Dynamic weighing system based on heavy truck

Abstract

The invention discloses a dynamic weighing system based on a heavy truck, relates to the technical field of dynamic weighing, and solves the technical problem that the prior art does not consider that a carriage can shake in the transportation process of a vehicle, so that the measurement of the vehicle-loaded weight is not accurate enough, and the early warning and misjudgment of the vehicle-loaded weight are caused. The method comprises the following steps: acquiring weighing data and vehicle state data of a vehicle; the initial vehicle load capacity of the vehicle is calculated based on the weighing data; and based on the vehicle state data, constructing a correction model, and based on the correction model, correcting the initial vehicle load capacity to obtain a target vehicle load capacity, and whether the early warning information is sent or not is judged based on the target vehicle load capacity and the preset vehicle load capacity threshold value, and the technical problem that vehicle load capacity early warning is misjudged due to the fact that measurement of the vehicle load capacity is not accurate enough in the prior art is solved.

Description

Technical Field

[0001] The invention belongs to the field of dynamic weighing, in particular to a dynamic weighing system based on a heavy truck. Background Art

[0002] With the rapid development of the logistics industry, heavy trucks are the main force in cargo transportation, and their load management is directly related to road safety, vehicle maintenance costs and environmental protection. The traditional static weighing method requires stopping at a specific location for inspection, which is not only time-consuming and labor-intensive, but also prone to traffic congestion.

[0003] The prior art (invention patent with application number 2020100019041) discloses a method and system for dynamic vehicle-mounted weighing, which includes: S1: collecting data, the data including the value of the strain gauge installed on the axle during the process of loading cargo on the vehicle; S2: using a linear regression model to establish a mapping relationship between the value of the strain gauge and the weight of the cargo; S3: extracting the difference as a feature vector; S4: according to the feature vector, training the support vector machine in the machine learning method to identify the vehicle operation status; S5: outputting the vehicle weight in real time according to the vehicle operation status. Avoiding the time to obtain the net weight by calculating the tare weight and gross weight of the truck shortens the time required for weighing the truck; improving the accuracy of the cargo load and alleviating the cargo backlog pressure at the transfer center; obtaining the vehicle-mounted weight of the vehicle in real time and tracing the loss of cargo; timely warning of overweight trucks; saving labor costs and improving transportation efficiency; avoiding interference from external factors such as fuel consumption and coolant. However, the existing technology does not take into account that the vehicle compartment will shake during transportation. The shaking of the compartment will cause changes in the vibration frequency and vibration amplitude of the compartment. The increase in vibration frequency will make the sensor's response speed insufficient to keep up with the rapidly changing pressure signal; and the increase in vibration amplitude will cause the sensor output signal to have greater fluctuations, so that the vehicle may be overweight for a moment during the vibration process, resulting in inaccurate measurement of the vehicle weight, thereby causing the problem of misjudgment of the vehicle weight warning.

[0004] Therefore, the present invention solves the above problems by proposing a dynamic weighing system based on heavy trucks. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a dynamic weighing system based on a heavy-duty truck, which is used to solve the technical problem that the prior art does not take into account the shaking of the vehicle compartment during transportation, the shaking of the compartment will cause the vibration frequency and vibration amplitude of the compartment to change, and the increase in the vibration frequency will make the response speed of the sensor insufficient to keep up with the rapidly changing pressure signal; and the increase in the vibration amplitude will cause the signal output by the sensor to have greater fluctuations, so that the vehicle will experience instantaneous overweight during the vibration process, resulting in inaccurate measurement of the vehicle weight, thereby causing misjudgment of the vehicle weight warning.

[0006] To achieve the above-mentioned object, a first aspect of the present invention provides a dynamic weighing system based on a heavy truck, comprising: a data acquisition module, a data processing module, a data correction module and a data early warning module;

[0007] Data acquisition module: obtain vehicle weighing data and vehicle status data;

[0008] Data processing module: calculates the initial vehicle weight based on the weighing data;

[0009] Data correction module: builds a correction model based on vehicle status data, and corrects the initial vehicle load based on the correction model to obtain the target vehicle load;

[0010] Data warning module: Determine whether to send a warning message based on the target vehicle weight and the preset vehicle weight threshold; if yes, send the warning message to the client; if not, continue to detect and judge.

[0011] Preferably, the data processing module communicates with and / or is electrically connected to the data acquisition module and the data correction module respectively; the data early warning module communicates with and / or is electrically connected to the data correction module.

[0012] Preferably, the obtaining of vehicle weighing data and vehicle status data includes:

[0013] The vehicle's weighing data and vehicle status data are collected in real time through data sensors; the weighing data includes: the pressure between the vehicle's front axle and the frame and the pressure between the vehicle's rear axle and the frame; the vehicle status data includes: the vibration frequency and vibration amplitude of the vehicle's compartment.

