Vehicle early warning monitoring method and device, electronic equipment and storage medium

Abstract

The invention discloses a vehicle early warning monitoring method and device, electronic equipment and a storage medium. The method comprises the steps that the load condition and driving environment parameters of a vehicle in the driving process are obtained; determining a target working condition monitoring strategy according to the load condition and the driving environment parameters; and monitoring the running stability condition of the vehicle according to the target working condition monitoring strategy. According to the technical scheme, by obtaining the load condition and the driving environment parameters of the vehicle in the driving process, the driving state of the vehicle can be monitored in real time, the working condition monitoring strategy of the vehicle is comprehensively determined according to the load condition and the driving environment parameters, the dynamic monitoring capacity of the vehicle for various road environments can be improved, and the driving safety is improved. The vehicle driving stability can be monitored in real time in different environments, the monitoring accuracy is remarkably improved, and the vehicle driving safety is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and in particular to a vehicle early warning monitoring method, device, electronic equipment, and storage medium. Background Technology

[0002] Currently, due to factors such as road conditions, driving routes, and driving speeds, articulated vehicles have a certain probability of deviating from their expected trajectory during operation, or even experiencing situations such as the trailer unit fishtailing, detaching, or overturning. These situations are difficult to correct through operator control, thus becoming a key factor affecting the driving safety of articulated vehicles.

[0003] The aforementioned early warning methods for instability mainly rely on accelerometers and steering sensors to collect data on the vehicle's lateral acceleration and yaw rate. This data is then compared with preset thresholds to determine vehicle stability. However, this monitoring method has significant drawbacks: when the system detects instability, the vehicle is already in a critical state, making it difficult for the operator to react, and it cannot cope with complex and changing road environments. Therefore, how to accurately and environmentally adaptable monitor the instability of articulated vehicles in real time has become a key technical problem urgently needing to be solved in this field. Summary of the Invention

[0004] This invention provides a vehicle early warning monitoring method, device, electronic device, and storage medium to solve the problem that vehicle early warning monitoring cannot adapt to changes in the environment.

[0005] According to one aspect of the present invention, a vehicle early warning monitoring method is provided, the method comprising:

[0006] Obtain the vehicle's load and driving environment parameters during operation;

[0007] Determine the target operating condition monitoring strategy based on the load conditions and the driving environment parameters;

[0008] The vehicle's driving stability is monitored according to the target operating condition monitoring strategy.

[0009] According to another aspect of the present invention, a vehicle early warning monitoring device is provided, the device comprising:

[0010] The parameter acquisition module is used to acquire the vehicle's load and driving environment parameters during the driving process;

[0011] The pattern matching module is used to determine the target working condition monitoring strategy based on the load conditions and the driving environment parameters;

[0012] The vehicle early warning module is used to monitor the driving stability of the vehicle according to the target operating condition monitoring strategy.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the vehicle early warning monitoring method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute the vehicle early warning monitoring method according to any embodiment of the present invention.

[0018] The technical solution of this invention acquires the vehicle's load and driving environment parameters during operation; then determines a target operating condition monitoring strategy based on the load and driving environment parameters; and finally monitors the vehicle's driving stability according to the target operating condition monitoring strategy. This technical solution, by acquiring the vehicle's load and driving environment parameters during operation, can monitor the vehicle's driving status in real time. By comprehensively determining the vehicle's operating condition monitoring strategy based on the load and driving environment parameters, it can improve the vehicle's dynamic monitoring capability in diverse road environments, enabling real-time monitoring of vehicle driving stability under different conditions, thus achieving the beneficial effects of improving monitoring accuracy and ensuring vehicle driving safety.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a vehicle early warning monitoring method provided in Embodiment 1 of the present invention;

[0022] Figure 2This is a flowchart of another vehicle early warning monitoring method provided in Embodiment 2 of the present invention;

[0023] Figure 3 This is a flowchart of another vehicle early warning monitoring method provided in Embodiment 3 of the present invention;

[0024] Figure 4 This is a working condition monitoring strategy diagram corresponding to different working conditions provided in Embodiment 3 of the present invention;

[0025] Figure 5 This is a flowchart of a threshold adjustment method for a working condition monitoring strategy according to Embodiment 3 of the present invention;

[0026] Figure 6 This is a schematic diagram of a vehicle early warning monitoring device according to Embodiment 4 of the present invention;

[0027] Figure 7 This is a schematic diagram of the structure of an electronic device that implements the vehicle early warning and monitoring method of Embodiment 5 of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1This is a flowchart of a vehicle early warning monitoring method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations requiring vehicle early warning monitoring adaptability to different operating conditions. The method can be executed by a vehicle early warning monitoring device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0032] S110. Obtain the vehicle's load and driving environment parameters during the driving process.

[0033] Here, load conditions refer to the weight of the cargo carried by the vehicle. Driving environment parameters refer to the physical characteristics of the road surface and surrounding environment in which the vehicle is located during driving.

[0034] Specifically, during the driving process, the vehicle's load status and driving environment parameters can be obtained in real time through the early warning and monitoring system deployed inside the vehicle.

