An internet of things-based vehicle intelligent management method and system

Abstract

The application discloses a kind of vehicle intelligent management method and system based on Internet of Things, method includes: generating including job route, job time period and speed limit job task;Real-time running data of vehicle is obtained, real-time running data of vehicle includes vehicle real-time position, vehicle job state, vehicle dynamic motion parameter;Route deviation result is generated by comparing job route and real-time position, effective / non-job time period result is generated by combining job time period and driving speed, job compliance result is generated based on job device parameter, actual driving mileage and basic job completion rate are calculated, and job state judgment result is comprehensively generated;Warning information and dispatching instruction are generated based on the result;Operation evaluation report is generated by summarizing data, judgment result and instruction execution situation.The system is correspondingly provided with task generation, data acquisition, state analysis, early warning and dispatching and report generation module, and realizes vehicle intelligent management.

Description

Technical Field

[0001] This invention relates to the field of technology, and in particular to a vehicle intelligent management method and system based on the Internet of Things. Background Technology

[0002] In traditional vehicle management, especially in the management of mechanized vehicles (such as municipal sanitation sweepers and garbage trucks), there is a core technical problem of low efficiency in operation supervision and dispatch.

[0003] Specifically, existing management methods rely heavily on manual recording of work routes and manual inspection of vehicle locations and work status. This makes it difficult to obtain accurate real-time vehicle location data and work status data, resulting in an inability to promptly determine whether vehicles have deviated from the preset work route, whether they are driving or operating illegally within the specified work period, or whether the operation of the work equipment meets the standards. At the same time, when situations such as vehicles deviating from the route, speeding, or abnormal operation occur, it is impossible to quickly generate accurate early warning information and corresponding dispatch instructions, thereby affecting the timely and high-quality completion of work tasks, increasing work management costs, and reducing overall work efficiency. Summary of the Invention

[0004] To address the technical problems existing in the prior art, this invention provides a vehicle intelligent management method and system based on the Internet of Things.

[0005] The technical solution adopted in this invention is:

[0006] The first aspect of this application provides a vehicle intelligent management method based on the Internet of Things, comprising the following steps:

[0007] Step 1: Generate a work task containing a work route, which includes a start point, an end point, intermediate waypoints, and a planned mileage. The work task also includes a work period and speed limits.

[0008] Step 2: Obtain real-time vehicle operation data, which includes real-time vehicle location information, vehicle operation status information, and vehicle dynamic motion parameters; wherein, the vehicle operation status information includes vehicle speed and vehicle operation device operation parameters.

[0009] Step 3: Compare the work route with the real-time vehicle location information to generate a vehicle route deviation judgment result; combine the work period from Step 1 with the vehicle's speed to generate a judgment result for the effective work period and non-work period; based on the vehicle's operating parameters, generate a judgment result for whether the work meets the standards; calculate the vehicle's actual mileage based on the real-time vehicle location information and dynamic motion parameters, and calculate the basic work completion rate based on the planned mileage of the work route from Step 1; based on the vehicle route deviation judgment result, the effective work period and non-work period judgment result, the judgment result for whether the work meets the standards, and the basic work completion rate, generate a work status judgment result.

[0010] Step 4: Based on the operation status judgment results, generate early warning information for situations such as vehicle route deviation, speeding, and non-compliance with operation standards, and generate dispatch instructions based on the early warning information and the real-time vehicle status reflected by the real-time vehicle operation data.

[0011] Step 5: Summarize real-time vehicle operation data, work status judgment results, and dispatch instruction execution status to generate a work evaluation report.

[0012] Preferably, the real-time vehicle location information includes the vehicle's current coordinates and driving direction. The step of comparing the work route with the real-time vehicle location information to generate a vehicle route deviation judgment result includes the following:

[0013] Preset route deviation thresholds for different road segment attributes, and preset corresponding route deviation thresholds for straight road segments and turning road segments respectively;

[0014] The vertical distance between the vehicle's current coordinates and the planned operation route is calculated in real time; the vertical distance is compared with the route deviation threshold of the corresponding road segment to obtain the comparison result of the vertical distance and the threshold; at the same time, the duration of the vehicle's deviation is obtained, and the angle between the vehicle's driving direction and the planned operation route direction is calculated to determine whether the angle exceeds the preset angle range, thus forming the angle relationship determination result.

[0015] Based on the comparison results of vertical distance and threshold, deviation duration and angle relationship, the deviation type is determined; and a vehicle route deviation judgment result containing deviation type, deviation duration, deviation segment attributes and real-time coordinates is generated.

[0016] Preferably, the results of determining the effective working period and non-working period by combining the working time period and vehicle speed in step 1 include the following:

[0017] When the vehicle travel time is within the preset work period in step 1, the vehicle speed is within the preset work speed range, and the real-time vehicle location is within the work route planning range, it is determined to be a valid work period.

[0018] If the vehicle travel time exceeds the preset working period in step 1, it is determined to be a non-working period;

[0019] If the vehicle travels within the work period, and the vehicle speed is lower than the preset non-work speed threshold and the duration of this state reaches the preset non-work stationary duration threshold, or the vehicle is outside the work route planning range and there is no dispatch instruction authorization, it is determined to be a non-work period.

[0020] Generate results for determining valid and non-work periods, including the start and end times of valid work periods, the start and end times of non-work periods, and the reasons for non-work periods.

[0021] Preferably, based on the operating parameters of the vehicle's operating device, the result for determining whether the operation meets the standard includes the following:

[0022] The system includes a preset range of standard parameters for the operating device and a set of qualified status codes. The standard parameter range includes the acceptable range of device operating time and device power. The set of qualified status codes includes the preset status codes corresponding to the normal operation of the operating device. The system also compares the vehicle operating device's operating parameters with the standard parameter range and the operating status codes with the set of qualified status codes in real time.

[0023] When the device running time and device power are both within the corresponding standard parameter range, and the operating status code of the working device belongs to the qualified status code set, the operation is judged to be in compliance with the standard; when the device running time exceeds the standard parameter range, or the device power exceeds the standard parameter range, or the operating status code of the working device does not belong to the qualified status code set, the operation is judged to be non-compliant with the standard, and the abnormal parameters and deviation values ​​are recorded.

[0024] Generate a result indicating whether a job meets the standard, including the job's compliance status, abnormal parameters, and duration of the abnormality.

[0025] Preferably, the calculation of the vehicle's actual mileage based on the vehicle's real-time location information and dynamic motion parameters includes the following:

[0026] Real-time vehicle location information and dynamic motion parameters are collected for continuous sampling periods to establish a trajectory sampling sequence; the real-time vehicle location information includes vehicle coordinates and driving direction, and the dynamic motion parameters include vehicle steering angular velocity and vehicle lateral acceleration.

