Green pass inspection method, device, equipment and storage medium for freight vehicles on highways

Abstract

The present application discloses a method, device, equipment and storage medium for green pass inspection of freight vehicles on highways. The method includes: obtaining basic data of freight vehicles to be inspected, the basic data including list data, highway traffic behavior data, historical green pass inspection result data, freight item type data and freight item weight data; obtaining a first inspection result according to the list data, the freight item weight data and the freight item type data; obtaining a second inspection result according to the highway traffic behavior data; obtaining a third inspection result according to the historical green pass inspection result data; determining a green pass inspection result according to the first inspection result, the second inspection result and the third inspection result; determining target processing measures according to the green pass inspection result. Obtaining a green pass inspection result by comparing list data, highway traffic behavior and historical green pass inspection results is conducive to improving the accuracy of green pass inspection.

Description

Technical Field

[0001] The present application relates to the technical field of highway green pass inspection, and in particular to a method, device, equipment and storage medium for highway freight vehicle green pass inspection. Background Art

[0002] In order to promote the supply of "vegetable basket" and ensure the basic living needs of the masses, there is a special "green channel" for the transportation of fresh agricultural products. However, some places have inconsistent understandings of fresh agricultural products and inconsistent inspection standards. For example, there are many categories of agricultural products and many aliases, and it is impossible to quickly identify the legitimacy of green channel goods. Some truck drivers use non-agricultural mixed loading, overloading of goods and other behaviors to impersonate green channel vehicles to evade tolls, which reduces the efficiency and accuracy of inspection. Summary of the invention

[0003] The embodiments of the present application provide a method, device, equipment and storage medium for green pass inspection of freight vehicles on highways. The green pass inspection results of the freight vehicles to be inspected are obtained by comprehensively comparing the freight item lists, types and weights of the freight items, high-speed behaviors of the freight vehicles and historical green pass inspection results. The handling measures for the freight vehicles to be inspected are determined according to the green pass inspection results, which is beneficial to improving the accuracy of green pass inspection while ensuring the efficiency of green pass inspection.

[0004] In a first aspect, an embodiment of the present application provides a method for checking a green pass for a freight vehicle on a highway, which is applied to a server of a system for checking a green pass for a freight vehicle on a highway, comprising:

[0005] Obtain basic data of the freight vehicle to be inspected, the basic data including freight item list data, expressway traffic behavior data, historical green pass inspection result data, freight item type data and freight item weight data; obtain a first inspection result based on the freight item list data, the freight item weight data and the freight item type data, the first inspection result is used to indicate whether the freight item is abnormal; obtain a second inspection result based on the expressway traffic behavior data, the second inspection result is used to indicate whether the freight vehicle to be inspected has abnormal traffic behavior; obtain a third inspection result based on the historical green pass inspection result data, the third inspection result is used to indicate whether the freight vehicle to be inspected is an abnormal green pass vehicle; determine the green pass inspection result of the freight vehicle to be inspected based on the first inspection result, the second inspection result and the third inspection result; determine target processing measures for the freight vehicle to be inspected based on the green pass inspection result.

[0006] In a second aspect, the embodiment of the present application provides a highway freight vehicle green pass inspection device, comprising:

[0007] An acquisition module is used to acquire basic data of the freight vehicle to be inspected, wherein the basic data includes freight item list data, high-speed traffic behavior data, historical green pass inspection result data, freight item type data, and freight item weight data;

[0008] A processing module, used to obtain a first inspection result based on the freight item list data, the freight item weight data and the freight item type data, the first inspection result is used to indicate whether the freight item is abnormal; and used to obtain a second inspection result based on the high-speed traffic behavior data, the second inspection result is used to indicate whether the freight vehicle to be inspected has abnormal traffic behavior; and used to obtain a third inspection result based on the historical green pass inspection result data, the third inspection result is used to indicate whether the freight vehicle to be inspected is an abnormal green pass vehicle; and used to determine the green pass inspection result of the freight vehicle to be inspected based on the first inspection result, the second inspection result and the third inspection result; and used to determine the target processing measures for the freight vehicle to be inspected based on the green pass inspection result.

[0009] In a third aspect, the embodiment of the present application provides a highway freight vehicle green pass inspection device, the device comprising:

[0010] A memory, a processor, and a data processing program stored in the memory and executable on the processor, wherein the data processing program is configured to implement part or all of the steps described in any method in the first aspect.

[0011] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a data processing program is stored. When the data processing program is executed by a processor, some or all of the steps described in any method in the first aspect are implemented.

[0012] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect of the embodiment of the present application. The computer program product can be a software installation package.

[0013] By implementing the embodiments of the present application, the server of the highway freight vehicle green pass inspection system first obtains the basic data of the freight vehicle to be inspected, the basic data including freight item list data, highway traffic behavior data, historical green pass inspection result data, freight item type data and freight item weight data; obtains a first inspection result based on the freight item list data, the freight item weight data and the freight item type data, and the first inspection result is used to indicate whether the freight item is abnormal; obtains a second inspection result based on the highway traffic behavior data, and the second inspection result is used to indicate whether the freight vehicle to be inspected has abnormal traffic behavior; obtains a third inspection result based on the historical green pass inspection result data, and the third inspection result is used to indicate whether the freight vehicle to be inspected is an abnormal green pass vehicle; determines the green pass inspection result of the freight vehicle to be inspected based on the first inspection result, the second inspection result and the third inspection result; determines the target processing measures for the freight vehicle to be inspected based on the green pass inspection result. The Green Pass inspection results of the freight vehicles to be inspected are obtained by comprehensively comparing the freight item lists, freight item types and weights, high-speed behavior of the freight vehicles and historical Green Pass inspection results. The handling measures for the freight vehicles to be inspected are determined based on the Green Pass inspection results, which is conducive to improving the accuracy of Green Pass inspection while ensuring the efficiency of Green Pass inspection. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0015] Figure 1 It is a schematic diagram of the architecture of a highway freight vehicle green pass inspection system provided by an embodiment of the present application;

[0016] Figure 2 It is a flow chart of a method for checking the green pass of freight vehicles on highways provided in an embodiment of the present application;

[0017] Figure 3 This is a hardware architecture diagram of a highway freight vehicle green pass inspection system provided by an embodiment of the present application;

[0018] Figure 4 It is a schematic diagram of the hardware architecture of a weighing system provided in an embodiment of the present application;

[0019] Figure 5 is a schematic diagram of a first prompt information provided in an embodiment of the present application;

[0020] Figure 6 It is a structural schematic diagram of a green pass inspection device for highway freight vehicles provided in an embodiment of the present application;

[0021] Figure 7It is a structural schematic diagram of a highway freight vehicle green pass inspection device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. According to the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0023] The terms "first", "second", "third", etc. in the specification, claims and drawings of this application are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0024] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0025] In order to promote the supply of "vegetable basket" and ensure the basic living needs of the masses, there is a special "green channel" for the transportation of fresh agricultural products. However, some places have inconsistent understandings of fresh agricultural products and inconsistent inspection standards. For example, there are many categories of agricultural products and many aliases, and it is impossible to quickly identify the legitimacy of green channel goods. Some truck drivers use non-agricultural mixed loading, overloading of goods and other behaviors to impersonate green channel vehicles to evade tolls, which reduces the efficiency and accuracy of inspection.