[0035] For example, the vehicle warning and monitoring system acquires the weight information of the cargo carried by the vehicle through pressure sensors deployed on the vehicle; acquires the angular velocities and accelerations of yaw, roll, and pitch during vehicle movement through a combination of sensors deployed around the vehicle body; and acquires the vehicle position and the relative offset between the vehicle's rear end and road markings through a dynamic tracking unit deployed on the vehicle. The cargo weight information during vehicle movement is used as the load information, and the set of angular velocities and accelerations of yaw, roll, and pitch, vehicle position, and the relative offset between the vehicle's rear end and road markings is used as the driving environment parameters.

[0036] S120. Determine the target operating condition monitoring strategy based on load conditions and driving environment parameters.

[0037] Among them, the target operating condition monitoring strategy refers to the strategy pre-designed for the current operating condition of the vehicle to guide the vehicle early warning monitoring system to make stability judgments on the vehicle.

[0038] Specifically, the acquired vehicle load and driving environment parameters can be compared with the standard parameter ranges corresponding to different operating condition monitoring strategies to determine the target operating condition monitoring strategy matching the current load and driving environment parameters. For example, preset operating condition monitoring strategies are associated with different standard operating condition types. The preprocessed vehicle load and driving environment parameters can be compared with the parameter ranges corresponding to the pre-set standard operating condition types to determine the current target operating condition type of the vehicle. Then, the target operating condition monitoring strategy corresponding to the target operating condition type can be searched and matched from the pre-set operating condition monitoring strategy dataset.

[0039] S130. Monitor the vehicle's driving stability according to the target operating condition monitoring strategy.

[0040] Among them, driving stability refers to the situation where the vehicle maintains the expected state of motion and trajectory during driving.

[0041] Specifically, the current vehicle load and driving environment parameters are compared with the various monitoring thresholds in the target operating condition monitoring strategy. If the current vehicle load and driving environment parameters do not exceed the various monitoring thresholds in the target operating condition monitoring strategy, the vehicle will operate normally. If any parameter of the current vehicle load or driving environment parameters exceeds the various monitoring thresholds in the target operating condition monitoring strategy, the vehicle will issue a warning signal.

[0042] The technical solution of this invention acquires the vehicle's load and driving environment parameters during operation; then determines a target operating condition monitoring strategy based on the load and driving environment parameters; and finally monitors the vehicle's driving stability according to the target operating condition monitoring strategy. This technical solution, by acquiring the vehicle's load and driving environment parameters during operation, can monitor the vehicle's driving status in real time. By comprehensively determining the vehicle's operating condition monitoring strategy based on the load and driving environment parameters, it can improve the vehicle's dynamic monitoring capability in diverse road environments, enabling real-time monitoring of vehicle driving stability under different conditions, significantly improving monitoring accuracy and ensuring vehicle driving safety.

[0043] Example 2

[0044] Figure 2 This is a flowchart of a vehicle early warning monitoring method provided in Embodiment 2 of the present invention. This embodiment further refines the above embodiment:

[0045] like Figure 2 As shown, the method includes:

[0046] S210. The central decision processor collects the first data collected by the vehicle's tractor perception unit and the second data collected by the vehicle's trailer dynamic tracking unit.

[0047] The central decision processor (CCPU) refers to the control unit deployed on the vehicle, used to receive, process data, and perform decision-making operations. The tractor unit (TUN) is a collection of sensors deployed on the tractor section of the vehicle to sense its own motion status. The TUN sensing unit may include forward / surround-view cameras, short / medium-range millimeter-wave radar, inertial measurement units, wheel speed sensors, and trailer angle sensors. The first data refers to the data collected by the TUN sensing unit that reflects the tractor's motion status. The trailer dynamic tracking unit is a collection of sensors deployed on the trailer section of the vehicle to sense the trailer's own motion status, real-time location, and cargo status. The trailer dynamic tracking unit may include articulation angle sensors, trailer wheel speed sensors, trailer inertial measurement units, and trailer integrated navigation and positioning units. The second data refers to the data collected by the trailer dynamic tracking unit that reflects the trailer's motion status, real-time location, and cargo status.

[0048] Specifically, the central decision processor deployed on the vehicle collects first data from the tractor's perception unit and second data from the trailer's dynamic tracking unit through the vehicle's internal communication network. The vehicle's internal communication network may include a controller area network, a local interconnect network, or an in-vehicle Ethernet network. The first data may include steering wheel angle, torque signal, and the vehicle's actual steering intention. The second data may include vehicle yaw rate, yaw acceleration, roll rate, roll acceleration, pitch rate, pitch acceleration, cargo mass distribution, vehicle position, and the relative offset between the vehicle's rear end and road markings.

[0049] S220. Obtain initial database parameters based on the central decision processor, and determine the vehicle's load and driving environment parameters according to the initial database parameters, the first data, and the second data.

[0050] The initial database parameters refer to the set of vehicle physical parameters and vehicle historical driving parameters that are pre-stored in the vehicle's internal information storage unit. Vehicle physical parameters may include vehicle weight, tire characteristic parameters, body specifications, wheelbase, etc., while vehicle historical driving parameters may include steering wheel angle, torque signal, vehicle yaw rate, yaw acceleration, roll rate, roll acceleration, pitch rate, pitch acceleration, cargo mass distribution, vehicle position, etc.

