Early warning system and method for traffic safety management and control of public electric vehicles

Abstract

The invention relates to the technical field of information, and particularly provides an early warning system and method for traffic safety management and control of a public electric vehicle, and the early warning system for traffic safety management and control of the public electric vehicle comprises five modules. The obtaining module collects the physical state of a driver and vehicle operation data by means of a sensor and intelligent equipment. The first data processing module applies a machine learning algorithm to analyze the vehicle operation data to obtain a driving early warning coefficient and quantify the operation risk. The second data processing module evaluates the state of the driver and determines a safety control coefficient in combination with the physiological and psychological principle. And the third data processing module fuses the two coefficients, generates early warning information and pushes the early warning information to a driving end. The circulation module dynamically adjusts the detection period according to the two coefficients, carries out high-frequency monitoring when the risk is high, and reduces the frequency when the risk is low. The five modules operate cooperatively, data redundancy is reduced, early warning timeliness and accuracy are improved, and public transport operation safety is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to an early warning system and method for public bus and tram driving safety management and control. Background Art

[0002] As urban public transportation systems grow in size, the safe operation of public buses and trams has become a core element in ensuring efficient and safe travel for citizens. Currently, safety assessment and early warning technologies for public buses and trams still have significant shortcomings. Traditional safety assessments largely rely on manual inspections and basic onboard equipment data. This is not only inefficient and inaccurate, but also incapable of achieving full-time, all-encompassing, real-time monitoring.

[0003] Existing technologies for safety risk warnings are, on the one hand, unable to accurately set differentiated alarms, resulting in delayed warnings or numerous false alarms, making them ineffective. On the other hand, detection frequency settings lack flexibility, or a fixed high-frequency detection mode is used, resulting in data redundancy, increased system burden and operating costs. Insufficient frequency can also prevent timely capture of risk signals, preventing potential safety risks from being identified and addressed promptly. This leads to the persistent existence of traffic accident risks, posing a serious threat to public transportation safety and urban traffic order. Summary of the Invention

[0004] One purpose of the present application is to provide an early warning system for public bus and tram driving safety management and control, at least to solve the problem of how to accurately provide early warning of the safety status of public buses and trams while reducing the detection frequency.

[0005] To achieve the above objectives, some embodiments of the present application provide the following aspects: In a first aspect, some embodiments of the present application further provide an early warning system for public bus and tram driving safety management and control, the system comprising an acquisition module, a first data processing module, a second data processing module, a third data processing module, and a circulation module; The acquisition module is configured to acquire the driver's physical condition data and bus and tram operation data within a preset time period; wherein the physical condition data includes heart rate, eye movement frequency and fatigue level; The first data processing module is configured to determine a driving warning coefficient of the public bus based on the operation data of the public bus; The second data processing module is configured to determine a driving safety control coefficient based on the driver's physical condition data; The third data processing module is configured to determine warning information based on the driving safety control coefficient and the driving warning coefficient, and send the warning information to the driver end; The loop module is configured to re-execute the step of "obtaining the driver's physical condition data and public bus and tram operation data within a preset time period" after a dynamic time interval; wherein, the dynamic time interval is determined based on the driving safety control coefficient and the driving warning coefficient.

[0006] In a second aspect, some embodiments of the present application further provide an early warning method for public bus and tram driving safety management and control, the method comprising: Acquiring the driver's physical condition data and bus and tram operation data within a preset time period; wherein the physical condition data includes heart rate, eye movement frequency, and fatigue level; Determining a driving warning coefficient of the public bus or tram based on the operating data of the public bus or tram; Determining a driving safety control coefficient based on the driver's physical condition data; Determining warning information based on the driving safety control coefficient and the driving warning coefficient, and sending the warning information to the driver end; After the dynamic time interval, the step of "obtaining the driver's physical condition data and the bus and tram operation data within the preset time period" is re-executed; wherein, the dynamic time interval is determined based on the driving safety control coefficient and the driving warning coefficient.

[0007] In a third aspect, some embodiments of the present application further provide an electronic device comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0008] In a fourth aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method described above.

