A risk identification management and control system and method for an operating vehicle

Abstract

The application discloses a kind of risk identification management and control system and method to deal with operating vehicle, it is related to operating vehicle management technical field;Including data acquisition module, basic analysis module, operation analysis module, speed adjustment module, dynamic adjustment module and risk identification module: data acquisition module: collection operating personnel driving data, operating route data, environmental data and operating vehicle data, and the data collected are divided, and the data collected except the data of the day constitute historical data set, its technical key points are: a plurality of key factors influencing operating safety are analyzed, the influence of relevant factors on risk is comprehensively analyzed, help operating personnel and passenger understand relevant risk, help operating platform better supervise operating vehicle, so as to effectively reduce the incidence of accident, use effect is good, with good use prospect.

Description

Technical Field

[0001] This invention relates to the field of commercial vehicle management technology, specifically to a risk identification and control system and method for commercial vehicles. Background Technology

[0002] Operating vehicles refer to vehicles that have been approved by the competent authority to participate in operations at the end of the reporting period. This includes vehicles that are in good technical condition, under repair, awaiting repair, have been out of service for a long time, and vehicles that are slated for scrapping but have not yet been approved by the superior competent authority. However, it does not include non-operating vehicles of enterprises (such as overhead line vehicles, tank trucks, and other special-purpose vehicles) and borrowed passenger vehicles.

[0003] Road traffic accidents have become one of the most serious threats to public safety, attracting widespread attention from countries around the world. Among road traffic accidents, commercial vehicles operate over wide areas for extended periods, carrying large quantities of goods and experiencing massive passenger and freight traffic; therefore, the safety of commercial vehicles accounts for a significant proportion of road traffic safety incidents.

[0004] With the rapid increase in the number of commercial vehicles, the existing manual supervision methods lack an understanding of the safety patterns of commercial vehicles and are no longer able to meet the needs of modern refined traffic safety management.

[0005] To better assist in the management of commercial vehicles, people have invented several management systems related to commercial vehicles, including the apparel supply chain management system.

[0006] The existing patent, CN109649396B, entitled "Method for Detecting the Safety of Drivers of Commercial Vehicles," describes an invention patent that establishes an initial detection model for the safety of commercial drivers based on data on vehicle motion status during driving and parameters of the relative relationship between the vehicle and the surrounding traffic environment, using the analytic hierarchy process (AHP). This model yields a total safety score for the driver and performs safety grading to assess driver safety. The method establishes a database based on drivers and can update indicator weights as the sample size increases. As the data volume increases, the accuracy of the safety detection for commercial drivers also increases. This method has the advantages of high intelligence, full automation, no manual operation required, and high reliability.

[0007] The central idea of ​​the aforementioned patent is to analyze the drivers of commercial vehicles, determine their daily driving habits, score them based on these habits, and then determine the safety of their driving. The focus is on the drivers.

[0008] However, the above solution is only applicable to scenarios where the external environment is simple and the impact on the driver's driving is relatively small. Under such conditions, the main source of operational risk is the driver. Therefore, the assessment of operational risk can be achieved by evaluating the driver's driving risk. However, in complex external environments, a driver's experience can improve safety to some extent, but it is not enough to reflect the overall risk. For example, the risks of driving the same road during the day and at night are completely different. Furthermore, as operational supervision becomes increasingly stringent and drivers' driving skills and behaviors become more standardized, the impact of drivers on operational risks is significantly reduced. In this scenario, existing solutions are completely unable to meet the requirements of users and regulatory platforms. Therefore, we have developed a risk identification and control system and method for commercial vehicles. Summary of the Invention

[0009] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a risk identification and control system and method for commercial vehicles. It analyzes multiple key factors affecting operational safety, comprehensively analyzes the impact of relevant factors on risks, helps operators and passengers understand the relevant risks, and helps operating platforms better supervise commercial vehicles, thereby effectively reducing the accident rate. It has good performance and promising application prospects, and solves the problems mentioned in the background technology.

[0010] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: A risk identification and control system for commercial vehicles includes a data acquisition module, a basic analysis module, an operation analysis module, a speed adjustment module, a dynamic adjustment module, and a risk identification module. Data acquisition module: Collects driving data of operators, route data, environmental data and vehicle data, and divides the collected data into historical data sets except for the data collected on the current day; Basic Analysis Module: Analyzes the operating vehicle data and operating personnel driving data in the historical dataset, calculates the vehicle risk value and personnel risk value, and sets the vehicle risk value and personnel risk value as the basic risk value; Operational analysis module: acquires real-time collected operating route data, extracts the operating routes from it, and retrieves operating route data with the same operating routes from the historical data set to calculate the standard safe speed range and basic road risk value; Speed ​​adjustment module: acquires environmental data of the operating route, calculates environmental risk value based on environmental data, calculates speed adjustment ratio by combining basic risk value, road basic risk value and environmental risk value, calculates real-time safe speed range, and marks the calculated safe speed range on the operating route; Dynamic adjustment module: Analyzes real-time collected environmental data and road congestion data, determines the amount of change in environmental data and road congestion data, compares the amount of change with the set adjustment value, and executes the corresponding strategy based on the comparison; Risk identification module: Collects driving data of operators and driving speed of operating vehicles in real time, determines whether there is dangerous driving operation, and determines whether the driving speed is within the corresponding real-time safe speed range. If there is dangerous driving operation or the driving speed is outside the real-time safe speed range, an early warning will be issued.

[0011] Furthermore, the operational route data includes basic road data, road event data, operational routes, and road congestion data; the operational personnel driving data includes total operational mileage data, annual operational mileage, driver's annual demerit points record, and real-time video data of operational personnel; the environmental data includes weather data and air visibility; the operational vehicle data includes the vehicle wear and tear value assessed at the time of the last inspection, the operational mileage at the time of vehicle inspection, and the current operational mileage; and the operational route is the navigation route planned by the operational system based on the vehicle's current location and the initial location of the order, and the navigation route planned based on the initial location of the order and the destination location of the order.

