A self-learning method for identifying and assessing the load capacity of commercial vehicles

Abstract

This invention discloses a self-learning method for identifying and assessing the load of commercial vehicles. First, it utilizes big data extraction technology to extract the vehicle operation data required by the model from multi-source datasets. Then, it identifies and preprocesses abnormal frames in the vehicle operation data according to a data cleaning strategy, and performs extended calculations on vehicle operation features and road structure features. A recursive least squares algorithm with a forgetting factor is used to construct a load benchmark generation model based on vehicle longitudinal dynamics. An optimized Informer neural network is used to construct a load identification output and confidence assessment model to complete the whole vehicle load identification and confidence assessment scheme. This invention's device utilizes vehicle network data to achieve real-time identification of the whole vehicle load, which is beneficial for standardizing the on-the-road monitoring of heavy freight vehicles and provides a reliable basis for optimizing vehicle trip utilization.

Description

Technical Field

[0001] This invention belongs to the field of vehicle parameter identification, specifically relating to a self-learning method for identifying the load capacity of commercial vehicles and assessing confidence levels. Background Technology

[0002] With the development of technologies such as big data science, 5G communication, and artificial intelligence, the electrification, connectivity, and intelligence of automobiles are gradually becoming the development direction of the automotive industry. Intelligent connected vehicles refer to vehicles equipped with advanced onboard sensors, controllers, actuators, and other devices, enabling them to perceive complex environments, make intelligent decisions, and coordinate control, greatly facilitating the acquisition of various parameters under vehicle operating conditions. Combined with modern communication and network technologies, the data storage capacity and accuracy of vehicle-to-everything (V2X) networks have significantly improved. Under this development trend, vehicle parameter identification and data mining based on massive amounts of V2X data have gradually become research hotspots.

[0003] In recent years, with the rapid development of the transportation industry, road freight transport has accounted for 73.8% of the total freight volume in my country. Currently, there are two main measures to achieve full-process on-the-road monitoring of freight vehicles: one is to install additional gravity sensors, and the other is to have drivers manually input and upload data. However, both methods have certain problems: installing gravity sensors will increase vehicle costs, making it difficult to widely implement for general vehicles and small fleets; while the authenticity and accuracy of data uploaded manually by drivers will be affected by factors such as individual driver factors.

[0004] With the increasing maturity of vehicle-to-everything (V2X) technology, identifying and estimating the vehicle weight based on vehicle operating condition measurement data from intelligent connected vehicles is one of the important application solutions for V2X data. Through the research and development of vehicle load estimation algorithms, the real-time cargo transportation status of vehicles can be obtained, enabling full-process monitoring of the vehicle's on-road status. This allows for effective supervision of vehicle overloading, thus preventing traffic accidents to a certain extent. Simultaneously, by obtaining real-time vehicle load information, abnormal loading and unloading behavior of trucks can be avoided, and the utilization rate of vehicles in the corresponding area can also be reflected, thereby helping freight companies to rationally allocate trucks, avoid wasting vehicle transport capacity resources, and improve transportation efficiency. Summary of the Invention

[0005] Purpose of the invention: This invention provides a self-learning method for identifying the load capacity of commercial vehicles and assessing their confidence level, which can realize full-process monitoring of freight vehicles en route.

[0006] Technical solution: The self-learning commercial vehicle load identification and confidence assessment method of the present invention includes the following steps:

[0007] (1) Vehicle operation initialization: includes GPS module, vehicle management system, vehicle communication module and vehicle static parameter initialization. The vehicle static parameters include the following fields: vehicle identification number, vehicle model, engine type, vehicle width, vehicle height, rear axle speed ratio, gear ratio of each gear, wheel rolling radius, and engine maximum torque.

[0008] (2) Multi-source data acquisition: Data acquired via GPS module includes timestamp, elevation, and vehicle speed; data acquired via CAN bus includes engine speed, engine torque, and gear information.

[0009] (3) Operation data detection and cleaning: Implement corresponding cleaning strategies for missing, noisy and other erroneous frame data in the operation data, and then take preprocessing measures such as interpolation and filtering for operation data that does not meet the continuity conditions;

[0010] (4) Vehicle operation characteristics extension: Based on the vehicle system transmission characteristics and road structure characteristics, the preprocessed vehicle operation data is used to obtain the road slope characteristics, vehicle acceleration and jerk characteristics of the entire road segment at all times.

