Remote control method and device for engineering vehicle, remote control equipment and medium

Abstract

The invention relates to the field of remote control sensing, in particular to an engineering vehicle remote control method and device, remote control equipment and a medium. The method comprises the following steps: obtaining a monitoring video corresponding to a target engineering vehicle in real time, carrying out detailed analysis on the monitoring video to obtain a vehicle form and current environment characteristics of the target engineering vehicle, obtaining a BIM (Building Information Model) model corresponding to the target engineering vehicle, and updating the BIM model based on the real-time vehicle form and environment characteristics, so as to obtain the real-time monitoring video of the target engineering vehicle. The BIM model is no longer a static and fixed model, but a dynamic model capable of reflecting the engineering vehicle and the environment change thereof in real time, and then the updated BIM model is displayed on the display module, so that the accuracy and intuition of remote control are enhanced, and an operator does not need to carry out remote control even if the operator is far away from the engineering vehicle. Accurate operation and control can also be carried out like on site, the number of on-site operators is reduced, and then the labor cost is reduced.

Description

Technical Field

[0001] The present application relates to the field of remote control sensing, and in particular to a remote control method, device, remote control equipment and medium for an engineering vehicle. Background Art

[0002] Engineering vehicles are the backbone of a construction project, mainly used for transportation, excavation, and repairs, and are one of the indispensable tools on the construction site. Common engineering vehicles mainly include the following categories: heavy transport vehicles, large cranes, excavators, bulldozers, road rollers, loaders, power repair vehicles, and engineering rescue vehicles. At present, most of the work and other actions of engineering vehicles are controlled by operators in the cab of the engineering vehicle. However, controlling the engineering vehicle through the driver not only requires the driver to have driving ability, but also requires the operator to be in the cab of the engineering vehicle for control, which requires a lot of manpower costs.

[0003] With the advancement of technology, the concept of remote intelligent control of engineering vehicles has emerged, allowing operators to use remote control devices such as handles or controllers to control them. However, in order to implement control more effectively and ensure operational safety, operators often need to use handles and controllers to directly control the engineering vehicles from a relatively close distance to the engineering vehicles. This method does not reduce the number of on-site operators, and therefore, does not reduce labor costs. Summary of the invention

[0004] In order to reduce the manpower cost in operating an engineering vehicle, the present application provides an engineering vehicle remote control method, device, remote control equipment and medium.

[0005] In the first aspect, the present application provides a remote control method for an engineering vehicle, which adopts the following technical solution: A remote control method for an engineering vehicle, comprising: Acquire a monitoring video corresponding to the target engineering vehicle in real time, wherein the monitoring video includes the complete target engineering vehicle and the environment; Analyze the monitoring video to obtain the vehicle shape and current environment characteristics corresponding to the target engineering vehicle; Obtaining a BIM model corresponding to the target engineering vehicle; Based on the vehicle form and the current environmental characteristics, the BIM model is updated, and the updated BIM model is displayed on a display module.

[0006] By adopting the above technical solution, the monitoring video corresponding to the target engineering vehicle is obtained in real time and analyzed in detail, so as to obtain the vehicle shape and current environmental characteristics of the target engineering vehicle, obtain the BIM (Building Information Model) model corresponding to the target engineering vehicle, and update it based on the real-time vehicle shape and environmental characteristics, so that the BIM model is no longer a static and fixed model, but a dynamic model that can reflect the changes of the engineering vehicle and its environment in real time. Then the updated BIM model is displayed on the display module, so that the operator can understand the current shape of the engineering vehicle (such as whether it is in normal working state, whether there is damage or abnormality, etc.) and the characteristics of the surrounding environment (such as terrain, obstacles, other vehicles or pedestrians, etc.) through the display module. This intuitive visual display method greatly enhances the accuracy and intuitiveness of remote control, so that the operator can perform precise operation and control as if on site even if he is far away from the engineering vehicle, reducing the number of on-site operators and thus reducing labor costs. At the same time, it improves the efficiency of operation, significantly enhances the safety of operation, and reduces the potential risks caused by information lag or misjudgment.

[0007] In a possible implementation, the monitoring video is analyzed to obtain the vehicle shape and current environment characteristics corresponding to the target engineering vehicle, including: The monitoring video is divided into a plurality of image frames, and the target engineering vehicle in each image frame is detected to obtain vehicle features in each image frame; Based on the vehicle features in each of the image frames, the vehicle features are separated from the corresponding image frames to obtain a vehicle foreground and an environmental background; Associating the vehicle foreground corresponding to each of the image frames to obtain the vehicle form corresponding to the surveillance video; Based on the environmental background corresponding to each of the image frames, current environmental features corresponding to the surveillance video are determined.

[0008] By adopting the above technical solution, by dividing the surveillance video into multiple image frames and detecting the target engineering vehicle in each image frame, the detailed features of the vehicle, such as size, shape, color, etc., can be accurately captured. Furthermore, by associating the vehicle foreground in each image frame, the continuous form of the target engineering vehicle in the surveillance video can be tracked. It helps to understand the vehicle's motion trajectory, speed changes, and possible behavior patterns, and provides a reliable data basis for subsequent vehicle behavior analysis or anomaly detection. Since each image frame is processed and associated separately, it can reduce the recognition error caused by poor video quality (such as blur, jitter, etc.), thereby improving the accuracy of vehicle form recognition.

