Electric bicycle intelligent cushion pressure monitoring riding posture and state alarm method and system

Abstract

The invention relates to the technical field of electric bicycles, and provides an electric bicycle intelligent cushion pressure monitoring and alarming method and system. The method comprises the following steps: acquiring initial pressure distribution data information and inertial measurement data information of the cushion in a preset time period in real time; optimizing the initial pressure distribution data information based on the inertial measurement data information to obtain intermediate pressure distribution data information, and normalizing the intermediate pressure distribution data information to obtain target pressure distribution data information; inputting the target pressure distribution data information into a preset riding posture and state prediction model to obtain a riding state and a riding posture of the user, and judging whether the riding state or the riding posture is normal or not; and if not, generating alarm information, and sending the alarm information to the user. According to the method, the problem of poor dynamic environment adaptability caused by dependence on static pressure data in a traditional scheme is solved by fusing multi-modal data of the pressure sensor array and the inertial measurement unit.

Description

Technical Field

[0001] The present application relates to the technical field of electric motorcycles, and in particular to a method and system for monitoring riding posture and status alarms using an intelligent electric motorcycle seat pressure. Background Art

[0002] As a green and convenient means of short-distance travel, motorcycles have rapidly gained popularity worldwide in recent years, particularly in shared mobility and urban commuting. With the development of intelligent transportation technology, the safety, comfort, and intelligence of motorcycles have become a focus of attention for users and manufacturers. Seat pressure monitoring is a key technology for enhancing the riding experience, used to detect riding posture, fatigue, and even potential safety hazards (such as imbalance and falls). Traditional motorcycle seat pressure monitoring solutions typically rely solely on static pressure data and lack adaptability to dynamic riding environments, resulting in a high rate of false alarms. Summary of the Invention

[0003] The present application provides a method and system for monitoring riding posture and status alarm using an intelligent motorcycle seat pressure to solve the problems raised by the above-mentioned background technology.

[0004] In a first aspect, the present application provides a method for monitoring riding posture and status alarm using an intelligent motorcycle seat pressure, comprising: Real-time acquisition of initial pressure distribution data and inertial measurement data of the seat cushion within a preset time period; Optimizing the initial pressure distribution data information based on the inertial measurement data information to obtain intermediate pressure distribution data information, and normalizing the intermediate pressure distribution data information to obtain target pressure distribution data information; Inputting the target pressure distribution data information into a preset riding posture and state prediction model to obtain the user's riding posture and state, and determining whether the riding posture and state are normal; If it is abnormal, an alarm message matching the riding status is generated and sent to the user.

[0005] In one possible implementation, the initial pressure distribution data information includes initial pressure change data information of multiple pressure sensors within the preset time period, the inertial measurement data information includes inertial data change information of multiple inertial data within the preset time period, and the optimizing the initial pressure distribution data information based on the inertial measurement data information to obtain the intermediate pressure distribution data information includes: For each of the pressure sensors, determine whether each initial pressure value in the initial pressure change data information corresponding to the pressure sensor is within a first threshold range corresponding to the pressure sensor; if any of the initial pressure values is not within the first threshold range, extract inertial data change information corresponding to the pressure sensor from the inertial measurement data information, and optimize the initial pressure distribution data information based on the inertial data change information to obtain intermediate pressure change data information corresponding to the pressure sensor.

[0006] In a possible implementation, the optimizing the initial pressure distribution data information based on the inertial data change information to obtain the intermediate pressure change data information corresponding to the pressure sensor includes: For each initial pressure value of the initial pressure distribution data information, if the initial pressure value is not within the first threshold range, determine whether the inertial data value corresponding to the inertial data change information at the time of collection of the initial pressure value is within the second threshold range corresponding to the inertial data change information; if not, replace the initial pressure value with the pressure value that appears most frequently in the initial pressure distribution data information.

