Muck truck management method and system

Abstract

The invention provides a muck truck management method and system, and the method comprises the steps: obtaining snapshot images before and after the flushing of a muck truck, and comparing the snapshot images before and after the flushing of the muck truck, so as to supervise the source of a construction site; when the comparison result is qualified, releasing the slag car, and controlling a camera arranged at the road bayonet to shoot a road transportation picture of the corresponding slag car; and road transportation pictures are analyzed, and early warning is carried out on yawing, dripping and dripping and uncovering transportation of the muck truck according to analysis results so as to ensure that the muck transportation process meets the requirements of environmental protection, traffic and urban management.

Description

Technical Field

[0001] The invention belongs to the field of intelligent transportation technology, and in particular relates to a muck truck management method and system. Background Art

[0002] With the rapid development of urban construction, the number of construction projects has continued to increase, and the amount of muck transportation has also increased significantly. In the past, the management of muck trucks mainly relied on manual inspections and simple rules and regulations, which was inefficient and prone to regulatory loopholes. For example, during the large-scale development stage of a city, a medium-sized city may have hundreds of muck trucks in transportation operations every day. It is almost impossible to rely solely on manpower to check the transportation routes and loading conditions of each vehicle.

[0003] In order to adapt to the process of urbanization, it is necessary to use advanced technical means to achieve efficient management of dump trucks and ensure that the dump transportation process meets the requirements of environmental protection, transportation, urban management and other aspects. Summary of the invention

[0004] Based on this, an embodiment of the present invention provides a method and system for managing a slag truck, aiming to ensure that the slag transportation process complies with the requirements of environmental protection, transportation and urban management.

[0005] A first aspect of an embodiment of the present invention provides a method for managing a muck truck, the method comprising:

[0006] Obtain snapshot images of the muck truck before and after flushing, and compare the snapshot images before and after flushing to monitor the source of the construction site;

[0007] When the comparison result is qualified, the muck truck is released, and the camera set at the road checkpoint is controlled to shoot the road transportation picture of the corresponding muck truck;

[0008] The road transport images are analyzed, and based on the analysis results, early warnings are issued for deviation, spillage, and open-cover transportation of the dump truck.

[0009] Furthermore, the step of obtaining the captured images of the muck truck before and after flushing and comparing the captured images of the muck truck before and after flushing includes:

[0010] Acquire captured images of the muck truck before and after flushing, wherein the captured images are images taken at the same viewing angle and the same distance;

[0011] Compare the captured image before flushing with the standard image of the muck truck to determine the stain area on the muck truck;

[0012] Obtaining the pixel value of each pixel point in the stain area on the standard image of the muck truck, and adjusting the pixel value of each pixel point in the stain area on the standard image of the muck truck according to a preset pixel value range to determine the standard pixel value range;

[0013] Obtaining the pixel value of each pixel point in the stain area on the washed captured image, comparing the pixel value of each pixel point in the stain area on the washed captured image with the standard pixel value range, and determining the target pixel point that is not within the standard pixel value range;

[0014] Acquire a first number of the target pixel points and a second number of the pixel points in the stain area, and calculate a first proportion according to the first number and the second number;

[0015] Determining whether the first proportion is greater than a first threshold;

[0016] If it is determined that the first proportion is greater than a first threshold, an alarm is issued.

[0017] Furthermore, the step of obtaining the captured images of the muck truck before and after flushing and comparing the captured images of the muck truck before and after flushing includes:

[0018] Identify the license plate, model and body color of the muck truck in the captured image, obtain the time of the captured images before and after the muck truck is washed, and determine the washing time according to the time of the captured images before and after the muck truck is washed;

[0019] The license plate, vehicle model, body color and flushing time of the muck truck are uploaded to the server, so that the muck truck information is queried in the server according to the license plate, the flushing time and the alarm record.

[0020] Furthermore, the step of determining the flushing time according to the time of the captured images before and after flushing of the muck truck includes:

[0021] The area of ​​the stain area corresponding to the dump truck is obtained, the flushing time is associated with the area of ​​the stain area, and the flushing time corresponding to the area of ​​the stain area is determined by big data analysis to optimize the flushing time of subsequent dump trucks.

