Electric scooter remote monitoring method and system based on Internet of Things

Abstract

The invention relates to an electric scooter remote monitoring method, device and equipment based on the Internet of Things and a storage medium, and the method comprises the following steps: if a scooter state feature vector represents that an electric scooter is abnormal, carrying out the multi-modal feature mapping of the electric scooter based on the scooter state feature vector, and carrying out the remote monitoring of the electric scooter based on the multi-modal feature mapping; vehicle abnormal behavior mode characteristics are obtained; performing risk prediction on the electric scooter based on the vehicle abnormal behavior mode characteristics to obtain a risk grade division result; performing distributed processing and priority ranking on the risk grade division result through an edge computing gateway to obtain a control strategy instruction set; the control strategy instruction set is input into the remote motor controller to control the electric scooter, and the technical problems that a traditional management mode mainly depends on manual inspection and regular maintenance, the efficiency is low, the state of the scooter is difficult to monitor in real time, and potential safety hazards are difficult to find in time are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric scooters, and in particular to a remote monitoring method and system for electric scooters based on the Internet of Things. Background Art

[0002] With the acceleration of urbanization and the diversification of people's travel needs, electric scooters have been rapidly popularized around the world as a convenient and environmentally friendly short-distance transportation tool. However, with this comes the growing demand for the management and safety monitoring of electric scooters. Traditional management methods mainly rely on manual inspections and regular maintenance, which is not only inefficient, but also difficult to monitor the status of vehicles in real time and detect potential safety hazards in a timely manner. Especially in the sharing economy model, a large number of electric scooters have been put on the market, and how to efficiently and accurately monitor the status of these vehicles has become an urgent problem to be solved.

[0003] On the technical level, although existing electric scooters are already equipped with basic sensor equipment, such as GPS positioning systems and battery management systems, the data from these sensors often work independently and lack effective integration and analysis mechanisms. This results in that even if a large amount of raw data can be collected, it is difficult to extract valuable information from it to guide actual operations. In addition, due to the complex and changeable environment in which electric scooters are used, a single type of sensor data is difficult to fully reflect the actual operating conditions of the vehicle, thereby limiting the accuracy of the vehicle health status assessment. Therefore, a more intelligent method is needed to achieve comprehensive monitoring and management of the status of electric scooters.

[0004] Based on the above background, researchers have begun to explore the use of IoT technology combined with advanced methods such as edge computing to improve the remote monitoring capabilities of electric scooters. By integrating data from multiple sensors and applying advanced data analysis algorithms for processing, abnormal vehicle behavior can be more accurately identified and potential risks can be predicted. This approach not only improves the safety of electric scooters, but also provides decision support for operators, optimizes resource allocation, and reduces operating costs. Despite this, there are still many challenges to achieving this goal, including but not limited to the maturity of sensor data fusion technology, the stability of edge computing performance, and data privacy protection. The effective solution of these problems is crucial to promoting the development of intelligent monitoring technology for electric scooters. Summary of the invention

[0005] The main purpose of the present invention is to provide an electric scooter remote monitoring method and system based on the Internet of Things, which solves the technical problem that the traditional management method mainly relies on manual inspection and regular maintenance, which is not only inefficient but also difficult to monitor the vehicle status in real time and discover potential safety hazards in a timely manner.

[0006] To achieve the above object, the present invention provides a remote monitoring method for an electric scooter based on the Internet of Things, wherein a motor controller is provided in the electric scooter, and the method comprises the following steps: Collect data from multiple source sensors on the electric scooter to obtain an original sensor data stream; wherein the original sensor data stream includes a gyroscope sensor, an acceleration sensor, a GPS positioning sensor, and a battery management sensor; Performing multi-dimensional feature extraction and data fusion on the original sensor data stream to obtain a scooter state feature vector; If the scooter state feature vector indicates that the electric scooter is abnormal, multimodal feature mapping is performed on the electric scooter based on the scooter state feature vector to obtain abnormal behavior pattern features of the vehicle; Based on the abnormal behavior pattern characteristics of the vehicle, risk prediction is performed on the electric scooter to obtain a risk level classification result; Distributed processing and priority sorting of the risk level classification results are performed through an edge computing gateway to obtain a control strategy instruction set; The control strategy instruction set is input into the remote motor controller to control the electric scooter.

[0007] Furthermore, the multi-dimensional feature extraction and data fusion are performed on the original sensor data stream to obtain the scooter state feature vector, including: Decomposing the original sensor data stream in the time-frequency domain to obtain a vibration characteristic sequence of the electric scooter, and decomposing the vibration characteristic sequence by wavelet packets to obtain a multi-scale vibration spectrum diagram; Performing scooter posture data analysis on the electric scooter based on the multi-scale vibration spectrum to obtain a posture feature set, performing time-series alignment on the posture feature set to obtain an aligned state sequence, and performing sparse coding on the aligned state sequence to obtain a compressed feature representation; Multimodal feature fusion is performed on the compressed feature representation to obtain a fused feature matrix, and tensor decomposition is performed on the fused feature matrix to obtain a scooter state feature vector.

[0008] Furthermore, the multimodal feature mapping of the electric scooter is performed based on the scooter state feature vector to obtain the abnormal behavior pattern feature of the vehicle, including: Performing multi-dimensional space projection on the scooter state feature vector to obtain a feature distribution mapping atlas, and performing nonlinear manifold embedding on the feature distribution mapping atlas to obtain a topological feature matrix; The topological feature matrix is ​​similarly measured by a preset local sensitive hashing technology to obtain a feature similarity spectrum, and the feature similarity spectrum is subjected to spectral clustering analysis to obtain a behavior pattern prototype set; wherein the behavior pattern prototype set includes an acceleration mode, a steering mode, and a braking mode; Extracting spatiotemporal features from the behavior pattern prototype set to obtain a spatiotemporal feature tensor, and performing tensor decomposition on the spatiotemporal feature tensor to obtain multimodal behavior features; The multimodal behavior characteristics are subjected to a time-series dependency analysis through a preset conditional random field technology to obtain a state transition sequence, and the state transition sequence is subjected to a Markov jump analysis to obtain a behavior pattern transition diagram; wherein the behavior pattern transition diagram includes state duration, state transition probability, and state stability; Based on the preset historical behavior data, the behavior pattern conversion graph is subjected to distributed association mining to obtain the behavior pattern association rules, and the behavior pattern association rules are subjected to probability graph reasoning to obtain the behavior prediction vector; wherein the behavior prediction vector includes the probability of behavior occurrence, the duration of the behavior, and the behavior conversion trend; Multi-scale feature fusion is performed on the behavior prediction vector to obtain a multi-dimensional behavior feature space, and abnormal behavior detection is performed based on the multi-dimensional behavior feature space to obtain vehicle abnormal behavior pattern characteristics; wherein the vehicle abnormal behavior pattern characteristics include abnormality occurrence probability, abnormality degree score and abnormal behavior type.

[0009] Furthermore, the risk prediction of the electric scooter based on the abnormal behavior pattern characteristics of the vehicle to obtain a risk level classification result includes: Performing structural decomposition on the abnormal behavior pattern characteristics of the vehicle to obtain a behavior characteristic component sequence, and performing dynamic evolution analysis on the behavior characteristic component sequence through a recursive quantitative analysis method to obtain a behavior evolution trajectory diagram, and performing fractal dimension calculation on the behavior evolution trajectory diagram to obtain a fault evolution feature set; wherein the fault evolution feature set includes component wear trend, fault diffusion rate, and system degradation degree; Performing causal chain analysis on the fault evolution feature set based on preset historical fault data to obtain a fault propagation network, and extracting topological features from the fault propagation network to obtain a set of key fault nodes; Probabilistic reasoning is performed on the key fault node set through a preset Bayesian network algorithm to obtain a component reliability evaluation value, and a fuzzy comprehensive evaluation is performed on the electric scooter based on the component reliability evaluation value to obtain a health status score sheet; wherein the health status score sheet includes motor health, battery life value and bearing wear; An operation risk analysis of the electric scooter is performed based on the health status score sheet to obtain a risk level classification result.

[0010] Furthermore, the risk level classification results are distributedly processed and prioritized by the edge computing gateway to obtain a control strategy instruction set, including: Performing data structure analysis on the risk level classification result to obtain a risk feature sequence, and performing multi-level threshold quantization on the risk feature sequence to obtain a graded risk numerical matrix; wherein the graded risk numerical matrix includes a motor control risk value, a battery safety threshold, and a bearing loss index; Performing distributed parallel decoupling on the hierarchical risk numerical matrix to obtain a risk priority mapping table, and performing adaptive allocation of network topology based on the risk priority mapping table to obtain a task scheduling sequence; wherein the task scheduling sequence includes an emergency task queue, a regular task queue, and a low-priority task queue; By using the edge computing gateway, resource allocation is optimized for the task scheduling sequence to obtain an edge node allocation scheme, and load balancing is adjusted for the edge node allocation scheme to obtain an edge computing optimization strategy; Based on the edge computing optimization strategy, the preset scooter control parameters are adjusted and mapped in real time to obtain a control strategy instruction set; wherein the control strategy instruction set includes speed limit instructions, steering angle constraints, battery power control, and emergency braking strategies.

[0011] Furthermore, the step of inputting the control strategy instruction set into the remote motor controller to control the electric scooter comprises: Decoding and restoring the control strategy instruction set to obtain a control parameter sequence, and performing parameter boundary check on the control parameter sequence to obtain a safety control threshold matrix; wherein the safety control threshold matrix includes a maximum speed limit, a maximum steering angle, a maximum power output, and a maximum braking force; The safety control threshold matrix is ​​mapped in parallel segments through a multi-thread channel to obtain a multi-channel control signal stream, and the multi-channel control signal stream is synchronized in real time to obtain a synchronous control instruction sequence; Performing waveform modulation transformation on the synchronous control instruction sequence to obtain a drive waveform cluster, and performing pulse width encoding on the drive waveform cluster to obtain a motor drive signal chain; Performing hardware timing analysis on the motor controller based on the motor drive signal chain to obtain a controller execution timing table, and performing parallel task allocation on the controller execution timing table to obtain a multi-core execution instruction group; The multi-core execution instruction group is mapped to a hardware driver to obtain a motor control drive sequence, and the electric scooter is controlled in real time based on the motor control drive sequence.

