Dangerous goods transport vehicle overload early warning method, system and computer storage medium

Abstract

This application discloses a method, system, and computer storage medium for early warning of overloaded dangerous goods transport vehicles. The method includes: collecting vehicle parameter information of a target vehicle, including at least the empty weight and the maximum load weight of the target vehicle; performing signal noise reduction and analysis on the vehicle parameter information to obtain the operating status information of the target vehicle; estimating the current weight of the target vehicle based on the operating status information and in conjunction with the vehicle dynamics model of the target vehicle to obtain the estimated weight of the target vehicle; comparing the empty weight and the maximum load weight with the estimated weight of the vehicle to obtain the empty or loaded status information of the target vehicle; generating a vehicle status warning for the target vehicle based on the empty or loaded status information of the target vehicle, and uploading the vehicle status warning.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of road transport safety monitoring technology for dangerous goods, and in particular to a method, system, and computer storage medium for early warning of overloaded dangerous goods transport vehicles. Background Technology

[0002] Dangerous goods transport vehicles are characterized by high risks and great hazards. Once a traffic accident occurs, it seriously affects the safety of people's lives and property. Overloading increases the axle load, accelerates brake wear, and changes the distribution of braking force, which can easily lead to longer braking distances, steering failure during braking, and vehicle fishtailing. In addition, overloading increases the total weight, making other vehicles relatively smaller, which greatly increases the risk of injury or death to occupants of the smaller vehicle in the event of a collision. Currently, dangerous goods transport vehicles are weighed by the source companies using weighbridges before departure, which suffers from problems such as lax supervision, susceptibility to human factors, and the need for regular equipment calibration. In the road transport sector, roadside inspections rely on the experience of personnel and lack effective technical supervision methods, making it difficult to detect and warn of overloading in real time.

[0003] While existing technologies include monitoring systems based on single or partial integration of weighbridges, GPS positioning, and video surveillance, there is still a lack of an overload warning scheme that can accurately estimate vehicle load in real time during transportation and perform closed-loop verification with weighbridge measurement data. Summary of the Invention

[0004] The purpose of this application is to provide a method, system, and computer storage medium for early warning of overloaded dangerous goods transport vehicles, so as to solve the problem of lack of accurate supervision of dangerous goods transport vehicles during real-time transportation in the prior art.

[0005] The embodiments of this application adopt the following technical solutions: This application provides a method for early warning of overloading of dangerous goods transport vehicles, the method comprising: Collect vehicle parameter information of the target vehicle, wherein the vehicle parameter information includes at least the empty load and the load capacity of the target vehicle; The vehicle parameter information is subjected to signal noise reduction and analysis to obtain the operating status information of the target vehicle; Based on the operating status information and combined with the vehicle dynamics model of the target vehicle, the current mass of the target vehicle is estimated to obtain the estimated mass of the target vehicle. The empty load and the load capacity are compared with the estimated load of the vehicle to obtain the empty or loaded status information of the target vehicle. Based on the empty or loaded status information of the target vehicle, generate a vehicle status alert for the target vehicle and upload the vehicle status alert.

[0006] This application embodiment also provides an overload warning system for dangerous goods transport vehicles, the dangerous goods transport vehicle overload warning system comprising: The acquisition module acquires vehicle parameter information of the target vehicle, including at least the empty load and the load capacity of the target vehicle. The analysis module performs signal noise reduction and analysis on the vehicle parameter information to obtain the operating status information of the target vehicle; The estimation module estimates the current mass of the target vehicle based on the operating status information and the vehicle dynamics model of the target vehicle, thereby obtaining the estimated mass of the target vehicle. The comparison module compares the empty load and the load capacity with the estimated vehicle load to obtain the empty or loaded status information of the target vehicle. The early warning module generates a vehicle status alert for the target vehicle based on its empty or loaded status information, and uploads the vehicle status alert.

[0007] This application also provides a computer storage medium, including a program for use in conjunction with an electronic device, the program being executed by a processor to complete the following steps: Collect vehicle parameter information of the target vehicle, wherein the vehicle parameter information includes at least the empty load and the load capacity of the target vehicle; The vehicle parameter information is subjected to signal noise reduction and analysis to obtain the operating status information of the target vehicle; Based on the operating status information and combined with the vehicle dynamics model of the target vehicle, the current mass of the target vehicle is estimated to obtain the estimated mass of the target vehicle. The empty load and the load capacity are compared with the estimated load of the vehicle to obtain the empty or loaded status information of the target vehicle. Based on the empty or loaded status information of the target vehicle, generate a vehicle status alert for the target vehicle and upload the vehicle status alert.

[0008] Based on the dangerous goods transport vehicle overload early warning method, system and computer storage medium in the embodiments of this application, the target vehicle's operating status information is obtained by performing signal noise reduction and analysis on the collected vehicle parameter information. Combined with the vehicle dynamics model, the target vehicle's mass is estimated. The empty mass and the maximum load mass are compared with the vehicle's estimated mass to determine the target vehicle's empty or loaded status information, thereby generating a vehicle status prompt early warning and uploading the prompt early warning information to the superior.