[0022] Furthermore, the step of analyzing the road transport picture and, based on the analysis result, issuing an early warning for deviation, spillage, or open-cover transport of the muck truck comprises:

[0023] Pre-mark the standard sequence of cameras set up at road checkpoints according to the planned route;

[0024] Obtaining the order in which the muck truck is photographed by the cameras, and determining whether it is consistent with the standard order;

[0025] If it is determined that the sequence is inconsistent with the standard, an early warning is issued;

[0026] If the judgment is consistent with the standard sequence, the outline of the muck truck is obtained, and the extreme points in the four directions of up, down, left and right are determined according to the outline of the muck map;

[0027] According to the extreme value points, a rectangular frame representing the muck truck is generated, wherein the size of the rectangular frame changes as the muck truck moves;

[0028] Determine the lower boundary of the rectangular frame, and determine the driving area swept out by the lower boundary on the road surface according to the movement of the muck truck;

[0029] Acquire a first image in the driving area after the muck truck exits the monitoring screen, and acquire a second image in the driving area when the muck truck does not enter the driving area;

[0030] The first image is compared with the second image to determine whether there is any spillage or dripping.

[0031] Furthermore, the step of comparing the first image with the second image to determine whether there is any spillage or dripping includes:

[0032] Acquire grayscale histograms of the first image and the second image, and perform normalization processing on the grayscale histograms of the first image and the second image respectively;

[0033] Calculating the Bhattacharyya distance of the grayscale histograms of the normalized first image and the second image;

[0034] Determining whether the Bhattacharyya distance is greater than a second threshold;

[0035] If it is determined that the Bhattacharyya distance is greater than the second threshold, it is determined that spillage and dripping exist.

[0036] Furthermore, after the step of obtaining the order of the cameras that successively photographed the muck truck and determining whether it is consistent with the standard order, the following steps are further included:

[0037] If it is determined that the sequence is consistent with the standard, the model of the current muck truck is obtained, and it is determined whether the model is a target model with a dump box;

[0038] If the vehicle type is determined to be a target vehicle type with a dump truck, a cargo image of the construction site corresponding to the muck truck is obtained, and a first pixel value representing the cargo in the cargo image is extracted;

[0039] Obtaining the geographic location of the camera where the muck truck is photographed, and obtaining weather conditions based on the geographic location, and determining a target pixel value range based on the weather conditions and the first pixel value;

[0040] Acquire target pixel points within the rectangular frame that are within the target pixel value range, and determine a target area composed of the target pixel points;

[0041] Acquire the area belonging to the dump bucket, and calculate a second proportion according to the target area and the area belonging to the dump bucket;

[0042] Determining whether the second proportion is greater than a third threshold;

[0043] If it is determined that the second proportion is greater than the third threshold, an alarm is issued.

[0044] A second aspect of an embodiment of the present invention provides a muck truck management system, which is used to implement the muck truck management method provided in the first aspect, and the system includes:

[0045] The comparison module is used to obtain the captured images of the muck truck before and after washing, and compare the captured images before and after washing the muck truck to supervise the source of the construction site;

[0046] The control module is used to release the muck truck when the comparison result is qualified, and control the camera set at the road checkpoint to shoot the road transportation picture of the corresponding muck truck;

[0047] The analysis module is used to analyze the road transport picture and, based on the analysis result, issue an early warning for the deviation, spillage, and open-cover transportation of the muck truck.

[0048] A third aspect of an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the muck truck management method provided in the first aspect.

[0049] A fourth aspect of an embodiment of the present invention provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the muck truck management method provided in the first aspect is implemented.

