Intelligent car navigation method integrating multiple navigation modes, equipment, medium and product

Abstract

The invention relates to the technical field of trolley navigation, in particular to an intelligent trolley navigation method and device integrating multiple navigation modes, a medium and a product, and the method comprises the following steps: carrying out transportation task path area analysis based on an automatic transportation task and factory area division information, and determining a plurality of working areas; then, performing navigation mode analysis based on each working area and factory area division information, determining a navigation mode corresponding to each working area, controlling the intelligent trolley to select a proper navigation mode in scenes of different working areas, and fully utilizing the advantages of different navigation modes for complementation; and the navigation accuracy of the intelligent trolley is improved to a great extent. Further, navigation planning is performed based on the target working area and the corresponding navigation mode, and target navigation information is determined. And integrating the target navigation information corresponding to each target working area, finally determining transportation navigation information, and sending the transportation navigation information to the intelligent trolley terminal.

Description

Technical Field

[0001] The present application relates to the technical field of car navigation, and in particular to a smart car navigation method, device, medium and product integrating multiple navigation modes. Background Art

[0002] The factory production environment is becoming increasingly complex, and the requirements for production efficiency and precision are becoming higher and higher. Traditional handling methods can no longer meet the needs of modern production. Therefore, with the rapid development of industrial automation and intelligent manufacturing technology, intelligent mobile carts have shown significant advantages in improving production efficiency and reducing labor costs. With its high efficiency, flexibility and reliability, intelligent carts have become a key tool to improve production efficiency and reduce labor costs.

[0003] In modern factories, smart mobile cars are a key component of automated production processes, and the performance of their navigation systems is directly related to the efficiency and safety of production lines. However, as factory environments become increasingly complex, smart car navigation methods that only have a single mode have the defect of low navigation accuracy and cannot meet the needs of car navigation in complex environments.

[0004] Therefore, how to improve the navigation accuracy of the smart car is an urgent problem to be solved by those skilled in the art. Summary of the invention

[0005] The purpose of this application is to provide a smart car navigation method, device, medium and product integrating multiple navigation modes, so as to solve at least one of the above technical problems.

[0006] The above invention objectives of the present application are achieved through the following technical solutions: In a first aspect, the present application provides a smart car navigation method integrating multiple navigation modes, which adopts the following technical solutions: A smart car navigation method integrating multiple navigation modes, comprising: Acquire automated transportation tasks and factory area division information, perform transportation task route area analysis based on the automated transportation tasks and the factory area division information, and determine multiple work areas; Perform navigation mode analysis based on each of the work areas and the factory area division information to determine a navigation mode corresponding to each of the work areas; Performing navigation planning based on a target working area and the corresponding navigation mode to determine target navigation information, wherein the target working area is any one of the multiple working areas; The target navigation information corresponding to each target working area is integrated to finally determine the transport navigation information, and the transport navigation information is sent to the smart car terminal to control the smart car terminal to perform the transport task according to the navigation mode and the transport navigation information.

[0007] By adopting the above technical solution, the automated transportation task and factory area division information are obtained, and the transportation task route area analysis is performed based on the automated transportation task and factory area division information, and multiple work areas are determined. By predetermining the work area that the smart car passes through, it is convenient to more flexibly select the navigation mode suitable for the work area. Then, based on each work area and factory area division information, the navigation mode corresponding to each work area is analyzed, and the smart car is controlled to select the appropriate navigation mode in the scene of different work areas, and the advantages of different navigation modes are fully utilized for complementarity, which greatly improves the accuracy of the smart car navigation. Then, based on the target work area and the corresponding navigation mode, navigation planning is performed to determine the target navigation information. The target navigation information provides accurate road guidance for the smart car to work in the target work area, which improves the work efficiency of the smart car. The target navigation information corresponding to each target work area is integrated, and the transportation navigation information is finally determined, and the transportation navigation information is sent to the smart car terminal to control the smart car terminal to perform the transportation task according to the navigation mode and transportation navigation information.

[0008] In a preferred example, the present application can be further configured as follows: the navigation modes include: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode. The performing of navigation mode analysis based on each of the work areas and the factory area division information to determine the navigation mode corresponding to each of the work areas includes: Based on the matching of the target work area and the factory area division information, the environmental condition characteristics, work task characteristics, and facility deployment characteristics are determined; When the facility deployment feature includes deploying a QR code tag, determining that the navigation mode corresponding to the target working area is the QR code navigation mode; When the environmental condition characteristics include complex dynamic changes, determining that the navigation mode corresponding to the target working area is the SLAM navigation mode; When the work task feature includes fixed-path transportation, determining that the navigation mode corresponding to the target work area is the magnetic navigation mode; Otherwise, obtain the navigation mode priority, and determine the navigation mode corresponding to the target working area based on the navigation mode priority.

[0009] In a preferred example, the present application may be further configured as follows: performing navigation planning based on the target working area and the corresponding navigation mode to determine the target navigation information includes: When the navigation mode is the SLAM navigation mode, a dynamic area map, a work starting point, and a work end point corresponding to the target work area are obtained, and a feasible path planning is performed based on the dynamic area map, the work starting point, and the work end point to determine the target navigation information; When the navigation mode is the QR code navigation mode, a QR code analysis is performed based on the automated transport task and the target work area to determine a QR code combination, and navigation planning is performed based on the QR code combination to determine target navigation information, wherein the QR code combination includes: a deceleration QR code and a positioning stop QR code; When the navigation mode is the magnetic navigation mode, the magnetic stripe laying information corresponding to the target working area is obtained, and the magnetic stripe path planning is performed based on the magnetic stripe laying information and the automated transportation task to determine the target navigation information.

[0010] In a preferred example, the present application may be further configured as follows: the target navigation information corresponding to each target working area is integrated to finally determine the transportation navigation information, including: Performing task operation analysis based on the automated transportation task and each of the target work areas, and determining a work task corresponding to each of the target work areas; Performing mode switching analysis based on the navigation mode corresponding to each of the working areas to determine navigation mode switching information; The transportation navigation information is finally determined based on the navigation mode switching information, the work tasks corresponding to each of the target work areas, and the target navigation information.