[0007] In a possible implementation, normalizing the intermediate pressure distribution data information to obtain target pressure distribution data information includes: extracting the intermediate pressure change data information set corresponding to each side of the seat cushion from the intermediate pressure distribution data information; For each side of the seat cushion, the pressure values corresponding to each pressure sensor on the side at the same moment are extracted from the intermediate pressure change data information corresponding to the side to obtain the pressure distribution data information corresponding to the side at multiple moments, and the pressure distribution data information corresponding to each moment is normalized respectively to obtain the normalized processing result corresponding to the side; the normalized processing result corresponding to each side of the seat cushion is the target pressure distribution data information.

[0008] In a possible implementation, normalizing the pressure distribution data information corresponding to each moment to obtain the corresponding normalized processing result includes: For each moment, use The pressure distribution data information corresponding to the moment is normalized; wherein, For the The pressure sensor corresponds to the normalized value at the moment, For the The pressure value corresponding to each pressure sensor in the pressure distribution data information, is the minimum pressure value in the pressure distribution data information, is the maximum pressure value in the pressure distribution data information.

[0009] In a possible implementation, the method further includes: The initial pressure distribution data information, the inertial measurement data information and the alarm information are bound to obtain a binding result, and the binding result is stored in a preset database.

[0010] In one possible implementation, the initial pressure distribution data information includes initial pressure change data information of multiple pressure sensors within the preset time period, and the inertial measurement data information includes inertial data change information of multiple inertial data within the preset time period. Binding the initial pressure distribution data information, the inertial measurement data information, and the alarm information to obtain a binding result includes: For each piece of initial pressure change data information, encoding the initial pressure change data information based on a preset first character encoding algorithm to obtain a first code sequence corresponding to the initial pressure change data information, and arranging each first code sequence in order from top to bottom in a preset blank matrix based on a serial number of a pressure sensor corresponding to each first code sequence to obtain a first matrix; the first code sequence being composed of characters in the same language; For each piece of inertial data change information, encoding the piece of inertial data change information based on a preset second character encoding algorithm to obtain a second code sequence corresponding to the piece of inertial data change information, and arranging the second code sequences in order from top to bottom in a preset blank matrix based on the sequence number of the inertial measurement unit corresponding to the second code sequence to obtain a second matrix; the second code sequences are composed of characters in the same language, and the characters constituting the first code sequence and the characters constituting the second code sequence are in completely different languages; Digitally encoding the alarm information based on a preset digital encoding algorithm to obtain a digital sequence corresponding to the alarm information; Determining whether the first matrix and the second matrix are isotype matrices; If yes, add the first matrix and the second matrix to obtain an intermediate matrix, and multiply the intermediate matrix by the value corresponding to the digital sequence to obtain a target matrix; the target matrix is the binding result; If not, the first matrix and the second matrix are adjusted to be isotype matrices based on the numerical values corresponding to the digital sequence, and the adjusted first matrix and the second matrix are added to obtain a target matrix; the target matrix is the binding result.

[0011] In a second aspect, the present application provides a motorcycle intelligent seat pressure monitoring riding posture and status alarm system, comprising: An acquisition module is used to obtain the initial pressure distribution data and inertial measurement data of the seat cushion in a preset time period in real time; an optimization processing module, configured to optimize the initial pressure distribution data information based on the inertial measurement data information to obtain intermediate pressure distribution data information, and perform normalization processing on the intermediate pressure distribution data information to obtain target pressure distribution data information; An input module, configured to input the target pressure distribution data information into a preset riding posture and state prediction model, obtain the user's riding posture and state, and determine whether the riding is normal; The sending module is used to generate an alarm message matching the riding posture and state if the riding posture and state are abnormal, and send the alarm message to the user through the APP.