[0012] Furthermore, the pulse width encoding of the driving waveform cluster is performed to obtain a motor driving signal chain, including: Performing waveform feature decomposition on the driving waveform cluster to obtain a fundamental wave parameter matrix, and performing multiple sampling quantization on the fundamental wave parameter matrix to obtain a waveform sampling sequence; Carrier signal synthesis is performed based on the waveform sampling sequence to obtain a carrier modulation parameter set, and phase compensation calibration is performed on the carrier modulation parameter set to obtain a phase correction matrix; Performing duty cycle modulation calculation on the phase correction matrix to obtain a PWM modulation sequence, and performing dead time compensation on the PWM modulation sequence to obtain a compensation control vector; The compensation control vector is edge-detected and synchronized by a preset synchronization trigger to obtain a trigger timing set, and the trigger timing set is multi-level interleaved encoded to obtain a drive waveform sequence, and the drive waveform sequence is output parameter mapped to obtain a motor drive signal chain; wherein the motor drive signal chain includes a PWM waveform, a phase trigger point, a duty cycle parameter, and a frequency modulation value.

[0013] The present invention also provides an electric scooter remote monitoring system based on the Internet of Things, wherein a motor controller is provided in the electric scooter, comprising: The acquisition module is used to collect data from the multi-source sensors carried by the electric scooter to obtain the original sensor data stream; wherein the original sensor data stream includes a gyroscope sensor, an acceleration sensor, a GPS positioning sensor, and a battery management sensor; An extraction module, used for performing multi-dimensional feature extraction and data fusion on the original sensor data stream to obtain a scooter state feature vector; A mapping module, used for performing multimodal feature mapping on the electric scooter based on the scooter state feature vector to obtain vehicle abnormal behavior pattern features; A prediction module, used to predict the risk of the electric scooter based on the abnormal behavior pattern characteristics of the vehicle, and obtain a risk level classification result; A sorting module, used for performing distributed processing and priority sorting on the risk level classification results through an edge computing gateway to obtain a control strategy instruction set; The control module is used to input the control strategy instruction set into the remote motor controller to control the electric scooter.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0016] The present invention provides a remote monitoring method for an electric scooter based on the Internet of Things, comprising the following steps: collecting data from multi-source sensors carried by the electric scooter to obtain an original sensor data stream; performing multi-dimensional feature extraction and data fusion on the original sensor data stream to obtain a scooter state feature vector; if the scooter state feature vector indicates that the electric scooter is abnormal, performing multimodal feature mapping on the electric scooter based on the scooter state feature vector to obtain a vehicle abnormal behavior pattern feature; performing risk prediction on the electric scooter based on the vehicle abnormal behavior pattern feature to obtain a risk level classification result; performing distributed processing and priority sorting on the risk level classification result through an edge computing gateway to obtain a control strategy instruction set; inputting the control strategy instruction set into the remote motor controller to control the electric scooter, and solving the technical problem that the traditional management method mainly relies on manual inspection and regular maintenance, which is not only inefficient, but also difficult to monitor the vehicle status in real time and timely discover potential safety hazards, and realizing the use of edge computing gateways for distributed processing and priority sorting, ensuring that even in poor network conditions, the control strategy can be quickly responded to and executed. This greatly improves the stability and reliability of the system, while also reducing latency and enhancing the technical effects of the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the steps of a remote monitoring method for an electric scooter based on the Internet of Things in one embodiment of the present invention; Figure 2 It is a structural block diagram of an electric scooter remote monitoring system based on the Internet of Things in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a remote monitoring method for an electric scooter based on the Internet of Things in one embodiment of the present invention; In one embodiment of the present invention, a remote monitoring method for an electric scooter based on the Internet of Things is provided. The electric scooter is provided with a motor controller, and the method comprises the following steps: Step S1, collecting data from multiple source sensors carried by the electric scooter to obtain an original sensor data stream; wherein the original sensor data stream includes a gyroscope sensor, an acceleration sensor, a GPS positioning sensor and a battery management sensor.

[0021] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, data collection of multi-source sensors carried by electric scooters is one of the key steps to achieve comprehensive monitoring. Specifically, this process involves obtaining raw sensor data streams from gyroscope sensors, accelerometers, GPS positioning sensors, and battery management sensors. First, the gyroscope sensor can detect the change in the angular velocity of the scooter, thereby helping to determine its posture and direction; the accelerometer can measure the linear acceleration of the scooter in three dimensions, which is crucial for understanding the dynamic behavior of the vehicle. At the same time, the GPS positioning sensor provides precise location information, allowing operators to track the specific location of each scooter in real time, which is particularly important in the sharing economy model because it helps to optimize scheduling and manage resources. In addition, the battery management sensor monitors the status of the battery, including parameters such as voltage, current, and temperature, to ensure the safe use of the battery and extend its life. To better illustrate this process, we can imagine an application scenario: in a busy urban environment, a shared scooter company needs to ensure that all scooters put on the market can operate efficiently and safely. Through the collaborative work of the above sensors, the system starts collecting data from these sensors every time a user starts a scooter. For example, when the scooter turns, the gyroscope and accelerometer will record the corresponding angular velocity and acceleration changes, while the GPS positioning sensor will continuously update the scooter's location information so that the background management system can monitor its driving path in real time. At the same time, the battery management sensor constantly monitors the battery status. Once the battery is too low or the temperature rises abnormally, the system will immediately notify the maintenance team to take corresponding measures. This not only improves the user experience, but also enhances safety and reduces potential risks. Throughout the process, the rich data streams provided by multi-source sensors lay a solid foundation for subsequent data processing and analysis, making the scooter's status assessment more accurate and management more efficient.

[0022] Step S2, performing multi-dimensional feature extraction and data fusion on the original sensor data stream to obtain a scooter state feature vector.

[0023] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, multi-dimensional feature extraction and data fusion of the original sensor data stream to generate the scooter state feature vector is an important step to achieve accurate monitoring. This process first requires extracting key features from the data collected by the gyroscope sensor, accelerometer, GPS positioning sensor and battery management sensor. For example, the change trend of angular velocity is extracted from the gyroscope sensor, the change of linear acceleration is extracted from the accelerometer, the speed and direction of position change are obtained from the GPS positioning sensor, and the parameters such as voltage, current and temperature are monitored from the battery management sensor. Next, these different types of raw data are fused through complex data processing algorithms in order to convert them into a feature vector that can fully describe the current state of the scooter. To understand this process more clearly, we can imagine such an application scenario: in a shared scooter service system, when the user drives the scooter, each sensor will continuously record its operating status. Suppose a scooter suddenly vibrates abnormally during driving. At this time, the gyroscope sensor will capture the abnormal angular velocity fluctuation, and the accelerometer will record the abnormal acceleration change. At the same time, the GPS positioning sensor provides the location information of the vehicle to help determine the specific location of the problem, while the battery management sensor ensures that the battery status is stable to avoid problems caused by low power or overheating. The data provided by all these sensors are fed into the data fusion module, where advanced algorithms such as machine learning models analyze and integrate the data to generate a state feature vector containing information in multiple dimensions. This feature vector not only reflects the current physical state of the scooter (such as whether it is in a stable driving state or whether there is a mechanical failure), but also contains important information such as environmental factors (such as position and speed) and battery health. This comprehensive status assessment provides a solid foundation for subsequent risk prediction and control strategy formulation, allowing operators to more accurately judge the operating status of the scooter and take corresponding measures to ensure user safety and service quality. In this way, not only the intelligence level of the system is improved, but also the user experience and safety are enhanced.

[0024] Step S3: If the scooter state feature vector indicates that the electric scooter is abnormal, multimodal feature mapping is performed on the electric scooter based on the scooter state feature vector to obtain abnormal behavior pattern features of the vehicle.

[0025] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, once the scooter state feature vector obtained by multi-dimensional feature extraction and data fusion shows an abnormal situation, the system will perform multimodal feature mapping on the electric scooter based on this feature vector to identify the abnormal behavior pattern characteristics of the vehicle. Specifically, this process first requires analyzing the various indicators in the state feature vector, including data provided by the gyroscope sensor, accelerometer sensor, GPS positioning sensor and battery management sensor. For example, if the state feature vector shows significant fluctuations in angular velocity and acceleration, accompanied by irregularities in position changes or abnormal battery parameters, this may mean that the scooter has a mechanical failure or is experiencing an abnormal usage scenario. In this case, the system will further map these features using a pre-trained model to convert different types of sensor data into unified abnormal behavior pattern characteristics. To better understand this process, we can imagine an application scenario: in a shared scooter operation environment, a scooter suddenly shakes violently and deviates from the scheduled route during driving. At this time, the gyroscope sensor records an abnormally high angular velocity value, the accelerometer detects frequent acceleration changes, the GPS positioning sensor shows an irregular position movement trajectory, and the battery management sensor reports an increase in battery temperature. All this information is integrated into the state feature vector of the scooter and passed to the multimodal feature mapping module. This module uses machine learning algorithms to conduct in-depth analysis of these features, trying to find the root cause of these abnormal phenomena. For example, the system may find that this abnormal behavior pattern matches a known mechanical failure (such as tire damage) or an extreme usage situation (such as high-speed sharp turns). In this way, the system can not only accurately identify specific abnormal behavior patterns, but also provide key basis for subsequent risk prediction and control strategies. For example, after detecting the above anomalies, the system can immediately notify the operation team for inspection and repair, thereby avoiding potential safety hazards and ensuring the riding safety and service quality of users. In addition, this intelligent monitoring mechanism can also help optimize maintenance plans, reduce unnecessary downtime, and improve resource utilization. By combining data from multi-source sensors and advanced data analysis technology, accurate assessment and timely response to the health status of electric scooters are achieved.

[0026] Step S4: predicting the risk of the electric scooter based on the abnormal behavior pattern characteristics of the vehicle to obtain a risk level classification result.