[0009] In this way, by fusing the recursive estimation model of the vehicle dynamics model with the collected multi-source data, the accuracy and reliability of mass estimation can be effectively improved; by comparing and verifying the estimated mass of the vehicle with the empty mass and the rated load mass, accurate warnings of overloading can be achieved; and through information interconnection, enterprises and traffic management departments can monitor the vehicle loading status in real time, which can greatly improve the level of safety management of dangerous goods transportation. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of the embodiments of this specification and form part of the embodiments of this specification, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 A flowchart illustrating an overload warning method for dangerous goods transport vehicles provided in this application embodiment; Figure 2 This is a schematic diagram of the system architecture corresponding to an overload warning method for dangerous goods transport vehicles provided in an embodiment of this application; Figure 3 A schematic diagram illustrating the specific application process of an overload warning method for dangerous goods transport vehicles provided in this application embodiment; Figure 4 A top view schematic diagram illustrating the specific application process of an overload warning method for dangerous goods transport vehicles provided in the embodiments of this specification; Figure 5 This is a schematic diagram of an axle load dynamic detection device in an overload early warning method for dangerous goods transport vehicles provided in the embodiments of this specification; Figure 6 A schematic diagram of an overload warning system for dangerous goods transport vehicles provided in this application embodiment; Figure 7 This is a schematic diagram of the structure of a computer storage medium corresponding to an overload warning method for dangerous goods transport vehicles provided in an embodiment of this application. Detailed Implementation

[0012] In existing technologies, some existing truck weighing technologies have problems in terms of real-time performance, reliability, convenience, scalability, and accuracy. In terms of monitoring overloaded vehicles transporting dangerous goods, the main problems are discontinuous monitoring coverage, lack of real-time performance and accuracy in detection methods, insufficient coordination of early warning system functions, and challenges in infrastructure construction and maintenance.

[0013] For example: regulatory blind spots and lag.

[0014] Existing regulatory methods rely on weighbridges at fixed locations (such as at the source company before departure), which suffers from lax supervision and susceptibility to subjective influence from testing personnel. Furthermore, weighbridge equipment requires regular calibration and maintenance, resulting in high costs and cumbersome procedures. In the road transport sector, roadside inspections largely depend on the experience of personnel, lacking effective and technically advanced real-time monitoring methods, making it difficult to detect and warn of overloading during transportation. In addition, fixed-point weighbridges cannot cover the entire road section, and in areas or time periods without weighbridges, overloaded vehicles can easily become regulatory blind spots, evading detection.

[0015] Furthermore, using a single sensor or acquiring cargo status parameters (such as load and speed) at a single measuring point in monitoring is prone to errors and even erroneous results, affecting the accuracy of alarms. For example, the reliability of relying solely on dynamic track scales or single speed measuring devices to detect overload and exceeding limits needs to be improved.

[0016] Therefore, this application provides a method, system, and computer storage medium for early warning of overloaded dangerous goods transport vehicles. By performing signal noise reduction and analysis on the collected vehicle parameter information, the operating status information of the target vehicle is obtained. Combined with the vehicle dynamics model, the mass of the target vehicle is estimated. The empty mass and the load capacity are compared with the estimated mass of the vehicle to determine the empty or loaded status information of the target vehicle, thereby generating a vehicle status warning and uploading the warning information to the superior authority.

[0017] In this way, by fusing the recursive estimation model of the vehicle dynamics model with the collected multi-source data, the accuracy and reliability of mass estimation can be effectively improved; by comparing and verifying the estimated mass of the vehicle with the empty mass and the rated load mass, accurate warnings of overloading can be achieved; and through information interconnection, enterprises and traffic management departments can monitor the vehicle loading status in real time, which can greatly improve the level of safety management of dangerous goods transportation.

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0020] Please see Figure 1 This is a flowchart illustrating an overload warning method for dangerous goods transport vehicles provided in this application embodiment.

[0021] In the embodiments of this specification, the method for early warning of overloaded dangerous goods transport vehicles may specifically include the following steps: S101: Collect vehicle parameter information of the target vehicle, wherein the vehicle parameter information includes at least the empty load and the load capacity of the target vehicle; S103: Perform signal noise reduction and analysis on the vehicle parameter information to obtain the operating status information of the target vehicle; S105: Based on the operating status information and combined with the vehicle dynamics model of the target vehicle, the current mass of the target vehicle is estimated to obtain the estimated mass of the target vehicle. S107: Compare the empty load and the load capacity with the estimated load of the vehicle to obtain the empty or loaded status information of the target vehicle. S109: Based on the empty or loaded status information of the target vehicle, generate a vehicle status alert for the target vehicle and upload the vehicle status alert.

[0022] In the embodiments of this specification, by integrating vehicle parameter information, and based on information interconnection analysis and comparison of the estimated vehicle mass, measured mass and overload threshold, and through single-vehicle estimation-reliable verification-communication interconnection, accurate early warning of overload of road transport vehicles carrying dangerous goods can be achieved. This can support enterprises and traffic regulatory departments to grasp the vehicle loading status in a timely manner and help the safe operation of road transport vehicles carrying dangerous goods.

