Various data scoring system based on vehicle control multi-dimensional fusion and scoring method thereof

Abstract

The invention discloses a system and method for scoring various data based on vehicle control multi-dimensional fusion, and the method comprises the steps: a data collection module collects driving data through a truck sensor, a data processing module receives, cleans, sorts and analyzes the data, and obtains driving behavior data after the data is standardized through a preset algorithm; the scoring module distributes scores based on the data and the weights to form a result. The feedback module feeds the score back to a driver and a vehicle management department, and the driver can check the score and improvement suggestions through a vehicle-mounted terminal or a mobile phone APP. All the modules are in signal connection in sequence, and efficient data circulation and processing are achieved.

Description

Technical Field

[0001] The patent of this invention relates to the field of vehicle networking technology, specifically a data scoring system and scoring method based on multi-dimensional fusion of vehicle control. Background Art

[0002] If the truck driving behavior is not standardized, it will often lead to increased vehicle energy consumption and greater safety risks. Therefore, developing a system that can scientifically and accurately evaluate truck driving behavior is of great significance to improving drivers' driving skills, reducing vehicle energy consumption and ensuring driving safety. In the past, many fleets set fuel consumption baselines based on experience to manage drivers, but fuel consumption is actually affected by many comprehensive factors. The fuel consumption difference caused by different driving habits for the same vehicle on the same route can exceed 10%. In pursuit of low fuel consumption, many drivers drive at high speeds and low speeds. Although they can achieve very low fuel consumption, they cause great damage to the vehicle, especially long-term driving at high speeds and low speeds, which will greatly reduce the service life of the vehicle.

[0003] Therefore, studying the driver's driving behavior is crucial to ensuring the safe driving of the vehicle, and intelligent driving behavior analysis is imperative.

[0004] Invention patent content

[0005] The purpose of this patent is to provide a data scoring system based on multi-dimensional fusion of vehicle control and its use method to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the patent of the present invention provides the following technical solutions: a scoring system for various data based on multi-dimensional fusion of vehicle control, including a data acquisition module, a data processing module, a scoring module and a feedback module. The data acquisition module is used to collect various data during the driving process of the truck. The data acquisition is realized by a sensor installed on the truck. The sensor is connected to the data acquisition module through a signal. The data processing module is used to receive the various data collected by the data acquisition module, and clean, organize and analyze the various data, and standardize the various data through the preset algorithm in the data processing module to obtain driving behavior data. The scoring module scores the various data based on the driving behavior data and weight distribution, and forms a scoring result. The feedback module is used to feed back the scoring result to the driver and the vehicle management department. The driver views his driving score and improvement suggestions through the vehicle terminal or mobile phone APP. The data acquisition module, the data processing module, the scoring module and the feedback module are connected by signals in sequence.

[0007] Furthermore, the feedback module can use the vehicle-mounted T-BOX to voice broadcast driving behavior data improvement suggestions.

[0008] The scoring method of the various data scoring systems based on the multi-dimensional fusion of vehicle control implemented by the emergency lane autonomous vehicle networking system includes the following steps:

[0009] S1: The data acquisition module collects various data in real time through the vehicle body sensors and uploads them to the data processing module;

[0010] The data include GPS time, longitude and latitude, GPS time difference, standard mileage difference, standard fuel consumption difference, standard speed, acceleration, vehicle current gear, engine speed, throttle opening percentage difference, coolant temperature °C, brake signal status, altitude, driving slope, calculated slope value, engine output torque, engine fuel consumption rate, steering wheel angle;

[0011] S2: The data processing module cleans, organizes and analyzes the data, and standardizes the data through a preset algorithm to obtain driving behavior data, which is then uploaded to the scoring module;

[0012] The standardized index is a standard threshold, and different models are configured with different thresholds;

[0013] S3: The scoring module assigns weights based on the driving behavior data, and scores the driving behavior according to the weight ratio of each indicator in the driving behavior data to obtain a scoring result;

[0014] The weight ratio of each indicator is used to determine whether the indicator participates in the scoring;

[0015] The weight ratio of the indicators includes three levels: high, medium and low.

