Intelligent monitoring method and system for vehicle overload and vehicle

Abstract

The invention discloses an intelligent monitoring method for vehicle overload. The intelligent monitoring method comprises the steps that physical response parameters generated by a vehicle stress part under the load effect and operation state parameters generated in the vehicle operation process are collected in real time; obtaining a first load mass of the vehicle based on the physical response parameter, and obtaining a second load mass of the vehicle based on the operation state parameter; under the condition that the first load mass and the second load mass are determined to be valid data, if the first load mass and / or the second load mass exceed / exceeds the check load mass of the vehicle, an overload monitoring result representing that the vehicle is in an overload state is generated, and an overload early warning signal is output through the vehicle and the supervision platform of the vehicle. Meanwhile, the invention further discloses intelligent monitoring equipment for vehicle overload and the vehicle.

Description

Technical Field

[0001] This application relates to the field of transportation safety monitoring, specifically to an intelligent monitoring method, system, and vehicle for vehicle overloading. Background Technology

[0002] Overloading is a major contributing factor to road traffic accidents. It not only accelerates wear and tear on vehicle components and significantly shortens vehicle lifespan, but also reduces road capacity, damages road infrastructure, and poses a serious threat to transportation safety. Therefore, accurate monitoring and efficient early warning of vehicle overloading have become a key requirement in the field of transportation safety management. Summary of the Invention

[0003] In view of this, the embodiments of this application aim to provide an intelligent monitoring method, system and vehicle for vehicle overloading, so as to at least solve the above-mentioned technical problems.

[0004] To achieve the above objectives, the technical solution of this application is implemented as follows:

[0005] According to one aspect of the embodiments of this application, an intelligent monitoring method for vehicle overloading is provided, the method comprising:

[0006] Real-time acquisition of physical response parameters of vehicle stress-bearing components under load and operating status parameters generated during vehicle operation;

[0007] The first load capacity of the vehicle is obtained based on the physical response parameters, and the second load capacity of the vehicle is obtained based on the operating state parameters;

[0008] If both the first load capacity and the second load capacity are valid data, and if the first load capacity and / or the second load capacity exceed the vehicle's rated load capacity, an overload monitoring result indicating that the vehicle is in an overloaded state is generated, and an overload warning signal is output through the vehicle and the vehicle's monitoring platform.

[0009] In the above scheme, obtaining the first load capacity of the vehicle based on the physical response parameters includes:

[0010] Based on the pre-stored mapping relationship between load and physical quantity in the sensing unit on the vehicle's force-bearing component, the first load mass corresponding to the physical response parameter is determined.

[0011] In the above scheme, obtaining the second load capacity of the vehicle based on the operating state parameters includes:

[0012] The operating status parameters are filtered to extract the smooth acceleration parameters during the smooth acceleration phase and the smooth deceleration parameters during the smooth deceleration phase of the vehicle.

[0013] The third load of the vehicle during the steady acceleration phase is calculated based on the steady acceleration parameters, and the fourth load of the vehicle during the steady deceleration phase is calculated based on the steady deceleration parameters.

[0014] The third load capacity and the fourth load capacity are weighted and calculated to obtain the second load capacity that represents the vehicle during the stable driving phase.

[0015] In the above scheme, determining that both the first load capacity and the second load capacity are valid data includes:

[0016] Calculate the load error between the first load capacity and the second load capacity;

[0017] If the load error remains within a reliable range for a preset time, both the first load and the second load are determined to be valid data.

[0018] In the above scheme, the step of determining that both the first load capacity and the second load capacity are valid data if the load capacity error remains within a reliable range for a preset time includes:

[0019] Within the preset time period, the average value and standard deviation of the load mass error are calculated in real time.

[0020] When the average value is less than the first threshold and the standard deviation is less than the second threshold, it indicates that the first load capacity and the second load capacity are continuously within the confidence range within the preset time period, and it is determined that both the first load capacity and the second load capacity are valid data.

[0021] The method in the above scheme further includes:

[0022] If the first load capacity and / or the second load capacity are determined to be invalid data, the physical response parameters of the vehicle's load-bearing components under load and the operating status parameters generated during vehicle operation are re-acquired to obtain the first load capacity and the second load capacity of the vehicle again, until the first load capacity and / or the second load capacity are determined to be valid data.