[0009] In a possible implementation, the vehicle feature includes a shape sub-feature, a color sub-feature, and a size sub-feature, and the detecting of the target engineering vehicle in each image frame to obtain the vehicle feature in each image frame includes: Extracting the target engineering vehicle in each of the image frames based on a contour extraction function to obtain a vehicle contour corresponding to each image frame, and determining a shape sub-feature corresponding to each of the image frames based on the vehicle contour corresponding to each image frame; Converting the pixels of each sub-image corresponding to the vehicle outline into an HSV color space, and calculating the HSV histogram of the pixels in each sub-image to obtain the color sub-feature corresponding to each image frame; Acquire a size ratio corresponding to the image frame, where the size ratio is used to represent a ratio between an actual size and an image size; Identifying a first size of each of the vehicle contours, and calculating a second size corresponding to each of the vehicle contours based on the size ratio, so as to obtain a size sub-feature corresponding to each of the image frames; The vehicle features in each of the image frames are determined based on the shape sub-features, color sub-features and size sub-features corresponding to each of the image frames.

[0010] By adopting the above technical solution, the shape features of the vehicle can be accurately captured by extracting the contour of the target engineering vehicle in each image frame based on the contour extraction function. This helps to distinguish different types of engineering vehicles, such as excavators, loaders, rollers, etc. The pixels of the sub-image corresponding to the vehicle contour are converted into the HSV color space, and the HSV histogram is calculated, so that the color features of the vehicle can be stably obtained. Compared with the RGB color space, the HSV color space has better color stability under lighting changes, so it can more accurately reflect the true color of the vehicle. Furthermore, by obtaining the size ratio of the image frame and calculating the actual size (second size) of the vehicle contour based on the ratio, the size features of the vehicle can be accurately obtained. Combining the three sub-features of shape, color and size, the vehicle characteristics of the target engineering vehicle can be fully and accurately described, providing reliable data support for subsequent vehicle identification, tracking and behavior analysis.

[0011] In a possible implementation, determining the current environment feature corresponding to the surveillance video based on the environment background corresponding to each image frame includes: A feature point detection algorithm is used to identify the salient elements in each environmental background, and the descriptor corresponding to each salient element is calculated; Extract texture features and color features from each environmental background; Based on the texture features, color features and descriptors corresponding to the salient elements in each of the environmental backgrounds, current environmental features corresponding to the surveillance video are determined.

[0012] By adopting the above technical solution and the feature point detection algorithm, it is possible to accurately identify the significant elements in the environmental background, such as buildings, trees, road signs, etc., which is helpful for the subsequent analysis and understanding of environmental features. For each identified significant element, the corresponding descriptor is calculated. These descriptors contain key information such as the shape, size, and position of the element. The precise calculation of the descriptor makes the environmental features more accurate and reliable in subsequent analysis and comparison. The texture features and color features in the environmental background are further extracted. The texture features reflect the surface structure and details of the environment, while the color features provide the overall tone and color distribution information of the environment. Since the scheme combines multiple features such as significant elements, textures, and colors, it can maintain high recognition accuracy and robustness even under certain environmental conditions (such as lighting changes, occlusion, etc.).

[0013] In a possible implementation, associating the vehicle foreground corresponding to each of the image frames to obtain the vehicle form corresponding to the surveillance video includes: Based on the surveillance video, a plurality of the image frames are sequenced to obtain a target sequence; According to the target sequence and the vehicle foreground corresponding to each of the image frames, a tracking algorithm is used to associate the various vehicle foregrounds to obtain the vehicle form corresponding to the monitoring video.

[0014] By adopting the above technical solution, by arranging multiple image frames in time order to form a target sequence, the continuity of the vehicle's morphology in the time dimension can be ensured. It is helpful to observe and analyze key information such as the position, speed, direction, etc. of the vehicle at different time points, and provide reliable data support for subsequent vehicle behavior analysis or anomaly detection. By using a tracking algorithm to associate the vehicle foreground in each image frame, the complete morphology of the vehicle in the surveillance video can be accurately reconstructed. This morphological reconstruction is not limited to the appearance characteristics of the vehicle, but also includes its motion trajectory and dynamic changes in the video. It is of great significance for understanding the behavior pattern of the vehicle, predicting its future position, or judging whether it has abnormal behavior.

[0015] In a possible implementation, before updating the BIM model, the method further includes: Receiving a connection signal with an engineering vehicle, and determining the engineering vehicle corresponding to the connection signal as a target engineering vehicle; Acquire a current image corresponding to the target engineering vehicle; Based on the current image, determine the vehicle coating and the environmental coating, and establish a three-dimensional coordinate system; Based on the vehicle coating, the environmental coating and the three-dimensional coordinate system, a BIM model corresponding to the target engineering vehicle is established.

[0016] By adopting the above technical solution, by receiving the connection signal with the engineering vehicle and obtaining the current image corresponding to the target engineering vehicle, the actual state of the vehicle can be captured in real time. Based on the vehicle coating and environmental coating determined by the current image, and the established three-dimensional coordinate system, the three-dimensional model of the target vehicle can be accurately constructed. This precise construction method ensures that the BIM model is highly consistent with the actual situation, providing a reliable foundation for subsequent construction management and maintenance. With the movement of engineering vehicles and changes in the construction environment, the BIM model needs to be continuously updated to reflect the latest situation. By continuously receiving the connection signal and obtaining the current image, key information such as the vehicle coating, environmental coating, and three-dimensional coordinate system in the BIM model can be updated in real time. This real-time update capability enables the BIM model to always keep in sync with the actual construction situation, improving the efficiency and accuracy of construction management.

[0017] In a possible implementation, the method further includes: Receiving operation instructions corresponding to the operator; Determine a standard vehicle action corresponding to the operation instruction, and determine an actual vehicle action corresponding to the operation instruction; Calculating the similarity between the standard vehicle action and the actual vehicle action; And based on the similarity, it is determined whether to generate an operation abnormality signal.

[0018] By adopting the above technical solution, the standard vehicle action and the actual vehicle action corresponding to the operation instruction can be determined, and the accuracy of the operation can be evaluated by calculating the similarity between them. This comparison method can promptly detect deviations or errors in the operation process, thereby avoiding potential safety hazards. When the similarity between the actual vehicle action and the standard vehicle action is lower than the preset threshold, the system will generate an abnormal operation signal. This early warning mechanism can remind the operator to adjust the operation in time to ensure that the vehicle operates in the expected manner, thereby greatly improving the safety and accuracy of the operation.