[0012] This application provides a method and system for intelligent motorcycle seat pressure monitoring, riding posture, and status alarms. The method includes: acquiring initial pressure distribution data and inertial measurement data of the seat in real time within a preset time period; optimizing the initial pressure distribution data based on the inertial measurement data to obtain intermediate pressure distribution data, and normalizing the intermediate pressure distribution data to obtain target pressure distribution data; inputting the target pressure distribution data into a preset riding posture and status prediction model to obtain the user's riding posture and status, and determining whether the riding posture and status are normal; if not, generating an alarm message matching the riding posture and status, and sending the alarm message to the user. This method, first, solves the problem of poor adaptability to dynamic environments caused by the traditional solution's reliance on static pressure data by fusing multimodal data from the pressure sensor array and the inertial measurement unit, significantly improving the accuracy of riding posture and status monitoring. Secondly, it uses inertial measurement data to dynamically optimize and normalize the initial pressure distribution, effectively eliminating noise interference introduced by vehicle bumps or posture adjustments, which helps to reduce the false alarm rate. Then, the riding posture and status prediction model constructed based on deep learning can accurately identify abnormal conditions such as fatigue driving, imbalance, and falls, and realize intelligent graded early warning. Finally, through the matching mechanism of abnormal conditions and alarm strategies, targeted alarm information is sent to the rider or emergency contact, which not only ensures user safety but also avoids the interference of invalid alarms on users, thereby improving the safety of electric motorcycles while optimizing the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0014] Figure 1 A flow chart of a method for monitoring riding posture and status alarm using an intelligent motorcycle seat pressure controller according to an embodiment of the present application; Figure 2 A schematic block diagram of the structure of the electric motorcycle intelligent seat pressure monitoring riding posture and status alarm system provided in an embodiment of the present application; Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0016] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0017] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0018] It should be further understood that the term "and / or" used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0019] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0020] See also Figure 1 , Figure 1This is a flow chart of the method for monitoring riding posture and status alarm of the motorcycle smart seat pressure provided in the embodiment of the present application, as shown in FIG. Figure 1 As shown, the electric motorcycle intelligent seat cushion pressure monitoring and alarm method provided in the embodiment of the present application includes steps S1 to S4.

[0021] Step S1: acquiring initial pressure distribution data and inertial measurement data of a seat cushion within a preset time period in real time.

[0022] Step S2: optimizing the initial pressure distribution data information based on the inertial measurement data information to obtain intermediate pressure distribution data information, and normalizing the intermediate pressure distribution data information to obtain target pressure distribution data information.

[0023] Step S3: input the target pressure distribution data information into a preset riding posture and state prediction model to obtain the user's riding posture and state, and determine whether the riding posture and state are normal.

[0024] Step S4: If it is abnormal, generate an alarm message that matches the riding posture and status, and send the alarm message to the user through the APP.

[0025] It should be noted that the executor of the embodiment of the present application is a server or an electric motorcycle intelligent seat pressure monitoring riding posture and status alarm system.

[0026] This embodiment specifically includes: As described in step S1 above, the initial pressure distribution data and inertial measurement data of the seat cushion within a preset time period are acquired in real time. Specifically, the initial pressure distribution data and inertial measurement data of the seat cushion within the preset time period are acquired in real time via a pressure sensor array preset in the seat cushion. Multiple pressure sensors are provided on the upper, left, and right sides of the seat cushion, and the multiple pressure sensors constitute the pressure sensor array. The initial pressure distribution data includes initial pressure change data from the multiple pressure sensors within the preset time period. The inertial measurement data includes inertial data change information of multiple inertial data within the preset time period. The inertial data change information includes three-axis acceleration change information and three-axis angular velocity change information. The three-axis acceleration change information includes linear acceleration change information corresponding to the X-axis, Y-axis, and Z-axis. The three-axis angular velocity change information includes rotational angular velocity change information corresponding to the X-axis, Y-axis, and Z-axis.

[0027] As described in step S3 above, the initial pressure distribution data information is optimized based on the inertial measurement data information to obtain intermediate pressure distribution data information, and the intermediate pressure distribution data information is normalized to obtain target pressure distribution data information. Specifically, first, for each of the pressure sensors, determine whether each initial pressure value in the initial pressure change data information corresponding to the pressure sensor is within the first threshold range corresponding to the pressure sensor; if any of the initial pressure values is not within the first threshold range, extract the inertial data change information corresponding to the pressure sensor from the inertial measurement data information, and optimize the initial pressure distribution data information based on the inertial data change information to obtain the intermediate pressure change data information corresponding to the pressure sensor; then, extract the intermediate pressure change data information set corresponding to each side of the seat cushion from the intermediate pressure distribution data information; finally, for each side of the seat cushion, extract the pressure values corresponding to each pressure sensor of the side at the same time from the intermediate pressure change data information set corresponding to the side to obtain the pressure distribution data information corresponding to the side at multiple times; and normalize the pressure distribution data information corresponding to each time to obtain the normalized processing result corresponding to the side; the normalized processing result corresponding to each side of the seat cushion is the target pressure distribution data information.