[0023] According to a second aspect of this application, an intelligent monitoring system for vehicle overloading is provided, the system comprising:

[0024] The data acquisition unit is used to collect in real time the physical response parameters of the vehicle's stressed components under load and the operating status parameters generated during vehicle operation;

[0025] A data calculation unit is used to obtain the first load capacity of the vehicle based on the physical response parameters and the second load capacity of the vehicle based on the operating state parameters;

[0026] An overload monitoring unit is used to generate an overload monitoring result indicating that the vehicle is in an overload state if the first load and / or the second load exceed the vehicle's rated load, provided that both the first load and the second load are valid data.

[0027] The collaborative early warning unit is used to output overload warning signals through the vehicle and the vehicle's monitoring platform.

[0028] In the above scheme, the system further includes:

[0029] The data cross-verification unit is used to calculate the load error between the first load capacity and the second load capacity; if the load error remains within a reliable range for a preset time, it is determined that both the first load capacity and the second load capacity are valid data.

[0030] In the above scheme, the data cross-calibration unit is specifically used to calculate the average value and standard deviation of the load mass error in real time within the preset time period; when the average value is less than a first threshold and the standard deviation is less than a second threshold, it indicates that the first load mass and the second load mass are continuously within the confidence range within the preset time period, and it is determined that the first load mass and the second load mass are both valid data.

[0031] In the above scheme, the data calculation unit is specifically used to filter the operating state parameters, extract the smooth acceleration parameters during the smooth acceleration phase and the smooth deceleration parameters during the smooth deceleration phase; calculate the third load mass of the vehicle during the smooth acceleration phase based on the smooth acceleration parameters, and calculate the fourth load mass of the vehicle during the smooth deceleration phase based on the smooth deceleration parameters; and perform a weighted calculation on the third load mass and the fourth load mass to obtain the second load mass representing the vehicle during the smooth driving phase.

[0032] According to a third aspect of this application, a vehicle is provided, the vehicle comprising:

[0033] Memory is used to store computer programs that can run on a processor;

[0034] The processor is configured to execute any of the above-described intelligent monitoring methods for vehicle overloading while running the computer program.

[0035] The intelligent monitoring method, system, and vehicle for vehicle overload provided in this application represent a solution for accurate monitoring and efficient early warning of vehicle overload by collecting and cross-verifying multi-dimensional data. Specifically, the solution includes: real-time acquisition of physical response parameters of the vehicle's load-bearing components under load and operational status parameters generated during vehicle operation; obtaining a first load capacity of the vehicle based on the physical response parameters and a second load capacity based on the operational status parameters; and, if both the first and second load capacities are valid data, generating an overload monitoring result indicating that the vehicle is in an overloaded state if the first and / or second load capacities exceed the vehicle's rated load capacity, and outputting an overload early warning signal through the vehicle and its monitoring platform. This not only enables real-time monitoring and collaborative early warning of vehicle overload but also avoids misjudgments of overload through a multi-dimensional data cross-verification mechanism, thereby improving the accuracy of overload monitoring. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating the intelligent monitoring method for vehicle overloading in this application. Figure 1 ;

[0037] Figure 2 This is a flowchart illustrating the intelligent monitoring method for vehicle overloading in this application. Figure 2 ;

[0038] Figure 3 This is a schematic diagram of the structural composition of the intelligent monitoring system for vehicle overloading in this application;

[0039] Figure 4 This is a schematic diagram of the structural composition of the vehicle in this application. Detailed Implementation

[0040] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0041] The specific technical features described in the various embodiments in the detailed implementation can be combined in various ways without contradiction. For example, different implementation methods can be formed by combining different specific technical features. In order to avoid unnecessary repetition, the various possible combinations of the specific technical features in this application will not be described separately.

[0042] It should be noted that the terms "first," "second," and "third" used in the embodiments of this application are merely to distinguish similar objects and do not represent a specific order of objects. It is understood that "first," "second," and "third" can be interchanged in a specific order or sequence where permitted. It should be understood that the objects distinguished by "first," "second," and "third" can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in an order other than those illustrated or described herein.

[0043] Figure 1 This is a flowchart illustrating the intelligent monitoring method for vehicle overloading in this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0044] Step 101: Real-time acquisition of physical response parameters of vehicle components under load and operating status parameters generated during vehicle operation;

[0045] Here, the load-bearing components of the vehicle specifically refer to load-bearing structural components and / or suspension system components whose physical state (deformation, stress, strain, pressure) changes regularly and measurably with the vehicle's load, including but not limited to axles, leaf springs, airbags, frames, tires, etc.