[0023] As an application embodiment of this specification, for step S101, collecting vehicle parameter information of the target vehicle, the vehicle parameter information includes the empty load capacity and the rated load capacity of the target vehicle, specifically including: Collect the basic vehicle information and dynamic parameters of the target vehicle. The basic vehicle information includes at least the license plate number, license plate color, vehicle registration photo, empty load capacity, and rated load capacity.

[0024] In the embodiments of this specification, the basic vehicle information is the static parameters of the target vehicle. By collecting the static and dynamic parameters of the target vehicle and combining static and dynamic data, multi-source data fusion of the target vehicle can effectively improve the accuracy and precision of vehicle quality prediction.

[0025] Among them, the load capacity is the maximum weight that the target vehicle can bear, which is the full load weight. When the weight of the target vehicle exceeds the load capacity, it will be judged as overloaded.

[0026] Furthermore, collecting the dynamic parameters of the target vehicle may specifically include: Sensors pre-installed on the target vehicle are used to collect data in real time on the suspension deformation caused by changes in the vehicle's load, the road slope where the vehicle is located, vehicle acceleration information, wheelbase, transmission ratio, transmission efficiency, wheel radius, vehicle speed, air drag coefficient, frontal area, and driving torque.

[0027] In the embodiments of this specification, by collecting multi-source data such as suspension deformation, road slope, and vehicle acceleration information in real time and performing multi-source data fusion, the accuracy and reliability of vehicle predicted mass estimation can be improved.

[0028] The sensors are installed at various locations on the target vehicle. For example, tire pressure sensors are pre-installed on the vehicle tires, etc., and no specific limitation is made here.

[0029] Furthermore, in step S103, the vehicle parameter information is subjected to signal noise reduction and analysis to obtain the operating status information of the target vehicle, which may specifically include: The vehicle parameter information is subjected to signal noise reduction, including adaptive filtering and time-frequency analysis of the vehicle parameter information; The vehicle parameter information is calibrated to ensure that the vehicle parameter information is collected at the same time. By denoising the signal and synchronizing the vehicle parameter information with the acquisition time, feature information is extracted to obtain multi-source feature data; The extracted multi-source feature data are fused to obtain the fusion result; Based on the fusion results, the operating status information is determined.

[0030] In the embodiments described in this specification, since the original sensor signals (e.g., suspension displacement, acceleration, vehicle speed, etc.) are usually mixed with a lot of noise and interference, direct use will lead to serious distortion of the vehicle mass estimation results.

[0031] By performing adaptive filtering and time-frequency analysis on the collected vehicle parameter information, the signal noise of the vehicle parameter information can be reduced, which can effectively improve the quality of the collected data signal.

[0032] For example, for the non-stationary characteristics of noise in the vehicle's interior environment, the Least Mean Square (LMS) adaptive filtering algorithm can be used for adaptive filtering. The LMS adaptive filtering algorithm can automatically adjust the filter coefficients according to the statistical characteristics of the input signal, track and suppress noise in real time, and has a small computational load, making it suitable for embedded deployment.

[0033] Furthermore, for more complex nonlinear and non-stationary noise (e.g., engine vibration, random road surface excitation, etc.), variational mode decomposition (VMD) combined with Hilbert-Huang transform (HHT) can be used for time-frequency analysis. This method can decompose signal data into modes with different center frequencies, effectively extracting useful feature components hidden in the noise.

[0034] In addition, since the vehicle parameter information collected comes from data from different sensors, such as suspension, slope, acceleration, CAN bus, etc., in order to ensure the consistency of the collection time of multi-source data and improve data accuracy, the vehicle parameter information needs to be calibrated for collection time.

[0035] In this way, the vehicle parameter information, which is synchronized with the signal noise reduction and acquisition time, will be used to extract key features that can reflect the vehicle's status.

[0036] Specifically, feature extraction can include time-domain feature extraction and frequency-domain feature extraction.

[0037] Temporal feature extraction can specifically include the mean and variance of suspension displacement sensors, which can reflect static load changes; and the peak and effective values ​​of acceleration signals, which can reflect the severity of vehicle vibration, etc., without specific limitations here.

[0038] Frequency domain feature extraction can convert time-domain signals to the frequency domain using fast Fourier transform to analyze the vehicle's response characteristics at different frequencies. For example, changes in power spectral density at a specific frequency may be related to road excitation or vehicle malfunction.

[0039] Furthermore, after extracting a large number of features, not all features are useful for vehicle quality prediction. Intelligent optimization algorithms such as adaptive genetic algorithms can be used to select the "optimal feature subset" that is most sensitive to and representative of vehicle operating states (such as acceleration, deceleration, constant speed, climbing, etc.) from high-dimensional features. This can effectively reduce the complexity of subsequent calculations and improve the generalization ability of the model.

[0040] To obtain a more robust prediction of vehicle operating status, it is necessary to fuse information collected from different sensors. Specifically, an adaptive weighted average algorithm can be used, which can dynamically allocate the weight of each sensor data in the final fusion result based on the confidence level or error of each sensor data. This results in more accurate and reliable fused data than that from a single sensor, and the operating status information of the target vehicle can be determined based on the fusion result.

