Multi-source data intelligent connected vehicle power consumption control system and method

Abstract

This invention discloses a multi-source data intelligent connected vehicle power control system and method, belonging to the field of data analysis technology. The system includes: deploying a high-definition monitoring camera on the target vehicle and a positioning sensor on the moving vehicle; obtaining the abnormal driving ratio of the vehicle model based on its type; extracting key frame moments to capture the location of abnormally moving vehicles in the monitoring footage; obtaining the mapping of the contact wire on the road surface of the receiving section to obtain the power-receiving range of the vehicle to be detected within the receiving section; and judging the impact of the abnormally moving vehicle on the vehicle to be detected based on its location and power-receiving range, intelligently outputting differentiated early warning prompts to the vehicle to be detected. This invention, by analyzing the receiving section and combining the monitoring video and positioning sensors deployed on the vehicle, intelligently determines whether there are abnormalities in vehicles moving on the road section, providing early warnings to the driver, effectively reducing driving risks and ensuring the safety of the driver and vehicle.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, specifically to a multi-source data intelligent connected vehicle power control system and method. Background Technology

[0002] Traditional road freight systems consume large amounts of fuel, making it difficult to meet the demands of smart and efficient development. Electrified road systems, powered by overhead contact networks and integrating core system components such as cloud-based traffic organization and scheduling, and vehicle-to-grid communication, can significantly improve freight efficiency. Compared with traditional freight systems, electrified road systems have significant advantages in multiple dimensions, including operating costs, energy conservation and environmental protection, and transportation efficiency. Electrified roads consist of two core components: ground systems and vehicle systems. By erecting electric traction networks on the road, electric transport vehicles equipped with pantographs can be charged in real time during transportation, ensuring the safety, efficiency, and stability of vehicle and cargo transportation from multiple dimensions.

[0003] In actual driving on electrified highways, drivers need to constantly observe whether there are any abnormalities in the vehicles ahead and determine whether to retract the pantograph to prevent damage to the vehicle or the pantograph. However, in actual driving, when drivers observe abnormalities in the vehicles ahead, they are often too close to the abnormal vehicles and may not have enough time to retract the pantograph, leading to pantograph or even contact network failure. If high-definition monitoring cameras and positioning sensors are deployed on the vehicle, combined with the actual road conditions, to intelligently determine whether there are any abnormalities in the vehicles and remind the driver in advance, driving risks can be effectively reduced and the safety of the driver and the vehicle can be ensured. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-source data intelligent connected vehicle power control system and method to solve the problems raised in the prior art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A multi-source data intelligent connected vehicle power control method includes the following steps:

[0007] The goal is to acquire the contact wire sections on electrified highways, which are sections along the highway where overhead contact lines and traction power supply systems are installed. These sections allow target vehicles equipped with pantographs to draw power or charge their devices by contacting the contact wires with the pantographs during their journey. High-definition surveillance cameras are then deployed on the target vehicles to enable full-section monitoring of the vehicles during their journey.

[0008] The vehicles traveling on the circuit segment include several models. A positioning sensor is deployed on each vehicle. Based on the speed of each vehicle during its travel, the abnormal driving ratio of each model is obtained.

[0009] The target vehicle currently driving and drawing power on the circuit receiving segment is taken as the vehicle to be detected. Based on the navigation platform, the planned driving trajectory of the vehicle to be detected is obtained. Positioning sensors are deployed on the vehicle to be detected to obtain the real-time positioning of the vehicle to be detected. According to the vehicle type of the vehicles driving on the circuit receiving segment in real time, and the abnormal driving ratio of each vehicle type, the corresponding key frame moment during the driving process of the vehicle to be detected is extracted in real time.

[0010] Obtain the mapping of the contact wire on the road surface in the circuit section, and obtain the power receiving range of the vehicle under test in the circuit section based on the pantograph on the vehicle under test.

[0011] The system acquires the speed of each vehicle at key frame moments. When a vehicle is in an abnormal driving state, it captures the location of the abnormal vehicle in the monitoring screen. Based on the location and the range of power available, it assesses the impact of the abnormal vehicle on the vehicle to be detected and intelligently outputs differentiated early warning prompts to the vehicle to be detected.

