An automatic detection system for overloading and over-limit of high-speed freight based on intelligent perception

Abstract

The present invention relates to the technical field of vehicle overloading detection, and particularly to an automatic detection system for overloading and over-limit of high-speed freight transportation based on intelligent perception. The present invention obtains the load of each axle and the image of the freight truck through the perception module, determines whether the freight truck is overloaded or over-limit through the category division module, and divides the over-limit tendency category of the freight truck. Through the detection and analysis module, the load fluctuation value is calculated and combined with the over-limit difference value of the freight truck to calculate the early warning characterization parameter. According to the early warning characterization parameter, it is determined whether to issue an over-limit early warning signal, or, according to the change value of the over-limit parameter of the freight truck obtained from the image of the freight truck, it is determined whether to issue an over-limit early warning signal. The signal sending unit issues an early warning signal. The present invention can improve the detection efficiency, reduce the detection cost while ensuring the detection reliability, prevent over-limit situations, and enhance the safety of the road.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle overloading detection, and particularly to an automatic detection system for overloading and over-limit of high-speed freight transportation based on intelligent perception. Background Art

[0002] The detection of overloading and over-limit of freight trucks is crucial for ensuring road traffic safety, maintaining road infrastructure, and improving transportation efficiency. Overloaded and over-limit freight trucks pose a threat to road safety and increase the risk of traffic accidents. By effectively identifying and preventing these vehicles from hitting the road, the accident rate can be reduced.

[0003] The prior art discloses a rapid detection system for overloading and over-limit of freight vehicles based on Zigbee network, including a freight vehicle terminal, a mobile law enforcement terminal, a fixed law enforcement terminal, and a remote monitoring center; the freight vehicle terminal includes a freight vehicle terminal microprocessor, a weight detection unit, a vehicle information storage module, and a freight vehicle terminal man-machine interface; the mobile law enforcement terminal includes a mobile law enforcement terminal microprocessor, a mobile terminal GPS module, a mobile video capture module, a remote communication module, and a mobile law enforcement terminal man-machine interface; the fixed law enforcement terminal includes a fixed law enforcement terminal microprocessor, a current section over-limit information storage unit, a fixed terminal GPS module, a fixed video capture module, and a remote communication module; the remote monitoring center includes a database server and a remote communication module. This system can not only give overloading and over-limit prompts to freight vehicle drivers, but also conduct load weight inspection and illegal evidence collection without disturbing the normal driving of freight vehicles. However, the prior art does not consider classifying the over-limit risk tendency of freight trucks, nor does it consider pre-interfering with the over-limit situation of freight trucks, resulting in low detection efficiency, high detection cost for overloading and over-limit of freight trucks, and inability to more effectively improve road safety.

[0004] Therefore, there is an urgent need for an automatic detection system for overloading and over-limit of high-speed freight transportation based on intelligent perception, which can classify the over-limit tendency categories of freight trucks according to the truck type combined with the truck image, and consider the changes in the truck state during the driving process of the freight truck, can improve the detection efficiency, reduce the detection cost while ensuring the detection reliability, prevent the over-limit situation, and improve the road safety. Summary of the Invention

[0005] The purpose of the present invention is to provide an automatic detection system for overloading and over-limit of high-speed freight transportation based on intelligent perception, which can classify the over-limit tendency categories of freight trucks according to the truck type combined with the truck image, and consider the changes in the truck state during the driving process of the freight truck, can improve the detection efficiency, reduce the detection cost while ensuring the detection reliability, prevent the over-limit situation, and improve the road safety.

[0006] The present invention provides an automatic detection system for overloading and over-limit of high-speed freight based on intelligent perception. This automatic detection system for overloading and over-limit of high-speed freight based on intelligent perception includes,

[0007] A perception module, including a weighing detection unit arranged on each axle of the freight vehicle to obtain the load of each axle and an image acquisition unit to obtain the image of the freight vehicle;

[0008] A category division module, connected to the perception module, is used to determine whether the freight vehicle is overloaded or over-limit according to the load of each axle and the image of the freight vehicle, and divide the over-limit tendency category of the freight vehicle in combination with the type of the freight vehicle and the image of the freight vehicle;

[0009] A detection and analysis module, connected to each of the perception modules and the category division module, includes a strategy selection unit and a signal emission unit;