[0011] In a preferred example, the present application may be further configured as follows: after sending the transport navigation information to the smart car terminal, the present application may further include: Acquire radar detection data sent by the smart car terminal in real time, perform obstacle analysis based on the radar detection data, determine obstacle distribution information, and perform position relationship evaluation based on the obstacle distribution information and the transport navigation information to determine a position relationship evaluation result; When the position relationship evaluation result is that it hinders driving, an obstacle avoidance analysis is performed based on the transportation navigation information, the obstacle position and the obstacle size in the obstacle distribution information, an obstacle avoidance control instruction is determined, and the obstacle avoidance control instruction is sent to the smart car terminal.

[0012] In a preferred example, the present application may be further configured as follows: after sending the transport navigation information to the smart car terminal, the present application may further include: Acquire the periodic working data sent by the intelligent car terminal, perform navigation mode feasibility analysis based on the periodic working data, and determine the navigation mode feasibility analysis result; When the feasibility analysis result of the navigation mode does not meet the requirements, a backup navigation mode is determined, and the smart car terminal is controlled to continue working after switching to the backup navigation mode.

[0013] In a second aspect, the present application provides an electronic device, which adopts the following technical solution: 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 above-mentioned smart car navigation method integrating multiple navigation modes.

[0014] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program, which, when executed in a computer, causes the computer to execute the above-mentioned intelligent car navigation method integrating multiple navigation modes.

[0015] In a fourth aspect, the present application provides a computer program product, which adopts the following technical solution: A computer program product includes a computer program. When the computer program is executed by a processor, the intelligent car navigation method integrating multiple navigation modes is implemented.

[0016] In summary, the present application includes at least one of the following beneficial technical effects: Obtain the automated transportation task and factory area division information, perform transportation task route area analysis based on the automated transportation task and factory area division information, determine multiple work areas, and predetermine the work area that the smart car passes through, so as to more flexibly select the navigation mode suitable for the work area. Then, perform navigation mode analysis based on each work area and factory area division information, determine the navigation mode corresponding to each work area, control the smart car to select the appropriate navigation mode in the scene of different work areas, make full use of the advantages of different navigation modes for complementarity, and greatly improve the accuracy of smart car navigation. Then, perform navigation planning based on the target work area and the corresponding navigation mode, determine the target navigation information, and the target navigation information provides accurate road guidance for the smart car to work in the target work area, thereby improving the work efficiency of the smart car. Combine the target navigation information corresponding to each target work area, finally determine the transportation navigation information, and send the transportation navigation information to the smart car terminal to control the smart car terminal to perform the transportation task according to the navigation mode and transportation navigation information.

[0017] A variety of navigation modes are provided, which helps the smart car to automatically select the most suitable navigation mode for the work area under different environments and task requirements, greatly enhancing the adaptability and flexibility of the smart car. Therefore, matching is performed based on the target work area and the factory area division information to determine the environmental condition characteristics, work task characteristics, and facility deployment characteristics. When the facility deployment characteristics include the deployment of QR code tags, the navigation mode corresponding to the target work area is determined to be the QR code navigation mode; when the environmental condition characteristics include complex dynamic changes, the navigation mode corresponding to the target work area is determined to be the SLAM navigation mode; when the work task characteristics include fixed path transportation, the navigation mode corresponding to the target work area is determined to be the magnetic navigation mode; otherwise, the navigation mode priority is obtained, and the navigation mode corresponding to the target work area is determined based on the navigation mode priority. Control the smart car to select the appropriate navigation mode in the scenarios of different work areas, make full use of the advantages of different navigation modes for complementarity, and greatly improve the accuracy and robustness of the smart car navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of a smart car navigation method integrating multiple navigation modes according to one embodiment of the present application; Figure 2 It is a structural schematic diagram of an intelligent car navigation device integrating multiple navigation modes according to one embodiment of the present application; Figure 3 It is a structural schematic diagram of an electronic device according to one embodiment of the present application. DETAILED DESCRIPTION

[0019] The following combination Figures 1 to 3 This application is described in further detail.

[0020] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope of the present application.

[0021] In order to make the purpose, technical scheme and advantages of the embodiment of the present application clearer, the technical scheme in the embodiment of the present application will be clearly and completely described in conjunction with the drawings in the embodiment of the present application. Obviously, the described embodiment is a part of the embodiment 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 the field without creative work are within the scope of protection of the present application. It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the data related to the object is involved in the embodiment of the present application, it needs to be obtained through the authorization and consent of the object, the authorization and consent of the relevant departments, and in accordance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiment, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained, and the embodiment also needs to be implemented with the authorization and consent of the object.

[0022] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0023] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0024] The embodiment of the present application provides a smart car navigation method integrating multiple navigation modes, which is executed by an electronic 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. Figure 1 As shown, the method includes step S101, step S102, step S103 and step S104, wherein: Step S101: Acquire automated transport tasks and factory area division information, perform transport task route area analysis based on the automated transport tasks and factory area division information, and determine multiple work areas.

[0025] For the embodiment of the present application, the specific work tasks that the smart car needs to complete are clearly recorded in detail in the automated transportation task. The specific work tasks include but are not limited to: cargo transportation from the starting point to the end point, material distribution, inspection operations, etc. Each specific work task clearly records the key information such as the starting position, target position, type and quantity of transported items, and time requirements of the smart car. The factory area division information records in detail the specific layout, functional division, obstacle location, and traffic route of each area inside the factory. The factory area division information is usually presented in the form of maps, CAD drawings, or real-time sensor data, etc., which provides the environment basis required for navigation for the smart car. Furthermore, based on the automated transportation task and the factory area division information, the transportation task path area analysis is performed to determine multiple work areas, wherein the multiple work areas are all areas that the smart car must pass through and may stay in order to complete the transportation task. For the transportation task path area analysis, a preliminary transportation path analysis is performed based on the automated transportation task and the factory area division information to determine the initial transportation path, and the factory area passed in the initial transportation path is recorded as the work area. Since different work areas in a factory have different characteristics, such as environmental conditions, work tasks, facility deployment, space layout, road conditions, lighting conditions, etc., by pre-determining the work area that the smart car will pass through, it is convenient to more flexibly select the navigation mode suitable for the work area, ensuring that the smart car can operate stably and accurately in different environments.

[0026] Step S102: performing navigation mode analysis based on each work area and factory area division information to determine the navigation mode corresponding to each work area.