[0012] Furthermore, the steps for analyzing the operating vehicle data in the historical dataset and calculating the vehicle risk value are as follows: Obtain the last inspection data of the operating vehicles and extract the vehicle wear value from it; Obtain the mileage data of the last inspection of the operating vehicle and the current operating mileage, analyze the difference in operating mileage, and calculate the loss value of the operating vehicle based on the difference in operating mileage. The vehicle risk value is calculated based on the vehicle wear and tear value and the operating wear and tear value.

[0013] Furthermore, the steps for analyzing the driving data of operating personnel in the historical dataset and calculating the personnel risk value are as follows: Obtain the driving data of operators for the past three years from the historical dataset; Extract annual operating mileage and demerit point records from the driving data of operating personnel over the past three years; The operational risk value is calculated based on the annual operating mileage and demerit points. Determine the percentage change between operational risk values, calculate the difference between the percentage changes, and compare the difference with the set standard value for change. If the difference is less than the standard value of change, then calculate the average of the change rates; If the difference is greater than or equal to the standard value of change, then the closest change ratio is extracted; The personnel risk value is calculated based on the average of the change ratios or the closest change ratio extracted.

[0014] Furthermore, the following steps are taken to retrieve and analyze the operational route data that is identical to the operational route from the historical dataset: Input the navigation routes into the database, find overlapping operating route data, and delete overlapping operating route data that will result in deductions. Extract the data from the identified operating routes, and extract the data from the overlapping routes in the operating route data; Arrange the extracted data in chronological order from most recent to oldest, and delete the data after the Kth group. Obtain the upper and lower speed limits required by the roads along the operating route, and extract the speed of the operating vehicles as they pass through the operating route. Then, extract the upper speed limit data. and lower limit speed The speed data was extracted, and the average speed was calculated. ; Average value based on velocity Calculate the preliminary safe speed range 5% < F < 8%; Then Compared with the lower limit speed, Compare with the upper limit speed; like Greater than or equal to the lower limit speed, and If the speed is less than or equal to the upper limit, then the standard safe speed range is: ; like Speed ​​less than the lower limit If the speed is less than or equal to the upper limit, then the standard safe speed range is: ; like Speed ​​greater than or equal to the lower limit If the speed is less than or equal to the upper limit, then the standard safe speed range is: .

[0015] Furthermore, when calculating the basic road risk value, the operating route data in the extracted historical dataset are arranged in chronological order from most recent to oldest. The top N groups of operating route data are selected, and the selected operating route data are analyzed. The specific steps are as follows: Obtain the operating route data of the top N groups and extract the vibration data detected by the vehicle vibration sensor from them; The operating route is divided into sections of H meters, and vibration data is used to assess the road grade of each section. Obtain the number of lanes for each segment of the operating route, and calculate the basic road risk value based on the number of lanes and road grade.

[0016] Furthermore, the vehicle speed adjustment ratio is analyzed by combining the basic risk value, road basic risk value, and environmental risk value; When calculating the real-time safe speed range, the standard safe speed range is adjusted using the vehicle speed adjustment ratio. The lower limit of the real-time safe speed range is calculated as the lower limit of the initial safe speed range multiplied by the vehicle speed adjustment ratio, and the upper limit of the real-time safe speed range is calculated as the upper limit of the initial safe speed range multiplied by the vehicle speed adjustment ratio.

[0017] Furthermore, the change in road congestion data is used to determine the change in road congestion level, and corresponding strategies are executed based on the comparison: If all changes are less than or equal to the set adjustment value, no adjustment will be made; If the change exceeds the set adjustment value, the environmental risk value is calculated based on real-time collected environmental data, the basic road risk value is calculated based on road congestion data, and the vehicle speed adjustment ratio is recalculated to adjust the real-time safe speed range.

[0018] Furthermore, the steps for conducting hazard analysis on the real-time collected driving data of operators to determine whether dangerous driving operations exist are as follows: The system processes real-time video data of operations personnel, capturing one image per second. The screenshot is transmitted to the hazard identification model, which uses blink detection algorithm, target detection algorithm and convolutional neural network to determine whether the driver has engaged in dangerous driving operations.

[0019] Furthermore, a risk identification and control method for commercial vehicles includes the following steps: Collect driving data of operators, route data, environmental data and vehicle data, and divide the collected data into historical data sets except for the data collected on the current day; Analyze the operating vehicle data and operating personnel driving data in the historical dataset, calculate the vehicle risk value and personnel risk value, and set the vehicle risk value and personnel risk value as the basic risk value; The system acquires real-time operational route data, extracts the operational routes from it, and retrieves operational route data with the same operational routes from the historical data set to calculate the standard safe speed range and basic road risk value. Obtain environmental data of the operating route, calculate the environmental risk value based on the environmental data, combine the basic risk value, the road basic risk value and the environmental risk value to calculate the vehicle speed adjustment ratio, calculate the real-time safe speed range, and mark the calculated safe speed range on the operating route; Analyze the real-time collected environmental data and road congestion data, determine the changes in the environmental data and road congestion data, and compare the changes with the set adjustment values; If all changes are less than or equal to the set adjustment value, no adjustment will be made; If the change exceeds the set adjustment value, the environmental risk value is calculated based on real-time collected environmental data, the basic road risk value is calculated based on road congestion data, and the vehicle speed adjustment ratio is recalculated to adjust the real-time safe speed range. The system collects real-time driving data from operators and the driving speed of operating vehicles to determine whether dangerous driving operations are occurring and whether the driving speed is within the corresponding real-time safe speed range. If dangerous driving operations are occurring or the driving speed is outside the real-time safe speed range, an early warning is issued.