[0026] In response to the above problems, an embodiment of the present application provides a method, device, equipment and storage medium for green pass inspection of freight vehicles on highways. The server of the green pass inspection system for freight vehicles on highways first obtains basic data of freight vehicles to be inspected, the basic data including freight item list data, highway traffic behavior data, historical green pass inspection result data, freight item type data and freight item weight data; a first inspection result is obtained based on the freight item list data, the freight item weight data and the freight item type data, and the first inspection result is used to indicate whether the freight item is abnormal; a second inspection result is obtained based on the highway traffic behavior data, and the second inspection result is used to indicate whether the freight vehicle to be inspected has abnormal traffic behavior; a third inspection result is obtained based on the historical green pass inspection result data, and the third inspection result is used to indicate whether the freight vehicle to be inspected is an abnormal green pass vehicle; the green pass inspection result of the freight vehicle to be inspected is determined based on the first inspection result, the second inspection result and the third inspection result; and target processing measures for the freight vehicle to be inspected are determined based on the green pass inspection result. The Green Pass inspection results of the freight vehicles to be inspected are obtained by comprehensively comparing the freight item lists, freight item types and weights, high-speed behavior of the freight vehicles and historical Green Pass inspection results. The handling measures for the freight vehicles to be inspected are determined based on the Green Pass inspection results, which is conducive to improving the accuracy of Green Pass inspection while ensuring the efficiency of Green Pass inspection.

[0027] The highway freight vehicle green pass inspection method, device, equipment and storage medium provided in the embodiments of the present application can be applied to Figure 1 In the highway freight vehicle green pass inspection system shown, please refer to Figure 1 , Figure 1 It is a schematic diagram of the architecture of a highway freight vehicle green pass inspection system provided by an embodiment of the present application. The highway freight vehicle green pass inspection system 100 includes a terminal 101 and a server 102. The terminal 101 can communicate with the server 102 through a network. The terminal 101 refers to a device used by a user, such as a smart phone, a computer, etc. In this solution, the terminal 101 is provided with a user interface, which is mainly responsible for interacting with the server 102, issuing green pass inspection instructions for freight vehicles to be inspected to the server 102, and submitting some basic data of the freight vehicles to be inspected to the server 102, such as freight item list data. The terminal 101 can obtain and display the processing results from the server 102, including the first inspection result, the second inspection result, the third inspection result, the final green pass inspection result, and the target processing measures for the freight vehicles to be inspected.

[0028] Server 102 refers to a remote computer used to process a large number of computing tasks and store data. In this solution, server 102 is responsible for storing basic data of freight vehicles to be inspected, including freight item list data, high-speed traffic behavior data, historical green pass inspection result data, freight item type data, and freight item weight data. Server 102 is also used to perform multiple green pass inspections and determine target processing measures for freight vehicles to be inspected based on multiple green pass inspection results.

[0029] Based on this, the present application provides a method, device, equipment and storage medium for green pass inspection of freight vehicles on highways. The present application is described in detail below in conjunction with the accompanying drawings.

[0030] See also Figure 2 , Figure 2 : is a flowchart of a method for checking a green pass for a freight vehicle on a highway provided by an embodiment of the present application, such as Figure 2 As shown, the method comprises the following steps:

[0031] S201, obtaining basic data of the freight vehicle to be inspected.

[0032] Among them, the basic data includes freight item list data, high-speed traffic behavior data, historical green pass inspection result data, freight item type data and freight item weight data.

[0033] Among them, the freight item list data is a detailed information list of all goods carried by the freight vehicle to be inspected, mainly including the name of the goods carried by the vehicle, the type of goods carried by the vehicle, the weight of the goods carried by the vehicle, the packaging method of the goods carried by the vehicle, the production and sales place of the goods carried by the vehicle, and the calibrated price of the goods carried by the vehicle.

[0034] Among them, the highway traffic behavior data includes the records of freight vehicles to be inspected getting on and off the highway, the time they stay in service areas, the driving speed, the route they pass, the number of stops, and abnormal driving behaviors. The records of getting on and off the highway are used to record the time, location, and frequency of vehicles entering and leaving the highway. The time they stay in service areas records the length of time the vehicle stays in each service area. Abnormal driving behaviors include sudden braking, sudden acceleration, and other driving behaviors.

[0035] Among them, the historical Green Pass inspection result data is the inspection result record obtained when the freight vehicles to be inspected underwent Green Pass inspections in the past, including the Green Pass inspection time, Green Pass inspection location, Green Pass inspection results and the treatment measures taken, etc. The Green Pass inspection results include that the freight vehicles to be inspected are normal Green Pass vehicles and that the freight vehicles to be inspected are abnormal Green Pass vehicles.

[0036] In a possible implementation, basic data of the freight vehicle to be inspected is obtained, including:

[0037] The license plate number and image data of the freight items of the freight vehicle to be inspected are obtained through an image acquisition device; the freight item list data submitted by the freight vehicle to be inspected at the highway entrance station is obtained according to the license plate number of the freight vehicle to be inspected; the highway traffic behavior data and historical green pass inspection result data of the freight vehicle to be inspected are obtained from the database according to the license plate number of the freight vehicle to be inspected; the freight item image data are subjected to feature extraction and label classification through a convolutional neural network to obtain the freight item type data of the freight vehicle to be inspected; the whole vehicle weight data of the freight vehicle to be inspected is obtained through a measuring sensor; the freight item weight data of the freight vehicle to be inspected is obtained according to the whole vehicle weight data and the calibrated weight of the freight vehicle to be inspected.

[0038] Among them, see Figure 3 , Figure 3 Schematic diagram of the hardware architecture of a highway freight vehicle green pass inspection system provided in an embodiment of the present application. Figure 3 As shown, the hardware architecture of the highway freight vehicle green pass inspection system includes an image acquisition device X and a metering sensor Y. The image acquisition device X can be various types of devices set at multiple locations, used to obtain the license plate number and freight image data of the freight vehicle to be inspected, such as a camera installed at a fixed position or an aerial photography device such as a drone. Specifically, the image acquisition device X can use optical character recognition (OCR) technology to shoot the front or rear of the freight vehicle to be inspected, and extract and identify the license plate number through an image processing algorithm. The image acquisition device uses a high-definition camera and other equipment to shoot the goods carried by the freight vehicle to be inspected. The shooting area includes multiple angles and perspectives such as the entire cargo hold, trunk or container, so as to obtain detailed information such as the appearance and quantity of the goods. The metering sensor Y is used to obtain the vehicle weight.

[0039] Among them, after the license plate number is extracted by the image acquisition device X, the server of the highway freight vehicle green pass inspection system compares the extracted license plate number with the record stored in advance in the database. The database contains all freight vehicles entering the highway entrance station and their corresponding freight inventory data. By executing the structured query language (SQL) query or calling the corresponding API interface, the server can quickly retrieve the data associated with the license plate.