[0041] Furthermore, regarding step S105, based on the operating status information and in conjunction with the vehicle dynamics model of the target vehicle, the current mass of the target vehicle is estimated to obtain the estimated mass of the target vehicle, which may specifically include: The estimated vehicle mass of the target vehicle is estimated according to the following formula (1): Formula (1), Where m is the estimated mass of the target vehicle. T tq This refers to engine torque. i g For the gearbox ratio, i o Main reduction ratio, y T For transmission efficiency, r For the wheel radius, C D The air drag coefficient, A The vehicle's frontal area. Where g is air resistance, g is gravitational acceleration, and f is the rolling resistance coefficient. α The slope angle, δ is the conversion factor for vehicle rotational mass, and 'a' is the vehicle acceleration.

[0042] In the embodiments of this specification, the above formula (1) is a vehicle dynamics model, which is a recursive estimation model that can be used to estimate the estimated mass of a vehicle and has adaptive capabilities to reduce environmental interference.

[0043] Specifically, the derivation process of the above formula (1) is as follows: Formula (2),

[0044] ) Formula (3),

[0045] Formula (4), Formula (1), In the above formula (2), F t For vehicle driving force, ; F f For vehicle rolling resistance, ; F w For vehicle air resistance, ; F i For vehicle gradient resistance, ; F j For vehicle acceleration resistance, .

[0046] Therefore, formula (3) can be derived from formula (2), and formula (4) can be derived from formula (3), thus obtaining formula (1).

[0047] As an application embodiment of this specification, step S107, comparing the empty load mass and the rated load mass with the estimated vehicle mass to obtain the empty or loaded status information of the target vehicle, may specifically include: If the unloaded mass is the same as the estimated mass of the vehicle, then the target vehicle is in an unloaded state. If the estimated weight of the vehicle is between the empty weight and the rated load weight, then the target vehicle is in a normal cargo-carrying state. If the estimated mass of the vehicle exceeds the rated load capacity, then the target vehicle is in an overloaded state.

[0048] In the embodiments of this specification, by comparing the unloaded mass and the load capacity with the estimated mass of the vehicle, and based on the comparison results, if the estimated mass of the vehicle is the same as the unloaded mass, it can be inferred that the target vehicle is currently unloaded. In this case, there is no need to issue an overload warning for the target vehicle.

[0049] If the estimated weight of the vehicle is between the empty weight and the rated load weight, it indicates that the target vehicle is in a normal cargo-carrying state and is not overloaded. In this case, there is no need to issue an overload warning for the target vehicle.

[0050] If the estimated weight of a vehicle exceeds its rated load capacity, it can be inferred that the target vehicle is currently overloaded. In this case, an overload warning needs to be issued for the target vehicle.

[0051] Furthermore, as an application embodiment of this specification, for step S109, generating a vehicle status alert for the target vehicle based on the empty or loaded status information of the target vehicle may specifically include: If the target vehicle is overloaded, a multi-level warning will be triggered for the target vehicle.

[0052] In the embodiments of this specification, in order to improve the accuracy of early warning, when the target vehicle is overloaded, a multi-level early warning mode is implemented according to the overload status of the target vehicle.

[0053] Furthermore, the multi-level early warning system for the target vehicle includes at least one of the following methods: If the overload ratio of the target vehicle is lower than the first threshold, a first-level warning for the target vehicle is triggered, and the first-level warning is uploaded to the system and the target vehicle. If the overload ratio of the target vehicle is higher than the first threshold but lower than the second threshold, a secondary warning for the target vehicle is triggered, and the secondary warning is uploaded to the system, the target vehicle, and the unit where the target vehicle is located. If the overload ratio of the target vehicle is higher than the second threshold, a level 3 warning for the target vehicle is triggered, and the level 3 warning is uploaded to the system, the target vehicle, the unit where the target vehicle is located, and the vehicle supervision department.

[0054] In the embodiments of this specification, the overload status of the target vehicle is divided into multiple levels by setting a first threshold and a second threshold, wherein the overload mass ratio is the percentage by which the estimated vehicle mass of the target vehicle exceeds the rated load mass.

[0055] If the overload ratio of the target vehicle is lower than the first threshold, a first-level warning for the target vehicle is triggered and uploaded to the system and the target vehicle. In this case, the overload situation of the target vehicle is not very serious. By triggering the first-level warning, the system and the target vehicle are informed of the overload situation and reminded to rectify it.

[0056] If the overload ratio of the target vehicle is higher than the first threshold but lower than the second threshold, a level two warning for the target vehicle will be triggered and uploaded to the system, the target vehicle, and the unit where the target vehicle is located. In this case, the overload status of the target vehicle is more serious. By triggering the level two warning, the target vehicle and relevant units are informed of the overload situation and reminded to rectify the overload.

[0057] If the overload ratio of the target vehicle exceeds the second threshold, a level 3 warning for the target vehicle is triggered. The level 3 warning is then uploaded to the system, the target vehicle, the unit to which the target vehicle belongs, and the vehicle supervision department. In this case, the overload status of the target vehicle is very serious. By triggering the level 3 warning, the target vehicle and relevant units and departments are informed of the overload situation, and the target vehicle and relevant units are reminded to rectify the overload. The relevant supervision department will closely supervise the process to ensure that the target vehicle rectifys the overload in a timely manner.