[0010] The strategy selection unit is used to select a detection strategy according to the over-limit tendency category, including,

[0011] Calculating a load fluctuation value according to the load of each axle and calculating a warning characterization parameter in combination with the over-limit difference of the freight vehicle. The over-limit difference of the freight vehicle includes the difference between the length of the freight vehicle and the preset comparison threshold of the length of the freight vehicle, the difference between the width of the freight vehicle and the preset comparison threshold of the width of the freight vehicle, and the difference between the height of the freight vehicle and the preset comparison threshold of the height of the freight vehicle. Determining whether to emit an over-limit warning signal according to the warning characterization parameter;

[0012] Or, obtaining the change value of the over-limit parameter of the freight vehicle according to the image of the freight vehicle. The over-limit parameters of the freight vehicle include the length, width and height of the freight vehicle, and determining whether to emit an over-limit warning signal;

[0013] The signal emission unit is used to emit a warning signal.

[0014] The category division module determines whether the freight vehicle is overloaded or over-limit according to the load of each axle and the image of the freight vehicle,

[0015] Obtaining the load of each axle and calculating the total weight of the freight vehicle according to the load of each axle;

[0016] Comparing the total weight of the freight vehicle with the preset overloading comparison threshold of the freight vehicle,

[0017] If the total weight of the freight vehicle is greater than the preset overloading comparison threshold of the freight vehicle, it is determined that the freight vehicle is overloaded;

[0018] Obtaining the image of the freight vehicle, extracting the outer contour image of the freight vehicle, and obtaining the length, width and height of the freight vehicle according to the outer contour image of the freight vehicle,

[0019] Comparing the length, width and height of the freight vehicle with the preset comparison threshold of the length of the freight vehicle, the preset comparison threshold of the width of the freight vehicle, and the preset comparison threshold of the height of the freight vehicle respectively,

[0020] If the first preset condition is met, it is determined that the truck is over-limit;

[0021] The first preset condition is that the length of the truck is greater than the preset truck length comparison threshold, or the width of the truck is greater than the preset truck width comparison threshold, or the height of the truck is greater than the preset truck height comparison threshold.

[0022] The category division module divides the over-limit tendency category of the truck according to the truck type in combination with the truck image.

[0023] The truck type is divided into a closed truck and a non-closed truck according to the truck image.

[0024] The length, width, and height of the truck are respectively compared with the preset warning truck length comparison threshold, the preset warning truck width comparison threshold, and the preset warning truck height comparison threshold.

[0025] If the second preset condition is met, the over-limit tendency category of the truck is divided into a high-warning over-limit category;

[0026] If the second preset condition is not met, the over-limit tendency category of the truck is divided into a low-warning over-limit category;

[0027] The second preset condition is that the truck type is a non-closed truck and the length of the truck is greater than the preset warning truck length comparison threshold, or the truck type is a non-closed truck and the width of the truck is greater than the preset warning truck width comparison threshold, or the truck type is a non-closed truck and the height of the truck is greater than the preset warning truck height comparison threshold.

[0028] The strategy selection unit is used to select a detection strategy according to the over-limit tendency category, where

[0029] If the over-limit tendency category of the truck is a high-warning over-limit category, the load fluctuation value is calculated according to the load of each axle, and the warning characterization parameter is calculated in combination with the truck over-limit difference. The truck over-limit difference includes the difference between the truck length and the preset truck length comparison threshold, the difference between the truck width and the preset truck width comparison threshold, and the difference between the truck height and the preset truck height comparison threshold. It is determined whether to issue an over-limit warning signal according to the warning characterization parameter;

[0030] If the over-limit tendency category of the truck is a low-warning over-limit category, the truck over-limit parameter change value is obtained according to the truck image. The truck over-limit parameters include the truck length, the truck width, and the truck height. It is determined whether to issue an over-limit warning signal.

[0031] The strategy selection unit calculates the load fluctuation value according to the load of each axle.

[0032] The load of each axle is obtained at every preset time;

[0033] Calculate the difference between the maximum and minimum values of the axle loads of each vehicle axle;

[0034] Select the maximum difference as the load fluctuation value.