[0046] In one embodiment of this application, the load-bearing component of the vehicle is a leaf spring, and the physical response parameters can be the deflection change of the leaf spring under load, the height change of the air spring, or the relative displacement of the axle relative to the vehicle frame, measured in real time by a load-bearing sensor. This load-bearing sensor includes, but is not limited to, a high-precision displacement sensor or a laser rangefinder.

[0047] In another embodiment of this application, the vehicle load-bearing component is the vehicle axle, and the physical response parameter can be a micro-strain signal measured by a strain gauge attached to the axle.

[0048] In another embodiment of this application, the force-bearing component of the vehicle is the vehicle's air suspension airbag, and the physical response parameter can be the internal pressure value of the airbag measured by a pressure sensor.

[0049] Here, the operating status parameters can refer to a set of multiple parameters based on vehicle dynamics principles, used to inversely calculate the total mass. These parameters reflect the vehicle's dynamic state and have a direct physical relationship with the vehicle's total mass, including but not limited to longitudinal acceleration, driving force, traction force, driving resistance, and braking force. These operating status parameters can be obtained through the vehicle's bus interface; this bus interface includes, but is not limited to, CAN bus, LIN bus, FlexRay bus, MOST bus, and in-vehicle Ethernet.

[0050] The driving force of the vehicle can be calculated by comprehensively considering parameters such as engine torque, transmission ratio, final drive ratio, and transmission efficiency. The vehicle's running resistance can be estimated using a model and parameters such as vehicle speed and gradient. This includes, but is not limited to, rolling resistance related to vehicle weight, tires, and road surface; air resistance related to vehicle speed and frontal area; and slope resistance related to vehicle weight and road gradient. The braking force of the vehicle can be estimated using the master cylinder pressure, brake pedal travel, and the command pressure of the electronic braking system (EBS).

[0051] Step 102: Obtain the first load capacity of the vehicle based on the physical response parameters, and obtain the second load capacity of the vehicle based on the operating state parameters;

[0052] In this application, when the first load capacity of the vehicle is obtained based on the physical response parameters, the collected physical response parameters can be preprocessed (including data noise reduction, outlier removal, data standardization, etc.), and then the first load capacity corresponding to the physical response parameters can be determined based on the mapping relationship between load and physical quantity pre-stored in the sensing unit on the force-bearing component of the vehicle.

[0053] Here, the mapping relationship between the load and the physical quantity can be represented in ways including but not limited to load-deformation calibration curves, mathematical models, databases, etc.

[0054] In this application, when obtaining the second load capacity of the vehicle based on the operating state parameters, the operating state parameters can be first filtered to extract the smooth acceleration parameters during the smooth acceleration phase and the smooth deceleration parameters during the smooth deceleration phase; then, the third load capacity of the vehicle during the smooth acceleration phase is calculated based on the smooth acceleration parameters, and the fourth load capacity of the vehicle during the smooth deceleration phase is calculated based on the smooth deceleration parameters; then, the third load capacity and the fourth load capacity are weighted and calculated to obtain the second load capacity corresponding to the smooth driving phase of the vehicle.

[0055] Here, the specific method for filtering the operating status parameters is not limited; for example, it can be achieved through window moving method, Kalman filtering method, etc.

[0056] Here, the third load m of the vehicle during the smooth acceleration phase can be calculated using formula (1). 载1 :

[0057]

[0058] Among them, T m For the engine driving torque, i c η is the transmission ratio. TFor transmission efficiency, r is the wheel radius, m0 is the unloaded mass of the truck, g is the acceleration due to gravity, f is the rolling resistance coefficient, θ is the road slope angle, ρ is the air density, and C d denoted as drag coefficient, A as the vehicle's frontal area, v as velocity, and a as acceleration.

[0059] Here, the fourth load m of the vehicle during the smooth deceleration phase can be calculated using formula (2). 载2 ;

[0060]

[0061] in, For the braking force on the j-th axis, μ(λ) j ) represents the friction coefficient related to slip ratio, h g L is the height of the vehicle's center of gravity. j The distance from the j-axis to the center of gravity, ρ is the air density, and C is the distance from the j-axis to the center of gravity. d Let A be the drag coefficient, v be the vehicle's frontal area, a be the velocity, a be the acceleration, and r be the wheel radius. Let η be the moment of inertia of the wheel on axis j, L be the wheelbase of the vehicle, and η be the moment of inertia of the wheel on axis j. b denoted as the brake temperature correction efficiency coefficient, m0 as the unloaded mass of the truck, g as the gravitational acceleration, f as the rolling resistance coefficient, and θ as the road slope angle.

