Multifunctional unmanned sweeper and control method thereof

Abstract

The invention relates to the field of sweeping robots, in particular to a multifunctional unmanned sweeper and a control method thereof.The multifunctional unmanned sweeper comprises a sweeper body, the sweeper body is provided with an automatic navigation system and an advertisement screen, and the automatic navigation system integrates a laser radar, a camera, an ultrasonic sensor and a GPS module; according to the intelligent cleaning robot, all-around environment perception and accurate positioning are achieved through the navigation algorithm processor, it is ensured that the sweeping robot efficiently and safely executes cleaning tasks, the advertising screen is arranged so that the sweeping robot can display advertising information to pedestrians and vehicles while executing cleaning work, and diversification of functions is achieved.

Description

Technical Field

[0001] The present application relates to the field of sweeping robots, and in particular to a multifunctional unmanned sweeping robot and a control method thereof. Background Art

[0002] With the development of intelligence, unmanned sweepers that can automatically navigate are usually used to clean the ground in outdoor places such as industrial parks, which can effectively save labor costs and make the cleaning effect better and more efficient.

[0003] In actual use, since the automatic navigation system of the unmanned sweeping robot is usually equipped with an automatic navigation system, the unmanned sweeping robot in the prior art cannot reasonably utilize the automatic navigation system, resulting in the unmanned sweeping robot having a relatively single function. Summary of the invention

[0004] In order to achieve performance diversification of an unmanned sweeping robot, the present application provides a multifunctional unmanned sweeping robot and a control method thereof.

[0005] In the first aspect, the present application provides a multifunctional unmanned sweeping machine, including a sweeping machine body, which is provided with an automatic navigation system and an advertising screen, the automatic navigation system including a laser radar, a camera, an ultrasonic sensor, a GPS module and a navigation algorithm processor, the laser radar being located on the top of the sweeping machine body for 360-degree environmental scanning, the camera being installed on the top of the sweeping machine to be located on one side of the laser radar for identifying obstacles and environmental features at a longer distance, the ultrasonic sensors being distributed around the sweeping machine body for close-range obstacle detection; the GPS module being located inside the sweeping machine body for determining the precise position of the unmanned sweeping machine, the navigation algorithm processor being built into the sweeping machine body, and the collected data being transmitted to the navigation algorithm processor, the navigation algorithm processor processing sensor data, executing a path planning algorithm, and generating navigation instructions, the advertising screen being installed at the front end of the sweeping machine body, facing pedestrians or vehicles.

[0006] A control method for a multifunctional unmanned sweeper is applied to the above-mentioned multifunctional unmanned sweeper, comprising the following steps: S1: Utilizes integrated multimodal sensors to perceive environmental changes in real time, including dynamic information of obstacles, pedestrian and vehicle flows, intelligently distinguish between cleaning and non-cleaning areas, and identify potential advertising display areas, thus achieving dynamic adjustment of cleaning paths and precise positioning of advertising displays; S2: Based on real-time environmental perception data, the cleaning path is dynamically planned to avoid obstacles. During non-cleaning periods, the system automatically switches to inspection mode: During intelligent inspections, abnormal situations are identified and reported in a timely manner. The intelligent scheduling system of the cloud platform automatically dispatches the unmanned sweeper to perform cleaning or inspection tasks based on real-time task requirements and equipment status. S3: By analyzing the flow of people and vehicles in the park and the time period, intelligently matching advertising content, dynamic display of advertisements is achieved, and the advertising display strategy is dynamically adjusted according to the real-time environment and crowd characteristics; S4: Real-time transmission of data such as equipment status, cleaning progress, and advertising display effects to the cloud platform. Through AI algorithms, the cloud platform can intelligently analyze data and achieve remote monitoring and maintenance of equipment; S5: When the battery is low, it automatically returns to the charging station to charge. At the same time, it intelligently adjusts the maintenance cycle and maintenance strategy to ensure long-term stable operation of the equipment.

