no need to formulate an early warning strategy;

[0028] If the target user's corresponding riding safety assessment coefficient ψ<ψ′ during riding, a warning strategy is formulated for the motorcycle riding process.

[0029] Preferably, the process of formulating an early warning strategy specifically includes:

[0030] Wrisk=ε1*(α′-α)+ε2*(β′-β)+ε3*(γ′-γ)

[0031] The comprehensive reference value Wrisk of the target user's safety risk during riding is calculated by the above formula, where α′, β′, and γ′ represent the preset speed safety assessment threshold, acceleration safety assessment threshold, and tilt angle safety assessment threshold, respectively, and ε1, ε2, and ε3 represent the preset correlation coefficients;

[0032] Compare the comprehensive reference value Wrisk with the preset standard reference value Wbz;

[0033] If Wrisk≤Wbz, a first warning signal is issued to the target user during riding;

[0034] If Wrisk>Wbz, a first warning signal is issued to the target user during the riding process, and the corresponding wearing specifications of the target user are further analyzed.

[0035] Preferably, further analyzing the wearing specifications corresponding to the target user includes:

[0036] Confirm the key parts corresponding to the target user, and then obtain the number of protective equipment worn by the target user corresponding to the key parts;

[0037] By formula Determine the wearing safety assessment coefficient σ corresponding to the target user, where n represents the number corresponding to each key part of the target user, n=1,2,3...m, Sn, S′n represent the number of protective gear worn and the number of reference protective gear worn corresponding to the nth key part, respectively, and cn represents the part weight coefficient corresponding to the nth key part;

[0038] Compare the wearing safety assessment coefficient σ corresponding to the target user with the preset wearing safety assessment threshold σ′;

[0039] If the wearing safety assessment coefficient σ<σ′ corresponding to the target user, a second warning signal is issued to the target user.

[0040] Preferably, the riding process of the target user in the next monitoring period is monitored, and the riding safety of the target user is managed based on the monitoring result, specifically including:

[0041] Mark the riding safety assessment coefficient corresponding to the target user during riding and the wearing safety assessment coefficient corresponding to the target user as the original riding safety assessment coefficient ψ original corresponding to the target user during riding and the original wearing safety assessment coefficient σ original corresponding to the target user;

[0042] Obtain the riding safety assessment coefficient corresponding to the target user during the riding process and the wearing safety assessment coefficient corresponding to the target user in the next monitoring period, and mark them as the current riding safety assessment coefficient ψ current corresponding to the target user during the riding process and the current wearing safety assessment coefficient σ current corresponding to the target user;

[0043] By formula Confirm the riding specification correction coefficient Correct corresponding to the target user, where △ψ and △σ represent the preset permitted riding safety assessment difference and permitted wearing safety assessment difference, respectively, and manage the riding safety of the target user based on the riding specification correction coefficient corresponding to the target user.

[0044] In summary, the present application includes at least one of the following beneficial technical effects:

[0045] 1. The present invention provides an intelligent riding management method based on a motorcycle recorder, which performs safety analysis on the video information and basic information of the target user during riding based on the motorcycle recorder within a monitoring period, confirms the riding safety assessment coefficient, formulates an early warning strategy based on the riding safety assessment coefficient, monitors the riding process of the target user in the next monitoring period, and manages the riding safety of the target user based on the monitoring results, thereby effectively analyzing the riding process of the target user and effectively improving the utilization rate of the motorcycle recorder;

[0046] 2. By analyzing the number of protective gear worn by the target users at each key part, the corresponding wearing safety assessment coefficient of the target users is confirmed, and the target users are managed in a standardized manner based on the corresponding wearing safety assessment coefficient of the target users, and then the riding safety of the target users is effectively analyzed, thereby effectively improving the riding safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flow chart of the method for intelligent riding management based on a motorcycle recorder in an embodiment of the present application. DETAILED DESCRIPTION

[0048] The present application is further described in detail below in conjunction with the accompanying drawings.

[0049] Example 1

[0050] The embodiment of the present application discloses an intelligent riding management method based on a motorcycle recorder.