[0027] For the embodiments of the present application, different working areas in the factory have different characteristics. If the smart car only adopts a single navigation mode, it cannot adapt to the multi-featured working environment, resulting in the problem of low navigation accuracy in the navigation method of the smart car. In order to improve the navigation accuracy of the smart car in the complex and changeable factory environment, the smart car is controlled to select a suitable navigation mode in the scenarios of different working areas, and the advantages of different navigation modes are fully utilized for complementarity, thereby greatly improving the accuracy and robustness of the smart car navigation. Therefore, a navigation mode analysis is performed based on each working area and factory area division information to determine the navigation mode corresponding to each working area, wherein the navigation mode includes but is not limited to: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode. There are many ways to analyze the navigation mode, which are no longer limited in the embodiments of the present application. In one feasible method, matching is performed based on the target work area and the factory area division information to determine the environmental condition characteristics, work task characteristics, and facility deployment characteristics; when the facility deployment characteristics include the deployment of a QR code tag, the navigation mode corresponding to the target work area is determined to be a QR code navigation mode. Preferably, the navigation modes in the smart car terminal in the target work area are arranged from high to low as follows: QR code navigation mode, magnetic navigation mode, and SLAM navigation mode; when the environmental condition characteristics include complex dynamic changes, the navigation mode corresponding to the target work area is determined to be a SLAM navigation mode. Preferably, the The navigation mode priorities in the smart car terminal in the target working area are arranged from high to low as follows: SLAM navigation mode, magnetic navigation mode, and QR code navigation mode; when the work task characteristics include fixed-path transportation, the navigation mode corresponding to the target working area is determined to be the magnetic navigation mode. Preferably, the navigation mode priorities in the smart car terminal in the target working area are arranged from high to low as follows: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode; otherwise, the navigation mode priority is obtained, and based on the navigation mode priority, the navigation mode corresponding to the target working area is determined, wherein the navigation mode priority is arranged from high to low as follows: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode. The specific sorting method of the navigation mode priorities corresponding to different target working areas is not limited in the embodiments of the present application, and users can adjust it according to actual conditions.

[0028] Step S103: performing navigation planning based on the target working area and the corresponding navigation mode to determine target navigation information, wherein the target working area is any one of the multiple working areas; Step S104: The target navigation information corresponding to each target working area is integrated to finally determine the transport navigation information, and the transport navigation information is sent to the smart car terminal to control the smart car terminal to perform the transport task according to the navigation mode and the transport navigation information.

[0029] For the embodiments of the present application, different types of navigation modes focus on different contents when executing navigation planning, and the execution process is also different. In order to control the smart car to autonomously complete automated transportation tasks in multiple working areas, navigation planning is performed based on the target working area and the corresponding navigation mode, and the target navigation information is determined. The target navigation information provides accurate road guidance for the smart car working in the target working area, ensuring that the smart car can follow a feasible path, avoiding unnecessary detours and waiting, thereby improving the work efficiency of the smart car. There are many ways to implement navigation rules, which are no longer limited in the embodiments of the present application. In one feasible way, when the navigation mode is the SLAM navigation mode, the dynamic area map, the work starting point and the work end point corresponding to the target working area are obtained, and feasible path planning is performed based on the dynamic area map, the work starting point and the work end point to determine the target navigation information; when the navigation mode is the QR code navigation mode, the QR code analysis is performed based on the automated transportation task and the target working area, the QR code combination is determined, and navigation planning is performed based on the QR code combination to determine the target navigation information, wherein the QR code combination includes: a deceleration QR code and a positioning stop QR code; when the navigation mode is the magnetic navigation mode, the magnetic stripe laying information corresponding to the target working area is obtained, and the magnetic stripe path planning is performed based on the magnetic stripe laying information and the automated transportation task to determine the target navigation information.

[0030] Then, the target navigation information corresponding to each target work area is integrated to finally determine the transport navigation information, which covers the work tasks that the smart car needs to perform in each target work area, the road guidance in each target work area, and the navigation mode switching information corresponding to the switching between different navigation modes. The specific implementation process of determining the transport navigation information based on the integrated target navigation information is as follows: based on the automated transport task and each target work area, the task operation analysis is performed to determine the work task corresponding to each target work area; based on the navigation mode corresponding to each work area, the mode switching analysis is performed to determine the navigation mode switching information; based on the navigation mode switching information, the work task corresponding to each target work area and the target navigation information, the transport navigation information is finally determined. After that, the transport navigation information is sent to the smart car terminal to control the smart car terminal to perform the transport task according to the navigation mode and the transport navigation information.

[0031] It can be seen that in the embodiment of the present application, the automated transportation task and the factory area division information are obtained, and the transportation task route area analysis is performed based on the automated transportation task and the factory area division information, and multiple work areas are determined. By predetermining the work area that the smart car passes through, it is convenient to more flexibly select the navigation mode suitable for the work area. Then, based on each work area and the factory area division information, the navigation mode corresponding to each work area is determined, and the smart car is controlled to select the appropriate navigation mode in the scene of different work areas, and the advantages of different navigation modes are fully utilized for complementarity, which greatly improves the accuracy of the smart car navigation. Then, based on the target work area and the corresponding navigation mode, navigation planning is performed to determine the target navigation information. The target navigation information provides accurate road guidance for the smart car to work in the target work area, which improves the work efficiency of the smart car. The target navigation information corresponding to each target work area is integrated, and the transportation navigation information is finally determined, and the transportation navigation information is sent to the smart car terminal to control the smart car terminal to perform the transportation task according to the navigation mode and transportation navigation information.

[0032] Furthermore, in order to improve the accuracy and robustness of the smart car navigation, in the embodiment of the present application, the navigation modes include: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode. Based on the information of each work area and factory area division, the navigation mode analysis is performed to determine the navigation mode corresponding to each work area, including: Match the target work area with the factory area division information to determine the environmental condition characteristics, work task characteristics, and facility deployment characteristics; When the facility deployment feature includes deploying a QR code tag, determining that the navigation mode corresponding to the target work area is a QR code navigation mode; When the environmental condition characteristics include complex dynamic changes, the navigation mode corresponding to the target working area is determined to be the SLAM navigation mode; When the work task characteristics include fixed-path transportation, it is determined that the navigation mode corresponding to the target work area is the magnetic navigation mode; Otherwise, obtain the navigation mode priority, and determine the navigation mode corresponding to the target working area based on the navigation mode priority.