[0027] Driving scenarios are identified based on the steering angular velocity, lateral acceleration, and driving direction in the trajectory sampling sequence. Driving scenarios include turning scenarios, low-speed driving scenarios, and normal driving scenarios. Among them, low-speed driving scenarios refer to scenarios where the vehicle speed is continuously below a preset low-speed threshold, which is determined according to the vehicle's operation type; normal driving scenarios refer to scenarios where the vehicle speed is above the preset low-speed threshold, and the changes in steering angular velocity, lateral acceleration, and driving direction do not meet the criteria for turning scenarios; turning scenarios refer to scenarios where the changes in steering angular velocity, lateral acceleration, and driving direction simultaneously meet preset characteristics.

[0028] For regular driving scenarios, the straight-line distance is calculated based on the vehicle coordinates of adjacent sampling points in the trajectory sampling sequence. For low-speed driving scenarios, virtual trajectory points are generated using directional constraint interpolation based on the vehicle coordinates of adjacent sampling points in the trajectory sampling sequence, and the cumulative distance between the actual sampling point vehicle coordinates and the virtual trajectory point coordinates is calculated. For turning scenarios, the vehicle coordinates, driving direction, and lateral acceleration features of key sampling points are extracted from the trajectory sampling sequence as curve fitting control points. The curve parameters are adjusted by combining the rate of change of driving direction and the trend of lateral acceleration. The curve arc length is calculated based on the vehicle coordinates of the control points as the mileage of the turning segment.

[0029] The actual mileage of the vehicle is obtained by summing up the mileage of each driving scenario and combining it with the historical mileage correction factor for the same road segment.

[0030] Preferably, for the turning scenario, extracting vehicle coordinates, driving direction, and lateral acceleration features of key sampling points from the trajectory sampling sequence as curve fitting control points, adjusting curve parameters based on the rate of change of driving direction and the trend of lateral acceleration, and calculating the curve arc length as the turning segment mileage based on the vehicle coordinates of the control points includes the following:

[0031] Key sampling points for turning scenarios are extracted from the trajectory sampling sequence. The key sampling points include the turning start point, the turning vertex, and the turning end point. The turning start point is the sampling point in the trajectory sampling sequence where the lateral acceleration first reaches a preset turning start threshold. The turning vertex is the sampling point in the trajectory sampling sequence where the steering angular velocity reaches its maximum value. The turning end point is the sampling point in the trajectory sampling sequence where the lateral acceleration falls back to a preset turning end threshold.

[0032] Using the vehicle coordinates of the extracted key sampling points as the geometric reference points and the driving direction of the key sampling points as the tangent direction of the curve, an initial fitting curve for the turning scene is constructed in the form of parametric equations. The parametric equations use sampling time as the parameter variable and associate the vehicle coordinates and driving direction of the key sampling points.

[0033] Based on the trend of lateral acceleration change between adjacent key sampling points in the trajectory sampling sequence, the lateral acceleration change rate between adjacent key sampling points is calculated; when the absolute value of the lateral acceleration change rate is greater than a preset sensitivity threshold, the curvature coefficient of the initial fitting curve parametric equation is adjusted, and the curve curvature adjustment weight between adjacent key sampling points is increased to obtain the parametric equation of the adjusted fitting curve.

[0034] Based on the parametric equations of the adjusted fitted curve, combined with the vehicle coordinates of the extracted key sampling points, the arc length of the curve segment between adjacent key sampling points is calculated by integration, and the arc lengths of each curve segment are summed to obtain the total mileage of the turning section.

[0035] Preferably, the mileage of each driving scenario is accumulated and combined with the historical mileage correction factor for the same road segment to obtain the actual mileage of the vehicle, which includes the following:

[0036] The identification information of the current vehicle travel segment is determined, and based on the identification information of the segment, historical driving data of the same segment within a preset period is retrieved from the historical database. The historical driving data includes the historical actual mileage of each driving scenario in the same segment, the historical planned mileage of the operation route, and the distribution ratio of the historical driving scenarios.

[0037] The historical mileage correction coefficient for the same road segment is calculated by weighting the current driving scenario distribution ratio with the historical driving scenario distribution ratio, based on the average deviation rate between the historical actual mileage and the historical planned mileage of the same road segment.

[0038] Multiply the cumulative mileage value for each driving scenario by the historical mileage correction coefficient for the same road segment to obtain the actual mileage of the vehicle after eliminating the statistical bias of historical mileage.

[0039] The second aspect of this application provides an Internet of Things (IoT)-based intelligent vehicle management system, which applies the aforementioned IoT-based intelligent vehicle management method, including:

[0040] The task generation module is used to generate a task containing a task route. The task route includes a start point, an end point, intermediate waypoints, and a planned mileage. The task also includes a task time period and speed limit.

[0041] The real-time data acquisition module is used to acquire real-time operating data of the vehicle, including real-time vehicle location information, vehicle operation status information, and vehicle dynamic motion parameters; wherein, the vehicle operation status information includes vehicle speed and vehicle operation device operating parameters.

[0042] The operation status analysis module is used to compare the operation route with the real-time location information of the vehicle to generate a vehicle route deviation judgment result; combine the operation period in step 1 with the vehicle's driving speed to generate a judgment result of the effective operation period and non-operation period; generate a judgment result of whether the operation meets the standard based on the operating parameters of the vehicle's operation device; calculate the actual mileage of the vehicle based on the real-time location information and dynamic motion parameters of the vehicle, and calculate the basic operation completion rate based on the mileage of the operation route planned in step 1; and generate an operation status judgment result based on the vehicle route deviation judgment result, the effective operation period and non-operation period judgment result, the judgment result of whether the operation meets the standard, and the basic operation completion rate.

[0043] The early warning and dispatch module is used to generate early warning information based on the operation status judgment result for situations such as vehicle route deviation, speeding, and non-compliance with operation standards, and to generate dispatch instructions based on the early warning information and the real-time vehicle status reflected by the real-time vehicle operation data.

[0044] The task evaluation report module is used to summarize real-time vehicle operation data, task status judgment results, and dispatch instruction execution status to generate a task evaluation report.

[0045] The beneficial effects of the present invention are at least one of the following:

[0046] By generating work tasks that include work routes, work periods, and speed limits, clear and unambiguous execution guidelines can be provided for vehicle operations, helping to avoid problems such as unclear vehicle operation directions and inconsistent execution standards caused by incomplete work task parameters.

[0047] By integrating multi-dimensional information such as the work route and real-time location, work period and driving speed, and work device parameters, the system generates judgment results including route deviation, effective / non-work periods, and work completion status. It also calculates the basic work completion rate based on actual mileage, ultimately forming a work status judgment result. This helps to comprehensively understand the vehicle's work status from multiple perspectives and, compared to a single-dimensional judgment, better reflects the overall work situation.

[0048] Based on the results of the work status assessment, early warning information and dispatch instructions are generated, which can respond in a timely manner to abnormal situations such as vehicle route deviation, speeding, and non-compliance with work standards. This helps managers or the system to adjust vehicle operation behavior in a timely manner and helps reduce the impact of abnormal situations on work quality or efficiency. At the same time, the relevant data is summarized to generate work evaluation reports, which can provide information support for work process review and subsequent work optimization. Attached Figure Description

[0049] Figure 1 This is a schematic flowchart of the method of the present invention;

[0050] Figure 2 This is a structural block diagram of the system of the present invention. Detailed Implementation

[0051] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0052] Example 1 provides a vehicle intelligent management method based on the Internet of Things, such as Figure 1 As shown, it includes the following steps:

[0053] Step 1: Generate a work task containing a work route. The work route includes a start point, an end point, intermediate waypoints, and a planned mileage. The work task also includes a work period and speed limits.

