Intelligent loading method and system based on artificial intelligence technology

Abstract

The invention provides an intelligent loading method and system based on an artificial intelligence technology. The method comprises the steps of obtaining basic information of a transport vehicle and storing the basic information in a database; according to the successful reservation information, guiding the transport vehicle to drive to a designated area to park the vehicle; the image recognition module is used for adjusting the number, the positions and the angles of the cameras by utilizing a preset guide rod according to the basic information of the vehicle, obtaining a carriage image in the complete loading process and transmitting the carriage image to the intelligent loading system; monitoring the collected carriage image in the loading process through an image recognition module of the intelligent loading system based on an artificial intelligence deep learning image recognition method; and truck loading analysis and early warning are carried out based on the constructed intelligent metering system. Through an artificial intelligence-based picture recognition algorithm, an intelligent metering system and an automatic control discharging device, rapid fixed-value loading is realized, the automatic and intelligent management level of loading tasks is improved, and the system has important significance in the aspects of improving the production efficiency and economic benefits of enterprises and the like.

Description

Technical Field

[0001] The present application relates to the technical field of logistics shipping equipment, and in particular to an intelligent loading method and system based on artificial intelligence technology. Background Art

[0002] The unattended quantitative loading system is a highly integrated loading solution based on automated control, intelligent metering and information technology. The system can achieve fast, accurate and automated loading processes and is suitable for loading operations of various bulk materials such as coal, oil, ore, grain, etc., aiming to improve the production efficiency and economic benefits of enterprises.

[0003] However, the traditional method of loading cargo transportation has the following problems:

[0004] Inefficient loading: Traditional loading methods often rely on manual operations and long waiting times, which results in long waiting times for customers. This inefficiency may lead to transportation delays and affect the efficiency of the entire supply chain.

[0005] Cooperation between drivers and loaders: During the loading process, the cooperation between drivers and loaders is also a major pain point. For example, loaders control traffic lights to instruct drivers to move vehicles, but due to low interaction and cooperation between the two parties, inadequate execution, and inability to perceive the loading progress, it is easy to cause repeated vehicle movement, further reducing loading efficiency.

[0006] Difficulty in observing the loading progress in the carriage: Due to the high height of the carriage, it is difficult for on-site personnel to observe the loading progress in the carriage in real time, which may lead to inaccurate loading volume, under-tonnage or over-tonnage. This not only reduces the vehicle loading efficiency and turnover efficiency, but also increases the difficulty of management and leads to irregular operations.

[0007] High operating costs: Manual loading requires a lot of manpower, which increases the operating costs of enterprises. At the same time, due to low efficiency, it may lead to higher transportation costs, further compressing the profit margins of enterprises.

[0008] Low level of intelligence: Currently, many cargo loading operations still remain in the traditional mode and lack the application of intelligent technology. This not only limits the improvement of loading efficiency, but also makes it difficult to adapt to the development needs of the modern transportation industry.

[0009] How to improve the automation level and accuracy of unmanned quantitative loading is a technical problem that technical personnel in this field need to urgently solve. Summary of the invention

[0010] In view of this, it is necessary to provide an intelligent loading method and system based on artificial intelligence technology. Through the artificial intelligence-based image recognition algorithm and intelligent metering system, the unloading device can be automatically controlled to achieve rapid fixed-value loading, which is of great significance to improving the production efficiency and economic benefits of the enterprise.

[0011] In a first aspect, an embodiment of the present application provides an intelligent loading method based on artificial intelligence technology, characterized in that the method comprises the following operating steps:

[0012] Obtain basic information of transport vehicles through the input module of the intelligent loading system and save it to the database;

[0013] The navigation module of the intelligent loading system guides the transport vehicle to the designated area to park the vehicle according to the successful reservation information;

[0014] According to the basic information of the vehicle, the number, position and angle of the cameras are adjusted using the preset guide rods to obtain the image of the carriage during the complete loading process and transmit it to the image recognition module of the intelligent loading system;

[0015] The image recognition module of the intelligent loading system monitors the collected carriage images during the loading process based on the artificial intelligence deep learning image recognition method;

[0016] Carry out loading analysis and early warning based on the constructed intelligent metering system.