[0035] The strategy selection unit calculates the early warning characterization parameter according to formula (1),

[0036]

[0037] In formula (1), D represents the early warning characterization parameter, B represents the load fluctuation value, B0 represents the preset load fluctuation value threshold, Lc represents the difference in the length of the freight car exceeding the limit, Lc0 represents the preset difference threshold for the length of the freight car exceeding the limit, Hc represents the difference in the height of the freight car exceeding the limit, Hc0 represents the preset difference threshold for the height of the freight car exceeding the limit, Wc represents the difference in the width of the freight car exceeding the limit, and Wc0 represents the preset difference threshold for the width of the freight car exceeding the limit.

[0038] The strategy selection unit determines whether to issue an over-limit early warning signal according to the early warning characterization parameter,

[0039] Compare the early warning characterization parameter with the preset comparison threshold of the early warning characterization parameter,

[0040] If the early warning characterization parameter is greater than the preset comparison threshold of the early warning characterization parameter, it is determined to issue an over-limit early warning signal.

[0041] The strategy selection unit obtains the change value of the over-limit parameters of the freight car according to the freight car image,

[0042] Extract the freight car images of two adjacent times;

[0043] The difference in the length of the freight car in the outer contour image of the freight car is the change value of the length of the freight car, the difference in the width of the freight car in the outer contour image of the freight car is the change value of the width of the freight car, and the difference in the height of the freight car in the outer contour image of the freight car is the change value of the height of the freight car.

[0044] The strategy selection unit determines whether to issue an over-limit early warning signal according to the change value of the over-limit parameters of the freight car obtained from the freight car image,

[0045] Compare the change value of the length of the freight car with the preset comparison threshold of the change value of the length of the freight car, compare the change value of the height of the freight car with the preset comparison threshold of the change value of the height of the freight car, and compare the change value of the width of the freight car with the preset comparison threshold of the change value of the width of the freight car,

[0046] If the third preset condition is satisfied, it is determined to issue an over-limit early warning signal;

[0047] The third preset condition is that the change value of the length of the freight car is greater than the preset comparison threshold of the change value of the length of the freight car, or the change value of the width of the freight car is greater than the preset comparison threshold of the change value of the width of the freight car, or the change value of the height of the freight car is greater than the preset comparison threshold of the change value of the height of the freight car.

[0048] The signal sending unit obtains the determination results of the category division module and the policy selection unit, and sends out an overload signal, or an overlimit signal, or a warning signal.

[0049] The beneficial effects of the present invention are as follows:

[0050] The present invention provides an intelligent perception-based automatic detection system for overloading and overlimiting of high-speed freight transportation. The intelligent perception-based automatic detection system for overloading and overlimiting of high-speed freight transportation divides the overlimiting tendency categories of freight trucks according to the truck type in combination with the truck image, and takes into account the changes in the truck state during the driving process of the truck. It can improve the detection efficiency, reduce the detection cost while ensuring the detection reliability, prevent overlimiting situations, and enhance the safety of the road.

[0051] Furthermore, the category division module of the present invention divides the overlimiting tendency categories of freight trucks according to the truck type in combination with the truck image. In actual situations, there are fixed limitations on the length, width, and height of enclosed trucks, while there are no fixed limitations on the length, width, or height of non-enclosed trucks, which may lead to the situation that the goods on non-enclosed trucks exceed the original length or height of the trucks. By recognizing the length, width, and height of the truck through the truck image, the current situation of the truck can be obtained. If the length, width, and height of the current truck are already relatively close to the overlimit values, it is more likely to occur overlimiting. By dividing the overlimiting tendency categories of freight trucks, different detection strategies can be adopted for trucks with different overlimiting tendencies, so as to improve the detection efficiency, reduce the detection cost while ensuring the detection reliability.

[0052] Furthermore, the policy selection unit of the present invention calculates the load fluctuation value based on the load of each axle and combines it with the overlimit difference of the freight truck to calculate the warning characterization parameter. During the operation of the freight truck, it is possible that the goods loaded on the truck shift or shake. When the goods shift or shake, the load of each axle obtained will also change to a certain extent. When the overlimit difference of the freight truck is small, the freight truck is more likely to occur overlimiting. If the goods loaded on the freight truck also have a certain degree of shift or shake, it may lead to the situation that the length or width of the freight truck exceeds the limit. By calculating the warning characterization parameter, the freight trucks that are more likely to occur overlimiting can be obtained, and warning signals are sent through such freight trucks, so as to prevent overlimiting situations and enhance the safety of the road.