[0058] Furthermore, to ensure that the target vehicle promptly corrects its overload status, its weight can be reassessed after a period of time. If the estimated weight of the replacement vehicle falls below its rated load capacity, a normal load status alert is issued. If the estimated weight of the replacement vehicle still exceeds its rated load capacity, multi-level warnings are issued, and an overload warning is sent to higher authorities. Staff will then intervene to correct the overload status of the target vehicle, ensuring it operates under normal load conditions and guaranteeing its safe operation.

[0059] In another embodiment of this specification, if the target vehicle is overloaded, the method may further include: Obtain the actual measured mass of the target vehicle at the checkpoint weighbridge; The measured mass of the weighbridge is compared with the estimated mass of the vehicle to obtain the mass difference between the measured mass of the weighbridge and the estimated mass of the vehicle. If the quality difference is within a preset range, a multi-level warning for the target vehicle will be directly triggered. If the quality difference exceeds the preset range, the actual measured quality of the weighbridge is verified, and a multi-level warning is triggered for the target vehicle based on the verification result.

[0060] In the embodiments of this specification, to further ensure the accuracy of the estimated vehicle weight, the actual weight of the target vehicle at the checkpoint can be introduced. By comparing the actual weight with the estimated vehicle weight, the weight difference is obtained. If the weight difference is small, the actual weight is taken as the standard, and multi-level warnings are implemented according to the above embodiments.

[0061] If the quality difference is large and exceeds the preset range, the superior regulatory department will be notified, and staff will intervene to reverse-verify whether the corresponding weighbridge is malfunctioning. If the weighbridge is malfunctioning, the estimated vehicle weight will be used as the standard for multi-level early warning. If the weighbridge is not malfunctioning, the actual weight measured by the weighbridge will be used as the standard for multi-level early warning, and the calculation process of the estimated vehicle weight will be checked to determine the fault point and rectify it.

[0062] Furthermore, the first threshold and the second threshold are determined based on the vehicle type of the target vehicle.

[0063] In the embodiments of this specification, the vehicle type of the target vehicle can be determined based on the collected basic vehicle information.

[0064] For the first threshold and the second threshold, the first threshold can be preset to 20% and the second threshold to 50%, without specific limitations.

[0065] This specification provides an embodiment of a method for early warning of overloading of dangerous goods transport vehicles. By performing signal noise reduction and analysis on the collected vehicle parameter information, the operating status information of the target vehicle is obtained. Combined with the vehicle dynamics model, the mass of the target vehicle is estimated. The empty mass and the load capacity are compared with the estimated mass of the vehicle to determine the empty or loaded status information of the target vehicle, thereby generating a vehicle status prompt warning and uploading the prompt warning information to the superior.

[0066] In this way, by fusing the recursive estimation model of the vehicle dynamics model with the collected multi-source data, the accuracy and reliability of mass estimation can be effectively improved; by comparing and verifying the estimated mass of the vehicle with the empty mass and the rated load mass, accurate warnings of overloading can be achieved; and through information interconnection, enterprises and traffic management departments can monitor the vehicle loading status in real time, which can greatly improve the level of safety management of dangerous goods transportation.

[0067] It should be noted that the above-described specific method for warning of overloaded dangerous goods transport vehicles is merely a specific application example and does not limit the scope of the embodiments in this specification. Other specific embodiments may also be included, which will not be elaborated here.

[0068] Based on the same inventive concept, this specification also provides specific application examples of the above-described method for early warning of overloaded dangerous goods transport vehicles.

[0069] like Figure 2 The diagram shown is a system architecture diagram corresponding to an overload warning method for dangerous goods transport vehicles provided in an embodiment of this specification.

[0070] In the embodiments of this specification, the system corresponding to the dangerous goods transport vehicle overload early warning method may specifically include the following different units: The sensor unit is installed on the road transport vehicle carrying dangerous goods to acquire information such as suspension deformation caused by changes in the vehicle's load, road gradient, and vehicle acceleration. The information processing unit, embedded in the computing center of dangerous goods road transport vehicles, obtains the estimated mass through signal noise reduction and analysis calculations; The information transmission unit uploads the estimated quality to the enterprise and the traffic management and supervision center via a communication protocol; The information interconnection unit is used to obtain the actual measured quality of the weighbridge at the checkpoint and to interact with the information of the enterprise and the traffic management and supervision center. The verification display unit is used to compare the deviation between the estimated quality and the measured quality. If the deviation between the estimated quality and the measured quality is within 5%, and the estimated quality exceeds the threshold of 5%, the unit displays the warning information of the dangerous goods road transport vehicle overload and reports the warning information to the traffic management and supervision center.

[0071] The sensor unit may specifically include displacement sensors, acceleration sensors, tire pressure sensors, etc., without being specifically limited here.

[0072] The information transmission unit can specifically use a 4G / 5G communication module or wireless communication to upload data to the cloud platform.