[0028] As described in step S3 above, the target pressure distribution data information is input into a preset riding posture and state prediction model to obtain the user's riding posture and state, and determine whether the riding posture and state are normal; wherein, the riding posture and state prediction model is a pre-trained classification model, and the riding posture and state prediction model includes an input layer, a feature extraction layer, a riding posture and state generation layer, and an output layer.

[0029] As described in step S4 above, if the riding posture and state are abnormal, an alarm message that matches the riding posture and state is generated, and the alarm message is sent to the user through the APP. Specifically, if the riding posture and state are abnormal, first, traverse the preset abnormal riding posture and state-related information matching table to obtain the relevant information corresponding to the riding posture and state, and the relevant information includes the alarm method and the user. Then, based on the alarm method, an alarm message is sent to the user, wherein each riding posture in the riding posture and state-related information matching table corresponds to multiple states, and the user includes the rider and the rider's related contacts. For example, if the riding state is fatigue driving, an alarm message to stop riding is sent to the rider; if the riding state is an unbalanced state, an alarm message to keep balance is sent to the rider; if the riding state is a fall, an alarm message of the rider's fall is sent to the rider's related contacts.

[0030] The method provided in this embodiment, first, solves the problem of poor adaptability to dynamic environments caused by the traditional solution's reliance on static pressure data by fusing multimodal data from a pressure sensor array and an inertial measurement unit, and significantly improves the accuracy of riding posture and status monitoring. Secondly, the initial pressure distribution is dynamically optimized and normalized using inertial measurement data, effectively eliminating noise interference introduced by vehicle bumps or posture adjustments, which helps to reduce the false alarm rate. Then, the riding posture and status prediction model constructed based on deep learning can accurately identify abnormal conditions such as fatigue driving, imbalance, and falls, and realize intelligent graded early warning. Finally, through the matching mechanism of abnormal conditions and alarm strategies, targeted alarm information is sent to the rider or emergency contact through the APP, which not only ensures user safety but also avoids interference from invalid alarms on users, thereby improving the safety of electric motorcycles while optimizing the user experience.

[0031] In some embodiments, the initial pressure distribution data information includes initial pressure change data information of multiple pressure sensors within the preset time period, and the inertial measurement data information includes inertial data change information of multiple inertial data within the preset time period. Optimizing the initial pressure distribution data information based on the inertial measurement data information to obtain the intermediate pressure distribution data information includes the following steps: For each pressure sensor, a determination is made as to whether each initial pressure value in the initial pressure change data corresponding to the pressure sensor is within a first threshold range corresponding to the pressure sensor. If any of the initial pressure values is not within the first threshold range, inertial data change information corresponding to the pressure sensor is extracted from the inertial measurement data information, and the initial pressure distribution data information is optimized based on the inertial data change information to obtain intermediate pressure change data information corresponding to the pressure sensor. The inertial data change information corresponding to the pressure sensor on the upper side of the seat cushion is linear acceleration change information corresponding to the Z axis, and the inertial data change information corresponding to the pressure sensors on the left and right sides of the seat cushion is rotational angular velocity change information corresponding to the Y axis.

[0032] The method provided in this embodiment first screens the effectiveness of the initial pressure value of each pressure sensor by setting a first threshold range to ensure the basic reliability of data optimization. Secondly, for abnormal pressure data exceeding the threshold range, the corresponding inertial measurement data change information is intelligently correlated and extracted to achieve multi-dimensional cross-validation of pressure data and motion status. Then, the abnormal pressure values are dynamically compensated and optimized based on the inertial data to effectively distinguish between real pressure changes and environmental interference such as vehicle bumps. Finally, through this targeted optimization mechanism, the effective pressure distribution characteristics are retained and the misjudgment rate is significantly reduced, providing a more accurate data basis for subsequent riding status analysis.