[0012] Preferably, obtaining the abnormal driving ratio for each vehicle model includes the following steps: obtaining the location trajectory of a certain vehicle over a certain period of time as a driving record; calculating the moving speed of the vehicle; if the moving speed is less than a preset speed threshold, then the driving record is considered an abnormal driving record; obtaining several abnormal driving records, summarizing the total number of records, and calculating the number of abnormal driving records for each vehicle model; dividing the number of abnormal driving records by the total number of records to obtain the abnormal driving ratio for each vehicle model.

[0013] Preferably, the key frame moments corresponding to the vehicle under test during its movement are extracted in real time, including the following steps:

[0014] Set the maximum time interval for extracting keyframes to G. max The minimum time interval is G min Based on the origin and destination of the vehicle to be detected, the planned driving trajectory of the vehicle to be detected is obtained through the navigation platform. Positioning sensors are deployed on the vehicle to be detected to monitor the position of the vehicle to be detected in real time. The position at a certain time T0 is taken as P0. The road segment with a length of m along the planned driving trajectory starting from position P0 is taken as the road segment to be detected. The vehicle at time T0 on the road segment to be detected is taken as the feature vehicle corresponding to time T0.

[0015] Take time T0 as the keyframe time, and let the duration after time T0 be G. max The moment as T max The duration after time T0 is G. min Take time T1 as the time, obtain all characteristic vehicles corresponding to time T1, and sum the abnormal driving ratios of all characteristic vehicles according to the abnormal driving ratio of each vehicle type to obtain the characteristic ratio corresponding to time T1.

[0016] The initial feature value is set to 0, and the ratio threshold is set to B0. The feature value is then added to the feature ratio corresponding to time T1. If the feature value is not less than B0, then time T1 is taken as the keyframe time; if the feature value is less than B0, then the time after time T1 is set to G. min Taking time T0 as T2, we obtain the characteristic proportion corresponding to time T2, where the duration between time T0 and time T2 is less than G. max Add the feature value to the feature ratio corresponding to time T2. If the feature value is not less than B0, then time T2 is the keyframe time. Continue this process until the feature value is added to time T. max The corresponding feature ratio, at this time T max As a keyframe moment;

[0017] To prevent unforeseen circumstances and ensure the reliability of the vehicles under test during charging and driving, time T1 needs to be used as the key frame time for the following analysis to achieve road surface monitoring. Since the characteristic vehicle models in the road segment under test are different, and the probability of anomalies varies for different models, it is of great significance to intelligently set the key frame time according to the vehicles and models in the road segment under test in this solution. The more models with a higher proportion of abnormal driving on the road segment under test, the earlier the key frame time should be set.

[0018] The final keyframe moments are obtained, and so on, to extract the corresponding keyframe moments during the driving process of the vehicle under test in real time.

[0019] Preferably, obtaining the power-receiving range of the vehicle under test in the power-receiving section includes the following steps:

[0020] The mapping of the contact wire on the road surface in the receiving section is taken as line segment X; the pantograph on the vehicle to be tested is deployed, and the rightmost end of the pantograph's power supply contact is brought into contact with the contact wire. The distance between line segment X and the left wheel of the vehicle to be tested at this time is taken as D1. The leftmost end of the pantograph's power supply contact is brought into contact with the contact wire, and the distance between line segment X and the right wheel of the vehicle to be tested at this time is taken as D2. The range of line segment X extending to the left by a length of D1 and extending to the right by a length of D2 in the receiving section is taken as the range of the vehicle to be tested that can receive electricity in the receiving section.

[0021] Preferably, the intelligent system outputs differentiated warning prompts to the vehicle to be detected, including the following steps:

[0022] The distance traveled by each vehicle within a certain period before a certain key frame is obtained, and the speed of each vehicle at a certain key frame is obtained. Vehicles with a speed less than a preset speed threshold are identified as abnormal vehicles. If a vehicle is identified as an abnormal vehicle, the monitoring screen of the vehicle to be detected at a certain key frame is extracted, and the location of the abnormal vehicle in the monitoring screen is captured as the abnormal vehicle location.

[0023] Obtain the width of the vehicle to be tested, preset the vehicle safety distance, and extend the vehicle safety distance along the left and right sides of the width to obtain the safe driving width of the vehicle to be tested.

[0024] The monitoring camera on the vehicle under test is also used to monitor the contact position between the pantograph and the overhead contact line in real time. Based on the contact position, the position of the pantograph on the vehicle under test and the safe driving width, the safe contour area of ​​the vehicle under test on the power receiving section is obtained. Based on the distance between the safe contour area and the left and right boundaries of the power receiving range, the virtual safe driving range formed on the road surface when the vehicle under test continues to move forward is predicted.