[0017] In one embodiment, the basic information of the vehicle includes at least vehicle model data, license plate information, vehicle length, width and height data, and vehicle body photo data.

[0018] In one embodiment, the method of adjusting the number, position and angle of cameras using a preset guide rod according to the basic information of the vehicle to obtain the image of the vehicle compartment during the complete loading process includes:

[0019] Obtain the length, width and height data of the vehicle box and calculate the projection range of each surface around it;

[0020] The shooting angle of view is calculated according to the mapping relationship between the shooting angle of view and the projection range;

[0021] The number, position and angle of the cameras on the preset guide rod are adjusted according to the shooting angle;

[0022] When the shooting angle of view exceeds the adjustment range, the vehicle image is zoomed and shot at a preset ratio.

[0023] In one embodiment, when the shooting angle of view exceeds the adjustment range, zooming and shooting the vehicle image at a preset ratio includes:

[0024] When the shooting angle of view is smaller than the projection range, the vehicle image is magnified and photographed at a first preset ratio;

[0025] When the shooting angle of view is larger than the projection range, the vehicle image is zoomed out and shot at a second preset ratio.

[0026] In one embodiment, before the image recognition module of the intelligent loading system monitors the collected carriage images during the loading process based on the artificial intelligence deep learning image recognition method, the method further includes:

[0027] Real-time collection of license plate images and body images of transport vehicles;

[0028] Perform OCR character recognition on the license plate image to obtain the license plate recognition result;

[0029] Compare the license plate information with the license plate recognition result, and when the two information are consistent, extract the fingerprint feature information of the vehicle body image;

[0030] Comparing the fingerprint feature information with the fingerprint feature information of the vehicle body photo data;

[0031] If the comparison result shows that the similarity is greater than the specified threshold, the license plate and vehicle model information are considered consistent, and the authentication result is passed, otherwise it is failed;

[0032] Among them, the similarity is calculated using the improved cosine distance, and the formula is as follows:

[0033]

[0034] Among them, A i , B i are the characteristic vectors of the tested samples and the control samples respectively, n is the number of partial samples, and N is the total number of samples.

[0035] In one embodiment, the image recognition module of the intelligent loading system monitors the collected carriage images during the loading process based on an artificial intelligence deep learning image recognition method, including:

[0036] Continuously collecting multiple carriage images at preset time intervals;

[0037] Segmenting and identifying the multiple carriage images using a three-dimensional image segmentation technology to obtain the quantity of cargo at different time intervals;

[0038] The loading time efficiency is calculated by the changing pattern of cargo quantity;

[0039] Real-time prediction of loading status and vehicle condition information based on vehicle volume and loading time efficiency prediction.

[0040] In one embodiment, the loading analysis and early warning based on the constructed intelligent metering system include:

[0041] Build an intelligent metering system based on the predicted loading situation and vehicle status data at different times;

[0042] When the intelligent metering system detects that the cargo is full, it will give a timely warning. At the same time, the warning level and warning time will be displayed in the alarm information window of the on-site terminal operation station. When the warning message is not fed back, the unloading device will be automatically controlled to stop unloading, realizing rapid fixed-value loading.

[0043] In a second aspect, an embodiment of the present application provides an intelligent loading system based on artificial intelligence technology, which is applied to the intelligent loading method based on artificial intelligence technology as described in the first aspect, and the system includes:

[0044] Static data acquisition module, used to obtain basic information of transport vehicles through the input module of the intelligent loading system and save it to the database;

[0045] The parking space navigation module is used to guide the transport vehicle to the designated area to park the vehicle according to the successful reservation information through the navigation module of the intelligent loading system;

[0046] Dynamic data acquisition module, used to adjust the number, position and angle of cameras using preset guide rods according to the basic information of the vehicle, obtain the image of the carriage during the complete loading process, and transmit it to the image recognition module of the intelligent loading system;

[0047] A data processing module is used to monitor the collected carriage images during the loading process through the image recognition module of the intelligent loading system based on the artificial intelligence deep learning image recognition method;

[0048] The analysis and early warning module is used for loading analysis and early warning based on the constructed intelligent metering system.