[0009] In a fifth aspect, some embodiments of the present application further provide a computer program product, comprising a computer program / instruction, which implements the steps of the above-described method when executed by a processor.

[0010] Compared to related technologies, the solution provided in the embodiments of this application is a warning system for public bus and tram operating safety management. The system includes an acquisition module, a first data processing module, a second data processing module, a third data processing module, and a circulation module. Each module has a clear division of labor and specific functions as follows: The acquisition module uses high-precision sensors and smart wearable devices to collect driver physical status data and public bus and tram operating data at a preset sampling frequency. Physical status data includes heart rate monitored by a smart bracelet, eye movement frequency captured by an eye tracker, and fatigue determined by a facial recognition algorithm. Operational data includes real-time vehicle speed feedback from the speed sensor, steering information recorded by the steering angle sensor, and brake pressure data collected by the brake pressure sensor. The first data processing module uses machine learning or deep learning algorithms to deeply mine and analyze the acquired public bus and tram operating data. Through data cleaning, feature extraction, and model training, it accurately calculates a driving warning coefficient that quantifies the vehicle's operating safety risk. A larger value indicates a higher vehicle operating risk. The second data processing module uses multi-dimensional data analysis to comprehensively evaluate the driver's physical status data. The driving safety control coefficient is then determined to measure the impact of the driver's physical condition on driving safety. The third data processing module integrates the driving safety control coefficient with the driving warning coefficient for analysis, generating warning information of varying levels. This warning information is then pushed in real time to the driver's human-machine interface via the onboard communication system, prompting the driver to take appropriate safety measures. This invention establishes a dynamic, adaptive warning mechanism by deeply integrating the driver's real-time physical condition data with public bus and tram operation data. Based on multi-dimensional data analysis and precise modeling, differentiated warning strategies can be flexibly and accurately set based on different driving scenarios, the driver's physical condition, and vehicle dynamics. This not only effectively avoids warning lags, ensuring timely triggering of warnings at the earliest stages of risk, but also significantly reduces false alarms through intelligent control, avoiding unnecessary disruption to the driver. The loop module dynamically adjusts the data collection cycle based on a dynamic time interval calculation formula, integrating the driving safety control coefficient and the driving warning coefficient. When the risk is high, the time interval is shortened to achieve frequent monitoring; when the risk is low, the time interval is extended to reduce data processing pressure. After the time interval ends, a new round of data collection and processing is automatically triggered, enabling full-time, intelligent monitoring of public bus and tram safety conditions. Through this modular design and collaborative working mechanism, this early warning system effectively reduces data detection frequency and system resource consumption while significantly improving the timeliness, accuracy, and reliability of public bus and tram safety warnings, providing a strong technical guarantee for the safe operation of urban public transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] One or more embodiments are exemplarily illustrated by pictures in the corresponding drawings. These exemplifications do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings are represented as similar elements. Unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0012] Figure 1 A schematic diagram of the structure of an early warning system for public bus and tram driving safety management provided in an embodiment of the present application; Figure 2 A flowchart of a warning method for public bus and tram driving safety management and control according to an embodiment of the present application; Figure 3 is a schematic diagram of an exemplary structure of a processor and memory according to the present application; Figure 4 Schematic diagram of an exemplary structure of an electronic device according to the present application.

[0013] Reference numerals: 11-Get module; 12-first data processing module; 13- second data processing module; 14- third data processing module; 15-Loop module. DETAILED DESCRIPTION

[0014] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0015] See attached Figure 1 , Figure 1 This is a schematic diagram of the structure of a warning system for public bus and tram driving safety management provided in an embodiment of the present application. Figure 1 As shown, the early warning system for public bus and tram driving safety management in the embodiment of the present application mainly includes an acquisition module 11, a first data processing module 12, a second data processing module 13, a third data processing module 14 and a circulation module 15; The acquisition module 11 is configured to acquire the driver's physical condition data and bus and tram operation data within a preset time period; wherein the physical condition data includes heart rate, eye movement frequency and fatigue level; The first data processing module 12 is configured to determine a driving warning coefficient of the public bus or tram based on the operation data of the public bus or tram; The second data processing module 13 is configured to determine a driving safety control coefficient based on the driver's physical condition data; The third data processing module 14 is configured to determine warning information based on the driving safety control coefficient and the driving warning coefficient, and send the warning information to the driver; The loop module 15 is configured to re-execute the step of "obtaining the driver's physical condition data and public bus and tram operation data within a preset time period" after a dynamic time interval; wherein the dynamic time interval is determined based on the driving safety control coefficient and the driving warning coefficient.