[0040] Exemplarily, the image acquisition device X is used to photograph the freight vehicle to be inspected, and the license plate number is extracted therefrom. The extracted license plate number is "Yue A12345". The server automatically queries the database to obtain the freight item list data of the vehicle, which shows the following content when the truck entered the green channel inspection point of the highway: Name of goods: Apples, Type of goods: Fruits, Quantity: 50 boxes, Weight: 1000 kg, Packaging method: Cartons.

[0041] Among them, the specific steps for feature extraction and labeling of freight item image data through a convolutional neural network (CNN) are as follows: First, obtain the freight item image of the freight vehicle to be inspected. Preprocess all input images, including but not limited to uniformly adjusting them to the same size (such as 224x224 pixels) to meet the input requirements of the CNN model, normalizing the images, converting the pixel values to the range [0, 1], and then using multiple convolutional layers to extract features from the preprocessed images. A pooling layer is added after convolution to reduce the feature dimension and retain important features. Finally, the extracted features are integrated through one or more fully connected layers, and the probability distribution corresponding to each category is finally output. The final output is a vector, where each element represents the probability corresponding to a cargo type. By selecting the category corresponding to the highest probability value as the prediction result, the data on the types of freight items carried on the vehicle is obtained.

[0042] Among them, when weighing the vehicle, please refer to Figure 4 , Figure 4 which is a schematic diagram of the hardware architecture of a weighing system provided by an embodiment of the present application. As Figure 4 shown, the weighing system can use a hardware architecture mainly composed of a weighing platform A, a tire identifier B, an axle number identifier C, a central processing cabinet D, and an infrared grating separator E. The weighing platform A is composed of a load-bearing tabletop, weighing sensors, limit devices, etc., and weighs the vehicle driving onto the load-bearing platform in real time and transmits the data to the central processing cabinet D through a communication interface; the axle identifier is composed of axle identification sensors, axle identification tabletops, and related cables and other components. It identifies the single or double situation of the vehicle tires based on the action of the driving vehicle on the sensors. Usually, a pressure induction type sensor is used as the signal acquisition unit, which has both tire identification function and axle identification function, can monitor the axle number of the vehicle driving onto the weighing platform in real time, judge whether the vehicle is overloaded, and transmit the data to the central processing cabinet D; the infrared grating separator E is mainly composed of an infrared light curtain emission device and a receiving device, a separator housing, and a controller, etc. It is installed on both sides of the approach road in front of the weighing platform A. When working, it continuously emits a high-density infrared light curtain, which can eliminate the phenomenon of following vehicles and ensure the one-to-one correspondence between the detection data and the passing vehicles.

[0043] It can be seen that in this example, by obtaining license plate numbers and freight item image data through image acquisition equipment and obtaining vehicle weight data through measuring sensors, it is possible to comprehensively collect information related to the freight vehicles to be inspected, effectively optimize the freight vehicle inspection process, and improve the efficiency of green pass inspection.

[0044] S202: Obtain a first inspection result according to the freight item list data, the freight item weight data, and the freight item type data.

[0045] The first inspection result is used to indicate whether the freight item is abnormal.

[0046] In a possible implementation, obtaining a first inspection result according to the freight item list data, the freight item weight data, and the freight item type data includes:

[0047] A freight item weight inspection result is obtained according to the freight item list data and the freight item weight data; a freight item type inspection result is obtained according to the freight item list data and the freight item type data; when the freight item inspection result is that the freight item weight is abnormal, and / or the freight item type inspection result is that the freight item type is abnormal, it is determined that the first inspection result is that the freight item is abnormal.

[0048] Among them, by comparing the actual weight of the freight items with the weight of the freight items calibrated in the freight item list data, it is determined whether the total mass of the actual freight items is accurate; if it is found that the difference between the actual weight of the freight items and the weight of the freight items calibrated in the freight item list data exceeds a preset threshold (such as more than 5%), or the actual weight of the freight items exceeds the calibrated load range, it can be determined that the freight item inspection result is abnormal freight item weight.

[0049] For example, in a green pass inspection, the freight vehicle to be inspected submitted the following freight list data and corresponding freight weight data: Cargo name: apples; Cargo type: fruit; Quantity: 50 boxes; Expected average weight per box: 20 kg. In the actual weighing, the total weight of the cargo is 1050 kg. Through calculation, it can be seen that the average weight per box is 21 kg. This result is within a reasonable range with the pre-calibrated average weight per box (the error is less than 5%). Therefore, in this case, it can be concluded that the weight of the freight is normal.

[0050] It can be seen that in this example, the corresponding inspection results are obtained by combining the cargo list data, weight data and cargo type data for comprehensive analysis. This can comprehensively consider different factors and improve the accuracy of green pass inspection.

[0051] S203: Obtain a second inspection result based on the high-speed traffic behavior data.

[0052] Among them, the second inspection result is used to indicate whether the freight vehicle to be inspected has abnormal traffic behavior.

[0053] In a possible implementation, obtaining a second inspection result according to the high-speed traffic behavior data includes:

[0054] When it is detected that one of the behaviors of multiple on-and-off highway behaviors, emergency lane stop behaviors, and long-term stay in the service area occurs in the highway traffic behavior data, the second inspection result is determined to be that the freight vehicle to be inspected has abnormal traffic behavior.

[0055] Among them, the highway traffic behavior data includes the highway on-and-off records, service area stay time, driving speed, route, number of stops and abnormal driving behavior of the freight vehicles to be inspected. After obtaining the highway traffic behavior data, the highway on-and-off records of the freight vehicles to be inspected can be compared with the preset number of on-and-off times (for example, 3 times). If the number of on-and-off times of the freight vehicles to be inspected exceeds the preset number of on-and-off times, it is considered that multiple on-and-off times have occurred; by comparing the service area stay time with the preset stay time (for example, 3 hours), if the service area stay time of the freight vehicles to be inspected exceeds the preset stay time, it is considered that a long-term detention in the service area has occurred; when it is detected that the vehicle stops in the emergency lane through the driving speed and route, it is considered that the emergency lane stops.

[0056] It can be seen that in this example, by analyzing the high-speed traffic behavior data, it is possible to automatically identify whether the freight vehicles to be inspected have abnormal traffic behavior, which is conducive to improving the efficiency and accuracy of green pass inspections.

[0057] S204, obtaining a third inspection result based on the historical green pass inspection result data.

[0058] Among them, the third inspection result is used to indicate whether the freight vehicle to be inspected is an abnormal green pass vehicle.

[0059] In a possible implementation, obtaining a third inspection result according to the historical green pass inspection result data includes:

[0060] When it is detected that the number of historical green pass inspection failures of the freight vehicle to be inspected in the historical green pass inspection result data is not less than a preset number threshold, the third inspection result is determined that the freight vehicle to be inspected is an abnormal green pass vehicle.

[0061] Among them, the preset quantity threshold can be set by the user in the terminal. Exemplarily, the preset quantity threshold is 3 times. The historical green pass inspection result data of the freight vehicles to be inspected are as follows: the first green pass inspection: passed; the second green pass inspection: failed; the third green pass inspection: failed; the fourth green pass inspection: failed. At this time, the server counts the number of times the freight vehicles to be inspected have failed in the historical records, and compares it with the preset quantity threshold. Since the number of times the freight vehicles to be inspected have failed in the history of green pass inspection is 3 times (not less than the preset quantity threshold), it can be determined that the third inspection result is that the freight vehicle to be inspected is an abnormal green pass vehicle.