[0016] The weight ratios of the high-level indicators are as follows: over-safe speed, over-economic speed, over-economic speed, high throttle, sudden braking, insufficient braking prediction, and excessive idling;

[0017] When the braking prediction and emergency braking occur at the same time, the system will issue an emergency braking signal;

[0018] The weight ratios of the intermediate indicators are as follows: too low speed, short-term throttle fluctuation, unreasonable neutral sliding, and throttle acceleration.

[0019] The weight ratios of the bottom-level indicators are as follows: cold vehicle running, too low speed for upshifting, too low speed for downshifting, and unreasonable gear prediction;

[0020] S4: The feedback module feeds back the scoring results to the driver and the vehicle management department;

[0021] S5: The driver can check his driving score and improvement suggestions through the vehicle terminal or mobile phone APP;

[0022] S6: The vehicle management department will conduct reward and punishment management on the driver based on the scoring results.

[0023] The beneficial effects achieved by the patent of this invention are: by collecting key data during the driving process of the truck in real time, and combining advanced algorithms and scoring mechanisms, the system can scientifically and accurately evaluate the driver's driving behavior. The system has real-time data processing and instant feedback functions, and can quickly convey the scoring results to the driver and the vehicle management department, so as to identify problems in time and take corresponding improvement measures. In addition, the system can flexibly customize the scoring rules according to different driving behaviors and scenarios to ensure that the evaluation results are more targeted and instructive. This system is compatible with various models of trucks and drivers, showing wide applicability and good market promotion potential. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is the process of the scoring method of the data scoring system based on multi-dimensional fusion of vehicle control in Example 2;

[0025] Figure 2 This is a data display diagram of the driving behavior analysis algorithm in Example 2. DETAILED DESCRIPTION

[0026] The present invention is further described below in conjunction with the accompanying drawings.

[0027] Embodiment 1

[0028] The patent of the present invention discloses a data scoring system based on multi-dimensional fusion of vehicle control, including: a data acquisition module, a data processing module, a scoring module and a feedback module. The data acquisition module is used to collect various data during the driving process of the truck. The data acquisition is realized by a sensor installed on the truck. The sensor is connected to the data acquisition module through a signal. The data processing module is used to receive various data collected by the data acquisition module, and clean, organize and analyze the various data, and standardize the various data through a preset algorithm in the data processing module to obtain driving behavior data. The scoring module scores the various data based on the driving behavior data and weight distribution, and forms a scoring result. The feedback module is used to feed back the scoring result to the driver and the vehicle management department. The driver views his driving score and improvement suggestions through the vehicle terminal or mobile phone APP. The data acquisition module, the data processing module, the scoring module and the feedback module are connected by signals in sequence.

[0029] Furthermore, the feedback module can use the vehicle-mounted T-BOX to voice broadcast driving behavior data improvement suggestions.

[0030] Embodiment 2

[0031] See also Figure 1 , a scoring method for various data scoring systems based on multi-dimensional fusion of vehicle control implemented by the emergency lane autonomous vehicle networking system includes the following steps:

[0032] S1: The data acquisition module collects various data in real time through the vehicle body sensors and uploads them to the data processing module;

[0033] The data include GPS time, longitude and latitude, GPS time difference, standard mileage difference, standard fuel consumption difference, standard speed, acceleration, vehicle current gear, engine speed, throttle opening percentage difference, coolant temperature °C, brake signal status, altitude, driving slope, calculated slope value, engine output torque, engine fuel consumption rate, steering wheel angle;

[0034] S2: The data processing module cleans, organizes and analyzes the data, and standardizes the data through a preset algorithm to obtain driving behavior data, which is then uploaded to the scoring module;

[0035] The standardized index is a standard threshold, and different models are configured with different thresholds;

[0036] For example: super-economic speed threshold, super-safe speed threshold, high-throttle throttle threshold, short-term throttle fluctuation amplitude.