[0027] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, once the abnormal behavior pattern characteristics of the vehicle are obtained through multimodal feature mapping, the system will predict the risk of the electric scooter based on these characteristics to obtain the risk level classification result. Specifically, this process first requires the use of machine learning models or statistical analysis methods to evaluate the impact of abnormal behavior pattern characteristics. For example, if the state feature vector shows that there is a significant fluctuation in the data of the accelerometer and gyroscope sensors, and the GPS positioning sensor records irregular position changes, and the battery management sensor reports abnormal battery temperature increases, then this information will be input into the pre-trained risk prediction model. The model will calculate the probability of a failure or accident that may occur in the current abnormal behavior mode based on historical data and preset risk thresholds. To understand this process more clearly, we can imagine an application scenario: in a shared scooter service system, a scooter has abnormal vibrations, frequent sharp turns, and battery overheating during driving. At this point, the system has identified these abnormal behavior pattern characteristics through multimodal feature mapping and passed them to the risk prediction module. In this module, the system uses historical data and known risk factors (such as mechanical failure, overuse, or environmental impact) to evaluate the potential risks under the current situation. Assuming that the system detects that this abnormal behavior pattern is highly similar to a high-risk event in the previous record, such as loss of control caused by tire damage or safety hazards caused by battery overheating, it will immediately start the risk prediction algorithm and calculate the corresponding risk level. For example, the system may classify the risk into three levels: low, medium, and high according to the severity of the abnormal behavior pattern. If the prediction result shows that the current scooter is in a high-risk state, the system will generate a high-risk level result and trigger the corresponding alarm mechanism. Further, based on the risk level classification results, the system can take different countermeasures. For example, for high-risk situations, the system will immediately notify the operation team for emergency inspection and repair; while for medium-risk situations, the system may suggest that the user slow down and end the trip as soon as possible. This intelligent risk prediction mechanism can not only detect potential safety hazards in a timely manner, but also provide operators with a scientific basis for formulating reasonable maintenance plans, reducing the occurrence of accidents, and improving user experience and safety. By combining advanced data analysis technology and real-time monitoring methods, accurate evaluation and effective management of the operating status of electric scooters are achieved. In this way, it can not only ensure the riding safety of users, but also improve resource utilization efficiency and reduce operating costs.

[0028] Step S5: Distributed processing and priority sorting of the risk level classification results are performed through the edge computing gateway to obtain a control strategy instruction set.

[0029] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, distributed processing and prioritization of risk level classification results through edge computing gateways to generate control strategy instruction sets are key steps to ensure efficient response and optimize resource management. Specifically, after the system completes risk prediction based on the abnormal behavior pattern characteristics of the vehicle and obtains risk level classification results, these results will be sent to the edge computing gateway. As a processing node close to the data source, the edge computing gateway can quickly analyze and process a large amount of data without uploading all data to the cloud, thereby reducing latency and improving the real-time performance of the system. In this process, the edge computing gateway first distributes the risk level classification results of each scooter, which means that it processes the data of multiple scooters at the same time and classifies them according to their risk levels. For example, in a shared scooter service system, assume that multiple scooters report different risk levels at the same time: some vehicles are in high-risk states (such as battery overheating or mechanical failure), while others are in medium or low-risk states (such as minor sensor abnormalities). The edge computing gateway will first identify those scooters in high-risk states and prioritize them. This sorting mechanism can determine which vehicles need to take immediate action based on the severity and urgency of the risk. For high-risk scooters, the system may generate an emergency stop command or advise the user to end the trip immediately; for medium-risk scooters, it may generate instructions to slow down or check the status of the device. All these control strategy instruction sets will be integrated together to form specific operating guidelines for each scooter. To better understand this process, we can imagine such an application scenario: In a busy urban environment, a shared scooter company operates thousands of scooters. One day, due to bad weather and frequent use, many scooters experienced varying degrees of abnormalities. After receiving the risk level classification results of these vehicles, the edge computing gateway quickly starts the distributed processing mechanism to evaluate the status of each vehicle. For those scooters with serious battery overheating or mechanical failure, the system immediately generates an emergency stop command and sends it to the corresponding scooter through the network to ensure the safety of users. At the same time, for those scooters with only minor abnormalities (such as unstable GPS signals), the system generates instructions to remind users to pay attention and recommend stopping nearby for inspection. In this way, the edge computing gateway can not only process a large amount of data in a short time, but also dynamically adjust the control strategy according to the actual situation to ensure that each scooter can be managed in a timely and effective manner. This efficient distributed processing and priority sorting mechanism significantly improves the system's response speed and reliability, while also optimizing resource allocation, reducing operating costs, and enhancing user experience and security.

[0030] Step S6, inputting the control strategy instruction set into the remote motor controller to control the electric scooter.

[0031] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, inputting the control strategy instruction set into the remote motor controller to control the electric scooter is a key step to achieve intelligent management and ensure user safety. Specifically, after the edge computing gateway completes the distributed processing and priority sorting of the risk level classification results, the generated control strategy instruction set will be sent to the motor controller of each scooter. This process relies on a stable and reliable communication network to ensure that the instructions can be delivered to the target vehicle in a timely and accurate manner. Once the motor controller receives these instructions, it will adjust the operation mode of the scooter according to the content of the instructions, thereby achieving real-time control of the vehicle. For example, in a shared scooter service system, assuming that a scooter is identified as a high-risk state by the system due to battery overheating, the edge computing gateway generates an emergency shutdown instruction as part of the control strategy. The instruction is then transmitted to the motor controller of the scooter through a wireless communication network. After receiving the instruction, the motor controller immediately performs the corresponding operation, such as gradually slowing down and eventually stopping the operation of the scooter, and at the same time sends a warning message to the user, prompting that there is a safety hazard in the current device and that the trip needs to be ended immediately and the customer service should be contacted for inspection. For scooters in a medium-risk state, if a minor mechanical failure or sensor abnormality is detected, the system may generate instructions to slow down or suggest that the user stop nearby for inspection. After receiving such instructions, the motor controller will adjust the speed of the scooter accordingly and notify the user to take appropriate measures through the on-board display or mobile phone application. To further illustrate this process, we can imagine such an application scenario: In a busy urban environment, a shared scooter company operates thousands of scooters. One of the scooters suddenly has a serious mechanical failure during use, causing abnormal fluctuations in the acceleration sensor and gyroscope sensor data. The edge computing gateway quickly analyzes this data, determines that the scooter is in a high-risk state, and generates an emergency stop instruction. The instruction is quickly transmitted to the scooter's motor controller through the wireless communication network, and the motor controller responds immediately, first gradually reducing the speed of the scooter and then stopping it smoothly. At the same time, the system sends an alarm message through the user's mobile phone application, informing the user that the current device has a safety hazard and recommending that he contact customer service as soon as possible for follow-up processing. This immediate response mechanism not only effectively avoids potential safety accidents, but also improves user experience and sense of security. In addition, for scooters with only minor abnormalities (such as unstable GPS signals), the system generates instructions to remind users to pay attention and recommends stopping nearby for inspection. The motor controller adjusts the vehicle status according to the instructions to ensure that users can continue to use the scooter in a safe manner. In this way, the entire system realizes closed-loop management from data collection, risk assessment to control strategy execution, significantly improving the safety and reliability of electric scooters. This efficient control mechanism not only optimizes resource utilization, but also reduces operating costs and enhances overall service quality.

[0032] In a specific embodiment, the multi-dimensional feature extraction and data fusion of the original sensor data stream to obtain the scooter state feature vector includes: Decomposing the original sensor data stream in the time-frequency domain to obtain a vibration characteristic sequence of the electric scooter, and decomposing the vibration characteristic sequence by wavelet packets to obtain a multi-scale vibration spectrum diagram; Performing scooter posture data analysis on the electric scooter based on the multi-scale vibration spectrum to obtain a posture feature set, performing time-series alignment on the posture feature set to obtain an aligned state sequence, and performing sparse coding on the aligned state sequence to obtain a compressed feature representation; Multimodal feature fusion is performed on the compressed feature representation to obtain a fused feature matrix, and tensor decomposition is performed on the fused feature matrix to obtain a scooter state feature vector.

[0033] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, it is a complex and critical process to extract multi-dimensional features and fuse data from the original sensor data stream to generate a scooter state feature vector. This process includes multiple steps, each of which involves complex signal processing and data analysis techniques to ensure that the state of the scooter can be fully and accurately described. First, the original sensor data stream obtained from the gyroscope sensor, acceleration sensor, GPS positioning sensor and battery management sensor needs to be decomposed in the time-frequency domain to obtain the vibration feature sequence of the electric scooter, and further these vibration feature sequences are decomposed by wavelet packets to generate a multi-scale vibration spectrum. Specifically, the time-frequency domain decomposition converts the original sensor data into a data representation in two dimensions, time and frequency, so that the vibration characteristics in different time periods can be analyzed simultaneously. For example, in a shared scooter service system, assuming that a scooter has abnormal vibration during driving, the specific time and frequency distribution of this vibration can be captured through time-frequency domain decomposition. Then, using wavelet packet decomposition technology, these vibration features can be further refined into multi-scale vibration spectrum diagrams, providing more detailed frequency component information to help identify potential problems. Next, the posture data of the electric scooter is analyzed based on the multi-scale vibration spectrum to obtain a posture feature set. In this process, the posture feature set not only contains the posture information of the scooter (such as tilt angle, rotation speed, etc.), but also combines the position and motion data provided by other sensors. In order to ensure the consistency and comparability of these data, the posture feature set needs to be time-series aligned, that is, the timestamps of different sensors are synchronized to generate an aligned state sequence. This step is crucial for subsequent data processing because it ensures that all sensor data can be compared and analyzed under the same time reference. For example, when the scooter is turning or accelerating or decelerating, the aligned state sequence can accurately capture its posture changes, so as to better evaluate the running state of the vehicle. Then, the aligned state sequence is sparsely encoded to obtain a compressed feature representation. Sparse coding is a data compression technology that achieves efficient data representation by finding a small number of basis vectors that best represent the original data. This method can not only reduce the amount of data, but also retain key information and improve the efficiency of subsequent processing. For example, in the above application scenario, by sparsely encoding the aligned state sequence, a large amount of raw sensor data can be compressed into a compact feature representation, which is convenient for fast transmission and processing. This compressed feature representation not only reduces the burden of data storage and transmission, but also improves the real-time response capability of the system. Subsequently, the compressed feature representation is subjected to multimodal feature fusion to generate a fused feature matrix. Multimodal feature fusion aims to integrate data from different sensors to form a unified feature representation. In this process, different types of sensor data (such as acceleration, angular velocity, position, etc.) are combined to form a multidimensional feature matrix.For example, in the application scenario of shared scooters, by fusing the data of the accelerometer, gyroscope sensor, and GPS positioning sensor, a feature matrix that comprehensively reflects the dynamic behavior of the scooter can be generated. This fused feature matrix can provide more comprehensive information and help to more accurately identify the status and potential problems of the scooter. Finally, the fused feature matrix is ​​tensor decomposed to obtain the scooter state feature vector. Tensor decomposition is an advanced data analysis method that can decompose high-dimensional data into multiple low-dimensional subspaces to reveal hidden structures and patterns in the data. In this way, the most representative feature vector can be extracted from the fused feature matrix for subsequent risk prediction and control strategy formulation. For example, in the above application scenario, by performing tensor decomposition on the fused feature matrix, a feature vector that can accurately describe the current state of the scooter can be obtained. This feature vector not only contains the posture, position, and motion information of the scooter, but also reflects its health status and potential risks. Based on this feature vector, the system can perform accurate risk assessment and early warning, and take corresponding control measures in time to ensure the safety of users and service quality. To better understand the whole process, we can imagine a specific application scenario: In a busy urban environment, a shared scooter company operates thousands of scooters. One day, due to bad weather and frequent use, many scooters experienced different degrees of abnormal conditions. Assume that one of the scooters suddenly shook violently and deviated from the scheduled route during driving. At this time, the system first collects the original sensor data stream from each sensor and generates a multi-scale vibration spectrum through time-frequency domain decomposition and wavelet packet decomposition. Then, posture data analysis is performed based on these spectrum graphs to obtain a posture feature set, and an aligned state sequence is generated through time alignment. Sparse coding is performed on the aligned state sequence to generate a compressed feature representation, thereby reducing the amount of data and retaining key information. Subsequently, the compressed feature representation is fused with other sensor data for multimodal features to generate a fused feature matrix. Finally, the fused feature matrix is ​​tensor-decomposed to generate a scooter state feature vector. Based on this feature vector, the system can accurately identify the abnormal conditions of the scooter and take corresponding control measures according to the risk level classification results, such as emergency shutdown or suggesting that users slow down to ensure user safety and normal operation of the equipment. Through this multi-level data processing and analysis mechanism, comprehensive monitoring and intelligent management of the status of electric scooters are achieved, significantly improving the reliability of the system and user experience.