[0011] (5) Load joint identification system design: Establish a load benchmark generation model based on recursive least squares method, and obtain the distribution benchmark of estimated load through vehicle operation data iteration; establish a load regression identification model based on improved Informer encoder, and output load identification results through GPS source data and extended operation features;

[0012] (6) Confidence assessment and model fine-tuning: Based on the dynamic load distribution benchmark, the output of the neural network load regression model is combined to complete the confidence assessment of the load identification results; store the vehicle operation data and total weight benchmark data at each moment of the entire journey, and perform offline data training and fine-tuning of the load regression identification model after the current journey ends, and redeploy the model after training is completed;

[0013] Furthermore, the implementation process of step (3) is as follows:

[0014] Considering the differences in the operating characteristics of non-same-source data, erroneous frames in GPS source data and CAN bus source data are manually feature extracted and labeled. Based on the continuity criterion, erroneous frame data samples are separated, and the following multi-source data erroneous frame feature and cleaning strategy are formulated.

[0015] GPS source data: Error frames mainly originate from data interruptions. Based on the data feedback timestamp, they can be categorized into short-term data loss and long-term signal interruptions, with a time threshold δ = 30 seconds. For short-term data loss less than this threshold, linear interpolation is performed using the nearest non-error data value before and after the error frame. For long-term signal interruptions exceeding this threshold, the data during that period is set to zero and discarded.

[0016] CAN bus source data: Error frame data is mainly characterized by short-term data loss, high-frequency measurement noise, and discontinuous abrupt changes.

[0017] Short-term data missing mainly targets vehicle gear data detection. Based on the principle of continuous change in vehicle gears, short-term single-point missing data is filled with non-abnormal data from the previous moment.

[0018] High-frequency measurement noise is mainly aimed at detecting engine speed and engine torque data. A Savitzky-Golay filter is designed to process the noise data.

[0019] Discontinuous abrupt changes are primarily targeted at engine torque data detection, and the characteristic detection standard is set as follows:

[0020]

[0021] Where R is the engine torque data at a certain moment, T is the timestamp at the corresponding moment, t is the calculation moment, z is the time step, and δ is the set detection threshold; for the data points obtained according to the above detection standards, a linear interpolation method is used to cover the data values ​​at the corresponding moment.

[0022] Furthermore, the implementation process of step (4) is as follows:

[0023] After the vehicle operation data is cleaned, based on the vehicle parameter attributes and operating characteristics, the preprocessed vehicle operation data is used to obtain vehicle operating speed characteristics:

[0024]

[0025] This allows us to obtain the vehicle's acceleration and jerk characteristics:

[0026]

[0027] Where n is the engine speed, r is the wheel rolling radius, and i g Where i is the gear ratio of the transmission, i0 is the rear axle speed ratio, Δt is the time step, and V is the transmission ratio. t Let a be the vehicle speed value. t is the acceleration value, and j is the jerk.

[0028] Based on the construction standards and changing characteristics of highways, the road slope is obtained by preprocessing vehicle speed and elevation data.

[0029]

[0030] In the formula, l is the time step for backward calculation; h t h is the elevation value at time t. t+nv is the elevation value at time t+n, in meters; i This is the vehicle speed value, in units of

[0031] Slope calculations are performed at points with a distance span of 100m, ensuring that elevation data changes monotonically within a single calculation step, and the time step k satisfies:

[0032]

[0033] (h i -h i-1 )·(h i+1 -h i )>0, (i=t+1,t+2,…,t+n-1)

[0034] Based on the above calculation strategy and formula, the data of all running segments are traversed to obtain the road gradient characteristics, vehicle acceleration and jerk characteristics of the entire journey.

[0035] Furthermore, the load-bearing joint identification system in step (5) is designed as follows:

[0036] Based on the vehicle dynamics model, a reasonable distribution range of the estimated load is obtained, and the longitudinal dynamics model of the vehicle under driving conditions is adopted:

[0037]

[0038] Where g is the acceleration due to gravity, M is the mass of the car, f is the coefficient of frictional resistance, α is the road slope, and C D Where A is the air resistance coefficient, ρ is the vehicle's frontal area, σ is the vehicle's moment of inertia conversion factor, v is the vehicle's speed, a is the vehicle's acceleration, and T is the air resistance coefficient. tq For engine torque, i g i is the gear ratio of the transmission, i0 is the rear axle speed ratio, and η is the transmission ratio of the gearbox. T The total efficiency of the transmission system is given by r, where r is the rolling radius of the wheel.