[0020] (III) Beneficial Effects This invention provides a risk identification and control system and method for commercial vehicles, which has the following beneficial effects: 1. This invention provides a risk identification and control system and method for commercial vehicles. It analyzes multiple key factors affecting operational safety, comprehensively analyzes the impact of relevant factors on risks, helps operators and passengers understand relevant risks, and helps operating platforms better supervise commercial vehicles, thereby effectively reducing the accident rate. Furthermore, it combines the analysis with the user's own driving habits, calculates risks based on the vehicle, driver, external environment, and road conditions, and has good results and promising application prospects.

[0021] 2. This invention provides a risk identification and control system and method for commercial vehicles. During operation, it analyzes historical data of the operating route and the current environmental conditions to calculate the risk of the operating route, thereby planning a reasonable safe speed range. Operating at the speed within this safe speed range can effectively improve operational safety. Furthermore, it collects external environmental data and lane congestion data in real time. When the external environment and lane congestion data change significantly, it automatically adjusts the safe speed range, ensuring that the generated safe speed range remains reasonable. This effectively reduces the risk of commercial vehicles, has good performance, and has promising application prospects. Attached Figure Description

[0022] Figure 1 This is a flowchart of a risk identification and control system for commercial vehicles according to the present invention; Figure 2 This is a flowchart of a dynamic adjustment module in a risk identification and control system for commercial vehicles according to the present invention. Figure 3 This invention relates to an operational route map that identifies a standard safe speed range in a risk identification and control system for commercial vehicles. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Research and development concept: The core idea of ​​the existing patent is to analyze the drivers of commercial vehicles, determine the daily driving habits of the relevant drivers, score the drivers based on the driving habits, and then determine the driving safety. The focus is on the drivers.

[0025] However, the above solution is only applicable to scenarios where the external environment is simple and the impact on the driver's driving is relatively small. Under such conditions, the main source of operational risk is the driver. Therefore, the assessment of operational risk can be achieved by evaluating the driver's driving risk. However, in complex external environments, a driver's experience can improve safety to some extent, but it is not enough to reflect the overall risk. For example, the risks of driving the same road during the day and at night are completely different. Moreover, as operational regulations become increasingly stringent, drivers' driving skills and behaviors become more standardized. As a result, the impact of drivers on operational risks is significantly reduced. In this scenario, existing solutions are completely unable to meet the requirements of users and regulatory platforms.

[0026] Therefore, in the early stages of research and development, it is necessary to develop a relatively scientific and comprehensive system. This system can comprehensively consider, evaluate, and analyze operational risks from multiple perspectives, helping relevant personnel and drivers to understand and provide timely warnings, thereby reducing operational risks.

[0027] However, in actual use, it was found that assessment and warning alone are not sufficient to effectively reduce risks. The main problem is that warnings issued too early or too late can easily affect the driving of commercial drivers, while warnings issued too late are ineffective. Therefore, this warning method has certain drawbacks and does not meet people's requirements, thus requiring further improvement.

[0028] Therefore, during the mid-stage of research and development, the system was further adjusted, and new warning methods were studied. After a long period of research, it was found that the best approach was to implement the corresponding plan at the beginning of operation, and for the drivers to review it before driving the vehicle. The drivers had a certain psychological expectation of the overall situation, and with the added warnings, the system's effectiveness was improved, and operational safety was greatly enhanced.

[0029] However, in actual use, it has been found that the external environment changes frequently. Therefore, it is not feasible to use a fixed plan for planning. Furthermore, there are certain differences in road traffic. Therefore, historical data cannot clearly reflect the current state. Thus, the system still has certain drawbacks in actual use, mainly in terms of flexibility.

[0030] Therefore, further research and planning were conducted in the later stages of development to further plan the entire system. Based on the initial planning, a real-time adjustment scheme was added, so that the entire system can not only meet the needs of drivers for initial viewing and analysis, but also make corresponding adjustments in response to external changes. Furthermore, by analyzing the adjustment situation, it can effectively avoid the situation of recalculating for minor changes in the external environment, thereby effectively reducing the computational load of the system operation. The overall performance is good and it has good application prospects.

[0031] Example 1: Please see Figure 1 This embodiment provides a risk identification and control system for operating vehicles, which mainly consists of hardware and software components. The hardware component mainly consists of computers and servers, as well as related equipment that supports the operation of the software component, such as network communication equipment.

[0032] The software section contains the following information: It includes a data acquisition module, a basic analysis module, an operational analysis module, a speed adjustment module, a dynamic adjustment module, and a risk identification module. For any system to operate with high precision, it must be based on sufficient data. Therefore, for this system to achieve high risk identification effectiveness, it must also have relevant and comprehensive data. Thus, data collection is necessary, and the data collection process is based on the data acquisition module.

[0033] Data acquisition module: Collects driving data of operators, route data, environmental data and vehicle data, and divides the collected data into historical data sets except for the data collected on the current day; The data acquisition module mainly connects to the existing operating vehicle management system, directly retrieving historical data and real-time data from the existing operating vehicle management system.

[0034] Since existing vehicle management systems can already collect most vehicle data, this system can be implemented without modifying the vehicles or adding additional sensor equipment. Most existing vehicles can meet the requirements, so the implementation cost of this system is relatively low.

[0035] Furthermore, this system is designed for commercial vehicles, which are numerous and can quickly collect relevant data. Therefore, it acquires data relatively quickly, and the analysis is mainly focused on commercial vehicles, resulting in good analysis results for them. However, this system is not suitable for analyzing private vehicles, mainly because it is more difficult to collect relevant data for private cars. Without sufficient data to support it, the accuracy of the analysis of private vehicles cannot be guaranteed.