[0016] In this embodiment, the early warning system for public bus and tram operating safety management consists of an acquisition module 11, a first data processing module 12, a second data processing module 13, a third data processing module 14, and a circulation module 15. Each module has a clear division of labor and specific functions as follows: Acquisition module 11: Leveraging high-precision sensors and smart wearable devices, it collects driver physical status data and public bus and tram operating data at a preset sampling frequency. Physical status data includes heart rate monitored by a smart bracelet, eye movement frequency captured by an eye tracker, and fatigue determined using a facial recognition algorithm. Operational data includes real-time vehicle speed feedback from the speed sensor, steering information recorded by the steering angle sensor, and brake pressure data collected by the brake pressure sensor. First data processing module 12: Utilizing machine learning or deep learning algorithms, it conducts in-depth mining and analysis of the acquired public bus and tram operating data. Through data cleaning, feature extraction, and model training, it accurately calculates a driving warning coefficient that quantifies the vehicle's operational safety risk. A larger value indicates a higher vehicle operational risk. Second data processing module 13: Utilizing multi-dimensional data analysis, it conducts a comprehensive assessment of the driver's physical status data. The driving safety control coefficient is then determined to measure the impact of the driver's physical condition on driving safety. The third data processing module 14 integrates the driving safety control coefficient with the driving warning coefficient for analysis, generating warning information of varying levels. Subsequently, the warning information is pushed in real time to the driver's human-machine interface via the onboard communication system, prompting the driver to take appropriate safety measures. This invention establishes a dynamic, adaptive warning mechanism by deeply integrating the driver's real-time physical condition data with public bus and tram operation data. Based on multi-dimensional data analysis and precise modeling, differentiated warning strategies can be flexibly and accurately set based on different driving scenarios, the driver's physical condition, and vehicle dynamics. This not only effectively avoids warning lags, ensuring timely triggering of warnings at the earliest stages of risk, but also significantly reduces false alarms through intelligent control, avoiding unnecessary disruption to the driver. The loop module 15 dynamically adjusts the data collection cycle based on the dynamic time interval calculation formula, integrating the driving safety control coefficient and the driving warning coefficient. When the risk is high, the time interval is shortened to achieve frequent monitoring; when the risk is low, the time interval is extended to reduce data processing pressure. After the time interval ends, a new round of data collection and processing is automatically triggered, enabling full-time, intelligent monitoring of public bus and tram safety conditions. Through this modular design and collaborative working mechanism, this early warning system effectively reduces data detection frequency and system resource consumption while significantly improving the timeliness, accuracy, and reliability of public bus and tram safety warnings, providing a strong technical guarantee for the safe operation of urban public transportation.

[0017] In one embodiment, the second data processing module 13 includes a second data receiving module and a second data processing submodule; a second data receiving module configured to receive the driver's physical condition data sent by the acquisition module 11; The second data processing submodule is configured to input the driver's physical condition data into a preset driving safety control coefficient evaluation equation group to obtain the driving safety control coefficient.

[0018] In this embodiment, the second data receiving module, serving as a key hub for data exchange, receives real-time driver physical condition data transmitted by acquisition module 11 via a stable and reliable communication protocol. The second data processing submodule, using a pre-set set of driving safety control coefficient evaluation equations as its core processing logic, substitutes the driver physical condition data transmitted by the second data receiving module into the equations. Through complex mathematical operations, it deeply explores the correlation characteristics between various data indicators, efficiently outputting a quantified driving safety control coefficient, which scientifically measures the potential impact of the driver's physical condition on driving safety.