[0014] Preferably, the calculating the initial vehicle weight of the vehicle based on the weighing data includes:

[0015] S1: Extract the pressure between the front axle and the beam and the pressure between the rear axle and the beam of the vehicle, and respectively QPi and HPj; where i = {1, 2, ..., m}, and m is the number of the pressure sensor between the front axle and the beam of the vehicle; j = {1, 2, ..., n}, and n is the number of the pressure sensor between the rear axle and the beam of the vehicle;

[0016] S2: The initial vehicle load is calculated by the formula: W = (A×∑QPi+B×∑HPj) / g; wherein W is the initial vehicle load; A and B are proportional coefficients, 0<A<1, 0<B<1, and g is the acceleration of gravity.

[0017] Preferably, the constructing of the correction model based on the vehicle status data includes:

[0018] S1: The vibration frequency of the carriage is marked as L, and the vibration amplitude of the carriage is marked as F;

[0019] S2: The vibration frequency and vibration amplitude of the carriage are taken as independent variables, and the correction coefficient is taken as the dependent variable, and is marked as XW;

[0020] S3: Fit the independent variables and dependent variables by polynomial fitting to establish a revised model; the revised model is specifically:

[0021] XW=[C×e^[L / (L+1)]+D×e^tanh(F)]×f;

[0022] Wherein, C and D are proportional coefficients, and C>0, D<0, tanh(*) is the hyperbolic tangent function, and f is the error coefficient.

[0023] It should be noted that as the vibration frequency of the vehicle compartment continues to increase, the sensor's response speed is not fast enough to keep up with the rapidly changing pressure signal, causing the peak value of the sensor output signal to be lower than the actual pressure peak value, which means that during dynamic weighing, the measured value may be lower;

[0024] When the vibration amplitude increases, the signal output by the sensor will fluctuate more. These fluctuations will cause the vehicle to be momentarily overweight during the vibration process. The momentary overweight will cause the pressure signal output by the sensor to temporarily rise, resulting in a higher measured value.

[0025] Preferably, the correction of the initial vehicle load based on the correction model includes:

[0026] S1: Input the vehicle status data into the correction model to obtain the correction coefficient;

[0027] S2: Multiply the initial vehicle load by the correction factor to obtain the correction value;

[0028] S3: Add the initial vehicle load and the correction value to obtain the target vehicle load.

[0029] The present invention takes the vibration frequency and vibration amplitude of the carriage as independent variables and the correction coefficient as the dependent variable; the independent variables and the dependent variables are fitted by polynomial fitting to establish a correction model, and the initial vehicle load weight is corrected based on the correction model to obtain the target vehicle load weight, thereby solving the problem that the prior art does not take into account the shaking of the vehicle carriage during transportation, resulting in inaccurate measurement of the vehicle load weight and thus causing misjudgment of the vehicle load weight warning.

[0030] Preferably, the determining whether to send warning information based on the target vehicle weight and a preset vehicle weight threshold includes:

[0031] Determine whether the target vehicle weight is greater than the preset vehicle weight threshold; if yes, generate a warning message and send it to the client; if no, continue to detect and judge; wherein, the preset vehicle weight threshold is set according to the actual vehicle weight limit standard; the client includes: mobile phone or computer.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The prior art does not take into account that the vehicle compartment will shake during transportation, and the shaking of the compartment will cause changes in the vibration frequency and vibration amplitude of the compartment. The increase in vibration frequency will make the response speed of the sensor insufficient to keep up with the rapidly changing pressure signal; and the increase in vibration amplitude will cause greater fluctuations in the signal output by the sensor, so that the vehicle will be momentarily overweight during vibration, thereby causing a technical problem of inaccurate measurement of the vehicle load weight. The present invention obtains vehicle weighing data and vehicle status data; calculates the initial vehicle load weight of the vehicle based on the weighing data; and constructs a correction model based on the vehicle status data, and corrects the initial vehicle load weight based on the correction model to obtain the target vehicle load weight, thereby solving the above technical problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 A schematic diagram of a system module according to an embodiment of the present invention;

[0036] Figure 2 A schematic diagram of a specific process of an embodiment of the present invention;

[0037] Figure 3 Schematic diagram of target vehicle load weight judgment and warning according to an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] See also Figure 1-Figure 2 , the first aspect of the present invention provides a dynamic weighing system based on a heavy truck, including: a data acquisition module, a data processing module, a data correction module and a data early warning module;

[0040] Data acquisition module: obtain vehicle weighing data and vehicle status data;

[0041] Data processing module: calculates the initial vehicle weight based on the weighing data;

[0042] Data correction module: builds a correction model based on vehicle status data, and corrects the initial vehicle load based on the correction model to obtain the target vehicle load;

[0043] Data warning module: Determine whether to send a warning message based on the target vehicle weight and the preset vehicle weight threshold; if yes, send the warning message to the client; if not, continue to detect and judge.