[0007] Optionally, the integrated multimodal sensor is used to perceive environmental changes in real time, including dynamic information of obstacles, pedestrian and vehicle flows, intelligently distinguish between cleaning areas and non-cleaning areas, and identify potential advertising display areas, thereby achieving dynamic adjustment of the cleaning path and precise positioning of advertising display, including the following steps: S11: Integrates lidar, camera, and ultrasonic sensors, and uses deep convolutional neural networks to deeply understand environmental information, especially for obstacle recognition and material perception in complex environments; S12: Through the support vector machine algorithm, combined with the machine learning model, it can intelligently distinguish between cleaning areas and non-cleaning areas, identify advertising display areas, and optimize cleaning strategies; S13: Detect and classify obstacles in real time, use the YOLOv4 deep learning model to accurately classify obstacles and predict obstacle behavior; S14: Based on the environmental information, a cleaning path and an advertisement display strategy are generated to optimize resource allocation and dynamically adjust cleaning and advertisement display priorities.

[0008] Optionally, the system dynamically plans the cleaning path based on real-time environmental perception data, avoids obstacles, and automatically switches to the inspection mode during non-cleaning periods: when performing intelligent inspections, it identifies abnormal situations and notifies them in a timely manner, and automatically dispatches the unmanned sweeper to perform cleaning or inspection tasks according to real-time task requirements and equipment status through the intelligent scheduling system of the cloud platform, including the following steps: S21: Use genetic algorithm to optimize the path and dynamically plan the cleaning path; S22: Based on the urgency of the task and the status of the equipment, the cleaning tasks are intelligently sorted by combining algorithms to predict equipment health; S23: Automatically switch to inspection mode during non-cleaning period; S24: Through the cloud platform, combined with the time series analysis model, the unmanned sweeper is automatically scheduled to perform tasks according to real-time demand and equipment status, optimizing resource allocation.

[0009] Optionally, the method of analyzing the park's pedestrian and vehicle flow data and time period, intelligently matching the advertising content, realizing dynamic display of advertisements, and dynamically adjusting the advertising display strategy according to the real-time environment and crowd characteristics includes the following steps: S31: Real-time analysis of park pedestrian and vehicle flow data, combined with social network analysis, to identify high-exposure time periods and hot spots to improve advertising accuracy; S32: intelligently matching and generating advertising content according to the analysis results; S33: Predict user behavior based on real-time environment and crowd characteristics, dynamically adjust ad display strategies, and optimize ad display frequency and duration; S34: Monitor advertising display effects, analyze advertising conversion rates, and dynamically adjust advertising strategies.

[0010] Optionally, the real-time transmission of data such as device status, cleaning progress, and advertising display effect to a cloud platform. Through AI algorithms, the cloud platform can intelligently analyze the data to achieve remote monitoring and maintenance of the device, including the following steps: S41: Real-time transmission of device status to the cloud platform to achieve low-latency data transmission and preliminary data processing; S42: Through AI algorithms, data is analyzed to achieve remote monitoring of equipment and predict equipment performance trends; S43: Automatically detect equipment failures, send early warning information, reduce downtime, and predict potential failures; S44: Intelligently adjust maintenance cycles and strategies based on equipment operating status.

[0011] Optionally, when the battery is low, the device automatically returns to the charging station for charging, and at the same time, intelligently adjusts the maintenance cycle and maintenance strategy to ensure long-term stable operation of the device, including the following steps: S51: When the battery is low, the route is optimized by combining the Dijkstra algorithm and the vehicle automatically returns to the charging station for charging; S52: Built-in self-check system, combined with DBN, regular self-check; S53: Send early warning information, combine remote diagnosis technology, and intelligently handle potential faults; S54: Intelligently adjust maintenance cycles based on equipment usage.

[0012] In a second aspect, the present application provides a control system for a multifunctional unmanned sweeper, which is applied to the above-mentioned multifunctional unmanned sweeper and includes the following modules: Environmental perception and decision-making module, which integrates multimodal sensors, uses deep learning technology to identify the environment, and intelligently generates cleaning and advertising display strategies; Path planning and dynamic scheduling module, used to optimize cleaning paths, intelligently schedule tasks, and ensure efficient operation of the equipment; Advertisement content dynamic matching and intelligent display module, used to analyze traffic data, intelligently match advertisement content, and dynamically adjust display strategies; Remote monitoring and intelligent maintenance module, used to monitor equipment status in real time, predict maintenance needs, and intelligently adjust maintenance plans; Autonomous charging and intelligent self-checking module is used for automatic charging, regular self-checking, and intelligent adjustment of maintenance cycles to ensure long-term stable operation of the equipment.