[0062] Here, the brake temperature correction efficiency coefficient η b It can be obtained through formula (3):

[0063] η b =0.92-0.0012·(T) brake -50) (3)

[0064] Among them, T brake This refers to the brake temperature.

[0065] Here, the braking force on the j-th axis It can be obtained through formula (4):

[0066]

[0067] Where, N j P represents the number of wheels on axis j. j B is the braking pressure on the j-axis. j R is the effective area of ​​a single brake chamber. j r is the effective radius of the j-axis brake drum. j Let J be the radius of the wheel on axis j.

[0068] Here, the slip ratio is related to the friction coefficient μ(λ). j It can be obtained through formula (5):

[0069]

[0070] Where, λ j Let be the slip ratio of the j-th axis.

[0071] Here, the slip ratio λ of the j-th axis j It can be obtained through formula (6):

[0072]

[0073] Where, ω j Let be the wheel angular velocity along axis j.

[0074] Here, the vehicle's center of gravity height h g It can be obtained through formula (7):

[0075]

[0076] Among them, h g0 The unloaded center of gravity height of the vehicle, in meters. max δ represents the maximum permissible gross vehicle weight, and δ is the confidence factor.

[0077] In this application, the engine driving torque T m transmission ratio i c Transmission efficiency η T Velocity v, acceleration a, braking pressure P on the j-axis j The wheel angular velocity ω on the j-axis j All of these can be obtained directly or indirectly via the CAN bus. Brake temperature T brake It can be obtained through a temperature sensor; the confidence factor δ can be obtained through a tire pressure sensor.

[0078] In this application, after obtaining the third load mass m 载1 and the fourth load mass m 载2 In this case, the third load mass m can also be determined using formula (8). 载1 and the fourth load mass m 载2 A weighted calculation is performed to obtain the second load m, which represents the vehicle's load during the stable driving phase. 载x :

[0079] m 载x =K t m 载1 +(1-K t )m 载2 (8)

[0080] Among them, K t This is a weighted value for velocity fluctuations.

[0081] Here, the velocity fluctuation weighting value K tIt can be obtained through formula (9):

[0082]

[0083] Among them, S (t) For smooth acceleration, e is the natural constant, with a value of approximately 2.71828182.

[0084] Here, the smooth acceleration S (t) It can be obtained through formula (10):

[0085] S (t) =0.3a + 0.7S (t-0.01) (10)

[0086] Where t is time, S (0) =0.

[0087] Step 103: If both the first load capacity and the second load capacity are valid data, and if the first load capacity and / or the second load capacity exceed the vehicle's rated load capacity, generate an overload monitoring result indicating that the vehicle is in an overloaded state, and output an overload warning signal through the vehicle and the vehicle's monitoring platform.

[0088] Here, the load error between the first load and the second load can first be calculated; if the load error remains within a reliable range for a preset time, it is determined that both the first load and the second load are valid data.

[0089] In one implementation of this application, the load mass error can be compared with a set error within the preset time period. If the comparison result indicates that the load mass error is less than the set error, it is determined that the first load mass and the second load mass are continuously within a reliable range within the preset time period, thereby determining that both the first load mass and the second load mass are valid data.

[0090] In another implementation of this application, the average value and standard deviation of the load mass error can be calculated in real time within the preset time period; when the average value is less than a preset first threshold and the standard deviation is less than a preset second threshold, it indicates that the first load mass and the second load mass are continuously within the reliable range within the preset time period, and it is determined that both the first load mass and the second load mass are valid data.

[0091] Here, the confidence interval is not limited; for example, it can be 3% of the vehicle's rated load limit, or it can be set according to actual needs.

[0092] In this application, if the first load capacity and / or the second load capacity are determined to be invalid data, the physical response parameters generated by the vehicle's load-bearing components under load and the operating state parameters generated during vehicle operation are re-acquired to obtain the first load capacity and the second load capacity of the vehicle again, until the first load capacity and / or the second load capacity are determined to be valid data.