[0051] Specifically, the central decision processor deployed on the vehicle retrieves the initial database parameters from the vehicle's internal information storage unit, and then fuses the initial database parameters with the first data collected by the vehicle's tractor perception unit and the second data collected by the vehicle's trailer dynamic tracking unit to obtain the vehicle's load status and driving environment parameters.

[0052] For example, the central decision processor calculates the vehicle's load status by combining the vehicle's own weight, body specification data, historical cargo weight distribution data, etc., with the cargo weight distribution data in the second data; and calculates the vehicle's driving environment parameters by combining the tire characteristic parameters, wheelbase, historical vehicle position, etc., with the steering wheel angle and torque signal in the first data.

[0053] S230: Perform timestamp alignment and spatial coordinate calibration on load conditions and driving environment parameters.

[0054] Timestamp alignment refers to the process of processing data collected at different times using a unified time reference. Spatial coordinate calibration refers to the process of transforming data collected from sensors at different locations on the vehicle using a unified standard coordinate system.

[0055] Specifically, after acquiring the initial database parameters, vehicle load information, and driving environment parameters, the central decision processor deployed in the vehicle adds a high-precision timestamp to all of the above data and synchronizes all of the above data to a unified time series based on this timestamp. The method for synchronizing all of the above data to a unified time series based on this timestamp may include interpolation techniques, resampling techniques, etc. After acquiring the initial database parameters, vehicle load information, and driving environment parameters, the central decision processor deployed in the vehicle converts all of the above data to a pre-calibrated standard coordinate system, which facilitates the unified processing of parameters by the vehicle early warning and monitoring system.

[0056] S240. Compare the load conditions and driving environment parameters with the parameters corresponding to the preset standard working condition type to determine the target working condition type.

[0057] The standard operating condition type refers to a pre-defined typical vehicle operating state, and each vehicle operating state has a specific parameter range. The target operating condition type refers to the standard operating condition type that matches the current actual operating state of the vehicle. The target operating condition type includes at least one of the following: unloaded, center of gravity centered, cargo offset, liquid transportation, ordinary road surface, low-adhesion road surface, straight driving, and cornering driving.

[0058] Specifically, the central decision processor deployed in the vehicle uses the vehicle's load and driving environment parameters, which have been timestamped and calibrated with spatial coordinates, as input feature vectors. It then matches these parameters with the range of feature parameters for each pre-stored standard operating condition type and selects the standard operating condition type with the highest matching degree as the current target operating condition type. The matching process can be achieved through deep neural networks, pattern recognition algorithms, and other methods.

[0059] For example, a deep neural network model is deployed in the vehicle early warning and monitoring system. This model adopts an encoder-decoder network architecture, where the encoding part contains 3×3 convolutional layers, 5×5 convolutional layers, and pooling layers; the decoding part contains upsampling layers and fully connected layers. The central decision processor deployed in the vehicle pre-collects a large amount of historical vehicle driving data covering various standard operating condition labels as a training set. This dataset is input into the deep neural network model deployed in the vehicle early warning and monitoring system for training. The trained deep neural network model can receive the input load conditions and driving environment parameter feature vectors in real time, and directly output the standard operating condition type that matches the input load conditions and driving environment parameter feature vectors with the highest degree of matching as the current target operating condition type.

[0060] S250. Search for the target operating condition monitoring strategy that matches the target operating condition type within the preset monitoring strategy set.

[0061] The preset monitoring strategy set refers to the set of strategies predefined and stored in the central decision processor or vehicle storage unit, which guide the vehicle early warning monitoring system to make stability judgments about the vehicle. The target operating condition monitoring strategy refers to the strategy pre-designed for the current operating condition of the vehicle, which guides the vehicle early warning monitoring system to make stability judgments about the vehicle.

[0062] Specifically, after determining the target operating condition type corresponding to the current vehicle, the central decision processor deployed in the vehicle searches for a target operating condition monitoring strategy that matches the target operating condition type in the preset monitoring strategy set. The search process can be completed through direct index matching, priority matching, or other methods.

[0063] S260. Monitor the vehicle's driving stability according to the target operating condition monitoring strategy.

[0064] Among them, driving stability refers to the situation where the vehicle maintains the expected state of motion and trajectory during driving.

[0065] Specifically, based on the threshold matching rules in the target operating condition monitoring strategy, the current vehicle load and driving environment parameters are compared with the various monitoring thresholds in the threshold matching rules of the target operating condition monitoring strategy. If neither the current vehicle load nor the driving environment parameters exceed the various monitoring thresholds in the threshold matching rules of the target operating condition monitoring strategy, the vehicle operates normally. If any parameter in the current vehicle load or driving environment parameters exceeds the various monitoring thresholds in the threshold matching rules of the target operating condition monitoring strategy, the vehicle issues a warning signal.