[0050] A method and system for managing a dump truck provided in an embodiment of the present invention obtains snapshot images of the dump truck before and after flushing, and compares the snapshot images before and after flushing to supervise the source of the construction site; when the comparison result is qualified, the dump truck is released, and the camera set at the road checkpoint is controlled to capture the road transportation picture of the corresponding dump truck; the road transportation picture is analyzed, and according to the analysis result, an early warning is issued for the yaw, spillage, and open-cover transportation of the dump truck to ensure that the dump truck transportation process meets the requirements of environmental protection, traffic and urban management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A flowchart of a method for managing a muck truck provided in the first embodiment of the present invention;

[0052] Figure 2 A structural block diagram of a muck truck management system provided in the second embodiment of the present invention;

[0053] Figure 3 This is a structural block diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0054] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0055] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0057] Embodiment 1

[0058] According to an embodiment of the present invention, a method for managing a garbage truck is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0059] In this embodiment 1, a method for managing a muck truck is provided, which can be used in an electronic device, such as a computer. Figure 1 , Figure 1 A flowchart of a method for managing a muck truck provided in the first embodiment of the present invention is shown, which specifically includes steps S01 to S03.

[0060] Step S01, obtaining captured images of the muck truck before and after flushing, and comparing the captured images of the muck truck before and after flushing to monitor the source of the construction site.

[0061] In order to prevent pollutants such as dirt and dust from the construction site from being brought into the city, it is necessary to supervise the source of the construction site. Specifically, a washing area is set at the exit of the construction site. When the muck truck enters the washing area, the water gun is controlled to discharge water to wash the periphery of the muck truck. In this embodiment, the captured images of the muck truck before and after washing are obtained, wherein the captured images are images taken at the same viewing angle and the same distance;

[0062] The captured image before flushing is compared with the standard image of the muck truck to determine the stain area on the muck truck. Since the working environment of the muck truck is relatively bad, it is not appropriate to use the muck truck image at the factory as the standard image. In this embodiment, the muck truck image is regularly updated and used as the standard image, which can improve the accuracy of determining the stain area on the muck truck.

[0063] Obtain the pixel value of each pixel point in the stain area on the standard image of the muck truck, and adjust the pixel value of each pixel point in the stain area on the standard image of the muck truck according to a preset pixel value range to determine a standard pixel value range. It can be understood that the standard pixel value range is a combined range of the pixel value range of the muck truck surface paint color and the pixel value range of the grayscale muck truck surface paint color;

[0064] Obtaining the pixel value of each pixel point in the stain area on the washed captured image, comparing the pixel value of each pixel point in the stain area on the washed captured image with the standard pixel value range, and determining the target pixel point that is not within the standard pixel value range;

[0065] Obtaining a first number of target pixels and a second number of pixels in the stain area, and calculating a first proportion according to the first number and the second number;

[0066] Determine whether the first proportion is greater than a first threshold;

[0067] If it is determined that the first proportion is greater than the first threshold, it means that the stain area is of a certain size and does not meet the cleaning standard, and an alarm is issued.

[0068] It should be noted that, in order to facilitate the information query of the muck truck under the whole process, the license plate, model and body color of the muck truck in the captured image are identified, and the time of the captured image before and after the muck truck is washed is obtained. According to the time of the captured image before and after the muck truck is washed, the washing time is determined, wherein the area of ​​the stain area of ​​the corresponding muck truck is obtained, the washing time is associated with the area of ​​the stain area, and the washing time corresponding to the area of ​​the stain area is determined by big data analysis to optimize the subsequent washing time of the muck truck. Specifically, the big data analysis can be, first, data cleaning is performed, that is, checking whether the collected data is complete, eliminating the data points with incorrect washing time records (such as negative time or unreasonable length) and abnormal stain area area measurement (such as negative area or too large to exceed the vehicle surface area), and then standardizing the data. Since the units and magnitudes of the washing time and the stain area area may be different, standardization processing is required. For example, the washing time is converted into seconds, and the stain area area is converted into square meters. And the data can be converted into data with a mean of 0 and a standard deviation of 1 through a standardization method (such as Z-score standardization), so that different variables are analyzed at the same level, and then the data is grouped and classified, that is, the data is grouped according to factors such as the type of muck truck (such as car size, vehicle purpose, etc.), stain type (such as soil stains, concrete stains, etc.), weather conditions (such as sunny days, rainy days), etc., so as to analyze the relationship between the flushing time and the area of ​​the stain area under different conditions in the future. Finally, regression analysis is used. It can be understood that a regression model is established to describe the relationship between the flushing time and the area of ​​the stain area. If the correlation analysis shows that there is a linear relationship between the two, a linear regression model can be used, such as y=a+bx, where y represents the flushing time, x represents the area of ​​the stain area, and a and b are regression coefficients. The regression coefficient is estimated by the least squares method and other methods, and the model can be used to predict the flushing time under a given stain area. If there is a nonlinear relationship, polynomial regression can be tried (such as quadratic regression y=a+bx+cx 2 ), exponential regression (such as y = a × e bx ) and other regression models, and through model evaluation indicators (such as mean square error, R 2 Values, etc.) to select the optimal regression model;