[0033] For the embodiment of the present application, a variety of navigation modes are provided, which help the smart car to automatically select the most suitable navigation mode for the working area under different environments and task requirements, greatly enhancing the adaptability and flexibility of the smart car. The navigation modes include: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode. For the magnetic stripe navigation mode, the smart car needs to travel along a fixed magnetic stripe laying route, and can provide stable and reliable navigation positioning in fixed path transportation and environmentally stable working areas, ensuring that the smart car can still maintain high-precision positioning at high speeds; for the SLAM navigation mode, in working areas with a large number of temporary obstacles or complex terrain, SLAM navigation can sense and respond quickly to environmental changes in real time, and effectively avoid obstacles; for the QR code navigation mode, by scanning densely arranged QR code tags, fast path planning and direction adjustment can be achieved, improving the operating efficiency of the smart car in complex environments.

[0034] In order to improve the navigation accuracy of the smart car in the complex and changeable factory environment, the smart car is controlled to select the appropriate navigation mode in the scenes of different work areas, and the advantages of different navigation modes are fully utilized to complement each other, which greatly improves the accuracy and robustness of the smart car navigation. Therefore, for any target work area in multiple work areas, matching is performed based on the target work area and the factory area division information to determine the environmental condition characteristics, work task characteristics, and facility deployment characteristics, among which the environmental condition characteristics are used to characterize whether the layout in the work area changes frequently, whether there are a large number of temporary obstacles or complex terrain, etc. The environmental condition characteristics include: complex dynamic changes and static stability; the work task characteristics are used to characterize whether the work tasks in the work area need to be transported along a fixed path, and the work task characteristics include: fixed path transportation and custom path transmission; the facility deployment characteristics are used to characterize whether there are dense QR code tags in the work area to control the smart car to accurately stop at a specific location, and the facility deployment characteristics include: deployed QR code tags and undeployed QR code tags.

[0035] Furthermore, when the facility deployment features include the deployment of QR code tags, the navigation mode corresponding to the target work area is determined to be the QR code navigation mode. The QR code tags provide clear coordinate information, so that the smart car can accurately identify and locate the location of each QR code, thereby achieving high-precision navigation and docking. When the environmental condition characteristics include complex dynamic changes, the navigation mode corresponding to the target work area is determined to be the SLAM navigation mode. The SLAM navigation mode can perceive and adapt to environmental changes in real time, including the appearance and disappearance of dynamic obstacles and temporary changes in the path. The strong adaptability enables the smart car to maintain stable navigation performance in a complex and dynamic working environment. When the work task characteristics include fixed-path transportation, the navigation mode corresponding to the target work area is determined to be the magnetic navigation mode. The magnetic navigation mode uses physical magnetic field signals for positioning, which is stable and not easily affected by the environment. It can provide stable and reliable navigation performance, ensuring that the smart car travels accurately along the predetermined path. Of course, when none of the above conditions are met, that is, when the environmental condition is characterized by static stability, the work task is characterized by custom path transmission, and the facility deployment is characterized by no QR code tag being deployed, the navigation mode priority is obtained. The navigation mode priority is a priority order of multiple navigation modes pre-set and stored in the electronic device. Preferably, they are arranged from high to low in order of priority: magnetic navigation mode, SLAM navigation mode, and based on the navigation mode priority, the navigation mode corresponding to the target working area is determined.

[0036] It can be seen that in the embodiment of the present application, a variety of navigation modes are provided, which helps the smart car to automatically select the most suitable navigation mode for the work area under different environments and task requirements, greatly enhancing the adaptability and flexibility of the smart car. Therefore, matching is performed based on the target work area and the factory area division information to determine the environmental condition characteristics, work task characteristics, and facility deployment characteristics. When the facility deployment characteristics include the deployment of a QR code tag, the navigation mode corresponding to the target work area is determined to be a QR code navigation mode; when the environmental condition characteristics include complex dynamic changes, the navigation mode corresponding to the target work area is determined to be a SLAM navigation mode; when the work task characteristics include fixed path transportation, the navigation mode corresponding to the target work area is determined to be a magnetic navigation mode; otherwise, the navigation mode priority is obtained, and the navigation mode corresponding to the target work area is determined based on the navigation mode priority. Control the smart car to select the appropriate navigation mode in the scenarios of different work areas, make full use of the advantages of different navigation modes for complementarity, and greatly improve the accuracy and robustness of the smart car navigation.

[0037] Furthermore, in order to improve the adaptability of the target navigation information and the navigation mode, and to help the smart car work safely in each navigation mode, in an embodiment of the present application, navigation planning is performed based on the target working area and the corresponding navigation mode, and the target navigation information is determined, including: When the navigation mode is the SLAM navigation mode, the dynamic area map, the work starting point and the work end point corresponding to the target work area are obtained, and a feasible path planning is performed based on the dynamic area map, the work starting point and the work end point to determine the target navigation information; When the navigation mode is the QR code navigation mode, the QR code is analyzed based on the automated transportation task and the target work area to determine the QR code combination, and navigation planning is performed based on the QR code combination to determine the target navigation information, wherein the QR code combination includes: a deceleration QR code and a positioning stop QR code; When the navigation mode is the magnetic navigation mode, the magnetic stripe laying information corresponding to the target working area is obtained, and the magnetic stripe path planning is performed based on the magnetic stripe laying information and the automated transportation task to determine the target navigation information.

[0038] For the embodiment of the present application, since a variety of navigation modes are integrated, in the process of executing navigation planning, it is necessary to perform navigation planning based on the working characteristics of each navigation mode, and determine the target navigation information under the navigation mode. Specifically, when the navigation mode is the SLAM navigation mode, the layout representing the target working area will change frequently, and there will be a large number of temporary obstacles or complex terrains. Therefore, the dynamic area map, the work starting point and the work end point corresponding to the target working area are obtained, wherein the work starting point is the starting point where the smart car enters the target working area, and the work end point is the final location where the smart car completes the work task in the target working area. The dynamic area map is a map representing the current environmental conditions of the target working area. The image data regularly collected by the camera in the target working area can be used to construct the area map to obtain a dynamic area map, which can timely display the layout changes, obstacle distribution and other conditions in the target working area. Then, based on the dynamic area map, the work starting point and the work end point, a path planning algorithm (for example, A* algorithm, Dijkstra algorithm, RRT algorithm, etc.) is used to search for feasible paths. The path planning algorithm will consider factors such as obstacles, road width, and turning restrictions in the dynamic area map to find an optimal path from the work starting point to the work end point. Then, based on the planned optimal path, specific target navigation information is generated, which includes but is not limited to: the driving speed, steering angle, acceleration and deceleration control information of the smart car. At the same time, when the smart car is in SLAM navigation mode, SLAM navigation can perceive environmental changes in real time and flexibly adjust the path based on the optimal path.