[0033] In some embodiments, the optimizing the initial pressure distribution data information based on the inertial data change information to obtain the intermediate pressure change data information corresponding to the pressure sensor includes the following steps: For each initial pressure value of the initial pressure distribution data information, if the initial pressure value is not within the first threshold range, determine whether the inertial data value corresponding to the inertial data change information at the time of collection of the initial pressure value is within the second threshold range corresponding to the inertial data change information; if not, replace the initial pressure value with the pressure value that appears most frequently in the initial pressure distribution data information.

[0034] The method provided in this embodiment, first, achieves accurate identification of abnormal pressure data through a dual-threshold judgment mechanism, effectively distinguishing between real pressure anomalies and motion interference. Second, when abnormalities are detected in both pressure data and inertial data, it is determined to be environmental noise rather than real pressure changes. Then, the most frequently occurring pressure value is used for data replacement, which not only eliminates the impact of transient interference but also maintains the overall trend characteristics of the pressure distribution. Finally, this dynamic optimization strategy significantly improves the reliability and consistency of pressure data, providing a more stable and accurate data foundation for subsequent riding status analysis.

[0035] In some embodiments, normalizing the intermediate pressure distribution data to obtain target pressure distribution data includes the following steps: extracting the intermediate pressure change data information set corresponding to each side of the seat cushion from the intermediate pressure distribution data information; For each side of the seat cushion, the pressure values corresponding to each pressure sensor on the side at the same moment are extracted from the intermediate pressure change data information corresponding to the side to obtain the pressure distribution data information corresponding to the side at multiple moments, and the pressure distribution data information corresponding to each moment is normalized respectively to obtain the normalized processing result corresponding to the side; the normalized processing result corresponding to each side of the seat cushion is the target pressure distribution data information.

[0036] The normalizing of the pressure distribution data information corresponding to each moment to obtain the normalized processing result corresponding to the side includes: For each moment, use The pressure distribution data information corresponding to the moment is normalized; wherein, For the The pressure sensor corresponds to the normalized value at the moment, For the The pressure value corresponding to each pressure sensor in the pressure distribution data information, is the minimum pressure value in the pressure distribution data information, is the maximum pressure value in the pressure distribution data information.

[0037] The method provided in this embodiment firstly extracts and analyzes the pressure data on each side of the seat cushion independently through a partitioning processing strategy, effectively maintaining the spatial characteristics of the pressure distribution. Then, the pressure sensor data on each side is synchronously processed using a time alignment method to ensure the temporal consistency of the pressure distribution data. Finally, the pressure data at each moment is standardized through a normalization algorithm to eliminate the influence of individual differences, which helps to improve the accuracy of the riding status prediction results.

[0038] In some embodiments, the method further comprises the following steps: The initial pressure distribution data information, the inertial measurement data information and the alarm information are bound to obtain a binding result, and the binding result is stored in a preset database.