[0049] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0050] processor;

[0051] a memory for storing processor-executable instructions;

[0052] Wherein, the processor is configured to implement the intelligent loading method based on artificial intelligence technology as described in the first aspect when executing the instructions.

[0053] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, wherein the instructions instruct a device to execute the intelligent loading method based on artificial intelligence technology as described in the first aspect.

[0054] The embodiments of the present application provide an intelligent loading method and system based on artificial intelligence technology, which can realize a fast, accurate and automated loading process. Through the image recognition technology based on artificial intelligence and the intelligent metering system, the unloading device is automatically controlled to realize fast fixed-value loading.

[0055] The beneficial effects of the system specifically include:

[0056] (1) Automatic quantitative loading. The system automatically controls the unloading device through artificial intelligence-based image recognition technology and intelligent metering system to achieve fast fixed-value loading. According to the preset loading parameters, the loading speed and quantity are automatically adjusted to ensure loading accuracy.

[0057] (2) Real-time data monitoring. Automatically collect and display loading data in real time, including loading weight, progress, etc. Through the LED screen or computer screen, managers and customers can understand the loading status in real time.

[0058] (3) Remote monitoring and control. Equipped with a remote video monitoring system, the loading process can be remotely monitored to ensure operational safety. Supports remote operation control to achieve unattended loading operations.

[0059] (4) Intelligent vehicle model identification. Automatically scan the vehicle model and license plate number, and select the appropriate loading capacity according to the vehicle model and loading requirements. The system supports matching multiple vehicle models to meet different loading requirements.

[0060] (5) Reduce environmental pollution. Equipped with a dust control system to reduce dust emissions during loading and achieve clean and environmentally friendly loading operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram of the process flow of an intelligent loading method based on artificial intelligence technology provided in one embodiment of the present application.

[0062] Figure 2 This is a schematic diagram of an intelligent loading system module based on artificial intelligence technology provided by an embodiment of the present application.

[0063] Figure 3 A schematic diagram of an electronic terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present application will be described clearly and completely 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, rather than all of the embodiments.

[0065] It should be noted that, in the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0066] It should be noted that, in the embodiments of the present application, words such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. Features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

[0067] Based on the implementations in this application, all other implementations obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0068] The intelligent loading system based on artificial intelligence technology has become an important development direction of the automatic loading industry with its high efficiency, intelligence and safety. By introducing this system, enterprises can greatly improve loading efficiency, reduce operating costs and enhance competitiveness. At the same time, the system also helps to improve the working environment, ensure personnel safety, and provide strong guarantees for the sustainable development of enterprises. Therefore, we strongly recommend that enterprises actively adopt the automatic loading system to promote the progress and development of the industry.

[0069] In view of this, the present application provides an intelligent loading method based on artificial intelligence technology, which can improve the level of automation and intelligent management of loading, so as to realize remote monitoring, intelligent control and safety protection, and ensure that the loading task can be carried out safely and efficiently even without human supervision.

[0070] Figure 1 This is a flow chart of an intelligent loading method based on artificial intelligence technology provided by an embodiment of the present application. Figure 1 An intelligent loading method based on artificial intelligence technology is shown, which includes at least the following steps:

[0071] S1: Obtain the basic information of the transport vehicle through the input module of the intelligent loading system and save it to the database.

[0072] Specifically, the basic information of the vehicle includes at least vehicle model data, license plate information, vehicle length, width and height data, vehicle body photo data and other data, which are used for subsequent authentication and analysis and identification functions.

[0073] It can be understood that in the embodiment of the present application, information can be entered through the user registration function by following the prompts. Newly opened companies or individual businesses need to register an account before they can log in to the intelligent loading system. In addition to registering on the computer web page, you can also register and enter information directly on your mobile phone.

[0074] In an embodiment of the present application, the central management system has a large database that stores information on a large amount of registered vehicles and users.

[0075] S2: The navigation module of the intelligent loading system guides the transport vehicle to the designated area to park the vehicle according to the successful reservation information.