[0039] A recursive least squares algorithm with a forgetting factor is used to obtain the distribution benchmark of the estimated load based on the above vehicle dynamics model filtering, and the loss function of the filtering algorithm is constructed as follows:

[0040]

[0041] Where λ represents the forgetting factor, z(i) represents the actual output of the system, and φ(k) represents the system input. This represents the vector of parameters to be estimated at time k.

[0042] The load reference generation model based on recursive least squares is designed as follows:

[0043]

[0044] A load regression identification model was built using an Informer-based neural network architecture. The model input parameters were selected as: vehicle speed, acceleration, jerk, road elevation, and road slope. The model output parameters were the load regression identification results.

[0045] Furthermore, the implementation process of step (6) is as follows:

[0046] The load distribution output by the recursive least squares algorithm is used to construct a 3σ distribution interval. The interval mean is used as the confidence evaluation benchmark to evaluate the confidence of the load regression identification model output of the Informer encoder. The MAE is used as the evaluation index.

[0047]

[0048] in, The output of the load regression identification model. This represents the average value within the load reference range;

[0049] The confidence level assessment criteria are as follows:

[0050]

[0051] The load regression identification results and confidence assessment results iterate over time and are continuously output as the vehicle runs until the end of the vehicle's working journey; the load regression identification results and confidence assessment results at each time dimension are recorded in the vehicle's storage unit;

[0052] After the vehicle's current working journey ends, the confidence level of the load identification results throughout the entire journey is statistically analyzed. Load identification data with a confidence level below 10% are marked. The average load identification benchmark at the corresponding time point is used as the fine-tuning label to generate the fine-tuning dataset of the Informer encoder for offline training. After the model is fine-tuned, it is redeployed.

[0053] Through the above model calculations and identification, the load identification results and confidence evaluation values ​​can be output, and the dataset generation, model self-learning and model redeployment can be completed after the vehicle trip ends.

[0054] Beneficial Effects: Compared with existing technologies, the beneficial effects of this invention are as follows: 1. This invention considers the technological advantages and levels of existing intelligent connected freight vehicles, combines the content and characteristics of vehicle network data, and adopts mature data preprocessing methods for typical vehicle network data types. Based on the known vehicle and road status information, it accurately identifies the total vehicle weight; 2. This invention uses a recursive least squares algorithm with a forgetting factor and an optimized Informer neural network model, which can achieve real-time identification of the total vehicle load and high-precision output of the result confidence level; 3. This invention can achieve automatic learning and fine-tuning of the model. With the accumulation of application scope and application time, the accuracy and versatility of the identification regression model will continue to improve; 4. This invention can be applied to many fields related to commercial vehicles. In the vehicle design stage, understanding the vehicle operating load conditions in the segmented market makes the vehicle development goals more accurate. In the vehicle usage stage, it enables precise matching of cargo and transport capacity, optimization of cargo loading rate, improvement of vehicle energy consumption assessment, calculation of intelligent maintenance strategies, calculation of residual value of used vehicles, and even participation in the calculation of intelligent predictive driving strategies. Attached Figure Description

[0055] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0056] 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.

[0057] The present invention provides a self-learning method for identifying and assessing the load capacity of commercial vehicles, comprising the following steps:

[0058] (1) Vehicle operation initialization: includes GPS module, vehicle management system, vehicle communication module and vehicle static parameter initialization. The vehicle static parameters include the following fields: vehicle identification number, vehicle model, engine type, vehicle width, vehicle height, rear axle speed ratio, gear ratio of each gear, wheel rolling radius, and engine maximum torque.

[0059] (2) Multi-source data acquisition: Data acquired via GPS module includes timestamp, elevation, and vehicle speed; data acquired via CAN bus includes engine speed, engine torque, and gear information.

[0060] (3) Operation data detection and cleaning: Implement corresponding cleaning strategies for missing, noisy and other erroneous frame data in the operation data, and then take preprocessing measures such as interpolation and filtering for operation data that does not meet the continuity conditions;

[0061] (4) Vehicle operation characteristics extension: Based on the vehicle system transmission characteristics and road structure characteristics, the preprocessed vehicle operation data is used to obtain the road slope characteristics, vehicle acceleration and jerk characteristics of the entire road segment at all times.