[0025] If the location of the abnormal vehicle is outside the virtual safe driving range, it will indicate that there is an abnormal vehicle ahead of the vehicle to be inspected, and you need to observe the road conditions and slow down.

[0026] If the abnormal vehicle is located within the virtual safe driving range, calculate the distance L1 between the abnormal vehicle and the left boundary of the power-receiving range, and the distance L2 between the abnormal vehicle and the right boundary of the power-receiving range. If both distance L1 and distance L2 are not greater than the safe driving width, then indicate that there is an abnormal vehicle in front of the vehicle to be detected, and the pantograph needs to be retracted and the abnormal vehicle avoided.

[0027] If the abnormal vehicle is located within the virtual safe driving range and the distance from L1 or L2 is greater than the safe driving width, a message will be displayed indicating that there is an abnormal vehicle ahead of the vehicle to be detected. The vehicle to be detected will be instructed to move to an area that avoids the abnormal vehicle without leaving the power-receiving range. Once the movement is successful, a message will be displayed indicating that the road conditions need to be observed and the vehicle should slow down.

[0028] A multi-source data intelligent connected vehicle power control system includes an abnormal driving ratio calculation module, a key frame moment extraction module, a power-receiving range extraction module, and an early warning module;

[0029] Abnormal Driving Proportion Calculation Module: Used to obtain the contact wire sections on electrified highways. Contact wire sections are sections along electrified highways where contact wires and traction power supply systems are installed, allowing target vehicles equipped with pantographs to obtain or charge power by contacting the contact wires with the pantographs during driving. High-definition monitoring cameras are deployed on the target vehicles to achieve full-section monitoring of the target vehicles during their driving process.

[0030] The vehicles traveling on the circuit segment include several models. A positioning sensor is deployed on each vehicle. Based on the speed of each vehicle during its travel, the abnormal driving ratio of each model is obtained.

[0031] Keyframe Moment Extraction Module: This module is used to identify the target vehicle currently driving and drawing power on the circuit segment as the vehicle to be detected. Based on the navigation platform, it obtains the planned driving trajectory of the vehicle to be detected, deploys positioning sensors on the vehicle to be detected, and obtains the real-time positioning of the vehicle to be detected. Based on the vehicle type of the vehicles driving on the circuit segment in real time and the abnormal driving ratio of each vehicle type, it extracts the corresponding keyframe moments during the driving process of the vehicle to be detected in real time.

[0032] Power-receiving range extraction module: used to obtain the mapping of the contact wire on the road surface on the power-receiving section, and to obtain the power-receiving range of the vehicle under test in the power-receiving section based on the pantograph on the vehicle under test;

[0033] Early warning module: used to obtain the moving speed of each vehicle at key frame moments. When a vehicle is in an abnormal driving state, it captures the location of the abnormal vehicle in the monitoring screen. Based on the location and the power-received range, it analyzes the impact of the abnormal vehicle on the vehicle to be detected and intelligently outputs differentiated early warning prompts to the vehicle to be detected.

[0034] Preferably, the abnormal driving ratio calculation module includes an abnormal driving ratio calculation unit;

[0035] Abnormal Driving Ratio Calculation Unit: Used to acquire the positioning trajectory of a certain vehicle over a certain period of time as a driving record, calculate the moving speed of the vehicle, and if the moving speed is less than a preset speed threshold, the driving record is regarded as an abnormal driving record; acquire several abnormal driving records, summarize the total number of records, calculate the number of abnormal driving records for each vehicle model, divide the number of abnormal driving records by the total number of records, and obtain the abnormal driving ratio for each vehicle model.

[0036] Preferably, the early warning module includes a virtual safe driving range prediction unit and an early warning unit;

[0037] Virtual safe driving range prediction unit: used to monitor the contact position between the pantograph and the overhead contact line in real time; based on the contact position, the position of the pantograph on the vehicle under test and the safe driving width, the safe contour area of ​​the vehicle under test on the power receiving section is obtained; based on the distance between the safe contour area and the left and right boundaries of the power receiving range, the virtual safe driving range formed on the road surface when the vehicle under test continues to move forward is predicted.

[0038] Early warning unit: Based on the location of abnormal vehicles and the virtual safe driving range, it analyzes the impact of abnormal vehicles on the vehicle to be inspected and intelligently outputs differentiated early warning prompts to the vehicle to be inspected.