[0051] Reference Figure 1 , an intelligent riding management method based on a motorcycle recorder includes the following steps:

[0052] S1. Obtain the video information and basic information of the target user during riding based on the motorcycle recorder during the monitoring period;

[0053] S2. Analyze and process the video information and basic information of the motorcycle during riding, conduct safety analysis on the motorcycle riding process according to the analysis and processing results, confirm the riding safety assessment coefficient, and then formulate an early warning strategy according to the riding safety assessment coefficient;

[0054] S3. Execute the early warning strategy, monitor the riding process of the target user in the next monitoring period, and manage the riding safety of the target user based on the monitoring results.

[0055] By adopting the above technical solution, a security analysis is performed on the video information and basic information of the target user during the riding process based on the motorcycle recorder during the monitoring period, the riding safety assessment coefficient is confirmed, and an early warning strategy is formulated based on the riding safety assessment coefficient. The riding process of the target user in the next monitoring period is monitored, and the riding safety of the target user is managed based on the monitoring results, thereby effectively analyzing the riding process of the target user and effectively improving the utilization rate of the motorcycle recorder.

[0056] Furthermore, the basic information of the target user during riding includes speed information, acceleration information and tilt angle information.

[0057] Specifically, by comprehensively analyzing the speed, acceleration and tilt angle information during motorcycle riding, the understanding of driving behavior and the accuracy of safety assessment can be significantly improved, and the above data can help to more accurately identify potential high-risk driving behaviors.

[0058] It should be noted that the analysis and processing of the video information and basic information of the motorcycle during riding specifically includes:

[0059] According to the video information of the target user during the riding process, the road section information corresponding to the target user during the riding process is confirmed, and the road section information corresponding to the target user during the riding process is marked as each riding section;

[0060] Obtain speed limit information corresponding to each riding section when the target user is riding, extract speed information corresponding to each riding section from the basic information corresponding to the target user during the riding process, and determine the maximum speed value corresponding to each riding section of the target user from the speed information;

[0061] Specifically, the speed limit information corresponding to each cycling section is different. For example, the speed limit of some cycling sections is 80km / h, and the speed limit of some cycling sections is 60km / h.

[0062] By formula The speed safety assessment coefficient α corresponding to the target user during riding is determined, where i represents the number corresponding to each riding section, i=1, 2, 3...j, and Vi and Vi′ represent the maximum speed value and the maximum allowed speed value corresponding to the i-th riding section, respectively.

[0063] Furthermore, the video information and basic information of the motorcycle during riding are analyzed and processed, including:

[0064] Confirming the basic information corresponding to the target user during the riding process, and extracting the acceleration information corresponding to the target user during the riding process from the basic information corresponding to the target user during the riding process;

[0065] Based on the acceleration information corresponding to the target user during the riding process, the sudden acceleration information and sudden braking information corresponding to the target user are identified, and the positive acceleration value corresponding to the sudden acceleration of the target user and the negative acceleration value corresponding to the sudden braking of the target user are respectively obtained;

[0066] The positive acceleration value corresponding to the sudden acceleration of the target user is compared with the preset positive acceleration threshold. If the positive acceleration value corresponding to the sudden acceleration of the target user exceeds the preset positive acceleration threshold, it is marked as a sudden acceleration event, and the corresponding number of sudden acceleration events Jjs in the monitoring period is obtained;

[0067] The negative acceleration value corresponding to the target user's emergency braking is compared with the preset negative acceleration threshold. If the negative acceleration value corresponding to the target user's emergency braking is lower than the preset negative acceleration threshold, it is marked as an emergency braking event, and the corresponding emergency braking event number Jsc within the monitoring period is obtained.

[0068] Confirm the number of sudden acceleration events Jjs and sudden braking events Jsc during the monitoring period, and confirm the corresponding riding distance D of the target user during the riding process, perform normalization, and use the formula The acceleration safety assessment coefficient β corresponding to the target user during riding is determined, where ω1 and ω2 represent preset weight coefficients respectively.

[0069] Specifically, by analyzing sudden acceleration and sudden braking and calculating the acceleration safety assessment coefficient, it is ensured that potential dangerous driving behaviors can be effectively identified and reduced.