[0062] It can be seen that in this example, by analyzing the historical green pass inspection result data, whether the freight vehicle to be inspected is an abnormal green pass vehicle is determined based on the preset quantity threshold and the number of historical green pass inspection failures of the freight vehicle to be inspected. This automated judgment process is conducive to reducing the risks of subjective judgment and human errors and improving the efficiency of green pass inspections.

[0063] S205: Determine the green pass inspection result of the freight vehicle to be inspected based on the first inspection result, the second inspection result and the third inspection result.

[0064] In a possible implementation, determining the green pass inspection result of the freight vehicle to be inspected according to the first inspection result, the second inspection result, and the third inspection result includes:

[0065] When it is detected that the first inspection result is that the freight item is abnormal, and / or the second inspection result is that the freight vehicle to be inspected has abnormal traffic behavior, and / or the third inspection result is that the freight vehicle to be inspected is an abnormal green pass vehicle, the green pass inspection result is determined to be that the freight vehicle to be inspected is an abnormal green pass vehicle; when it is detected that the first inspection result is that the freight item is normal, the second inspection result is that the freight vehicle to be inspected does not have abnormal traffic behavior, and the third inspection result is that the freight vehicle to be inspected is not an abnormal green pass vehicle, the green pass inspection result is determined to be that the freight vehicle to be inspected is a normal green pass vehicle.

[0066] Among them, the first inspection concluded that there were abnormalities with the cargo by analyzing the freight inventory data and weight data; the second inspection detected abnormal situations such as multiple on-and-off highway behaviors, stops in emergency lanes, or long-term stays in service areas based on the highway traffic behavior data; the third inspection judged that the freight vehicle to be inspected was an abnormal green pass vehicle based on the number of historical green pass inspection failures.

[0067] It can be seen that in this example, by comprehensively considering multiple green pass inspection factors including cargo information, highway driving status and historical green pass inspection records, judging whether the freight items to be inspected meet the green pass requirements based on this information is conducive to improving the accuracy of green pass inspection.

[0068] In another example, the green pass inspection results of the freight vehicle to be inspected include that the freight vehicle to be inspected is an abnormal green pass vehicle, the freight vehicle to be inspected is a suspected abnormal green pass vehicle, and the freight vehicle to be inspected is not an abnormal green pass vehicle.

[0069] Specifically, when it is detected that one of the first inspection result, the second inspection result and the third inspection result is abnormal, the freight vehicle to be inspected is a suspected abnormal green pass vehicle; when it is detected that the first inspection result is abnormal, the second inspection result is that the freight vehicle to be inspected has abnormal traffic behavior, and the third inspection result is that the freight vehicle to be inspected is an abnormal green pass vehicle, the green pass inspection result is determined to be that the freight vehicle to be inspected is an abnormal green pass vehicle; when it is detected that the first inspection result is normal, the second inspection result is that the freight vehicle to be inspected does not have abnormal traffic behavior, and the third inspection result is that the freight vehicle to be inspected is not an abnormal green pass vehicle, the green pass inspection result is determined to be that the freight vehicle to be inspected is a normal green pass vehicle.

[0070] It can be seen that in this example, the introduction of the "suspected abnormal green pass vehicle" result can help the system capture those freight vehicles to be inspected that may have low risks or unclear situations, and improve the accuracy of green pass inspections.

[0071] In another example, the server determines the green pass inspection result for the freight vehicle to be inspected through the vehicle portrait.

[0072] Among them, vehicle profiling is a vehicle characteristic model established based on a large amount of relevant data and information, which is used to describe and analyze the attributes, historical behaviors, records, etc. of the freight vehicles to be inspected.

[0073] Specifically, the process of constructing a vehicle portrait is as follows: collect data related to the freight vehicles to be inspected through various channels, including but not limited to license plate numbers, driver information, transportation history records, violations, etc. Extract meaningful key features that can represent the characteristics of the vehicle from the collected data, including the number of past violations, frequent changes of drivers or carriers, and overloading. Then, based on the extracted feature data, use machine learning algorithms or other related technologies to build a comprehensive model, namely the vehicle portrait model, which can predict the possible problems or risks of the freight vehicles to be inspected in the green pass inspection based on the input parameters.

[0074] For example, assuming that the system server has established a freight truck portrait model, when a freight truck to be inspected enters the system, the system will use the information obtained during the previous submission process of the truck and the data collected in real time as input parameters to send to the portrait model for evaluation. The freight truck portrait model recognizes that the freight truck has multiple violation records in the past and the current weight exceeds the normal range, then it can be determined that the green pass inspection result is abnormal.

[0075] It can be seen that in this example, the vehicle portrait model established based on a large amount of relevant data and information can comprehensively consider multiple factors and use technologies such as machine learning algorithms to make accurate predictions, which is conducive to improving the accuracy of green pass inspections.

[0076] S206, determining target processing measures for the freight vehicle to be inspected based on the green pass inspection result.

[0077] In a possible implementation, determining a target processing measure for the freight vehicle to be inspected according to the green pass inspection result includes:

[0078] When it is detected that the green pass inspection result is that the freight vehicle to be inspected is an abnormal green pass vehicle, the target processing measure is determined to be the use of quantum sensors in conjunction with manual inspection; when it is detected that the green pass inspection result is that the freight vehicle to be inspected is a normal green pass vehicle, the target processing measure is determined to be releasing the freight vehicle to be inspected.

[0079] Among them, vehicles that the system determines to be normal green pass can be quickly released. Vehicles that are determined to be abnormal green pass will be manually inspected, and those that refuse to cooperate with the inspection and are likely to cause public opinion can be released. Abnormal vehicles will be tracked and judged afterwards to determine whether the vehicles have been replaced or mixed, and will be added to the black / grey list or recovered afterwards.

[0080] VR can be used to assist in manual inspections. VR equipment can be provided to inspectors to achieve remote immersive cargo inspections through real-time transmission of high-definition images and three-dimensional models. Combined with motion capture technology, inspectors can operate and mark in a virtual environment, achieving remote command and collaborative work.

[0081] Among them, machine inspection is introduced, and quantum sensing technology is used to detect the weight and composition of goods. Quantum sensors have extremely high precision and sensitivity, and can measure the weight of goods more accurately, while quickly distinguishing the chemical composition of goods and judging whether they meet the green pass standards.

[0082] It can be seen that in this example, when the green pass inspection result is determined that the freight vehicle to be inspected is an abnormal green pass vehicle, a secondary inspection through manual cooperation with the machine can reduce the risk of misjudgment of the green pass inspection and further improve the inspection accuracy and efficiency.

[0083] In a possible implementation, the method further includes:

[0084] It is detected that the green pass inspection result of the freight vehicle to be inspected at the previous green pass inspection point is that the freight vehicle to be inspected is a normal green pass vehicle, and there is no abnormal traffic behavior during the driving process of the freight vehicle to be inspected between the previous green pass inspection point and the current green pass inspection point, and / or the vehicle weight data of the freight vehicle to be inspected at the previous green pass inspection point and the current green pass inspection point are the same, it is determined that the freight vehicle to be inspected is a normal green pass vehicle, and the target processing measure is determined to release the freight vehicle to be inspected.