[0069] The license plate, model, body color and flushing time of the muck truck are uploaded to the server, so that the muck truck information can be queried in the server according to the license plate, flushing time and alarm records.

[0070] Step S02: When the comparison result is qualified, the muck truck is released, and the camera set at the road checkpoint is controlled to capture the road transportation picture of the corresponding muck truck.

[0071] Among them, cameras are reused / newly built at road checkpoints on the planned routes of dump trucks to monitor the dump trucks.

[0072] Step S03, analyzing the road transport picture, and based on the analysis result, issuing an early warning for deviation, spillage, and cover-opening of the muck truck.

[0073] Specifically, the standard sequence of cameras set up at road checkpoints is pre-marked according to the planned route;

[0074] Obtain the order in which the cameras successively photographed the muck truck, and determine whether it is consistent with the standard order, wherein the license plate number of the muck truck can be identified to determine the tracking of a muck truck;

[0075] If it is judged that it is inconsistent with the standard sequence, it means that the muck truck is deviating, and an early warning is issued;

[0076] If the judgment is consistent with the standard order, the outline of the muck truck is obtained, and the extreme points in the four directions of up, down, left and right are determined according to the outline of the muck map;

[0077] According to the extreme value points, a rectangular frame representing the muck truck is generated, wherein the size of the rectangular frame changes as the muck truck moves. It is understandable that the rectangular frame will also change as the muck truck moves farther and farther and turns.

[0078] Determine the lower boundary of the rectangular frame, and determine the driving area of ​​the lower boundary swept out on the road surface according to the movement of the muck truck;

[0079] A first image is obtained in the driving area after the muck truck exits the monitoring screen, and a second image is obtained in the driving area when the muck truck has not entered the driving area. It should be noted that in the picture taken by the camera, all vehicles can be drawn with rectangular frames, and the rectangular frames are given colors. For example, the rectangular frame color of qualified vehicles is green, and the rectangular frame color of unqualified vehicles is red. The first image is formed by splicing multiple images in the video screen taken by the camera. Specifically, when it is determined that the muck truck is in the monitoring screen and the lower boundary is in the driving area scanned flat on the road surface without interference from the rectangular frames drawn by other vehicles, the most accurate image of the muck truck when it exits the monitoring screen is obtained. The last frame image is taken, and according to the driving area, the last frame image is intercepted to obtain the first image; when it is determined that the muck truck is in the monitoring screen, and the lower boundary of the driving area swept out by the road surface is interfered by the rectangular frame drawn by other vehicles, the key frame image in the driving area in the video is determined, and the key frame images are spliced ​​to obtain the first image, wherein the key frame image refers to the image containing the ground, and all the images containing the ground in the driving area are taken by the union method to obtain the first image. It can be understood that the final first image does not contain a complete ground and does not affect the final spillage judgment, because the subsequent comparison is a similarity comparison between images. In addition, the second image is obtained by determining the image of the muck truck at the moment before it enters the monitoring screen, and according to the driving area, the image of the muck truck at the moment before it enters the monitoring screen is intercepted, and at the same time, according to the actual situation of the first image, it is intercepted again to finally obtain the second image;

[0080] The first image and the second image are compared to determine whether there is spillage or dripping. Specifically, the grayscale histograms of the first image and the second image are obtained, and the grayscale histograms of the first image and the second image are normalized respectively. The grayscale histogram of the first image can be expressed as H A (i), the grayscale histogram of the first image can be expressed as H B (i), i represents the gray level, which usually ranges from 0 to 255. The normalized expression is: and