[0037] S3: The scoring module assigns weights based on the driving behavior data, and scores the driving behavior according to the weight ratio of each indicator in the driving behavior data to obtain a scoring result;

[0038] The weight ratio of each indicator is used to determine whether the indicator participates in the scoring;

[0039] The weight ratio of the indicators includes three levels: high, medium and low;

[0040] The weight ratios of the high-level indicators are as follows: over-safe speed, over-economic speed, over-economic speed, high throttle, sudden braking, insufficient braking prediction, and excessive idling;

[0041] When the braking prediction and emergency braking occur at the same time, the system will issue an emergency braking signal;

[0042] The weight ratios of the intermediate indicators are as follows: too low speed, short-term throttle fluctuation, unreasonable neutral sliding, and throttle acceleration;

[0043] The weight ratios of the bottom-level indicators are as follows: cold vehicle running, too low speed for upshifting, too low speed for downshifting, and unreasonable gear prediction;

[0044] For example: in the proportion of the weight ratio of each index, the super-economic vehicle speed is divided into the proportion of the super-economic vehicle speed mileage being 65% and the average speed of the super-economic vehicle speed being 35%; in the habit of large throttle, the proportion of the large throttle mileage is 65% and the average opening of the large throttle is 35%.

[0045] As Figure 2 shown, the driving behavior analysis algorithm is specifically as follows:

[0046] Super-economic vehicle speed: Vehicle speed determination: v_current > v_threshold, Threshold determination: v = [(Vmax - Vmin) * 0.8 + Vmin], General cargo: v < 88 km / h, v, 88 <= v < 95 km / h, 88.5 <= v, for the vehicle with a speed limit of 89, calculate according to 88, for the vehicle without a speed limit of 89, calculate according to 95 (if it cannot be identified whether there is a speed limit, calculate according to the vehicle without a speed limit model). For hazardous chemicals: take the smaller value of v and 79 km / h; Throttle determination: Throttle opening > 0%; Engine actual torque percentage > 20%; Duration > 60 s (default); After the event is established, if any parameter does not meet the requirements continuously for 2 s, it will end. 6 The vehicle speed threshold can be configured. 7 The duration can be configured;

[0047] Super-safe vehicle speed: Vehicle speed determination: For hazardous chemicals, the default is > 80 km / h; for general cargo, the default is > 100 km / h; Duration: & > 10 s (the duration can be uniformly configured); After the event is established, if the vehicle speed is less than the full value continuously for 2 s, it will terminate; It supports the vehicle speed configuration within a certain vehicle range;

[0048] Insufficient brake anticipation: 1 s, there is throttle, no brake, speed > 50 km / h; 2 s - 5 s, there is no throttle, there is a brake (brake switch = 1 or brake stroke > 5%) and there is a point where the acceleration < -0.56 m / s2 (the brake and acceleration determination meet the event establishment and end);

[0049] Flat road braking: Vehicle speed > 30 km / h; Brake switch = 1 or brake stroke > 5%; Throttle opening = 0; Acceleration < -0.56 m / s2; Gradient > 0.8%. 6 Duration > 1 s. 7 If any of the above does not meet the requirements, it will be interrupted;

[0050] Emergency braking: Initial speed > 40 km / h; The minimum final speed is 30 km / h; Acceleration < y'a (a: default 1.0); For the specific rules, see the formula: ---- During the braking process, brake = 1 or brake stroke > 5%, Throttle opening = 0;

[0051] Too long idling: Vehicle speed = 0; 500 rpm < engine speed < 1100 rpm; Throttle opening = 0; Water temperature > 10 degrees (for vehicles with abnormal water temperature, cancel the water temperature judgment); Duration > 600 s (default); Ambient temperature; After the event is established, if any field does not meet the requirements continuously for 2 s, it will end; The duration can be configured;

[0052] Ultra - economic speed driving: The whole - journey vehicle speed > 20 km / h; The current engine speed > upper - limit economic speed * (1 + slope) ----- When slope < 0, assign 0, when slope > 5%, assign 5%; Throttle > 0%; Engine actual torque percentage > 20%; Duration > 60 s (default); After the event is established, if any field does not meet the requirements for 2 consecutive seconds, it ends; The duration is configurable

[0053] Economic speed driving: The whole - journey vehicle speed > 20 km / h; Any of the following conditions is satisfied: [The current engine speed <= (lower - limit economic speed - 50) and throttle = 0%; (lower - limit economic speed - 50) < current engine speed < upper - limit economic speed * (1 + slope); The current engine speed >= upper - limit economic speed * (1 + slope) and throttle = 0%]; ---- When slope < 0, assign 0, when slope > 5%, assign 5%; Note: This index is a statistical index, not an event;