[0085] It can be seen that in this example, the previous and current green pass detection results, driving process and vehicle weight data are analyzed and compared to determine whether the freight vehicle to be inspected is considered a normal green pass vehicle. If the above conditions are met, the freight vehicle can be released directly, which is conducive to improving the efficiency of green pass inspection.

[0086] In a possible implementation, the method further includes:

[0087] Determine the bad weather tolerance level of the freight items of the freight vehicle to be inspected and the bad weather tolerance time of the freight items of the freight vehicle to be inspected according to the freight item list data of the freight vehicle to be inspected and the preset bad weather tolerance data of the freight items; determine the bad weather level of the Green Pass inspection point according to the meteorological data of the Green Pass inspection point and the preset bad weather level; when it is detected that the bad weather level of the Green Pass inspection point is not less than the bad weather tolerance level of the freight items of the freight vehicle to be inspected, obtain the license plate number and freight item image data of the freight vehicle to be inspected through the image acquisition device, obtain the whole vehicle weight data of the freight vehicle to be inspected through the measuring sensor, and determine the target processing measures to be taken for the freight items of the freight vehicle to be inspected. The freight vehicle to be inspected is released within the inclement weather tolerance time; the freight list data submitted by the freight vehicle to be inspected at the highway entrance station is obtained according to the license plate number of the freight vehicle to be inspected; the freight image data is subjected to feature extraction and label classification by a convolutional neural network to obtain the freight type data of the freight vehicle to be inspected; the freight weight data of the freight vehicle to be inspected is obtained according to the vehicle weight data and the rated weight of the freight vehicle to be inspected; the inclement weather green pass inspection result is obtained according to the freight list data, the freight type data and the freight weight data, and the inclement weather green pass inspection result and the license plate number of the freight vehicle to be inspected are stored in a database.

[0088] Among them, the meteorological data of the current Green Pass inspection point, such as temperature, rainfall, wind speed, etc., is obtained, and the severe weather level in the area is judged based on these factors. These parameters may be converted into corresponding levels using preset standards, such as "mild", "medium", "high", etc. Each freight item has a corresponding tolerance level (such as high, medium, low) and tolerance time (the maximum time it can be stored at the Green Pass inspection point in severe weather).

[0089] Among them, each freight item is provided with a freight item severe weather tolerance data table, which may include the weather tolerance time of the freight item under different weather conditions. For example, the weather tolerance time of Fuji apples at a temperature of 35 degrees Celsius is 40 minutes, and the weather tolerance time of Fuji apples at a temperature of 40 degrees Celsius is 30 minutes.

[0090] Among them, cargo storage parameters and cargo storage environment parameters can also be obtained, wherein the cargo storage parameters include at least one of the following: cargo packaging method, cargo stacking density, which are not limited here, and the cargo storage environment parameters include at least one of the following: ambient temperature, ambient humidity, ambient atmospheric pressure, etc., which are not limited here. In a specific implementation, the bad weather tolerance level of the freight items of the freight vehicle to be inspected and the initial bad weather tolerance time of the freight items of the freight vehicle to be inspected can be preliminarily determined based on the freight item list data of the freight vehicle to be inspected and the preset freight items bad weather tolerance data. Different cargo storage environment parameters can correspond to different adjustment parameters. For example, the mapping relationship between the preset cargo storage parameters and the adjustment parameters can be pre-stored, and the adjustment parameters corresponding to the corresponding acquired cargo storage parameters can be determined based on the mapping relationship. The mapping relationship between the preset cargo storage environment parameters and the fine-tuning parameters can also be pre-stored. Based on the mapping relationship, the fine-tuning parameters corresponding to the corresponding cargo storage environment parameters are determined, and the initial freight items' bad weather tolerance time is adjusted based on the adjustment parameters and the fine-tuning parameters to obtain the actual freight items' bad weather tolerance time. Due to different cargo storage parameters, the pressure, friction, degree of damage, and ability to resist vehicle bumps on the cargo are different, which leads to different bad weather tolerance of the freight items. Usually, the cargo storage parameters can also dynamically adjust the bad weather tolerance of the freight items to a certain extent, while the cargo storage environment parameters are closely related to the actual environment. To a certain extent, the cargo storage environment parameters will also affect the bad weather tolerance of the freight items. Furthermore, the bad weather tolerance time of the freight items can be dynamically optimized based on the cargo storage parameters and the cargo storage environment parameters, so that the actual bad weather tolerance time of the freight items is in line with the actual situation, which can ensure the efficiency of the green pass inspection and the safety of the cargo.

[0091] For example, suppose the freight vehicle to be inspected is loaded with food Z. According to the meteorological data analysis of the current green pass inspection point, it is found that the rainfall exceeds the preset standard and the wind speed is high. The bad weather level in the area is judged to be "high". The bad weather tolerance time of food Z when the bad weather level is "high" is 4 hours. The freight vehicle to be inspected is allowed to continue driving within 4 hours. The server determines the target processing measure to release the freight vehicle to be inspected within 4 hours.

[0092] It can be seen that in this example, by comprehensively considering factors such as the type and weight of the goods and severe weather conditions, the Green Pass inspection vehicles are released in a timely manner under severe weather conditions, which is conducive to avoiding cargo losses caused by excessively long Green Pass inspection time.

[0093] In a possible implementation, the method further includes:

[0094] After determining the target processing measures for the freight vehicle to be inspected according to the green pass inspection result, a first prompt information is sent, and the first prompt information is used to inform the user of the target processing measures for the freight vehicle to be inspected.

[0095] The first prompt information may be sent to the user via email, text message, mobile application push notification, online platform message, and telephone notification, and the first prompt information may be text information or graphic information.

[0096] Among them, see Figure 5 , Figure 5 is a schematic diagram of a first prompt information provided in an embodiment of the present application, such as Figure 5 As shown, after determining the target processing measures for the freight vehicle to be inspected, the server can send a first prompt message to the terminal, wherein the first prompt message can inform the target processing measures for the freight vehicle to be inspected.

[0097] It can be seen that in this example, by sending the first prompt information, the user can promptly take targeted processing measures for the freight vehicle to be inspected, which is conducive to the Green Pass inspector to make corresponding decisions in a timely manner.

[0098] See also Figure 6 , Figure 6 : is a schematic diagram of a structure of a green pass inspection device for freight vehicles on a highway provided in an embodiment of the present application, such as Figure 6 As shown, the highway freight vehicle green pass inspection device 600 includes:

[0099] The acquisition module 601 is used to acquire basic data of the freight vehicle to be inspected, wherein the basic data includes freight item list data, high-speed traffic behavior data, historical green pass inspection result data, freight item type data, and freight item weight data;

[0100] Processing module 602 is used to obtain a first inspection result based on the freight item list data, the freight item weight data and the freight item type data, the first inspection result is used to indicate whether the freight item is abnormal; and to obtain a second inspection result based on the high-speed traffic behavior data, the second inspection result is used to indicate whether the freight vehicle to be inspected has abnormal traffic behavior; and to obtain a third inspection result based on the historical green pass inspection result data, the third inspection result is used to indicate whether the freight vehicle to be inspected is an abnormal green pass vehicle; and to determine the green pass inspection result of the freight vehicle to be inspected based on the first inspection result, the second inspection result and the third inspection result; and to determine the target processing measures for the freight vehicle to be inspected based on the green pass inspection result.