[0062] (5) Load joint identification system design: Establish a load benchmark generation model based on recursive least squares method, and obtain the distribution benchmark of estimated load through vehicle operation data iteration; establish a load regression identification model based on improved Informer encoder, and output load identification results through GPS source data and extended operation features;

[0063] (6) Confidence assessment and model fine-tuning: Based on the dynamic load distribution benchmark, the output of the neural network load regression model is combined to complete the confidence assessment of the load identification results; store the vehicle operation data and total weight benchmark data at each moment of the entire journey, and perform offline data training and fine-tuning of the load regression identification model after the current journey ends, and redeploy the model after training is completed;

[0064] Furthermore, the implementation process of step (3) is as follows:

[0065] Considering the differences in the operating characteristics of non-same-source data, erroneous frames in GPS source data and CAN bus source data are manually feature extracted and labeled. Based on the continuity criterion, erroneous frame data samples are separated, and the following multi-source data erroneous frame feature and cleaning strategy are formulated.

[0066] GPS source data: Error frames mainly originate from data interruptions. Based on the data feedback timestamp, they can be categorized into short-term data loss and long-term signal interruptions, with a time threshold δ = 30 seconds. For short-term data loss less than this threshold, linear interpolation is performed using the nearest non-error data value before and after the error frame. For long-term signal interruptions exceeding this threshold, the data during that period is set to zero and discarded.

[0067] CAN bus source data: Error frame data is mainly characterized by short-term data loss, high-frequency measurement noise, and discontinuous abrupt changes.

[0068] Short-term data missing mainly targets vehicle gear data detection. Based on the principle of continuous change in vehicle gears, short-term single-point missing data is filled with non-abnormal data from the previous moment.

[0069] High-frequency measurement noise is mainly aimed at detecting engine speed and engine torque data. A Savitzky-Golay filter is designed to process the noise data. The filter is constructed using a quadratic polynomial, and the filter window length is set to 19.

[0070] Discontinuous abrupt changes are primarily targeted at engine torque data detection, and the characteristic detection standard is set as follows:

[0071]

[0072] Where R is the engine torque data at a certain moment, T is the timestamp at the corresponding moment, t is the calculation moment, z is the time step, and δ is the set detection threshold; for the data points obtained according to the above detection standards, a linear interpolation method is used to cover the data values ​​at the corresponding moment.

[0073] Furthermore, the implementation process of step (4) is as follows:

[0074] After the vehicle operation data is cleaned, based on the vehicle parameter attributes and operating characteristics, the preprocessed vehicle operation data is used to obtain vehicle operating speed characteristics:

[0075]

[0076] This allows us to obtain the vehicle's acceleration and jerk characteristics:

[0077]

[0078] Where n is the engine speed, r is the wheel rolling radius, and i g Where i is the gear ratio of the transmission, i0 is the rear axle speed ratio, Δt is the time step, and V is the transmission ratio. t Let a be the vehicle speed value. t is the acceleration value, and j is the jerk.

[0079] Based on the construction standards and changing characteristics of highways, the road slope is obtained by preprocessing vehicle speed and elevation data.

[0080]

[0081] In the formula, l is the time step for backward calculation; h t h is the elevation value at time t. t+n v is the elevation value at time t+n, in meters; i This is the vehicle speed value, in units of

[0082] Slope calculations are performed at points with a distance span of 100m, ensuring that elevation data changes monotonically within a single calculation step, and the time step k satisfies:

[0083]

[0084] (h i -h i-1 )·(h i+1 -h i )>0, (i=t+1,t+2,…,t+n-1)

[0085] Based on the above calculation strategy and formula, the data of all running segments are traversed to obtain the road gradient characteristics, vehicle acceleration and jerk characteristics of the entire journey.

[0086] Furthermore, the load-bearing joint identification system in step (5) is designed as follows:

[0087] Based on the vehicle dynamics model, a reasonable distribution range of the estimated load is obtained, and the longitudinal dynamics model of the vehicle under driving conditions is adopted:

[0088]

[0089] Where g is the acceleration due to gravity, M is the mass of the car, f is the coefficient of frictional resistance, α is the road slope, and C D Where A is the air resistance coefficient, ρ is the vehicle's frontal area, σ is the vehicle's moment of inertia conversion factor, v is the vehicle's speed, a is the vehicle's acceleration, and T is the air resistance coefficient. tq For engine torque, i g i is the gear ratio of the transmission, i0 is the rear axle speed ratio, and η is the transmission ratio of the gearbox. T The total efficiency of the transmission system is given by r, where r is the rolling radius of the wheel.