[0013] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a control method of a multifunctional unmanned sweeping robot as described in any one of the above items are implemented.

[0014] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a control method of a multifunctional unmanned sweeping robot as described in any one of the above items are implemented.

[0015] In summary, this application includes the following beneficial technical effects: 1. By integrating laser radar, camera, ultrasonic sensor and GPS module, the unmanned sweeper can achieve 360-degree environmental perception, accurately identify obstacles and environmental features at long and short distances, and combine the path planning algorithm of the navigation algorithm processor to achieve efficient and safe cleaning path planning, avoid collisions, and improve cleaning efficiency and safety; 2. The advertising screen installed on the front of the sweeper faces pedestrians or vehicles. Combined with intelligent analysis technology, it can intelligently match and display advertising content based on real-time pedestrian and vehicle flow data and environmental characteristics, thereby increasing the exposure and accuracy of advertising, thereby improving advertising marketing effects and realizing the diversification of the sweeper's functions; 3. Through the automatic navigation system and intelligent decision-making algorithm, the unmanned sweeper can dynamically adjust the cleaning and advertising display strategies according to environmental changes and task requirements, achieve optimal allocation of resources, reduce manual intervention, and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a structural schematic diagram of a multifunctional unmanned sweeper in an embodiment of the present application.

[0017] Figure 2 It is a flow chart of a control method of a multifunctional unmanned sweeper in an embodiment of the present application.

[0018] Figure 3 It is a flowchart of the sub-steps of step S1 in the embodiment of the present application.

[0019] Figure 4 It is a flowchart of the sub-steps of step S2 in the embodiment of the present application.

[0020] Figure 5 It is a flowchart of the sub-steps of step S3 in the embodiment of the present application.

[0021] Figure 6 It is a flowchart of the sub-steps of step S4 in the embodiment of the present application.

[0022] Figure 7 It is a flowchart of the sub-steps of step S5 in the embodiment of the present application.

[0023] Figure numerals: 1. Sweeping robot body; 2. Advertising screen; 3. LiDAR; 4. Camera; 5. Ultrasonic sensor. DETAILED DESCRIPTION

[0024] The following is combined with Figure 1-Figure 7 This application is described in further detail.

[0025] Reference Figure 1 The embodiment of the present application discloses a multifunctional unmanned sweeping machine, including a sweeping machine body 1. The sweeping machine body 1 is provided with an automatic navigation system and an advertising screen 2. The automatic navigation system is used to realize the cruising function of the unmanned sweeping machine. The advertising screen 2 is installed at the front end of the sweeping machine body 1 for displaying advertisements. The advertising screen 2 improves the functional diversity of the unmanned sweeping machine.

[0026] The automatic navigation system includes a laser radar 3, a camera 4, an ultrasonic sensor 5, a GPS module (not shown in the figure) and a navigation algorithm processor (not shown in the figure). The laser radar 3 is located on the top of the sweeping machine body 1 and is used for 360-degree environmental scanning. The camera 4 is installed on the top of the sweeping machine and is located on the side of the laser radar 3 to identify obstacles and environmental features at a longer distance. The ultrasonic sensor 5 is distributed around the sweeping machine body 1 and is used for short-range obstacle detection; the GPS module is located inside the sweeping machine body 1 and is used to determine the precise position of the unmanned sweeping machine. The navigation algorithm processor is built into the sweeping machine body 1, and the collected data is transmitted to the navigation algorithm processor, which processes the sensor data, executes the path planning algorithm, and generates navigation instructions. The advertising screen 2 is installed at the front end of the sweeping machine body 1, facing pedestrians or vehicles.

[0027] Among them, the laser radar 3 is located on the top of the sweeper body 1, and performs a 360-degree full-range scan to capture detailed information about the surrounding environment, including the shape, size and position of obstacles. The camera 4 is installed on the side of the laser radar 3 to identify obstacles and environmental features at a longer distance, such as pedestrians, vehicles, garbage, etc. The ultrasonic sensor 5 is distributed around the sweeper body 1 to detect close-range obstacles and ensure safe operation in complex environments. The GPS module is located inside the sweeper and provides accurate geographic location information to ensure accurate positioning of the unmanned sweeper in the park.