[0053] Furthermore, the strategy selection unit of the present invention obtains the change value of the over-limit parameters of the truck based on the truck image to determine whether to issue an over-limit warning signal. For trucks in the low-warning over-limit category, it is less likely to have over-limit situations. By obtaining the change value of the over-limit parameters of the truck, the changes in the truck state during the driving process of the truck can be obtained. When the change value of the over-limit parameters of the truck is large, there are significant changes in the truck state during the driving process of the truck. Even if it is difficult for the truck to have over-limit situations, a warning signal should still be issued to the truck, so as to prevent over-limit situations and improve the safety of the road. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 FIG. is a system structure diagram of the automatic detection system for overloading and over-limit of high-speed freight transportation based on intelligent perception in the embodiment of the present invention;

[0055] Figure 2 FIG. is a structure diagram of the detection and analysis module in the embodiment of the present invention;

[0056] Figure 3 FIG. is a logical decision diagram for the category division module in the embodiment of the present invention to divide the over-limit tendency categories of trucks;

[0057] Figure 4 FIG. is a logical decision diagram for the strategy selection unit in the embodiment of the present invention to determine whether to issue an over-limit warning signal based on the warning characterization parameters. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0058] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0059] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. These are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. Among them, the terms "first position" and "second position" are two different positions. Moreover, the first feature being "above", "over" and "on" the second feature includes the first feature being directly above and obliquely above the second feature, or merely indicating that the first feature has a higher horizontal height than the second feature. The first feature being "below", "under" and "beneath" the second feature includes the first feature being directly below and obliquely below the second feature, or merely indicating that the first feature has a lower horizontal height than the second feature.

[0060] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "mounted", "connected" and "coupled" shall be construed in a broad sense. For example, it may be a fixed connection, a detachable connection, or an integral connection; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0061] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are only used to explain the present invention and should not be construed as a limitation of the present invention.

[0062] Please refer to Figure 1 - Figure 2 as shown in the system structure diagram of the intelligent perception-based high-speed freight overload and over-limit automatic detection system in the embodiments of the present invention. Figure 2 This is the structure diagram of the detection and analysis module in the embodiments of the present invention. The embodiments of the present invention provide an intelligent perception-based high-speed freight overload and over-limit automatic detection system, and this intelligent perception-based high-speed freight overload and over-limit automatic detection system includes:

[0063] A sensing module, including a weighing detection unit arranged on each axle of the freight vehicle to obtain the load of each axle and an image acquisition unit to obtain the image of the freight vehicle;

[0064] A category classification module, connected to the perception module, is used to determine whether a freight car is overloaded or over-limit according to the load of each axle and the freight car image, and classify the over-limit tendency category of the freight car in combination with the freight car type and the freight car image;

[0065] A detection and analysis module, connected to each of the perception modules and the category classification module, includes a strategy selection unit and a signal sending unit;

[0066] The strategy selection unit is used to select a detection strategy according to the over-limit tendency category, including,

[0067] Calculating a load fluctuation value based on the load of each axle and calculating a warning characterization parameter in combination with the freight car over-limit difference value. The freight car over-limit difference value includes the difference between the freight car length and the preset freight car length comparison threshold, the difference between the freight car width and the preset freight car width comparison threshold, and the difference between the freight car height and the preset freight car height comparison threshold, and determining whether to issue an over-limit warning signal according to the warning characterization parameter;

[0068] Or, obtaining the change value of the freight car over-limit parameter from the freight car image. The freight car over-limit parameter includes the freight car length, the freight car width, and the freight car height, and determining whether to issue an over-limit warning signal;

[0069] The signal sending unit is used to send a warning signal.

[0070] Specifically, the present invention does not limit the specific structure of the perception module. The weighing detection unit can be a weighing sensor, and the image acquisition unit can be a camera installed on the highway.

[0071] Specifically, the present invention does not limit the specific structures of the category classification module and the detection and analysis module. They can both be composed of logic components or combinations of logic components. The logic components include field programmable processors, computers, or microprocessors in computers. This is prior art and will not be elaborated here.