[0039] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a multi-source data intelligent connected vehicle power control system and method, including: deploying a high-definition monitoring camera on the target vehicle and a positioning sensor on the moving vehicle; obtaining the abnormal driving ratio of the vehicle model based on the vehicle type; extracting key frame moments to capture the location of abnormally driving vehicles in the monitoring image; obtaining the mapping of the contact wire on the road surface of the receiving section to obtain the power-receiving range of the vehicle to be detected in the receiving section; and judging the impact of the abnormally driving vehicle on the vehicle to be detected based on the location and power-receiving range, and intelligently outputting differentiated early warning prompts to the vehicle to be detected. This invention, by analyzing the receiving section and combining the monitoring video and positioning sensors deployed on the vehicle, intelligently determines whether there are abnormalities in vehicles driving on the road section, providing early warnings to the driver, effectively reducing driving risks and ensuring the safety of the driver and vehicle. Attached Figure Description

[0040] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating a multi-source data intelligent connected vehicle power control method according to the present invention. Detailed Implementation

[0042] 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.

[0043] Example: Figure 1 As shown, this invention provides a technical solution for a multi-source data intelligent connected vehicle power control method, comprising the following steps:

[0044] (1) Obtain the contact line section on the electrified highway. The contact line section is the section along the electrified highway where the contact network and traction power supply system are installed. The target vehicle with the pantograph is connected to the contact network through the pantograph to complete the power collection or charging during the driving process. Deploy high-definition monitoring cameras on the target vehicle to realize full-section monitoring of the target vehicle during the driving process.

[0045] (2) The vehicles traveling on the circuit segment include several models. A positioning sensor is deployed on each vehicle. The abnormal driving ratio of each model is obtained based on the speed of each vehicle during its travel.

[0046] The system acquires the location trajectory of a vehicle over a certain period of time, recording it as a driving record. It calculates the vehicle's speed; if the speed is less than a preset speed threshold, the driving record is considered an abnormal driving record. Several abnormal driving records are acquired, the total number of records is summed, and the number of abnormal driving records for each vehicle model is calculated. The abnormal driving ratio for each vehicle model is obtained by dividing the number of abnormal driving records by the total number of records. In this embodiment, the speed threshold is 10 km / h. This means that when a vehicle's speed on the road is less than the speed threshold, it indicates that the vehicle may have encountered a malfunction or other accident, and is therefore considered an abnormal driving record. However, due to differences in factors such as load and road conditions, the probability of abnormal driving varies for different vehicle models. Therefore, this solution can obtain the abnormal driving ratio for different vehicle models based on different vehicle conditions.

[0047] (3) The target vehicle currently driving and drawing power on the circuit segment is taken as the vehicle to be detected. Based on the navigation platform, the planned driving trajectory of the vehicle to be detected is obtained. Positioning sensors are deployed on the vehicle to be detected to obtain the positioning position of the vehicle to be detected in real time. According to the vehicle type of the vehicle driving on the circuit segment in real time and the abnormal driving ratio of each vehicle type, the corresponding key frame moment during the driving process of the vehicle to be detected is extracted in real time.

[0048] Set the maximum time interval for extracting keyframes to G. max The minimum time interval is G min Based on the origin and destination of the vehicle to be detected, the planned driving trajectory of the vehicle to be detected is obtained through the navigation platform. Positioning sensors are deployed on the vehicle to be detected to monitor the position of the vehicle to be detected in real time. The position at a certain time T0 is taken as P0. The road segment with a length of m along the planned driving trajectory starting from position P0 is taken as the road segment to be detected. The vehicle at time T0 on the road segment to be detected is taken as the feature vehicle corresponding to time T0.

[0049] Take time T0 as the keyframe time, and let the duration after time T0 be G. max The moment as T max The duration after time T0 is G.min Take time T1 as the time, obtain all characteristic vehicles corresponding to time T1, and sum the abnormal driving ratios of all characteristic vehicles according to the abnormal driving ratio of each vehicle type to obtain the characteristic ratio corresponding to time T1.