[0028] Furthermore, all the collected sensor data is transmitted to the built-in navigation algorithm processor. The navigation algorithm processor is the "brain" of the unmanned sweeper. It processes this data and executes a complex path planning algorithm. By analyzing environmental information, the navigation algorithm processor can generate the optimal cleaning path to avoid obstacles while considering cleaning efficiency and safety. The path planning algorithm comprehensively considers the size, shape, obstacle distribution and priority of the cleaning area to ensure that the unmanned sweeper can complete the cleaning task efficiently and safely.

[0029] In addition, the navigation algorithm processor generates navigation instructions based on the processed data. These instructions include cleaning path, speed control, steering angle, etc., to ensure that the unmanned sweeper can clean according to the planned path. After receiving the navigation instructions, the unmanned sweeper automatically performs the cleaning task, and achieves efficient cleaning of the ground through the coordinated work of motor control, steering system and cleaning mechanism.

[0030] Reference Figure 2 On the other hand, the embodiment of the present application also provides a control method for a multifunctional unmanned sweeper, which is applied to the above-mentioned multifunctional unmanned sweeper, and includes the following steps: S1: Utilizes integrated multimodal sensors to perceive environmental changes in real time, including dynamic information of obstacles, pedestrian and vehicle flows, intelligently distinguish between cleaning and non-cleaning areas, and identify potential advertising display areas, thus achieving dynamic adjustment of cleaning paths and precise positioning of advertising displays.

[0031] Reference Figure 3 , step S1 correspondingly includes the following sub-steps.

[0032] S11: Integrates LiDAR 3, camera 4, and ultrasonic sensor 5, and uses deep convolutional neural network to deeply understand environmental information, especially for obstacle recognition and material perception in complex environments.

[0033] S12: Through the support vector machine algorithm combined with the machine learning model, it can intelligently distinguish between cleaning areas and non-cleaning areas, identify advertising display areas, and optimize cleaning strategies.

[0034] S13: Detect and classify obstacles in real time, use the YOLOv4 deep learning model to accurately classify obstacles and predict obstacle behavior.

[0035] S14: Based on the environmental information, a cleaning path and an advertisement display strategy are generated to optimize resource allocation and dynamically adjust cleaning and advertisement display priorities.

[0036] The following examples are given for steps S11 to S14.

[0037] First, the laser radar 3 on the top of the unmanned sweeper performs a 360-degree scan to obtain point cloud data inside the mall and build a three-dimensional model of the environment. The high precision and long-distance detection capabilities of the laser radar 3 enable it to effectively identify fixed obstacles in the mall, such as pillars and counters.

[0038] At the same time, the camera 4 installed on the side of the laser radar 3 performs image recognition through a deep convolutional neural network (DCNN), especially for obstacle recognition and material perception in complex environments. For example, DCNN can identify garbage such as paper scraps and beverage bottles on the ground, as well as floors of different materials such as carpets and tiles, providing a basis for cleaning strategies.

[0039] In addition, the ultrasonic sensors 5 distributed around the sweeper are used to detect obstacles at close range, such as customers, shopping carts and other moving obstacles, to ensure a safe distance and avoid collisions.

[0040] Then, by combining the machine learning model, it can intelligently distinguish between cleaning areas (such as aisles and open areas) and non-cleaning areas (such as store interiors and rest areas) in the mall, as well as advertising display areas (such as aisles with high traffic volume and elevator entrances). In this embodiment, the SVM algorithm learns historical data to identify the characteristics of different areas, such as traffic volume and dwell time, so as to optimize the cleaning strategy, avoid cleaning in non-cleaning areas, and determine the best location for advertising display.

[0041] Furthermore, obstacles such as customers, shopping carts, and baby strollers are detected and classified in real time. In this embodiment, YOLOv4 is a real-time target detection model that can quickly identify multiple types of obstacles while maintaining high accuracy. In addition, by analyzing the movement trajectory of obstacles and predicting their behavior, such as the walking direction of customers and the moving speed of shopping carts, the cleaning path can be planned in advance to avoid collisions.