[0073] Furthermore, the information transmission unit and the verification display unit communicate wirelessly.

[0074] In practical applications, the system can acquire vehicle state parameters through suspension displacement sensors, inertial measurement units (IMUs), etc. Vehicle powertrain parameters are obtained via the CAN bus; The estimated vehicle mass is calculated based on the formula (1), and recursive optimization is performed by combining historical estimates. Furthermore, road slope information is indirectly obtained through tire pressure sensors for model correction.

[0075] Based on the same inventive concept, this specification also provides a specific application embodiment of the method for early warning of overloaded dangerous goods transport vehicles.

[0076] like Figure 3 The diagram shown is a structural schematic of the specific application process of an overload warning method for dangerous goods transport vehicles provided in an embodiment of this specification.

[0077] like Figure 4 The diagram shown is a top view of the structure of a specific application process of an overload warning method for dangerous goods transport vehicles provided in an embodiment of this specification.

[0078] like Figure 5 The diagram shown is a schematic of the axle load dynamic detection device in an overload warning method for dangerous goods transport vehicles provided in an embodiment of this specification.

[0079] In the embodiments described in this specification, the sensors pre-installed in the vehicle may specifically include displacement sensors, etc., and are not specifically limited herein. The displacement sensor transmits signals via a signal harness and is connected to the display module of the target vehicle.

[0080] Specifically, the displacement sensor is located in the middle of the vehicle body connecting plate and the guide rail and slide. An overload protection device is installed on the guide rail and slide. The frame connecting plate is connected to the other side of the guide rail and slide.

[0081] In addition, an axle load dynamic detection device is installed at the wheels of the target vehicle to monitor the axle load dynamics of the vehicle in real time. The ideal center of gravity of the target vehicle is located at the center of the vehicle body.

[0082] The specific implementation process of the embodiments in this specification can be referred to the various implementation steps corresponding to the above embodiments, and will not be repeated here.

[0083] Based on the same inventive concept, embodiments of this specification also provide an overload warning system for dangerous goods transport vehicles. For example... Figure 6 The diagram shown is a structural schematic of an overload warning system for dangerous goods transport vehicles provided in an embodiment of this specification.

[0084] Specifically, the dangerous goods transport vehicle overload warning system may include: The acquisition module 601 acquires vehicle parameter information of the target vehicle, wherein the vehicle parameter information includes at least the empty load and the load capacity of the target vehicle. The analysis module 602 performs signal noise reduction and analysis on the vehicle parameter information to obtain the operating status information of the target vehicle; The estimation module 603 estimates the current mass of the target vehicle based on the operating status information and in conjunction with the vehicle dynamics model of the target vehicle, thereby obtaining the estimated mass of the target vehicle. The comparison module 604 compares the empty load mass and the load capacity with the estimated vehicle mass to obtain the empty or loaded status information of the target vehicle. The early warning module 605 generates a vehicle status alert for the target vehicle based on the target vehicle's empty or loaded status information, and uploads the vehicle status alert.

[0085] based on Figure 6 The system described in this specification also provides some specific implementation schemes of the system, which will be described below.

[0086] Furthermore, vehicle parameter information of the target vehicle is collected, including: Collect the basic vehicle information and dynamic parameters of the target vehicle. The basic vehicle information includes at least the license plate number, license plate color, vehicle registration photo, empty load capacity, and rated load capacity.

[0087] Furthermore, the dynamic parameters of the target vehicle are collected, including: Sensors pre-installed on the target vehicle are used to collect data in real time on the suspension deformation caused by changes in the vehicle's load, the road slope where the vehicle is located, vehicle acceleration information, wheelbase, transmission ratio, transmission efficiency, wheel radius, vehicle speed, air drag coefficient, frontal area, and driving torque.

[0088] Furthermore, based on the operational status information and in conjunction with the vehicle dynamics model of the target vehicle, the current mass of the target vehicle is estimated to obtain the estimated mass of the target vehicle, including: The estimated vehicle mass of the target vehicle is estimated using the following formula: , Where m is the estimated mass of the target vehicle. T tq This refers to engine torque. i g For the gearbox ratio, i o Main reduction ratio, y T For transmission efficiency, r For the wheel radius, C D The air drag coefficient, A The vehicle's frontal area. Where g is air resistance, g is gravitational acceleration, and f is the rolling resistance coefficient. α The slope angle, δ is the conversion factor for vehicle rotational mass, and 'a' is the vehicle acceleration.

[0089] Furthermore, the empty load and the rated load are compared with the estimated vehicle load to obtain the empty or loaded status information of the target vehicle, including: If the unloaded mass is the same as the estimated mass of the vehicle, then the target vehicle is in an unloaded state. If the estimated weight of the vehicle is between the empty weight and the rated load weight, then the target vehicle is in a normal cargo-carrying state. If the estimated mass of the vehicle exceeds the rated load capacity, then the target vehicle is in an overloaded state.

[0090] Furthermore, based on the empty or loaded status information of the target vehicle, a vehicle status alert is generated for the target vehicle, including: If the target vehicle is overloaded, a multi-level warning will be triggered for the target vehicle.