[0054] For example, taking urban sanitation cleaning operations as an example, for a sanitation sweeper vehicle of model ZLJ5160TXSDFE5, the vehicle intelligent management system generates corresponding work tasks based on the area cleaning requirements issued by the municipal sanitation department:

[0055] The starting point of the operation route is set at the intersection of Road A and Street B in Chengdong New District (latitude and longitude: N30°15′22″, E120°08′35″), and the ending point is set at the intersection of Road A and Lane F in Chengdong New District (latitude and longitude: N30°17′18″, E120°10′21″). The intermediate points are the intersection of Road A and Street C, the intersection of Road A and Street D, and the intersection of Road A and Street E. Based on the actual length of each road segment, the planned mileage of the operation route is calculated to be 5.2 kilometers.

[0056] The operating hours are set from 03:00 to 06:00 daily based on the characteristics of urban traffic flow (avoiding the morning rush hour); speed limits are differentiated between operating and transfer states. During the operating state (when the sweeping equipment is started), the speed limit is 5-10 km / h, and during the transfer state (when the sweeping equipment is turned off, heading to the starting point of the operation or leaving the end point of the operation), the speed limit is no more than 20 km / h, to ensure a balance between operation quality and road traffic safety.

[0057] Step 2: Obtain real-time vehicle operation data, which includes real-time vehicle location information, vehicle operation status information, and vehicle dynamic motion parameters; wherein, the vehicle operation status information includes vehicle speed and vehicle operation device operation parameters.

[0058] For example, for the sanitation sweeper truck with model number ZLJ5160TXSDFE5, the vehicle intelligent management system collects real-time operating data through the on-board IoT terminal. The specific collection method and data content are as follows:

[0059] The vehicle's real-time location information is obtained through the onboard Beidou / GPS dual-mode positioning module, with a sampling frequency of 10 seconds / time. The system outputs the vehicle's current latitude and longitude coordinates (e.g., when working near the intersection of Road A and Street D, the coordinates are N30°16′05″, E120°09′18″) and driving direction (e.g., when driving from north to south, the direction angle is 185°). The positioning error is controlled within 10 meters, which meets the requirements for comparing the work route.

[0060] The vehicle speed in the vehicle operation status information is read through the vehicle's CAN bus and the current speed is fed back in real time (e.g., the speed is 7km / h in operation mode and 18km / h in transfer mode); the operating parameters of the vehicle operation device are collected by the device controller, including the sweeping roller brush rotation speed (280r / min during normal operation), the high-pressure water gun working pressure (3.2MPa), and the device status code (e.g., the status code is 00H when the roller brush is running normally and 01H when the water gun is spraying water normally).

[0061] Vehicle dynamic motion parameters are collected by an onboard inertial measurement unit (IMU), including steering angular velocity (approximately 0.5° / s when driving straight, increasing to 8° / s when turning) and lateral acceleration (approximately 0.2m / s² when driving normally, and approximately 0.1m / s² when operating at low speed). The data sampling frequency is synchronized with the position information to ensure the time matching between motion state and position information.

[0062] Step 3: Compare the work route with the real-time vehicle location information to generate a vehicle route deviation judgment result; combine the work period from Step 1 with the vehicle's speed to generate a judgment result for the effective work period and non-work period; based on the vehicle's operating parameters, generate a judgment result for whether the work meets the standards; calculate the vehicle's actual mileage based on the real-time vehicle location information and dynamic motion parameters, and calculate the basic work completion rate based on the planned mileage of the work route from Step 1; generate a work status judgment result based on the vehicle route deviation judgment result, the effective work period and non-work period judgment result, the judgment result for whether the work meets the standards, and the basic work completion rate.

[0063] In one possible implementation, the real-time vehicle location information includes the vehicle's current coordinates and driving direction. The step of comparing the work route with the real-time vehicle location information to generate a vehicle route deviation judgment result includes the following:

[0064] Preset route deviation thresholds for different road segment attributes, and preset corresponding route deviation thresholds for straight road segments and turning road segments respectively;

[0065] The vertical distance between the vehicle's current coordinates and the planned operation route is calculated in real time; the vertical distance is compared with the route deviation threshold of the corresponding road segment to obtain the comparison result of the vertical distance and the threshold; at the same time, the duration of the vehicle's deviation is obtained, and the angle between the vehicle's driving direction and the planned operation route direction is calculated to determine whether the angle exceeds the preset angle range, thus forming the angle relationship determination result.

[0066] Based on the comparison results of vertical distance and threshold, deviation duration and angle relationship, the deviation type is determined; and a vehicle route deviation judgment result containing deviation type, deviation duration, deviation segment attributes and real-time coordinates is generated.

[0067] For example, for a sanitation sweeper of model ZLJ5160TXSDFE5, combining the work task parameters from step 1 and the real-time operation data from step 2, the vehicle route deviation is judged by a preset road segment attribute threshold (1.5 meters for straight road segments and 2.0 meters for turning road segments (such as the intersection of Road A and Street D)). When the vehicle is operating near the intersection of Road A and Street D, the real-time coordinates are N30°16′08″, E120°09′25″, the vertical distance from the planned work route trajectory (center line of Road A) is 1.8 meters, and the vehicle's driving direction angle is 200° (planned direction angle is 185°), with the included angle exceeding the preset included angle range (≤10°) by 15°. This state lasts for 2 minutes, which is judged as a slight deviation from the turning road segment, and a judgment result including the deviation type, duration, and real-time coordinates is generated.

[0068] In one possible implementation, the determination of effective working time and non-working time by combining the working period of step 1 with the vehicle speed includes the following:

[0069] When the vehicle travel time is within the preset work period in step 1, the vehicle speed is within the preset work speed range, and the vehicle's real-time location is within the planned work route, it is determined to be a valid work period.

[0070] If a vehicle's travel time exceeds the preset work period in step 1, it is determined to be a non-work period. If the vehicle's travel time is within the work period, but the vehicle's speed is lower than a preset non-work speed threshold and this state lasts for a duration equal to a preset non-work stationary duration threshold, or the vehicle is outside the planned work route and has no dispatch instruction authorization, it is determined to be a non-work period. A result is generated that includes the start and end times of valid work periods, the start and end times of non-work periods, and the reason for the non-work period.

[0071] For example, the determination of a valid / non-operational period means that if a vehicle travels at a speed of 7 km / h (which meets the operating speed of 5-10 km / h) at 04:30 (within the preset operating period of 03:00-06:00 in step 1), and its real-time location is within the operating route of Route A, it is determined to be a valid operating period; if the vehicle is still operating at 06:10 (outside the operating period), or if its speed drops to 2 km / h at 05:00 (below the preset non-operational speed threshold of 3 km / h) for 5 minutes, or if it deviates from the operating route of Street C without authorization from the dispatching authority, it is determined to be a non-operational period, and the reason for non-operational operation is recorded (timeout / low-speed stationary / unauthorized deviation).