[0044] Obtain vehicle weighing data and vehicle status data, including:

[0045] The vehicle's weighing data and vehicle status data are collected in real time through data sensors; the weighing data includes: the pressure between the vehicle's front axle and the frame and the pressure between the vehicle's rear axle and the frame; the vehicle status data includes: the vibration frequency and vibration amplitude of the vehicle's compartment.

[0046] The initial vehicle weight is calculated based on the weighing data, including:

[0047] S1: Extract the pressure between the front axle and the beam and the pressure between the rear axle and the beam of the vehicle, and respectively QPi and HPj; where i = {1, 2, ..., m}, and m is the number of the pressure sensor between the front axle and the beam of the vehicle; j = {1, 2, ..., n}, and n is the number of the pressure sensor between the rear axle and the beam of the vehicle;

[0048] S2: The initial vehicle load is calculated by the formula: W = (A×∑QPi+B×∑HPj) / g; wherein W is the initial vehicle load; A and B are proportional coefficients, 0<A<1, 0<B<1, and g is the acceleration of gravity.

[0049] Build a correction model based on vehicle status data, including:

[0050] S1: The vibration frequency of the carriage is marked as L, and the vibration amplitude of the carriage is marked as F;

[0051] S2: The vibration frequency and vibration amplitude of the carriage are taken as independent variables, and the correction coefficient is taken as the dependent variable, and is marked as XW;

[0052] S3: Fit the independent variables and dependent variables by polynomial fitting to establish a revised model; the revised model is specifically:

[0053] XW=[C×e^[L / (L+1)]+D×e^tanh(F)]×f;

[0054] Wherein, C and D are proportional coefficients, and C>0, D<0, tanh(*) is the hyperbolic tangent function, and f is the error coefficient.

[0055] The initial vehicle load is corrected based on the correction model, including:

[0056] S1: Input the vehicle status data into the correction model to obtain the correction coefficient;

[0057] S2: Multiply the initial vehicle load by the correction factor to obtain the correction value;

[0058] S3: Add the initial vehicle load and the correction value to obtain the target vehicle load.

[0059] See also Figure 3 , based on the target vehicle weight and the preset vehicle weight threshold, determining whether to send a warning message includes:

[0060] Determine whether the target vehicle weight is greater than the preset vehicle weight threshold; if yes, generate a warning message and send it to the client; if no, continue to detect and judge; wherein, the preset vehicle weight threshold is set according to the actual vehicle weight limit standard; the client includes: mobile phone or computer.

[0061] For example, suppose a truck has a weight limit of 5 tons. In order to monitor its load in real time, two pressure sensors (numbered 1 and 2) are installed between the front axle and the beam of the truck, and four pressure sensors (numbered 1 to 4) are installed between the rear axle and the beam. At the same time, sensors for monitoring the vibration frequency (L) and vibration amplitude (F) of the vehicle compartment are also installed. The details are as follows:

[0062] 1. Data collection;

[0063] Weighing data: The pressure values ​​between the front axle and the beam collected in real time by the pressure sensor are QP1=14000N and QP2=13500N respectively; the pressure values ​​between the rear axle and the beam are HP1=7000N, HP2=7100N, HP3=7050N and HP4=7020N respectively.

[0064] Vehicle status data: The vibration frequency of the car is L = 5 Hz, and the vibration amplitude is F = 0.1 m.

[0065] 2. Data processing;

[0066] Initial vehicle weight calculation:

[0067] Assume that the proportionality coefficients A = 0.8, B = 0.9, g = 9.8 m / s 2 The initial vehicle load W is calculated as follows:

[0068] W=[0.8×(14000+13500)+0.9×(7000+7100+7050+7020)] / 9.8≈4832.35kg;

[0069] 3. Data correction;

[0070] Modified model parameters: Assume C = 0.1, D = -0.05, f = 0.9.

[0071] Correction factor calculation:

[0072] XW=[0.1×e^[5 / (5+1)]+(-0.05)×e^tanh(0.1)]×0.9≈0.157;

[0073] 4. Revised vehicle weight;

[0074] Target vehicle load = 4832.35 + 4832.35 × 0.157 ≈ 5592.29 kg = 5.59229 tons

[0075] 5. Data early warning;

[0076] Preset vehicle weight threshold: 5 tons.

[0077] Judgment: Since the target vehicle load weight (5.59229 tons) is greater than the preset vehicle load weight threshold (5 tons), an overweight warning message is sent to the driver's mobile phone or computer for reminder.