[0072] Specifically, the category classification module determines whether a freight car is overloaded or over-limit according to the load of each axle and the freight car image,

[0073] Obtaining the load of each axle and calculating the total weight of the freight car according to the load of each axle;

[0074] Comparing the total weight of the freight car with the preset freight car overload comparison threshold,

[0075] If the total weight of the freight car is greater than the preset freight car overload comparison threshold, it is determined that the freight car is overloaded;

[0076] Obtaining the freight car image, extracting the outer contour image of the freight car, and obtaining the length, width, and height of the freight car according to the outer contour image of the freight car,

[0077] Compare the length, width, and height of the truck with the preset truck length comparison threshold L0, the preset truck width comparison threshold W0, and the preset truck height comparison threshold H0 respectively.

[0078] If the first preset condition is met, it is determined that the truck is over-limit.

[0079] The first preset condition is that the length of the truck is greater than the preset truck length comparison threshold, or the width of the truck is greater than the preset truck width comparison threshold, or the height of the truck is greater than the preset truck height comparison threshold.

[0080] In this embodiment, the image recognition software recognizes the outer contour image of the truck according to the truck image, and extracts the length, width, and height of the truck according to the outer contour image of the truck. The image recognition software can be matlab.

[0081] In this embodiment, various truck data and various road information data are stored in the database. The preset truck overload comparison threshold is the sum of the maximum load capacity of the corresponding truck selected according to the truck image and the self-weight of the corresponding truck. The preset truck length comparison threshold, the preset truck width comparison threshold, and the preset truck height comparison threshold are the truck length, truck width, and truck height specified for the corresponding truck on the road selected according to the truck image.

[0082] Please refer to Figure 3 As shown, specifically, the category division module divides the over-limit tendency category of the truck according to the truck type and the truck image.

[0083] The truck type is divided into a closed truck and a non-closed truck according to the truck image.

[0084] Compare the length, width, and height of the truck with the preset warning truck length comparison threshold, the preset warning truck width comparison threshold, and the preset warning truck height comparison threshold respectively.

[0085] If the second preset condition is met, the over-limit tendency category of the truck is divided into a high-warning over-limit category.

[0086] If the second preset condition is not met, the over-limit tendency category of the truck is divided into a low-warning over-limit category.

[0087] The second preset condition is that the truck type is a non-closed truck and the length of the truck is greater than the preset warning truck length comparison threshold, or the truck type is a non-closed truck and the width of the truck is greater than the preset warning truck width comparison threshold, or the truck type is a non-closed truck and the height of the truck is greater than the preset warning truck height comparison threshold.

[0088] In this embodiment, trucks with fixed limitations on their length, width, and height are classified as enclosed trucks, such as van trucks. For van trucks, since their length, width, and height are restricted, the goods usually do not exceed the box body, so it is not easy to exceed the limit. Trucks with no fixed limitations on their length, width, or height are classified as non-enclosed trucks, such as flatbed trucks. The goods may exceed the original height or length of the vehicle, so there may be a greater risk of exceeding the limit.

[0089] In this embodiment, the truck images are used by image recognition software to classify the truck types into enclosed trucks and non-enclosed trucks. The image recognition software can be Matlab.

[0090] In this embodiment, a preset warning truck length comparison threshold Ly0 = a×L0 is set, where a is the length warning coefficient and 0.7 < a < 0.9. A preset warning truck width comparison threshold Wy0 = b×W0 is set, where b is the width warning coefficient and 0.7 < b < 0.9. A preset warning truck height comparison threshold Hy0 = c×H0 is set, where c is the height warning coefficient and 0.7 < c < 0.9.

[0091] Specifically, the category classification module of the present invention classifies the truck over-limit tendency categories according to the truck type in combination with the truck images. In actual situations, the length, width, and height of enclosed trucks have fixed limitations, while the length, width, or height of non-enclosed trucks has no fixed limitations, which leads to the situation that the goods of non-enclosed trucks are likely to exceed the original length or height of the truck. By recognizing the length, width, and height of the truck through the truck images, the current situation of the truck can be obtained. If the length, width, and height of the current truck are already relatively close to the over-limit values, it is more likely to occur the situation of exceeding the limit. By classifying the truck over-limit tendency categories, different detection strategies can be adopted for trucks with different over-limit tendencies, so that while ensuring the detection reliability, the detection efficiency can be improved and the detection cost can be reduced.