[0101] In one possible implementation, in terms of acquiring basic data of freight vehicles to be inspected, the acquisition module 601 is specifically used to: acquire the license plate number and freight image data of the freight vehicle to be inspected through an image acquisition device; acquire the freight list data submitted by the freight vehicle to be inspected at the highway entrance station according to the license plate number of the freight vehicle to be inspected; acquire the highway traffic behavior data and historical green pass inspection result data of the freight vehicle to be inspected from the database according to the license plate number of the freight vehicle to be inspected; perform feature extraction and label classification on the freight image data through a convolutional neural network to obtain the freight type data of the freight vehicle to be inspected; acquire the whole vehicle weight data of the freight vehicle to be inspected through a measuring sensor; and obtain the freight weight data of the freight vehicle to be inspected according to the whole vehicle weight data and the calibrated weight of the freight vehicle to be inspected.

[0102] In one possible implementation, in terms of obtaining a first inspection result based on the freight item list data, the freight item weight data and the freight item type data, the processing module 602 is specifically used to: obtain a freight item weight inspection result based on the freight item list data and the freight item weight data; obtain a freight item type inspection result based on the freight item list data and the freight item type data; and determine that the first inspection result is a freight item abnormality when the freight item inspection result is an abnormal freight item weight and / or the freight item type inspection result is an abnormal freight item type.

[0103] In one possible implementation, in terms of obtaining the second inspection result based on the highway traffic behavior data, the processing module 602 is specifically used to: when one of the multiple highway on and off behaviors, emergency lane stop behaviors, and long-term stay behaviors in the service area is detected in the highway traffic behavior data, determine that the second inspection result is that the freight vehicle to be inspected has abnormal traffic behavior.

[0104] In one possible implementation, in terms of obtaining the third inspection result based on the historical green pass inspection result data, the processing module 602 is specifically used to: when it is detected that the number of historical green pass inspection failures of the freight vehicle to be inspected in the historical green pass inspection result data is not less than a preset number threshold, determine that the third inspection result is that the freight vehicle to be inspected is an abnormal green pass vehicle.

[0105] In one possible implementation, in terms of determining the green pass inspection result of the freight vehicle to be inspected based on the first inspection result, the second inspection result and the third inspection result, the processing module 602 is specifically used to: when it is detected that the first inspection result is that the freight item is abnormal, and / or the second inspection result is that the freight vehicle to be inspected has abnormal passage behavior, and / or the third inspection result is that the freight vehicle to be inspected is an abnormal green pass vehicle, determine that the green pass inspection result is that the freight vehicle to be inspected is an abnormal green pass vehicle; when it is detected that the first inspection result is that the freight item is normal, the second inspection result is that the freight vehicle to be inspected does not have abnormal passage behavior and the third inspection result is that the freight vehicle to be inspected is not an abnormal green pass vehicle, determine that the green pass inspection result is that the freight vehicle to be inspected is a normal green pass vehicle.

[0106] In one possible implementation, in terms of determining the target processing measures for the freight vehicle to be inspected based on the green pass inspection result, the processing module 602 is specifically used to: when it is detected that the green pass inspection result is that the freight vehicle to be inspected is an abnormal green pass vehicle, determine the target processing measures to be inspected using quantum sensors in conjunction with manual inspection; when it is detected that the green pass inspection result is that the freight vehicle to be inspected is a normal green pass vehicle, determine the target processing measures to be released the freight vehicle to be inspected.

[0107] In one possible implementation, the processing module 602 is also used to: detect that the green pass inspection result of the freight vehicle to be inspected at the previous green pass inspection point is that the freight vehicle to be inspected is a normal green pass vehicle, and that there is no abnormal traffic behavior during the driving process of the freight vehicle to be inspected between the previous green pass inspection point and the current green pass inspection point, and / or, when the vehicle weight data of the freight vehicle to be inspected at the previous green pass inspection point and the current green pass inspection point are the same, determine that the freight vehicle to be inspected is a normal green pass vehicle, and determine that the target processing measure is to release the freight vehicle to be inspected.

[0108] In a possible implementation, the processing module 602 is also used to: determine the bad weather tolerance level of the freight items of the freight vehicle to be inspected and the bad weather tolerance time of the freight items of the freight vehicle to be inspected according to the freight item list data of the freight vehicle to be inspected and the preset bad weather tolerance data of the freight items; determine the bad weather level of the green pass inspection point according to the meteorological data of the green pass inspection point and the preset bad weather level; when it is detected that the bad weather level of the green pass inspection point is not less than the bad weather tolerance level of the freight items of the freight vehicle to be inspected, obtain the license plate number and freight item image data of the freight vehicle to be inspected through the image acquisition device, obtain the whole vehicle weight data of the freight vehicle to be inspected through the measuring sensor, and determine the target processing measures as in the The freight vehicle to be inspected is released within the bad weather tolerance time of its freight items; the freight list data submitted by the freight vehicle to be inspected at the highway entrance station is obtained according to the license plate number of the freight vehicle to be inspected; feature extraction and label classification are performed on the freight image data through a convolutional neural network to obtain the freight type data of the freight vehicle to be inspected; the freight weight data of the freight vehicle to be inspected is obtained according to the vehicle weight data and the rated weight of the freight vehicle to be inspected; the bad weather green pass inspection result is obtained according to the freight list data, the freight type data and the freight weight data, and the bad weather green pass inspection result and the license plate number of the freight vehicle to be inspected are stored in a database.

[0109] It is worth noting that the specific functional implementation of the highway freight vehicle green pass inspection device 600 is shown in the above Figure 2 The description of the method for checking the green pass for freight vehicles on highways shown in the figure, for example, the acquisition module 601 is used to implement the relevant content of executing S201, and the processing module 602 is used to implement the relevant content of executing S202-S206. The various units or modules in the green pass inspection device 600 for freight vehicles on highways can be respectively or completely combined into one or several other units or modules to constitute, or one (some) of the units or modules can be further divided into multiple functionally smaller units or modules to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided according to logical functions. In actual applications, the functions of one unit (or module) are implemented by multiple units (or modules), or the functions of multiple units (or modules) are implemented by one unit (or module).

[0110] According to the description of the above method embodiment and related device embodiment, please refer to Figure 7 , an embodiment of the present invention also provides a structural schematic diagram of a highway freight vehicle green pass inspection device 700. Figure 7 The highway freight vehicle green pass inspection device 700 shown includes a processor 701, a memory 702, a communication interface 703 and a bus 704. The processor 701, the memory 702 and the communication interface 703 are connected to each other through the bus 704.

[0111] Optionally, the memory 702 is a ROM, a static storage device, a dynamic storage device or a RAM.

[0112] The memory 702 can store executable program codes. When the executable program codes stored in the memory 702 are executed by the processor 701, the processor 701 and the communication interface 703 are used to execute Figure 2 The various steps of the method for green pass inspection of highway freight vehicles in the illustrated embodiment.

