Intelligent analysis and auxiliary decision method, device, equipment and system for large-size transportation

Abstract

The application discloses a kind of big piece transport's intelligent analysis and auxiliary decision-making method, device, equipment and system, comprising: real-time acquisition laser radar data, camera image data, GNSS data and inertial sensor data, and according to each data perception obtains the situational information of big piece transport and the dynamic monitoring information of big piece cargo and transport;The situational information of transport and the dynamic monitoring information of big piece and transport are intelligently analyzed, and the passable envelope information of transport and big piece is calculated, the passability of big piece transport and big piece is judged, and the crash of transport and big piece and space target object;According to the dynamic monitoring information of big piece transport and big piece cargo and intelligent analysis result, form big piece transport auxiliary decision-making information, carry out big piece safe, reliable transport;The application realizes the intelligent analysis and auxiliary decision-making of big piece transport by digitization, intelligent means, to reduce the risk of big piece transport process accident, improve safe transport efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of navigation, guidance and control, geodetic surveying and remote sensing mapping, and more particularly to an intelligent analysis and auxiliary decision method, device, equipment and system for large piece transportation. BACKGROUND

[0002] In recent years, with the acceleration of national key projects and the rapid development of energy economy, the demand for large piece transportation has increased significantly, especially in the construction of infrastructure such as power, chemical industry, transportation and aerospace, which undertakes the transportation support task of key equipment.

[0003] Large piece transportation is an important guarantee for national key engineering construction and a high-end field of the transportation industry. However, these large piece goods or equipment are indivisible, and are super-long, super-wide, super-high and super-heavy, resulting in the characteristics of large piece transportation, i.e. high safety risk, high transportation cost, high service requirement, high transportation difficulty, high support difficulty and high supervision difficulty of the competent department.

[0004] It is of great significance to realize intelligent analysis and auxiliary decision of large piece transportation through digital and intelligent means to improve the safety, efficiency and convenience of large piece transportation and reduce the cost of large piece transportation.

[0005] Therefore, how to realize intelligent analysis and auxiliary decision of large piece transportation through digital and intelligent means to reduce the risk of accidents in the process of large piece transportation and improve the efficiency of safe transportation is a problem that needs to be solved by those skilled in the art. SUMMARY

[0006] Therefore, the present application provides an intelligent analysis and auxiliary decision method, device, equipment and system for large piece transportation to solve some of the technical problems mentioned in the background art.

[0007] In order to achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0008] An intelligent analysis and auxiliary decision method for large piece transportation, comprising the following steps:

[0009] S1. Real-time acquisition of laser radar data, camera image data, GNSS data and inertial sensor data, and according to the data, sensing and acquiring situational information of large piece transportation and dynamic monitoring information of large piece goods and vehicles;

[0010] S2. Intelligent analysis of situational information of large piece transportation and dynamic monitoring information of large piece goods and vehicles, and calculation of passable envelope information of vehicles and large piece goods, judgment of passability of large piece vehicles and large piece goods, and collision of large piece vehicles and large piece goods with space target objects;

[0011] S3. Forming an auxiliary decision-making information of the heavy cargo transportation according to the dynamic monitoring information of the heavy cargo and the heavy carrier and the intelligent analysis result, and performing safe and reliable transportation of the heavy cargo.

[0012] Preferably, in step S1, laser radar data, camera image data, GNSS data and inertial sensor data are acquired in real time by using cameras, laser radars, GNSS receivers and inertial sensors installed on the heavy cargo and the heavy carrier.

[0013] Preferably, the context information of the heavy carrier transportation includes motion state information of the heavy transportation carrier and environmental information around the carrier.

[0014] The motion state information of the carrier includes acceleration, angular velocity, attitude, speed and position information of the carrier; and the environmental information around the carrier is real-time environmental digital twin model information.

[0015] The dynamic monitoring information of the heavy cargo and the carrier includes image, video, laser point cloud information monitored by the heavy cargo and the carrier, and attitude information of the heavy cargo.

[0016] Preferably, the specific content of step S2 includes:

[0017] S21. According to the real-time acquired motion state information of the carrier and the environmental information around the carrier, the target objects in the environmental space are extracted by using clustering, machine learning or deep learning method, and the three-dimensional coordinates of the target objects are determined.