[0042] Finally, based on the environmental information, the navigation algorithm processor of the unmanned sweeper generates a cleaning path and advertising display strategy. For example, during periods of high traffic, the main aisles are cleaned first, while promotional information is displayed on the advertising screen 2 to attract customers' attention. During periods of low traffic, deep cleaning can be performed while displaying brand stories or product introductions to enhance customer experience.

[0043] Then, according to the real-time traffic data and device status, the cleaning and advertising display priorities are dynamically adjusted. For example, when a sudden increase in traffic in a certain area is detected, the unmanned sweeper can temporarily adjust the cleaning path and prioritize advertising display to maximize the advertising effect.

[0044] Replay Figure 2 , the following steps are included after step S1.

[0045] S2: Based on real-time environmental perception data, dynamically plan cleaning paths, avoid obstacles, and automatically switch to inspection mode during non-cleaning periods: During intelligent inspections, identify abnormal situations and report them in a timely manner, and automatically dispatch unmanned sweepers to perform cleaning or inspection tasks based on real-time task requirements and equipment status through the intelligent scheduling system of the cloud platform.

[0046] Reference Figure 4 , step S2 correspondingly includes the following sub-steps.

[0047] S21: Genetic algorithm is used to optimize the path and dynamically plan the cleaning path; S22: Based on the urgency of the task and the status of the equipment, the cleaning tasks are intelligently sorted by combining algorithms to predict equipment health; S23: Automatically switch to inspection mode during non-cleaning period; S24: Through the cloud platform, combined with the time series analysis model, the unmanned sweeper is automatically scheduled to perform tasks according to real-time demand and equipment status, optimizing resource allocation.

[0048] The following examples are given for steps S21 to S24.

[0049] First, a genetic algorithm is used to optimize the cleaning path. The algorithm simulates natural selection and genetic processes to find the optimal or nearly optimal cleaning path. In the complex environment of the industrial park, the genetic algorithm can consider multiple constraints, such as obstacle distribution, cleaning efficiency, equipment energy consumption, etc., and dynamically plan the shortest and most efficient cleaning path to reduce repeated cleaning and improve cleaning efficiency.

[0050] Furthermore, according to the urgency of the task and the current status of the equipment, the algorithm is combined to predict the health status of the equipment and intelligently sort the cleaning tasks. For example, when the amount of garbage in a certain area reaches a threshold, the unmanned sweeper will prioritize the cleaning task in that area. At the same time, by predicting the health status of the equipment, such as battery power, degree of wear, etc., the equipment is intelligently scheduled to avoid failures caused by excessive use of the equipment and ensure that the equipment performs tasks in the best condition.

[0051] On the other hand, during non-cleaning periods, the unmanned sweeper automatically switches to patrol mode to conduct environmental monitoring and abnormality detection. For example, at night or during periods with low traffic, the unmanned sweeper can conduct security patrols to detect abnormal conditions such as intrusions, fires, etc., while collecting environmental data to provide a basis for decision-making for the next day's cleaning tasks.

[0052] In addition, through the connection with the cloud platform, the unmanned sweeper can automatically schedule tasks and optimize resource allocation according to real-time demand and equipment status. The cloud platform combines time series analysis models to predict future demand changes, such as traffic flow and garbage generation, and plans cleaning tasks in advance to ensure the rational allocation of resources. At the same time, by real-time monitoring of equipment status, such as location, power, task execution, etc., the unmanned sweeper can be intelligently scheduled to avoid resource waste and improve overall operational efficiency.

[0053] Replay Figure 2 , after step S2, the following steps are also included.

[0054] S3: By analyzing the flow of people and vehicles in the park and the time period, the advertising content is intelligently matched to achieve dynamic display of advertisements, and the advertising display strategy is dynamically adjusted according to the real-time environment and crowd characteristics.

[0055] Reference Figure 5 , step S3 correspondingly includes the following sub-steps.

