Transportation process overtime monitoring alarm system based on Internet of Vehicles technology and design method thereof

Abstract

The invention relates to a transportation process timeout monitoring and alarming system based on an Internet of Vehicles technology and a design method thereof, and solves the problems of untimely management, large manual troubleshooting workload and incomplete supervision in the prior art. The system comprises a mobile terminal intelligent alarm APP, a vehicle-mounted integrated alarm display system and a cloud intelligent alarm management system. According to the design method of the system, unnecessary alarm triggering caused by data fluctuation, temporary abnormity or sensor false alarm is reduced through a denoising strategy, so that the effectiveness of alarm and the credibility of a user are improved, and the design method specifically comprises the following steps: 1) a time window smoothing method; 2) repeatedly alarming and filtering; 3) intelligent learning and self-adaptive threshold value; 4) a dual-acknowledgement mechanism; and 5) performing user feedback circulation. According to the invention, real-time, comprehensive and intelligent supervision of the vehicle transportation process is realized, the supervision efficiency and comprehensiveness of the vehicle transportation process are improved, and the safety and operation efficiency of the transportation industry are further improved.

Description

Technical Field

[0001] This invention relates to a monitoring and alarm system, specifically to a transportation process overtime monitoring and alarm system based on vehicle networking technology and its design method. Background Technology

[0002] In the current domestic transportation industry, the supervision of vehicle transportation mainly relies on traditional manual management methods and basic vehicle-to-everything (V2X) technology. Traditional methods, such as communicating with drivers via telephone and radio, suffer from response delays and information lags, making it difficult to monitor vehicle dynamics in real time. While some companies have introduced V2X systems for vehicle location and trajectory recording, they can only monitor simple behaviors such as speeding and illegal parking, and require manual screening for abnormal data. Specifically, the following shortcomings exist:

[0003] 1. Insufficient real-time performance: Human communication cannot provide timely feedback on vehicle status. Although the vehicle networking system can collect data, alarm delays or false alarm rates are high.

[0004] 2. Low management efficiency: The massive amount of vehicle data relies on manual analysis, which is costly and prone to overlooking key risks.

[0005] 3. One-sided supervision: Traditional spot check models cannot cover the entire process, and it is difficult to fully identify violations. Summary of the Invention

[0006] This invention provides a transportation process overtime monitoring and alarm system based on vehicle networking technology and its design method, which solves the problems of untimely management, large workload of manual investigation, and incomplete supervision.

[0007] This invention is achieved through the following technical solutions:

[0008] A transportation process overtime monitoring and alarm system based on vehicle networking technology includes a mobile smart alarm APP, an in-vehicle integrated alarm display system, and a cloud-based smart alarm management system;

[0009] The mobile smart alarm APP integrates a vehicle monitoring alarm module, allowing users to register and log in to view and receive alarm information during vehicle transportation in real time;

[0010] The in-vehicle integrated alarm display system includes a high-definition in-vehicle alarm display module and an in-vehicle alarm voice broadcast module, which are used to convey alarm information to the driver in real time so as to respond quickly and correct possible driving problems;

[0011] The cloud-based intelligent alarm management system includes user management, device management, vehicle management, alarm settings, alarm reports, and intelligent alarm algorithm modules. By acquiring vehicle information from the vehicle network system in real time and combining it with preset alarm rules, it calculates and triggers corresponding alarm actions to ensure the accuracy and timeliness of alarm information.

[0012] A design method for a transportation process overtime monitoring and alarm system based on vehicle networking technology is presented. This method reduces unnecessary alarm triggering caused by data fluctuations, temporary anomalies, or sensor false alarms through a noise reduction strategy, thereby improving the effectiveness of alarms and user trust. Specifically, it includes:

[0013] 1) Time window smoothing method

[0014] Define a continuous monitoring interval: To avoid false alarms caused by instantaneous data fluctuations, a time window T is set to smooth out dynamically changing parameters;

[0015] Trend analysis: Analyze the parameter change trend within a time window, and trigger an alarm only when T1 exceeds the threshold for a sustained period of time;

[0016] 2) Repeated alarm filtering

[0017] Alarm cooldown time: For the same alarm type, no new alarm will be triggered within a period of time M after it has been acknowledged or automatically reset, even if the conditions are met again.

[0018] Alarm frequency limit: Set the maximum number of alarms for M1 per unit time for a single vehicle or user. If the limit is exceeded, the alarm will be temporarily suppressed unless the situation is extremely serious.

[0019] 3) Intelligent learning and adaptive threshold

[0020] Historical data analysis: Utilize machine learning technology to analyze historical alarm data, identify normal fluctuation ranges and abnormal patterns, and dynamically adjust alarm thresholds to better reflect actual operating conditions;

[0021] Environmental factor correction: Considering the impact of external factors on vehicle performance, adjust alarm parameters in a timely manner and appropriately reduce the overspeed alarm threshold;

[0022] 4) Dual confirmation mechanism

[0023] Multiple sensor verification: Dual or multiple sensor verification is used for critical alarms, and the alarm is only triggered when most sensor data consistently point to an anomaly.