[0018] S22. The width, slope and radius of curvature of the road are calculated in real time by using the real-time acquired motion state information of the carrier and the environmental information around the carrier.

[0019] S23. The passable envelope information of the carrier and the heavy cargo is calculated in real time by using the context information of the heavy carrier transportation and the dynamic monitoring information of the heavy cargo and the carrier. The passable envelope is a cuboid formed by the outermost boundary of the carrier and the heavy cargo.

[0020] S24. When the N times size of the passable envelope cuboid does not conflict with the target objects in the environmental space, the carrier and the heavy cargo can pass through; when the N times size of the passable envelope cuboid conflicts with the target objects in the environmental space, the carrier and the heavy cargo collide with the target objects in the environmental space, which is not passable.

[0021] Preferably, the specific content of step S3 includes:

[0022] S31. According to the pre-acquired survey information and basic information of the heavy carrier and the heavy cargo, the road survey data of the heavy transportation, and the basic data of the roads and bridges along the heavy transportation route, the transportation route of the heavy transportation is planned, and then the heavy transportation scheme and emergency strategy are formulated.

[0023] S32. According to the transport situation information of the large-size carrier, the dynamic monitoring information of the large-size and the carrier, and the passable envelope information of the carrier and the large-size, the passability and the collision property of the carrier and the large-size are judged and predicted, and early warning and alarm are given, and then the operation strategy of the carrier and the adjustment strategy of the large-size are given.

[0024] Preferably, the specific content of step S3 further includes:

[0025] S33. The dynamic monitoring information of the large-size and the carrier, the operation strategy of the carrier and the adjustment strategy of the large-size are displayed to the driver in the form of video pictures and three-dimensional space models, and supplemented by voice information.

[0026] An intelligent analysis and auxiliary decision device for large-size transportation, comprising: a multi-sensor data acquisition module, a multi-sensor error modeling compensation module, a multi-sensor fusion environment perception and positioning navigation module, an intelligent analysis module, a large-size transportation auxiliary decision module, and a display module;

[0027] The multi-sensor data acquisition module is used to acquire laser radar data, camera image data, GNSS data and inertial sensor data in real time;

[0028] The multi-sensor error modeling compensation module is used for modeling and compensation of the self-error of laser radar, camera, GNSS receiver and inertial sensor, and the error between sensors, and the modeling compensation of laser radar point cloud motion distortion;

[0029] The multi-sensor fusion environment perception and positioning navigation module is used to acquire the situation information of the large-size carrier transportation and the dynamic monitoring information of the large-size goods and the carrier according to the error-compensated data of laser radar data, camera image data, GNSS data and inertial sensor data;

[0030] The intelligent analysis module is used to intelligently analyze the situation information of the large-size carrier transportation and the dynamic monitoring information of the large-size goods and the carrier, judge the passability of the large-size carrier and the large-size goods, and the collision property of the large-size carrier and the large-size goods and the space target object;

[0031] The large-size transportation auxiliary decision module is used to form large-size transportation auxiliary decision information according to the dynamic monitoring information of the large-size carrier and the large-size goods and the intelligent analysis result, and to realize safe and reliable transportation of the large-size;

[0032] The display module displays the large-size transportation auxiliary decision information to the driver in the form of video pictures and three-dimensional space models, and supplements it with voice information.

[0033] The computer device comprises at least one memory and at least one processor; the memory is used for storing a program for implementing the intelligent analysis and auxiliary decision method for large piece transportation; and the processor is used for calling and executing the program stored in the memory to implement the intelligent analysis and auxiliary decision method for large piece transportation.

[0034] The intelligent analysis and auxiliary decision system for large piece transportation comprises:

[0035] The computer device;

[0036] and at least one data acquisition device and at least one display device;

[0037] The data acquisition device is used for being deployed at a large piece transportation site to acquire data information of large piece goods and carriers; the display device is used for being deployed at the large piece transportation site to display the dynamic monitoring information of the carriers and large pieces obtained by the computer device, the large piece and carrier collision warning and alarm information with the environmental target objects, and the large piece transportation auxiliary decision information, so as to guide the driver to safely and reliably perform the large piece transportation.