[0076] In an embodiment of the present application, the intelligent loading system can be downloaded and installed in the form of a mini program, or it can be provided and used in the form of a microservice, such as a WeChat mini program.

[0077] Optionally, the navigation function can choose the on-board navigation system, whose functions include: GPS satellite navigation positioning, electronic map browsing and query, intelligent route planning, and voice prompts throughout the journey.

[0078] Specifically, when the reservation is successful, the vacant parking space can be allocated through the intelligent algorithm. At this time, the navigation function of the parking space can be performed through the guide button. The navigation function can be realized through the in-vehicle navigation system, by calling the mobile phone navigation software, or through the navigation web page function embedded in the mini program.

[0079] S3: According to the basic information of the vehicle, the number, position and angle of the cameras are adjusted using the preset guide rods to obtain the image of the carriage during the complete loading process and transmit it to the image recognition module of the intelligent loading system.

[0080] It can be understood that the method of adjusting the number, position and angle of the cameras using the preset guide rods according to the basic information of the vehicle to obtain the image of the carriage during the complete loading process includes:

[0081] Obtain the length, width and height data of the vehicle box and calculate the projection range of each surface around it;

[0082] The shooting angle of view is calculated according to the mapping relationship between the shooting angle of view and the projection range;

[0083] The number, position and angle of the cameras on the preset guide rod are adjusted according to the shooting angle;

[0084] When the shooting angle of view exceeds the adjustment range, the vehicle image is zoomed and shot at a preset ratio.

[0085] Specifically, when the shooting angle of view exceeds the adjustment range, the vehicle image is zoomed in and out at a preset ratio, including: when the shooting angle of view is smaller than the projection range, the vehicle image is zoomed in and out at a first preset ratio; when the shooting angle of view is larger than the projection range, the vehicle image is zoomed out and out at a second preset ratio.

[0086] It is understandable that the ultimate purpose of zooming in and zooming out is to obtain an image of a complete vehicle or vehicle compartment. In the embodiment of the present application, since multiple cameras are used for shooting, a suitable viewing angle can also be selected to fuse the images taken by the multiple cameras to obtain an image of a complete vehicle or vehicle compartment for subsequent analysis or recognition.

[0087] Specifically, before the image recognition module of the intelligent loading system monitors the collected carriage images during the loading process based on the artificial intelligence deep learning image recognition method, it also includes:

[0088] Real-time collection of license plate images and body images of transport vehicles;

[0089] Perform OCR character recognition on the license plate image to obtain the license plate recognition result;

[0090] Compare the license plate information with the license plate recognition result, and when the two information are consistent, extract the fingerprint feature information of the vehicle body image;

[0091] Comparing the fingerprint feature information with the fingerprint feature information of the vehicle body photo data;

[0092] If the comparison result shows that the similarity is greater than the specified threshold, the license plate and vehicle model information are considered consistent, and the authentication result is passed, otherwise it is failed;

[0093] Among them, the similarity is calculated using the improved cosine distance, and the formula is as follows:

[0094]

[0095] Among them, A i , B i are the characteristic vectors of the tested samples and the control samples respectively, n is the number of partial samples, and N is the total number of samples.

[0096] It is understandable that OCR technology can use the opencv algorithm library to extract text information from images through image processing and statistical machine learning methods, including binarization, noise filtering, correlation domain analysis, AdaBoost, etc. Traditional OCR technology can be divided into three stages according to the processing method: image preparation, text recognition and post-processing. Image preparation preprocessing can be text area positioning, text correction, text segmentation, such as binarization, noise filtering and other technical means.

[0097] Specifically, the license plate information is compared with the license plate recognition result, which can be a character-by-character comparison. Only when all the characters are exactly the same, the comparison result is considered consistent, otherwise, it is inconsistent.