[0070] It should be noted that the analysis and processing of the video information and basic information of the motorcycle during riding also includes:

[0071] Confirming the basic information corresponding to the target user during the riding process, and extracting the inclination angle information corresponding to the target user during the riding process from the basic information corresponding to the target user during the riding process;

[0072] Based on the corresponding inclination angle information of the target user during riding, the maximum inclination angle Mmax and the average inclination angle Mavg corresponding to the target user are identified;

[0073] By formula The corresponding inclination angle safety assessment coefficient γ of the target user during riding is identified, where Tmax and Tsafe represent the preset maximum safe inclination angle and average safe inclination angle respectively, and Tmax>Tsafe, and k1 and k2 represent the preset adjustment parameters respectively, which are used to control the influence of the maximum inclination angle and the average inclination angle on the overall safety assessment.

[0074] Further, a safety analysis is performed on the motorcycle riding process according to the results of the analysis and processing, and a riding safety assessment coefficient is confirmed, and then an early warning strategy is formulated according to the riding safety assessment coefficient, specifically including:

[0075] Substitute the speed safety assessment coefficient α corresponding to the target user during riding, the acceleration safety assessment coefficient β corresponding to the target user during riding, and the tilt angle safety assessment coefficient γ corresponding to the target user during riding into the formula Calculate the riding safety assessment coefficient ψ corresponding to the target user during riding, where a1, a2, and a3 represent the weight coefficients of the preset speed safety assessment coefficient, acceleration safety assessment coefficient, and tilt angle safety assessment coefficient, respectively;

[0076] Compare the riding safety assessment coefficient ψ corresponding to the target user during riding with the preset riding safety assessment threshold ψ′;

[0077] If the target user's corresponding riding safety assessment coefficient ψ≥ψ′ during riding, there is no need to formulate an early warning strategy;

[0078] If the target user's corresponding riding safety assessment coefficient ψ<ψ′ during riding, a warning strategy is formulated for the motorcycle riding process.

[0079] It should be noted that the process of formulating early warning strategies specifically includes:

[0080] Wrisk=ε1*(α′-α)+ε2*(β′-β)+ε3*(γ′-γ)

[0081] The comprehensive reference value Wrisk of the target user's safety risk during riding is calculated by the above formula, where α′, β′, and γ′ represent the preset speed safety assessment threshold, acceleration safety assessment threshold, and tilt angle safety assessment threshold, respectively, and ε1, ε2, and ε3 represent the preset correlation coefficients;

[0082] Compare the comprehensive reference value Wrisk with the preset standard reference value Wbz;

[0083] If Wrisk≤Wbz, a first warning signal is issued to the target user during the riding process; wherein the first warning signal is used to warn the target person to standardize riding actions.

[0084] If Wrisk>Wbz, a first warning signal is issued to the target user during the riding process, and the corresponding wearing specifications of the target user are further analyzed.

[0085] Furthermore, the wearing specifications corresponding to the target users are further analyzed, including:

[0086] Confirm the key parts corresponding to the target user, and then obtain the number of protective equipment worn by the target user corresponding to the key parts;

[0087] By formula Determine the wearing safety assessment coefficient σ corresponding to the target user, where n represents the number corresponding to each key part of the target user, n=1,2,3...m, Sn, S′n represent the number of protective gear worn and the number of reference protective gear worn corresponding to the nth key part, respectively, and cn represents the part weight coefficient corresponding to the nth key part;

[0088] Compare the wearing safety assessment coefficient σ corresponding to the target user with the preset wearing safety assessment threshold σ′;

[0089] If the wearing safety assessment coefficient σ<σ′ corresponding to the target user, a second warning signal is issued to the target user, wherein the second warning signal is used to warn the target person to properly wear protective equipment.

[0090] It should be noted that the target user is monitored during the riding process in the next monitoring period, and the riding safety of the target user is managed based on the monitoring results, specifically including:

[0091] Mark the riding safety assessment coefficient corresponding to the target user during riding and the wearing safety assessment coefficient corresponding to the target user as the original riding safety assessment coefficient ψ original corresponding to the target user during riding and the original wearing safety assessment coefficient σ original corresponding to the target user;

[0092] Obtain the riding safety assessment coefficient corresponding to the target user during the riding process and the wearing safety assessment coefficient corresponding to the target user in the next monitoring period, and mark them as the current riding safety assessment coefficient ψ current corresponding to the target user during the riding process and the current wearing safety assessment coefficient σ current corresponding to the target user;

[0093] By formula Confirm the riding specification correction coefficient Correct corresponding to the target user, where △ψ and △σ represent the preset permitted riding safety assessment difference and permitted wearing safety assessment difference, respectively, and manage the riding safety of the target user based on the riding specification correction coefficient corresponding to the target user.