[0081] Calculate the Bhattacharyya distance of the grayscale histogram of the first image and the second image after normalization. The calculation expression of Bhattacharyya distance is d B (A, B) = -ln(BC(A, B)), where It is the Bhattacharyya Coefficient. The value of the Bhattacharyya Coefficient is between 0 and 1. When the two histograms are exactly the same, BC(A, B) = 1. At this time, the Bhattacharyya distance d B (A, B) = 0; when the two histograms are very different, BC (A, B) approaches 0, and the Bhattacharyya distance dB (A, B) approaches infinity;

[0082] Determine whether the Bhattacharyya distance is greater than a second threshold;

[0083] If it is determined that the Bhattacharyya distance is greater than the second threshold, it is determined that spillage and dripping exist.

[0084] In some other embodiments of the present invention, if the judgment is consistent with the standard sequence, the model of the current muck truck is obtained, and it is judged whether the model is a target model with a dump bucket. It should be noted that the dump bucket here is not limited to the model of the muck truck, and the target model is a model that can transport goods and needs to be closed.

[0085] If the vehicle type is determined to be a target vehicle type with a dump truck, the cargo image of the construction site corresponding to the muck truck is obtained, and the first pixel value representing the cargo in the cargo image is extracted. It is understandable that the construction site is generally monitored, and the pixel value of the photographed cargo can be obtained in real time, wherein the cargo may be sand, stone, etc.;

[0086] Obtain the geographic location of the camera where the muck truck is photographed, and obtain the weather conditions based on the geographic location, and determine the target pixel value range based on the weather conditions and the first pixel value. The purpose of this step is to consider the impact of weather factors on the pixels in the captured image and improve the recognition accuracy. It is understandable that the color of the cargo in the captured image is brighter when there is sufficient sunlight.

[0087] Obtain target pixel points within the target pixel value range in the rectangular frame, and determine the target area composed of the target pixel points;

[0088] Obtain an area belonging to the dump bucket, and calculate a second proportion according to the target area and the area belonging to the dump bucket. It should be noted that the dump bucket area is determined by obtaining a three-dimensional digital model of the muck truck, and moving the three-dimensional digital model of the muck truck according to the shooting angle of the camera and the distance between the camera and the muck truck, wherein the distance between the camera and the muck truck can be estimated according to the shooting angle of the camera and ground information (zebra crossings, indicator lines and other reference objects). Specifically, according to the shooting angle of the camera, the placement angle of the three-dimensional digital model of the muck truck is adjusted, and at the same time, a rectangular frame is generated, and the rectangular frame of the three-dimensional digital model of the muck truck is scaled in proportion until it is consistent with the rectangular frame of the muck truck in the monitoring screen, and the area of ​​the dump bucket area in the three-dimensional digital model of the muck truck is determined, that is, the area of ​​the dump bucket area of ​​the muck truck in the monitoring screen. In addition, the second proportion can be determined by calculating the pixel areas of the two regions;

[0089] Determine whether the second proportion is greater than a third threshold;

[0090] If it is determined that the second proportion is greater than the third threshold, an alarm is issued.

[0091] In summary, the dump truck management method in the above-mentioned embodiment of the present invention obtains the captured images of the dump truck before and after washing, and compares the captured images before and after washing to supervise the source of the construction site; when the comparison result is qualified, the dump truck is released, and the camera set at the road checkpoint is controlled to capture the road transportation picture of the corresponding dump truck; the road transportation picture is analyzed, and according to the analysis result, the yaw, spillage, and open-cover transportation of the dump truck are warned to ensure that the dump truck transportation process meets the requirements of environmental protection, traffic and urban management.

[0092] Embodiment 2

[0093] See also Figure 2 , Figure 2 This is a block diagram of a muck truck management system provided by Embodiment 2 of the present invention. The muck truck management system 200 is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0094] Specifically, the muck truck management system 200 includes: a comparison module 21, a control module 22 and an analysis module 23, wherein:

[0095] The comparison module 21 is used to obtain the captured images of the muck truck before and after the washing, and compare the captured images of the muck truck before and after the washing to supervise the source of the construction site;

[0096] The control module 22 is used to release the muck truck when the comparison result is qualified, and control the camera set at the road checkpoint to shoot the road transportation picture of the corresponding muck truck;

[0097] The analysis module 23 is used to analyze the road transport picture and, based on the analysis result, issue an early warning for deviation, spillage, or open-cover transportation of the muck truck.