[0091] Furthermore, the multi-level early warning system for the target vehicle includes at least one of the following methods: If the overload ratio of the target vehicle is lower than the first threshold, a first-level warning for the target vehicle is triggered, and the first-level warning is uploaded to the system and the target vehicle. If the overload ratio of the target vehicle is higher than the first threshold but lower than the second threshold, a secondary warning for the target vehicle is triggered, and the secondary warning is uploaded to the system, the target vehicle, and the unit where the target vehicle is located. If the overload ratio of the target vehicle is higher than the second threshold, a level 3 warning for the target vehicle is triggered, and the level 3 warning is uploaded to the system, the target vehicle, the unit where the target vehicle is located, and the vehicle supervision department.

[0092] Furthermore, if the target vehicle is overloaded, the system further includes: Obtain the actual measured mass of the target vehicle at the checkpoint weighbridge; The measured mass of the weighbridge is compared with the estimated mass of the vehicle to obtain the mass difference between the measured mass of the weighbridge and the estimated mass of the vehicle. If the quality difference is within a preset range, a multi-level warning for the target vehicle will be directly triggered. If the quality difference exceeds the preset range, the actual measured quality of the weighbridge is verified, and a multi-level warning is triggered for the target vehicle based on the verification result.

[0093] Furthermore, the first threshold and the second threshold are determined based on the vehicle type of the target vehicle.

[0094] This specification provides an overload warning system for dangerous goods transport vehicles. By performing signal noise reduction and analysis on the collected vehicle parameter information, the system obtains the target vehicle's operating status information. Combined with the vehicle dynamics model, the system estimates the target vehicle's weight. The system compares the empty weight and the maximum load weight with the estimated vehicle weight to determine the target vehicle's empty or loaded status information, thereby generating a vehicle status warning and uploading the warning information to the superior authority.

[0095] In this way, by fusing the recursive estimation model of the vehicle dynamics model with the collected multi-source data, the accuracy and reliability of mass estimation can be effectively improved; by comparing and verifying the estimated mass of the vehicle with the empty mass and the rated load mass, accurate warnings of overloading can be achieved; and through information interconnection, enterprises and traffic management departments can monitor the vehicle loading status in real time, which can greatly improve the level of safety management of dangerous goods transportation.

[0096] Based on the same inventive concept, embodiments of this specification also provide an electronic device, including at least one processor and a memory, wherein the memory stores a program and is configured to be executed by the at least one processor in the following steps: Collect vehicle parameter information of the target vehicle, wherein the vehicle parameter information includes at least the empty load and the load capacity of the target vehicle; The vehicle parameter information is subjected to signal noise reduction and analysis to obtain the operating status information of the target vehicle; Based on the operating status information and combined with the vehicle dynamics model of the target vehicle, the current mass of the target vehicle is estimated to obtain the estimated mass of the target vehicle. The empty load and the load capacity are compared with the estimated load of the vehicle to obtain the empty or loaded status information of the target vehicle. Based on the empty or loaded status information of the target vehicle, generate a vehicle status alert for the target vehicle and upload the vehicle status alert.

[0097] Other functions of the processor can be found in the above embodiments, and will not be repeated here.

[0098] Based on the same inventive concept, embodiments of this specification also provide a computer-readable storage medium, including a program for use in conjunction with an electronic device, the program being executable by a processor to perform the following steps: Collect vehicle parameter information of the target vehicle, wherein the vehicle parameter information includes at least the empty load and the load capacity of the target vehicle; The vehicle parameter information is subjected to signal noise reduction and analysis to obtain the operating status information of the target vehicle; Based on the operating status information and combined with the vehicle dynamics model of the target vehicle, the current mass of the target vehicle is estimated to obtain the estimated mass of the target vehicle. The empty load and the load capacity are compared with the estimated load of the vehicle to obtain the empty or loaded status information of the target vehicle. Based on the empty or loaded status information of the target vehicle, generate a vehicle status alert for the target vehicle and upload the vehicle status alert.

[0099] Other functions of the processor can be found in the above embodiments, and will not be repeated here.

[0100] like Figure 7 As shown in the figure, this specification also provides a schematic diagram of the structure of a computer storage medium.

[0101] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0102] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0103] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0107] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0108] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0109] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0110] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0111] This application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0112] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0113] The above description is merely an embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of this application should be included within the scope of protection of the claims of this application.

Claims

1. A method for early warning of overloading of dangerous goods transport vehicles, characterized in that, The method for early warning of overloaded dangerous goods transport vehicles includes: Collect vehicle parameter information of the target vehicle, wherein the vehicle parameter information includes at least the empty load and the load capacity of the target vehicle; The vehicle parameter information is subjected to signal noise reduction and analysis to obtain the operating status information of the target vehicle; Based on the operating status information and combined with the vehicle dynamics model of the target vehicle, the current mass of the target vehicle is estimated to obtain the estimated mass of the target vehicle. The empty load and the load capacity are compared with the estimated load of the vehicle to obtain the empty or loaded status information of the target vehicle. Based on the empty or loaded status information of the target vehicle, generate a vehicle status alert for the target vehicle and upload the vehicle status alert.