[0039] When the navigation mode is the QR code navigation mode, the work task analysis is performed based on the automated transportation task and the target work area to determine the precise parking position of the smart car and the positioning stop QR code corresponding to the position, so that the smart car can accurately and smoothly dock at a specific position after scanning the positioning stop QR code. However, since the running speed of the smart car is relatively fast when the smart car performs the navigation operation according to the QR code mode, if only one positioning stop QR code is used, the smart car cannot be stopped immediately from the high-speed running state, causing the smart car to continue to slide for a distance near the positioning stop QR code, increasing the risk of collision with surrounding obstacles, personnel or other vehicles, and unable to achieve accurate docking of the smart car. In order to improve the accuracy of the smart car's docking, a deceleration QR code is selected at a preset distance from the positioning stop QR code, wherein the size of the preset distance is set by the technician according to the actual situation, and the embodiment of the present application is no longer limited, so that when the smart car scans the deceleration QR code, the deceleration operation is performed to ensure that the smart car can be smoothly and accurately docked at the specific position corresponding to the positioning stop QR code.

[0040] When the navigation mode is the magnetic navigation mode, the magnetic stripe laying information corresponding to the target working area is obtained. The magnetic stripe laying information is used to characterize the starting point, end point, path shape, connection relationship, etc. of the magnetic stripe laying in the target working area. The magnetic stripe laying information can be expressed in the form of a chart. Furthermore, the automated transportation task lists in detail the locations where the smart car needs to stop or perform operations in the target working area. Such locations can be called waypoints. Then, based on the magnetic stripe laying information, waypoints, work starting point and work end point, magnetic stripe path planning is performed to determine the magnetic stripe driving path. In the process of executing the magnetic stripe path planning, the kinematic characteristics of the smart car are comprehensively considered, such as the maximum speed, steering ability, etc. Then, according to the planned magnetic stripe driving path, specific target navigation information is generated. The target navigation information includes but is not limited to: the driving speed, steering angle, acceleration and deceleration and other control information of the smart car.

[0041] It can be seen that in an embodiment of the present application, when the navigation mode is the SLAM navigation mode, the dynamic area map, the work starting point and the work end point corresponding to the target working area are obtained, and feasible path planning is performed based on the dynamic area map, the work starting point and the work end point, and the target navigation information is determined. When the navigation mode is the QR code navigation mode, the QR code analysis is performed based on the automated transportation task and the target working area, the QR code combination is determined, and navigation planning is performed based on the QR code combination to determine the target navigation information, wherein the QR code combination includes: a deceleration QR code and a positioning stop QR code. At the same time, when the navigation mode is the magnetic navigation mode, the magnetic stripe laying information corresponding to the target working area is obtained, and the magnetic stripe path planning is performed based on the magnetic stripe laying information and the automated transportation task to determine the target navigation information. In the process of executing navigation planning, it is necessary to perform navigation planning based on the working characteristics of each navigation mode, which improves the adaptability of the target navigation information and the navigation mode, and helps the smart car to work safely in each navigation mode.

[0042] Furthermore, in order to improve the accuracy and reliability of the automated transportation task, in the embodiment of the present application, the target navigation information corresponding to each target working area is integrated to finally determine the transportation navigation information, including: Perform task operation analysis based on automated transportation tasks and each target work area, and determine the work tasks corresponding to each target work area; Performing mode switching analysis based on the navigation mode corresponding to each work area to determine navigation mode switching information; Based on the navigation mode switching information, the work tasks corresponding to each target work area and the target navigation information, the transportation navigation information is finally determined.

[0043] For the embodiments of the present application, in order to ensure that the smart car can successfully complete the automated transportation task, the smart car can smoothly switch to the appropriate navigation mode in different working areas to improve the navigation accuracy of the smart car in the factory. When the smart car works according to the navigation information, it can greatly reduce errors and deviations in the task execution process and improve the accuracy and reliability of the automated transportation task.

[0044] Specifically, the automated transportation task records in detail the specific work tasks that the smart car needs to complete, including but not limited to: cargo transportation from the starting point to the end point, material distribution, inspection operations, etc. Since different specific work tasks will be distributed in different target areas, task operation analysis is performed based on the automated transportation task and each target work area to determine the work tasks corresponding to each target work area. At the same time, in order to improve the navigation accuracy of the smart car in the complex and changeable factory environment, the smart car is controlled to select a suitable navigation mode in the scene of different work areas. Therefore, a mode switching analysis is performed based on the navigation mode corresponding to each work area to determine the navigation mode switching information, wherein the navigation mode switching information is used to characterize the location information where the navigation mode switching needs to be executed, so that the smart car can complete the navigation mode switching suitable for the work area when it just drives to the work area. Finally, based on the navigation mode switching information, the work tasks corresponding to each target work area and the target navigation information, the transportation navigation information is finally determined, so that the smart car can successfully complete the work task execution and navigation mode switching in the target work area after receiving the transportation navigation information, thereby improving the accuracy and reliability of the automated transportation task.

[0045] It can be seen that in the embodiment of the present application, task operation analysis is performed based on the automated transportation task and each target work area to determine the work task corresponding to each target work area. Then, a mode switching analysis is performed based on the navigation mode corresponding to each work area to determine the navigation mode switching information. Finally, based on the navigation mode switching information, the work task corresponding to each target work area and the target navigation information, the transportation navigation information is finally determined. In the process of determining the transportation navigation information, the work task of the smart car in the target work area and the switching problem between different navigation modes are comprehensively considered, thereby improving the accuracy and reliability of the automated transportation task.