[0093] In this application, assuming both the first and second load capacities are valid data, they can be compared with the vehicle's rated load capacity. If the comparison results indicate that the first and / or second load capacities exceed the vehicle's rated load capacity, an overload monitoring result indicating that the vehicle is in an overloaded state is generated. At this time, an overload warning signal can be output through the vehicle and its monitoring platform.

[0094] Here, the method of overload warning signal is not limited, including but not limited to sound warning, light warning, telephone warning, speed limit warning, pop-up warning, etc.

[0095] Here, after receiving the overload monitoring results, the vehicle can also send the overload monitoring results to the monitoring platform. After receiving the overload monitoring results, the monitoring platform will store the data and output an overload warning signal. At the same time, it will monitor the vehicle for overload in real time. If necessary, it will inform the vehicle driver via voice that the vehicle is overloaded and that necessary measures need to be taken in a timely manner.

[0096] In this application, when comparing the first load capacity and the second load capacity with the vehicle's rated load capacity, it can be specifically determined whether both the first load capacity and the second load capacity exceed a preset limit of the vehicle's rated load capacity (e.g., 5% of the vehicle's rated load limit). If it is determined that both the first load capacity and the second load capacity exceed the preset limit of the vehicle's rated load capacity, then the vehicle is determined to be in an overloaded state, and an overload monitoring result is generated. This overload monitoring result includes at least the overload occurrence time, current load value, rated load limit, and overload percentage.

[0097] Here, the vehicle can also upload the generated overload monitoring results to the vehicle's monitoring platform in real time.

[0098] Here, the preset limit of the rated load capacity is not limited, and can be set through the vehicle terminal or monitoring platform.

[0099] The intelligent monitoring method for vehicle overload provided in this application collects data on the stress on the vehicle's load-bearing components under load.

[0100] The system generates physical response parameters and operational status parameters during vehicle operation, and then fuses and verifies the validity of these parameters. Based on this valid data, it enables precise monitoring of vehicle overload status. This not only improves the accuracy and reliability of overload monitoring but also reduces the risk of false overload detection. Furthermore, when a vehicle is overloaded, collaborative early warnings can be issued between the vehicle and the monitoring platform, significantly enhancing the effectiveness of overload supervision.

[0101] Figure 2 This is a flowchart illustrating the intelligent monitoring method for vehicle overloading in this application. Figure 2 ,like Figure 2 As shown, the method includes:

[0102] Step 201: Real-time acquisition of physical response parameters of the vehicle's stressed components under load using load sensors;

[0103] Step 202: Obtain the operating status parameters generated during vehicle operation through the bus interface;

[0104] Step 203: Obtain the first load capacity of the vehicle based on the physical response parameters;

[0105] Specifically, the first load mass corresponding to the physical response parameter can be determined based on the pre-stored mapping relationship between load and physical quantity in the sensing unit on the force-bearing component of the vehicle.

[0106] Step 204: Obtain the second load capacity of the vehicle based on the operating status parameters;

[0107] Specifically, the operating state parameters can be filtered to extract the smooth acceleration parameters during the smooth acceleration phase and the smooth deceleration parameters during the smooth deceleration phase. Based on the smooth acceleration parameters, the third load mass of the vehicle during the smooth acceleration phase can be calculated, and based on the smooth deceleration parameters, the fourth load mass of the vehicle during the smooth deceleration phase can be calculated. The third load mass and the fourth load mass can be weighted to obtain the second load mass that represents the vehicle during the smooth driving phase.

[0108] Step 205: Determine whether both the first load capacity and the second load capacity are valid data;

[0109] If one of the data is invalid, repeat steps 201 and 202; if both are valid data, proceed to step 206.

[0110] Step 206: Determine whether the first load capacity and / or the second load capacity exceed the vehicle's rated load capacity;

[0111] If the first load capacity and / or the second load capacity exceed the vehicle's rated load capacity, proceed to step 207; if neither the first load capacity nor the second load capacity exceeds the vehicle's rated load capacity, proceed to steps 201 and 202 to continuously monitor the vehicle for overload.

[0112] Step 207: Determine that the vehicle is overloaded, generate overload monitoring results, and output an overload warning signal.