[0019] In one embodiment, the driving safety control coefficient evaluation equation group is constructed by the following formula: , , , , , , Where, is the driving safety control coefficient, is the driver’s i-th physical condition data at time t, is the lower limit of the normal range of the i-th physical condition data, is the upper limit of the normal range of the i-th physical condition data, is the standard deviation of the i-th physical state data in the preset time period, is the attenuation factor, t is the current moment, is the starting time point of the preset time period, The end time of the preset time period. is the average value of the i-th physical state data within the preset time period, is the standard deviation of the ith physical state data within a preset time period, and m is the dimension of the physical state data.

[0020] In this embodiment, The driving safety control coefficient is a comprehensive indicator calculated from the various parameters in the equations. It is used to assess the impact of the driver's physical condition on driving safety during a given time period. A larger S value indicates a worse driver's physical condition and a higher driving safety risk, requiring appropriate control measures to ensure driving safety. is the i-th physical state data of the driver at time t, for example, i=1 represents heart rate, i=2 represents eye movement frequency, i=3 represents fatigue, etc. m is the total dimension of the physical state data, that is, the number of different physical state indicators included. is the lower limit of the normal range of the i-th physical condition data, is the upper limit of the normal range of the i-th physical state data. For example, for heart rate, It's 60 times per minute. It is 100 times / minute, which is used to judge whether the body status data is within the normal range. is the standard deviation of the i-th physical state data in the preset time period, is the decay factor, which is used to control the decay speed of the data and reflect the timeliness of the data. t is the current time. is the starting time point of the preset time period, The end time of the preset time period. is the average value of the i-th physical state data within the preset time period, is the standard deviation of the i-th physical state data within the preset time period, which is used to measure the fluctuation of the physical state data within the observation period. The larger the standard deviation, the greater the data fluctuation and the more unstable the driver's physical state. m is the dimension of the physical state data, In one embodiment, the first data processing module 12 includes a first data receiving module and a first data processing submodule; The first data receiving module is configured to receive the operation data of the public bus or tram sent by the acquisition module 11; wherein the operation data includes vehicle speed, steering angle, and brake pressure; The first data processing submodule is configured to arrange the operation data of the public bus and tram in ascending order according to the timestamp to obtain the sorted operation data of the public bus and tram; perform feature extraction on the sorted operation data of the public bus and tram to obtain the operation data features; and input the operation data features into a preset deep learning model to obtain the driving warning coefficient of the public bus and tram.

[0021] In this embodiment, the first data receiving module: as the primary link in data transmission, this module adopts a high-speed and stable communication protocol to receive the bus and tram operation data from the acquisition module 11 in real time. The first data processing submodule: first, using the time series processing algorithm, the received bus and tram operation data are strictly arranged in ascending order according to the timestamp to construct a time series data sequence. Then, through the data feature extraction technology, the sorted data is deeply mined to identify and extract the key features that can effectively reflect the vehicle operation status, forming an accurate operation data feature set. Finally, these feature data are input into a preset deep learning model that has been trained and optimized with a large amount of historical data. With the help of the model's powerful self-learning and pattern recognition capabilities, through complex neural network calculations and parameter adjustments, a quantitative bus and tram driving warning coefficient is output to accurately characterize the safety risk level during vehicle operation.

[0022] In one embodiment, the system further comprises a correction module; The correction module is configured to correct the driving warning coefficient according to the warning coefficient correction factor.

[0023] In this embodiment, considering the limitations of the driving warning coefficient based solely on the vehicle's own status and its inability to fully reflect actual driving risks, a warning coefficient correction factor is introduced for optimization. By multiplying the initial driving warning coefficient with the correction factor, a wider range of factors influencing driving safety can be comprehensively considered, including but not limited to road conditions, traffic flow, and the status of the preceding vehicle. This results in a more accurate and reliable final driving warning coefficient, effectively improving the accuracy and comprehensiveness of safety risk assessments.