[0092] Specifically, the strategy selection unit is used to select the detection strategy according to the over-limit tendency category, where,

[0093] If the truck over-limit tendency category is a high-warning over-limit category, the load fluctuation value is calculated according to the load of each axle and combined with the truck over-limit difference value to calculate the warning characterization parameter. The truck over-limit difference value includes the difference between the truck length and the preset truck length comparison threshold, the difference between the truck width and the preset truck width comparison threshold, and the difference between the truck height and the preset truck height comparison threshold. Whether to issue an over-limit warning signal is determined according to the warning characterization parameter;

[0094] If the truck over-limit tendency category is a low-warning over-limit category, the truck over-limit parameter change value is obtained according to the truck image. The truck over-limit parameters include the truck length, the truck width, and the truck height. Whether to issue an over-limit warning signal is determined.

[0095] Specifically, the policy selection unit calculates the load fluctuation value according to the load of each axle,

[0096] and obtains the load of each axle at every preset time interval;

[0097] calculates the difference between the maximum value and the minimum value of the load of each axle;

[0098] and selects the maximum difference as the load fluctuation value.

[0099] In this embodiment, the preset time interval is selected within the range of [5 min, 10 min].

[0100] It can be understood that the load of each axle is obtained at every preset time interval, the maximum value and the minimum value are obtained from the load data of the same axle obtained at different times, and the difference is calculated.

[0101] Specifically, the policy selection unit calculates the early warning characterization parameter according to formula (1),

[0102]

[0103] In formula (1), D represents the early warning characterization parameter, B represents the load fluctuation value, B0 represents the preset load fluctuation value threshold, Lc represents the difference in the length of the freight car exceeding the limit, Lc0 represents the preset difference threshold for the length of the freight car exceeding the limit, Hc represents the difference in the height of the freight car exceeding the limit, Hc0 represents the preset difference threshold for the height of the freight car exceeding the limit, Wc represents the difference in the width of the freight car exceeding the limit, and Wc0 represents the preset difference threshold for the width of the freight car exceeding the limit.

[0104] In this embodiment, the preset load fluctuation value threshold B0 = d×F, where d is the axle load coefficient, F is the axle load corresponding to the maximum difference selected, 0.05 < d < 0.15, Lc0 = e×L0, where e is the difference coefficient for the length exceeding the limit, 0.05 < e < 0.15, Wc0 = f×W0, where f is the difference coefficient for the width exceeding the limit, 0.05 < f < 0.15, and Hc0 = g×H0, where g is the difference coefficient for the height exceeding the limit, 0.05 < g < 0.15.

[0105] Specifically, the strategy selection unit of the present invention calculates the load fluctuation value based on the load of each axle and combines it with the over-limit difference of the freight car to calculate the early warning characterization parameter. During the operation of the freight car, it is possible that the goods loaded on the freight car shift or shake. When the goods shift or shake, the load of each axle obtained will also change to a certain extent. When the over-limit difference of the freight car is small, the freight car is more likely to be over-limit. If the goods loaded on the freight car also have a certain degree of shift or shake, it may cause the length or width of the freight car to be over-limit. By calculating the early warning characterization parameter, the freight cars that are more likely to be over-limit can be obtained, and early warning signals can be sent through such freight cars, so as to prevent over-limit situations and improve the safety of the road.

[0106] Please refer to Figure 4 As shown, specifically, the strategy selection unit determines whether to issue an over-limit early warning signal according to the early warning characterization parameter.

[0107] Compare the early warning characterization parameter with the preset comparison threshold of the early warning characterization parameter.

[0108] If the early warning characterization parameter is greater than the preset comparison threshold of the early warning characterization parameter, it is determined to issue an over-limit early warning signal.

[0109] In this embodiment, the preset comparison threshold of the early warning characterization parameter is selected within the range of [1.82, 1.90].

[0110] Specifically, the strategy selection unit obtains the change value of the over-limit parameter of the freight car according to the freight car image.

[0111] Extract the freight car images of two adjacent times.