[0034] In a specific embodiment, the multimodal feature mapping of the electric scooter based on the scooter state feature vector to obtain the abnormal behavior pattern feature of the vehicle includes: Performing multi-dimensional space projection on the scooter state feature vector to obtain a feature distribution mapping atlas, and performing nonlinear manifold embedding on the feature distribution mapping atlas to obtain a topological feature matrix; The topological feature matrix is ​​similarly measured by a preset local sensitive hashing technology to obtain a feature similarity spectrum, and the feature similarity spectrum is subjected to spectral clustering analysis to obtain a behavior pattern prototype set; wherein the behavior pattern prototype set includes an acceleration mode, a steering mode, and a braking mode; Extracting spatiotemporal features from the behavior pattern prototype set to obtain a spatiotemporal feature tensor, and performing tensor decomposition on the spatiotemporal feature tensor to obtain multimodal behavior features; The multimodal behavior characteristics are subjected to a time-series dependency analysis through a preset conditional random field technology to obtain a state transition sequence, and the state transition sequence is subjected to a Markov jump analysis to obtain a behavior pattern transition diagram; wherein the behavior pattern transition diagram includes state duration, state transition probability, and state stability; Based on the preset historical behavior data, the behavior pattern conversion graph is subjected to distributed association mining to obtain the behavior pattern association rules, and the behavior pattern association rules are subjected to probability graph reasoning to obtain the behavior prediction vector; wherein the behavior prediction vector includes the probability of behavior occurrence, the duration of the behavior, and the behavior conversion trend; Multi-scale feature fusion is performed on the behavior prediction vector to obtain a multi-dimensional behavior feature space, and abnormal behavior detection is performed based on the multi-dimensional behavior feature space to obtain vehicle abnormal behavior pattern characteristics; wherein the vehicle abnormal behavior pattern characteristics include abnormality occurrence probability, abnormality degree score and abnormal behavior type.

[0035] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, it is a complex and multi-level process to perform multimodal feature mapping on electric scooters based on the scooter state feature vector to obtain the abnormal behavior pattern characteristics of the vehicle. First, the system will perform multidimensional space projection on the scooter state feature vector to generate a feature distribution mapping map, and further perform nonlinear manifold embedding on these maps to obtain a topological feature matrix. Specifically, multidimensional space projection converts high-dimensional feature vectors into representations in low-dimensional space, making it possible to more intuitively observe and analyze the distribution characteristics of data. For example, in a shared scooter service system, assuming that a scooter has abnormal vibration during driving, the state feature vector of the scooter can be mapped to a two-dimensional or three-dimensional space through multidimensional space projection to form a feature distribution mapping map. Then, using nonlinear manifold embedding technology (such as t-SNE or LLE), these features can be further embedded into a topological structure to reveal hidden data relationships and generate a topological feature matrix. Next, the system will use the preset local sensitive hashing (LSH) technology to measure the similarity of the topological feature matrix, obtain the feature similarity spectrum, and perform spectral clustering analysis on the feature similarity spectrum to generate a set of behavioral pattern prototypes. In this process, the local sensitive hashing technology can efficiently calculate the similarity between different features, and the spectral clustering analysis can group samples with similar features to form different behavioral pattern prototypes. For example, in the above application scenario, the similarity between the topological feature matrices of each vehicle is calculated by the local sensitive hashing technology, and the spectral clustering algorithm is used to divide them into several clusters, each cluster representing a typical behavioral pattern, such as acceleration mode, steering mode and braking mode. This classification not only helps to identify normal driving behavior, but also helps to discover potential abnormal behavior patterns. Subsequently, the system extracts spatiotemporal features from the behavioral pattern prototype set, generates a spatiotemporal feature tensor, and performs tensor decomposition on it to obtain multimodal behavioral features. Spatiotemporal feature extraction aims to capture the dynamic changes of behavioral patterns in time and space, while tensor decomposition can decompose high-dimensional spatiotemporal features into multiple low-dimensional subspaces to reveal key information in the data. For example, in the above application scenario, by extracting the spatiotemporal features of the acceleration mode, steering mode, and braking mode, a spatiotemporal feature tensor containing time series and spatial position information can be generated. Then, through the tensor decomposition technology, this high-dimensional tensor can be decomposed into multiple low-dimensional features to form multimodal behavior features to better describe the behavior pattern of the scooter. Then, the system uses the preset conditional random field (CRF) technology to perform temporal dependency analysis on the multimodal behavior features, generate a state transition sequence, and perform Markov jump analysis on the state transition sequence to obtain a behavior pattern transition diagram. Conditional random field technology can effectively model dependencies in sequence data, while Markov jump analysis can reveal the transition probability and duration between states.For example, in the above application scenario, the time dependency of multimodal behavior characteristics is analyzed by conditional random field technology to generate a state transition sequence, such as the transition process from acceleration mode to steering mode and then to braking mode. Then, through Markov jump analysis, the duration, state transition probability and state stability of each state can be calculated to form a behavior pattern transition graph. Next, the system performs distributed association mining on the behavior pattern transition graph based on the preset historical behavior data, generates behavior pattern association rules, and performs probabilistic graph reasoning on the behavior pattern association rules to obtain a behavior prediction vector. Distributed association mining can discover potential behavior pattern association rules from a large amount of historical data, while probabilistic graph reasoning can quantify the occurrence probability and trend of these rules. For example, in the above application scenario, by performing distributed association mining on historical behavior data, the association between certain specific behavior pattern combinations (such as frequent sudden acceleration and sudden braking) and the occurrence rate of faults can be found. Then, through probabilistic graph reasoning technology, the probability, duration and conversion trend of these behavior patterns in the future can be calculated to form a behavior prediction vector. Finally, the system performs multi-scale feature fusion on the behavior prediction vector to generate a multi-dimensional behavior feature space, and performs abnormal behavior detection based on the multi-dimensional behavior feature space to generate abnormal behavior pattern features of the vehicle. Multi-scale feature fusion can integrate feature information at different levels, while abnormal behavior detection can identify behaviors that do not conform to the normal pattern. For example, in the above application scenario, by performing multi-scale feature fusion on the behavior prediction vector, a multi-dimensional behavior feature space can be generated, which contains feature representations of multiple behavior patterns. Then, through the abnormal behavior detection algorithm, behaviors that deviate from the normal pattern, such as abnormal acceleration fluctuations or irregular steering behaviors, can be identified, and abnormal behavior pattern features of the vehicle can be generated, including the probability of abnormal occurrence, abnormality degree score and abnormal behavior type. Through this multi-level data processing and analysis mechanism, comprehensive monitoring and intelligent management of the status of electric scooters are achieved, significantly improving the reliability of the system and user experience. This meticulous analysis can not only detect potential safety hazards in a timely manner, but also provide a scientific basis for subsequent risk prediction and control strategies to ensure user safety and service quality.

[0036] In a specific embodiment, the risk prediction of the electric scooter based on the abnormal behavior pattern characteristics of the vehicle to obtain the risk level classification result includes: Performing structural decomposition on the abnormal behavior pattern characteristics of the vehicle to obtain a behavior characteristic component sequence, and performing dynamic evolution analysis on the behavior characteristic component sequence through a recursive quantitative analysis method to obtain a behavior evolution trajectory diagram, and performing fractal dimension calculation on the behavior evolution trajectory diagram to obtain a fault evolution feature set; wherein the fault evolution feature set includes component wear trend, fault diffusion rate, and system degradation degree; Performing causal chain analysis on the fault evolution feature set based on preset historical fault data to obtain a fault propagation network, and extracting topological features from the fault propagation network to obtain a set of key fault nodes; Probabilistic reasoning is performed on the key fault node set through a preset Bayesian network algorithm to obtain a component reliability evaluation value, and a fuzzy comprehensive evaluation is performed on the electric scooter based on the component reliability evaluation value to obtain a health status score sheet; wherein the health status score sheet includes motor health, battery life value and bearing wear; An operation risk analysis of the electric scooter is performed based on the health status score sheet to obtain a risk level classification result.