[0036] The types of data collected are mainly as follows: route data, including basic road data, road event data, route data, and road congestion data; driver data, including total operating mileage, annual operating mileage, driver's annual demerit points record, and real-time video data of drivers; environmental data, including weather data and air visibility; and vehicle data, including vehicle wear and tear values ​​assessed at the time of the last inspection, operating mileage at the time of vehicle inspection, and current operating mileage.

[0037] It mainly considers four aspects: driver, vehicle, route, and environment. Under normal circumstances, the route and environment have the greatest impact.

[0038] The main reason is that, under normal circumstances, drivers receive specialized training and their driving skills are relatively stable. In actual use, vehicles undergo comprehensive maintenance at short intervals. Therefore, the impact of vehicle condition and driver on driving risk is relatively limited. Thus, the risk factors to be considered are mainly external factors, requiring the collection of data on external factors.

[0039] After collecting data on external factors, it is necessary to analyze the collected data. The first thing to analyze is the vehicle and the driver, as these two are the most basic and generally do not fluctuate in a short period of time. Therefore, analyzing these two data first can clearly and explicitly understand the basic risks of operating vehicles. This step relies on the basic analysis module.

[0040] Basic Analysis Module: Analyzes the operating vehicle data and operating personnel driving data in the historical dataset, calculates the vehicle risk value and personnel risk value, and sets the vehicle risk value and personnel risk value as the basic risk value; The steps for analyzing the operational vehicle data in the historical dataset and calculating the vehicle risk value are as follows: Obtain the last inspection data of the operating vehicle and extract the vehicle wear value LSRc from it; To ensure safety, commercial vehicles are usually inspected and maintained regularly. The data obtained is the data assessed during inspection and maintenance. The vehicle wear and tear value LSRc is assessed by relevant personnel. This is existing technology and can be achieved by general car manufacturer software, so it will not be described in detail.

[0041] Obtain the mileage data from the last inspection of the operating vehicle and the current operating mileage, calculate the difference in operating mileage, and then calculate the vehicle wear and tear value based on the difference in operating mileage. The specific formula is as follows: In the formula, LSRy represents the wear and tear value of the operating vehicle. This refers to the current operating mileage of the vehicles. This represents the mileage data of the operating vehicle at the time of its last inspection. A is a constant value, where 1 < A < 1.5. This is the initial wear coefficient for operating vehicles; This step mainly involves calculating the actual risk. Since there is a certain time gap between the two maintenance operations, usually 5-6 months, the data from the assessment is more accurate if it falls within the first two months, but the accuracy will be greatly reduced if it falls within the last two months.

[0042] Therefore, if the evaluation data is used directly, there will be some error. In order to reduce the impact of the error, the actual loss is calculated.

[0043] The vehicle risk value is calculated based on vehicle wear and tear values ​​and operational wear and tear values. In the formula, hsxs is the conversion factor between the loss value and the vehicle risk value.

[0044] Risk assessment can be achieved by measuring vehicle wear and tear. Since the operating company monitors the status of the vehicles, there is no risk of vehicle damage. By converting wear and tear values, the risks of vehicle operation can be analyzed relatively accurately, and the method is quite convenient.

[0045] The steps for analyzing the driving data of operating personnel in the historical dataset and calculating the personnel risk value are as follows: Obtain the driving data of operators for the past three years from the historical dataset; The driving data of operators over the past three years is sufficient to reflect their situation. If the time frame is too short, the response will not be enough to show the specific situation. If the data collection period is too long, driving skills and habits will have changed significantly. Using this data for analysis will actually reduce the accuracy. Therefore, using three years of data is more appropriate.

[0046] Extract annual operating mileage and demerit point records from the driving data of operating personnel over the past three years; The amount of data analyzed related to driving behavior is very large, so it is mainly done through feedback on demerit point deductions. This method analyzes a smaller amount of data, but the results are more accurate.

[0047] The main reason is that the existing operating personnel have high requirements, their driving skills are relatively high and stable, so the standards for personnel-related risk gaps can be lowered, hence the above-mentioned solution is adopted.

[0048] The operational risk value is calculated based on the annual operating mileage and demerit points. In the formula, Let this be the operating risk value for year i. For the operating mileage in year i, This represents the total deductions for year i. For the i-th year and j-th demerit point record, the above calculation method can clearly reflect the driver's situation.

[0049] Determine and calculate and The ratio of change between and and The ratio of change between Calculate The difference is compared with a set standard value for change. This is the operating risk value for the previous year. This is the operating risk value for the previous two years, and The operating risk value is the value for the previous three years; If the current year is 2022, then For 2021, For 2020, It was in 2019.

[0050] If the difference is less than the standard value of change, then calculate the percentage change. and average ; If the difference is less than the standard value for change, it means that the change is within the normal range and is considered normal. The effect reflected by the average is more accurate.

[0051] If the difference is greater than or equal to the standard value of change, then extract... ; This indicates that the technology has improved, or that unexpected circumstances have occurred, at which point the following approach is adopted. That would be more accurate.

[0052] Using point deductions instead of accident counts is a more effective way to filter out the impact of other drivers' actions.

[0053] The personnel risk value is calculated using the following formula: In the formula, Here, B represents the personnel risk value, and B is a constant value, where 300,000 < B < 500,000. For constant data, SYz represents the total operating mileage of the operators, and zhxsr represents the personnel conversion coefficient.

[0054] The above method is mainly based on two change ratios to obtain the personnel risk value under two different conditions. The calculation is performed separately for each condition, and the result is more accurate.