[0113] Figure 3 This is a schematic diagram of the structural composition of the intelligent monitoring system for vehicle overloading in this application, as shown below. Figure 3 As shown, the system includes:

[0114] Data acquisition unit 301 is used to collect in real time the physical response parameters of the vehicle's stressed components under load and the operating status parameters generated during vehicle operation;

[0115] The data calculation unit 302 is used to obtain the first load capacity of the vehicle based on the physical response parameters and the second load capacity of the vehicle based on the operating state parameters;

[0116] The overload monitoring unit 303 is used to generate an overload monitoring result indicating that the vehicle is in an overload state if the first load and / or the second load exceed the vehicle's rated load, provided that both the first load and the second load are valid data.

[0117] The collaborative early warning unit 304 is used to output an overload early warning signal through the vehicle and the vehicle's monitoring platform.

[0118] Here, the data acquisition unit 301 includes a load cell (accuracy ±3%, with a waterproof and dustproof IP67 enclosure structure), which can be fixedly installed on the bottom of the vehicle's frame longitudinal beam, the surface of the axle, or the stress area of ​​the leaf spring, avoiding welds and weak deformation points. This load cell can be a strain gauge type load cell, which can have a built-in signal amplification module and a 16-bit A / D conversion module. Its sampling frequency can be set to 10Hz, and it can be connected to the data processing unit 302 via an RS485 interface.

[0119] Here, the data acquisition unit 301 may also include a bus transceiver connected to the vehicle's bus interface, which may include, but is not limited to, CAN bus, LIN bus, FlexRay bus, MOST bus and vehicle Ethernet.

[0120] For example, the bus transceiver can obtain the engine driving torque T via the CAN bus. m transmission ratio i c Transmission efficiency η TVelocity v, acceleration a, braking pressure P j Wheel angular velocity ω j The parameters are set, and bidirectional communication is established with the data computing unit 302 through the SPI interface. The communication rate can be 1Mbps, and the data reception delay can be ≤50ms.

[0121] In a preferred embodiment of this application, the data calculation unit 302 can specifically determine the first load mass corresponding to the physical response parameter based on the pre-stored mapping relationship between load and physical quantity in the sensing unit on the force-bearing component of the vehicle.

[0122] In a preferred embodiment of this application, the data calculation unit 302 can specifically filter the operating state parameters to extract the smooth acceleration parameters and smooth deceleration parameters of the vehicle during the smooth acceleration phase; calculate the third load mass of the vehicle during the smooth acceleration phase based on the smooth acceleration parameters, and calculate the fourth load mass of the vehicle during the smooth deceleration phase based on the smooth deceleration parameters; and perform a weighted calculation on the third load mass and the fourth load mass to obtain the second load mass representing the vehicle during the smooth driving phase.

[0123] Here, the data calculation unit 302 can use an industrial-grade embedded processor as the core computing unit. The data calculation unit 302 pre-stores a load estimation model based on the vehicle dynamics model. By obtaining the third load of the vehicle during the smooth acceleration phase and the fourth load of the vehicle during the smooth deceleration phase through different load estimation models, the third load and the fourth load are then weighted and calculated using an adaptive weighting algorithm to obtain the second load corresponding to the vehicle during the smooth driving phase. In this way, the estimation error of the calculated load can be further reduced.

[0124] Here, you can set the rate of change of acceleration. To determine the smooth deceleration criteria, a 10-second smooth deceleration segment was extracted. A moving window method (window size 50ms, step size 20ms) was used to filter the parameters of this segment, eliminating segments with acceleration fluctuations exceeding ±0.5m / s². 2 The data was used to obtain the smooth deceleration window data.

[0125] Here, you can also set the rate of change of acceleration. To determine the acceleration and deceleration transition phase, a moving window method (window size 50ms, step size 20ms) was used to filter the parameters of the segment, removing data that frequently switched between acceleration and deceleration (data with alternating positive and negative acceleration within the window), thus obtaining the acceleration and deceleration smooth transition window data.

[0126] In a preferred embodiment of this application, the system further includes:

[0127] The data cross-calibration unit 305 is used to calculate the load error between the first load and the second load; if the load error remains within the confidence range for a preset time, it is determined that both the first load and the second load are valid data.

[0128] In one implementation of this application, the data cross-checking unit 305 can compare the load mass error with a set error within the preset time period. If the comparison result indicates that the load mass error is less than the set error, it is determined that the first load mass and the second load mass are continuously within the reliable range within the preset time period, thereby determining that both the first load mass and the second load mass are valid data.