[0037] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, risk prediction of electric scooters based on abnormal behavior pattern characteristics of vehicles to obtain risk level classification results is a multi-level and complex process. First, the system will perform structural decomposition on the abnormal behavior pattern characteristics of the vehicle to generate a behavior feature component sequence, and dynamically evolve these sequences through a recursive quantitative analysis method to obtain a behavior evolution trajectory diagram. Specifically, structural decomposition decomposes complex abnormal behavior pattern characteristics into multiple independent behavior feature components, so that the change trend of each component can be analyzed more carefully. For example, in a shared scooter service system, assuming that a scooter has frequent sudden acceleration and sudden braking during driving, these abnormal behavior pattern characteristics can be decomposed into independent behavior feature component sequences such as acceleration changes and steering angle changes through structural decomposition. Then, a recursive quantitative analysis method (such as a recursive graph or recursive quantitative analysis) is used to dynamically evolve these behavior feature component sequences to generate a behavior evolution trajectory diagram. This trajectory diagram can not only show the changes of behavior characteristics over time, but also reveal potential trends and patterns. Next, the system calculates the fractal dimension of the behavior evolution trajectory diagram to obtain the fault evolution feature set. Fractal dimension calculation is a mathematical tool used to describe the dynamic characteristics of complex systems, which can quantify the self-similarity and irregularity of the system. In this process, by calculating the fractal dimension of the behavior evolution trajectory diagram, key features reflecting the degree of system degradation, such as component wear trend, fault diffusion rate, and system degradation degree, can be extracted. For example, in the above application scenario, through fractal dimension calculation, it can be found that some key components of the scooter (such as motors or bearings) have a trend of gradual deterioration, and a fault evolution feature set is generated. These feature sets not only provide detailed fault information, but also provide a scientific basis for subsequent risk assessment. Subsequently, the system performs causal chain analysis on the fault evolution feature set based on the preset historical fault data, generates a fault propagation network, and extracts topological features from the fault propagation network to obtain a set of key fault nodes. Causal chain analysis aims to mine the causal relationship between faults from historical fault data, while topological feature extraction can identify key nodes in the fault propagation network. For example, in the above application scenario, through causal chain analysis, it can be found that there is an obvious causal relationship between sudden acceleration and sudden braking and problems such as motor overheating and bearing wear, forming a fault propagation network. Then, by extracting the topological features of the network, the key fault nodes that have the greatest impact on system reliability can be identified, such as motor overheating or battery life attenuation. Then, the system uses the preset Bayesian network algorithm to perform probabilistic reasoning on the set of key fault nodes to obtain component reliability assessment values, and based on these assessment values, performs fuzzy comprehensive evaluation on the electric scooter to generate a health status score sheet. Bayesian network is a graphical model based on probabilistic reasoning that can effectively handle uncertainty and complex dependencies.In this process, by performing probabilistic reasoning on key fault nodes, the reliability assessment value of each component, such as motor health, battery life value and bearing wear, can be calculated. For example, in the above application scenario, the Bayesian network algorithm can be used to determine that the current motor health is 80%, the battery life value is 75%, and the bearing wear is 60%. Then, using the fuzzy comprehensive evaluation method, these specific values ​​are converted into an overall health status score table to fully reflect the operating status of the scooter. Finally, the system performs an operation risk analysis on the electric scooter based on the health status score table and generates a risk level classification result. The operation risk analysis aims to assess the possibility of future failures of the scooter based on its overall health status and classify it into different risk levels. For example, in the above application scenario, based on the health status score table (motor health 80%, battery life value 75%, bearing wear 60%), the system can assess that the probability of major failures of this scooter in the future is high, so its risk level is classified as high risk. On the contrary, if the health status score table shows that the status of all key components is at a good level, its risk level may be classified as low risk. In this way, the system can not only identify potential safety hazards in a timely manner, but also provide scientific maintenance suggestions to operators to ensure the safety and reliability of scooters. To better understand the whole process, we can imagine a specific application scenario: In a busy urban environment, a shared scooter company operates thousands of scooters. One day, due to bad weather and frequent use, many scooters experienced abnormal conditions to varying degrees. Assuming that one of the scooters suddenly experienced frequent rapid acceleration and braking during driving, the system first structured decomposed the abnormal behavior pattern characteristics of the scooter to generate a behavioral feature component sequence. Then, the dynamic evolution analysis of these sequences was performed using a recursive quantitative analysis method to generate a behavioral evolution trajectory graph, and the fractal dimension was calculated to obtain a fault evolution feature set. These feature sets reveal the wear trend and system degradation degree of key components such as scooter motors, batteries, and bearings. Subsequently, the system performed a causal chain analysis on the fault evolution feature set based on historical fault data, generated a fault propagation network, and identified key fault nodes such as motor overheating and battery life attenuation through topological feature extraction. Then, the Bayesian network algorithm is used to perform probabilistic reasoning on these key fault nodes to obtain the reliability evaluation values ​​of each component, and a health status score sheet is generated based on these evaluation values ​​to fully reflect the operating status of the scooter. Finally, the system performs an operation risk analysis on the scooter based on the health status score sheet, generates risk level classification results, and helps operators take corresponding maintenance measures in a timely manner to ensure user safety and service quality. Through this multi-level data processing and analysis mechanism, comprehensive monitoring and intelligent management of the status of electric scooters are achieved, significantly improving the reliability of the system and user experience.This detailed analysis can not only detect potential safety hazards in a timely manner, but also provide a scientific basis for subsequent risk prediction and control strategies to ensure user safety and service quality.

[0038] In a specific embodiment, the risk level classification results are distributedly processed and prioritized by the edge computing gateway to obtain a control strategy instruction set, including: Performing data structure analysis on the risk level classification result to obtain a risk feature sequence, and performing multi-level threshold quantization on the risk feature sequence to obtain a graded risk numerical matrix; wherein the graded risk numerical matrix includes a motor control risk value, a battery safety threshold, and a bearing loss index; Performing distributed parallel decoupling on the hierarchical risk numerical matrix to obtain a risk priority mapping table, and performing adaptive allocation of network topology based on the risk priority mapping table to obtain a task scheduling sequence; wherein the task scheduling sequence includes an emergency task queue, a regular task queue, and a low-priority task queue; By using the edge computing gateway, resource allocation is optimized for the task scheduling sequence to obtain an edge node allocation scheme, and load balancing is adjusted for the edge node allocation scheme to obtain an edge computing optimization strategy; Based on the edge computing optimization strategy, the preset scooter control parameters are adjusted and mapped in real time to obtain a control strategy instruction set; wherein the control strategy instruction set includes speed limit instructions, steering angle constraints, battery power control, and emergency braking strategies.

[0039] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, it is a complex and multi-level process to perform distributed processing and priority sorting of risk level classification results through edge computing gateways to generate control strategy instruction sets. First, the system will perform data structure analysis on the risk level classification results, generate risk feature sequences, and perform multi-level threshold quantization on these sequences to obtain a graded risk numerical matrix. Specifically, data structure analysis decomposes the complex and multi-level risk level classification results into multiple independent risk feature components, so that the specific values ​​of each component can be analyzed more carefully. For example, in a shared scooter service system, assuming that a scooter is assessed to be in a high-risk state, the system first analyzes the risk level classification results of the scooter and generates a risk feature sequence including motor control risk value, battery safety threshold and bearing loss index. Then, these risk feature sequences are quantified using a multi-level threshold quantization method (such as setting different risk threshold intervals) to generate a graded risk numerical matrix. This matrix can not only clearly show the risk level of each key component, but also provide a scientific basis for subsequent task scheduling. Next, the system performs distributed parallel decoupling on the hierarchical risk numerical matrix, generates a risk priority mapping table, and performs network topology adaptive allocation based on the mapping table to generate a task scheduling sequence. Distributed parallel decoupling aims to separate the risk feature components in the hierarchical risk numerical matrix to form an independent risk priority mapping table for subsequent parallel processing and optimization. For example, in the above application scenario, by performing distributed parallel decoupling on the hierarchical risk numerical matrix, it is possible to identify which components have the highest risk and map them to the corresponding priority list. Next, based on these priority mapping tables, network topology adaptive allocation is performed to generate a task scheduling sequence including emergency task queues, regular task queues, and low-priority task queues. This scheduling mechanism ensures that high-risk tasks can be processed first, thereby minimizing potential safety hazards. Then, the system optimizes the resource allocation of the task scheduling sequence through the edge computing gateway, generates an edge node allocation plan, and performs load balancing adjustment on the plan to generate an edge computing optimization strategy. Resource allocation optimization aims to reasonably allocate the computing resources of the edge computing gateway according to the needs of the task scheduling sequence to ensure efficient task processing capabilities. For example, in the above application scenario, the edge computing gateway dynamically adjusts the allocation of its computing resources according to the emergency task queue, regular task queue, and low-priority task queue in the task scheduling sequence to ensure that emergency tasks can respond quickly. At the same time, through load balancing adjustment, the workload of edge nodes is further optimized to avoid the situation where some nodes are overloaded while other nodes are idle. This optimization strategy not only improves the overall efficiency of the system, but also enhances the reliability and stability of the system. Finally, based on the edge computing optimization strategy, the preset scooter control parameters are adjusted and mapped in real time to generate a control strategy instruction set.Specifically, the control strategy instruction set is a series of control instructions generated in real time by the edge computing gateway according to the current state and risk level of the scooter, which is used to guide the specific operation of the scooter. For example, in the above application scenario, assuming that a scooter is assessed as a high-risk state due to battery overheating, the edge computing gateway generates a control strategy instruction set including speed limit instructions, steering angle constraints, battery power control and emergency braking strategies according to the edge computing optimization strategy. These instructions are transmitted to the motor controller of the scooter through the wireless communication network, and the corresponding operations are immediately performed, such as gradually slowing down and eventually stopping the operation of the scooter, and a warning message is issued to the user, prompting that there are safety hazards in the current device, and the trip needs to be ended immediately and the customer service is contacted for inspection. For scooters in a medium-risk state, if a minor mechanical failure or sensor abnormality is detected, the system may generate instructions to slow down or suggest that the user stop nearby for inspection. After receiving such instructions, the motor controller will adjust the speed of the scooter accordingly and notify the user to take appropriate measures through the on-board display or mobile phone application. To better understand the whole process, we can imagine a specific application scenario: In a busy urban environment, a shared scooter company operates thousands of scooters. One day, due to bad weather and frequent use, many scooters experienced different degrees of abnormal conditions. Assume that one of the scooters suddenly had a serious mechanical failure during driving, resulting in abnormal fluctuations in the data of the acceleration sensor and gyroscope sensor. First, the system performs data structure analysis on the risk level classification results of the scooter to generate a risk feature sequence containing motor control risk value, battery safety threshold and bearing loss index. Then, a hierarchical risk numerical matrix is ​​generated through a multi-level threshold quantification method, revealing that the key components of the scooter have a high risk level. Subsequently, the system performs distributed parallel decoupling on the hierarchical risk numerical matrix to generate a risk priority mapping table, and performs network topology adaptive allocation based on the mapping table to generate a task scheduling sequence. In this process, the system identifies that the scooter is in a high-risk state and adds its task to the emergency task queue to ensure priority processing. Then, the edge computing gateway optimizes the resource allocation of the task scheduling sequence, generates an edge node allocation plan, and generates an edge computing optimization strategy through load balancing adjustment. In this case, the edge computing gateway allocates more computing resources to tasks in the emergency task queue to ensure that high-risk scooters can be processed in a timely manner. Finally, based on the edge computing optimization strategy, the system adjusts and maps the preset scooter control parameters in real time to generate a control strategy instruction set. For example, for this high-risk scooter, the system generates a control strategy instruction set including speed limit instructions, steering angle constraints, battery power control, and emergency braking strategies, and sends it to the scooter's motor controller through a wireless communication network.After receiving the command, the motor controller immediately performs the corresponding operation, gradually slows down and eventually stops the scooter. At the same time, it sends an alarm message through the user's mobile phone application to inform the user that the current device has a safety hazard and recommends that he contact customer service as soon as possible for follow-up processing. Through this multi-level data processing and optimization mechanism, comprehensive monitoring and intelligent management of the status of electric scooters are achieved, significantly improving the reliability of the system and user experience. This efficient control strategy can not only detect and respond to potential safety hazards in a timely manner, but also provide operators with scientific maintenance suggestions to ensure user safety and service quality. The entire process, from the analysis of risk level classification results, to the generation of task scheduling sequences, to the formulation of edge computing optimization strategies, and finally to the generation of control strategy instruction sets, forms a complete closed-loop management system to ensure that each scooter can be managed and maintained in a timely and effective manner.