[0066] The technical solution of this invention involves collecting first data from the vehicle's tractor perception unit and second data from the vehicle's trailer dynamic tracking unit via a central decision processor. This data is then used to obtain initial database parameters based on the central decision processor. The vehicle's load condition and driving environment parameters are determined according to the initial database parameters, the first data, and the second data. The load condition and driving environment parameters are then time-stamp aligned and spatially calibrated. Finally, the load condition and driving environment parameters are compared with parameters corresponding to a preset standard operating condition type to determine the target operating condition type. A target operating condition monitoring strategy matching the target operating condition type is then searched within a preset monitoring strategy set. Finally, the vehicle's driving stability is monitored according to the target operating condition monitoring strategy. The above technical solution, by acquiring initial database parameters based on a central decision processor and determining the vehicle's load and driving environment parameters according to the initial database parameters, first data, and second data, can monitor the vehicle's driving status in real time. By comparing the load and driving environment parameters with the parameters corresponding to preset standard operating conditions, the target operating condition type is determined, and then a target operating condition monitoring strategy matching the target operating condition type is searched within a preset monitoring strategy set, which can improve the vehicle's dynamic monitoring capability for diverse road environments. By monitoring the vehicle's driving stability according to the target operating condition monitoring strategy, real-time monitoring of vehicle driving stability under different environments can be achieved, significantly improving monitoring accuracy and ensuring vehicle driving safety.

[0067] Furthermore, based on the above embodiments of the invention, the data collected by the central decision processor from the first data collected by the vehicle's tractor perception unit and the second data collected by the vehicle's trailer dynamic tracking unit includes at least one of the following:

[0068] The vehicle's steering wheel angle and torque signals are acquired, and the actual steering intention of the vehicle is determined based on the steering wheel angle and torque signals as the first data.

[0069] The second data is obtained from the combined sensors deployed on the longitudinal beams of the vehicle frame, which collect the angular velocities and accelerations of the vehicle's yaw, roll, and pitch.

[0070] A pressure sensor array is deployed in the air spring chamber of the vehicle to obtain the vehicle's cargo weight distribution as a second data point.

[0071] The vehicle position, the relative offset of the vehicle's rear end and the road markings are obtained as secondary data through the trailer dynamic tracking unit.

[0072] Among these, yaw, roll, and pitch angular velocities and accelerations refer to the velocity parameters of a vehicle's rotational and linear motion in three-dimensional space. Cargo mass distribution refers to the weight of the cargo carried by the vehicle and its distribution along the longitudinal and lateral directions of the vehicle body. Relative offset refers to the deviation between the vehicle's actual position and its expected position on the road during operation.

[0073] Specifically, the central decision processor obtains the vehicle's steering wheel angle and torque signals from the vehicle's tractor perception unit, and uses a built-in algorithm to parse the vehicle's actual steering intention as the first data. The central decision processor obtains the vehicle's yaw, roll, and pitch angular velocities and accelerations from the combined sensors deployed on the vehicle's longitudinal beams as the second data. The central decision processor obtains the vehicle's cargo weight distribution from the pressure sensor array deployed in the vehicle's air spring chambers as the second data. The central decision processor obtains the vehicle's position, the relative offset of the vehicle's rear end, and road markings from the vehicle's trailer dynamic tracking unit as the second data. The combined sensors deployed on the vehicle's longitudinal beams may include angular velocity sensors, acceleration sensors, displacement sensors, etc.; the trailer dynamic tracking unit may include a global satellite navigation system, a lidar array, etc.

[0074] Furthermore, based on the above embodiments of the invention, the target operating condition monitoring strategy includes at least one of the following:

[0075] In no-load mode, the first threshold is used as the monitoring threshold;

[0076] Center-of-gravity mode uses the second threshold as the monitoring threshold;

[0077] In the cargo offset mode, the second threshold is increased and used as the monitoring threshold.

[0078] In liquid transport mode, the second threshold is reduced and used as the monitoring threshold.

[0079] In the normal road surface mode, the third threshold is used as the monitoring threshold;

[0080] For low-adhesion road surface mode, the third threshold is increased and used as the monitoring threshold.

[0081] In straight-line driving mode, the fourth threshold is used as the monitoring threshold;

[0082] In the cornering driving mode, the fourth threshold is reduced and used as the monitoring threshold.

[0083] The four modes are as follows: Empty mode (no cargo), Centered center of gravity mode (the cargo weight is evenly distributed longitudinally and laterally, and the vehicle's center of gravity is approximately at its geometric center), Cargo offset mode (the cargo weight is concentrated on one side of the vehicle, and the vehicle's center of gravity is off-center), Liquid transport mode (the cargo is liquid), Normal road mode (driving on dry, smooth asphalt or concrete surfaces with a normal road adhesion coefficient), Low adhesion road mode (driving on wet, slippery, or icy surfaces with a road adhesion coefficient below normal), Straight-line driving mode (driving in a straight line), and Curved driving mode (driving along a curve). The first threshold is a pre-set indicator value for vehicle stability assessment in empty mode. The second threshold is a pre-set indicator value for vehicle stability assessment in cargo-related modes, including centered center of gravity mode, cargo offset mode, and liquid transport mode. The third threshold refers to the pre-set index value for judging vehicle driving stability in road-related modes, which include normal road mode and low-adhesion road mode. The fourth threshold refers to the pre-set index value for judging vehicle driving stability in trajectory-related modes, which include straight-line driving mode and curve driving mode. Monitoring thresholds are specific numerical standards used in different operating conditions to compare with real-time collected vehicle load and driving environment parameters to determine whether the vehicle is in a stable state.