[0038] According to the above technical solution, compared with the prior art, the present application provides an intelligent analysis and auxiliary decision method, device, equipment and system for large piece transportation, through multi-sensor information fusion of laser radar, camera, GNSS and inertial sensor, the carrier and large piece transportation situation information dynamic perception, large piece transportation passability intelligent judgment, carrier and large piece and environmental target object collision analysis and timely collision warning and alarm can be realized, the problem that the large piece transportation process relies on human monitoring and judgment is solved, and the large piece safe transportation efficiency is improved;

[0039] The multi-sensor data fusion environment perception, positioning navigation, large piece transportation digital twin model and intelligent analysis result are used to give large piece transportation auxiliary decision information, especially in narrow, turning, mountainous, bridge, tunnel and other restrictive road sections to give suggestions for safe passing of carriers and large pieces, reduce the risk caused by inaccurate human decision in the large piece transportation process, and reduce the large piece transportation cost. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only embodiments of the present application, and those skilled in the art can obtain other drawings according to the provided drawings without creative labor.

[0041] Figure 1A large piece of transport intelligent analysis and auxiliary decision method schematic diagram provided by the present application;

[0042] Figure 2 A large piece of goods and large piece of transport sensor installation schematic diagram provided by the present application;

[0043] Figure 3 A transportable envelope diagram of the large piece of goods and the large piece of transport provided by the present application;

[0044] Figure 4 A large piece of transport intelligent analysis and auxiliary decision device schematic diagram provided by the present application;

[0045] Figure 5 A large piece of transport intelligent analysis and auxiliary decision system schematic diagram provided by the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0047] Embodiment one of the present application discloses a large piece of transport intelligent analysis and auxiliary decision method, like Figure 1 , comprising the following steps:

[0048] S1. Real-time acquisition of laser radar data, camera image data, GNSS data and inertial sensor data, and according to the data, sensing acquisition of the context information of the large piece of transport, and the dynamic monitoring information of the large piece of goods and the transport;

[0049] S2. Intelligent analysis of the context information of the large piece of transport, and the dynamic monitoring information of the large piece of goods and the transport, and calculation of the passable envelope information of the transport and the large piece of goods, judgment of the passability of the large piece of transport and the large piece of goods, and the collision of the large piece of transport and the large piece of goods with the space target object;

[0050] S3. According to the dynamic monitoring information and intelligent analysis result of the large piece of transport and the large piece of goods, forming the large piece of transport auxiliary decision information, and carrying out the safe and reliable transport of the large piece.

[0051] In order to further implement the above technical solutions, like Figure 2 In step S1, the camera, laser radar, GNSS receiver and inertial sensor installed on the large piece of goods and the large piece of transport are used to acquire laser radar data, camera image data, GNSS data and inertial sensor data in real time.

[0052] In order to further implement the above technical solutions, the situational information of the large-size transport tool includes motion state information of the large-size transport tool and environmental information around the transport tool.

[0053] The motion state information of the transport tool includes acceleration, angular velocity, attitude, speed and position information of the transport tool; and the environmental information around the transport tool is real-time environmental digital twin model information.

[0054] The dynamic monitoring information of the large-size cargo and the transport tool includes images, videos, laser point cloud information monitored by the large-size cargo and the transport tool, and attitude information of the large-size cargo.

[0055] In order to further implement the above technical solutions, the specific content of step S2 includes:

[0056] S21. According to the real-time acquired motion state information of the transport tool and the environmental information around the transport tool, the target objects in the environmental space are extracted by using clustering, machine learning or deep learning method, and the three-dimensional coordinates of the target objects are determined.

[0057] In this embodiment, the target objects in the environmental space include but are not limited to road boundary lines, lamp poles, line towers, traffic signboards, height limit bars, guardrails, overhead cables, vehicles, pedestrians, trees, bridges, tunnels, highway toll gates, mountains and other environmental space target objects.

[0058] S22. The width, slope and radius of curvature of the road are calculated in real time by using the real-time acquired motion state information of the transport tool and the environmental information around the transport tool.