[0040] In a specific embodiment, the step of inputting the control strategy instruction set into the remote motor controller to control the electric scooter includes: Decoding and restoring the control strategy instruction set to obtain a control parameter sequence, and performing parameter boundary check on the control parameter sequence to obtain a safety control threshold matrix; wherein the safety control threshold matrix includes a maximum speed limit, a maximum steering angle, a maximum power output, and a maximum braking force; The safety control threshold matrix is ​​mapped in parallel segments through a multi-thread channel to obtain a multi-channel control signal stream, and the multi-channel control signal stream is synchronized in real time to obtain a synchronous control instruction sequence; Performing waveform modulation transformation on the synchronous control instruction sequence to obtain a drive waveform cluster, and performing pulse width encoding on the drive waveform cluster to obtain a motor drive signal chain; Performing hardware timing analysis on the motor controller based on the motor drive signal chain to obtain a controller execution timing table, and performing parallel task allocation on the controller execution timing table to obtain a multi-core execution instruction group; The multi-core execution instruction group is mapped to a hardware driver to obtain a motor control drive sequence, and the electric scooter is controlled in real time based on the motor control drive sequence.

[0041] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, it is a complex and multi-level process to input the control strategy instruction set into the remote motor controller to realize the real-time motion control of the electric scooter. First, the system will decode and restore the control strategy instruction set, generate a control parameter sequence, and perform parameter boundary verification on these sequences to obtain a safety control threshold matrix. Specifically, instruction decoding and restoration is to convert a high-level control strategy instruction set into a specific control parameter sequence so that it can be directly applied to the motor controller. For example, in a shared scooter service system, assuming that a scooter is assessed as a high-risk state due to battery overheating, the system generates a control strategy instruction set including speed limit instructions, steering angle constraints, battery power control and emergency braking strategies. By decoding and restoring these instructions, a control parameter sequence containing specific values ​​such as maximum speed limit, maximum steering angle, maximum power output and maximum braking force can be generated. Then, through parameter boundary verification, it is ensured that these control parameters are within the safe range and a safety control threshold matrix is ​​generated. This verification mechanism not only ensures the safety of the system, but also prevents potential dangers caused by misoperation. Next, the system performs parallel segment mapping of the safety control threshold matrix through multi-threaded channels to generate multi-channel control signal streams, and performs real-time timing synchronization on these signal streams to generate synchronous control instruction sequences. Multi-threaded channels allow multiple control tasks to be processed simultaneously, thereby improving the response speed and efficiency of the system. For example, in the above application scenario, assuming that the speed, steering angle, and braking force of the scooter need to be adjusted at the same time, the system can process these tasks in parallel through multi-threaded channels to generate multi-channel control signal streams. Then, by performing real-time timing synchronization on these signal streams, it is ensured that each control signal can reach the motor controller at the correct time point to generate a synchronous control instruction sequence. This synchronization mechanism not only improves the real-time performance of the system, but also ensures the coordination and consistency between various control signals. Subsequently, the system performs waveform modulation transformation on the synchronous control instruction sequence to generate drive waveform clusters, and performs pulse width encoding on these waveform clusters to generate a motor drive signal chain. Waveform modulation transformation is a key step in converting digital control signals into analog drive waveforms, and pulse width encoding further converts these waveforms into pulse signals suitable for processing by the motor controller. For example, in the above application scenario, by performing waveform modulation transformation on the synchronous control instruction sequence, a series of waveform clusters suitable for motor drive can be generated. Then, through pulse width encoding technology, these waveforms are converted into specific pulse signals to form a motor drive signal chain. These signal chains not only contain specific control information of speed, steering angle and braking force, but also can accurately guide the motor controller to perform corresponding operations. Then, the system performs hardware timing analysis on the motor controller based on the motor drive signal chain, generates a controller execution timing table, and performs parallel task allocation on the table to generate a multi-core execution instruction group.Hardware timing analysis aims to generate a detailed controller execution timing table based on the specific content of the motor drive signal chain to ensure that each control signal can be executed at the correct time point. For example, in the above application scenario, by performing hardware timing analysis on the motor drive signal chain, a detailed execution timing table can be generated to clarify the trigger time and duration of each control signal. Then, through parallel task allocation technology, these timing tasks are allocated to multiple processor cores to generate a multi-core execution instruction group. This allocation mechanism not only improves the processing capability of the system, but also ensures the coordination and synchronization between various control tasks. Finally, the system performs hardware driver mapping on the multi-core execution instruction group, generates a motor control drive sequence, and performs real-time motion control on the electric scooter based on the sequence. Hardware driver mapping is a key step in converting abstract control instructions into specific hardware operations to ensure that the motor controller can accurately execute control commands. For example, in the above application scenario, by performing hardware driver mapping on the multi-core execution instruction group, a detailed motor control drive sequence can be generated to clarify the specific operation details of each control signal. Then, based on this drive sequence, the motor controller can adjust the speed, steering angle and braking force of the scooter in real time to ensure the safe operation of the vehicle. For example, when the system detects that the scooter battery is overheating, the motor controller will gradually reduce the speed of the scooter and eventually stop it. At the same time, it will send an alarm message to the user, prompting him that there is a safety hazard in the current device and he needs to end the trip immediately and contact customer service for inspection. To better understand the whole process, we can imagine a specific application scenario: In a busy urban environment, a shared scooter company operates thousands of scooters. One day, due to bad weather and frequent use, many scooters experienced different degrees of abnormal conditions. Suppose one of the scooters suddenly had a serious mechanical failure during driving, resulting in abnormal fluctuations in the data of the acceleration sensor and gyroscope sensor. First, the system parses the risk level classification results of the scooter and generates a control strategy instruction set, including speed limit instructions, steering angle constraints, battery power control, and emergency braking strategies. By decoding and restoring these instructions, a control parameter sequence containing specific values ​​such as maximum speed limit, maximum steering angle, maximum power output, and maximum braking force is generated, and a safety control threshold matrix is ​​generated through parameter boundary verification. Then, the system performs parallel segmentation mapping of the safety control threshold matrix through multi-threaded channels to generate a multi-channel control signal stream, and generates a synchronous control instruction sequence through real-time timing synchronization. For example, if the speed, steering angle, and braking force of the scooter need to be adjusted at the same time, the system can process these tasks in parallel through multi-threaded channels to generate multi-channel control signal streams. Then, by performing real-time timing synchronization on these signal streams, it ensures that each control signal can reach the motor controller at the right time point, generating a synchronous control instruction sequence.Subsequently, the system performs waveform modulation transformation on the synchronous control instruction sequence to generate a drive waveform cluster, and generates a motor drive signal chain through pulse width coding. For example, by performing waveform modulation transformation on the synchronous control instruction sequence, a series of waveform clusters suitable for motor drive are generated. Then, through pulse width coding technology, these waveforms are converted into specific pulse signals to form a motor drive signal chain. These signal chains not only contain specific control information of speed, steering angle and braking force, but also can accurately guide the motor controller to perform corresponding operations. Then, based on the motor drive signal chain, the system performs hardware timing analysis on the motor controller, generates a controller execution timing table, and generates a multi-core execution instruction group through parallel task allocation. For example, by performing hardware timing analysis on the motor drive signal chain, a detailed execution timing table is generated to clarify the trigger time and duration of each control signal. Then, through parallel task allocation technology, these timing tasks are assigned to multiple processor cores to generate a multi-core execution instruction group. Finally, the system performs hardware driver mapping on the multi-core execution instruction group, generates a motor control drive sequence, and performs real-time motion control on the electric scooter based on the sequence. For example, by mapping the multi-core execution instruction group to a hardware driver, a detailed motor control drive sequence is generated to clarify the specific operation details of each control signal. Then, based on this drive sequence, the motor controller can adjust the speed, steering angle and braking force of the scooter in real time to ensure the safe operation of the vehicle. For example, when the system detects that the scooter battery is overheated, the motor controller will gradually reduce the speed of the scooter and eventually stop its operation. At the same time, it will send an alarm message to the user, prompting him that there is a safety hazard in the current device and he needs to end the trip immediately and contact customer service for inspection. Through this multi-level data processing and optimization mechanism, comprehensive monitoring and intelligent management of the status of the electric scooter are achieved, significantly improving the reliability of the system and user experience. This efficient control strategy can not only detect and respond to potential safety hazards in a timely manner, but also provide operators with scientific maintenance suggestions to ensure user safety and service quality. The entire process, from decoding and restoring the control strategy instruction set, to parallel processing of multi-threaded channels, to hardware driver mapping and real-time motion control, forms a complete closed-loop management system to ensure that each scooter can be managed and maintained in a timely and effective manner.