[0019] In the second aspect, the present application provides a remote control device for an engineering vehicle, which adopts the following technical solution: A remote control device for an engineering vehicle, comprising: A video acquisition module is used to acquire the monitoring video corresponding to the target engineering vehicle in real time, wherein the monitoring video includes the complete target engineering vehicle and the environment; An analysis module, used to analyze the monitoring video to obtain the vehicle shape and current environment characteristics corresponding to the target engineering vehicle; A model acquisition module is used to acquire the BIM model corresponding to the target engineering vehicle; The display module is used to update the BIM model based on the vehicle form and the current environmental characteristics, and display the updated BIM model on the display module.

[0020] In a third aspect, the present application provides a remote control device, which adopts the following technical solution: A remote control device, comprising: Display module, used to display the BIM model; at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the engineering vehicle remote control method described in the first aspect above.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program that can be loaded by a processor and execute the engineering vehicle remote control method described in the first aspect.

[0022] In summary, this application includes the following beneficial technical effects: By acquiring the corresponding surveillance video of the target engineering vehicle in real time and analyzing it in detail, the vehicle shape and current environmental characteristics of the target engineering vehicle are obtained, and the BIM (Building Information Model) model corresponding to the target engineering vehicle is obtained and updated based on the real-time vehicle shape and environmental characteristics, so that the BIM model is no longer a static and fixed model, but a dynamic model that can reflect the changes of the engineering vehicle and its environment in real time. Then the updated BIM model is displayed on the display module, so that the operator can understand the current shape of the engineering vehicle (such as whether it is in normal working condition, whether there is damage or abnormality, etc.) and the characteristics of the surrounding environment (such as terrain, obstacles, other vehicles or pedestrians, etc.) through the display module. This intuitive visual display method greatly enhances the accuracy and intuitiveness of remote control, so that even if the operator is far away from the engineering vehicle, he can perform precise operation and control as if he is on site, reducing the number of on-site operators and thus reducing labor costs. At the same time, it improves the efficiency of operation, significantly enhances the safety of operation, and reduces the potential risks caused by information lag or misjudgment. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of the connection and interaction between a remote control device and a terminal on an engineering vehicle provided in an embodiment of the present application; Figure 2 It is a flowchart of a remote control method for an engineering vehicle provided in an embodiment of the present application; Figure 3 It is a block diagram of a remote control device for an engineering vehicle provided in an embodiment of the present application; Figure 4 It is a schematic diagram of a remote control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following is combined with Figure 1-4 This application is described in further detail.

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, 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 part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In order to facilitate understanding of the technical solution proposed in this application, several elements introduced in the description of this application are first introduced here. It should be understood that the following introduction is only for the convenience of understanding these elements, so as to understand the content of the embodiments of this application, and does not necessarily cover all possible situations.

[0027] The principle of remote control car is mainly based on radio technology and electromagnetic wave transmission to achieve remote control of the car. Specifically, the remote control car system is mainly composed of a remote control and a car control part. Remote control: The remote control is a handheld device consisting of a transmitter and a receiver (a part of the handheld device here, used to send signals, not a device that receives feedback signals from the car). The transmitter transmits radio signals through operating controllers such as buttons or joysticks, and these signals are transmitted through the antenna. Car control part: mainly includes receivers, decoders, control modules and actuators (such as motors, steering gears, etc.). The receiver is built into the car and receives the signal sent by the transmitter through the antenna. The decoder decodes the received signal and converts it into an electrical signal. The control module controls the various functions of the car, such as forward, backward, left turn, right turn, etc., according to the received signal instructions. The actuator drives the car to perform corresponding actions according to the instructions of the control module.

[0028] Engineering vehicles are the backbone of a construction project, mainly used for transportation, excavation, and repairs, and are one of the indispensable tools on the construction site. Common engineering vehicles mainly include the following categories: heavy transport vehicles, large cranes, excavators, bulldozers, road rollers, loaders, power repair vehicles, and engineering rescue vehicles. At present, most of the work and other actions of engineering vehicles are controlled by operators in the cab of the engineering vehicle. However, controlling the engineering vehicle through the driver not only requires the driver to have driving ability, but also requires the operator to be in the cab of the engineering vehicle for control, which requires a lot of manpower costs.

[0029] With the advancement of technology, the concept of remote intelligent control of engineering vehicles has emerged, allowing operators to use remote control devices such as handles or controllers to control them. However, in order to implement control more effectively and ensure operational safety, operators often need to use handles and controllers to directly control the engineering vehicles from a relatively close distance to the engineering vehicles. This method does not reduce the number of on-site operators, and therefore, does not reduce labor costs.

[0030] In view of this, an embodiment of the present application provides a remote control method for an engineering vehicle, which is executed by a remote control device, on which a transmitter, a receiver and a display screen are installed, and each engineering vehicle is equipped with a receiver. Figure 1 A remote control device can be connected to at least one engineering vehicle 10 through wireless communication technology, such as Bluetooth, Wi-Fi, ZigBee or more advanced communication technology, to achieve remote control of the engineering vehicle through the remote control device. The remote control device can be an electronic device, such as a mobile phone, a tablet or a computer, etc., which is not limited in the present embodiment of the application.

[0031] The present application embodiment provides a remote control method for an engineering vehicle, such as Figure 2 As shown, the method provided in the embodiment of the present application is executed by a remote control device, which can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in the embodiment of the present application. The method includes steps S201 to S204, wherein: Step S201: Acquire the monitoring video corresponding to the target engineering vehicle in real time.