[0024] In one embodiment, the warning coefficient correction factor is determined by the following formula: , Where, is the correction factor of the warning coefficient, The average speed of the current road section can be obtained through traffic data statistics or sensor collection. The real-time speed of the vehicle ahead can be obtained through the vehicle's radar, camera and other sensors. For safe distance, The actual distance to the vehicle in front can be measured by the vehicle's radar, camera and other sensors. The braking status of the preceding vehicle: if the preceding vehicle is braking, it is 1; if the preceding vehicle is driving normally, it is 0; is the acceleration of the preceding vehicle, which can be calculated by the rate of change of the preceding vehicle's speed. A positive acceleration indicates that the preceding vehicle is accelerating, and a negative acceleration indicates that the preceding vehicle is decelerating. is a positive constant used to adjust the influence of distance factors on the correction factor. It can be determined by fitting based on actual data. , , , are the weight coefficients of each item, and their sum is one.

[0025] In one embodiment, the third data processing module 14 includes a third data receiving module, a third data processing submodule and an early warning module; The third data receiving module is configured to receive the driving warning coefficient of the public bus or tram sent by the first data processing module 12; The third data processing submodule is configured to divide the driving safety control coefficient by the driving warning coefficient to obtain a warning level; determine warning information based on the warning level, and adjust the warning sensitivity threshold of the warning module based on the warning level; The warning module is used for sudden braking warning, sharp turn warning, and blind spot collision warning.

[0026] In this embodiment, the third data processing module 14 comprises a third data receiving module, a third data processing submodule, and an early warning module. These modules work together to implement precise driving safety warning functions. Specifically, the third data receiving module serves as the key interface for data exchange. This module receives the public bus and tram driving warning coefficient output by the first data processing module 12 in real time via a stable communication protocol. The third data processing submodule divides the driving safety control coefficient output by the second data processing module by the received driving warning coefficient to calculate a quantitative warning level. Subsequently, the system matches and determines the corresponding warning content based on a pre-set warning level and warning information comparison table. If the warning level is high, indicating a significant driving safety risk, the system will generate a warning message such as "High Risk! Please slow down and check your vehicle's condition immediately." If the warning level is low, a relatively mild warning message is generated, such as "There is a certain risk at this time. Please drive with caution." Furthermore, this submodule dynamically adjusts the warning module's warning sensitivity threshold based on the resulting warning level. When the warning level is high, the warning sensitivity threshold is lowered so that the warning module can respond to potential risks more quickly; when the warning level is low, the warning sensitivity threshold is appropriately increased to reduce unnecessary false alarms. Warning module: As the terminal execution unit of the driving safety warning system, it integrates multiple core warning functions such as sudden braking warning, sharp turn warning, and blind spot collision warning. This module monitors the vehicle's operating status in real time based on the warning sensitivity threshold adjusted by the third data processing sub-module. Once it detects that the vehicle is braking or turning suddenly, or an obstacle is detected in the blind spot, and the corresponding warning trigger conditions are met, it will promptly send clear and unambiguous warning information to the driver through various means such as sound and light alarms and vibration prompts, effectively reducing driving safety risks.

[0027] In one embodiment, the dynamic time interval is determined by the following formula: ,

[0028] Wherein, T is the dynamic time interval, is the first proportional coefficient, is the second proportional coefficient, is the driving safety control coefficient, W is the driving warning coefficient, is the first dynamic offset, is the second dynamic offset, is the third dynamic offset, is the fourth dynamic offset, is the first weight coefficient, is the second weight coefficient, is the third weight coefficient, is the fourth weight coefficient, is the first power adjustment parameter, is the second power adjustment parameter, is the third power adjustment parameter, is the fourth power adjustment parameter, is the road condition coefficient, is the weather condition coefficient, To correct the parameters, To adjust the parameters, is the threshold parameter.