[0050] In this embodiment, G max =20 seconds, G min =4 seconds. When the feature proportion corresponding to time T1 is relatively large (not less than the proportion threshold B0), it indicates that the vehicle is more likely to be driving abnormally at time T1. In order to prevent sudden situations and ensure the reliability of the vehicle under test during charging and driving, time T1 needs to be used as the key frame time for the following analysis to realize road monitoring. Since the feature vehicle models in the road segment under test are different, and the probability of abnormality is also different for different models, it is of great significance to intelligently set the key frame time according to the vehicles and models in the road segment under test in this scheme. The more models with a high proportion of abnormal driving pass through the road segment under test, the earlier the key frame time should be set.

[0051] The initial feature value is set to 0, and the ratio threshold is set to B0. The feature value is then added to the feature ratio corresponding to time T1. If the feature value is not less than B0, then time T1 is taken as the keyframe time; if the feature value is less than B0, then the time after time T1 is set to G. min Taking time T0 as T2, we obtain the characteristic proportion corresponding to time T2, where the duration between time T0 and time T2 is less than G. max Add the feature value to the feature ratio corresponding to time T2. If the feature value is not less than B0, then time T2 is the keyframe time. Continue this process until the feature value is added to time T. max The corresponding feature ratio, at this time T max As a keyframe moment;

[0052] The final keyframe moments are obtained, and so on, to extract the corresponding keyframe moments during the driving process of the vehicle under test in real time.

[0053] (4) Obtain the mapping of the contact wire on the road surface in the circuit section, and obtain the power receiving range of the vehicle under test in the circuit section based on the pantograph on the vehicle under test.

[0054] The mapping of the contact wire on the road surface in the receiving section is taken as line segment X; the pantograph on the vehicle to be tested is deployed, and the rightmost end of the pantograph's power supply contact is brought into contact with the contact wire. The distance between line segment X and the left wheel of the vehicle to be tested at this time is taken as D1. The leftmost end of the pantograph's power supply contact is brought into contact with the contact wire, and the distance between line segment X and the right wheel of the vehicle to be tested at this time is taken as D2. The range of line segment X extending to the left by a length of D1 and extending to the right by a length of D2 in the receiving section is taken as the range of the vehicle to be tested that can receive electricity in the receiving section.

[0055] The pantograph has a certain length. For example, if the width of the vehicle to be tested is 3 meters (that is, the length between the left and right wheels), the pantograph on the vehicle to be tested will be 1 meter long when extended and positioned in the middle of the vehicle. At this time, the length between the leftmost power supply contact of the pantograph and the left wheel of the vehicle to be tested is 1 meter, and the length between the rightmost power supply contact of the pantograph and the right wheel of the vehicle to be tested is 1 meter. In this scheme, when the rightmost end of the power supply contact of the pantograph contacts the contact wire, the distance between line segment X and the left wheel of the vehicle to be tested is D1 = 3 - 1 = 2 meters. When the leftmost end of the power supply contact of the pantograph contacts the contact wire, the distance between line segment X and the right wheel of the vehicle to be tested is D2 = 3 - 1 = 2 meters. The resulting range of power reception is 4 meters in length.

[0056] (5) Obtain the moving speed of each vehicle at the key frame moment. When there is a vehicle in an abnormal driving state, capture the location of the abnormal vehicle in the monitoring screen. Based on the location and the power-received range, analyze the impact of the abnormal vehicle on the vehicle to be detected, and intelligently output differentiated early warning prompts to the vehicle to be detected.

[0057] The distance traveled by each vehicle within a certain period before a certain key frame is obtained, and the speed of each vehicle at a certain key frame is obtained. Vehicles with a speed less than a preset speed threshold are identified as abnormal vehicles. If a vehicle is identified as an abnormal vehicle, the monitoring screen of the vehicle to be detected at a certain key frame is extracted, and the location of the abnormal vehicle in the monitoring screen is captured as the abnormal vehicle location.

[0058] Obtain the width of the vehicle to be tested, preset the vehicle safety distance, and extend the vehicle safety distance along the left and right sides of the width to obtain the safe driving width of the vehicle to be tested.

[0059] The monitoring camera on the vehicle under test is also used to monitor the contact position between the pantograph and the overhead contact line in real time. Based on the contact position, the position of the pantograph on the vehicle under test and the safe driving width, the safe contour area of ​​the vehicle under test on the power receiving section is obtained. Based on the distance between the safe contour area and the left and right boundaries of the power receiving range, the virtual safe driving range formed on the road surface when the vehicle under test continues to move forward is predicted.

[0060] Here's an example of this step: Set the safe distance between vehicles to 0.2 meters. Since the width of the vehicle being tested is 3 meters, the safe driving width is 3.4 meters.