[0084] Specifically, the central decision processor retrieves the corresponding first, second, third, or fourth threshold from the target operating condition monitoring strategy as the base threshold, and then adjusts the base threshold according to the determined target operating condition type of the current vehicle. In cargo offset mode, the second threshold is increased by a preset ratio and used as the monitoring threshold; in liquid transport mode, the second threshold is decreased by a preset ratio and used as the monitoring threshold; in low-adhesion road surface mode, the third threshold is increased by a preset ratio and used as the monitoring threshold; and in curve driving mode, the fourth threshold is decreased by a preset ratio and used as the monitoring threshold. The preset ratio can be adjusted according to the vehicle's load, driving environment parameters, etc.

[0085] Furthermore, based on the above embodiments of the invention, the vehicle's driving stability is monitored according to the target operating condition monitoring strategy, including:

[0086] Obtain the threshold matching rules corresponding to the target operating condition monitoring strategy;

[0087] The vehicle operates normally when the load and driving environment parameters do not exceed the monitoring threshold of the corresponding threshold matching rule;

[0088] When the load and driving environment parameters exceed the monitoring threshold of the corresponding threshold matching rule, the vehicle issues a warning signal.

[0089] Among them, threshold matching rules refer to the different threshold determination rules corresponding to different target working condition monitoring strategies.

[0090] Specifically, the central decision processor acquires and compares the current vehicle load and driving environment parameters with the various monitoring thresholds of the threshold matching rules in the target operating condition monitoring strategy, based on the threshold matching rules corresponding to the target operating condition monitoring strategy. If neither the current vehicle load nor the driving environment parameters exceed the various monitoring thresholds of the threshold matching rules in the target operating condition monitoring strategy, the vehicle operates normally. If any parameter of the current vehicle load or driving environment parameters exceeds the various monitoring thresholds of the threshold matching rules in the target operating condition monitoring strategy, the vehicle issues a warning signal.

[0091] Furthermore, based on the above embodiments, the invention also includes:

[0092] The monitoring threshold of the target operating condition monitoring strategy is dynamically updated based on the false alarm rate and the number of missed alarms for vehicle warnings.

[0093] The false alarm rate refers to the proportion of warning signals issued by the vehicle warning and monitoring system within a set period, where the vehicle did not actually experience instability. The missed alarm rate refers to the number of times the vehicle warning and monitoring system should have issued a warning signal within the set period, but the vehicle did not actually issue a warning signal.

[0094] Specifically, the vehicle early warning monitoring system retrieves the corresponding monitoring thresholds from the target operating condition monitoring strategy and monitors vehicle driving stability in real time based on these thresholds. During vehicle operation, the system calculates the false alarm rate and the number of missed alarms. If these figures exceed a set range, the system dynamically updates the monitoring thresholds of the target operating condition monitoring strategy.

[0095] For example, the vehicle early warning monitoring system retrieves the corresponding monitoring threshold from the target operating condition monitoring strategy and monitors the vehicle's driving stability in real time based on the monitoring threshold retrieved from the target operating condition monitoring strategy. During vehicle operation, the system statistically analyzes and determines whether the false alarm rate within a set period is greater than 10%. If so, it automatically raises the judgment threshold for that operating condition to reduce the system's sensitivity. If not, it further determines whether the number of missed alarms within the same set period is greater than or equal to two. If so, it automatically lowers the judgment threshold for that operating condition to enhance the system's risk identification capability. If not, it maintains the threshold parameters corresponding to the current driving condition. The set values ​​for the false alarm rate and the number of missed alarms can be adjusted according to the vehicle's load, driving environment parameters, etc.

[0096] Example 3

[0097] Figure 3 This is a flowchart of a vehicle early warning monitoring method provided in Embodiment 3 of the present invention.

[0098] For details, see Figure 3 The flowchart of a vehicle early warning monitoring method described in this embodiment shows the following steps for implementing vehicle early warning monitoring actions based on the vehicle early warning monitoring system:

[0099] S1. Input initial database parameters.

[0100] First, a pre-set multi-condition monitoring strategy database stored in the central decision processor is loaded into the vehicle early warning and monitoring system. This database contains monitoring strategies corresponding to different condition types. The condition types include at least one of the following: unloaded, center of gravity centered, cargo offset, liquid transport, ordinary road surface, low-adhesion road surface, straight driving, and curve driving; the condition monitoring strategies include at least one of the following: unloaded mode, center of gravity centered mode, cargo offset mode, liquid transport mode, ordinary road surface mode, low-adhesion road surface mode, straight driving mode, and curve driving mode.

[0101] S2. Real-time acquisition of power parameters of the traction unit and trailer unit.

[0102] The tractor's sensing unit captures the vehicle's yaw, roll, and pitch angular velocities and acceleration in real time through a combination of sensors deployed on the longitudinal beams of the chassis; it obtains steering wheel angle and torque signals through the CAN bus and calculates the actual steering intention by combining wheel speed pulses; and it samples the cargo mass distribution at a frequency of 0.5Hz through a pressure sensor group integrated in the air spring chamber.