[0039] The step of binding the initial pressure distribution data information, the inertial measurement data information, and the alarm information to obtain a binding result includes the following steps: For each piece of initial pressure change data information, encoding the initial pressure change data information based on a preset first character encoding algorithm to obtain a first code sequence corresponding to the initial pressure change data information, and arranging each first code sequence in order from top to bottom in a preset blank matrix based on a serial number of a pressure sensor corresponding to each first code sequence to obtain a first matrix; the first code sequence being composed of characters in the same language; For each piece of inertial data change information, encoding the piece of inertial data change information based on a preset second character encoding algorithm to obtain a second code sequence corresponding to the piece of inertial data change information, and arranging the second code sequences in order from top to bottom in a preset blank matrix based on the sequence number of the inertial measurement unit corresponding to the second code sequence to obtain a second matrix; the second code sequences are composed of characters in the same language, and the characters constituting the first code sequence and the characters constituting the second code sequence are in completely different languages; Digitally encoding the alarm information based on a preset digital encoding algorithm to obtain a digital sequence corresponding to the alarm information; Determining whether the first matrix and the second matrix are isotype matrices; If yes, add the first matrix and the second matrix to obtain an intermediate matrix, and multiply the intermediate matrix by the value corresponding to the digital sequence to obtain a target matrix; the target matrix is the binding result; for example, if the digital sequence is 2, 3, 4, then the value corresponding to the digital sequence is 234; If not, the first matrix and the second matrix are adjusted to be isotype matrices based on the values corresponding to the numerical sequence, and the adjusted first matrix and the second matrix are added to obtain a target matrix; the target matrix is the binding result. For example, if the first matrix includes three rows and four columns, and the second matrix includes four rows and three columns, the first matrix and the second matrix are adjusted to matrices with four rows and four columns, that is, the elements in the fourth row are added to the first matrix, and the elements in the fourth column are added to the second matrix, and the elements in the fourth column are added to the second matrix, and the elements in the fourth column are added to the second matrix, and the elements in the fourth column are added to the second matrix, and the elements in the fifth column are added to the second matrix, and the elements in the fourth row and the fifth column are added to the second matrix, and the elements in the fourth row and the fifth column are added to the second matrix, and the elements in the fourth row and the fifth column are added to the first matrix, and the elements in the fourth row and the fifth column are added to the second ...

[0040] The method provided in this embodiment, on the one hand, realizes the structured storage of associated data and saves storage space; on the other hand, it provides an effective data basis for the further intelligent development of the subsequent electric motorcycle intelligent seat pressure monitoring riding posture and status alarm method; on the other hand, the initial pressure change data information is encoded based on the preset first character encoding algorithm, and the inertial data change information is encoded based on the preset second character encoding algorithm, and the alarm information is digitally encoded based on the preset digital encoding algorithm, which provides an effective theoretical basis for the subsequent disassembly of the target matrix to obtain the initial pressure distribution data information, the inertial measurement data information and the alarm information, and can prevent data confusion.

[0041] See also Figure 2 , Figure 2 This is a schematic block diagram of the structure of the motorcycle intelligent seat pressure monitoring and alarm system 100 provided in the embodiment of the present application, as shown in FIG. Figure 2 As shown, the motorcycle intelligent seat cushion pressure monitoring and alarm system 100 provided in the embodiment of the present application includes: The acquisition module 110 is used to acquire the initial pressure distribution data information and inertial measurement data information of the seat cushion in a preset time period in real time.

[0042] The optimization processing module 120 is configured to optimize the initial pressure distribution data information based on the inertial measurement data information to obtain intermediate pressure distribution data information, and perform normalization processing on the intermediate pressure distribution data information to obtain target pressure distribution data information.

[0043] The input module 130 is used to input the target pressure distribution data information into a preset riding posture and state prediction model to obtain the user's riding state and determine whether the riding posture and state are normal.

[0044] The sending module 140 is used to generate an alarm message matching the riding posture and state if the riding posture and state are abnormal, and send the alarm message to the user through the APP. It should be noted that, those skilled in the art will clearly understand that for the sake of convenience and brevity in description, the specific working processes of the above-described devices and modules can refer to the processes in the aforementioned electric motorcycle intelligent seat pressure monitoring riding posture and status alarm method embodiment, and will not be repeated here.

[0045] The motorcycle intelligent seat pressure monitoring riding posture and status alarm system 100 provided in the above embodiment can be implemented in the form of a computer program. The computer program can be used in Figure 3 The system runs on the terminal device 200 shown.

[0046] See also Figure 3 , Figure 3 This is a schematic block diagram of the structure of a terminal device 200 provided in an embodiment of the present application. The terminal device 200 includes a processor 201 and a memory 202. The processor 201 and the memory 202 are connected via a system bus 203, wherein the memory 202 may include a non-volatile storage medium and an internal memory.