[0055] After understanding the risks to personnel and vehicles, it is necessary to further analyze the external environment. However, the environment varies in different areas and locations. If a comprehensive calculation method is used, the amount of data would be very large. Therefore, in order to avoid excessive calculations, we first analyze the operating routes and then analyze the environmental data of the operating routes to understand the external impact. This step relies on the operation analysis module.

[0056] Operational analysis module: acquires real-time collected operating route data, extracts the operating routes from it, and retrieves operating route data with the same operating routes from the historical data set to calculate the standard safe speed range and basic road risk value; The operating route is a navigation route planned by the operating system based on the vehicle's current location and the initial location of the order, and a navigation route planned based on the initial location of the order and the destination location of the order. If the operating vehicle uses a fixed route, there is no need to analyze the navigation route planned based on the vehicle's current location and the initial location of the order; you can directly analyze the navigation route planned based on the initial location and the final location of the order.

[0057] The navigation route is obtained using the existing ride-hailing platform's navigation software. This can be directly obtained from the existing operating platform's data, which is existing technology, so it will not be described in detail.

[0058] The steps for retrieving and analyzing the operational route data that is identical to the operational route from the historical dataset are as follows: Input the navigation routes into the database, find overlapping operating route data, and delete overlapping operating route data that will result in deductions. The most basic criterion for finding overlapping operating route data is that the overlapping portion accounts for more than one-third of the operating route. This avoids having too much data after filtering and processing too large a volume of data.

[0059] Deleting overlapping route data that result in point deductions primarily reduces the impact of these incidents and improves the accuracy of calculations.

[0060] Extract the data from the identified operating routes, and extract the data from the overlapping routes in the operating route data; For example, if one-third of the route overlaps with another route, then we can extract the data from that one-third overlap, and the rest of the data does not need to be extracted. This reduces the amount of data analysis and improves the overall usability.

[0061] Arrange the extracted data in chronological order from most recent to oldest, and delete the data after the Kth group. Time filtering can remove older data to prevent it from causing interference.

[0062] When a road surface undergoes upgrades, such as expansion or extensive repairs, the previous data on that route should be deleted and the data updated to avoid interference from historical data.

[0063] For example, replacing a cement road with an asphalt road, or a two-lane road with a four-lane road, has a significant impact on the road. Therefore, removing historical data and collecting new data will result in more accurate calculations.

[0064] If it is just a minor pothole repair, historical data can continue to be used.

[0065] Obtain the upper and lower speed limits required by the roads along the operating route, and extract the speeds of the operating vehicles as they pass through the route. Extract the speed data for the upper and lower speed limits from the data, and calculate the average speed. Where D represents the amount of data extracted from one segment of the operating route after filtering. The average speed is the sum of the upper limit speed and the lower limit speed / 2. For example, the speed limit on elevated roads in urban areas is generally 60-80 km / h. The average speeds recorded in 10 historical data points for a certain elevated road in the city are 72, 78, 68, 70, 79, 76, 62, 69, 71 and 77 km / h, respectively. The calculated average speed is 72.2 km / h.

[0066] The selected data is based on data from normal, uncongested conditions. Data from congested conditions is not used as it is a special case.

[0067] Average value based on velocity Calculate the preliminary safe speed range 5% < F < 8%; Then Compared with the lower limit speed, Compare with the upper limit speed; like Greater than or equal to the lower limit speed, and If the speed is less than or equal to the upper limit, then the standard safe speed range is: ; In the above scenario, the calculated data all fall within the set speed range. For example, if the average calculated speed is 70 and F is 6%, then the calculated standard safe speed range is (65.8, 74.2).

[0068] like Speed ​​less than the lower limit If the speed is less than or equal to the upper limit, then the standard safe speed range is: ; The above situation occurs when the calculated lower limit speed is too low. For example, if the average calculated speed is 62, and F is taken as 6%, then the calculated speed will be too low. The value is 58.28, which is less than 60. The standard safe speed range in this case is (60, 65.28).

[0069] like Speed ​​greater than or equal to the lower limit If the speed is less than or equal to the upper limit, then the standard safe speed range is: .

[0070] The above situation occurs when the calculated upper limit speed is too high. For example, if the average calculated speed is 76, and F is taken as 6%, then the calculated upper limit speed will be too high. The value is 80.56, which is greater than 80. The standard safe speed range in this case is (71.44, 80).

[0071] After calculating the driving speed, it is necessary to further understand the road conditions and calculate the basic risk value of the roads along the route.

[0072] When calculating the basic risk value of the road, the operating route data in the extracted historical data set are arranged in ascending order of time. The top N groups of operating route data are selected and analyzed. The specific steps are as follows: Arranging the data in order from nearest to farthest results in more accurate data collection.

[0073] To further improve the accuracy of the data, it is necessary to add further limiting conditions, such as setting external environmental factors like temperature and weather to not affect driving. By eliminating the influence of external environmental factors, the basic risk values ​​of the road analyzed will be more accurate.

[0074] Obtain the operating route data of the top N groups and extract the vibration data detected by the vehicle vibration sensor from them; To improve accuracy, the collected vibration data is based on data within the standard safe speed range calculated above. This is because different speeds result in different vibrations, and limiting the speed will improve the accuracy of the data collection. The vibration data is obtained from data of the same operating vehicles.

[0075] The main difference lies in the vehicles themselves, which result in variations in shock absorption structures and performance. Since vehicles used by the same company are essentially identical, the relevant data provided by the operating company can be directly adopted without further categorization. Therefore, no further description is necessary.