[0090] A recursive least squares algorithm with a forgetting factor is used to obtain the distribution benchmark of the estimated load based on the above vehicle dynamics model filtering, and the loss function of the filtering algorithm is constructed as follows:

[0091]

[0092] Where λ represents the forgetting factor, z(i) represents the actual output of the system, and φ(k) represents the system input. This represents the vector of parameters to be estimated at time k.

[0093] The load reference generation model based on recursive least squares is designed as follows:

[0094]

[0095] in:

[0096]

[0097] φ(k)=gα(k)+gf+σ(k)u

[0098] θ=M

[0099] A load regression identification model was built using an Informer-based neural network architecture. The model input parameters were selected as: vehicle speed, acceleration, jerk, road elevation, and road slope. The model output parameters were the load regression identification results.

[0100] The Informer encoder structure is designed as follows: encoding feature dimension (120, 7), extended feature dimension (120, 128), number of encoder layers 4, number of fully connected layers 256, number of sampling factors 10, number of multi-head attention 8, and output feature dimension (1, 1).

[0101] Furthermore, the implementation process of step (6) is as follows:

[0102] The load distribution output by the recursive least squares algorithm is used to construct a 3σ distribution interval. The interval mean is used as the confidence evaluation benchmark to evaluate the confidence of the load regression identification model output of the Informer encoder. The MAE is used as the evaluation index.

[0103]

[0104] in, The output of the load regression identification model. This represents the average value within the load reference range;

[0105] The confidence level assessment criteria are as follows:

[0106]

[0107] The load regression identification results and confidence assessment results iterate over time and are continuously output as the vehicle runs until the end of the vehicle's working journey; the load regression identification results and confidence assessment results at each time dimension are recorded in the vehicle's storage unit;

[0108] After the vehicle's current working journey ends, the confidence level of the load identification results throughout the entire journey is statistically analyzed. Load identification data with a confidence level below 10% are marked. The average load identification benchmark at the corresponding time point is used as the fine-tuning label to generate the fine-tuning dataset of the Informer encoder for offline training. After the model is fine-tuned, it is redeployed.

[0109] Through the above model calculations and identification, the load identification results and confidence evaluation values ​​can be output. After the vehicle journey ends, the dataset generation, model self-learning, and model redeployment are completed. The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or changes made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A self-learning method for identifying and assessing the load capacity of commercial vehicles, characterized in that, Includes the following steps: (1) Vehicle operation initialization: including the initialization of the on-board GPS module, vehicle management system, vehicle communication module and vehicle static parameters; (2) Multi-source data acquisition: Data acquired through GPS module includes timestamp, elevation, and vehicle speed; data acquired through CAN bus includes engine speed, engine torque, and gear information. (3) Operation data detection and cleaning: Implement corresponding cleaning strategies for missing, noisy and other erroneous frame data in the operation data, and then take preprocessing measures such as interpolation and filtering for operation data that does not meet the continuity conditions; The process of step (3) is as follows: Considering the differences in the operating characteristics of non-same-source data, the error frames in GPS source data and CAN bus source data are manually extracted and labeled. Based on the continuity criterion, the error frame data samples are separated, and the error frame features and cleaning strategies for multi-source data are formulated as follows. GPS source data: Error frames mainly originate from data interruptions. Combined with the data feedback timestamp, they can be divided into short-term data loss and long-term signal interruption. The time threshold δ = 30s is set. For short-term data missing values ​​less than this threshold, linear interpolation is performed using the non-error data value that is closest to the frame before and after the error. For long-term signal interruptions exceeding this threshold, the data during that period is set to zero and discarded. CAN bus source data: Error frame data is mainly characterized by short-term data loss, high-frequency measurement noise, and discontinuous abrupt changes; Short-term data missing mainly targets vehicle gear data detection. Based on the principle of continuous change in vehicle gears, short-term single-point missing data is filled with non-abnormal data from the previous moment. High-frequency measurement noise is mainly aimed at engine speed and engine torque data detection. A Savitzky-Golay filter is designed to process the noise data. Discontinuous abrupt changes are primarily targeted at engine torque data detection, and the characteristic detection standard is set as follows: Where R represents the engine torque data at a certain moment, T is the timestamp at the corresponding moment, t is the calculation time, z is the time step, and δ is the set detection threshold; for the data points obtained according to the above detection standards, a linear interpolation method is used to cover the data values ​​at the corresponding moments. (4) Vehicle operation characteristics extension: Based on the vehicle system transmission characteristics and road structure characteristics, the preprocessed vehicle operation data is used to obtain the road slope characteristics, vehicle acceleration and jerk characteristics of the entire road segment at all times. (5) Load joint identification system design: Establish a load benchmark generation model based on recursive least squares method, and obtain the distribution benchmark of estimated load through vehicle operation data iteration; establish a load regression identification model based on improved Informer encoder, and output load identification results through GPS source data and extended operation features; (6) Confidence assessment and model fine-tuning: Based on the dynamic load distribution benchmark, the confidence assessment of the load identification result is completed by combining the output of the neural network load regression model; store the vehicle operation data and total weight benchmark data at each moment of the entire journey, and perform offline data training and fine-tuning of the load regression identification model after the current journey ends, and redeploy the model after training is completed.