[0056] S31: Real-time analysis of park pedestrian and vehicle flow data, combined with social network analysis, to identify high-exposure time periods and hot spots to improve advertising accuracy; S32: intelligently matching and generating advertising content according to the analysis results; S33: Predict user behavior based on real-time environment and crowd characteristics, dynamically adjust ad display strategies, and optimize ad display frequency and duration; S34: Monitor advertising display effects, analyze advertising conversion rates, and dynamically adjust advertising strategies.

[0057] The following examples are given for steps S31 to S34.

[0058] Among them, the unmanned sweeper collects real-time data on the flow of people and vehicles in the park through integrated sensors and cameras4, and combines social network analysis to identify high-exposure time periods and hot spots. For example, by analyzing location check-ins, comments and sharing on social media, it can identify which areas have a large flow of people during a specific period of time, and which activities or attractions have attracted a lot of attention, providing precise positioning for advertising.

[0059] Based on the analysis results, we intelligently match and generate advertising content that matches the interests of the target audience. For example, if we detect that there are many young people in a certain area, we can generate advertisements for fashion and technology-related products; if we detect that there are many family tourists, we can display advertisements for parent-child activities or family packages to increase the attractiveness and conversion rate of the advertisements.

[0060] Furthermore, by analyzing the real-time environment and crowd characteristics, user behavior can be predicted and the advertising display strategy can be adjusted dynamically. For example, if it is predicted that the flow of people will increase significantly during a certain period of time, the frequency and duration of advertising display can be increased in advance to ensure high exposure of the advertisement; if it is detected that the user stays in a certain area for a long time, more detailed product information or promotional activities can be displayed to improve the interactivity and conversion rate of the advertisement.

[0061] In addition, the integrated sensors and cameras 4 can monitor the advertising display effect, analyze the advertising conversion rate, and dynamically adjust the advertising strategy. For example, by analyzing the user's stay time in front of the advertising screen 2 and interactive behavior (such as scanning, clicking, etc.), the attractiveness and effect of the advertisement can be evaluated. According to the feedback results, the advertising content, display frequency and duration can be adjusted in real time to optimize the advertising strategy and improve the accuracy and effect of the advertisement.

[0062] Replay Figure 2 , the following steps are also included after step S3.

[0063] S4: Transmit data such as device status, cleaning progress, and advertising display effects to the cloud platform in real time. Through AI algorithms, the cloud platform can intelligently analyze data and achieve remote monitoring and maintenance of equipment.

[0064] Reference Figure 6 , for step S4, it includes the following sub-steps.

[0065] S41: Real-time transmission of device status to the cloud platform to achieve low-latency data transmission and preliminary data processing; S42: Through AI algorithms, data is analyzed to achieve remote monitoring of equipment and predict equipment performance trends; S43: Automatically detect equipment failures, send early warning information, reduce downtime, and predict potential failures; S44: Intelligently adjust maintenance cycles and strategies based on equipment operating status.

[0066] The embodiment of the present application provides the following examples to illustrate steps S41-S44.

[0067] First, the unmanned sweeper transmits device status data (such as location, power, cleaning efficiency, sensor status, etc.) to the cloud platform in real time through the integrated wireless communication module, achieving low-latency data transmission. This ensures the real-time and accuracy of the data, providing a basis for subsequent analysis and decision-making.

[0068] Furthermore, through AI algorithms, the cloud platform can analyze the operation data of the equipment, realize remote monitoring, and predict the performance trend of the equipment. For example, by analyzing the energy consumption, cleaning efficiency and other data of the equipment, in this embodiment, the AI ​​algorithm can predict the performance changes of the equipment, identify the possible performance degradation trend in advance, and provide a basis for the maintenance strategy.

[0069] During this process, the unmanned sweeper can automatically detect equipment failures, such as sensor failure, low battery, and wear of cleaning parts, and send early warning information through the cloud platform. This helps reduce downtime, achieve preventive maintenance by predicting potential failures, and avoid operational interruptions caused by equipment failure.

[0070] Based on the equipment's operating status, the cloud platform can intelligently adjust maintenance cycles and strategies. For example, if it is detected that the cleaning parts of a certain device are wearing out quickly, maintenance can be scheduled in advance to avoid failures caused by excessive wear. At the same time, by analyzing the frequency of use of the equipment, environmental conditions, etc., the maintenance cycle can be intelligently adjusted to ensure that the equipment operates in the best condition, extend the equipment life, and reduce maintenance costs.