[0078] The present invention obtains vehicle weighing data and vehicle status data; calculates the initial vehicle load weight of the vehicle based on the weighing data; and constructs a correction model based on the vehicle status data, and corrects the initial vehicle load weight based on the correction model to obtain a target vehicle load weight; and determines whether to send an early warning message based on the target vehicle load weight and a preset vehicle load weight threshold. This solves the problem that the prior art does not take into account the shaking of the vehicle compartment during transportation, resulting in inaccurate measurement of the vehicle load weight, and also avoids the problem of misjudgment of an overweight early warning due to inaccurate measurement.

[0079] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0080] Working principle of the present invention:

[0081] The present invention obtains vehicle weighing data and vehicle status data; calculates the initial vehicle load weight of the vehicle based on the weighing data; and constructs a correction model based on the vehicle status data, and corrects the initial vehicle load weight based on the correction model to obtain a target vehicle load weight; determines whether to send an early warning message based on the target vehicle load weight and a preset vehicle load weight threshold, thereby solving the technical problems that the prior art does not take into account the shaking of the vehicle compartment during transportation, the shaking of the compartment will cause the vibration frequency and vibration amplitude of the compartment to change, and the increase in the vibration frequency will make the response speed of the sensor insufficient to keep up with the rapidly changing pressure signal; and the increase in the vibration amplitude will cause the signal output by the sensor to have greater fluctuations, so that the vehicle will experience instantaneous overweight during the vibration process, thereby resulting in inaccurate measurement of the vehicle load weight.

[0082] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A dynamic weighing system based on heavy trucks, characterized in that: include: Data acquisition module, data processing module, data correction module and data early warning module; Data acquisition module: obtain vehicle weighing data and vehicle status data; Data processing module: calculates the initial vehicle weight based on the weighing data; Data correction module: builds a correction model based on vehicle status data, and corrects the initial vehicle load based on the correction model to obtain the target vehicle load; Data warning module: determines whether to send warning information based on the target vehicle weight and the preset vehicle weight threshold; If yes, a warning message is sent to the client; If not, continue to test and judge.

2. A dynamic weighing system based on a heavy truck according to claim 1, characterized in that: The data processing module is in communication and / or electrically connected with the data acquisition module and the data correction module respectively; the data early warning module is in communication and / or electrically connected with the data correction module.

3. A dynamic weighing system based on heavy trucks according to claim 1, characterized in that: The obtaining of vehicle weighing data and vehicle status data includes: The vehicle's weighing data and vehicle status data are collected in real time through data sensors; the weighing data includes: the pressure between the vehicle's front axle and the frame and the pressure between the vehicle's rear axle and the frame; the vehicle status data includes: the vibration frequency and vibration amplitude of the vehicle's compartment.

4. A dynamic weighing system based on a heavy truck according to claim 1, characterized in that: The calculating the initial vehicle weight of the vehicle based on the weighing data includes: S1: Extract the pressure between the front axle and the beam and the pressure between the rear axle and the beam of the vehicle, and respectively QPi and HPj; where i = {1, 2, ..., m}, and m is the number of the pressure sensor between the front axle and the beam of the vehicle; j = {1, 2, ..., n}, and n is the number of the pressure sensor between the rear axle and the beam of the vehicle; S2: The initial vehicle load is calculated by the formula: W = (A×∑QPi+B×∑HPj) / g; wherein W is the initial vehicle load; A and B are proportional coefficients, 0<A<1, 0<B<1, and g is the acceleration of gravity.

5. A dynamic weighing system based on heavy trucks according to claim 1, characterized in that: The constructing of the correction model based on the vehicle status data comprises: S1: The vibration frequency of the carriage is marked as L, and the vibration amplitude of the carriage is marked as F; S2: The vibration frequency and vibration amplitude of the carriage are taken as independent variables, and the correction coefficient is taken as the dependent variable, and is marked as XW; S3: Fit the independent variables and dependent variables by polynomial fitting to establish a revised model; the revised model is specifically: XW=[C×e^[L / (L+1)]+D×e^tanh(F)]×f; Wherein, C and D are proportional coefficients, and C>0, D<0, tanh(*) is the hyperbolic tangent function, and f is the error coefficient.

6. A dynamic weighing system based on heavy trucks according to claim 1, characterized in that: The correcting the initial vehicle load based on the correction model includes: S1: Input the vehicle status data into the correction model to obtain the correction coefficient; S2: Multiply the initial vehicle load by the correction factor to obtain the correction value; S3: Add the initial vehicle load and the correction value to obtain the target vehicle load.

7. A dynamic weighing system based on a heavy truck according to claim 1, characterized in that: The determining whether to send warning information based on the target vehicle weight and the preset vehicle weight threshold includes: Determine whether the target vehicle weight is greater than the preset vehicle weight threshold; if yes, generate a warning message and send it to the client; if no, continue to detect and judge; wherein, the preset vehicle weight threshold is set according to the actual vehicle weight limit standard; the client includes: mobile phone or computer.

Images