[0112] The difference in the length of the freight car in the outer contour image of the freight car is the length change value of the freight car, the difference in the width of the freight car in the outer contour image of the freight car is the width change value of the freight car, and the difference in the height of the freight car in the outer contour image of the freight car is the height change value of the freight car.

[0113] In this embodiment, the freight car images of two adjacent times are the freight car images obtained by adjacent image acquisition units during the driving process of the freight car.

[0114] Specifically, the strategy selection unit determines whether to issue an over-limit early warning signal according to the change value of the over-limit parameter of the freight car obtained from the freight car image.

[0115] Compare the length change value of the freight car with the preset comparison threshold of the length change value of the freight car, compare the height change value of the freight car with the preset comparison threshold of the height change value of the freight car, and compare the width change value of the freight car with the preset comparison threshold of the width change value of the freight car.

[0116] If the third preset condition is satisfied, it is determined to issue an over-limit early warning signal.

[0117] The third preset condition is that the change value of the truck length is greater than the preset truck length change value comparison threshold Lb0, or the change value of the truck width is greater than the preset truck width change value comparison threshold Wb0, or the change value of the truck height is greater than the preset truck height change value comparison threshold Hb0.

[0118] In this embodiment, Lb0 = h×L0, where h is the length change value coefficient and 0.05 < h < 0.1, Wb0 = j×W0, where j is the width change value coefficient and 0.05 < j < 0.1, Hb0 = g×H0, where g is the height change value coefficient and 0.05 < g < 0.1.

[0119] Specifically, the strategy selection unit of the present invention determines whether to issue an overlimit warning signal according to the truck overlimit parameter change value obtained from the truck image. For trucks in the low-warning overlimit category, it is less likely to have an overlimit situation. By obtaining the truck overlimit parameter change value, the changes in the truck state during driving can be obtained. When the truck overlimit parameter change value is large, the truck state has changed significantly during driving. Even if it is difficult for the truck to have an overlimit situation, a warning signal should still be issued to the truck, so as to prevent overlimit situations and improve road safety.

[0120] Specifically, the signal sending unit obtains the determination results of the category division module and the strategy selection unit, and sends an overload signal, or an overlimit signal, or a warning signal.

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of devices, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based device for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0122] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, rather than limitations on the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation manners here. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the claims of the present invention.

Claims

1. An automatic detection system for overloading and over-limit of high-speed freight based on intelligent perception, characterized in that, Including: A perception module, including a weighing detection unit arranged on each axle of the truck for obtaining the load of each axle and an image acquisition unit for obtaining an image of the truck; A category classification module, connected to the perception module, for determining whether the truck is overloaded or over-limit according to the load of each axle and the truck image, and classifying the over-limit tendency category of the truck in combination with the truck type and the truck image; A detection and analysis module, connected to each of the perception modules and the category classification module, including a strategy selection unit and a signal sending unit; The strategy selection unit is used to select a detection strategy according to the over-limit tendency category, including, If the over-limit tendency category of the truck is a high-warning over-limit category, calculate the load fluctuation value according to the load of each axle, combine it with the truck over-limit difference value to calculate the warning characterization parameter. The truck over-limit difference value includes the difference between the truck length and the preset truck length comparison threshold, the difference between the truck width and the preset truck width comparison threshold, and the difference between the truck height and the preset truck height comparison threshold, and determine whether to send an over-limit warning signal according to the warning characterization parameter; If the over-limit tendency category of the truck is a low-warning over-limit category, obtain the truck over-limit parameter change value according to the truck image. The truck over-limit parameters include the truck length, the truck width, and the truck height, and determine whether to send an over-limit warning signal; A signal sending unit for sending a warning signal; The category classification module determines whether the truck is overloaded or over-limit according to the load of each axle and the truck image, Obtain the load of each axle, and calculate the total weight of the truck according to the load of each axle; Compare the total weight of the truck with the preset truck overloading comparison threshold, If the total weight of the truck is greater than the preset truck overloading comparison threshold, it is determined that the truck is overloaded; Obtain the truck image, extract the outer contour image of the truck, and obtain the length, width, and height of the truck according to the outer contour image of the truck, Compare the length, width, and height of the truck with the preset truck length comparison threshold, the preset truck width comparison threshold, and the preset truck height comparison threshold respectively, If the first preset condition is met, it is determined that the truck is over-limit; The first preset condition is that the length of the truck is greater than the preset truck length comparison threshold, or the width of the truck is greater than the preset truck width comparison threshold, or the height of the truck is greater than the preset truck height comparison threshold; The category classification module classifies the over-limit tendency category of the truck in combination with the truck type and the truck image, Classify the truck type into a closed-type truck and a non-closed-type truck according to the truck image, Compare the length, width, and height of the truck with the preset warning truck length comparison threshold, the preset warning truck width comparison threshold, and the preset warning truck height comparison threshold respectively, If the second preset condition is met, classify the over-limit tendency category of the truck as a high-warning over-limit category; If the second preset condition is not met, classify the over-limit tendency category of the truck as a low-warning over-limit category; The second preset condition is that the truck type is a non-closed-type truck and the length of the truck is greater than the preset warning truck length comparison threshold, or the truck type is a non-closed-type truck and the width of the truck is greater than the preset warning truck width comparison threshold, or the truck type is a non-closed-type truck and the height of the truck is greater than the preset warning truck height comparison threshold.