2. The method as described in claim 1, characterized in that, Collect vehicle parameter information of the target vehicle, including: Collect the basic vehicle information and dynamic parameters of the target vehicle. The basic vehicle information includes at least the license plate number, license plate color, vehicle registration photo, empty load capacity, and rated load capacity.

3. The method as described in claim 2, characterized in that, Collect the dynamic parameters of the target vehicle, including: Sensors pre-installed on the target vehicle are used to collect data in real time on the suspension deformation caused by changes in the vehicle's load, the road slope where the vehicle is located, vehicle acceleration information, wheelbase, transmission ratio, transmission efficiency, wheel radius, vehicle speed, air drag coefficient, frontal area, and driving torque.

4. The method as described in claim 3, characterized in that, Based on the operational status information and in conjunction with the vehicle dynamics model of the target vehicle, the current mass of the target vehicle is estimated to obtain the estimated mass of the target vehicle, including: The estimated vehicle mass of the target vehicle is estimated using the following formula: ， Where m is the estimated mass of the target vehicle. T tq This refers to engine torque. i g For the gearbox ratio, i o Main reduction ratio, y T For transmission efficiency, r For the wheel radius, C D The air drag coefficient, A The vehicle's frontal area. Where g is air resistance, g is gravitational acceleration, and f is the rolling resistance coefficient. α The slope angle, δ is the conversion factor for vehicle rotational mass, and 'a' is the vehicle acceleration.

5. The method as described in claim 1, characterized in that, The empty load and the rated load are compared with the estimated vehicle load to obtain the empty or loaded status information of the target vehicle, including: If the unloaded mass is the same as the estimated mass of the vehicle, then the target vehicle is in an unloaded state. If the estimated weight of the vehicle is between the empty weight and the rated load weight, then the target vehicle is in a normal cargo-carrying state. If the estimated mass of the vehicle exceeds the rated load capacity, then the target vehicle is in an overloaded state.

6. The method as described in claim 5, characterized in that, Based on the empty or loaded status information of the target vehicle, a vehicle status alert is generated for the target vehicle, including: If the target vehicle is overloaded, a multi-level warning will be triggered for the target vehicle.

7. The method as described in claim 6, characterized in that, The multi-level early warning system for the target vehicle includes at least one of the following methods: If the overload ratio of the target vehicle is lower than the first threshold, a first-level warning for the target vehicle is triggered, and the first-level warning is uploaded to the system and the target vehicle. If the overload ratio of the target vehicle is higher than the first threshold but lower than the second threshold, a secondary warning for the target vehicle is triggered, and the secondary warning is uploaded to the system, the target vehicle, and the unit where the target vehicle is located. If the overload ratio of the target vehicle is higher than the second threshold, a level 3 warning for the target vehicle is triggered, and the level 3 warning is uploaded to the system, the target vehicle, the unit where the target vehicle is located, and the vehicle supervision department.

8. The method as described in claim 6, characterized in that, If the target vehicle is overloaded, the method further includes: Obtain the actual measured mass of the target vehicle at the checkpoint weighbridge; The measured mass of the weighbridge is compared with the estimated mass of the vehicle to obtain the mass difference between the measured mass of the weighbridge and the estimated mass of the vehicle. If the quality difference is within a preset range, a multi-level warning for the target vehicle will be directly triggered. If the quality difference exceeds the preset range, the actual measured quality of the weighbridge is verified, and a multi-level warning is triggered for the target vehicle based on the verification result.

9. The method as described in claim 7, characterized in that, The first threshold and the second threshold are determined based on the vehicle type of the target vehicle.

10. An overload warning system for dangerous goods transport vehicles, characterized in that, The dangerous goods transport vehicle overload warning system includes: The acquisition module acquires vehicle parameter information of the target vehicle, including the empty load and the load capacity of the target vehicle. The analysis module performs signal noise reduction and analysis on the vehicle parameter information to obtain the operating status information of the target vehicle; The estimation module estimates the current mass of the target vehicle based on the operating status information and the vehicle dynamics model of the target vehicle, thereby obtaining the estimated mass of the target vehicle. The comparison module compares the empty load and the load capacity with the estimated vehicle load to obtain the empty or loaded status information of the target vehicle. The early warning module generates a vehicle status alert for the target vehicle based on its empty or loaded status information, and uploads the vehicle status alert.

11. A computer storage medium comprising a program for use in conjunction with an electronic device, the program being executable by a processor to perform the following steps: Collect vehicle parameter information of the target vehicle, wherein the vehicle parameter information includes at least the empty load and the load capacity of the target vehicle; The vehicle parameter information is subjected to signal noise reduction and analysis to obtain the operating status information of the target vehicle; Based on the operating status information and combined with the vehicle dynamics model of the target vehicle, the current mass of the target vehicle is estimated to obtain the estimated mass of the target vehicle. The empty load and the load capacity are compared with the estimated load of the vehicle to obtain the empty or loaded status information of the target vehicle. Based on the empty or loaded status information of the target vehicle, generate a vehicle status alert for the target vehicle and upload the vehicle status alert.

Images