[0103] The trailer dynamic tracking unit achieves centimeter-level heading positioning through carrier phase differential technology; it scans the relative offset between the trailer's rear end and road markings using 77GHz millimeter-wave radar; and it uses anti-interference frequency hopping technology to transmit the data back to the tractor control center in real time.

[0104] S3: Process real-time data and adopt different strategies for different operating conditions.

[0105] After receiving the power parameters collected in real time by the traction unit and trailer unit, the central decision processor performs timestamp alignment and spatial coordinate calibration on the inertial data of the tractor and the trajectory data of the trailer. It then compares the power parameters with the parameters corresponding to the preset standard working condition type to determine the target working condition type. Finally, it searches for the target working condition monitoring strategy that matches the target working condition type in the preset multi-working condition monitoring strategy database.

[0106] S4. Monitor driving conditions and issue an alarm if the data exceeds the corresponding threshold.

[0107] The vehicle warning and monitoring system monitors the power parameters corresponding to the current operating condition in real time and compares them with the standard monitoring strategy threshold corresponding to the current operating condition. If the power parameter value corresponding to the current operating condition exceeds the standard monitoring strategy threshold, the vehicle warning and monitoring system determines that the vehicle is at risk of driving instability and issues an alarm signal; if the power parameter value corresponding to the current operating condition does not exceed the standard monitoring strategy threshold, the vehicle continues to drive normally.

[0108] S5. Adjust the database based on misjudgments.

[0109] The vehicle warning and monitoring system adopts self-learning threshold optimization based on the different operating habits of drivers and the actual condition of vehicles. The system automatically generates a working condition performance report every 100 hours of operation and dynamically adjusts the threshold parameters: when the false alarm rate is >10%, the system increases the threshold for that working condition by 5%; when the number of missed alarms is ≥2, the system decreases the threshold by 3% and strengthens sensor verification.

[0110] Figure 4 This is a working condition monitoring strategy diagram corresponding to different working conditions provided in Embodiment 3 of the present invention.

[0111] For details, see Figure 4 The working condition monitoring strategy described in this embodiment, corresponding to different working conditions, mainly consists of three parts: load condition, road surface condition, and driving condition. The specific details are as follows:

[0112] The load conditions are divided into two categories: unloaded and loaded. The loaded condition is further subdivided into three scenarios: centered center of gravity, cargo offset, and liquid transport. The monitoring strategy for the unloaded condition is a lightweight sensitive mode; the monitoring strategy for the centered center of gravity condition is the standard monitoring mode within the high inertial stability mode; the monitoring strategy for the cargo offset condition is the enhanced offset monitoring mode within the high inertial stability mode; and the monitoring strategy for the liquid transport condition is the reduced lateral change monitoring sensitivity mode within the high inertial stability mode. Specifically, when the load sensing array detects that the trailer is unloaded, the system automatically activates the lightweight sensitive mode, lowers the trigger threshold for the yaw angle change rate, and enables radar lateral displacement weighted analysis. Conversely, in the fully loaded condition, it switches to the high inertial stability mode, focusing on monitoring the articulation angle change rate and introducing a 1.5-second trend prediction window to avoid false triggers.

[0113] Road surface conditions are categorized into two types: ordinary road surface and low-adhesion road surface. The monitoring strategy for ordinary road surface conditions uses a standard database deviation threshold mode, while the strategy for low-adhesion road surface conditions uses an increased deviation threshold mode. Specifically, the system identifies low-adhesion road surfaces using braking dynamic data, raising the trend deviation threshold by 30% to prevent false alarms caused by minor disturbances on icy / wet road surfaces. Frequency domain characteristics are enhanced, focusing on the low-frequency oscillation component (0.8-1.2Hz) of the instability main frequency of wet / slippery road surfaces.

[0114] Driving conditions are divided into two types: straight driving and cornering. For straight driving, the monitoring strategy is an optimized wind direction monitoring mode, while for cornering, it's a corner radius compensation mode. Specifically, when optimizing straight road conditions, the system activates a crosswind feature recognition engine and monitors for characteristics such as no warning change in the articulation angle but continuous drift in the trailer's heading angle and pulse-like fluctuations in lateral acceleration. When these characteristics are detected simultaneously, the system activates anti-crosswind control, performs slight reverse steering compensation, and displays a wind direction indicator icon. For false offset signals caused by centrifugal force in corners, the system employs a triple filtering method: steering intention verification, corner radius compensation, and load transfer correction. Steering intention verification compares the matching degree between the steering wheel angle and the vehicle's actual turning radius; corner radius compensation uses satellite trajectory curvature inversion to theoretically calculate the offset, monitoring only abnormal deviations; and load transfer correction dynamically adjusts the lateral acceleration tolerance based on the pressure difference between the inner and outer air springs.

[0115] Figure 5 This is a flowchart of a threshold adjustment method for a working condition monitoring strategy provided in Embodiment 3 of the present invention.

[0116] For details, see Figure 5 The threshold adjustment process for a working condition monitoring strategy described in this embodiment is as follows:

[0117] First, the system retrieves the threshold parameters corresponding to the vehicle's current driving condition from the database, and then monitors the vehicle's driving stability in real time based on these threshold parameters. During vehicle operation, the system calculates and determines whether the false alarm rate within a set period is greater than 10%. If so, it automatically raises the judgment threshold for that condition to reduce the system's sensitivity. If not, it further determines whether the number of missed alarms within the same set period is greater than or equal to two. If so, it automatically lowers the judgment threshold for that condition to enhance the system's risk identification capability. If not, it maintains the threshold parameters corresponding to the current driving condition.