[0098] Specifically, the fingerprint feature information is compared with the fingerprint feature information of the vehicle body photo data, wherein the fingerprint feature information can be the binary information of the image or the pixel information of the image, or the edge information of the image. In the machine, the similarity is calculated by extracting features. The features in the image include texture, contour, semantic information, etc., which are extracted by using a convolutional neural network. After continuous convolution and maxpool operations, the image is continuously reduced. After each operation here, it is a feature map, with the contour information at the bottom layer and the semantic information at the top layer, and then the image feature data is obtained. The Backbone model here can be vgg, resnet, SwinTransform, etc.

[0099] Specifically, similarity algorithms include: Euclidean geometric distance algorithm, Manhattan distance, edit distance, cosine distance, etc. If the traditional cosine distance is used for calculation, the formula is as follows:

[0100]

[0101] Among them, A i , B i are the feature vectors of the test sample and the control sample respectively, and n is the number of samples.

[0102] Specifically, if the improved cosine distance is used for calculation, the formula is as follows:

[0103]

[0104] Among them, A i , B i are the characteristic vectors of the tested samples and the control samples respectively, n is the number of partial samples, and N is the total number of samples.

[0105] S4: The image recognition module of the intelligent loading system is used to monitor the carriage images collected during the loading process based on the artificial intelligence deep learning image recognition method.

[0106] Specifically, the image recognition module of the intelligent loading system monitors the collected carriage images during the loading process based on an artificial intelligence deep learning image recognition method, including:

[0107] Continuously collecting multiple carriage images at preset time intervals;

[0108] Segmenting and identifying the multiple carriage images using a three-dimensional image segmentation technology to obtain the quantity of cargo at different time intervals;

[0109] The loading time efficiency is calculated by the changing pattern of cargo quantity;

[0110] Real-time prediction of loading status and vehicle condition information based on vehicle volume and loading time efficiency prediction.

[0111] Specifically, in order to improve the accuracy of image detection, before the multiple carriage images are segmented and recognized by the three-dimensional image segmentation technology, the following steps are performed: using a geometric correction model to construct a geometric relationship between the image and the ground coordinates; and calibrating the image data.

[0112] It is understandable that the image correction may include various types of correction such as geographic coordinate correction, geometric correction, and jitter correction of the image data, which may be selected according to actual needs.

[0113] Specifically, image segmentation is a common technology in image processing and is the basis for recognizing and understanding images. The purpose of loading image segmentation is to separate the target goods from the entire image in preparation for the next step of identifying the quantity of goods. When segmenting the image, the required goods are regarded as the foreground and the rest are regarded as the background. The foreground and background of the cargo image are separated through segmentation.

[0114] Specifically, in order to further identify goods of different forms, it is necessary to separate spots from the classified images, and the classified images based on the support vector machine can be used to extract the form of the goods.

[0115] In addition, through the early segmentation of the image, the image often contains redundant parts such as impurities, and these unnecessary information needs to be processed using morphology. In order to improve the accuracy of recognition, the image is also subjected to morphological processing, including corrosion and expansion operations.

[0116] S5: Loading analysis and early warning based on the constructed intelligent metering system.

[0117] In the embodiment of the present application, the loading analysis and early warning are performed based on the constructed intelligent metering system, including: constructing an intelligent metering system according to the predicted loading conditions and vehicle status data at different times; when the intelligent metering system detects that the goods are full, it promptly gives an early warning, and at the same time displays its early warning level and early warning time in the alarm information window of the on-site terminal operation station; when the early warning message is not fed back, the unloading device is automatically controlled to stop unloading, thereby realizing rapid fixed-value loading.

[0118] It can be understood that the intelligent loading method based on artificial intelligence technology provided in the embodiment of the present application can be displayed through a large LED screen or a computer screen, so that managers and customers can understand the loading situation in real time.

[0119] Specifically, in order to further accurately control the operation of the unloading device, an early warning can be given through a display screen or text message before the predicted time arrives, for example, 2 minutes before the predicted time arrives, or when the goods are about to be filled, for example, when they are filled to 99%. When the early warning message is fed back, the staff will control to stop unloading. When no feedback is received, the unloading device is automatically controlled to stop unloading, so as to achieve fast fixed-value loading.