[0024] Manual review: For certain high-risk but false alarms, a manual review process is added. After an automatic alarm is triggered, back-end personnel quickly review the alarm and confirm it before pushing the final alarm.

[0025] 5) User feedback loop

[0026] Alarm feedback mechanism: Users can confirm received alarms or report false alarms through the APP, and the system will optimize alarm logic and thresholds based on user feedback;

[0027] Dynamic adjustment strategy: Based on user feedback and alarm effectiveness analysis, the noise reduction strategy parameters are adjusted regularly, and the algorithm is continuously optimized to improve the accuracy of the alarm system and user satisfaction.

[0028] In the time window smoothing method (1), the dynamically changing parameters include power.

[0029] In 3) Intelligent learning and adaptive threshold, the external factors include weather and road conditions.

[0030] In the dual confirmation mechanism (4), the key alarms include overspeed and low battery, and the high-risk but false alarms include serious violations.

[0031] This invention not only fully utilizes the real-time and accuracy of vehicle networking technology, but also combines identification algorithms and alarm systems to achieve real-time, comprehensive, and intelligent supervision of the vehicle transportation process, thereby improving the efficiency and comprehensiveness of vehicle transportation supervision and enhancing the safety and operational efficiency of the transportation industry. Detailed Implementation

[0032] This invention provides a vehicle transportation monitoring and alarm system based on vehicle networking technology, which is essentially a transportation process overtime early warning algorithm that is different from existing transportation process supervision methods.

[0033] This includes mobile smart alarm apps, vehicle-mounted integrated alarm display systems, and cloud-based smart alarm management systems.

[0034] 1. The mobile smart alarm APP integrates a vehicle monitoring and alarm module, allowing users to register and log in to view and receive alarm information during vehicle transportation in real time.

[0035] 2. In-vehicle integrated alarm display system: This system includes a high-definition in-vehicle alarm display module and an in-vehicle alarm voice broadcast module, which are used to convey alarm information to the driver in real time so as to respond quickly and correct possible driving problems.

[0036] 3. Cloud-based intelligent alarm management system: This system includes user management, device management, vehicle management, alarm settings, alarm reports, and intelligent alarm algorithm modules. By acquiring vehicle information from the vehicle network system in real time and combining it with preset alarm rules, it calculates and triggers corresponding alarm actions to ensure the accuracy and timeliness of alarm information.

[0037] The specific design method and steps of the system are as follows:

[0038] Noise reduction strategies are a key component in ensuring the accuracy of alarm systems. They aim to reduce unnecessary alarm triggers caused by data fluctuations, temporary anomalies, or sensor false alarms, thereby improving alarm effectiveness and user trust. For the aforementioned vehicle transportation process monitoring alarm system, the following noise reduction strategies can be implemented:

[0039] 1. Time window smoothing method

[0040] Define a continuous monitoring interval: To avoid false alarms caused by instantaneous data fluctuations, set a time window T (such as the average data over the past 5 minutes) to smooth out dynamic parameters such as speed and power consumption.

[0041] Trend analysis: Analyze the parameter change trend within the time window, and trigger an alarm only when the threshold is exceeded for a period of time (such as more than 3 minutes), so as to avoid false alarms caused by instantaneous peak values.

[0042] 2. Repeated alarm filtering

[0043] Alarm cooldown time: For the same alarm type, a new alarm will not be triggered for a period of time M (e.g., 15 minutes) after it is acknowledged or automatically reset, even if the conditions are met again, in order to avoid continuous repeated alarms.

[0044] Alarm frequency limit: Set the maximum number of alarms per unit time M1 (e.g., one hour) for a single vehicle or user. If the limit is exceeded, the alarm will be temporarily suppressed unless the situation is extremely serious.

[0045] 3. Intelligent learning and adaptive threshold

[0046] Historical data analysis: Utilize machine learning technology to analyze historical alarm data, identify normal fluctuation ranges and abnormal patterns, and dynamically adjust alarm thresholds to better reflect actual operating conditions.

[0047] Environmental factor correction: Consider the impact of external factors such as weather and road conditions on vehicle performance, and adjust alarm parameters in a timely manner. For example, appropriately reduce the overspeed alarm threshold in rainy or snowy weather.

[0048] 4. Dual confirmation mechanism

[0049] Multiple sensor verification: Dual or multiple sensor verifications are used for critical alarms (such as overspeed and low battery). The alarm is only triggered when most sensor data consistently point to an abnormality.

[0050] Manual review: For certain high-risk but easily false alarms (such as major violations), a manual review process is added. After an automatic alarm is triggered, back-end personnel quickly review the alarm and confirm it before pushing the final alarm.