[0061] Since the pantograph is 1 meter long, the monitoring camera shows that the contact position of the overhead contact line on the pantograph is 0.7 meters from left to right and 0.3 meters from right to left. Therefore, the length between line segment X and the left side of the safe driving width of the vehicle under test is 1 + 0.2 + 0.7 = 1.9 meters, and the length between line segment X and the right side of the safe driving width of the vehicle under test is 1 + 0.2 + 0.3 = 1.5 meters. Then, the safe outline area of ​​the vehicle under test on the overhead contact line segment can be obtained. The distance between the safe outline area and the left boundary of the electrified range is 4 ÷ 2 - 1.9 = 0.1 meters, and the distance between the safe outline area and the right boundary of the electrified range is 4 ÷ 2 - 1.5 = 0.5 meters. Therefore, the predicted virtual safe driving range formed on the road surface when the vehicle under test continues to move forward is: a range where the left side of the safe driving width of the vehicle is 0.1 meters from the left boundary of the electrified range, and the right side is 0.5 meters from the right boundary of the electrified range.

[0062] If the location of the abnormal vehicle is outside the virtual safe driving range, it will indicate that there is an abnormal vehicle ahead of the vehicle to be inspected, and you need to observe the road conditions and slow down.

[0063] If the abnormal vehicle is located within the virtual safe driving range, calculate the distance L1 between the abnormal vehicle and the left boundary of the power-receiving range, and the distance L2 between the abnormal vehicle and the right boundary of the power-receiving range. If both distance L1 and distance L2 are not greater than the safe driving width, then indicate that there is an abnormal vehicle in front of the vehicle to be detected, and the pantograph needs to be retracted and the abnormal vehicle avoided.

[0064] If the abnormal vehicle is located within the virtual safe driving range and the distance from L1 or L2 is greater than the safe driving width, a message will be displayed indicating that there is an abnormal vehicle ahead of the vehicle to be detected. The vehicle to be detected will be instructed to move to an area that avoids the abnormal vehicle without leaving the power-receiving range. Once the movement is successful, a message will be displayed indicating that the road conditions need to be observed and the vehicle should slow down.

[0065] This embodiment also provides a multi-source data intelligent connected vehicle power control system, including an abnormal driving ratio calculation module, a key frame moment extraction module, a power-receiving range extraction module, and a warning prompt module. The abnormal driving ratio calculation module includes an abnormal driving ratio calculation unit, and the warning prompt module includes a virtual safe driving range prediction unit and a warning prompt unit. When the system executes the computer program, it implements the above-mentioned multi-source data intelligent connected vehicle power control method. Since this multi-source data intelligent connected vehicle power control method has been described in detail above, it will not be repeated here.