[0113] Processor 701 uses a general-purpose CPU, microprocessor, application-specific integrated circuit ASIC, GPU or one or more integrated circuits to execute relevant programs to execute the green pass inspection method for highway freight vehicles in the method embodiment of the present application.

[0114] Processor 701 can also be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the highway freight vehicle green pass inspection method of the present application can be completed by the hardware integrated logic circuit or software instructions in the processor 701. Optionally, processor 701 is a general-purpose processor, DSP, ASIC, FPGA or other programmable logic device, discrete gate or transistor logic device, discrete hardware component. The processor can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor is a microprocessor or the processor is any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to execute, or a combination of hardware and software modules in the decoding processor to execute. The optional software module is located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register or other mature storage medium in the art. The storage medium is located in the memory 702, and the processor 701 reads the information in the memory 702, and combines its hardware to complete the functions required to be executed by the modules included in a highway freight vehicle green pass inspection device 600 of an embodiment of the present application, or executes the highway freight vehicle green pass inspection method of the method embodiment of the present application.

[0115] The communication interface 703 uses a transceiver or other related device such as, but not limited to, a transceiver.

[0116] The bus 704 may include a path for transmitting information between various components of the highway freight vehicle green pass inspection device 700 (eg, the memory 702 , the processor 701 , the communication interface 703 ).

[0117] It should be noted that although Figure 7 The highway freight vehicle green pass inspection device 700 shown only shows a memory, a processor, and a communication interface. However, in the specific implementation process, those skilled in the art should understand that the highway freight vehicle green pass inspection device 700 also includes other devices necessary for normal operation. At the same time, according to specific needs, those skilled in the art should understand that the highway freight vehicle green pass inspection device 700 may also include hardware devices for implementing other additional functions. In addition, those skilled in the art should understand that the highway freight vehicle green pass inspection device 700 may also only include the devices necessary to implement the embodiments of the present application, and does not necessarily include Figure 7 All devices shown in .

[0118] An embodiment of the present application provides a computer-readable storage medium, which stores a computer program for electronic data exchange. The computer program includes execution instructions, and the execution instructions are used to execute part or all of the steps of any one of the highway freight vehicle green pass inspection methods recorded in the above-mentioned highway freight vehicle green pass inspection method embodiments. The above-mentioned computer includes an electronic terminal device.

[0119] An embodiment of the present application provides a computer program product, wherein the computer program product includes a computer program, and the computer program can be operated to enable a computer to perform part or all of the steps of any one of the highway freight vehicle green pass inspection methods recorded in the above method embodiments. The computer program product can be a software installation package.

[0120] It should be noted that, for the sake of simplicity, for any of the aforementioned embodiments of the method for checking the green pass for freight vehicles on highways, they are all described as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the described order of actions, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by this application.

[0121] The above is a detailed introduction to the embodiments of the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application's method, device, equipment and storage medium for green pass inspection of highway freight vehicles. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, based on the idea of ​​the present application's method, device, equipment and storage medium for green pass inspection of highway freight vehicles, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0122] The present application is described with reference to the flowcharts and / or block diagrams of the methods, hardware products, and computer program products of the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable highway freight vehicle green pass inspection device to produce a machine, so that the instructions executed by the processor of the computer or other programmable highway freight vehicle green pass inspection device generate instructions for implementing the processes in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0123] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable highway freight vehicle green pass inspection equipment to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 The memory may include: a flash disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0124] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "one" does not exclude multiple situations. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0125] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of any of the above-mentioned methods for the green pass inspection of freight vehicles on highways can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0126] It can be understood that any product that is controlled or configured to execute the processing method of the flowchart described in an embodiment of a method for green pass inspection of highway freight vehicles in this application, such as the device and computer program product of the above flowchart, falls within the scope of related products described in this application.

[0127] Obviously, those skilled in the art can make various changes and modifications to the method, device, equipment and storage medium for checking the green pass of freight vehicles on highways provided by this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include these modifications and variations.

Claims

1. A method for checking the green pass of freight vehicles on highways, characterized in that: A server applied to a green pass inspection system for freight vehicles on highways, the method comprising: Determine whether the weather at the Green Pass inspection point is severe; It is detected that the weather at the green pass inspection point is not bad weather, and basic data of the freight vehicle to be inspected is obtained, wherein the basic data includes freight item list data, high-speed traffic behavior data, historical green pass inspection result data, freight item type data, and freight item weight data; Obtaining a first inspection result according to the freight item list data, the freight item weight data, and the freight item type data, wherein the first inspection result is used to indicate whether the freight item is abnormal; Obtaining a second inspection result according to the high-speed traffic behavior data, wherein the second inspection result is used to indicate whether the freight vehicle to be inspected has abnormal traffic behavior; Obtaining a third inspection result according to the historical green pass inspection result data, wherein the third inspection result is used to indicate whether the freight vehicle to be inspected is an abnormal green pass vehicle; Determine the green pass inspection result of the freight vehicle to be inspected according to the first inspection result, the second inspection result and the third inspection result; Determine the target treatment measures for the freight vehicle to be inspected according to the green pass inspection results; It is detected that the weather at the green pass inspection point is bad weather, and the bad weather tolerance level of the freight items of the freight vehicle to be inspected and the theoretical bad weather tolerance time of the freight items of the freight vehicle to be inspected are determined according to the freight item list data of the freight vehicle to be inspected and the preset bad weather tolerance data of the freight items; The theoretical adverse weather tolerance time of the freight items of the freight vehicle to be inspected is adjusted according to the cargo storage parameters and cargo storage environment parameters of the freight vehicle to be inspected, so as to obtain the actual adverse weather tolerance time of the freight items of the freight vehicle to be inspected, wherein the cargo storage parameters include at least one of cargo packaging method and cargo stacking density, and the cargo storage environment parameters include at least one of ambient temperature, ambient humidity and ambient atmospheric pressure; Determine the severe weather level of the green pass inspection point according to the meteorological data of the green pass inspection point and the preset severe weather level; When it is detected that the bad weather level at the green pass inspection point is not less than the bad weather tolerance level of the freight items of the freight vehicle to be inspected, the license plate number and freight item image data of the freight vehicle to be inspected are obtained through an image acquisition device, and the vehicle weight data of the freight vehicle to be inspected is obtained through a metering sensor, and the target processing measure is determined to release the freight vehicle to be inspected within the actual bad weather tolerance time of the freight items of the freight vehicle to be inspected; Obtaining the cargo list data submitted by the cargo vehicle to be inspected at the highway entrance station according to the license plate number of the cargo vehicle to be inspected; Performing feature extraction and label classification on the freight item image data through a convolutional neural network to obtain freight item type data of the freight vehicle to be inspected; Obtaining weight data of freight items of the freight vehicle to be inspected according to the vehicle weight data and the calibrated weight of the freight vehicle to be inspected; Obtain the bad weather green pass inspection result based on the freight item list data, the freight item type data and the freight item weight data, and store the bad weather green pass inspection result and the license plate number of the freight vehicle to be inspected in a database.