[0072] In one possible implementation, the result of determining whether the operation meets the standard based on the operating parameters of the vehicle's operating device includes the following:

[0073] The system includes a preset range of standard parameters for the operating device and a set of qualified status codes. The standard parameter range includes the acceptable range of device operating time and device power. The set of qualified status codes includes the preset status codes corresponding to the normal operation of the operating device. The system also compares the vehicle operating device's operating parameters with the standard parameter range and the operating status codes with the set of qualified status codes in real time.

[0074] When the device running time and device power are both within the corresponding standard parameter range, and the operating status code of the working device belongs to the qualified status code set, the operation is judged to meet the standard; when the device running time exceeds the standard parameter range, or the device power exceeds the standard parameter range, or the operating status code of the working device does not belong to the qualified status code set, the operation is judged to not meet the standard, and the abnormal parameters and deviation values ​​are recorded; a result is generated that includes the operation's compliance status, abnormal parameters, and the duration of the abnormality to determine whether the operation meets the standard.

[0075] For example, whether the operation meets the standard is determined by preset standard parameters of the operating device (cleaning roller brush speed 250-300 r / min, high-pressure water gun pressure 3.0-3.5 MPa, qualified status code 00H / 01H). When the real-time data is collected that the roller brush speed is 280 r / min, the water gun pressure is 3.2 MPa, and the status code is 00H, the operation is determined to meet the standard; if the roller brush speed drops to 220 r / min (exceeding the lower limit), the operation is determined to not meet the standard, and the abnormal parameters (roller brush speed) and deviation value (-30 r / min) are recorded.

[0076] In one possible implementation, calculating the vehicle's actual mileage based on the vehicle's real-time location information and dynamic motion parameters includes the following:

[0077] Real-time vehicle location information and dynamic motion parameters are collected for continuous sampling periods to establish a trajectory sampling sequence; the real-time vehicle location information includes vehicle coordinates and driving direction, and the dynamic motion parameters include vehicle steering angular velocity and vehicle lateral acceleration.

[0078] Driving scenarios are identified based on the steering angular velocity, lateral acceleration, and driving direction in the trajectory sampling sequence. Driving scenarios include turning scenarios, low-speed driving scenarios, and normal driving scenarios. Among them, low-speed driving scenarios refer to scenarios where the vehicle speed is continuously below a preset low-speed threshold, which is determined according to the vehicle's operation type; normal driving scenarios refer to scenarios where the vehicle speed is above the preset low-speed threshold, and the changes in steering angular velocity, lateral acceleration, and driving direction do not meet the criteria for turning scenarios; turning scenarios refer to scenarios where the changes in steering angular velocity, lateral acceleration, and driving direction simultaneously meet preset characteristics.

[0079] For regular driving scenarios, the straight-line distance is calculated based on the vehicle coordinates of adjacent sampling points in the trajectory sampling sequence. For low-speed driving scenarios, virtual trajectory points are generated using directional constraint interpolation based on the vehicle coordinates of adjacent sampling points in the trajectory sampling sequence, and the cumulative distance between the actual sampling point vehicle coordinates and the virtual trajectory point coordinates is calculated. For turning scenarios, the vehicle coordinates, driving direction, and lateral acceleration features of key sampling points are extracted from the trajectory sampling sequence as curve fitting control points. The curve parameters are adjusted by combining the rate of change of driving direction and the trend of lateral acceleration. The curve arc length is calculated based on the vehicle coordinates of the control points as the mileage of the turning segment.

[0080] The actual mileage of the vehicle is obtained by summing up the mileage of each driving scenario and combining it with the historical mileage correction factor for the same road segment.

[0081] For example, taking a sanitation sweeper truck with model number ZLJ5160TXSDFE5 as an example, combining the operation route in step 1 (from A Road to B Street to F Lane) and the real-time operation data in step 2, the actual mileage of the vehicle is calculated according to the following process:

[0082] Real-time vehicle location information (latitude and longitude coordinates, driving direction) and dynamic motion parameters (steering angular velocity, lateral acceleration) were collected continuously for 30 minutes at a sampling frequency of 10 seconds per sampling point to form a trajectory sampling sequence. Examples of key sampling point data are shown below:

[0083] Sampling Point 1 (04:00): Coordinates N30°15′22″, E120°08′35″ (starting point of operation), driving direction angle 185°, turning angular velocity 0.5° / s, lateral acceleration 0.2m / s². Sampling Point 180 (04:30): Coordinates N30°16′05″, E120°09′18″ (near the intersection of Road A and Street D), driving direction angle 190°, turning angular velocity 7.8° / s, lateral acceleration 0.35m / s². Sampling Point 240 (04:40): Coordinates N30°16′30″, E120°09′40″ (section from Road A to Street D to Street E), driving direction angle 188°, turning angular velocity 0.6° / s, lateral acceleration 0.1m / s² (reduced speed due to roadside obstacle).

[0084] The preset low-speed threshold is 3 km / h (determined based on the operational characteristics of sanitation sweepers). The criteria for determining turning scenarios are a turning angular velocity > 5° / s, a lateral acceleration > 0.3 m / s², and a change in driving direction angle > 10°. Based on these criteria, scenarios for each road segment are identified.

[0085] The section from B Street to C Street on Road A (sampling points 1-90) had a vehicle speed of 8 km / h (>3 km / h), a steering angular velocity of 0.5-0.8° / s, and a lateral acceleration of 0.2-0.25 m / s². The conditions for turning were not met, and the scenario was classified as a normal driving scenario.

[0086] The intersection of Road A and Street D (sampling points 170-190) has a turning angular velocity of 7.5-8° / s, a lateral acceleration of 0.32-0.35m / s², and a driving direction angle that changes from 185° to 195° (a change of 10°). This meets the conditions for determining a turning scenario and is therefore identified as a turning scenario.

[0087] The section from D Street to E Street on Road A (sampling points 200-260) was characterized as a low-speed driving scenario, where vehicles reduced their speed to 2.5 km / h (<3 km / h) for 2 minutes to avoid obstacles.

[0088] Mileage calculation by scenario: Normal driving scenario (B Street to C Street): Based on the latitude and longitude coordinates of adjacent sampling points in the trajectory sampling sequence, the straight-line distance is calculated using a geographical distance calculation formula (such as the Haversine formula), and the mileage of this scenario is accumulated to 1.2 kilometers (such as the cumulative straight-line distance from sampling point 1 to sampling point 90).

[0089] Low-speed driving scenario (D Street to E Street section): For adjacent sampling points (such as sampling points 200 and 201, with small coordinate differences), the directional constraint interpolation method is used (with the driving direction angle of 188° as a constraint, two virtual trajectory points are generated between the two sampling points), the coordinate distance between the actual sampling point and the virtual trajectory point is calculated and accumulated, and the mileage of this scenario is 0.4 kilometers.