[0032] The monitoring video includes the complete target engineering vehicle and the environment. Specifically, each engineering vehicle is equipped with multiple image acquisition devices, and each image acquisition device can perform video monitoring. Through the image acquisition device on the target engineering vehicle, a monitoring video including the complete body of the engineering vehicle and its surrounding environment can be acquired.

[0033] Before the remote control device controls the engineering vehicle, each engineering vehicle connected to the remote control device is obtained, and a selection signal is received to determine an engineering vehicle to be controlled from multiple engineering vehicles connected to the remote control device, and the engineering vehicle to be controlled is the target engineering vehicle. Further, after the target engineering vehicle is determined, the remote control device receives the monitoring video collected by each image acquisition device on the target engineering vehicle in real time to obtain the monitoring video corresponding to the target engineering vehicle.

[0034] Step S202: Analyze the monitoring video to obtain the vehicle shape and current environment characteristics corresponding to the target engineering vehicle.

[0035] The vehicle form includes the vehicle shape and running state of the target engineering vehicle. The running state includes the driving sub-state and the working sub-state. The driving sub-state is driving or stopping. The working sub-state is related to the type of the engineering vehicle. Different engineering vehicles have different corresponding working states. For example, when the target engineering vehicle is an excavator, the working sub-state of the target engineering vehicle may include bucket excavation work, bucket stationary, bucket other work, etc. Therefore, one engineering vehicle type corresponds to one working sub-state set, and the working sub-state corresponding to the target engineering vehicle belongs to the corresponding working sub-state set.

[0036] The current environment features include road surface sub-features and obstacle sub-features.

[0037] Specifically, after receiving the surveillance video of the target engineering vehicle, the deep learning algorithm can be used to analyze the surveillance video in real time to obtain the vehicle shape and current environmental characteristics corresponding to the target engineering vehicle. More specifically, the received surveillance video of the target engineering vehicle is framed to obtain multiple frames of surveillance images, and each two adjacent frames of surveillance images are input into the trained deep network model in chronological order, and the vehicle shape and current environmental characteristics output by the deep network model are obtained. Among them, the deep network model is trained with a large number of adjacent frame image samples, vehicle shape samples, and current environmental feature samples.

[0038] Step S203: Obtain the BIM model corresponding to the target engineering vehicle.

[0039] After determining the target construction vehicle corresponding to the remote control device, the monitoring video corresponding to the target construction vehicle is received in real time, and a BIM model corresponding to the target construction vehicle is established based on the monitoring video. That is, the target construction vehicle corresponds to a BIM model, and the BIM model corresponding to the target construction vehicle can be directly obtained.

[0040] Step S204: based on the vehicle form and current environmental characteristics, update the BIM model, and display the updated BIM model on the display module.

[0041] After obtaining the vehicle shape and current environmental characteristics of the target engineering vehicle, the BIM model can be updated based on the vehicle shape and current environmental characteristics, so that the BIM model shows the current state of the target engineering vehicle, which is convenient for the operator to operate. Specifically, the vehicle shape data obtained by video analysis is compared with the vehicle shape in the BIM model to identify the parts of the vehicle shape in the BIM model that need to be updated, such as whether the height and width of the vehicle have changed, whether the color of the vehicle is consistent with that in the video, etc. At the same time, the environmental feature data obtained by video analysis is compared with the environmental information in the BIM model to identify the changes in environmental features that need to be reflected in the BIM model, such as whether the road surface is smooth and whether there are new obstacles.

[0042] Furthermore, the vehicle form in the BIM model is dynamically adjusted based on the results of the video analysis. For example, if the video shows that the height of the vehicle increases, the vehicle height in the BIM model is adjusted accordingly; if the color of the vehicle changes, the material properties in the BIM model are updated. Similarly, tags or properties related to environmental features are added or updated in the BIM model. For example, if the video shows that the road is slippery, a tag indicating that the road is slippery is added to the BIM model; if a new obstacle appears in the video, the location and shape information of the obstacle is added to the BIM model accordingly.

[0043] Furthermore, after the BIM model of the target engineering vehicle is updated, the updated BIM model is displayed on a display module of the remote control device.

[0044] The embodiment of the present application provides a remote control method for an engineering vehicle, which obtains the monitoring video corresponding to the target engineering vehicle in real time and analyzes it in detail, thereby obtaining the vehicle shape and current environmental characteristics of the target engineering vehicle, obtaining the BIM (Building Information Model) model corresponding to the target engineering vehicle, and updating it based on the real-time vehicle shape and environmental characteristics, so that the BIM model is no longer a static, fixed model, but a dynamic model that can reflect the changes of the engineering vehicle and its environment in real time, and then displays the updated BIM model on the display module, so that the operator can understand the current shape of the engineering vehicle (such as whether it is in a normal working state, whether it is damaged or abnormal, etc.) and the characteristics of the surrounding environment (such as terrain, obstacles, other vehicles or pedestrians, etc.) through the display module. This intuitive visual display method greatly enhances the accuracy and intuitiveness of remote control, so that the operator can perform precise operation and control as if on site even if he is far away from the engineering vehicle, reducing the number of on-site operators and thus reducing labor costs. At the same time, it improves the efficiency of operation, significantly enhances the safety of operation, and reduces the potential risks caused by information lag or misjudgment.

[0045] A possible implementation of the embodiment of the present application is that in the above step S202, the monitoring video is analyzed to obtain the vehicle shape and current environmental characteristics corresponding to the target engineering vehicle, including: The surveillance video is divided into multiple image frames, and the target engineering vehicle in each image frame is detected to obtain the vehicle features in each image frame; Based on the vehicle features in each image frame, the vehicle features are separated from the corresponding image frame to obtain the vehicle foreground and the environment background; Correlate the vehicle foreground corresponding to each image frame to obtain the vehicle shape corresponding to the surveillance video; Based on the environmental background corresponding to each image frame, the current environmental features corresponding to the surveillance video are determined.