[0059] The specific content is:

[0060] The laser radar point cloud data and image data are compensated for motion distortion by using IMU data (acceleration and angular velocity);

[0061] The road boundary lines and lane lines are detected by using the laser radar electric fish and image point cloud data, and the boundary lines are fitted, and the distance between the two road boundary lines is the width of the road;

[0062] The ground point cloud data within 30 meters in front of the vehicle is collected by using the laser radar, and the point cloud data on the road is fitted in different regions, and the relative slope of the road is calculated by calculating the change of the vertical coordinates of the radar point cloud data in different regions in front;

[0063] The lane line data extracted by using the laser radar point cloud and image data are fitted, and the radius of curvature of the lane line, i.e. the radius of curvature of the road, is calculated;

[0064] S23. Real-time calculation of the passable envelope information of the large piece and the large piece carrier based on the large piece carrier transportation situation information and the dynamic monitoring information of the large piece and the carrier. The passable envelope is a cuboid formed by the outermost boundary of the large piece and the large piece carrier, as shown in Figure 3 ;

[0065] The cuboid formed by the planes of the front end, the rear end, the upper end, the left end and the right end of the vehicle or the large piece and the ground is the passable envelope.

[0066] S24. When the N times size of the passable envelope cuboid does not conflict with the environmental space target, the carrier and the large piece can pass through; when the N times size of the passable envelope cuboid conflicts with the environmental space target, the carrier and the large piece collide with the environmental space target, which is not passable.

[0067] In practical applications, when N is 2 or 3, the passable envelope cuboid does not collide with the environmental space target, which means that the carrier and the large piece can safely pass through; otherwise, the passable envelope cuboid does not collide with the environmental space target, and the carrier and the large piece cannot safely pass through.

[0068] In order to further implement the above technical solutions, the specific content of step S3 includes:

[0069] S31. According to the survey information and basic information of the large piece carrier and the large piece collected in advance, the road survey data of the large piece transportation, and the basic data of the road and bridge along the large piece transportation obtained from the transportation department or the road construction department, the transportation route planning of the large piece transportation is completed, and then the large piece transportation scheme and emergency strategy are formulated;

[0070] S32. According to the transportation situation information of the large piece carrier, the dynamic monitoring information of the large piece and the carrier, the passable envelope information of the large piece and the carrier, the passability and collision of the large piece and the carrier are judged and predicted, and early warning and alarm are given, and then the operation strategy of the carrier and the adjustment strategy of the large piece are given, especially for the road sections with strong restrictions such as narrow, turning, mountainous area, bridge and tunnel.

[0071] In order to further implement the above technical solutions, the specific content of step S3 also includes:

[0072] S33. The dynamic monitoring information of the large piece and the carrier, the operation strategy of the carrier and the adjustment strategy of the large piece are displayed to the driver in the form of video pictures and three-dimensional space models, and supplemented by voice information prompts.

[0073] Embodiment two

[0074] An intelligent analysis and auxiliary decision device for large piece transportation, as shown inFigure 4 , comprising: a multi-sensor data acquisition module, a multi-sensor error modeling compensation module, a multi-sensor fusion environment perception and positioning navigation module, an intelligent analysis module, a large-transport auxiliary decision module, and a display module;

[0075] The multi-sensor data acquisition module is configured to acquire laser radar data, camera image data, GNSS data, and inertial sensor data in real time.

[0076] The multi-sensor error modeling compensation module is configured to model and compensate for the self-error of the laser radar, the camera, the GNSS receiver, and the inertial sensor, the error between sensors, and the motion distortion of the laser radar point cloud.

[0077] The multi-sensor fusion environment perception and positioning navigation module is configured to acquire situational information of large-transport and dynamic monitoring information of large cargo and transport based on the error-compensated data of the laser radar data, the camera image data, the GNSS data, and the inertial sensor data.

[0078] The intelligent analysis module is configured to intelligently analyze the situational information of large-transport and the dynamic monitoring information of large cargo and transport, determine the passability of large-transport and large cargo, and determine the collision of large-transport and large cargo with a space target.

[0079] The large-transport auxiliary decision module is configured to form large-transport auxiliary decision information based on the dynamic monitoring information of large-transport and large cargo and the intelligent analysis result, and perform safe and reliable large-transport.

[0080] The display module displays the large-transport auxiliary decision information to the driver in the form of a video picture and a three-dimensional space model, and provides voice information as an aid.