[0076] The operating route is divided into segments of H meters, and the road grade of each segment is assessed using vibration data. The specific formula is as follows: In the formula, DJdl represents the road grade, and G represents the number of vibration data points extracted from each road segment. For the i-th set of vibration data, This represents the difference between the upper and lower limits of road vibration for each level. Typically, H is set to 500 meters. The larger H is, the lower the accuracy; the smaller H is, the higher the accuracy, but the amount of data to be processed will be very large.

[0077] Obtain the number of lanes for each segment of the operating route, and calculate the basic road risk value based on the number of lanes and road grade. The specific formula is as follows: In the formula This represents the basic risk value for the road. This is the risk adjustment coefficient corresponding to the lane. The level of road congestion along the operating route. This is the road conversion coefficient.

[0078] The road congestion levels for the operating routes are the same as those provided by existing navigation systems, mainly ranging from smooth traffic, moderate congestion, traffic jams, and severe congestion.

[0079] Different levels of congestion naturally lead to different risks.

[0080] In actual use, the route will be switched in this situation, and the basic road risk value will be recalculated based on the new route.

[0081] After calculating the impact of the road, further analysis is needed to consider the influence of the external environment, such as insufficient light. In this case, the driving speed needs to be reduced. Therefore, it is necessary to further calculate the environmental risk value, which depends on the speed adjustment module.

[0082] Speed ​​Adjustment Module: Acquires environmental data for the operating route, calculates environmental risk values ​​based on this data, and combines these with the baseline risk value, road baseline risk value, and environmental risk value to calculate the speed adjustment ratio. It then calculates the real-time safe speed range and marks this range on the operating route. Please refer to [link / reference]. Figure 3 The formula for calculating environmental risk values ​​based on environmental data is as follows: In the formula, Here, Q represents the environmental risk value, and Q is the total number of categories of weather-related factors affecting vehicle driving. This refers to the level of the i-th factor affecting car driving, collected in real time. Let be the adjustment ratio for the i-th factor affecting car driving, and e be the natural constant. The minimum visibility level set so as not to affect vehicle driving. Visibility data collected when calculating environmental risk values. The adjustment ratio for visibility. This is the environmental conversion coefficient; The main categories of weather factors that affect car driving are humidity, temperature, wind speed, and rainfall rate.

[0083] There is no need to consider snowfall, mainly because operations usually stop when it snows, or the relevant city departments will handle the roads after snowfall.

[0084] The formula for calculating the vehicle speed adjustment ratio by combining the basic risk value, the basic road risk value, and the environmental risk value is as follows: In the formula, Adjust the vehicle speed ratio. Here, xshj represents the preset maximum risk value, xsc represents the weighting coefficient for environmental risk, xsr represents the weighting coefficient for vehicle risk, xsdl represents the weighting coefficient for personnel risk, and xsdl represents the weighting coefficient for road infrastructure risk. To adjust the coefficient, ; When calculating the real-time safe speed range, the standard safe speed range should be adjusted using the vehicle speed adjustment ratio. The lower limit of the calculated real-time safe speed range is the lower limit of the initial safe speed range multiplied by... The upper limit of the real-time safe speed range is the upper limit of the initial safe speed range multiplied by [the value of the upper limit]. .

[0085] Less than 1, when the calculated A value less than 0.8 usually indicates an abnormal situation caused by external influences.

[0086] In actual use, the level of congestion, routes, and external environment are not static. For example, abnormal events may cause congestion, requiring route changes. Congestion may worsen during rush hour, and visibility may decrease in the evening. At these times, the risks may change. Therefore, in actual use, adjustments need to be made in response to external changes, and the adjustment process is based on the dynamic adjustment module.

[0087] Please see Figure 2 Dynamic adjustment module: Analyzes real-time collected environmental data and road congestion data, judges the changes in environmental data and road congestion data, and compares the changes with the set adjustment values; If all changes are less than or equal to the set adjustment value, no adjustment will be made; If the change exceeds the set adjustment value, the environmental risk value is calculated based on real-time collected environmental data, the basic road risk value is calculated based on road congestion data, and the vehicle speed adjustment ratio is recalculated to adjust the real-time safe speed range.

[0088] The formula for determining changes in road congestion levels is as follows: The change in road congestion data is used to determine changes in environmental data. In the formula, ROChj represents the change in environmental data. This refers to the level of the i-th factor affecting car driving, collected in real time. This refers to the visibility data collected in real time.

[0089] The above can reflect changes in the external environment.

[0090] This invention provides a risk identification and control system and method for commercial vehicles. During operation, it analyzes historical data of the operating route and the current environmental conditions to calculate the risk of the operating route, thereby planning a reasonable safe speed range. Operating within this safe speed range can effectively improve operational safety. Furthermore, it collects external environmental data and lane congestion data in real time. When the external environment and lane congestion data change significantly, it automatically adjusts the safe speed range, ensuring that the generated safe speed range remains reasonable. This effectively reduces the risk of commercial vehicles, has good performance, and shows promising application prospects.

[0091] Risk identification module: Collects driving data of operators and driving speed of operating vehicles in real time, determines whether there is dangerous driving operation, and determines whether the driving speed is within the corresponding real-time safe speed range. If there is dangerous driving operation or the driving speed is outside the real-time safe speed range, an early warning will be issued.

[0092] The steps for performing hazard analysis on real-time collected driving data from operators to determine whether dangerous driving operations exist are as follows: The system processes real-time video data of operations personnel, capturing one image per second. The screenshot is transmitted to the hazard identification model, which uses blink detection algorithm, target detection algorithm and convolutional neural network to determine whether the driver has engaged in dangerous driving operations.

[0093] There are many existing technologies that document the existence of dangerous driving operations. For example, the paper "Design of Dangerous Driving Behavior Detection System Based on Image Recognition" describes a system for identifying dangerous driving operations. Therefore, it belongs to the existing technology and will not be described in detail.