[0042] In a specific embodiment, the pulse width encoding of the driving waveform cluster to obtain the motor driving signal chain includes: Performing waveform feature decomposition on the driving waveform cluster to obtain a fundamental wave parameter matrix, and performing multiple sampling quantization on the fundamental wave parameter matrix to obtain a waveform sampling sequence; Carrier signal synthesis is performed based on the waveform sampling sequence to obtain a carrier modulation parameter set, and phase compensation calibration is performed on the carrier modulation parameter set to obtain a phase correction matrix; Performing duty cycle modulation calculation on the phase correction matrix to obtain a PWM modulation sequence, and performing dead time compensation on the PWM modulation sequence to obtain a compensation control vector; The compensation control vector is edge-detected and synchronized by a preset synchronization trigger to obtain a trigger timing set, and the trigger timing set is multi-level interleaved encoded to obtain a drive waveform sequence, and the drive waveform sequence is output parameter mapped to obtain a motor drive signal chain; wherein the motor drive signal chain includes a PWM waveform, a phase trigger point, a duty cycle parameter, and a frequency modulation value.

[0043] Specifically, in the remote monitoring method of electric scooters based on the Internet of Things, pulse width encoding of the drive waveform cluster to generate a motor drive signal chain is a complex and multi-level process. First, the system performs waveform feature decomposition on the drive waveform cluster, generates a fundamental parameter matrix, and performs multiple sampling quantization on the matrix to obtain a waveform sampling sequence. Specifically, waveform feature decomposition is the process of disassembling a complex drive waveform cluster into multiple basic waveform components, so that the specific characteristics of each component can be analyzed more carefully. For example, in a shared scooter service system, assuming that a scooter needs to adjust its speed and steering angle, the system first generates a drive waveform cluster containing these control information. By performing waveform feature decomposition on these waveform clusters, the specific parameters of each basic waveform component (such as sine wave, square wave, etc.) can be extracted to form a fundamental parameter matrix. Then, through multiple sampling quantization technology, these fundamental parameters are further converted into discrete waveform sampling sequences to ensure that the characteristics of the original waveform can be accurately represented. Next, the system performs carrier signal synthesis based on the waveform sampling sequence, generates a carrier modulation parameter set, and performs phase compensation calibration on the set to obtain a phase correction matrix. Carrier signal synthesis aims to group multiple basic waveforms into carrier signals suitable for motor controller processing. For example, in the above application scenario, the system synthesizes a carrier signal suitable for motor drive based on the parameters in the waveform sampling sequence and generates a carrier modulation parameter set. Then, by performing phase compensation calibration on these parameter sets, the phase consistency between each carrier signal is ensured and a phase correction matrix is ​​generated. This calibration mechanism not only improves the accuracy of the system, but also prevents potential problems caused by phase asynchrony. Subsequently, the system performs duty cycle modulation calculation on the phase correction matrix to generate a PWM modulation sequence, and performs dead time compensation on the sequence to generate a compensation control vector. Duty cycle modulation calculation is a key step in converting the phase-corrected carrier signal into a specific PWM (pulse width modulation) signal, while dead time compensation further optimizes these PWM signals to ensure their stability and reliability in practical applications. For example, in the above application scenario, by performing duty cycle modulation calculation on the phase correction matrix, a series of PWM modulation sequences can be generated to clarify the duty cycle parameters of each PWM signal. Then, by performing dead time compensation on these PWM modulation sequences, a compensation control vector is generated to ensure that there is no conflict or overlap between each PWM signal, thereby improving the stability of the system. Then, the system uses the preset synchronization trigger to synchronize the edge detection of the compensation control vector, generates a trigger timing set, and performs multi-level interleaving encoding on the set to generate a drive waveform sequence. The function of the synchronization trigger is to ensure that each PWM signal can be triggered at the correct time point and generate an accurate trigger timing set. For example, in the above application scenario, the system uses the synchronization trigger to synchronize the edge detection of the compensation control vector, generates a trigger timing set, and clarifies the trigger time and duration of each PWM signal.Next, by performing multi-level interleaving encoding on the set, a driving waveform sequence is generated to ensure that each PWM signal can be output at the appropriate time point to form a continuous and stable driving waveform. Finally, the system performs output parameter mapping on the driving waveform sequence to generate a motor drive signal chain. Output parameter mapping is a key step to convert the specific control information in the driving waveform sequence into a signal format suitable for processing by the motor controller. For example, in the above application scenario, by performing output parameter mapping on the driving waveform sequence, a motor drive signal chain including PWM waveform, phase trigger point, duty cycle parameter and frequency modulation value can be generated. These signal chains not only contain specific control instructions, but also can accurately guide the motor controller to perform corresponding operations to ensure the safe operation of the scooter. For example, when the system detects that the scooter battery is overheated, the motor controller will gradually reduce the speed of the scooter and eventually stop its operation, while sending an alarm message to the user, prompting him that there is a safety hazard in the current device and he needs to end the trip immediately and contact customer service for inspection. To better understand the whole process, we can imagine such a specific application scenario: In a busy urban environment, a shared scooter company operates thousands of scooters. One day, due to bad weather and frequent use, many scooters experienced different degrees of abnormal conditions. Assume that one of the scooters suddenly had a serious mechanical failure during driving, resulting in abnormal fluctuations in the data of the acceleration sensor and gyroscope sensor. First, the system parses the risk level classification results of the scooter and generates a control strategy instruction set, including speed limit instructions, steering angle constraints, battery power control, and emergency braking strategies. By decoding and restoring these instructions, a control parameter sequence containing specific values ​​such as maximum speed limit, maximum steering angle, maximum power output, and maximum braking force is generated, and a safety control threshold matrix is ​​generated through parameter boundary verification. Then, the system parallel-segment maps the safety control threshold matrix through multi-threaded channels, generates a multi-channel control signal stream, and generates a synchronous control instruction sequence through real-time timing synchronization. For example, assuming that the speed, steering angle, and braking force of the scooter need to be adjusted at the same time, the system can process these tasks in parallel through multi-threaded channels to generate a multi-channel control signal stream. Then, by performing real-time timing synchronization on these signal streams, it is ensured that each control signal can reach the motor controller at the correct time point to generate a synchronous control instruction sequence. Subsequently, the system performs waveform modulation transformation on the synchronous control instruction sequence to generate a drive waveform cluster, and generates a motor drive signal chain through pulse width encoding. Specifically, the system first performs waveform feature decomposition on the drive waveform cluster to generate a fundamental wave parameter matrix, and generates a waveform sampling sequence through multiple sampling quantization. For example, in the above application scenario, the system generates a drive waveform cluster based on the synchronous control instruction sequence, performs waveform feature decomposition on it, extracts the specific parameters of each basic waveform component, and forms a fundamental wave parameter matrix.Then, through the multi-sampling quantization technology, these fundamental wave parameters are converted into discrete waveform sampling sequences to ensure that the characteristics of the original waveform can be accurately represented. Then, the system synthesizes the carrier signal based on the waveform sampling sequence, generates a carrier modulation parameter set, and performs phase compensation calibration on the set to generate a phase correction matrix. For example, in the above application scenario, the system synthesizes a carrier signal suitable for motor drive based on the parameters in the waveform sampling sequence to generate a carrier modulation parameter set. Then, by performing phase compensation calibration on these parameter sets, the phase consistency between each carrier signal is ensured to generate a phase correction matrix. Then, the system performs duty cycle modulation calculation on the phase correction matrix to generate a PWM modulation sequence, and performs dead time compensation on the sequence to generate a compensation control vector. For example, in the above application scenario, the system generates a series of PWM modulation sequences by performing duty cycle modulation calculation on the phase correction matrix to clarify the duty cycle parameters of each PWM signal. Then, by performing dead time compensation on these PWM modulation sequences, a compensation control vector is generated to ensure that there is no conflict or overlap between each PWM signal, thereby improving the stability of the system. Then, the system performs edge detection synchronization on the compensation control vector through a preset synchronization trigger, generates a trigger timing set, and performs multi-level interleaving encoding on the set to generate a drive waveform sequence. For example, in the above application scenario, the system uses a synchronization trigger to perform edge detection synchronization on the compensation control vector, generates a trigger timing set, and clarifies the trigger time and duration of each PWM signal. Then, by performing multi-level interleaving encoding on the set, a drive waveform sequence is generated to ensure that each PWM signal can be output at the appropriate time point to form a continuous and stable drive waveform. Finally, the system performs output parameter mapping on the drive waveform sequence to generate a motor drive signal chain. For example, in the above application scenario, by performing output parameter mapping on the drive waveform sequence, a motor drive signal chain including PWM waveform, phase trigger point, duty cycle parameter and frequency modulation value is generated. These signal chains not only contain specific control instructions, but also can accurately guide the motor controller to perform corresponding operations to ensure the safe operation of the scooter. For example, when the system detects that the scooter battery is overheating, the motor controller will gradually reduce the speed of the scooter and eventually stop it. At the same time, it will send an alarm message to the user, reminding him that there is a safety hazard in the current device and he needs to end the trip immediately and contact customer service for inspection. Through this multi-level data processing and optimization mechanism, comprehensive monitoring and intelligent management of the status of electric scooters are achieved, significantly improving the reliability of the system and user experience. This efficient control strategy can not only detect and respond to potential safety hazards in a timely manner, but also provide operators with scientific maintenance suggestions to ensure user safety and service quality.The entire process, from waveform feature decomposition, to carrier signal synthesis, to PWM modulation and dead time compensation, finally generates the motor drive signal chain, forming a complete closed-loop management system to ensure that each scooter can be managed and maintained in a timely and effective manner.