[0046] Furthermore, in order to reduce the risk of accidents caused by collisions and protect the safety of personnel and property, in the embodiment of the present application, after the transportation navigation information is sent to the smart car terminal, the following steps are also included: Acquire radar detection data sent by the smart car terminal in real time, perform obstacle analysis based on the radar detection data, determine obstacle distribution information, and perform position relationship evaluation based on the obstacle distribution information and transportation navigation information to determine the position relationship evaluation result; When the position relationship evaluation result is an obstacle to driving, an obstacle avoidance analysis is performed based on the transportation navigation information, the obstacle position and the obstacle size in the obstacle distribution information, and the obstacle avoidance control command is determined and sent to the smart car terminal.

[0047] For the embodiment of the present application, the smart car is equipped with a laser radar, so the smart car will detect the surrounding obstacles in time during the driving process through the laser radar detection, which is very important for avoiding collisions and ensuring the safety of personnel and goods. Therefore, the radar detection data sent by the smart car terminal is obtained in real time, and the obstacle analysis is performed based on the radar detection data to determine the obstacle distribution information, wherein the smart car and the electronic device communicate data through a wireless connection, and the obstacle distribution information includes but is not limited to: obstacle position and obstacle size and other information. The specific implementation process for obstacle analysis is as follows: the radar detection data is filtered to remove noise and reduce the amount of data, and the pre-processed point cloud data is divided into different modules to distinguish between ground points and non-ground points, and an unsupervised clustering algorithm (for example, Euclidean clustering, density clustering, etc.) is used to divide the non-ground point cloud into multiple clusters, each cluster representing an obstacle. Then, the attribute calculation is performed for each obstacle to determine the location and size of the obstacle. Preferably, the map is generated in combination with the relevant information of each obstacle to obtain the obstacle distribution information in the form of a distributed map. Furthermore, a position relationship assessment is performed based on the obstacle distribution information and the transport navigation information to determine the position relationship assessment result, wherein the position relationship assessment result includes: obstructed driving and normal driving. The specific implementation process for the position relationship assessment is as follows: using coordinate transformation and geometric calculation methods, the coordinate system of the obstacle distribution information and the transport navigation information is unified to calculate the spatial position relationship between the obstacle and the path. The position relationship assessment standard is obtained, which is an assessment standard set by technicians based on factors such as the performance indicators, safety standards, and traffic rules of the smart car. Users can adjust it according to actual conditions, and the embodiments of this application are no longer limited. Furthermore, the calculated spatial position relationship is compared with the position relationship assessment standard to determine the position relationship assessment result between the obstacle and the transport navigation path.

[0048] When the position relationship evaluation result is normal driving, no other operations are performed, and the smart car is controlled to drive normally according to the transportation navigation path. When the position relationship evaluation result is obstructed driving, obstacle avoidance analysis is performed based on the transportation navigation information, the obstacle position and obstacle size in the obstacle distribution information, and the obstacle avoidance control instruction is determined, wherein the obstacle avoidance control instruction includes but is not limited to: steering instruction, speed instruction, braking instruction. For obstacle avoidance analysis, based on the size of the smart car and the obstacle size, as well as traffic safety standards, the minimum safe distance between the smart car and the obstacle is calculated, and an obstacle avoidance area is delineated based on the obstacle position as the center and the minimum safe distance and obstacle size. The area should completely cover the obstacle and expand outward with a certain safety margin. Then, the obstacle avoidance starting point is selected according to the transportation navigation information and the obstacle avoidance area. The obstacle avoidance starting point should be as close to the obstacle as possible but will not immediately enter the obstacle avoidance area, and the obstacle avoidance path is planned according to the obstacle avoidance starting point and the obstacle avoidance area to determine the curvature of the obstacle path. Finally, based on the obstacle avoidance area, obstacle avoidance starting point, obstacle avoidance path curvature and obstacle avoidance speed, instructions are generated to determine the obstacle avoidance control instructions to guide the smart car to perform obstacle avoidance operations. Through the above methods, timely identification and avoidance of obstacles can significantly reduce the risk of accidents caused by collisions and protect the safety of people and property.

[0049] It can be seen that in the embodiment of the present application, the radar detection data sent by the smart car terminal is acquired in real time, obstacle analysis is performed based on the radar detection data, obstacle distribution information is determined, and position relationship evaluation is performed based on the obstacle distribution information and transportation navigation information to determine the position relationship evaluation result. Furthermore, when the position relationship evaluation result is an obstruction to driving, obstacle avoidance analysis is performed based on the transportation navigation information, the obstacle position and obstacle size in the obstacle distribution information, the obstacle avoidance control command is determined, and the obstacle avoidance control command is sent to the smart car terminal. Timely identification and avoidance of obstacles can significantly reduce the risk of accidents caused by collisions and protect the safety of people and property.

[0050] Furthermore, in order to improve the reliability and stability of the operation of the smart car, in the embodiment of the present application, after the transportation navigation information is sent to the smart car terminal, the following steps are also included: Obtain the periodic working data sent by the intelligent car terminal, perform feasibility analysis of the navigation mode based on the periodic working data, and determine the feasibility analysis result of the navigation mode; When the feasibility analysis result of the navigation mode does not meet the requirements, the backup navigation mode is determined, and the intelligent car terminal is controlled to switch to the backup navigation mode and continue to work.

[0051] For the embodiments of the present application, when the smart car is driving in the navigation mode, a navigation mode failure may occur, which may affect the normal operation of the smart car and the completion of the task. Therefore, in order to ensure the continuous and reliable operation of the smart car's navigation system, it is necessary to switch to the backup navigation mode in time when there is a failure in the navigation mode, so as to quickly restore the navigation function, ensure that the smart car continues to perform the task, and improve the reliability and stability of the smart car's operation.

[0052] Specifically, the periodic working data sent by the intelligent car terminal is obtained, and the periodic working data includes but is not limited to: the real-time status of the navigation module, the measurement data during the navigation process, the communication status and other information, and the periodic working data is cleaned, verified and formatted to ensure the accuracy and consistency of the data. Then, the key state parameters related to the navigation mode are extracted from the preprocessed data, such as GPS signal strength, laser radar scanning results, visual sensor image clarity, etc., and the standard key state parameters of the navigation mode under normal conditions are obtained. A feasibility analysis is performed based on the standard key state parameters and the key states actually extracted to determine the feasibility analysis result of the navigation mode. When any one of the items does not meet the requirements of the standard key state parameters, the feasibility analysis result of the navigation mode is determined to be unsatisfactory; otherwise, the feasibility analysis result of the navigation mode is determined to be satisfactory. When the feasibility analysis result of the navigation mode does not meet the requirements, it is characterized that the current navigation mode of the intelligent car has a fault, so the backup navigation mode is selected, and the intelligent car terminal is controlled to continue working after changing to the backup navigation mode. The backup navigation mode is pre-stored in the electronic device, and the user can set the backup navigation mode according to actual needs, and the embodiment of the present application is no longer limited.