[0098] Further, in some optional embodiments of the present invention, the comparison module 21 includes:

[0099] A first acquisition unit is used to acquire captured images of the muck truck before and after flushing, wherein the captured images are images taken at the same viewing angle and the same distance;

[0100] The first determination unit is used to compare the captured image before flushing with the standard image of the muck truck to determine the stain area on the muck truck;

[0101] The second determination unit is used to obtain the pixel value of each pixel point in the stain area on the standard image of the muck truck, and adjust the pixel value of each pixel point in the stain area on the standard image of the muck truck according to a preset pixel value range to determine the standard pixel value range;

[0102] A third determination unit is used to obtain the pixel value of each pixel point in the stain area of ​​the washed snapshot image, compare the pixel value of each pixel point in the stain area of ​​the washed snapshot image with the standard pixel value range, and determine the target pixel point that is not within the standard pixel value range;

[0103] A first calculation unit is used to obtain a first number of the target pixel points and a second number of the pixel points in the stain area, and calculate a first proportion according to the first number and the second number;

[0104] A first judging unit, configured to judge whether the first proportion is greater than a first threshold;

[0105] The first alarm unit is configured to issue an alarm if it is determined that the first proportion is greater than a first threshold.

[0106] Furthermore, in some optional embodiments of the present invention, the comparison module 21 further includes:

[0107] The recognition unit is used to recognize the license plate, vehicle model and body color of the muck truck in the captured image, and obtain the time of the captured images before and after the muck truck is washed, and determine the washing time according to the time of the captured images before and after the muck truck is washed;

[0108] The uploading unit is used to upload the license plate, model, body color and flushing time of the muck truck to the server, so as to query the muck truck information in the server according to the license plate, the flushing time and the alarm record.

[0109] Furthermore, in some optional embodiments of the present invention, the comparison module 21 further includes:

[0110] The fourth determination unit is used to obtain the area of ​​the stain area corresponding to the dump truck, associate the flushing time with the area of ​​the stain area, and determine the flushing time corresponding to the area of ​​the stain area through big data analysis to optimize the flushing time of subsequent dump trucks.

[0111] Furthermore, in some optional embodiments of the present invention, the analysis module 23 includes:

[0112] A marking unit, used to pre-mark the standard sequence of cameras set at road checkpoints according to the planned route;

[0113] A second judgment unit is used to obtain the order in which the muck truck is photographed by the cameras, and judge whether it is consistent with the standard order;

[0114] A first warning unit, configured to issue a warning if it is determined that the sequence is inconsistent with the standard sequence;

[0115] A fifth determination unit is used to obtain the outline of the muck truck if it is determined to be consistent with the standard sequence, and determine the extreme points in the four directions of up, down, left and right according to the outline of the muck map;

[0116] A generating unit, used for generating a rectangular frame representing the muck truck according to the extreme value point, wherein the size of the rectangular frame changes with the movement of the muck truck;

[0117] A sixth determining unit, configured to determine the lower boundary of the rectangular frame, and determine the driving area swept out by the lower boundary on the road surface according to the movement of the muck truck;

[0118] A second acquisition unit is used to acquire a first image in the driving area after the muck truck drives out of the monitoring screen, and to acquire a second image in the driving area when the muck truck does not drive into the driving area;

[0119] The comparison unit is used to compare the first image with the second image to determine whether there is any spillage or dripping.

[0120] Further, in some optional embodiments of the present invention, the comparison unit includes:

[0121] a normalization processing subunit, used for acquiring grayscale histograms of the first image and the second image, and performing normalization processing on the grayscale histograms of the first image and the second image respectively;

[0122] A calculation subunit, used for calculating the Bhattacharyya distance of the grayscale histograms of the first image and the second image after normalization;

[0123] A judging subunit, used to judge whether the Bhattacharyya distance is greater than a second threshold;

[0124] If it is determined that the Bhattacharyya distance is greater than the second threshold, it is determined that spillage and dripping exist.