[0046] After obtaining the surveillance video of the target engineering vehicle, the preprocessed surveillance video can be segmented into multiple continuous image frames to facilitate the subsequent extraction of vehicle form and current environmental features, wherein one image frame corresponds to a time point in the surveillance video.

[0047] Furthermore, after obtaining the segmented image frames, the target engineering vehicle in each image frame and the environment in which the target engineering vehicle is located can be detected using a target detection algorithm. Specifically, the processing process for each image frame can be: obtaining the color and material corresponding to the target engineering vehicle, and using the target detection algorithm to identify the target engineering vehicle in the image frame based on the color and material of the target engineering vehicle, so as to obtain the overall vehicle features of the target engineering vehicle in the image frame. After obtaining the vehicle features in the image frame, the identified target engineering vehicle can be segmented along the edge to obtain the vehicle foreground and environmental background in the image frame. Among them, one engineering vehicle corresponds to one color and material.

[0048] Furthermore, after obtaining the vehicle foreground corresponding to each image frame, the vehicle foreground corresponding to each image frame can be associated, and the time continuity between the image frames can be used to analyze the vehicle foreground in the image frames between adjacent frames to determine whether the vehicle foregrounds between adjacent frames are completely consistent. If they are completely consistent, the vehicle shape of the target engineering vehicle is obtained based on the edge of the vehicle foreground, and the driving sub-state of the target engineering vehicle is determined to be not driving and the working sub-state is determined to be not working; when there is inconsistency in the vehicle foreground between adjacent frames, it is determined that there are inconsistent vehicle parts, and based on the inconsistent vehicle parts, the driving sub-state and the working sub-state of the target engineering vehicle are determined, thereby obtaining the complete vehicle shape of the target engineering vehicle in the monitoring video.

[0049] More specifically, based on the inconsistent vehicle parts, the process of determining the driving sub-state and the working sub-state of the target engineering vehicle can be: if the inconsistent vehicle parts include wheels, the driving sub-state of the target engineering vehicle is determined to be driving; if the inconsistent vehicle parts do not include wheels, the driving sub-state of the target engineering vehicle is determined to be not driving; if the inconsistent vehicle parts include working parts, the working sub-state of the target engineering vehicle is determined to be working; if the inconsistent vehicle parts do not include working parts, the working sub-state of the target engineering vehicle is determined to be not working. Different types of engineering vehicles have different corresponding working parts.

[0050] Furthermore, based on the environmental background corresponding to each image frame, road features and obstacle features in the environmental background are identified to obtain current environmental features in the surveillance video.

[0051] A possible implementation of the embodiment of the present application, in the above embodiment, when the vehicle feature includes a shape sub-feature, a color sub-feature, and a size sub-feature, the target engineering vehicle in each image frame is detected to obtain the vehicle feature in each image frame, including: Extract the target engineering vehicle in each image frame based on the contour extraction function to obtain the vehicle contour corresponding to each image frame, and determine the shape sub-feature corresponding to each image frame based on the vehicle contour corresponding to each image frame; The pixels of the sub-image corresponding to each vehicle outline are converted into the HSV color space, and the HSV histogram of the pixels in each sub-image is calculated to obtain the color sub-features corresponding to each image frame; Obtaining a size ratio corresponding to an image frame, where the size ratio is used to represent the ratio between the actual size and the image size; Identify a first size of each vehicle outline, and calculate a second size corresponding to each vehicle outline based on the size ratio to obtain a size sub-feature corresponding to each image frame; The vehicle features in each image frame are determined based on the shape sub-features, color sub-features and size sub-features corresponding to each image frame.

[0052] Specifically, the contour extraction function in the image processing library (such as the findContours function in OpenCV) can be used to extract the contour of the target engineering vehicle in each image frame to identify and extract the contours of all objects in the image, and then filter out the contours that match the target engineering vehicle. Then, based on the extracted vehicle contours, the shape features are calculated. For example, the perimeter, area, aspect ratio of the circumscribed rectangle, and other parameters of the contour can be calculated as shape sub-features.

[0053] The pixels of the sub-image corresponding to each vehicle outline are converted from the RGB color space to the HSV color space. The HSV color space is more suitable for color feature extraction because it decomposes color information into three independent components: hue, saturation, and value. By counting the number of pixels with different values ​​in each HSV channel, the HSV histogram of the pixels in each sub-image is calculated to obtain the statistical information of the color distribution, and the statistical information of the color distribution is used as the color sub-feature of the sub-image corresponding to the vehicle outline.

[0054] The size ratio of the image frame, i.e., the ratio between the actual size and the image size, is obtained, and the first size of each vehicle outline, i.e., the size in the image (such as the width and height of the circumscribed rectangle of the outline), is identified. Then, based on the size ratio, the first size is converted into the actual size, i.e., the second size, which is the size sub-feature.

[0055] Furthermore, by splicing these feature vectors together, the shape sub-features, color sub-features and size sub-features corresponding to each image frame are fused to form a complete vehicle feature vector. The extracted vehicle features are stored in the feature database for subsequent vehicle identification or classification. In the identification stage, the type or identity of the vehicle in the image frame to be identified can be determined by calculating the similarity between the vehicle features of the image frame to be identified and the features in the feature database.

[0056] A possible implementation of the embodiment of the present application, in the above embodiment, based on the environmental background corresponding to each image frame, determining the current environmental features corresponding to the surveillance video includes: A feature point detection algorithm is used to identify the salient elements in each environmental background, and the descriptor corresponding to each salient element is calculated; Extract texture features and color features from each environmental background; Based on the texture features, color features and descriptors corresponding to the salient elements in each environmental background, the current environmental features corresponding to the surveillance video are determined.

[0057] Among them, the salient elements are elements with prominent colors, shapes or other features in the environment. The salient elements are more likely to be obstacles in the environment. Therefore, the salient elements in the environmental background can be identified to obtain the current environmental features in the surveillance video.