[0120] The loading method of the present invention can improve loading efficiency: the automatic loading system greatly shortens the loading time through accurate measurement and automated operation. The advanced control system can monitor the changes in loading capacity in real time, making the loading process more accurate and efficient. In addition, the system can optimize the loading operation according to real-time data and algorithms through the reservation system, minimize the empty driving and loading waiting time, and further improve the loading efficiency.

[0121] The loading method of the present invention can reduce labor costs: the traditional coal loading process requires a lot of manpower, which not only increases the operating costs of the enterprise, but also has safety hazards caused by human factors. The automatic coal loading system can reduce the number of workers on site and reduce labor costs. At the same time, due to the stability and accuracy of the system, the risk of accidents caused by human operating errors is also reduced.

[0122] The loading method of the present invention can improve the working environment: automated loading reduces manual operations, reduces noise and dust pollution in the working environment, and provides a more comfortable and healthy working environment for workers.

[0123] The loading method of the present invention can enhance data management capabilities: the coal automatic loading system can record and analyze loading data in real time, providing enterprises with more accurate and comprehensive data support. Through data analysis, enterprises can better understand the loading operation, optimize resource allocation, and improve overall operational efficiency.

[0124] Figure 2 This is a schematic diagram of an intelligent loading system module based on artificial intelligence technology provided by an embodiment of the present application, which is applied to Figure 1 The intelligent loading method based on artificial intelligence technology is shown. The system modules specifically include: static data acquisition module 11, parking space navigation module 12, dynamic data acquisition module 13, data processing module 14, analysis and early warning module 15. Its functions are similar to Figure 1 The parts shown in FIG. 1 are the same or similar and will not be described in detail here.

[0125] In the embodiment of the present application, the static data acquisition module 11 is used to obtain the basic information of the transport vehicle through the input module of the intelligent loading system and save it to the database. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0126] In the embodiment of the present application, the parking space navigation module 12 is used to guide the transport vehicle to the designated area to park the vehicle according to the successful reservation information through the navigation module of the intelligent loading system. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0127] In the embodiment of the present application, the dynamic data acquisition module 13 is used to adjust the number, position and angle of the cameras according to the basic information of the vehicle using the preset guide rod, obtain the image of the carriage during the complete loading process, and transmit it to the image recognition module of the intelligent loading system. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0128] In the embodiment of the present application, the data processing module 14 is used to monitor the collected carriage images during the loading process through the image recognition module of the intelligent loading system based on the artificial intelligence deep learning image recognition method. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0129] In the embodiment of the present application, the analysis and warning module 15 is used to perform loading analysis and warning based on the constructed intelligent metering system. Figure 1 And the corresponding description thereof, this application will not repeat them here.

[0130] See also Figure 3 , Figure 3 This is an electronic terminal device provided by an embodiment of the present application. Figure 3The electronic terminal device shown includes at least the following parts: one or more processors, one or more input devices, one or more output devices and one or more memories. The processors, input devices, output devices and memories communicate with each other via a communication bus. The memory is used to store computer programs, which include program instructions. The processor is used to execute the program instructions stored in the memory. The processor is configured to call the program instructions to perform the following operations to perform the functions of the modules / units in the above-mentioned device embodiments, such as Figure 2 Functionality of the modules shown.

[0131] In an embodiment of the present application, a computer-readable storage medium includes instructions, and the instructions instruct a device to execute the method of the first aspect. For example, the instructions instruct the device to execute Figure 1 The steps in the figure show an intelligent loading method based on artificial intelligence technology.

[0132] It should be understood that in the embodiment of the present invention, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. It should be noted that a part of the electronic device of the above-mentioned embodiment may also be implemented by a computer. In this case, the program for implementing the control function may be recorded in a computer-readable recording medium, and the program recorded in the recording medium may be read into a computer and executed.

[0133] Input devices may include a touchpad, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and output devices may include a display (LCD, etc.), a speaker, etc.

[0134] It should be noted that the "computer" mentioned here refers to a computer built into an electronic device, and uses a computer including hardware such as an OS and peripheral devices. In addition, "computer-readable recording medium" refers to removable media such as floppy disks, magneto-optical disks, ROMs, CD-ROMs, and storage devices such as hard disks built into computers.