[0053] It can be seen that in the embodiment of the present application, in order to ensure the continuous and reliable operation of the smart car navigation system, the periodic working data sent by the smart car terminal is obtained, and the feasibility analysis of the navigation mode is performed based on the periodic working data to determine the feasibility analysis result of the navigation mode. When the feasibility analysis result of the navigation mode does not meet the requirements, the backup navigation mode is determined, and the smart car terminal is controlled to switch to the backup navigation mode and continue to work. Switch to the backup navigation mode in time to quickly restore the navigation function, ensure that the smart car continues to perform the task, and improve the reliability and stability of the smart car.

[0054] The above embodiment introduces an intelligent car navigation method integrating multiple navigation modes from the perspective of method flow, and the following embodiment introduces an intelligent car navigation device integrating multiple navigation modes from the perspective of virtual module or virtual unit. For details, please refer to the following embodiment.

[0055] The present application embodiment provides a smart car navigation device integrating multiple navigation modes, such as Figure 2As shown, the smart car navigation device integrating multiple navigation modes may specifically include: A regional analysis module 210 is used to obtain automated transportation tasks and factory area division information, perform transportation task route area analysis based on the automated transportation tasks and factory area division information, and determine multiple work areas; A navigation mode analysis module 220, for performing navigation mode analysis based on each work area and factory area division information, and determining a navigation mode corresponding to each work area; A navigation planning module 230, configured to perform navigation planning based on a target working area and a corresponding navigation mode, and determine target navigation information, wherein the target working area is any one of the multiple working areas; The navigation information confirmation module 240 is used to integrate the target navigation information corresponding to each target working area, finally determine the transportation navigation information, and send the transportation navigation information to the smart car terminal to control the smart car terminal to perform the transportation task according to the navigation mode and the transportation navigation information.

[0056] For the embodiment of the present application, the automated transport task and the factory area division information are obtained, and the transport task route area analysis is performed based on the automated transport task and the factory area division information, and multiple work areas are determined. By predetermining the work area that the smart car passes through, it is convenient to more flexibly select the navigation mode suitable for the work area. Then, based on each work area and the factory area division information, the navigation mode corresponding to each work area is determined, and the smart car is controlled to select the appropriate navigation mode in the scene of different work areas, and the advantages of different navigation modes are fully utilized for complementarity, which greatly improves the accuracy of the smart car navigation. Then, based on the target work area and the corresponding navigation mode, navigation planning is performed to determine the target navigation information. The target navigation information provides accurate road guidance for the smart car to work in the target work area, which improves the work efficiency of the smart car. The target navigation information corresponding to each target work area is integrated, and the transportation navigation information is finally determined, and the transportation navigation information is sent to the smart car terminal to control the smart car terminal to perform the transportation task according to the navigation mode and the transportation navigation information.

[0057] In a possible implementation of the embodiment of the present application, the navigation modes include: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode. When performing navigation mode analysis based on each work area and factory area division information to determine the navigation mode corresponding to each work area, the navigation mode analysis module 220 is used to: Match the target work area with the factory area division information to determine the environmental condition characteristics, work task characteristics, and facility deployment characteristics; When the facility deployment feature includes deploying a QR code tag, determining that the navigation mode corresponding to the target work area is a QR code navigation mode; When the environmental condition characteristics include complex dynamic changes, the navigation mode corresponding to the target working area is determined to be the SLAM navigation mode; When the work task characteristics include fixed-path transportation, it is determined that the navigation mode corresponding to the target work area is the magnetic navigation mode; Otherwise, obtain the navigation mode priority, and determine the navigation mode corresponding to the target working area based on the navigation mode priority.

[0058] In a possible implementation of the embodiment of the present application, when the navigation planning module 230 performs navigation planning based on the target working area and the corresponding navigation mode and determines the target navigation information, it is used to: When the navigation mode is the SLAM navigation mode, the dynamic area map, the work starting point and the work end point corresponding to the target work area are obtained, and a feasible path planning is performed based on the dynamic area map, the work starting point and the work end point to determine the target navigation information; When the navigation mode is the QR code navigation mode, the QR code is analyzed based on the automated transportation task and the target work area to determine the QR code combination, and navigation planning is performed based on the QR code combination to determine the target navigation information, wherein the QR code combination includes: a deceleration QR code and a positioning stop QR code; When the navigation mode is the magnetic navigation mode, the magnetic stripe laying information corresponding to the target working area is obtained, and the magnetic stripe path planning is performed based on the magnetic stripe laying information and the automated transportation task to determine the target navigation information.

[0059] In a possible implementation of the embodiment of the present application, the navigation information confirmation module 240 is used to: Perform task operation analysis based on automated transportation tasks and each target work area, and determine the work tasks corresponding to each target work area; Performing mode switching analysis based on the navigation mode corresponding to each work area to determine navigation mode switching information; Based on the navigation mode switching information, the work tasks corresponding to each target work area and the target navigation information, the transportation navigation information is finally determined.

[0060] A possible implementation of the embodiment of the present application is a smart car navigation device integrating multiple navigation modes, further comprising: The intelligent obstacle avoidance module is used to obtain the radar detection data sent by the intelligent car terminal in real time, perform obstacle analysis based on the radar detection data, determine the obstacle distribution information, and perform position relationship evaluation based on the obstacle distribution information and transportation navigation information to determine the position relationship evaluation result; When the position relationship evaluation result is an obstacle to driving, an obstacle avoidance analysis is performed based on the transportation navigation information, the obstacle position and the obstacle size in the obstacle distribution information, and the obstacle avoidance control command is determined and sent to the smart car terminal.

[0061] A possible implementation of the embodiment of the present application is a smart car navigation device integrating multiple navigation modes, further comprising: The navigation mode switching module is used to obtain the periodic working data sent by the intelligent car terminal, perform navigation mode feasibility analysis based on the periodic working data, and determine the feasibility analysis result of the navigation mode; When the feasibility analysis result of the navigation mode does not meet the requirements, the backup navigation mode is determined, and the intelligent car terminal is controlled to switch to the backup navigation mode and continue to work.