[0129] In another implementation of this application, the data cross-calibration unit 305 can calculate the average value and standard deviation of the load mass error in real time within the preset time period; when the average value is less than a first threshold and the standard deviation is less than a second threshold, it indicates that the first load mass and the second load mass are continuously within the reliable range within the preset time period, and it is determined that both the first load mass and the second load mass are valid data.

[0130] In a preferred embodiment of this application, the data acquisition unit 301 may also re-acquire the physical response parameters generated by the vehicle's load-bearing components under load and the operating state parameters generated during vehicle operation if the first load capacity and / or the second load capacity are determined to be invalid data, so that the data calculation unit 302 can re-obtain the first load capacity and the second load capacity of the vehicle until the first load capacity and / or the second load capacity are determined to be valid data.

[0131] In this application, the collaborative early warning unit 304 can have a built-in 128KB Flash to store the approved load limit and calibration parameters; the approved load limit can be initially set to 18 tons, and it supports receiving update instructions issued by the regulatory platform through the 4G / 5G network, with an update response time of ≤1 second.

[0132] It should be noted that the intelligent vehicle overload monitoring system provided in the above embodiments is different from the one described above. Figure 1 and Figure 2 The intelligent monitoring method for vehicle overloading provided is based on the same concept. The specific implementation process can be found in the above method implementation examples, and will not be repeated here.

[0133] The intelligent vehicle overload monitoring system provided in this application constructs a dual monitoring system of "on-board weighing sensor + CAN bus dynamic data fusion." Through a data cross-calibration mechanism, the two sets of load data are mutually verified, effectively offsetting the errors of a single monitoring method. A targeted calculation model is adopted based on different vehicle operating states (acceleration, deceleration, transition phases), and an adaptive weighted algorithm is used to optimize weight parameters, further reducing the impact of environmental factors on estimation accuracy and ensuring the precision of overload monitoring. When the load error between the first and second loads exceeds a set error, the physical response parameters of the vehicle's load-bearing components under load and the operating state parameters generated during vehicle operation are re-collected until the first and / or second loads are valid data, ensuring the reliability of the monitoring data and improving the accuracy of overload monitoring, reducing the risk of false overload judgments. Furthermore, when a vehicle is overloaded, collaborative early warnings can be issued through the vehicle and its monitoring platform, constructing a collaborative closed loop of "instant driver reminder - real-time monitoring by the monitoring end." This avoids the monitoring lag problem caused by traditional early warnings only targeting the driver, improving the response efficiency and control effect of overload management.

[0134] Figure 4 This is a schematic diagram illustrating the structural composition of the vehicle in this application, which includes, but is not limited to, trucks and buses. Figure 4 As shown, the vehicle 400 includes at least one processor 401 and a memory 402 for storing a computer program that can run on the processor 401. When the processor 401 runs the computer program, it executes the intelligent vehicle overload monitoring method described in the above embodiments of this application. The vehicle 400 also includes at least one network interface 404 and a user interface 403. Various components in the vehicle 400 are coupled together via a bus system 405. It is understood that the bus system 405 is used to implement communication between these components. In addition to a data bus, the bus system 405 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 4 The general designated all buses as Bus System 405.

[0135] The user interface 403 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0136] It is understood that memory 402 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 402 described in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0137] In this embodiment, the memory 402 is used to store various types of data to support the operation of the vehicle 400. Examples of this data include any computer programs used to operate on the vehicle 400, such as the operating system 4021 and application programs 4022. The operating system 4021 includes various system programs, such as a framework layer, a core library layer, and a driver layer, used to implement various basic services and handle hardware-based tasks. The application program 4022 may include various applications, such as a media player and a browser, used to implement various application services. Programs implementing the methods of this embodiment may be included in the application program 4022.

[0138] Processor 401 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 401 or by instructions in software form. Processor 401 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 401 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, specifically memory 402. Processor 401 reads information from memory 402 and, in conjunction with its hardware, completes the steps of the aforementioned method.

[0139] In an exemplary embodiment, the vehicle 400 may be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to perform the aforementioned method.

[0140] In an exemplary embodiment, this application also provides a computer-readable storage medium, such as a memory 402 including a computer program, which can be executed by a processor 401 of a vehicle 400 to complete the steps described in the aforementioned method. The computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM; it may also be various devices including one or any combination of the above-mentioned memories, such as AR devices, CR devices, VR devices, MR devices, etc.