[0047] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor 201, the processor 201 can execute any of the above-mentioned methods for monitoring riding posture and status alarm of the intelligent motorcycle seat pressure.

[0048] The processor 201 is used to provide computing and control capabilities to support the operation of the entire terminal device 200.

[0049] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 201, the processor 201 can execute any of the above-mentioned methods for monitoring riding posture and status alarm of the intelligent seat pressure of the electric motorcycle.

[0050] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the terminal device 200 involved in the solution of the present application. The specific terminal device 200 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0051] It should be understood that the processor 201 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0052] In some embodiments, the processor 201 is configured to execute a computer program stored in the memory to implement the following steps: Real-time acquisition of initial pressure distribution data and inertial measurement data of the seat cushion within a preset time period; Optimizing the initial pressure distribution data information based on the inertial measurement data information to obtain intermediate pressure distribution data information, and normalizing the intermediate pressure distribution data information to obtain target pressure distribution data information; Inputting the target pressure distribution data information into a preset riding posture and state prediction model to obtain the user's riding posture and state, and determining whether the riding posture and state are normal; If it is abnormal, an alarm message matching the riding posture and status is generated and sent to the user through the APP.

[0053] It should be noted that, those skilled in the art can clearly understand that, for the sake of convenience and brevity of description, the specific working process of the terminal device 200 described above can refer to the corresponding process of the aforementioned electric motorcycle intelligent seat pressure monitoring riding posture and status alarm method, and will not be repeated here.

[0054] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors implement the electric motorcycle smart seat pressure monitoring riding posture and status alarm method provided in the embodiment of the present application.

[0055] The computer-readable storage medium may be an internal storage unit of the terminal device 200 in the aforementioned embodiment, such as a hard disk or memory of the terminal device 200. The computer-readable storage medium may also be an external storage device of the terminal device 200, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped with the terminal device 200.

[0056] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for monitoring riding posture and status alarm by using an intelligent seat pressure on an electric motorcycle, characterized in that: include: Real-time acquisition of initial pressure distribution data and inertial measurement data of the seat cushion within a preset time period; Optimizing the initial pressure distribution data information based on the inertial measurement data information to obtain intermediate pressure distribution data information, and normalizing the intermediate pressure distribution data information to obtain target pressure distribution data information; Inputting the target pressure distribution data information into a preset riding posture and state prediction model to obtain the user's riding posture and state, and determining whether the riding posture and state are normal; If it is abnormal, an alarm message matching the riding posture and status is generated and sent to the user through the APP.

2. The method for monitoring riding posture and status alarm by using an intelligent seat pressure for an electric motorcycle according to claim 1, characterized in that: The initial pressure distribution data information includes initial pressure change data information of multiple pressure sensors within the preset time period, the inertial measurement data information includes inertial data change information of multiple inertial data within the preset time period, and the initial pressure distribution data information is optimized based on the inertial measurement data information to obtain the intermediate pressure distribution data information, including: For each of the pressure sensors, determine whether each initial pressure value in the initial pressure change data information corresponding to the pressure sensor is within a first threshold range corresponding to the pressure sensor; if any of the initial pressure values is not within the first threshold range, extract inertial data change information corresponding to the pressure sensor from the inertial measurement data information, and optimize the initial pressure distribution data information based on the inertial data change information to obtain intermediate pressure change data information corresponding to the pressure sensor.

3. The method for monitoring riding posture and status alarm by using an intelligent seat pressure for an electric motorcycle according to claim 2, characterized in that: The optimizing the initial pressure distribution data information based on the inertial data change information to obtain the intermediate pressure change data information corresponding to the pressure sensor includes: For each initial pressure value of the initial pressure distribution data information, if the initial pressure value is not within the first threshold range, determine whether the inertial data value corresponding to the inertial data change information at the time of collection of the initial pressure value is within the second threshold range corresponding to the inertial data change information; if not, replace the initial pressure value with the pressure value that appears most frequently in the initial pressure distribution data information.