[0062] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the intelligent car navigation device with integrated multiple navigation modes described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0063] An electronic device is provided in an embodiment of the present application, such as Figure 3 As shown, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0064] The processor 301 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. The processor 301 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.

[0065] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but it does not mean that there is only one bus or only one type of bus.

[0066] The memory 303 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.

[0067] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0068] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0069] 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.

[0070] The embodiment of the present application provides a computer program product, which includes a computer program, which implements the method in any of the above embodiments when executed by a processor. Compared with the related art, the embodiment of the present application obtains the automated transportation task and the factory area division information, performs the transportation task route area analysis based on the automated transportation task and the factory area division information, determines multiple work areas, and predetermines the work area that the smart car passes through, so as to more flexibly select the navigation mode suitable for the work area. Then, based on each work area and the factory area division information, the navigation mode corresponding to each work area is analyzed, and the smart car is controlled to select the appropriate navigation mode in the scene of different work areas, making full use of the advantages of different navigation modes for complementarity, and greatly improving the accuracy of the smart car navigation. Then, based on the target work area and the corresponding navigation mode, navigation planning is performed to determine the target navigation information, and the target navigation information provides accurate road guidance for the smart car to work in the target work area, thereby improving the work efficiency of the smart car. The target navigation information corresponding to each target work area is integrated, and the transportation navigation information is finally determined, and the transportation navigation information is sent to the smart car terminal to control the smart car terminal to perform the transportation task according to the navigation mode and the transportation navigation information.

[0071] 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.

[0072] 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 smart car navigation method integrating multiple navigation modes, characterized in that: include: Acquire automated transportation tasks and factory area division information, perform transportation task route area analysis based on the automated transportation tasks and the factory area division information, and determine multiple work areas; Perform navigation mode analysis based on each of the work areas and the factory area division information to determine a navigation mode corresponding to each of the work areas; Performing navigation planning based on a target working area and the corresponding navigation mode to determine target navigation information, wherein the target working area is any one of the multiple working areas; The target navigation information corresponding to each target working area is integrated to finally determine the transport navigation information, and the transport navigation information is sent to the smart car terminal to control the smart car terminal to perform the transport task according to the navigation mode and the transport navigation information.

2. The intelligent car navigation method integrating multiple navigation modes according to claim 1 is characterized in that: The navigation modes include: magnetic navigation mode, SLAM navigation mode, and QR code navigation mode. The performing of navigation mode analysis based on each of the work areas and the factory area division information to determine the navigation mode corresponding to each of the work areas includes: Based on the matching of the target work area and the factory area division information, the environmental condition characteristics, work task characteristics, and facility deployment characteristics are determined; When the facility deployment feature includes deploying a QR code tag, determining that the navigation mode corresponding to the target working area is the QR code navigation mode; When the environmental condition characteristics include complex dynamic changes, determining that the navigation mode corresponding to the target working area is the SLAM navigation mode; When the work task feature includes fixed-path transportation, determining that the navigation mode corresponding to the target work area is the magnetic navigation mode; Otherwise, obtain the navigation mode priority, and determine the navigation mode corresponding to the target working area based on the navigation mode priority.

3. The intelligent car navigation method integrating multiple navigation modes according to claim 2 is characterized in that: The performing navigation planning based on the target working area and the corresponding navigation mode to determine the target navigation information includes: When the navigation mode is the SLAM navigation mode, a dynamic area map, a work starting point, and a work end point corresponding to the target work area are obtained, and a feasible path planning is performed based on the dynamic area map, the work starting point, and the work end point to determine the target navigation information; When the navigation mode is the QR code navigation mode, a QR code analysis is performed based on the automated transport task and the target work area to determine a QR code combination, and navigation planning is performed based on the QR code combination to determine target navigation information, wherein the QR code combination includes: a deceleration QR code and a positioning stop QR code; When the navigation mode is the magnetic navigation mode, the magnetic stripe laying information corresponding to the target working area is obtained, and the magnetic stripe path planning is performed based on the magnetic stripe laying information and the automated transportation task to determine the target navigation information.

4. The intelligent car navigation method integrating multiple navigation modes according to claim 1 is characterized in that: The step of integrating the target navigation information corresponding to each target working area to finally determine the transportation navigation information includes: Performing task operation analysis based on the automated transportation task and each of the target work areas, and determining a work task corresponding to each of the target work areas; Performing mode switching analysis based on the navigation mode corresponding to each of the working areas to determine navigation mode switching information; The transportation navigation information is finally determined based on the navigation mode switching information, the work tasks corresponding to each of the target work areas, and the target navigation information.

5. The intelligent car navigation method integrating multiple navigation modes according to claim 1 is characterized in that: After sending the transport navigation information to the smart car terminal, the method further includes: Acquire radar detection data sent by the smart car terminal in real time, perform obstacle analysis based on the radar detection data, determine obstacle distribution information, and perform position relationship evaluation based on the obstacle distribution information and the transport navigation information to determine a position relationship evaluation result; When the position relationship evaluation result is that it hinders driving, an obstacle avoidance analysis is performed based on the transportation navigation information, the obstacle position and the obstacle size in the obstacle distribution information, an obstacle avoidance control instruction is determined, and the obstacle avoidance control instruction is sent to the smart car terminal.

6. The intelligent car navigation method integrating multiple navigation modes according to claim 1 is characterized in that: After sending the transport navigation information to the smart car terminal, the method further includes: Acquire the periodic working data sent by the intelligent car terminal, perform navigation mode feasibility analysis based on the periodic working data, and determine the navigation mode feasibility analysis result; When the feasibility analysis result of the navigation mode does not meet the requirements, a backup navigation mode is determined, and the smart car terminal is controlled to continue working after switching to the backup navigation mode.

7. An electronic device, characterized in that: include: at least one processor; Memory; At least one application, wherein 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 intelligent car navigation method integrating multiple navigation modes as described in any one of claims 1 to 6.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed in a computer, the computer is caused to execute the intelligent car navigation method integrating multiple navigation modes as described in any one of claims 1 to 6.

9. A computer program product, characterized in that The method comprises a computer program, wherein the computer program is executed by a processor to implement the intelligent car navigation method integrating multiple navigation modes as claimed in any one of claims 1 to 6.

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