[0090] Turning scenario (intersection of D Street): The starting point of the turn (sampling point 170, lateral acceleration reaches 0.3 m / s² for the first time), the turning apex (sampling point 180, turning angular velocity reaches 8° / s), and the turning end point (sampling point 190, lateral acceleration drops back to 0.28 m / s²) were extracted from the trajectory sampling sequence as curve fitting control points. An initial fitting curve was constructed with the coordinates of each point as the reference and the driving direction as the tangent direction. The curve curvature coefficient was adjusted by combining the lateral acceleration change rate (0.02 m / s³), and the final calculated curve arc length was 0.3 km.

[0091] Accumulation and Correction: First, the mileage of each scenario is accumulated (1.2 + 0.4 + 0.3 = 1.9 km). Then, historical driving data of the same section of Route A for the past 30 days is retrieved from the historical database (20 records in total, with an average deviation rate of 1.01 between the historical actual mileage and the planned mileage). The correction factor for the historical mileage of the same section is determined to be 1.01. The accumulated mileage is multiplied by the correction factor (1.9 × 1.01 = 1.919 km), and the actual mileage traveled by the vehicle during this period is finally obtained as 1.919 km.

[0092] In one possible implementation, for the turning scenario, extracting vehicle coordinates, driving direction, and lateral acceleration features of key sampling points from the trajectory sampling sequence as curve fitting control points, adjusting curve parameters based on the rate of change of driving direction and the trend of lateral acceleration, and calculating the curve arc length as the turning segment mileage based on the vehicle coordinates of the control points includes the following:

[0093] Key sampling points for turning scenarios are extracted from the trajectory sampling sequence. The key sampling points include the turning start point, the turning vertex, and the turning end point. The turning start point is the sampling point in the trajectory sampling sequence where the lateral acceleration first reaches a preset turning start threshold. The turning vertex is the sampling point in the trajectory sampling sequence where the steering angular velocity reaches its maximum value. The turning end point is the sampling point in the trajectory sampling sequence where the lateral acceleration falls back to a preset turning end threshold.

[0094] Using the vehicle coordinates of the extracted key sampling points as the geometric reference points and the driving direction of the key sampling points as the tangent direction of the curve, an initial fitting curve for the turning scene is constructed in the form of parametric equations. The parametric equations use sampling time as the parameter variable and associate the vehicle coordinates and driving direction of the key sampling points.

[0095] Based on the trend of lateral acceleration change between adjacent key sampling points in the trajectory sampling sequence, the lateral acceleration change rate between adjacent key sampling points is calculated; when the absolute value of the lateral acceleration change rate is greater than a preset sensitivity threshold, the curvature coefficient of the initial fitting curve parametric equation is adjusted, and the curve curvature adjustment weight between adjacent key sampling points is increased to obtain the parametric equation of the adjusted fitting curve.

[0096] Based on the parametric equations of the adjusted fitted curve, combined with the vehicle coordinates of the extracted key sampling points, the arc length of the curve segment between adjacent key sampling points is calculated by integration, and the arc lengths of each curve segment are summed to obtain the total mileage of the turning section.

[0097] For example, in the scenario of a sanitation sweeper truck of model ZLJ5160TXSDFE5 turning at the intersection of Road A and Street D (sampling points 170-190), the specific calculation process is as follows:

[0098] The preset turning start threshold is 0.3 m / s², and the turning end threshold is 0.28 m / s². When the lateral acceleration at sampling point 170 first reaches 0.3 m / s², it is determined to be the turning start point, with coordinates N30°16′02″, E120°09′15″ and a driving direction angle of 185°. When the steering angular velocity at sampling point 180 reaches its maximum value of 8° / s, it is determined to be the turning apex, with coordinates N30°16′05″, E120°09′18″ and a driving direction angle of 190°. When the lateral acceleration at sampling point 190 drops back to 0.28 m / s², it is determined to be the turning end point, with coordinates N30°16′08″, E120°09′22″ and a driving direction angle of 195°.

[0099] Using the coordinates of three key sampling points as the geometric reference points and the driving direction angle as the tangent direction of the curve, an initial curve is constructed using parametric equations. The parametric equations use the sampling time t (t=0 corresponding to the starting point, t=100 seconds corresponding to the ending point) as the variable. An example of the expression is as follows:

[0100] x(t) = x0 + (x1 - x0)·t / 100 + k x sin(πt / 100)

[0101] y(t) = y0 + (y1 - y0) · t / 100 + k y sin(πt / 100)

[0102] x(t) represents the lateral coordinate of the vehicle at time t, which changes with time t. The longitude coordinate of the vehicle at any time t can be calculated using the expression for x(t), filling the positional gaps between discrete sampling points. y(t) represents the longitudinal coordinate of the vehicle at time t, which also changes with time t. The latitude value corresponding to the vehicle's real-time position, together with x(t), determines the vehicle's specific location in geographic space at time t. Here, (x0, y0) and (x1, y1) are the coordinates of the starting and ending points, respectively, t / 100 is the time standardization coefficient, and the total duration of the entire turning process is 100 seconds (this is determined based on the time interval between the starting and ending points of the turn in the actual sampling sequence; the total duration may vary in different scenarios, and 100 seconds is a specific setting in this example).

[0103] k x k y The initial curvature coefficient (calculated based on the change in direction angle, with an initial value of 0.5) is used to make the tangent direction of the curve 185° at the starting point and 195° at the ending point, with the vertex transitioning naturally through the equation.

[0104] The rate of change of lateral acceleration between adjacent key sampling points was calculated. From the starting point to the apex, the acceleration increased from 0.3 m / s² to 0.35 m / s², a rate of change of 0.0005 m / s³; from the apex to the end point, the acceleration decreased from 0.35 m / s² to 0.28 m / s², a rate of change of -0.0007 m / s³. The preset sensitivity threshold was 0.0006 m / s³. Since the absolute value of the rate of change from the apex to the end point (0.0007) was greater than the threshold, the curvature coefficient kᵧ for this segment was adjusted to 0.7 (increasing the weight) to make the curve more closely resemble the actual turning trajectory (due to the rapid decrease in acceleration, the actual trajectory curvature slightly increased).

[0105] Based on the adjusted parametric equations, the arc length of the curve segment between adjacent key sampling points is calculated by integration:

[0106] From the starting point to the vertex (t=0 to t=50 seconds), using the integral formula The arc length is calculated to be 0.12 kilometers.

[0107] From vertex to end point (t=50 to t=100 seconds): Calculated in the same way, the arc length is 0.18 kilometers due to the adjustment of the curvature coefficient; the total mileage of the turning section is 0.3 kilometers when the two arc lengths are added together.