[0054] Too - low engine speed: The whole - journey vehicle speed > 20 km / h; The current engine speed < speed threshold (speed threshold = lower - limit economic speed - ω) (ω: default 50); Throttle > 0%; Engine actual torque percentage changes from > 60% to > 20%; Duration > 10 s; After the event is established, if any field does not meet the requirements for 2 consecutive seconds, it ends; The duration is configurable;

[0055] Unreasonable gear - shift prediction: Vehicle speed > 20 km / h, from gear A to gear A + 1 (the duration of gear A + 1 is less than 10 s and greater than 2 s) and then back to gear A, and A is not the neutral gear;

[0056] Too - low up - shift engine speed: Vehicle speed > 20 km / h; Engine speed before up - shift < max economic speed; After up - shift: Engine speed < min economic speed - ω (ω: default 50); (When the current engine speed < max economic speed, if there are 3 consecutive gear increases later, and the last point is less than min economic speed - 50 rpm, and the total time < 6 s, the event is established);

[0057] Too - low down - shift engine speed: Vehicle speed > 20 km / h; Before down - shift: Engine speed < min economic speed - 50 rpm; After down - shift: Engine speed < min economic speed + ω (ω: default 100); (When the current engine speed < min economic speed - 50 rpm, if there are 3 consecutive gear decreases later, and the last point is less than min economic speed + 100 rpm, and the total time < 6 s, the event is established) (excluding the gear - shift process points, no more than 3 points)

[0058] Unreasonable neutral - gear coasting: For vehicles with abnormal brake - state data (throttle > 0 and brake = 1 for more than 1 s), use the braking - stroke judgment. Braking - stroke > 5%, and add a scenario where the neutral - gear coasting time is 2 s < t < 7 s;

[0059] Coasting in neutral gear: vehicle speed>1km / h; throttle opening<6%; 0<actual engine torque percentage<15%; speed<800rpm; duration>5s; interruption if any of the above is not met;

[0060] Habitual high throttle: Vehicle speed judgment: >20km / h; Throttle torque judgment: Throttle opening >80% and torque >80%; Duration: Duration >60s (default); After the event is established, the event will be terminated if the vehicle speed is less than 2s continuously or the throttle opening is less than 2s continuously; the duration is configurable;

[0061] Unstable throttle control - standard deviation: standard deviation of throttle opening: refueling driving is required to determine if the throttle opening > 0% time or mileage ratio > 20%; throttle standard deviation calculation for throttle opening > 0% data, the standard deviation value of the opening ratio that does not meet the requirements is assigned 0;

[0062] Unstable throttle control - throttle stability level: throttle stability level %, y = -1.4839x + 109.35, (standard deviation <9, is 9 standard deviation > 40, is 40);

[0063] Short-term throttle fluctuation: vehicle speed judgment>50km / h; gear judgment: the event process is not neutral, and the gear remains unchanged; throttle judgment (large fluctuation): within 3s (including the third second data), the throttle change amplitude I>65%, and it changes alternately >3 times, and the initial interval of the change is less than <13s; event termination conditions, interval>=13s, or the vehicle speed does not meet the requirements for 2 consecutive seconds, or the gear changes;

[0064] Accelerate in situ: vehicle speed = 0; speed > 1500rpm; throttle > 10%; duration > 3s; after the event is established, if the vehicle speed or throttle opening is less than 2s continuously or the speed is less than 2s continuously, the event will be terminated;

[0065] Cold car driving: speed>0; speed>10km / h; throttle>50%; -40 degrees < water temperature<20 degrees; duration>10s; event end: trip end or throttle<20% & lasts for 10s or water temperature>20 degrees & lasts for 10s;

[0066] Uphill cruise: CC activated (unable to identify the cruise state, throttle opening = 0 instead); vehicle speed > 50km / h; actual engine torque percentage > 85%; duration > 10s; after the event is established, if the vehicle speed or torque is less than full for 2s continuously, it will be terminated;

[0067] S4: The feedback module feeds back the scoring results to the driver and the vehicle management department;

[0068] S5: The driver can check his driving score and improvement suggestions through the vehicle terminal or mobile phone APP;

[0069] S6: The vehicle management department will conduct reward and punishment management on the driver based on the scoring results.