[0094] This invention provides a risk identification and control system and method for commercial vehicles. It analyzes multiple key factors affecting operational safety, comprehensively analyzes the impact of relevant factors on risks, helps operators and passengers understand relevant risks, and helps operating platforms better supervise commercial vehicles, thereby effectively reducing the accident rate. Furthermore, it combines the analysis with the user's own driving habits, calculates risks based on the vehicle, driver, external environment, and road conditions, and has good results and promising application prospects.

[0095] Dynamic speed limiting can effectively reduce the risks posed by the external environment to operations, improve operational safety, and has proven to be effective.

[0096] The weighting coefficients are determined using the coefficient of variation method, which assigns weights to each indicator based on the degree of variation between the current value and the target value. If the numerical difference of an indicator is large, clearly distinguishing each evaluated object, it indicates that the indicator has rich discriminative information and should therefore be given a larger weight. Conversely, if the numerical difference of each evaluated object on a certain indicator is small, then the indicator's ability to distinguish each evaluated object is weak, and therefore it should be given a smaller weight. This method directly utilizes the information contained in each indicator to calculate the weight of the indicator, thus possessing objectivity.

[0097] Example 2: Based on Example 1, a method for risk identification and control of commercial vehicles includes the following steps: Collect driving data of operators, route data, environmental data and vehicle data, and divide the collected data into historical data sets except for the data collected on the current day; Analyze the operating vehicle data and operating personnel driving data in the historical dataset, calculate the vehicle risk value and personnel risk value, and set the vehicle risk value and personnel risk value as the basic risk value; The system acquires real-time operational route data, extracts the operational routes from it, and retrieves operational route data with the same operational routes from the historical data set to calculate the standard safe speed range and basic road risk value. Obtain environmental data of the operating route, calculate the environmental risk value based on the environmental data, combine the basic risk value, the road basic risk value and the environmental risk value to calculate the vehicle speed adjustment ratio, calculate the real-time safe speed range, and mark the calculated safe speed range on the operating route; Analyze the real-time collected environmental data and road congestion data, determine the changes in the environmental data and road congestion data, and compare the changes with the set adjustment values; If all changes are less than or equal to the set adjustment value, no adjustment will be made; If the change exceeds the set adjustment value, the environmental risk value is calculated based on real-time collected environmental data, the basic road risk value is calculated based on road congestion data, and the vehicle speed adjustment ratio is recalculated to adjust the real-time safe speed range. The system collects real-time driving data from operators and the driving speed of operating vehicles to determine whether dangerous driving operations are occurring and whether the driving speed is within the corresponding real-time safe speed range. If dangerous driving operations are occurring or the driving speed is outside the real-time safe speed range, an early warning is issued.

[0098] In the application, the various formulas mentioned are all calculated by removing dimensions and taking their numerical values. The formulas are established by collecting a large amount of data and simulating the most recent real situation. Some coefficients or weights in the formulas are set by those skilled in the art according to the actual situation, so they will not be elaborated here.

[0099] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0100] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A risk identification and management system for dealing with commercial vehicles, characterized by, include: Data acquisition module: Collects driving data of operators, route data, environmental data and vehicle data, and divides the collected data into historical data sets except for the data collected on the current day; Basic Analysis Module: Analyzes the operating vehicle data and operating personnel driving data in the historical dataset, calculates the vehicle risk value and personnel risk value, and sets the vehicle risk value and personnel risk value as the basic risk value; The steps for analyzing the driving data of operating personnel in the historical dataset and calculating the personnel risk value are as follows: Obtain the driving data of operators for the past three years from the historical dataset; Extract annual operating mileage and demerit point records from the driving data of operating personnel over the past three years; An operating risk value is calculated based on the annual operating mileage and the penalty record : wherein is the operational risk value for year i, is the operational mileage for year i, is the total deduction for year i; Determine and calculate and The ratio of change between and and The ratio of change between Calculate The difference is compared with a set standard value for change. This is the operating risk value for the previous year; If the difference is less than the standard value of change, then calculate the percentage change. and average ; If the difference is greater than or equal to the change standard value, the extraction ; The personnel risk value is calculated using the following formula: ； In the formula, Here, B represents the personnel risk value, and B is a constant value, where 300,000 < B < 500,000. For constant data, SYz represents the total operating mileage of the operators, and zhxsr represents the personnel conversion coefficient; Operational analysis module: acquires real-time collected operating route data, extracts the operating routes from it, and retrieves operating route data with the same operating routes from the historical data set to calculate the standard safe speed range and basic road risk value; When calculating the basic risk value of the road, the operating route data in the extracted historical dataset are arranged in ascending order of time. The top N operating route data are selected and analyzed. The specific steps are as follows: Obtain the top N routes in the operational data and extract the vibration data detected by the vehicle vibration sensor from them; The operating route is divided into segments of H meters, and the road grade of each segment is evaluated using vibration data. The specific formula is as follows; ； In the formula, DJdl represents the road grade, and G represents the number of vibration data points extracted from each road segment. For the i-th set of vibration data, This represents the difference between the upper and lower limits of road vibration for each level. Obtain the number of lanes for each segment of the operating route, and calculate the basic road risk value based on the number of lanes and road grade. The specific formula is as follows: In the formula This represents the basic risk value for the road. This is the risk adjustment coefficient corresponding to the lane. The level of road congestion along the operating route. This refers to the road conversion coefficient. Speed ​​adjustment module: acquires environmental data of the operating route, calculates environmental risk value based on environmental data, calculates speed adjustment ratio by combining basic risk value, road basic risk value and environmental risk value, calculates real-time safe speed range, and marks the calculated safe speed range on the operating route; Dynamic adjustment module: Analyzes real-time collected environmental data and road congestion data, determines the amount of change in environmental data and road congestion data, compares the amount of change with the set adjustment value, and executes the corresponding strategy based on the comparison; Risk identification module: Collects driving data of operators and driving speed of operating vehicles in real time, determines whether there is dangerous driving operation, and determines whether the driving speed is within the corresponding real-time safe speed range. If there is dangerous driving operation or the driving speed is outside the real-time safe speed range, an early warning will be issued.