[0108] In one possible implementation, the mileage of each driving scenario is accumulated and combined with a correction factor for the mileage of the same road segment in the past to obtain the actual mileage of the vehicle, which includes the following:

[0109] The identification information of the current vehicle travel segment is determined, and based on the identification information of the segment, historical driving data of the same segment within a preset period is retrieved from the historical database. The historical driving data includes the historical actual mileage of each driving scenario in the same segment, the historical planned mileage of the operation route, and the distribution ratio of the historical driving scenarios.

[0110] The historical mileage correction coefficient for the same road segment is calculated by weighting the current driving scenario distribution ratio with the historical driving scenario distribution ratio, based on the average deviation rate between the historical actual mileage and the historical planned mileage of the same road segment.

[0111] Multiply the cumulative mileage value for each driving scenario by the historical mileage correction coefficient for the same road segment to obtain the actual mileage of the vehicle after eliminating the statistical bias of historical mileage.

[0112] For example, for the operation scenario of the sanitation sweeper vehicle with model number ZLJ5160TXSDFE5 on the section from B Street to F Lane of Road A, the mileage accumulation and correction are completed according to the following process:

[0113] Determine current road segment signs and retrieve historical data:

[0114] The current vehicle's route identification information is set as Chengdong New Area-A Road-001 (Road Section ID), with corresponding start and end coordinates ranging from the starting point N30°15′22″, E120°08′35″ to the ending point N30°17′18″, E120°10′21″, which perfectly matches the operation route in Step 1. Based on this identification, historical driving data for the same route over the past 30 days (preset period) is retrieved from the historical database, totaling 20 valid records, among which:

[0115] Historical distribution of driving scenarios: regular driving scenarios accounted for approximately 63% (average mileage 3.3 km), low-speed driving scenarios accounted for approximately 21% (average mileage 1.1 km), and turning scenarios accounted for approximately 16% (average mileage 0.8 km).

[0116] The average deviation rate between the historical actual mileage and the historical planned mileage (5.2 km) is 1.01 (that is, the average historical actual mileage is 5.2 × 1.01 = 5.252 km, which is slightly higher than the planned value due to errors in the calculation of arc length in turning scenarios and low-speed interpolation deviations).

[0117] First, calculate the current driving scenario distribution percentage: combining the previous scenario-based mileage (normal 1.2 km, low speed 0.4 km, turning 0.3 km, totaling 1.9 km), the current normal scenario accounts for approximately 63.2% (1.2 / 1.9), the low speed scenario accounts for approximately 21.1% (0.4 / 1.9), and the turning scenario accounts for approximately 15.8% (0.3 / 1.9).

[0118] By comparing the current and historical percentages of each scenario (regular 63.2% vs 63%, low speed 21.1% vs 21%, turning 15.8% vs 16%), the scenario distribution is highly consistent, and the matching degree is determined to be 98%.

[0119] Finally, the correction coefficient is calculated by weighting according to the preset weights (scene matching degree weight 0.7, basic coefficient weight 0.3): Correction coefficient = basic coefficient (1.01) × (scene matching degree (0.98) × 0.7 + 0.3) = 1.01 × (0.686 + 0.3) = 1.01 × 0.986 ≈ 1.006.

[0120] Multiply the cumulative mileage for each driving scenario (1.2 + 0.4 + 0.3 = 1.9 km) by the correction factor (1.006) to get the actual mileage = 1.9 × 1.006 ≈ 1.911 km.

[0121] Step 4: Based on the operation status judgment results, generate early warning information for situations such as vehicle route deviation, speeding, and non-compliance with operation standards, and generate dispatch instructions based on the early warning information and the real-time vehicle status reflected by the real-time vehicle operation data.

[0122] For example, for a sanitation sweeper of model ZLJ5160TXSDFE5, based on the overall operation status judgment result generated in step 3, which shows that the slight deviation at the intersection of Road A and Street D (04:25-04:27) has been corrected, the current effective operation period is (04:45), the operation device parameters meet the standards, and the basic operation completion rate is approximately 36.7% (1.911 km / 5.2 km), an early warning and dispatch instruction are generated as follows:

[0123] For minor deviations from turning sections in the past: A text warning was generated between 04:25 and 04:27, indicating that the vehicle had slightly deviated from the turning section at the intersection of Road A and Street D (coordinates N30°16′08″, E120°09′25″), with a vertical distance of 1.8 meters and a directional angle of 15°. The deviation has since recovered on its own. Drivers should pay attention to the stability of the vehicle on the turning section. Simultaneously, a pop-up window and voice prompt (please keep the vehicle close to the road on the turning section) were pushed to the driver via the vehicle terminal.

[0124] If, at 04:50, the vehicle speed is detected to have increased to 11 km / h (exceeding the operating speed limit of 5-10 km / h), an overspeed warning will be generated immediately. The current speed is 11 km / h, which exceeds the operating speed limit. The speed must be reduced to below 10 km / h. The warning information will be uploaded to the management backend and marked as Real-time Warning - Pending Processing.

[0125] If the cleaning roller brush speed drops to 240 r / min at 04:55 (below the standard lower limit of 250 r / min), an operation non-compliance warning will be generated. The roller brush speed is 240 r / min, which is below the standard range of 250-300 r / min, with a deviation value of -10 r / min. The roller brush drive device needs to be checked. The attached is a speed change curve of the past 5 minutes (from 280 r / min to 240 r / min).

[0126] For speeding warnings (speed 11km / h): Based on the real-time location (section from D Street to E Street on Road A, no obstacles), a dispatch instruction is generated to immediately reduce the driving speed to 8-10km / h, maintain a constant speed during operation, and avoid affecting the cleaning quality. The instruction is transmitted through the vehicle terminal with the instruction number: 202405XX001-Speeding Adjustment Identifier. After the driver confirms the execution, feedback indicates that the speed has been reduced to 9km / h.

[0127] Based on the vehicle's real-time location (near Road A and Street E, close to the work route), a dispatch instruction is generated suggesting a temporary stop at the intersection of Road A and Street E (coordinates N30°16′45″, E120°10′05″). The instruction should check the roller brush drive motor voltage (standard 12V) and the tension of the drive belt. After troubleshooting, the operation should be restarted. The stop time should not exceed 10 minutes (to avoid affecting the work schedule). The instruction should simultaneously inform the backend of the estimated recovery time for progress tracking. Regarding the work progress (completion rate 36.7%, current time 04:55, 1 hour and 5 minutes until the end of the work period): A progress dispatch instruction is generated. The current work completion rate is 36.7%, with 3.289 kilometers remaining. It is recommended to maintain a work speed of 8 km / h. If there are no abnormalities, the remaining work can be completed before 05:50 to avoid exceeding the time limit.

[0128] Step 5: Summarize real-time vehicle operation data, work status judgment results, and dispatch instruction execution status to generate a work evaluation report.