[0118] The technical solution of this invention can monitor the vehicle's driving status in real time by collecting the power parameters of the traction unit and the trailer unit; by processing real-time data and adopting different strategies for different working conditions, it can improve the vehicle's dynamic monitoring capability for diverse road environments; by monitoring driving conditions, triggering alarms when data exceeds corresponding thresholds and adjusting the database according to misjudgments, it can achieve real-time monitoring of vehicle driving stability in different environments, significantly improving monitoring accuracy and ensuring vehicle driving safety.

[0119] Example 4

[0120] Figure 6 This is a schematic diagram of a vehicle early warning and monitoring device provided in Embodiment 4 of the present invention. Figure 6 As shown, the device includes: a parameter acquisition module 610, a pattern matching module 620, and a vehicle warning module 630, wherein,

[0121] The parameter acquisition module 610 is used to acquire the vehicle's load and driving environment parameters during the driving process.

[0122] The pattern matching module 620 is used to determine the target working condition monitoring strategy based on the load conditions and driving environment parameters.

[0123] The vehicle early warning module 630 is used to monitor the driving stability of the vehicle according to the target operating condition monitoring strategy.

[0124] The technical solution of this invention acquires the vehicle's load and driving environment parameters during operation through a parameter acquisition module; then, a pattern matching module determines a target operating condition monitoring strategy based on the load and driving environment parameters; and finally, a vehicle early warning module monitors the vehicle's driving stability according to the target operating condition monitoring strategy. This technical solution, by acquiring the vehicle's load and driving environment parameters during operation, enables real-time monitoring of the vehicle's driving status; by determining a target operating condition monitoring strategy based on the load and driving environment parameters, it improves the vehicle's dynamic monitoring capability in diverse road environments; and by monitoring the vehicle's driving stability according to the target operating condition monitoring strategy, it achieves real-time monitoring of vehicle driving stability under different environments, significantly improving monitoring accuracy and ensuring vehicle driving safety.

[0125] Optional, parameter acquisition module 610, specifically used for:

[0126] It obtains the vehicle's load and driving environment parameters during the driving process.

[0127] Optionally, the vehicle's load and driving environment parameters during operation can be obtained, including:

[0128] The data is collected by the central decision processor from the first data collected by the vehicle's tractor perception unit and the second data collected by the vehicle's trailer dynamic tracking unit.

[0129] The initial database parameters are obtained based on the central decision processor, and the vehicle's load and driving environment parameters are determined according to the initial database parameters, the first data, and the second data.

[0130] Optionally, based on the first data collected by the vehicle's tractor-trailer sensing unit and the second data collected by the vehicle's trailer dynamic tracking unit, the data may also include at least one of the following:

[0131] The vehicle's steering wheel angle and torque signals are acquired, and the actual steering intention of the vehicle is determined based on the steering wheel angle and torque signals as the first data.

[0132] The second data is obtained from the combined sensors deployed on the longitudinal beams of the vehicle frame, which collect the angular velocities and accelerations of the vehicle's yaw, roll, and pitch.

[0133] A pressure sensor array is deployed in the air spring chamber of the vehicle to obtain the vehicle's cargo weight distribution as a second data point.

[0134] The vehicle position, the relative offset of the vehicle's rear end and the road markings are obtained as secondary data through the trailer dynamic tracking unit.

[0135] Optional, the pattern matching module 620 is specifically used for:

[0136] The target operating condition monitoring strategy is determined based on the load conditions and driving environment parameters.

[0137] Optionally, a target operating condition monitoring strategy can be determined based on load conditions and driving environment parameters, including:

[0138] Time stamp alignment and spatial coordinate calibration are performed on load conditions and driving environment parameters;

[0139] The load conditions and driving environment parameters are compared with the parameters corresponding to the preset standard working conditions to determine the target working condition type. The target working condition type includes at least one of the following: unloaded, center of gravity centered, cargo offset, liquid transportation, ordinary road surface, low-adhesion road surface, straight driving and curve driving.

[0140] Search for the target operating condition monitoring strategy that matches the target operating condition type within the preset monitoring strategy set.

[0141] Optional target condition monitoring strategies include at least one of the following:

[0142] In no-load mode, the first threshold is used as the monitoring threshold;

[0143] Center-of-gravity mode uses the second threshold as the monitoring threshold;

[0144] In the cargo offset mode, the second threshold is increased and used as the monitoring threshold.

[0145] In liquid transport mode, the second threshold is reduced and used as the monitoring threshold.

[0146] In the normal road surface mode, the third threshold is used as the monitoring threshold;

[0147] For low-adhesion road surface mode, the third threshold is increased and used as the monitoring threshold.

[0148] In straight-line driving mode, the fourth threshold is used as the monitoring threshold;

[0149] In the cornering driving mode, the fourth threshold is reduced and used as the monitoring threshold.