2. The automatic detection system for overloading and over-limit of high-speed freight based on intelligent perception according to claim 1, wherein The strategy selection unit calculates the load fluctuation value according to the loads of each axle, and obtains the loads of each axle at preset time intervals; calculates the difference between the maximum value and the minimum value of the loads of each axle; and selects the maximum difference as the load fluctuation value.

3. The automatic detection system for overloading and over-limit of high-speed freight based on intelligent perception according to claim 2, characterized in that The strategy selection unit calculates the early warning characterization parameter according to formula (1), In formula (1), D represents the early warning characterization parameter, B represents the load fluctuation value, B0 represents the preset load fluctuation value threshold, Lc represents the difference in the length of the freight car exceeding the limit, Lc0 represents the preset difference threshold for the length of the freight car exceeding the limit, Hc represents the difference in the height of the freight car exceeding the limit, Hc0 represents the preset difference threshold for the height of the freight car exceeding the limit, Wc represents the difference in the width of the freight car exceeding the limit, and Wc0 represents the preset difference threshold for the width of the freight car exceeding the limit.

4. The automatic detection system for overloading and over-limit of high-speed freight based on intelligent perception according to claim 3, characterized in that, The strategy selection unit determines whether to issue an over-limit early warning signal according to the early warning characterization parameter, compares the early warning characterization parameter with the preset comparison threshold of the early warning characterization parameter, and if the early warning characterization parameter is greater than the preset comparison threshold of the early warning characterization parameter, it is determined to issue an over-limit early warning signal.

5. The automatic detection system for overloading and over-limit of high-speed freight based on intelligent perception according to claim 1, wherein, The strategy selection unit obtains the change value of the over-limit parameter of the freight car according to the freight car image, and extracts the freight car images of two adjacent times; The difference in the length of the freight car in the outer contour image of the freight car is the change value of the length of the freight car, the difference in the width of the freight car in the outer contour image of the freight car is the change value of the width of the freight car, and the difference in the height of the freight car in the outer contour image of the freight car is the change value of the height of the freight car.

6. The automatic detection system for overloading and over-limit of high-speed freight based on intelligent perception according to claim 5, characterized in that, The strategy selection unit determines whether to issue an over-limit early warning signal according to the change value of the over-limit parameter of the freight car obtained from the freight car image, compares the change value of the length of the freight car with the preset comparison threshold of the change value of the length of the freight car, compares the change value of the height of the freight car with the preset comparison threshold of the change value of the height of the freight car, and compares the change value of the width of the freight car with the preset comparison threshold of the change value of the width of the freight car, and if the third preset condition is met, it is determined to issue an over-limit early warning signal; The third preset condition is that the change value of the length of the freight car is greater than the preset comparison threshold of the change value of the length of the freight car, or the change value of the width of the freight car is greater than the preset comparison threshold of the change value of the width of the freight car, or the change value of the height of the freight car is greater than the preset comparison threshold of the change value of the height of the freight car.

7. The automatic detection system for overloading and over-limit of high-speed freight based on intelligent perception according to claim 1, wherein, The signal sending unit obtains the determination results of the category division module and the strategy selection unit, and sends an overloading signal, or an over-limit signal, or an early warning signal.

Images