2. The risk identification and control system for commercial vehicles according to claim 1, characterized in that: The operational route data includes basic road data, road event data, operational routes, and road congestion data. The operational personnel driving data includes total operational mileage data, annual operational mileage, driver's annual demerit points record, and real-time video data of operational personnel. The environmental data includes weather data and air visibility. The operational vehicle data includes the vehicle wear and tear value assessed at the time of the last inspection, the operational mileage at the time of vehicle inspection, and the current operational mileage. The operational route is the navigation route planned by the operational system based on the vehicle's current location and the initial location of the order, and the navigation route planned based on the initial location of the order and the destination location of the order.

3. The risk identification and management system for commercial vehicles of claim 2, wherein: The steps for analyzing the operational vehicle data in the historical dataset and calculating the vehicle risk value are as follows: Obtain the last inspection data of the operating vehicles and extract the vehicle wear value from it; Obtain the mileage data of the last inspection of the operating vehicle and the current operating mileage, analyze the difference in operating mileage, and calculate the loss value of the operating vehicle based on the difference in operating mileage. The vehicle risk value is calculated based on the vehicle wear and tear value and the operating wear and tear value.

4. The risk identification and management system for commercial vehicles of claim 3, wherein: The steps for retrieving and analyzing the operational route data that is identical to the operational route from the historical dataset are as follows: Input the navigation routes into the database, find overlapping operating route data, and delete overlapping operating route data that will result in deductions. Extract the data from the identified operating routes, and extract the data from the overlapping routes in the operating route data; Arrange the extracted data in chronological order from most recent to oldest, and delete the data after the Kth group. Obtain the upper and lower speed limits required by the roads along the operating route, and extract the speed of the operating vehicles as they pass through the operating route. Then, extract the upper speed limit data. and lower limit speed The speed data was extracted, and the average speed was calculated. ; Average value based on speed Calculate preliminary safe speed interval 5% < F < 8% Then the With the lower speed ratio, the With the upper speed ratio; If greater than or equal to a lower limit speed, and less than or equal to an upper limit speed, then the standard safety speed interval is ; If the speed is less than the lower limit speed, the speed is less than or equal to the upper limit speed, the standard safety speed interval is ; If greater than or equal to a lower limit speed, less than or equal to an upper limit speed, then the standard safety speed interval is .

5. A risk identification and control system for commercial vehicles according to claim 4, characterized in that: The vehicle speed adjustment ratio is analyzed by combining the basic risk value, the basic road risk value, and the environmental risk value. When calculating the real-time safe speed range, the standard safe speed range is adjusted using the vehicle speed adjustment ratio. The lower limit of the real-time safe speed range is calculated as the lower limit of the initial safe speed range multiplied by the vehicle speed adjustment ratio, and the upper limit of the real-time safe speed range is calculated as the upper limit of the initial safe speed range multiplied by the vehicle speed adjustment ratio.

6. The risk identification and control system for commercial vehicles according to claim 5, characterized in that: To determine the change in road congestion level, we analyze the changes in road congestion data and then implement the corresponding strategy based on the comparison. If all changes are less than or equal to the set adjustment value, no adjustment will be made; If the change exceeds the set adjustment value, the environmental risk value is calculated based on real-time collected environmental data, the basic road risk value is calculated based on road congestion data, and the vehicle speed adjustment ratio is recalculated to adjust the real-time safe speed range.

7. A risk identification and control system for commercial vehicles according to claim 6, characterized in that: The steps for performing hazard analysis on real-time collected driving data from operators to determine whether dangerous driving operations exist are as follows: The system processes real-time video data of operations personnel, capturing one image per second. The screenshot is transmitted to the hazard identification model, which uses blink detection algorithm, target detection algorithm and convolutional neural network to determine whether the driver has engaged in dangerous driving operations.

8. A method for risk identification and control of commercial vehicles, using the system described in any one of claims 1 to 7, characterized in that: Includes the following steps: Collect driving data of operators, route data, environmental data and vehicle data, and divide the collected data into historical data sets except for the data collected on the current day; Analyze the operating vehicle data and operating personnel driving data in the historical dataset, calculate the vehicle risk value and personnel risk value, and set the vehicle risk value and personnel risk value as the basic risk value; The system acquires real-time operational route data, extracts the operational routes from it, and retrieves operational route data with the same operational routes from the historical data set to calculate the standard safe speed range and basic road risk value. Obtain environmental data of the operating route, calculate the environmental risk value based on the environmental data, combine the basic risk value, the road basic risk value and the environmental risk value to calculate the vehicle speed adjustment ratio, calculate the real-time safe speed range, and mark the calculated safe speed range on the operating route; Analyze the real-time collected environmental data and road congestion data, determine the changes in the environmental data and road congestion data, and compare the changes with the set adjustment values; If all changes are less than or equal to the set adjustment value, no adjustment will be made; If the change exceeds the set adjustment value, the environmental risk value is calculated based on real-time collected environmental data, the basic road risk value is calculated based on road congestion data, and the vehicle speed adjustment ratio is recalculated to adjust the real-time safe speed range. The system collects real-time driving data from operators and the driving speed of operating vehicles to determine whether dangerous driving operations are occurring and whether the driving speed is within the corresponding real-time safe speed range. If dangerous driving operations are occurring or the driving speed is outside the real-time safe speed range, an early warning is issued.

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