4. The method for monitoring riding posture and status alarm by using an intelligent seat pressure for an electric motorcycle according to claim 1, characterized in that: Normalizing the intermediate pressure distribution data information to obtain target pressure distribution data information includes: extracting the intermediate pressure change data information set corresponding to each side of the seat cushion from the intermediate pressure distribution data information; For each side of the seat cushion, the pressure values corresponding to each pressure sensor on the side at the same moment are extracted from the intermediate pressure change data information corresponding to the side to obtain the pressure distribution data information corresponding to the side at multiple moments, and the pressure distribution data information corresponding to each moment is normalized respectively to obtain the normalized processing result corresponding to the side; the normalized processing result corresponding to each side of the seat cushion is the target pressure distribution data information.

5. The method for monitoring riding posture and status alarm by using an intelligent motorcycle seat pressure as claimed in claim 4, characterized in that: Normalizing the pressure distribution data information corresponding to each moment to obtain the normalized processing result corresponding to the side includes: For each moment, use The pressure distribution data information corresponding to the moment is normalized; wherein, For the The pressure sensor corresponds to the normalized value at the moment, For the The pressure value corresponding to each pressure sensor in the pressure distribution data information, is the minimum pressure value in the pressure distribution data information, is the maximum pressure value in the pressure distribution data information.

6. The method for monitoring riding posture and status alarm by using an intelligent motorcycle seat pressure as claimed in claim 1, characterized in that: The method further comprises: The initial pressure distribution data information, the inertial measurement data information and the alarm information are bound to obtain a binding result, and the binding result is stored in a preset database.

7. The method for monitoring riding posture and status alarm by using an intelligent motorcycle seat pressure as claimed in claim 6, characterized in that: The initial pressure distribution data information includes initial pressure change data information of multiple pressure sensors within the preset time period, and the inertial measurement data information includes inertial data change information of multiple inertial data within the preset time period. The initial pressure distribution data information, the inertial measurement data information, and the alarm information are bound to obtain a binding result, including: For each piece of initial pressure change data information, encoding the initial pressure change data information based on a preset first character encoding algorithm to obtain a first code sequence corresponding to the initial pressure change data information, and arranging each first code sequence in order from top to bottom in a preset blank matrix based on a serial number of a pressure sensor corresponding to each first code sequence to obtain a first matrix; the first code sequence being composed of characters in the same language; For each piece of inertial data change information, encoding the piece of inertial data change information based on a preset second character encoding algorithm to obtain a second code sequence corresponding to the piece of inertial data change information, and arranging the second code sequences in order from top to bottom in a preset blank matrix based on the sequence number of the inertial measurement unit corresponding to the second code sequence to obtain a second matrix; the second code sequences are composed of characters in the same language, and the characters constituting the first code sequence and the characters constituting the second code sequence are in completely different languages; Digitally encoding the alarm information based on a preset digital encoding algorithm to obtain a digital sequence corresponding to the alarm information; Determining whether the first matrix and the second matrix are isotype matrices; If yes, add the first matrix and the second matrix to obtain an intermediate matrix, and multiply the intermediate matrix by the value corresponding to the digital sequence to obtain a target matrix; the target matrix is the binding result; If not, the first matrix and the second matrix are adjusted to be isotype matrices based on the numerical values corresponding to the digital sequence, and the adjusted first matrix and the second matrix are added to obtain a target matrix; the target matrix is the binding result.

8. An intelligent motorcycle seat pressure monitoring riding posture and status alarm system, characterized by: include: An acquisition module is used to obtain the initial pressure distribution data and inertial measurement data of the seat cushion in a preset time period in real time; an optimization processing module, configured to optimize the initial pressure distribution data information based on the inertial measurement data information to obtain intermediate pressure distribution data information, and perform normalization processing on the intermediate pressure distribution data information to obtain target pressure distribution data information; an input module, configured to input the target pressure distribution data information into a preset riding posture and state prediction model, obtain the user's riding posture and state, and determine whether the riding posture and state are normal; The sending module is used to generate an alarm message matching the riding posture and state if the riding posture and state are abnormal, and send the alarm message to the user through the APP.

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