2. The self-learning commercial vehicle load identification and confidence assessment method according to claim 1, characterized in that, The implementation process of step (4) is as follows: After the vehicle operation data is cleaned, based on the vehicle parameter attributes and operating characteristics, the preprocessed vehicle operation data is used to obtain vehicle operating speed characteristics: This allows us to obtain the vehicle's acceleration and jerk characteristics: Where n is the engine speed, r is the wheel rolling radius, and i g Where i is the gear ratio of the transmission, i0 is the rear axle speed ratio, Δt is the time step, and V is the transmission ratio. t Let a be the vehicle speed value. t Here, j represents the acceleration value, and j represents the jerk. Based on the construction standards and changing characteristics of highways, the road slope is obtained by preprocessing vehicle speed and elevation data. In the formula, l is the time step for backward calculation; h t h is the elevation value at time t. t+n v is the elevation value at time t+n, in meters; i This is the vehicle speed value, in units of Slope calculations are performed at points with a distance span of 100m, ensuring that elevation data changes monotonically within a single calculation step, and the time step k satisfies: (h i -h i-1 )·(h i+1 -h i )>0, (i=t+1,t+2,…,t+n-1) Based on the above calculation strategy and formula, traverse all running segment data to obtain the road gradient characteristics, vehicle acceleration and jerk characteristics of the entire journey.

3. The self-learning commercial vehicle load identification and confidence assessment method according to claim 1, characterized in that, The load-bearing joint identification system designed in step (5) is as follows: Based on the vehicle dynamics model, a reasonable distribution range of the estimated load is obtained, and the longitudinal dynamics model of the vehicle under driving conditions is adopted: Where g is the acceleration due to gravity, M is the mass of the car, f is the coefficient of frictional resistance, α is the road slope, and C D Where A is the air resistance coefficient, ρ is the vehicle's frontal area, σ is the vehicle's moment of inertia conversion factor, v is the vehicle's speed, a is the vehicle's acceleration, and T is the air resistance coefficient. tq For engine torque, i g i is the gear ratio of the transmission, i0 is the rear axle speed ratio, and η is the transmission ratio of the gearbox. T The total efficiency of the transmission system is given by r, where r is the rolling radius of the wheel. A recursive least squares algorithm with a forgetting factor is used to obtain the distribution benchmark of the estimated load based on the above vehicle dynamics model filtering, and the loss function of the filtering algorithm is constructed as follows: Where λ represents the forgetting factor, z(i) represents the actual output of the system, and φ(k) represents the system input. This represents the vector of parameters to be estimated at time k; The load reference generation model based on recursive least squares is designed as follows: A load regression identification model was built using an Informer-based neural network architecture. The model input parameters were selected as: vehicle speed, acceleration, jerk, road elevation, and road slope. The model output parameters were the load regression identification results.

4. The self-learning commercial vehicle load identification and confidence assessment method according to claim 1, characterized in that, The implementation process of step (6) is as follows: The load distribution output by the recursive least squares algorithm is used to construct a 3σ distribution interval. The interval mean is used as the confidence evaluation benchmark to evaluate the confidence of the load regression identification model output of the Informer encoder. The MAE is used as the evaluation index. in, The output of the load regression identification model. This represents the average value within the load reference range; The confidence level assessment criteria are as follows: The load regression identification results and confidence assessment results iterate over time and are continuously output as the vehicle runs until the end of the vehicle's working journey; the load regression identification results and confidence assessment results at each time dimension are recorded in the vehicle's storage unit; After the vehicle's current working journey ends, the confidence level of the load identification results throughout the entire journey is statistically analyzed. Load identification data with a confidence level below 10% are marked. The average load identification benchmark at the corresponding time point is used as the fine-tuning label to generate the fine-tuning dataset of the Informer encoder for offline training. After the model is fine-tuned, it is redeployed.

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