[0129] For example, for the operation of a sanitation sweeper with model number ZLJ5160TXSDFE5 on the section from B Street to F Lane on Road A on the same day (03:00-05:45), a work evaluation report is generated by summarizing relevant data. The work evaluation report includes: the working vehicle is a ZLJ5160TXSDFE5 sanitation sweeper; the working route is from the intersection of Road A and B Street in Chengdong New District (starting point) to the intersection of F Lane (ending point), with a planned mileage of 5.2 kilometers; the working time is preset to 03:00-06:00, the actual completion time is 03:00-05:45, ending 15 minutes in advance; the data collection cycle is consistent with the actual working time (03:00-05:45), and the sampling frequency is 10 seconds / time. The average operating speed was 7.8 km / h (meeting the limit of 5-10 km / h), the average roller brush speed was 272 r / min, and the average water gun pressure was 3.2 MPa. All operating parameters were within a reasonable range. The effective operating period was from 03:02 to 05:45, with a cumulative duration of 2 hours and 43 minutes. The non-operating period was only from 03:00 to 03:02, which was the vehicle start-up preparation phase, with no additional invalid time periods. Two minor abnormalities occurred during the operation, one from 04:25 to 04:27, during a slight deviation from the turning section (Road A and Road D). Near the street intersection (which has since been restored), and from 04:55 to 05:02, there was an abnormal roller brush speed (which was resolved and returned to normal), with no serious abnormalities occurring. The actual mileage traveled by the vehicle was 5.18 kilometers (calculated by summing the mileage of each scenario to 5.12 kilometers and multiplying it by a historical mileage correction factor of 1.012 for the same road segment). Combined with the planned mileage of 5.2 kilometers, the basic operation completion rate was calculated to be approximately 99.6%. During the operation, a total of 3 dispatch instructions were generated, with an instruction execution rate of 100%. The average driver response time was 1.5 minutes, indicating high instruction implementation efficiency. Overall, this sanitation cleaning operation met the standards, with core indicators such as speed, equipment parameters, and operation completion rate all meeting the requirements.

[0130] Example 2 provides an IoT-based intelligent vehicle management system, applying the aforementioned IoT-based intelligent vehicle management method, such as... Figure 2 As shown, it includes:

[0131] The task generation module is used to generate task assignments that include a task route. The task route includes a start point, an end point, intermediate waypoints, and a planned mileage. The task assignment also includes a task time period and speed limit.

[0132] The real-time data acquisition module is used to acquire real-time operating data of the vehicle, including real-time vehicle location information, vehicle operation status information, and vehicle dynamic motion parameters; wherein, the vehicle operation status information includes vehicle speed and operating parameters of vehicle operation devices.

[0133] The operation status analysis module is used to compare the operation route with the real-time location information of the vehicle to generate a vehicle route deviation judgment result; combine the operation period in step 1 with the vehicle's driving speed to generate a judgment result of the effective operation period and non-operation period; generate a judgment result of whether the operation meets the standard based on the operating parameters of the vehicle's operation device; calculate the actual mileage of the vehicle based on the real-time location information and dynamic motion parameters of the vehicle, and calculate the basic operation completion rate based on the mileage of the operation route planned in step 1; and generate an operation status judgment result based on the vehicle route deviation judgment result, the effective operation period and non-operation period judgment result, the judgment result of whether the operation meets the standard, and the basic operation completion rate.

[0134] The early warning and dispatch module is used to generate early warning information based on the operation status judgment results for situations such as vehicle route deviation, speeding, and non-compliance with operation standards, and to generate dispatch instructions based on the early warning information and the real-time vehicle status reflected by the real-time vehicle operation data.

[0135] The task evaluation report module is used to summarize real-time vehicle operation data, task status judgment results, and dispatch instruction execution status to generate a task evaluation report.

[0136] The embodiments described above are merely illustrative of specific implementations of the present invention, and while the descriptions are detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A vehicle intelligent management method based on the Internet of Things, characterized in that, Includes the following steps: Step 1: Generate a work task containing a work route, which includes a start point, an end point, intermediate waypoints, and a planned mileage. The work task also includes a work period and speed limits. Step 2: Obtain real-time vehicle operation data, which includes real-time vehicle location information, vehicle operation status information, and vehicle dynamic motion parameters; wherein, the vehicle operation status information includes vehicle speed and vehicle operation device operation parameters. Step 3: Compare the work route with the real-time vehicle location information to generate a vehicle route deviation judgment result; combine the work period from Step 1 with the vehicle's speed to generate a judgment result for the effective work period and non-work period; based on the vehicle's operating parameters, generate a judgment result for whether the work meets the standards; calculate the vehicle's actual mileage based on the real-time vehicle location information and dynamic motion parameters, and calculate the basic work completion rate based on the planned mileage of the work route from Step 1; based on the vehicle route deviation judgment result, the effective work period and non-work period judgment result, the judgment result for whether the work meets the standards, and the basic work completion rate, generate a work status judgment result. Step 4: Based on the operation status judgment results, generate early warning information for situations such as vehicle route deviation, speeding, and non-compliance with operation standards, and generate dispatch instructions based on the early warning information and the real-time vehicle status reflected by the real-time vehicle operation data. Step 5: Summarize real-time vehicle operation data, work status judgment results, and dispatch instruction execution status to generate a work evaluation report; The calculation of the vehicle's actual mileage based on the vehicle's real-time location information and dynamic motion parameters includes the following: Real-time vehicle location information and dynamic motion parameters are collected for continuous sampling periods to establish a trajectory sampling sequence; the real-time vehicle location information includes vehicle coordinates and driving direction, and the dynamic motion parameters include vehicle steering angular velocity and vehicle lateral acceleration. Driving scenarios are identified based on the steering angular velocity, lateral acceleration, and driving direction in the trajectory sampling sequence. Driving scenarios include turning scenarios, low-speed driving scenarios, and normal driving scenarios. Among them, low-speed driving scenarios refer to scenarios where the vehicle speed is continuously below a preset low-speed threshold, which is determined according to the vehicle's operation type; normal driving scenarios refer to scenarios where the vehicle speed is above the preset low-speed threshold, and the changes in steering angular velocity, lateral acceleration, and driving direction do not meet the criteria for turning scenarios; turning scenarios refer to scenarios where the changes in steering angular velocity, lateral acceleration, and driving direction simultaneously meet preset characteristics. For regular driving scenarios, the straight-line distance is calculated based on the vehicle coordinates of adjacent sampling points in the trajectory sampling sequence. For low-speed driving scenarios, virtual trajectory points are generated using directional constraint interpolation based on the vehicle coordinates of adjacent sampling points in the trajectory sampling sequence, and the cumulative distance between the actual sampling point vehicle coordinates and the virtual trajectory point coordinates is calculated. For turning scenarios, the vehicle coordinates, driving direction, and lateral acceleration features of key sampling points are extracted from the trajectory sampling sequence as curve fitting control points. The curve parameters are adjusted by combining the rate of change of driving direction and the trend of lateral acceleration. The curve arc length is calculated based on the vehicle coordinates of the control points as the mileage of the turning segment. The actual mileage of the vehicle is obtained by summing up the mileage of each driving scenario and combining it with the historical mileage correction factor for the same road segment. For the turning scenario, the vehicle coordinates, driving direction, and lateral acceleration features of key sampling points are extracted from the trajectory sampling sequence as curve fitting control points. Curve parameters are adjusted by combining the rate of change of driving direction and the trend of lateral acceleration. The curve arc length is calculated based on the vehicle coordinates of the control points as the mileage of the turning segment, including the following: Key sampling points for turning scenarios are extracted from the trajectory sampling sequence. The key sampling points include the turning start point, the turning vertex, and the turning end point. The turning start point is the sampling point in the trajectory sampling sequence where the lateral acceleration first reaches a preset turning start threshold. The turning vertex is the sampling point in the trajectory sampling sequence where the steering angular velocity reaches its maximum value. The turning end point is the sampling point in the trajectory sampling sequence where the lateral acceleration falls back to a preset turning end threshold. Using the vehicle coordinates of the extracted key sampling points as the geometric reference points and the driving direction of the key sampling points as the tangent direction of the curve, an initial fitting curve for the turning scene is constructed in the form of parametric equations. The parametric equations use sampling time as the parameter variable and associate the vehicle coordinates and driving direction of the key sampling points. Based on the trend of lateral acceleration change between adjacent key sampling points in the trajectory sampling sequence, the lateral acceleration change rate between adjacent key sampling points is calculated; when the absolute value of the lateral acceleration change rate is greater than a preset sensitivity threshold, the curvature coefficient of the initial fitting curve parametric equation is adjusted, and the curve curvature adjustment weight between adjacent key sampling points is increased to obtain the parametric equation of the adjusted fitting curve. Based on the parametric equations of the adjusted fitted curve, combined with the vehicle coordinates of the extracted key sampling points, the arc length of the curve segment between adjacent key sampling points is calculated by integration, and the arc lengths of each curve segment are summed to obtain the total mileage of the turning section. The mileage of each driving scenario is summed up and combined with the historical mileage correction factor for the same road segment to obtain the actual mileage of the vehicle, which includes the following: The identification information of the current vehicle travel segment is determined, and based on the identification information of the segment, historical driving data of the same segment within a preset period is retrieved from the historical database. The historical driving data includes the historical actual mileage of each driving scenario in the same segment, the historical planned mileage of the operation route, and the distribution ratio of the historical driving scenarios. The historical mileage correction coefficient for the same road segment is calculated by weighting the current driving scenario distribution ratio with the historical driving scenario distribution ratio, based on the average deviation rate between the historical actual mileage and the historical planned mileage of the same road segment. Multiply the cumulative mileage value for each driving scenario by the historical mileage correction coefficient for the same road segment to obtain the actual mileage of the vehicle after eliminating the statistical bias of historical mileage.