[0150] Optional, the vehicle warning module 630 is specifically used for:

[0151] The vehicle's driving stability is monitored according to the target operating condition monitoring strategy.

[0152] Optionally, the vehicle's driving stability can be monitored according to the target operating condition monitoring strategy, including:

[0153] Obtain the threshold matching rules corresponding to the target operating condition monitoring strategy;

[0154] The vehicle operates normally when the load and driving environment parameters do not exceed the monitoring threshold of the corresponding threshold matching rule;

[0155] When the load and driving environment parameters exceed the monitoring threshold of the corresponding threshold matching rule, the vehicle issues a warning signal.

[0156] Furthermore, based on the above embodiments, the invention also includes:

[0157] The monitoring threshold of the target operating condition monitoring strategy is dynamically updated based on the false alarm rate and the number of missed alarms for vehicle warnings.

[0158] The vehicle warning and monitoring device provided in this embodiment of the invention can execute the vehicle warning and monitoring method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0159] Example 5

[0160] Figure 7 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0161] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0162] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0163] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as vehicle warning and monitoring methods.

[0164] In some embodiments, the vehicle warning monitoring method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle warning monitoring method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle warning monitoring method by any other suitable means (e.g., by means of firmware).

[0165] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0166] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0167] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0168] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0169] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0170] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0171] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0172] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A vehicle early warning monitoring method, characterized in that, The method includes: Obtain the vehicle's load and driving environment parameters during operation; Determine the target operating condition monitoring strategy based on the load conditions and the driving environment parameters; The vehicle's driving stability is monitored according to the target operating condition monitoring strategy.

2. The method according to claim 1, characterized in that, The acquisition of the vehicle's load and driving environment parameters during operation includes: The data is collected by the central decision processor from the first data collected by the vehicle's tractor perception unit and the second data collected by the vehicle's trailer dynamic tracking unit. The initial database parameters are obtained based on the central decision processor, and the vehicle's load and driving environment parameters are determined according to the initial database parameters, the first data, and the second data.

3. The method according to claim 2, characterized in that, The first data collected by the vehicle's tractor perception unit and the second data collected by the vehicle's trailer dynamic tracking unit, collected by the central decision processor, include at least one of the following: The steering wheel angle and torque signal of the vehicle are acquired, and the actual steering intention of the vehicle is determined according to the steering wheel angle and torque signal as the first data; The angular velocities and accelerations of the vehicle's yaw, roll, and pitch, collected by the combined sensors deployed on the vehicle's frame longitudinal beams, are used as the second data. A pressure sensor array is deployed in the air spring chamber of the vehicle to obtain the cargo weight distribution of the vehicle as the second data; The second data is obtained by using a trailer dynamic tracking unit to acquire the vehicle position, the relative offset of the vehicle's rear end and the road markings.

4. The method according to claim 1, characterized in that, The step of determining the target operating condition monitoring strategy based on the load condition and the driving environment parameters includes: The load conditions and driving environment parameters are time-stamp aligned and spatial coordinates calibrated. The load conditions and driving environment parameters are compared with the parameters corresponding to the preset standard working condition types to determine the target working condition type, wherein the target working condition type includes at least one of the following: unloaded, center of gravity centered, cargo offset, liquid transportation, ordinary road surface, low adhesion road surface, straight driving and curve driving. Search within the preset monitoring strategy set for the target operating condition monitoring strategy that matches the target operating condition type.

5. The method according to claim 1, characterized in that, The target operating condition monitoring strategy includes at least one of the following: In no-load mode, the first threshold is used as the monitoring threshold; Center-of-gravity mode uses the second threshold as the monitoring threshold; In the cargo offset mode, the second threshold is increased and used as the monitoring threshold. In liquid transport mode, the second threshold is reduced and used as the monitoring threshold. In the normal road surface mode, the third threshold is used as the monitoring threshold; In the low-adhesion road surface mode, the third threshold is increased and used as the monitoring threshold. In straight-line driving mode, the fourth threshold is used as the monitoring threshold; In the curve driving mode, the fourth threshold is reduced and used as the monitoring threshold.

6. The method according to claim 1, characterized in that, The vehicle's driving stability is monitored according to the target operating condition monitoring strategy, including: Obtain the threshold matching rules corresponding to the target operating condition monitoring strategy; The vehicle operates normally when the load conditions and driving environment parameters do not exceed the monitoring threshold of the corresponding threshold matching rule; When the load conditions and driving environment parameters exceed the monitoring threshold of the corresponding threshold matching rule, the vehicle issues a warning signal.

7. The method according to any one of claims 1, 5, or 6, characterized in that, Also includes: The monitoring threshold of the target operating condition monitoring strategy is dynamically updated based on the false alarm rate and the number of missed alarms for vehicle warnings.

8. A vehicle early warning and monitoring device, characterized in that, The device includes: The parameter acquisition module is used to acquire the vehicle's load and driving environment parameters during the driving process; The pattern matching module is used to determine the target working condition monitoring strategy based on the load conditions and the driving environment parameters; The vehicle early warning module is used to monitor the driving stability of the vehicle according to the target operating condition monitoring strategy.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle early warning monitoring method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a processor to execute the vehicle early warning monitoring method according to any one of claims 1-7.

Images