[0058] Specifically, a feature point detection algorithm (such as SIFT, SURF or ORB, etc.) is applied to the environmental background part of each image frame to achieve automatic detection of feature points of significant elements in the image. For each detected feature point, its corresponding descriptor is calculated. Among them, the descriptor is a vector containing the image information around the feature point, which is used to describe the uniqueness and position relationship of the feature point.

[0059] Furthermore, texture analysis techniques (such as gray-level co-occurrence matrix, local binary pattern, etc.) are used to extract texture features in each environmental background, and color space conversion and color histogram are used to extract color features in each environmental background. Among them, texture features describe the arrangement and distribution of pixels in the image, reflecting the visual texture of the image, and color features describe the distribution and proportion of colors in the image, which are important clues to distinguish different environments.

[0060] Furthermore, by concatenating feature vectors, the descriptors, texture features, and color features corresponding to the salient elements are fused to form a comprehensive feature vector containing a variety of environmental information. The fused feature vector is analyzed to identify the current environmental features in the surveillance video.

[0061] A possible implementation of the embodiment of the present application, in the above embodiment, the vehicle foreground corresponding to each image frame is associated to obtain the vehicle form corresponding to the monitoring video, including: Based on the surveillance video, multiple image frames are sequenced to obtain the target sequence; According to the target sequence and the vehicle foreground corresponding to each image frame, the tracking algorithm is used to associate the various vehicle foregrounds to obtain the vehicle shape corresponding to the surveillance video.

[0062] After obtaining the surveillance video of the target engineering vehicle, the surveillance video can be parsed and split into a series of continuous image frames. Then, from the parsed image frames, image frames containing the foreground of the target engineering vehicle are selected and sorted in the chronological order of their appearance in the video to form a target sequence.

[0063] Furthermore, according to the characteristics of the surveillance video and the motion characteristics of the target engineering vehicle, a tracking algorithm (feature matching-based tracking, template matching-based tracking, detection-based tracking, etc.) is used to associate the vehicle foregrounds in each image frame by utilizing the temporal continuity between image frames and the similarity of the vehicle foregrounds. Based on the selected tracking algorithm, the vehicle foreground is tracked for each image frame in the target sequence, thereby obtaining a continuous sequence containing the position and motion trajectory of the vehicle at different time points. Furthermore, based on the associated vehicle foreground sequence, and by superimposing, fusing or interpolating the vehicle foreground in each image frame, the vehicle shape corresponding to the surveillance video is constructed to form a complete and continuous three-dimensional vehicle shape or motion trajectory, thereby obtaining the vehicle shape corresponding to the surveillance video.

[0064] In a possible implementation of the embodiment of the present application, before the above step S204, updating the BIM model, the method may further include: Receiving a connection signal with an engineering vehicle, and determining the engineering vehicle corresponding to the connection signal as a target engineering vehicle; Get the current image corresponding to the target engineering vehicle; Based on the current image, determine the vehicle coating and the environmental coating, and establish a three-dimensional coordinate system; Based on vehicle coating, environmental coating and three-dimensional coordinate system, a BIM model corresponding to the target engineering vehicle is established.

[0065] When the remote control device establishes the current control connection with the engineering vehicle, the engineering vehicle is determined as the target engineering vehicle, and the current image of the target engineering vehicle is obtained using the camera installed on the target engineering vehicle or the remote monitoring camera. The current image should be able to clearly show the appearance of the target engineering vehicle, including details such as coating color and texture.

[0066] Furthermore, after obtaining the current image of the target engineering vehicle, the acquired current image is processed, and the color, texture and other characteristics of the vehicle coating and the environmental coating are determined by using image processing technology (such as color recognition, texture analysis, etc.). And based on the acquired image information, a three-dimensional coordinate system is constructed. Among them, the three-dimensional coordinate system can accurately reflect the position, direction and size of the target engineering vehicle in the real world. When constructing the three-dimensional coordinate system, the characteristic points, edges or the size of known objects in the image can be used for calibration. Exemplarily, the three-dimensional coordinate system can be the left front tire of the target engineering vehicle as the origin, the direction from east to west as the positive direction of the x-axis, the direction from east to north as the positive direction of the y-axis, and the direction from the ground to the sky as the positive direction of the z-axis as the reference.

[0067] Furthermore, a basic BIM model framework is initialized based on the basic information such as the type and size of the target engineering vehicle, and the identified vehicle coating and environmental coating features are mapped to the corresponding components of the BIM model by adjusting the material properties, color, texture and other parameters in the BIM model. The established three-dimensional coordinate system is matched with the BIM model to ensure that the positions of the various components in the BIM model in three-dimensional space are consistent with the target engineering vehicle in the real world, thereby obtaining the BIM model corresponding to the target engineering vehicle.

[0068] A possible implementation manner of the embodiment of the present application, based on the above embodiment, the method may further include: receiving an operation instruction corresponding to the operator; Determine the standard vehicle action corresponding to the operation instruction, and determine the actual vehicle action corresponding to the operation instruction; Calculating the similarity between the standard vehicle action and the actual vehicle action; And determine whether to generate an operation abnormality signal based on the similarity.

[0069] Among them, one operation instruction corresponds to a standard vehicle action. The standard action is a set of standard actions pre-defined based on factors such as vehicle design, operation specifications and safety requirements. During the operation of the operator on the remote control device, each operation step is regarded as an operation instruction. The remote control device sends the operation instruction to the corresponding target engineering vehicle to control the target engineering vehicle to perform specific actions, such as forward, backward, steering, acceleration, deceleration, etc.

[0070] When an operation instruction sent by an operator on a remote control device is received, the standard vehicle action corresponding to the operation instruction is obtained according to the received operation instruction, and the actual action state of the target engineering vehicle is obtained.