[0029] In this embodiment, T is a dynamic time interval, is the first proportional coefficient, is the second proportional coefficient, is the driving safety control coefficient, and W is the driving warning coefficient, ranging from [0 to 1], reflecting the safety risks posed by the operation of public buses and trams. Values ​​closer to 1 indicate safer operation and better warning effectiveness; values ​​closer to 0 indicate more safety hazards. is the first dynamic offset, is the second dynamic offset, is the third dynamic offset, The fourth dynamic offset is the dynamic offset introduced by each influencing coefficient. Its value will be dynamically adjusted according to the fluctuation of historical data. These dynamic offsets are used to fine-tune the impact of various factors on the results to better adapt to various changes in actual conditions. For example, the standard deviation of the corresponding coefficient is calculated through a sliding window and then multiplied by the adjustment factor f. These dynamic offsets are used to fine-tune the impact of various factors on the results to better adapt to various changes in actual conditions. is the first weight coefficient, is the second weight coefficient, is the third weight coefficient, are the fourth weight coefficients, and these weights are used to indicate the relative importance of various factors in determining the dynamic time interval T. is the first power adjustment parameter, is the second power adjustment parameter, is the third power adjustment parameter, is the fourth power adjustment parameter, The road condition coefficient is a parameter that performs a power transformation on each influencing coefficient, and is used to nonlinearly adjust the impact of each factor on the dynamic time interval. For example, when the first power adjustment parameter is greater than 1, the impact of changes in the driving safety control coefficient S on T will be amplified; when the first power adjustment parameter is less than 1, the impact of changes in S on T will be reduced. By adjusting these power parameters, the complex impact of different factors on the dynamic time interval can be more flexibly simulated. The weather condition coefficient has a value range of [0,1]. The closer the value is to 1, the worse the weather is, such as rain, snow, fog, etc. The closer it is to 0, the clearer the weather is, the better the visibility is, which is conducive to driving safety. To correct the parameter, it is used to avoid the situation where the denominator is zero. In order to adjust the parameters, in the second calculation branch, the parameters of the fractional result are adjusted for the overall power, further controlling the changing trend of the dynamic time interval. It can make more detailed adjustments to the calculation results according to the actual situation to meet the needs of different application scenarios. is the threshold parameter, which is a number between 0 and 1 and is used to determine which formula branch to use for calculation. Greater than or equal to When , the first formula branch is adopted; otherwise, the second formula branch is adopted. The value can balance the applicable scenarios of different calculation logics, so that the formula can better adapt to different actual situations.

[0030] See attached Figure 2 , Figure 2 This is a flow chart of a warning method for public bus and tram driving safety management and control according to the embodiment of this application. Figure 2 As shown, the early warning method for public bus and tram driving safety management in the embodiment of the present invention mainly includes the following steps S200 to S208: Step S200: Acquiring the driver's physical condition data and bus and tram operation data within a preset time period; wherein the physical condition data includes heart rate, eye movement frequency, and fatigue level; Step S202: determining a driving warning coefficient of the public bus or tram based on the operation data of the public bus or tram; Step S204: Determine a driving safety control coefficient based on the driver's physical condition data; Step S206: Determine warning information based on the driving safety control coefficient and the driving warning coefficient, and send the warning information to the driver; Step S208: After the dynamic time interval, re-execute the step of "obtaining the driver's physical condition data and public bus and tram operation data within a preset time period"; wherein the dynamic time interval is determined based on the driving safety control coefficient and the driving warning coefficient.

[0031] The steps of the various methods above are divided only for the purpose of clear description. During implementation, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0032] It is not difficult to find that this embodiment is a method embodiment corresponding to the system embodiment, and this embodiment can be implemented in conjunction with the system embodiment. The relevant technical details mentioned in the system embodiment are still valid in this embodiment, and to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the system embodiment.

[0033] It is worth mentioning that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovation of this application, this embodiment does not include units that are not closely related to solving the technical problems proposed by this application. However, this does not mean that other units do not exist in this embodiment.

[0034] In addition, some embodiments of the present application further provide an electronic device. The electronic device may be various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device may also be various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0035] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, enable the processor to perform the steps of the method provided in any one or more of the above embodiments. Figure 4 An exemplary structural diagram of the electronic device is disclosed. Figure 3 As shown, the electronic device includes: one or more processors 1101, memory 1102, and interfaces for connecting various components, including high-speed and low-speed interfaces. The various components are interconnected using different buses and can be mounted on a common motherboard or in other ways as needed. The processor can process instructions executed within the electronic device, including instructions stored in or on the memory for displaying graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if desired, multiple processors and / or multiple buses can be used with multiple memories and multiple storage devices. Similarly, multiple electronic devices can be connected, with each device providing some of the necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0036] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 4 The bus connection is taken as an example.