[0094] Specifically, the riding specification correction coefficient corresponding to the target user is compared with the preset riding specification correction threshold. If the riding specification correction coefficient corresponding to the target user is lower than the preset riding specification correction threshold, a third warning signal is further issued to the target user, wherein the warning level of the warning signal is: the third warning signal is greater than the second warning signal and the first warning signal.

[0095] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0096] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0097] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent riding management method based on a motorcycle recorder, characterized in that: The following steps are involved: During the monitoring period, the video information and basic information of the target user during riding are obtained based on the motorcycle recorder; Analyze and process the video information and basic information of the motorcycle during riding, conduct safety analysis on the motorcycle riding process based on the analysis and processing results, confirm the riding safety assessment coefficient, and then formulate an early warning strategy based on the riding safety assessment coefficient; The early warning strategy is executed, and the riding process of the target user in the next monitoring cycle is monitored, and the riding safety of the target user is managed based on the monitoring results.

2. The intelligent riding management method based on a motorcycle recorder according to claim 1 is characterized in that: The basic information of the target user during riding includes speed information, acceleration information and tilt angle information.

3. The intelligent riding management method based on a motorcycle recorder according to claim 2 is characterized in that: Analyze and process the video information and basic information of the motorcycle during riding, including: According to the video information of the target user during the riding process, the road section information corresponding to the target user during the riding process is confirmed, and the road section information corresponding to the target user during the riding process is marked as each riding section; Obtain speed limit information corresponding to each riding section when the target user is riding, extract speed information corresponding to each riding section from the basic information corresponding to the target user during the riding process, and determine the maximum speed value corresponding to each riding section of the target user from the speed information; By formula The speed safety assessment coefficient α corresponding to the target user during riding is determined, where i represents the number corresponding to each riding section, i=1, 2, 3...j, and Vi and Vi′ represent the maximum speed value and the maximum allowed speed value corresponding to the i-th riding section, respectively.

4. The intelligent riding management method based on a motorcycle recorder according to claim 2 is characterized in that: Analyze and process the video information and basic information of the motorcycle during riding, including: Confirming the basic information corresponding to the target user during the riding process, and extracting the acceleration information corresponding to the target user during the riding process from the basic information corresponding to the target user during the riding process; Based on the acceleration information corresponding to the target user during the riding process, the sudden acceleration information and sudden braking information corresponding to the target user are identified, and the positive acceleration value corresponding to the sudden acceleration of the target user and the negative acceleration value corresponding to the sudden braking of the target user are respectively obtained; The positive acceleration value corresponding to the sudden acceleration of the target user is compared with the preset positive acceleration threshold. If the positive acceleration value corresponding to the sudden acceleration of the target user exceeds the preset positive acceleration threshold, it is marked as a sudden acceleration event, and the corresponding number of sudden acceleration events Jjs in the monitoring period is obtained; The negative acceleration value corresponding to the target user's sudden braking is compared with the preset negative acceleration threshold. If the negative acceleration value corresponding to the target user's sudden braking is lower than the preset negative acceleration threshold, it is marked as an emergency braking event, and the corresponding number of emergency braking events Jsc in the monitoring period is obtained. Confirm the number of sudden acceleration events Jjs and sudden braking events Jsc during the monitoring period, and confirm the corresponding riding distance D of the target user during the riding process, perform normalization, and use the formula The acceleration safety assessment coefficient β corresponding to the target user during riding is determined, where ω1 and ω2 represent preset weight coefficients respectively.

5. The intelligent riding management method based on a motorcycle recorder according to claim 2 is characterized in that: Analyze and process the video information and basic information of the motorcycle during riding, including: Confirming the basic information corresponding to the target user during the riding process, and extracting the inclination angle information corresponding to the target user during the riding process from the basic information corresponding to the target user during the riding process; Based on the corresponding inclination angle information of the target user during riding, the maximum inclination angle Mmax and the average inclination angle Mavg corresponding to the target user are identified; By formula The corresponding inclination angle safety assessment coefficient γ of the target user during riding is identified, where Tmax and Tsafe represent the preset maximum safe inclination angle and average safe inclination angle, respectively, and Tmax>Tsafe, and k1 and k2 represent the preset adjustment parameters, respectively.