2. The vehicle intelligent management method based on the Internet of Things according to claim 1, characterized in that, The real-time vehicle location information includes the vehicle's current coordinates and driving direction. The process of comparing the work route with the real-time vehicle location information to generate a vehicle route deviation judgment result includes the following: Preset route deviation thresholds for different road segment attributes, and preset corresponding route deviation thresholds for straight road segments and turning road segments respectively; Calculate the vertical distance between the vehicle's current coordinates and the planned work route trajectory in real time; The vertical distance is compared with the route deviation threshold of the corresponding road segment to obtain the comparison result of the vertical distance and the threshold; at the same time, the duration of the vehicle's deviation is obtained, and the angle between the vehicle's driving direction and the planned direction of the operation route is calculated to determine whether the angle exceeds the preset angle range, thus forming the angle relationship determination result. Based on the comparison results of vertical distance and threshold, the duration of deviation and the angle relationship, the deviation type is determined; Generate vehicle route deviation judgment results that include deviation type, deviation duration, deviation segment attributes, and real-time coordinates.

3. The vehicle intelligent management method based on the Internet of Things according to claim 1, characterized in that, Combining the work period and vehicle speed from step 1, the results for determining valid work periods and non-work periods include the following: When the vehicle travel time is within the preset work period in step 1, the vehicle speed is within the preset work speed range, and the real-time vehicle location is within the work route planning range, it is determined to be a valid work period. If the vehicle travel time exceeds the preset working period in step 1, it is determined to be a non-working period; If the vehicle travels within the work period, and the vehicle speed is lower than the preset non-work speed threshold and the duration of this state reaches the preset non-work stationary duration threshold, or the vehicle is outside the work route planning range and there is no dispatch instruction authorization, it is determined to be a non-work period. Generate results for determining valid and non-work periods, including the start and end times of valid work periods, the start and end times of non-work periods, and the reasons for non-work periods.

4. The vehicle intelligent management method based on the Internet of Things according to claim 1, characterized in that, Based on the operating parameters of the vehicle's operating equipment, the result of whether the operation meets the standard includes the following: The system includes a preset range of standard parameters for the operating device and a set of qualified status codes. The standard parameter range includes the acceptable range of device operating time and device power. The set of qualified status codes includes the preset status codes corresponding to the normal operation of the operating device. The system also compares the vehicle operating device's operating parameters with the standard parameter range and the operating status codes with the set of qualified status codes in real time. When the device running time and device power are both within the corresponding standard parameter range, and the operating status code of the working device belongs to the qualified status code set, the operation is judged to be in compliance with the standard; when the device running time exceeds the standard parameter range, or the device power exceeds the standard parameter range, or the operating status code of the working device does not belong to the qualified status code set, the operation is judged to be non-compliant with the standard, and the abnormal parameters and deviation values ​​are recorded. Generate a result indicating whether a job meets the standard, including the job's compliance status, abnormal parameters, and duration of the abnormality.

5. A vehicle intelligent management system based on the Internet of Things, characterized in that, The vehicle intelligent management method based on the Internet of Things according to any one of claims 1-4 includes: The task generation module is used to generate a task containing a task route. The task route includes a start point, an end point, intermediate waypoints, and a planned mileage. The task also includes a task time period and speed limit. The real-time data acquisition module is used to acquire real-time operating data of the vehicle, including real-time vehicle location information, vehicle operation status information, and vehicle dynamic motion parameters; wherein, the vehicle operation status information includes vehicle speed and vehicle operation device operating parameters. The operation status analysis module is used to compare the operation route with the real-time location information of the vehicle to generate a vehicle route deviation judgment result; combine the operation period in step 1 with the vehicle's driving speed to generate a judgment result of the effective operation period and non-operation period; generate a judgment result of whether the operation meets the standard based on the operating parameters of the vehicle's operation device; calculate the actual mileage of the vehicle based on the real-time location information and dynamic motion parameters of the vehicle, and calculate the basic operation completion rate based on the mileage of the operation route planned in step 1; and generate an operation status judgment result based on the vehicle route deviation judgment result, the effective operation period and non-operation period judgment result, the judgment result of whether the operation meets the standard, and the basic operation completion rate. The early warning and dispatch module is used to generate early warning information based on the operation status judgment result for situations such as vehicle route deviation, speeding, and non-compliance with operation standards, and to generate dispatch instructions based on the early warning information and the real-time vehicle status reflected by the real-time vehicle operation data. The task evaluation report module is used to summarize real-time vehicle operation data, task status judgment results, and dispatch instruction execution status to generate a task evaluation report.

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