[0135] Furthermore, "computer-readable recording media" may include: media that dynamically store programs for a short period of time, such as communication lines when sending programs via networks such as the Internet or communication lines such as telephone lines; and media that store programs for a fixed period of time, such as volatile memories inside computers that serve as servers or clients in this case. In addition, the above-mentioned program may be a program for realizing a part of the above-mentioned functions, or a program that can realize the above-mentioned functions by combining with a program already recorded in a computer.

[0136] In addition, the electronic device in the above-mentioned embodiment can also be implemented as a collection (device group) composed of multiple devices. Each device constituting the device group can have a part or all of the functions or functional blocks of the electronic device in the above-mentioned embodiment. As a device group, it is sufficient to have all the functions or functional blocks of the electronic device.

[0137] It is understandable that the intelligent loading method, system, electronic device and storage medium based on artificial intelligence technology provided in the embodiments of the present application can effectively improve the automation level of loading tasks and solve the problem that traditional manual inspection cannot solve loading problems in real time and in a timely manner. It can also realize functions such as multi-model identification, vehicle deviation correction and positioning, and vehicle moving reminder to improve loading accuracy.

[0138] It is understandable that the intelligent loading method, system, electronic device and storage medium based on artificial intelligence technology provided in the embodiments of the present application can automatically load quantitatively. The system automatically controls the unloading device through the image recognition technology based on artificial intelligence and the intelligent metering system to achieve fast fixed-value loading. According to the preset loading parameters, the loading speed and quantity are automatically adjusted to ensure loading accuracy.

[0139] It is understandable that the intelligent loading method, system, electronic device and storage medium based on artificial intelligence technology provided in the embodiments of the present application can monitor data in real time, automatically collect and display loading data in real time, including loading weight, progress, etc. Through the LED large screen or computer screen, it is convenient for managers and customers to understand the loading situation in real time.

[0140] It is understandable that the intelligent loading method, system, electronic device and storage medium based on artificial intelligence technology provided in the embodiments of the present application can be remotely monitored and controlled, equipped with a remote video monitoring system, to achieve remote monitoring of the loading process and ensure operational safety. It supports remote operation control and realizes unattended loading operations.

[0141] It is understandable that the intelligent loading method, system, electronic device and storage medium based on artificial intelligence technology provided in the embodiments of the present application can perform intelligent vehicle model recognition, automatically scan the vehicle model and license plate number, and select the appropriate loading amount according to the vehicle model and loading requirements. The system supports matching of multiple vehicle models to meet different loading requirements.

[0142] It is understandable that the intelligent loading method, system, electronic device and storage medium based on artificial intelligence technology provided in the embodiments of the present application can reduce environmental pollution, be equipped with a dust control system, reduce dust emissions during loading, and achieve clean and environmentally friendly loading operations.

[0143] Those skilled in the art should recognize that the above embodiments are only used to illustrate the present application and are not intended to be limiting of the present application. As long as they are within the spirit and scope of the present application, appropriate changes and modifications to the above embodiments are within the scope of protection claimed in the present application.

Claims

1. An intelligent loading method based on artificial intelligence technology, characterized in that: The method comprises the following steps: Obtain basic information of transport vehicles through the input module of the intelligent loading system and save it to the database; The navigation module of the intelligent loading system guides the transport vehicle to the designated area to park the vehicle according to the successful reservation information; According to the basic information of the vehicle, the number, position and angle of the cameras are adjusted using the preset guide rods to obtain the image of the carriage during the complete loading process and transmit it to the image recognition module of the intelligent loading system; The image recognition module of the intelligent loading system monitors the collected carriage images during the loading process based on the artificial intelligence deep learning image recognition method; Carry out loading analysis and early warning based on the constructed intelligent metering system.

2. The intelligent loading method based on artificial intelligence technology according to claim 1 is characterized in that: The basic information of the vehicle at least includes vehicle model data, license plate information, vehicle length, width and height data, and vehicle body photo data.