6. The intelligent riding management method based on a motorcycle recorder according to claim 5 is characterized in that: According to the results of the analysis and processing, a safety analysis is conducted on the motorcycle riding process, and the riding safety assessment coefficient is confirmed, and then an early warning strategy is formulated according to the riding safety assessment coefficient, including: Substitute the speed safety assessment coefficient α corresponding to the target user during riding, the acceleration safety assessment coefficient β corresponding to the target user during riding, and the tilt angle safety assessment coefficient γ corresponding to the target user during riding into the formula Calculate the riding safety assessment coefficient ψ corresponding to the target user during riding, where a1, a2, and a3 represent the weight coefficients of the preset speed safety assessment coefficient, acceleration safety assessment coefficient, and tilt angle safety assessment coefficient, respectively; Compare the riding safety assessment coefficient ψ corresponding to the target user during riding with the preset riding safety assessment threshold ψ′; If the target user's corresponding riding safety assessment coefficient ψ≥ψ′ during riding, there is no need to formulate an early warning strategy; If the target user's corresponding riding safety assessment coefficient ψ<ψ′ during riding, a warning strategy is formulated for the motorcycle riding process.

7. The intelligent riding management method based on a motorcycle recorder according to claim 6 is characterized in that: The process of developing an early warning strategy includes: Wrisk=ε1*(α′-α)+ε2*(β′-β)+ε3*(γ′-γ) The comprehensive reference value Wrisk of the target user's safety risk during riding is calculated by the above formula, where α′, β′, and γ′ represent the preset speed safety assessment threshold, acceleration safety assessment threshold, and tilt angle safety assessment threshold, respectively, and ε1, ε2, and ε3 represent the preset correlation coefficients; Compare the comprehensive reference value Wrisk with the preset standard reference value Wbz; If Wrisk≤Wbz, a first warning signal is issued to the target user during riding; If Wrisk>Wbz, a first warning signal is issued to the target user during the riding process, and the corresponding wearing specifications of the target user are further analyzed.

8. The intelligent riding management method based on a motorcycle recorder according to claim 7 is characterized in that: Further analysis of the wearing specifications corresponding to the target users includes: Confirm the key parts corresponding to the target user, and then obtain the number of protective equipment worn by the target user corresponding to the key parts; By formula Determine the wearing safety assessment coefficient σ corresponding to the target user, where n represents the number corresponding to each key part of the target user, n=1,2,3...m, Sn, S′n represent the number of protective gear worn and the number of reference protective gear worn corresponding to the nth key part, respectively, and cn represents the part weight coefficient corresponding to the nth key part; Compare the wearing safety assessment coefficient σ corresponding to the target user with the preset wearing safety assessment threshold σ′; If the wearing safety assessment coefficient σ<σ′ corresponding to the target user, a second warning signal is issued to the target user.

9. The intelligent riding management method based on a motorcycle recorder according to claim 8, characterized in that: Monitor the target user's riding process in the next monitoring cycle, and manage the target user's riding safety based on the monitoring results, including: Mark the riding safety assessment coefficient corresponding to the target user during riding and the wearing safety assessment coefficient corresponding to the target user as the original riding safety assessment coefficient ψ original corresponding to the target user during riding and the original wearing safety assessment coefficient σ original corresponding to the target user; Obtain the riding safety assessment coefficient corresponding to the target user during the riding process and the wearing safety assessment coefficient corresponding to the target user in the next monitoring period, and mark them as the current riding safety assessment coefficient ψ current corresponding to the target user during the riding process and the current wearing safety assessment coefficient σ current corresponding to the target user; By formula Confirm the riding specification correction coefficient Correct corresponding to the target user, where △ψ and △σ represent the preset permitted riding safety assessment difference and permitted wearing safety assessment difference, respectively, and manage the riding safety of the target user based on the riding specification correction coefficient corresponding to the target user.

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