[0051] 5. User feedback loop

[0052] Alarm feedback mechanism: Users are encouraged to confirm received alarms or report false alarms through the APP. The system optimizes alarm logic and thresholds based on user feedback.

[0053] Dynamic adjustment strategy: Based on user feedback and alarm effectiveness analysis, the noise reduction strategy parameters are adjusted regularly, and the algorithm is continuously optimized to improve the accuracy of the alarm system and user satisfaction.

[0054] By comprehensively applying the above noise reduction strategies, the accuracy and practicality of the vehicle transportation monitoring and alarm system can be significantly improved, the false alarm rate can be reduced, and the reliability and importance of alarm information can be ensured.

[0055] The present invention has the following advantages:

[0056] 1. Improve real-time monitoring: By combining vehicle networking with intelligent algorithms, achieve second-level response to vehicle status.

[0057] 2. Reduce manual intervention: Replace manual investigation with automated alarms to reduce management costs.

[0058] 3. Enhance the comprehensiveness of supervision: Cover multiple dimensions of data such as driving behavior, vehicle status, and environmental risks to eliminate regulatory blind spots.

[0059] 4. Optimize alarm accuracy: Reduce false alarms and improve the reliability of alarm information through noise reduction strategies.

Claims

1. A transportation process overtime monitoring and alarm system based on vehicle networking technology, characterized in that: This includes mobile smart alarm apps, vehicle-mounted integrated alarm display systems, and cloud-based smart alarm management systems; The mobile smart alarm APP integrates a vehicle monitoring alarm module, allowing users to register and log in to view and receive alarm information during vehicle transportation in real time; The vehicle-mounted integrated alarm display system includes a high-definition vehicle-mounted alarm display module and a vehicle-mounted alarm voice broadcast module, which are used to convey alarm information to the driver in real time so as to respond quickly and correct possible driving problems; The cloud-based intelligent alarm management system includes user management, device management, vehicle management, alarm settings, alarm reports, and intelligent alarm algorithm modules. By acquiring vehicle information from the vehicle network system in real time and combining it with preset alarm rules, it calculates and triggers corresponding alarm actions to ensure the accuracy and timeliness of alarm information.

2. A design method for a transportation process overtime monitoring and alarm system based on vehicle networking technology, characterized by: By employing noise reduction strategies to reduce unnecessary alarm triggers caused by data fluctuations, temporary anomalies, or sensor false alarms, the effectiveness of alarms and user trust can be improved. Specifically, this includes: 1) Time window smoothing method Define a continuous monitoring interval: To avoid false alarms caused by instantaneous data fluctuations, a time window T is set to smooth out dynamically changing parameters; Trend analysis: Analyze the parameter change trend within a time window, and trigger an alarm only when T1 exceeds the threshold for a sustained period of time; 2) Repeated alarm filtering Alarm cooldown time: For the same alarm type, no new alarm will be triggered within a period of time M after it has been acknowledged or automatically reset, even if the conditions are met again. Alarm frequency limit: Set the maximum number of alarms for M1 per unit time for a single vehicle or user. If the limit is exceeded, the alarm will be temporarily suppressed unless the situation is extremely serious. 3) Intelligent learning and adaptive threshold Historical data analysis: Utilize machine learning technology to analyze historical alarm data, identify normal fluctuation ranges and abnormal patterns, and dynamically adjust alarm thresholds to better reflect actual operating conditions; Environmental factor correction: Considering the impact of external factors on vehicle performance, adjust alarm parameters in a timely manner and appropriately reduce the overspeed alarm threshold; 4) Dual confirmation mechanism Multiple sensor verification: Dual or multiple sensor verification is used for critical alarms, and the alarm is only triggered when most sensor data consistently point to an anomaly. Manual review: For certain high-risk but false alarms, a manual review process is added. After an automatic alarm is triggered, back-end staff will quickly review the alarm and confirm it before pushing the final alarm. 5) User feedback loop Alarm feedback mechanism: Users can confirm received alarms or report false alarms through the APP, and the system will optimize alarm logic and thresholds based on user feedback; Dynamic adjustment strategy: Based on user feedback and alarm effectiveness analysis, the noise reduction strategy parameters are adjusted regularly, and the algorithm is continuously optimized to improve the accuracy of the alarm system and user satisfaction.

3. The design method of a transportation process overtime monitoring and alarm system based on vehicle networking technology according to claim 2, characterized in that: In the time window smoothing method (1), the dynamically changing parameters include power.

4. The design method of a transportation process overtime monitoring and alarm system based on vehicle networking technology according to claim 2, characterized in that: In 3) Intelligent learning and adaptive threshold, the external factors include weather and road conditions.

5. The design method of a transportation process overtime monitoring and alarm system based on vehicle networking technology according to claim 2, characterized in that: In the dual confirmation mechanism (4), the key alarms include overspeed and low battery, and the high-risk but false alarms include serious violations.