3. The intelligent loading method based on artificial intelligence technology according to claim 2 is characterized in that: The method of adjusting the number, position and angle of the cameras using a preset guide rod according to the basic information of the vehicle to obtain the image of the carriage during the complete loading process includes: Obtain the length, width and height data of the vehicle box and calculate the projection range of each surface around it; The shooting angle of view is calculated according to the mapping relationship between the shooting angle of view and the projection range; The number, position and angle of the cameras on the preset guide rod are adjusted according to the shooting angle; When the shooting angle of view exceeds the adjustment range, the vehicle image is zoomed and shot at a preset ratio.

4. The intelligent loading method based on artificial intelligence technology according to claim 3 is characterized in that: When the shooting angle of view exceeds the adjustment range, zooming and shooting the vehicle image at a preset ratio comprises: When the shooting angle of view is smaller than the projection range, the vehicle image is magnified and photographed at a first preset ratio; When the shooting angle of view is larger than the projection range, the vehicle image is zoomed out and shot at a second preset ratio.

5. The intelligent loading method based on artificial intelligence technology according to claim 2 is characterized in that: Before the image recognition module of the intelligent loading system monitors the collected carriage images during the loading process based on the artificial intelligence deep learning image recognition method, the method further includes: Real-time collection of license plate images and body images of transport vehicles; Perform OCR character recognition on the license plate image to obtain the license plate recognition result; Compare the license plate information with the license plate recognition result, and when the two information are consistent, extract the fingerprint feature information of the vehicle body image; Comparing the fingerprint feature information with the fingerprint feature information of the vehicle body photo data; If the comparison result shows that the similarity is greater than the specified threshold, the license plate and vehicle model information are considered consistent, and the authentication result is passed, otherwise it is failed; Among them, the similarity is calculated using the improved cosine distance, and the formula is as follows: Among them, A i , B i are the characteristic vectors of the tested samples and the control samples respectively, n is the number of partial samples, and N is the total number of samples.

6. The intelligent loading method based on artificial intelligence technology according to claim 4 is characterized in that: The image recognition module of the intelligent loading system monitors the collected carriage images during the loading process based on an artificial intelligence deep learning image recognition method, including: Continuously collecting multiple carriage images at preset time intervals; Segmenting and identifying the multiple carriage images using a three-dimensional image segmentation technology to obtain the quantity of cargo at different time intervals; The loading time efficiency is calculated by the changing pattern of cargo quantity; Real-time prediction of loading status and vehicle condition information based on vehicle volume and loading time efficiency prediction.

7. The intelligent loading method based on artificial intelligence technology according to claim 6 is characterized in that: The intelligent metering system constructed above performs loading analysis and early warning, including: Build an intelligent metering system based on the predicted loading situation and vehicle status data at different times; When the intelligent metering system detects that the cargo is full, it will give a timely warning. At the same time, the warning level and warning time will be displayed in the alarm information window of the on-site terminal operation station. When the warning message is not fed back, the unloading device will be automatically controlled to stop unloading, realizing rapid fixed-value loading.

8. An intelligent loading system based on artificial intelligence technology, applied to the intelligent loading method based on artificial intelligence technology as claimed in any one of claims 1 to 7, characterized in that: The system comprises: Static data acquisition module, used to obtain basic information of transport vehicles through the input module of the intelligent loading system and save it to the database; The parking space navigation module is used to guide the transport vehicle to the designated area to park the vehicle according to the successful reservation information through the navigation module of the intelligent loading system; Dynamic data acquisition module, used to adjust the number, position and angle of cameras using preset guide rods according to the basic information of the vehicle, obtain the image of the carriage during the complete loading process, and transmit it to the image recognition module of the intelligent loading system; A data processing module is used to monitor the collected carriage images during the loading process through the image recognition module of the intelligent loading system based on the artificial intelligence deep learning image recognition method; The analysis and early warning module is used for loading analysis and early warning based on the constructed intelligent metering system.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the intelligent loading method based on artificial intelligence technology as described in claim 8 when executing the instructions.

10. A computer-readable storage medium, characterized in that: Including instructions, which instruct the device to execute the intelligent loading method based on artificial intelligence technology as described in claim 8.

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