[0070] The patented system of this invention can scientifically and accurately evaluate the driver's driving behavior by collecting key data during the driving process of the truck in real time and combining it with advanced algorithms and scoring mechanisms. The system has real-time data processing and instant feedback functions, and can quickly convey the scoring results to the driver and vehicle management department, so as to identify problems in time and take corresponding improvement measures. In addition, the system can flexibly customize the scoring rules according to different driving behaviors and scenarios to ensure that the evaluation results are more targeted and instructive. This system is compatible with various models of trucks and drivers, showing wide applicability and good market promotion potential.

[0071] The above are only preferred specific implementation methods of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and invention concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A data scoring system based on multi-dimensional fusion of vehicle control, characterized by: include: A data acquisition module, a data processing module, a scoring module and a feedback module. The data acquisition module is used to collect various data during the driving process of the truck. The data acquisition is realized by sensors installed on the truck. The sensors are connected to the data acquisition module through signals. The data processing module is used to receive various data collected by the data acquisition module, and clean, organize and analyze the various data, and standardize the various data through the preset algorithm in the data processing module to obtain driving behavior data. The scoring module scores the various data based on the driving behavior data and weight distribution, and forms a scoring result. The feedback module is used to feed back the scoring result to the driver and the vehicle management department. The driver checks his driving score and improvement suggestions through the vehicle terminal or mobile phone APP. The data acquisition module, the data processing module, the scoring module and the feedback module are connected by signals in sequence.

2. The data scoring system based on multi-dimensional fusion of vehicle control according to claim 1 is characterized by: The feedback module can use the vehicle-mounted T-BOX to voice broadcast driving behavior data improvement suggestions.

3. A scoring method for various data scoring systems based on multi-dimensional fusion of vehicle control, characterized by: The steps include: S1: The data acquisition module collects various data in real time through the vehicle body sensors and uploads them to the data processing module; The data include GPS time, longitude and latitude, GPS time difference, standard mileage difference, standard fuel consumption difference, standard speed, acceleration, vehicle current gear, engine speed, throttle opening percentage difference, coolant temperature °C, brake signal status, altitude, driving slope, calculated slope value, engine output torque, engine fuel consumption rate, steering wheel angle; S2: The data processing module cleans, organizes and analyzes the data, and standardizes the data through a preset algorithm to obtain driving behavior data, which is then uploaded to the scoring module; The standardized index is a standard threshold, and different models are configured with different thresholds; S3: The scoring module assigns weights based on the driving behavior data, and scores the driving behavior according to the weight ratio of each indicator in the driving behavior data to obtain a scoring result; The weight ratio of each indicator is used to determine whether the indicator participates in the scoring; S4: The feedback module feeds back the scoring results to the driver and the vehicle management department; S5: The driver can check his driving score and improvement suggestions through the vehicle terminal or mobile phone APP; S6: The vehicle management department will conduct reward and punishment management on the driver based on the scoring results.

4. The scoring method of the various data scoring system based on multi-dimensional fusion of vehicle control according to claim 3 is characterized by: In step S3, the weight ratio of the indicator includes three levels: high, medium and low. The weight ratios of the high-level indicators are as follows: over-safe speed, over-economic speed, over-economic speed, high throttle, sudden braking, insufficient braking prediction, and excessive idling; The standardized index is a standard threshold, and different models are configured with different thresholds; The weight ratios of the intermediate indicators are as follows: too low speed, short-term throttle fluctuation, unreasonable neutral sliding, and throttle acceleration. The weight ratios of the bottom-level indicators are as follows: cold vehicle running, too low speed for upshifting, too low speed for downshifting, and unreasonable gear prediction.

5. The scoring method of the data scoring system based on multi-dimensional fusion of vehicle control according to claim 4 is characterized by: In step S3, in the weight ratio of the intermediate index, when the braking prediction and emergency braking occur at the same time, the system reports an emergency braking.

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