[0141] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the intelligent monitoring method for vehicle overloading as described in the above embodiments of this application.

[0142] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. Furthermore, the features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0143] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for intelligent monitoring of vehicle overload, characterized in that, The method comprises: Real-time acquisition of physical response parameters generated by vehicle force components under the action of load and running state parameters generated during vehicle operation; Based on the physical response parameters, the first load mass of the vehicle is obtained, and based on the running state parameters, the second load mass of the vehicle is obtained; If the first load mass and / or the second load mass exceeds the rated load mass of the vehicle, an overload monitoring result indicating that the vehicle is in an overload state is generated, and an overload warning signal is output through the vehicle and the supervision platform of the vehicle.

2. The method of claim 1, wherein, The first load mass of the vehicle based on the physical response parameters comprises: Based on the mapping relationship between the load and the physical quantity pre-stored in the sensing unit on the vehicle force component, the first load mass corresponding to the physical response parameter is determined.

3. The method of claim 1, wherein, The second load mass of the vehicle based on the running state parameters comprises: The running state parameters are screened, and the smooth acceleration parameters of the vehicle in the smooth acceleration stage and the smooth deceleration parameters of the vehicle in the smooth deceleration stage are extracted; Based on the smooth acceleration parameters, the third load mass of the vehicle in the smooth acceleration stage is calculated, and based on the smooth deceleration parameters, the fourth load mass of the vehicle in the smooth deceleration stage is calculated; The third load mass and the fourth load mass are weighted and calculated to obtain the second load mass corresponding to the vehicle in the smooth driving stage.

4. The method of claim 1, wherein, The determination that the first load mass and the second load mass are both valid data comprises: Calculate the load mass error between the first load mass and the second load mass; If the load mass error continuously stays in the confidence interval within a preset time, it is determined that the first load mass and the second load mass are both valid data.

5. The method of claim 4, wherein, If the load mass error continuously stays in the confidence interval within a preset time, it is determined that the first load mass and the second load mass are both valid data, comprising: Within the preset time, the average value and the standard deviation of the load mass error are calculated in real time; When the average value is less than a first threshold value, and the standard deviation is less than a second threshold value, it is indicated that the first load mass and the second load mass continuously stay in the confidence interval within the preset time, and it is determined that the first load mass and the second load mass are both valid data.

6. The method of claim 1, wherein, The method further comprises: In the case that the first load mass and / or the second load mass is invalid data, the physical response parameters generated by the vehicle force components under the action of load and the running state parameters generated during vehicle operation are re-acquired to re-obtain the first load mass and the second load mass of the vehicle until the first load mass and / or the second load mass is determined to be valid data.

7. An intelligent monitoring system for vehicle overloading, characterized by, The system comprises: A data acquisition unit for real-time acquisition of physical response parameters generated by vehicle force components under the action of load and running state parameters generated during vehicle operation; A data calculation unit for obtaining the first load mass of the vehicle based on the physical response parameters, and obtaining the second load mass of the vehicle based on the running state parameters; The overload monitoring unit is configured to, in a case where the first load mass and the second load mass are both valid data, generate an overload monitoring result indicating that the vehicle is in an overload state if the first load mass and / or the second load mass exceeds a rated load mass of the vehicle. The cooperative early warning unit is configured to output an overload early warning signal through the vehicle and a supervision platform of the vehicle.

8. The system of claim 7, wherein, The system further comprises: The data mutual checking unit is configured to calculate a load mass error between the first load mass and the second load mass, and determine that the first load mass and the second load mass are both valid data if the load mass error continuously stays in a credible interval within a preset time.

9. The system of claim 7, wherein, The data calculation unit is specifically configured to filter the running state parameters, extract a smooth acceleration parameter of the vehicle in a smooth acceleration stage and a smooth deceleration parameter of the vehicle in a smooth deceleration stage, calculate a third load mass of the vehicle in the smooth acceleration stage based on the smooth acceleration parameter, calculate a fourth load mass of the vehicle in the smooth deceleration stage based on the smooth deceleration parameter, and perform weighted calculation on the third load mass and the fourth load mass to obtain the second load mass corresponding to the vehicle in a smooth driving stage.

10. A vehicle characterized by comprising: The vehicle comprises: A memory configured to store a computer program capable of running on the processor. The processor is configured to execute the intelligent monitoring method for vehicle overload according to any one of claims 1 to 6 when the computer program is running.

Images