[0125] Furthermore, in some optional embodiments of the present invention, the analysis module 23 further includes:

[0126] A third judgment unit is used to obtain the model of the current muck truck if the judgment is consistent with the standard sequence, and judge whether the model is a target model with a dump truck;

[0127] an extraction unit, configured to obtain a cargo image of the construction site corresponding to the muck truck if the vehicle type is determined to be a target vehicle type with a dump truck, and extract a first pixel value representing the cargo in the cargo image;

[0128] a seventh determination unit, configured to obtain a geographical location of a camera where the muck truck is photographed, obtain weather conditions based on the geographical location, and determine a target pixel value range based on the weather conditions and the first pixel value;

[0129] an eighth determining unit, configured to obtain target pixel points within the rectangular frame that are within the target pixel value range, and determine a target area formed by the target pixel points;

[0130] A second calculation unit is used to obtain an area belonging to the dump box, and calculate a second proportion according to the target area and the area belonging to the dump box;

[0131] A fourth judging unit, configured to judge whether the second proportion is greater than a third threshold;

[0132] The second alarm unit is configured to issue an alarm if it is determined that the second proportion is greater than a third threshold.

[0133] Embodiment 3

[0134] Another aspect of the present invention provides an electronic device, see Figure 3 , shown is an electronic device in Embodiment 3 of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, the above-mentioned method for managing a muck truck is implemented.

[0135] In some embodiments, the processor 10 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor or other data processing chip, used to run program codes or process data stored in the memory 20, such as executing access restriction programs.

[0136] Among them, the memory 20 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory 20 may be an internal storage unit of an electronic device in some embodiments, such as a hard disk of the electronic device. The memory 20 may also be an external storage device of an electronic device in other embodiments, such as a plug-in hard disk equipped on the electronic device, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. Further, the memory 20 may also include both an internal storage unit and an external storage device of the electronic device. The memory 20 may be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or is to be output.

[0137] It should be pointed out that Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than those shown in the figure, or combine certain components, or arrange the components differently.

[0138] The embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the above-mentioned method for managing a muck truck is implemented.

[0139] Those skilled in the art will appreciate that the logic and / or steps represented in the flowchart or otherwise described herein, for example, may be considered as an ordered list of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0140] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0141] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or a combination thereof: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0142] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0143] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the attached claims.

Claims

1. A method for managing a muck truck, characterized in that: The method comprises: Obtain snapshot images of the muck truck before and after flushing, and compare the snapshot images before and after flushing to monitor the source of the construction site; When the comparison result is qualified, the muck truck is released, and the camera set at the road checkpoint is controlled to shoot the road transportation picture of the corresponding muck truck; The road transport images are analyzed, and based on the analysis results, early warnings are issued for deviation, spillage, and open-cover transportation of the dump truck.

2. The method for managing a muck truck according to claim 1, characterized in that: The steps of obtaining the captured images of the muck truck before and after flushing and comparing the captured images of the muck truck before and after flushing include: Acquire captured images of the muck truck before and after flushing, wherein the captured images are images taken at the same viewing angle and the same distance; Compare the captured image before flushing with the standard image of the muck truck to determine the stain area on the muck truck; Obtaining the pixel value of each pixel point in the stain area on the standard image of the muck truck, and adjusting the pixel value of each pixel point in the stain area on the standard image of the muck truck according to a preset pixel value range to determine the standard pixel value range; Obtaining the pixel value of each pixel point in the stain area on the washed captured image, comparing the pixel value of each pixel point in the stain area on the washed captured image with the standard pixel value range, and determining the target pixel point that is not within the standard pixel value range; Acquire a first number of the target pixel points and a second number of the pixel points in the stain area, and calculate a first proportion according to the first number and the second number; Determining whether the first proportion is greater than a first threshold; If it is determined that the first proportion is greater than a first threshold, an alarm is issued.