[0066] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0067] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0068] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-source data intelligent connected vehicle power control method, characterized in that, Includes the following steps: The goal is to acquire the contact wire sections on electrified highways, which are sections along the highway where overhead contact lines and traction power supply systems are installed. These sections allow target vehicles equipped with pantographs to draw power or charge their devices by contacting the contact wires with the pantographs during their journey. High-definition surveillance cameras are then deployed on the target vehicles to enable full-section monitoring of the vehicles during their journey. The vehicles traveling on the circuit segment include several models. A positioning sensor is deployed on each vehicle. Based on the speed of each vehicle during its travel, the abnormal driving ratio of each model is obtained. The target vehicle currently driving and drawing power on the circuit receiving segment is taken as the vehicle to be detected. Based on the navigation platform, the planned driving trajectory of the vehicle to be detected is obtained. Positioning sensors are deployed on the vehicle to be detected to obtain the real-time positioning of the vehicle to be detected. According to the vehicle type of the vehicles driving on the circuit receiving segment in real time, and the abnormal driving ratio of each vehicle type, the corresponding key frame moment during the driving process of the vehicle to be detected is extracted in real time. Obtain the mapping of the contact wire on the road surface in the circuit section, and obtain the power receiving range of the vehicle under test in the circuit section based on the pantograph on the vehicle under test. The system acquires the speed of each vehicle at key frame moments. When a vehicle is in an abnormal driving state, it captures the location of the abnormal vehicle in the monitoring screen. Based on the location and the power-received range, it assesses the impact of the abnormal vehicle on the vehicle to be detected and intelligently outputs differentiated warning prompts to the vehicle to be detected. The real-time extraction of key frame moments corresponding to the vehicle under test during its driving process includes the following steps: Set the maximum time interval for extracting keyframes to G. max The minimum time interval is G min Based on the origin and destination of the vehicle to be detected, the planned driving trajectory of the vehicle to be detected is obtained through the navigation platform. Positioning sensors are deployed on the vehicle to be detected to monitor the position of the vehicle to be detected in real time. The position at a certain time T0 is taken as P0. The road segment with a length of m along the planned driving trajectory starting from position P0 is taken as the road segment to be detected. The vehicle at time T0 on the road segment to be detected is taken as the feature vehicle corresponding to time T0. Take time T0 as the keyframe time, and let the duration after time T0 be G. max The moment as T max Let the duration after time T0 be G. min The time is taken as T1. All characteristic vehicles corresponding to time T1 are obtained. Based on the abnormal driving ratio of each vehicle type, the sum of the abnormal driving ratios of all characteristic vehicles is obtained to get the characteristic ratio corresponding to time T1. The initial feature value is set to 0, and the ratio threshold is set to B0. The feature value is then added to the feature ratio corresponding to time T1. If the feature value is not less than B0, then time T1 is taken as the keyframe time; if the feature value is less than B0, then the time after time T1 is set to G. min The time T0 is taken as T2, and the characteristic proportion corresponding to the time T2 is obtained, wherein the duration between time T0 and time T2 is less than G. max Add the feature value to the feature ratio corresponding to time T2. If the feature value is not less than B0, then time T2 is the keyframe time. Continue this process until the feature value is added to time T. max The corresponding feature ratio, at this time T max As a keyframe moment; The final keyframe moments are obtained, and so on, to extract the corresponding keyframe moments during the driving process of the vehicle under test in real time.

2. The multi-source data intelligent connected vehicle power control method according to claim 1, characterized in that, To obtain the abnormal driving ratio for each vehicle model, the following steps are included: obtaining the location trajectory of a certain vehicle over a certain period of time as a driving record; calculating the speed of the vehicle; if the speed is less than a preset speed threshold, the driving record is considered an abnormal driving record; obtaining several abnormal driving records, summarizing the total number of records, and calculating the number of abnormal driving records for each vehicle model; dividing the number of abnormal driving records by the total number of records to obtain the abnormal driving ratio for each vehicle model.

3. The multi-source data intelligent connected vehicle power control method according to claim 1, characterized in that, Determining the power-receiving range of the vehicle under test in the power-receiving section includes the following steps: The mapping of the contact wire on the road surface in the receiving section is taken as line segment X; the pantograph on the vehicle to be tested is deployed, and the rightmost end of the pantograph's power supply contact is brought into contact with the contact wire. The distance between line segment X and the left wheel of the vehicle to be tested at this time is taken as D1. The leftmost end of the pantograph's power supply contact is brought into contact with the contact wire, and the distance between line segment X and the right wheel of the vehicle to be tested at this time is taken as D2. The range of line segment X extending to the left by a length of D1 and extending to the right by a length of D2 in the receiving section is taken as the range of the vehicle to be tested that can receive electricity in the receiving section.

4. The multi-source data intelligent connected vehicle power control method according to claim 3, characterized in that, The system intelligently outputs differentiated warning prompts to the vehicle to be detected, including the following steps: The distance traveled by each vehicle within a certain period before a certain key frame time is obtained, and the speed of each vehicle at the certain key frame time is obtained. Vehicles with a speed less than a preset speed threshold are identified as abnormal vehicles. If a vehicle is identified as an abnormal vehicle, the monitoring screen of the vehicle to be detected at the certain key frame time is extracted, and the location of the abnormal vehicle in the monitoring screen is captured as the abnormal vehicle location. The width of the vehicle to be tested is obtained, a safe distance for the vehicle is preset, and the safe distance for the vehicle is extended along the left and right sides of the vehicle's width to obtain the safe driving width of the vehicle to be tested. The monitoring camera on the vehicle under test is also used to monitor the contact position between the pantograph and the overhead contact line in real time. Based on the contact position, the position of the pantograph on the vehicle under test and the safe driving width, the safe contour area of ​​the vehicle under test on the power receiving section is obtained. Based on the distance between the safe contour area and the left and right boundaries of the power receiving range, the virtual safe driving range formed on the road surface when the vehicle under test continues to move forward is predicted. If the location of the abnormal vehicle is outside the virtual safe driving range, it will indicate that there is an abnormal vehicle ahead of the vehicle to be inspected, and you need to observe the road conditions and slow down. If the abnormal vehicle is located within the virtual safe driving range, calculate the distance L1 between the abnormal vehicle and the left boundary of the power-receiving range, and the distance L2 between the abnormal vehicle and the right boundary of the power-receiving range. If both distance L1 and distance L2 are not greater than the safe driving width, then indicate that there is an abnormal vehicle in front of the vehicle to be detected, and the pantograph needs to be retracted and the abnormal vehicle avoided. If the abnormal vehicle is located within the virtual safe driving range and the distance from L1 or L2 is greater than the safe driving width, a message will be displayed indicating that there is an abnormal vehicle ahead of the vehicle to be detected. The vehicle to be detected will be instructed to move to an area that avoids the abnormal vehicle without leaving the power-receiving range. Once the movement is successful, a message will be displayed indicating that the road conditions need to be observed and the vehicle should slow down.