[0037] Input device 1103 can receive input digital or character information and generate key signal input related to user settings and function control of the electronic device. Examples include a touch screen, keypad, mouse, trackpad, touchpad, pointing stick, one or more mouse buttons, trackball, joystick, and other input devices. Output device 1104 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibration motors). Display devices may include, but are not limited to, liquid crystal displays (LCDs), light-emitting diode (LED) displays, and plasma displays. In some embodiments, the display device may be a touch screen.

[0038] To provide user interaction, the electronic device may be a computer. The computer includes a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user, as well as a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices may also be used to provide user interaction; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback), and input from the user may be received in any form, including acoustic input, voice input, or tactile input.

[0039] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium. When executed by a processor, the computer program / instruction implements the steps of the method provided in any one or more of the above embodiments. The computer-readable medium may be included in the electronic device described in the above embodiments, or it may exist independently and not be incorporated into the device. The computer-readable medium carries one or more computer-readable instructions.

[0040] The memory 1102 can be used as a non-transitory computer-readable storage medium to store non-transitory software programs, non-transitory computer executable programs, and modules. The processor 1101 executes the non-transitory software programs, instructions, and modules stored in the memory 1102 to execute various functional applications and data processing of the server, thereby implementing the program instructions / modules corresponding to the method provided in any one or more of the above embodiments of the present application.

[0041] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely located relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0042] It should be noted that the computer-readable medium described in this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable media may include, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device, or component.

[0043] Computer-readable media include both permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc-read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0044] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0045] In the above embodiments, all or part of the embodiments may be implemented using software, hardware, firmware, or any combination thereof. For example, implementation may be achieved using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of the present application may be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including related data structures) may be stored in a computer-readable recording medium, such as a RAM memory, a magnetic or optical drive, a floppy disk, or the like. In addition, some steps or functions of the present application may be implemented using hardware, for example, as a circuit that cooperates with a processor to perform the various steps or functions.

[0046] The computer program product provided in the embodiments of the present application includes one or more computer programs / instructions that, when executed by a processor, fully or partially produce the processes or functions described in the embodiments of the present application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0047] The flowcharts or block diagrams in the accompanying drawings illustrate the possible architectures, functions and operations of the devices, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, as well as the combination of boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-specific system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0048] The scope of this application is defined by the appended claims rather than the foregoing description and is therefore intended to encompass within this application all changes that come within the meaning and range of equivalents of the claims. Any reference signs in the claims should not be construed as limiting the claims to which they relate. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in a device claim may also be implemented by one unit or device through software or hardware. Words such as "first" and "second" are only used to distinguish the description and do not indicate any particular order, nor should they be understood as indicating or implying relative importance.

[0049] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art may easily propose variations or substitutions within the technical scope disclosed in the present application, and such variations or substitutions shall be encompassed within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be subject to the scope of protection of the claims, and the above embodiments shall be regarded as exemplary and non-limiting.

Claims

1. An early warning system for public bus and tram driving safety management and control, characterized by: The system includes an acquisition module, a first data processing module, a second data processing module, a third data processing module and a circulation module; The acquisition module is configured to acquire the driver's physical condition data and bus and tram operation data within a preset time period; wherein the physical condition data includes heart rate, eye movement frequency and fatigue level; The first data processing module is configured to determine a driving warning coefficient of the public bus based on the operation data of the public bus; The second data processing module is configured to determine a driving safety control coefficient based on the driver's physical condition data; The third data processing module is configured to determine warning information based on the driving safety control coefficient and the driving warning coefficient, and send the warning information to the driver end; The loop module is configured to re-execute the step of "obtaining the driver's physical condition data and the public bus and tram operation data within a preset time period" after a dynamic time interval; wherein the dynamic time interval is determined based on the driving safety control coefficient and the driving warning coefficient.