[0081] Embodiment Three

[0082] A computer device includes at least one memory and at least one processor; the memory is configured to store a program for implementing an intelligent analysis and auxiliary decision method for large-transport; the processor is configured to call and execute the program stored in the memory to implement the intelligent analysis and auxiliary decision method for large-transport.

[0083] In this example, the processor can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), ready-to-program gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any other conventional processor.

[0084] The memory can include read-only memory and random access memory, and provide instructions and data to the processor, a portion of the memory can also include non-volatile random access memory; for example, the memory can also store device type information.

[0085] Embodiment four

[0086] An intelligent analysis and auxiliary decision system for heavy goods transportation, such as Figure 5 , comprising:

[0087] A computer device;

[0088] And at least one data acquisition device and at least one display device;

[0089] Among them: the data acquisition device is used to be deployed in the heavy goods transportation site to collect data information of heavy goods and vehicles; the display device is used to be deployed in the heavy goods transportation site to display the dynamic monitoring information of the vehicles and heavy goods obtained by the computer device, the heavy goods and the vehicles and the environment target object can collide warning and alarm information, and the heavy goods transportation auxiliary decision information, so as to guide the driver to safely and reliably transport the heavy goods.

[0090] In this embodiment, the data acquisition device includes cameras, laser radars, GNSS receivers and inertial sensors installed on the heavy goods and heavy vehicles;

[0091] The camera includes an industrial camera, a fisheye camera or a panoramic camera, including one camera, multiple cameras or a combination of different types of cameras;

[0092] The laser radar includes a solid-state laser radar, a mechanical scanning radar or a blind-filling laser radar, including one radar, multiple radars or a combination of different types of laser radars;

[0093] The GNSS receiver includes a single Beidou receiver, GPS, GLONASS, Galileo or Beidou multi-mode receiver, including one GNSS antenna or multiple GNSS antennas;

[0094] The inertial sensor includes a low-precision inertial sensor or a high-precision inertial sensor, including one inertial sensor, multiple inertial sensors or a combination of different precision inertial sensors.

[0095] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same and similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the related parts can be referred to the method part.

[0096] The foregoing description of the disclosed embodiments enables a person skilled in the art to make or use the application. Modifications of these embodiments will occur to persons of skill in the art, and that the appended claims are intended to cover all such modifications that do not depart from the true spirit and scope of the application. Therefore, the application is not limited to the embodiments shown but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for intelligent analysis and aided decision of heavy goods transport, characterized in that, The method comprises the following steps: S1. Real-time acquisition of laser radar data, camera image data, GNSS data and inertial sensor data by using cameras, laser radars, GNSS receivers and inertial sensors installed on large cargos and large carriers, and sensing of context information of large carrier transportation and dynamic monitoring information of large cargos and carriers according to the data; S2. Intelligent analysis of the context information of large carrier transportation and the dynamic monitoring information of large cargos and carriers, calculation of the passable envelope information of the carriers and large cargos, judgment of the passability of large carriers and large cargos, and collision of large carriers and large cargos with space targets; S3. Formation of large transportation auxiliary decision information and display according to the dynamic monitoring information of large carriers and large cargos and the intelligent analysis results, and assistance of the driver to perform safe and reliable large transportation; The specific content of step S2 comprises: S21. Extraction of target objects in the environment space and determination of the three-dimensional coordinates of the target objects by using clustering, machine learning or deep learning methods according to the real-time acquired carrier motion state information and carrier surrounding environment information; S22. Real-time calculation of the width, slope and radius of curvature of the road by using the real-time acquired carrier motion state information and carrier surrounding environment information; The specific content is: Motion distortion compensation of the laser radar point cloud data and image data by using the acceleration and angular velocity of the IMU data; detection of the road boundary line and lane line by using the laser radar point cloud and image point cloud data, and fitting of the boundary line, the distance between the two road boundary lines being the width of the road; ground point cloud data within 30 meters in front of the vehicle is collected by using the laser radar, and the point cloud data on the road is fitted in different regions, and the relative slope of the road is calculated by calculating the vertical coordinate changes of the radar point cloud data in different regions in front; the lane line data extracted by using the laser radar point cloud and image data are fitted, and the radius of curvature of the lane line, i.e. the radius of curvature of the road, is calculated; S23. Real-time calculation of the passable envelope information of the carriers and large cargos by using the context information of large carrier transportation and the dynamic monitoring information of large cargos and carriers, the passable envelope being a cuboid formed by the outermost boundary of the carriers and large cargos; S24. When the N times size of the passable envelope cuboid does not conflict with the environment space target object, the carriers and large cargos can pass through; when the N times size of the passable envelope cuboid conflicts with the environment space target object, the carriers and large cargos collide with the environment space target object, which is not passable; The context information of large carrier transportation comprises the motion state information of the large transportation carrier and the environment information around the carrier; wherein the motion state information of the carrier comprises the acceleration, angular velocity, attitude, speed and position information of the carrier; the environment information around the carrier is real-time environment digital twin model information; the dynamic monitoring information of large cargos and carriers comprises image, video, laser point cloud information and attitude information of large cargos and carriers; The specific content of step S3 comprises: S31. According to the pre-acquired survey information and basic information of the large piece of equipment and the large piece, the road survey data of the large piece transportation, and the basic data of the road and bridge along the large piece transportation route, the transportation route of the large piece transportation is planned, and then the large piece transportation scheme and emergency strategy are formulated; S32. According to the transportation situation information of the large piece of equipment, the dynamic monitoring information of the large piece and the equipment, and the passable envelope information of the equipment and the large piece, the passability and collision property of the equipment and the large piece are judged and predicted, and early warning and alarm are given, and then the operation strategy of the equipment and the adjustment strategy of the large piece are given.