[0071] Furthermore, key features are extracted from standard actions and actual actions. These features can be time series data of physical quantities such as position, velocity, acceleration, etc., or they can be processed feature vectors.

[0072] The similarity between the standard action features and the actual action features is calculated using specific algorithms (such as dynamic time warping, cosine similarity, Euclidean distance, etc.). The higher the similarity value, the closer the actual action is to the standard action; the lower the similarity value, the greater the difference between the actual action and the standard action.

[0073] If the calculated similarity is lower than the set threshold, it is considered that the operator's operation is significantly different from the standard action, which may constitute an abnormal operation. At this time, an abnormal operation signal is generated and notified to the operator through sound and light alarms, display screen prompts, etc. Among them, the similarity threshold is set according to actual needs and safety requirements. The similarity threshold is used to determine whether the difference between the actual action and the standard action reaches an abnormal level.

[0074] The above embodiment introduces a remote control method for an engineering vehicle from the perspective of a method flow, and the following embodiment introduces a remote control device for an engineering vehicle from the perspective of a virtual module or a virtual unit. For details, please refer to the following embodiment.

[0075] See also Figure 3 The engineering vehicle remote control device 30 may specifically include: a video acquisition module 301, an analysis module 302, a model acquisition module 303 and a display module 304. Specifically: A remote control device 30 for an engineering vehicle, comprising: The video acquisition module 301 is used to acquire the monitoring video corresponding to the target engineering vehicle in real time, and the monitoring video includes the complete target engineering vehicle and the environment; The analysis module 302 is used to analyze the monitoring video to obtain the vehicle shape and current environment characteristics corresponding to the target engineering vehicle; The model acquisition module 303 is used to acquire the BIM model corresponding to the target engineering vehicle; The display module 304 is used to update the BIM model based on the vehicle form and current environmental characteristics, and display the updated BIM model on the display module.

[0076] In a possible implementation of the embodiment of the present application, the analysis module 302 analyzes the surveillance video to obtain the vehicle shape and current environmental characteristics corresponding to the target engineering vehicle, including: The surveillance video is divided into multiple image frames, and the target engineering vehicle in each image frame is detected to obtain the vehicle features in each image frame; Based on the vehicle features in each image frame, the vehicle features are separated from the corresponding image frame to obtain the vehicle foreground and the environment background; Correlate the vehicle foreground corresponding to each image frame to obtain the vehicle shape corresponding to the surveillance video; Based on the environmental background corresponding to each image frame, the analysis module 302 determines the current environmental features corresponding to the surveillance video.

[0077] In a possible implementation of the embodiment of the present application, the vehicle features include shape sub-features, color sub-features, and size sub-features. The analysis module 302 detects the target engineering vehicle in each image frame to obtain the vehicle features in each image frame, including: Extract the target engineering vehicle in each image frame based on the contour extraction function to obtain the vehicle contour corresponding to each image frame, and determine the shape sub-feature corresponding to each image frame based on the vehicle contour corresponding to each image frame; The pixels of the sub-image corresponding to each vehicle outline are converted into the HSV color space, and the HSV histogram of the pixels in each sub-image is calculated to obtain the color sub-features corresponding to each image frame; Obtaining a size ratio corresponding to an image frame, where the size ratio is used to represent the ratio between the actual size and the image size; Identify a first size of each vehicle outline, and calculate a second size corresponding to each vehicle outline based on the size ratio to obtain a size sub-feature corresponding to each image frame; The vehicle features in each image frame are determined based on the shape sub-features, color sub-features and size sub-features corresponding to each image frame.

[0078] In a possible implementation of the embodiment of the present application, the analysis module 302 determines the current environmental features corresponding to the surveillance video based on the environmental background corresponding to each image frame, including: A feature point detection algorithm is used to identify the salient elements in each environmental background, and the descriptor corresponding to each salient element is calculated; Extract texture features and color features from each environmental background; Based on the texture features, color features and descriptors corresponding to the salient elements in each environmental background, the current environmental features corresponding to the surveillance video are determined.

[0079] In a possible implementation of the embodiment of the present application, the analysis module 302 associates the vehicle foreground corresponding to each image frame to obtain the vehicle form corresponding to the surveillance video, including: Based on the surveillance video, multiple image frames are sequenced to obtain the target sequence; According to the target sequence and the vehicle foreground corresponding to each image frame, the tracking algorithm is used to associate the various vehicle foregrounds to obtain the vehicle shape corresponding to the surveillance video.

[0080] In a possible implementation of the embodiment of the present application, the engineering vehicle remote control device 30 may further include: A first receiving module is used to receive a connection signal with an engineering vehicle, and determine the engineering vehicle corresponding to the connection signal as a target engineering vehicle; An image acquisition module is used to acquire a current image corresponding to a target engineering vehicle; A first determination module is used to determine the vehicle coating and the environmental coating based on the current image and establish a three-dimensional coordinate system; A module is established to establish a BIM model corresponding to a target engineering vehicle based on vehicle coating, environmental coating and three-dimensional coordinate system.

[0081] In a possible implementation of the embodiment of the present application, the engineering vehicle remote control device 30 may further include: The second receiving module is used to receive the operation instructions corresponding to the operator; A second determination module is used to determine a standard vehicle action corresponding to the operation instruction and determine an actual vehicle action corresponding to the operation instruction; A calculation module, used for calculating the similarity between the standard vehicle action and the actual vehicle action; The third determination module is used to determine whether to generate an operation abnormality signal based on the similarity.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0083] See also Figure 4 , the embodiment of the present application also introduces a remote control device from the perspective of a physical device, such as Figure 4 As shown, Figure 4 The remote control device 40 shown includes: a processor 401 and a memory 403. The processor 401 and the memory 403 are connected, such as through a bus 402. Optionally, the remote control device 40 may also include a transceiver 404 and a display module 405. It should be noted that in actual applications, the transceiver 404 is not limited to one, and the structure of the remote control device 40 does not constitute a limitation on the embodiments of the present application.