2. The system according to claim 1, wherein: The second data processing module includes a second data receiving module and a second data processing submodule; The second data receiving module is configured to receive the driver's physical status data sent by the acquisition module; The second data processing submodule is configured to input the driver's physical condition data into a preset driving safety control coefficient evaluation equation group to obtain a driving safety control coefficient.

3. The system according to claim 2, characterized in that The driving safety control coefficient evaluation equation group is constructed by the following formula: ， ， ， ， ， ， Where, is the driving safety control coefficient, is the driver’s i-th physical condition data at time t, is the lower limit of the normal range of the i-th physical condition data, is the upper limit of the normal range of the i-th physical condition data, is the standard deviation of the i-th physical state data in the preset time period, is the attenuation factor, t is the current moment, is the starting time point of the preset time period, is the end time point of the preset time period, is the average value of the i-th physical condition data within the preset time period, is the standard deviation of the i-th body state data within a preset time period, and m is the dimension of the body state data.

4. The system according to claim 1, wherein: The first data processing module includes a first data receiving module and a first data processing submodule; The first data receiving module is configured to receive the operating data of the bus sent by the acquisition module; wherein the operating data includes vehicle speed, steering angle, and brake pressure; The first data processing submodule is configured to arrange the operation data of the public bus and tram in ascending order according to timestamps to obtain the sorted operation data of the public bus and tram; perform feature extraction on the sorted operation data of the public bus and tram to obtain operation data features; and input the operation data features into a preset deep learning model to obtain the driving warning coefficient of the public bus and tram.

5. The system according to claim 1, wherein: The system also includes a correction module; The correction module is configured to correct the driving warning coefficient according to the warning coefficient correction factor.

6. The system according to claim 1, wherein: The third data processing module includes a third data receiving module, a third data processing submodule and an early warning module; The third data receiving module is configured to receive the driving warning coefficient of the public bus or tram sent by the first data processing module; The third data processing submodule is configured to divide the driving safety control coefficient by the driving warning coefficient to obtain a warning level; Determine warning information according to the warning level, and adjust the warning sensitivity threshold of the warning module according to the warning level; The warning module is used for sudden braking warning, sharp turn warning, and blind spot collision warning.

7. The system according to claim 1, wherein: The dynamic time interval is determined by the following formula: ， Wherein, T is the dynamic time interval, is the first proportional coefficient, is the second proportional coefficient, is the driving safety control coefficient, W is the driving warning coefficient, is the first dynamic offset, is the second dynamic offset, is the third dynamic offset, is the fourth dynamic offset, is the first weight coefficient, is the second weight coefficient, is the third weight coefficient, is the fourth weight coefficient, is the first power adjustment parameter, is the second power adjustment parameter, is the third power adjustment parameter, is the fourth power adjustment parameter, is the road condition coefficient, is the weather condition coefficient, To correct the parameters, To adjust the parameters, is the threshold parameter.

8. A warning method for public bus and tram driving safety management and control, characterized in that: Applied to the system according to any one of claims 1 to 7, the method comprises: Acquiring the driver's physical condition data and bus and tram operation data within a preset time period; wherein the physical condition data includes heart rate, eye movement frequency, and fatigue level; Determining a driving warning coefficient of the public bus or tram based on the operating data of the public bus or tram; Determining a driving safety control coefficient based on the driver's physical condition data; Determining warning information based on the driving safety control coefficient and the driving warning coefficient, and sending the warning information to the driver end; After the dynamic time interval, the step of "obtaining the driver's physical condition data and the bus and tram operation data within the preset time period" is re-executed; wherein, the dynamic time interval is determined based on the driving safety control coefficient and the driving warning coefficient.

9. An electronic device, characterized in that: The device comprises: one or more processors; and A memory storing computer program instructions which, when executed, cause the processor to perform the method of claim 8.

10. A computer-readable medium, characterized in that Computer program instructions are stored thereon, and the computer program instructions can be executed by a processor to implement the method according to claim 8.

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