2. The method for intelligent analysis and aided decision of heavy haulage according to claim 1, characterized in that, The specific content of step S3 further includes: S33. The dynamic monitoring information of the large piece and the equipment, the operation strategy of the equipment and the adjustment strategy of the large piece are displayed to the driver in the form of video pictures and three-dimensional space models, and voice information is prompted.

3. An intelligent analysis and aided decision device for the transport of large pieces, characterized by, The intelligent analysis and auxiliary decision method for large piece transportation according to any one of claims 1-2 comprises: a multi-sensor data acquisition module, a multi-sensor error modeling compensation module, a multi-sensor fusion environment perception and positioning navigation module, an intelligent analysis module, a large piece transportation auxiliary decision module and a display module; The multi-sensor data acquisition module is used to acquire laser radar data, camera image data, GNSS data and inertial sensor data in real time; The multi-sensor error modeling compensation module is used to model and compensate the errors of laser radar, camera, GNSS receiver and inertial sensor, and the errors between sensors, and model and compensate the motion distortion of laser radar point cloud; The multi-sensor fusion environment perception and positioning navigation module is used to perceive and acquire the situation information of the large piece equipment transportation and the dynamic monitoring information of the large piece and the equipment according to the error-compensated data of laser radar data, camera image data, GNSS data and inertial sensor data; The intelligent analysis module is used to intelligently analyze the situation information of the large piece equipment transportation and the dynamic monitoring information of the large piece and the equipment, judge the passability of the large piece equipment and the large piece, and the collision property of the large piece equipment and the large piece with space target objects; The large piece transportation auxiliary decision module is used to form large piece transportation auxiliary decision information according to the dynamic monitoring information of the large piece equipment and the large piece and the intelligent analysis result, and realize safe and reliable large piece transportation; The display module displays the large piece transportation auxiliary decision information to the driver in the form of video pictures and three-dimensional space models, and prompts voice information.

4. A computer device, comprising: It comprises: At least one memory and at least one processor; The memory is used to store the program for realizing the intelligent analysis and auxiliary decision method for large piece transportation according to any one of claims 1-2; the processor is used to call and execute the program stored in the memory, so as to realize the intelligent analysis and auxiliary decision method for large piece transportation according to any one of claims 1-2.

5. An intelligent analysis and aided decision system for overdimensioned transport, characterized in that, It comprises: The computer device according to claim 4; At least one data acquisition device and at least one display device; The data acquisition device is arranged at the site of the large-size transportation to collect data information of the large-size goods and the carrier; the display device is arranged at the site of the large-size transportation to display the dynamic monitoring information of the carrier and the large-size obtained by the computer device, the collision warning and alarm information of the large-size and the carrier with the environmental target objects, and the large-size transportation auxiliary decision information, so as to guide the driver to safely and reliably perform the large-size transportation.

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