[0044] The above describes the electric scooter remote monitoring method based on the Internet of Things in the embodiment of the present invention. The following describes the electric scooter remote monitoring system based on the Internet of Things in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, an electric scooter remote monitoring system based on the Internet of Things includes: The acquisition module 21 is used to collect data from multiple source sensors carried by the electric scooter to obtain an original sensor data stream; wherein the original sensor data stream includes a gyroscope sensor, an acceleration sensor, a GPS positioning sensor, and a battery management sensor; An extraction module 22, used for performing multi-dimensional feature extraction and data fusion on the original sensor data stream to obtain a scooter state feature vector; A mapping module 23, configured to perform multimodal feature mapping on the electric scooter based on the scooter state feature vector to obtain vehicle abnormal behavior pattern features; A prediction module 24, configured to perform risk prediction on the electric scooter based on the abnormal behavior pattern characteristics of the vehicle to obtain a risk level classification result; A sorting module 25 is used to perform distributed processing and priority sorting on the risk level classification results through an edge computing gateway to obtain a control strategy instruction set; The control module 26 is used to input the control strategy instruction set into the remote motor controller to control the electric scooter.

[0045] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0046] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0047] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0048] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0049] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0050] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0051] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A remote monitoring method for electric scooters based on the Internet of Things, characterized in that: The electric scooter is provided with a motor controller, comprising the following steps: Collect data from multiple source sensors on the electric scooter to obtain an original sensor data stream; wherein the original sensor data stream includes a gyroscope sensor, an acceleration sensor, a GPS positioning sensor, and a battery management sensor; Performing multi-dimensional feature extraction and data fusion on the original sensor data stream to obtain a scooter state feature vector; If the scooter state feature vector indicates that the electric scooter is abnormal, multimodal feature mapping is performed on the electric scooter based on the scooter state feature vector to obtain abnormal behavior pattern features of the vehicle; Based on the abnormal behavior pattern characteristics of the vehicle, risk prediction is performed on the electric scooter to obtain a risk level classification result; Distributed processing and priority sorting of the risk level classification results are performed through an edge computing gateway to obtain a control strategy instruction set; The control strategy instruction set is input into the remote motor controller to control the electric scooter.

2. The electric scooter remote monitoring method based on the Internet of Things according to claim 1 is characterized in that: The multi-dimensional feature extraction and data fusion of the original sensor data stream to obtain the scooter state feature vector includes: Decomposing the original sensor data stream in the time-frequency domain to obtain a vibration characteristic sequence of the electric scooter, and decomposing the vibration characteristic sequence by wavelet packets to obtain a multi-scale vibration spectrum diagram; Performing scooter posture data analysis on the electric scooter based on the multi-scale vibration spectrum to obtain a posture feature set, performing time-series alignment on the posture feature set to obtain an aligned state sequence, and performing sparse coding on the aligned state sequence to obtain a compressed feature representation; Multimodal feature fusion is performed on the compressed feature representation to obtain a fused feature matrix, and tensor decomposition is performed on the fused feature matrix to obtain a scooter state feature vector.

3. The electric scooter remote monitoring method based on the Internet of Things according to claim 1 is characterized in that: The multimodal feature mapping of the electric scooter based on the scooter state feature vector to obtain the abnormal behavior pattern feature of the vehicle includes: Performing multi-dimensional space projection on the scooter state feature vector to obtain a feature distribution mapping atlas, and performing nonlinear manifold embedding on the feature distribution mapping atlas to obtain a topological feature matrix; The topological feature matrix is ​​similarly measured by a preset local sensitive hashing technology to obtain a feature similarity spectrum, and the feature similarity spectrum is subjected to spectral clustering analysis to obtain a behavior pattern prototype set; wherein the behavior pattern prototype set includes an acceleration mode, a steering mode, and a braking mode; Extracting spatiotemporal features from the behavior pattern prototype set to obtain a spatiotemporal feature tensor, and performing tensor decomposition on the spatiotemporal feature tensor to obtain multimodal behavior features; The multimodal behavior characteristics are subjected to a time-series dependency analysis through a preset conditional random field technology to obtain a state transition sequence, and the state transition sequence is subjected to a Markov jump analysis to obtain a behavior pattern transition diagram; wherein the behavior pattern transition diagram includes state duration, state transition probability, and state stability; Based on the preset historical behavior data, the behavior pattern conversion graph is subjected to distributed association mining to obtain the behavior pattern association rules, and the behavior pattern association rules are subjected to probability graph reasoning to obtain the behavior prediction vector; wherein the behavior prediction vector includes the probability of behavior occurrence, the duration of the behavior, and the behavior conversion trend; Multi-scale feature fusion is performed on the behavior prediction vector to obtain a multi-dimensional behavior feature space, and abnormal behavior detection is performed based on the multi-dimensional behavior feature space to obtain vehicle abnormal behavior pattern characteristics; wherein the vehicle abnormal behavior pattern characteristics include abnormality occurrence probability, abnormality degree score and abnormal behavior type.

4. The method for remote monitoring of electric scooters based on the Internet of Things according to claim 1, characterized in that: The risk prediction of the electric scooter based on the abnormal behavior pattern characteristics of the vehicle to obtain a risk level classification result includes: Performing structural decomposition on the abnormal behavior pattern characteristics of the vehicle to obtain a behavior characteristic component sequence, and performing dynamic evolution analysis on the behavior characteristic component sequence through a recursive quantitative analysis method to obtain a behavior evolution trajectory diagram, and performing fractal dimension calculation on the behavior evolution trajectory diagram to obtain a fault evolution feature set; wherein the fault evolution feature set includes component wear trend, fault diffusion rate, and system degradation degree; Performing causal chain analysis on the fault evolution feature set based on preset historical fault data to obtain a fault propagation network, and extracting topological features from the fault propagation network to obtain a set of key fault nodes; Probabilistic reasoning is performed on the key fault node set through a preset Bayesian network algorithm to obtain a component reliability evaluation value, and a fuzzy comprehensive evaluation is performed on the electric scooter based on the component reliability evaluation value to obtain a health status score sheet; wherein the health status score sheet includes motor health, battery life value and bearing wear; An operation risk analysis of the electric scooter is performed based on the health status score sheet to obtain a risk level classification result.

5. The method for remote monitoring of electric scooters based on the Internet of Things according to claim 1, characterized in that: The risk level classification results are distributedly processed and prioritized by the edge computing gateway to obtain a control strategy instruction set, including: Performing data structure analysis on the risk level classification result to obtain a risk feature sequence, and performing multi-level threshold quantization on the risk feature sequence to obtain a graded risk numerical matrix; wherein the graded risk numerical matrix includes a motor control risk value, a battery safety threshold, and a bearing loss index; Performing distributed parallel decoupling on the hierarchical risk numerical matrix to obtain a risk priority mapping table, and performing adaptive allocation of network topology based on the risk priority mapping table to obtain a task scheduling sequence; wherein the task scheduling sequence includes an emergency task queue, a regular task queue, and a low-priority task queue; By using the edge computing gateway, resource allocation is optimized for the task scheduling sequence to obtain an edge node allocation scheme, and load balancing is adjusted for the edge node allocation scheme to obtain an edge computing optimization strategy; Based on the edge computing optimization strategy, the preset scooter control parameters are adjusted and mapped in real time to obtain a control strategy instruction set; wherein the control strategy instruction set includes speed limit instructions, steering angle constraints, battery power control, and emergency braking strategies.

6. The method for remote monitoring of electric scooters based on the Internet of Things according to claim 1, characterized in that: The step of inputting the control strategy instruction set into the remote motor controller to control the electric scooter comprises: Decoding and restoring the control strategy instruction set to obtain a control parameter sequence, and performing parameter boundary check on the control parameter sequence to obtain a safety control threshold matrix; wherein the safety control threshold matrix includes a maximum speed limit, a maximum steering angle, a maximum power output, and a maximum braking force; The safety control threshold matrix is ​​mapped in parallel segments through a multi-thread channel to obtain a multi-channel control signal stream, and the multi-channel control signal stream is synchronized in real time to obtain a synchronous control instruction sequence; Performing waveform modulation transformation on the synchronous control instruction sequence to obtain a drive waveform cluster, and performing pulse width encoding on the drive waveform cluster to obtain a motor drive signal chain; Performing hardware timing analysis on the motor controller based on the motor drive signal chain to obtain a controller execution timing table, and performing parallel task allocation on the controller execution timing table to obtain a multi-core execution instruction group; The multi-core execution instruction group is mapped to a hardware driver to obtain a motor control drive sequence, and the electric scooter is controlled in real time based on the motor control drive sequence.

7. The method for remote monitoring of an electric scooter based on the Internet of Things according to claim 6, characterized in that: The pulse width encoding of the driving waveform cluster is performed to obtain a motor driving signal chain, including: Performing waveform feature decomposition on the driving waveform cluster to obtain a fundamental wave parameter matrix, and performing multiple sampling quantization on the fundamental wave parameter matrix to obtain a waveform sampling sequence; Carrier signal synthesis is performed based on the waveform sampling sequence to obtain a carrier modulation parameter set, and phase compensation calibration is performed on the carrier modulation parameter set to obtain a phase correction matrix; Performing duty cycle modulation calculation on the phase correction matrix to obtain a PWM modulation sequence, and performing dead time compensation on the PWM modulation sequence to obtain a compensation control vector; The compensation control vector is edge-detected and synchronized by a preset synchronization trigger to obtain a trigger timing set, and the trigger timing set is multi-level interleaved encoded to obtain a drive waveform sequence, and the drive waveform sequence is output parameter mapped to obtain a motor drive signal chain; wherein the motor drive signal chain includes a PWM waveform, a phase trigger point, a duty cycle parameter, and a frequency modulation value.

8. An electric scooter remote monitoring system based on the Internet of Things, characterized in that: The electric scooter is provided with a motor controller, including: The acquisition module is used to collect data from the multi-source sensors carried by the electric scooter to obtain the original sensor data stream; wherein the original sensor data stream includes a gyroscope sensor, an acceleration sensor, a GPS positioning sensor, and a battery management sensor; An extraction module, used for performing multi-dimensional feature extraction and data fusion on the original sensor data stream to obtain a scooter state feature vector; A mapping module, used for performing multimodal feature mapping on the electric scooter based on the scooter state feature vector to obtain vehicle abnormal behavior pattern features; A prediction module, used to predict the risk of the electric scooter based on the abnormal behavior pattern characteristics of the vehicle, and obtain a risk level classification result; A sorting module, used for performing distributed processing and priority sorting on the risk level classification results through an edge computing gateway to obtain a control strategy instruction set; The control module is used to input the control strategy instruction set into the remote motor controller to control the electric scooter.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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