3. The method for managing a muck truck according to claim 2, characterized in that: The step of obtaining the captured images of the muck truck before and after flushing and comparing the captured images of the muck truck before and after flushing comprises: Identify the license plate, model and body color of the muck truck in the captured image, obtain the time of the captured images before and after the muck truck is washed, and determine the washing time according to the time of the captured images before and after the muck truck is washed; The license plate, vehicle model, body color and flushing time of the muck truck are uploaded to the server, so that the muck truck information is queried in the server according to the license plate, the flushing time and the alarm record.

4. The method for managing a muck truck according to claim 3, characterized in that: The step of determining the flushing time according to the time of the captured images before and after the flushing of the muck truck comprises: The area of ​​the stain area corresponding to the dump truck is obtained, the flushing time is associated with the area of ​​the stain area, and the flushing time corresponding to the area of ​​the stain area is determined by big data analysis to optimize the flushing time of subsequent dump trucks.

5. The method for managing a muck truck according to claim 4, characterized in that: The step of analyzing the road transport picture and, based on the analysis result, giving an early warning of the deviation, spillage, or cover-opening of the muck truck comprises: Pre-mark the standard sequence of cameras set up at road checkpoints according to the planned route; Obtaining the order in which the muck truck is photographed by the cameras, and determining whether it is consistent with the standard order; If it is determined that the sequence is inconsistent with the standard, an early warning is issued; If the judgment is consistent with the standard sequence, the outline of the muck truck is obtained, and the extreme points in the four directions of up, down, left and right are determined according to the outline of the muck map; According to the extreme value points, a rectangular frame representing the muck truck is generated, wherein the size of the rectangular frame changes as the muck truck moves; Determine the lower boundary of the rectangular frame, and determine the driving area swept out by the lower boundary on the road surface according to the movement of the muck truck; Acquire a first image in the driving area after the muck truck exits the monitoring screen, and acquire a second image in the driving area when the muck truck does not enter the driving area; The first image is compared with the second image to determine whether there is any spillage or dripping.

6. The method for managing a muck truck according to claim 5, characterized in that: The step of comparing the first image with the second image to determine whether there is spillage or dripping includes: Acquire grayscale histograms of the first image and the second image, and perform normalization processing on the grayscale histograms of the first image and the second image respectively; Calculating the Bhattacharyya distance of the grayscale histograms of the normalized first image and the second image; Determining whether the Bhattacharyya distance is greater than a second threshold; If it is determined that the Bhattacharyya distance is greater than the second threshold, it is determined that spillage and dripping exist.

7. The method for managing a muck truck according to claim 6, characterized in that: The step of obtaining the sequence of cameras that successively photograph the muck truck and determining whether it is consistent with the standard sequence further includes: If it is determined that the sequence is consistent with the standard, the model of the current muck truck is obtained, and it is determined whether the model is a target model with a dump box; If the vehicle type is determined to be a target vehicle type with a dump truck, a cargo image of the construction site corresponding to the muck truck is obtained, and a first pixel value representing the cargo in the cargo image is extracted; Obtaining the geographic location of the camera where the muck truck is photographed, and obtaining weather conditions based on the geographic location, and determining a target pixel value range based on the weather conditions and the first pixel value; Acquire target pixel points within the rectangular frame that are within the target pixel value range, and determine a target area composed of the target pixel points; Acquire the area belonging to the dump bucket, and calculate a second proportion according to the target area and the area belonging to the dump bucket; Determining whether the second proportion is greater than a third threshold; If it is determined that the second proportion is greater than the third threshold, an alarm is issued.

8. A muck truck management system, characterized in that: Used to implement the muck truck management method according to any one of claims 1 to 7, the system comprises: The comparison module is used to obtain the captured images of the muck truck before and after washing, and compare the captured images before and after washing the muck truck to supervise the source of the construction site; The control module is used to release the muck truck when the comparison result is qualified, and control the camera set at the road checkpoint to shoot the road transportation picture of the corresponding muck truck; The analysis module is used to analyze the road transport picture and, based on the analysis result, issue an early warning for the deviation, spillage, and open-cover transportation of the muck truck.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the muck truck management method as described in any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for managing a muck truck as claimed in any one of claims 1 to 7 is implemented.

Images