[0084] Processor 401 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 401 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0085] The bus 402 may include a path to transmit information between the above components. The bus 402 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 402 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0086] The memory 403 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0087] The memory 403 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 401. The processor 401 is used to execute the application code stored in the memory 403 to implement the contents shown in the above method embodiment.

[0088] The display module 405 is used to display the BIM model. The display module 405 can be a variety of technologies and components that are responsible for converting digital signals into visual images or texts. Specifically, it can be an LED (Light Emitting Diode) display module, an LCD (Liquid Crystal Display) liquid crystal display module, an OLED (Organic Light Emitting Diode) organic light emitting diode display module or an E Ink electronic paper display module, which is not limited in the present application embodiment.

[0089] Among them, the remote control devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc., and can also be servers, etc. Figure 4 The remote control device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0090] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0091] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0092] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A remote control method for an engineering vehicle, characterized in that: The automated engineering vehicle remote control method is performed by a remote control device, and the method comprises: Acquire a monitoring video corresponding to the target engineering vehicle in real time, wherein the monitoring video includes the complete target engineering vehicle and the environment; Analyze the monitoring video to obtain the vehicle shape and current environment characteristics corresponding to the target engineering vehicle; Obtaining a BIM model corresponding to the target engineering vehicle; Based on the vehicle form and the current environmental characteristics, the BIM model is updated, and the updated BIM model is displayed on a display module.

2. The remote control method for an engineering vehicle according to claim 1, characterized in that: The analyzing the monitoring video to obtain the vehicle shape and current environment characteristics corresponding to the target engineering vehicle includes: The monitoring video is divided into a plurality of image frames, and the target engineering vehicle in each image frame is detected to obtain vehicle features in each image frame; Based on the vehicle features in each of the image frames, the vehicle features are separated from the corresponding image frames to obtain a vehicle foreground and an environmental background; Associating the vehicle foreground corresponding to each of the image frames to obtain the vehicle form corresponding to the surveillance video; Based on the environmental background corresponding to each of the image frames, current environmental features corresponding to the surveillance video are determined.

3. The remote control method for an engineering vehicle according to claim 2, characterized in that: The vehicle features include shape sub-features, color sub-features, and size sub-features. The target engineering vehicle in each image frame is detected to obtain the vehicle features in each image frame, including: Extracting the target engineering vehicle in each of the image frames based on a contour extraction function to obtain a vehicle contour corresponding to each image frame, and determining a shape sub-feature corresponding to each of the image frames based on the vehicle contour corresponding to each image frame; Converting the pixels of each sub-image corresponding to the vehicle outline into an HSV color space, and calculating the HSV histogram of the pixels in each sub-image to obtain the color sub-feature corresponding to each image frame; Acquire a size ratio corresponding to the image frame, where the size ratio is used to represent a ratio between an actual size and an image size; Identifying a first size of each of the vehicle contours, and calculating a second size corresponding to each of the vehicle contours based on the size ratio, so as to obtain a size sub-feature corresponding to each of the image frames; The vehicle features in each of the image frames are determined based on the shape sub-features, color sub-features and size sub-features corresponding to each of the image frames.

4. The remote control method for an engineering vehicle according to claim 2 or 3, characterized in that: The determining, based on the environmental background corresponding to each of the image frames, the current environmental features corresponding to the surveillance video includes: A feature point detection algorithm is used to identify the salient elements in each environmental background, and the descriptor corresponding to each salient element is calculated; Extract texture features and color features from each environmental background; Based on the texture features, color features and descriptors corresponding to the salient elements in each of the environmental backgrounds, current environmental features corresponding to the surveillance video are determined.

5. The remote control method for an engineering vehicle according to claim 2 or 3, characterized in that: The step of associating the vehicle foreground corresponding to each of the image frames to obtain the vehicle form corresponding to the surveillance video includes: Based on the surveillance video, a plurality of the image frames are sequenced to obtain a target sequence; According to the target sequence and the vehicle foreground corresponding to each of the image frames, a tracking algorithm is used to associate the various vehicle foregrounds to obtain the vehicle form corresponding to the monitoring video.

6. The remote control method for an engineering vehicle according to claim 1, characterized in that: Before updating the BIM model, the method further includes: Receiving a connection signal with an engineering vehicle, and determining the engineering vehicle corresponding to the connection signal as a target engineering vehicle; Acquire a current image corresponding to the target engineering vehicle; Based on the current image, determine the vehicle coating and the environmental coating, and establish a three-dimensional coordinate system; Based on the vehicle coating, the environmental coating and the three-dimensional coordinate system, a BIM model corresponding to the target engineering vehicle is established.

7. The remote control method for an engineering vehicle according to claim 1, characterized in that: The method further comprises: Receiving operation instructions corresponding to the operator; Determine a standard vehicle action corresponding to the operation instruction, and determine an actual vehicle action corresponding to the operation instruction; Calculating the similarity between the standard vehicle action and the actual vehicle action; And based on the similarity, it is determined whether to generate an operation abnormality signal.

8. A remote control device for an engineering vehicle, characterized in that: include: A video acquisition module is used to acquire the monitoring video corresponding to the target engineering vehicle in real time, wherein the monitoring video includes the complete target engineering vehicle and the environment; An analysis module, used to analyze the monitoring video to obtain the vehicle shape and current environment characteristics corresponding to the target engineering vehicle; A model acquisition module is used to acquire the BIM model corresponding to the target engineering vehicle; The display module is used to update the BIM model based on the vehicle form and the current environmental characteristics, and display the updated BIM model on the display module.

9. A remote control device, characterized in that: The remote control device includes: Display module, used to display the BIM model; at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the engineering vehicle remote control method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed in a computer, the computer is caused to execute the remote control method for an engineering vehicle according to any one of claims 1 to 7.

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