2. The method according to claim 1, characterized in that The basic data of the freight vehicle to be inspected is obtained, including: Acquire the license plate number of the freight vehicle to be inspected and the image data of the freight item through an image acquisition device; Obtaining the cargo list data submitted by the cargo vehicle to be inspected at the highway entrance station according to the license plate number of the cargo vehicle to be inspected; According to the license plate number of the freight vehicle to be inspected, the high-speed traffic behavior data and historical green pass inspection result data of the freight vehicle to be inspected are obtained from the database; Performing feature extraction and label classification on the freight item image data through a convolutional neural network to obtain freight item type data of the freight vehicle to be inspected; Obtaining the vehicle weight data of the freight vehicle to be inspected through a measuring sensor; The weight data of the freight items of the freight vehicle to be inspected is obtained according to the whole vehicle weight data and the calibrated weight of the freight vehicle to be inspected.

3. The method according to claim 1, characterized in that The first inspection result is obtained according to the freight item list data, the freight item weight data and the freight item type data, including: Obtaining a freight item weight inspection result according to the freight item list data and the freight item weight data; Obtaining a freight item type inspection result according to the freight item list data and the freight item type data; When the freight item weight inspection result is that the freight item weight is abnormal, and / or the freight item type inspection result is that the freight item type is abnormal, it is determined that the first inspection result is that the freight item is abnormal.

4. The method according to claim 1, characterized in that The obtaining of the second inspection result according to the high-speed traffic behavior data includes: When it is detected that one of the behaviors of multiple on-and-off highway behaviors, emergency lane stop behaviors, and long-term stay in the service area occurs in the highway traffic behavior data, the second inspection result is determined to be that the freight vehicle to be inspected has abnormal traffic behavior.

5. The method according to claim 1, characterized in that The third inspection result is obtained according to the historical green pass inspection result data, including: When it is detected that the number of historical green pass inspection failures of the freight vehicle to be inspected in the historical green pass inspection result data is not less than a preset number threshold, the third inspection result is determined that the freight vehicle to be inspected is an abnormal green pass vehicle.

6. The method according to claim 1, characterized in that The determining the green pass inspection result of the freight vehicle to be inspected according to the first inspection result, the second inspection result and the third inspection result includes: When it is detected that the first inspection result is that the freight item is abnormal, and / or the second inspection result is that the freight vehicle to be inspected has abnormal passing behavior, and / or the third inspection result is that the freight vehicle to be inspected is an abnormal green pass vehicle, the green pass inspection result is determined to be that the freight vehicle to be inspected is an abnormal green pass vehicle; When it is detected that the first inspection result is that the freight items are normal, the second inspection result is that the freight vehicle to be inspected has no abnormal traffic behavior, and the third inspection result is that the freight vehicle to be inspected is not an abnormal green pass vehicle, the green pass inspection result is determined to be that the freight vehicle to be inspected is a normal green pass vehicle.

7. The method according to claim 1, characterized in that Determining the target processing measures for the freight vehicle to be inspected according to the green pass inspection result includes: When it is detected that the green pass inspection result is that the freight vehicle to be inspected is an abnormal green pass vehicle, the target processing measure is determined to be to use quantum sensors to cooperate with manual inspection; When it is detected that the green pass inspection result is that the freight vehicle to be inspected is a normal green pass vehicle, the target processing measure is determined to be releasing the freight vehicle to be inspected.

8. The method according to any one of claims 1 to 7, characterized in that: The method further comprises: It is detected that the green pass inspection result of the freight vehicle to be inspected at the previous green pass inspection point is that the freight vehicle to be inspected is a normal green pass vehicle, and there is no abnormal traffic behavior during the driving process of the freight vehicle to be inspected between the previous green pass inspection point and the current green pass inspection point, and / or the vehicle weight data of the freight vehicle to be inspected at the previous green pass inspection point and the current green pass inspection point are the same, it is determined that the freight vehicle to be inspected is a normal green pass vehicle, and the target processing measure is determined to release the freight vehicle to be inspected.

9. A green pass inspection device for freight vehicles on highways, characterized in that: The device comprises: A processing module is used to determine whether the weather at the green pass inspection point is severe weather; An acquisition module is used to detect that the weather at the green pass inspection point is not bad weather, and to acquire basic data of the freight vehicle to be inspected, wherein the basic data includes freight item list data, high-speed traffic behavior data, historical green pass inspection result data, freight item type data, and freight item weight data; The processing module is further used to obtain a first inspection result based on the freight item list data, the freight item weight data and the freight item type data, the first inspection result is used to indicate whether the freight item is abnormal; and to obtain a second inspection result based on the high-speed passage behavior data, the second inspection result is used to indicate whether the freight vehicle to be inspected has abnormal passage behavior; and to obtain a third inspection result based on the historical green pass inspection result data, the third inspection result is used to indicate whether the freight vehicle to be inspected is an abnormal green pass vehicle; and to determine the green pass inspection result of the freight vehicle to be inspected based on the first inspection result, the second inspection result and the third inspection result; and to obtain a third inspection result based on the historical green pass inspection result data, the third inspection result is used to indicate whether the freight vehicle to be inspected is an abnormal green pass vehicle. for determining the target treatment measures for the freight vehicle to be inspected according to the green pass inspection result; and for detecting that the weather at the green pass inspection point is bad weather, determining the bad weather tolerance level of the freight items of the freight vehicle to be inspected and the theoretical bad weather tolerance time of the freight items of the freight vehicle to be inspected according to the freight item list data of the freight vehicle to be inspected and the preset bad weather tolerance data of the freight items; and for adjusting the theoretical bad weather tolerance time of the freight items of the freight vehicle to be inspected according to the cargo storage parameters and cargo storage environment parameters of the freight vehicle to be inspected to obtain the actual bad weather tolerance time of the freight items of the freight vehicle to be inspected, wherein the cargo storage parameters include cargo packaging method and At least one of the cargo stacking density, the cargo storage environment parameters include at least one of the ambient temperature, ambient humidity and ambient atmospheric pressure; and is used to determine the severe weather level of the Green Pass inspection point according to the meteorological data of the Green Pass inspection point and the preset severe weather level; and is used to detect that the severe weather level of the Green Pass inspection point is not less than the severe weather tolerance level of the cargo items of the freight vehicle to be inspected, obtain the license plate number and cargo image data of the freight vehicle to be inspected through an image acquisition device, obtain the vehicle weight data of the freight vehicle to be inspected through a measuring sensor, and determine the target processing measure to release the freight vehicle to be inspected within the actual severe weather tolerance time of the cargo items of the freight vehicle to be inspected. ; and for obtaining the cargo list data submitted by the cargo vehicle to be inspected at the highway entrance station according to the license plate number of the cargo vehicle to be inspected; and for performing feature extraction and label classification on the cargo image data through a convolutional neural network to obtain the cargo type data of the cargo vehicle to be inspected; and for obtaining the cargo weight data of the cargo vehicle to be inspected according to the vehicle weight data and the rated weight of the cargo vehicle to be inspected; and for obtaining the bad weather green pass inspection result according to the cargo list data, the cargo type data and the cargo weight data, and storing the bad weather green pass inspection result and the license plate number of the cargo vehicle to be inspected in a database.

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