5. A multi-source data intelligent connected vehicle power control system, used to execute the multi-source data intelligent connected vehicle power control method according to any one of claims 1-4, characterized in that, The system includes an abnormal driving ratio calculation module, a key frame moment extraction module, a power-received range extraction module, and an early warning module. Abnormal Driving Proportion Calculation Module: Used to obtain the contact wire sections on electrified highways. Contact wire sections are sections along electrified highways where contact wires and traction power supply systems are installed, allowing target vehicles equipped with pantographs to obtain or charge power by contacting the contact wires with the pantographs during driving. High-definition monitoring cameras are deployed on the target vehicles to achieve full-section monitoring of the target vehicles during their driving process. The vehicles traveling on the circuit segment include several models. A positioning sensor is deployed on each vehicle. Based on the speed of each vehicle during its travel, the abnormal driving ratio of each model is obtained. Keyframe Moment Extraction Module: This module is used to identify the target vehicle currently driving and drawing power on the circuit segment as the vehicle to be detected. Based on the navigation platform, it obtains the planned driving trajectory of the vehicle to be detected, deploys positioning sensors on the vehicle to be detected, and obtains the real-time positioning of the vehicle to be detected. Based on the vehicle type of the vehicles driving on the circuit segment in real time and the abnormal driving ratio of each vehicle type, it extracts the corresponding keyframe moments during the driving process of the vehicle to be detected in real time. Power-receiving range extraction module: used to obtain the mapping of the contact wire on the road surface on the power-receiving section, and to obtain the power-receiving range of the vehicle under test in the power-receiving section based on the pantograph on the vehicle under test; Early warning module: used to obtain the moving speed of each vehicle at key frame moments; when a vehicle is in an abnormal driving state, it captures the location of the abnormal vehicle in the monitoring screen; based on the location and the power-received range, it judges the impact of the abnormal vehicle on the vehicle to be detected, and intelligently outputs differentiated early warning prompts to the vehicle to be detected.

6. The multi-source data intelligent connected vehicle power control system according to claim 5, characterized in that, The abnormal driving ratio calculation module includes an abnormal driving ratio calculation unit. Abnormal driving ratio calculation unit: used to obtain the positioning trajectory of a certain vehicle over a certain period of time as a driving record, calculate the moving speed of the certain vehicle, and if the moving speed is less than a preset speed threshold, the driving record is regarded as an abnormal driving record. Obtain several abnormal driving records, summarize the total number of records, calculate the number of abnormal driving records for each vehicle model, divide the number of abnormal driving records by the total number of records, and obtain the abnormal driving ratio for each vehicle model.

7. The multi-source data intelligent connected vehicle power control system according to claim 5, characterized in that, The early warning module includes a virtual safe driving range prediction unit and an early warning unit; Virtual safe driving range prediction unit: used to monitor the contact position between the pantograph and the overhead contact line in real time; based on the contact position, the position of the pantograph on the vehicle under test and the safe driving width, the safe contour area of ​​the vehicle under test on the power receiving section is obtained; based on the distance between the safe contour area and the left and right boundaries of the power receiving range, the virtual safe driving range formed on the road surface when the vehicle under test continues to move forward is predicted. Early warning unit: Based on the location of abnormal vehicles and the virtual safe driving range, it analyzes the impact of abnormal vehicles on the vehicle to be inspected and intelligently outputs differentiated early warning prompts to the vehicle to be inspected.

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