[0071] Replay Figure 2 , after step S4, the following steps are included.

[0072] S5: When the battery is low, it automatically returns to the charging station to charge. At the same time, it intelligently adjusts the maintenance cycle and maintenance strategy to ensure long-term stable operation of the equipment.

[0073] Reference Figure 7 , for step S5, it includes the following sub-steps.

[0074] S51: When the battery is low, the route is optimized by combining the Dijkstra algorithm and the vehicle automatically returns to the charging station for charging; S52: Built-in self-check system, combined with DBN, regular self-check; S53: Send early warning information, combine remote diagnosis technology, and intelligently handle potential faults; S54: Intelligently adjust maintenance cycles based on equipment usage.

[0075] For steps S51-S54, the embodiment of the present application is described as follows.

[0076] When the unmanned sweeper detects that the battery is low, it optimizes the path by combining the Dijkstra algorithm and automatically returns to the nearest charging station for charging. In this embodiment, the Dijkstra algorithm is an algorithm for finding the shortest path between two points in a graph. By calculating the shortest path from the current location to the charging station, it ensures that the device can return to charge efficiently and quickly, reducing downtime caused by low battery.

[0077] At the same time, the unmanned sweeper has a built-in self-checking system, combined with machine learning algorithms such as deep belief networks (DBN), to conduct regular self-checks and evaluate the health status of the equipment. DBN can learn the characteristics of the equipment status from the equipment's operating data, and through pattern recognition and anomaly detection, it can promptly discover potential problems with the equipment and ensure that the equipment operates in the best condition.

[0078] Furthermore, when the self-check system detects a potential fault in the equipment, the unmanned sweeper can send an early warning message to the operation and maintenance personnel or the cloud platform. Combined with remote diagnosis technology, the operation and maintenance personnel or the system can remotely diagnose the equipment, intelligently handle potential faults, avoid downtime caused by equipment failure, and reduce maintenance costs and time.

[0079] Finally, the unmanned sweeper can intelligently adjust the maintenance cycle according to the equipment usage, such as operating time, cleaning task volume, environmental conditions, etc. By analyzing the equipment's operating data, predicting the equipment's wear and maintenance needs, and intelligently adjusting the maintenance cycle, the equipment can be ensured to operate in the best condition, extend the equipment life, and reduce maintenance costs.

[0080] This embodiment also provides a control system for a multifunctional unmanned sweeper, which is applied to the above-mentioned multifunctional unmanned sweeper and includes the following modules: Environmental perception and decision-making module, which integrates multimodal sensors, uses deep learning technology to identify the environment, and intelligently generates cleaning and advertising display strategies; Path planning and dynamic scheduling module, used to optimize cleaning paths, intelligently schedule tasks, and ensure efficient operation of the equipment; Advertisement content dynamic matching and intelligent display module, used to analyze traffic data, intelligently match advertisement content, and dynamically adjust display strategies; Remote monitoring and intelligent maintenance module, used to monitor equipment status in real time, predict maintenance needs, and intelligently adjust maintenance plans; Autonomous charging and intelligent self-checking module is used for automatic charging, regular self-checking, and intelligent adjustment of maintenance cycles to ensure long-term stable operation of the equipment.

[0081] This embodiment also provides an electronic device. The electronic device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the electronic device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a multifunctional unmanned sweeper and a control method thereof are implemented: S11: Integrates LiDAR 3, camera 4, and ultrasonic sensor 5, and uses deep convolutional neural networks to deeply understand environmental information, especially for obstacle recognition and material perception in complex environments; S12: Through the support vector machine algorithm, combined with the machine learning model, it can intelligently distinguish between cleaning areas and non-cleaning areas, identify advertising display areas, and optimize cleaning strategies; S13: Detect and classify obstacles in real time, use the YOLOv4 deep learning model to accurately classify obstacles and predict obstacle behavior; S14: Based on the environmental information, a cleaning path and an advertisement display strategy are generated to optimize resource allocation and dynamically adjust cleaning and advertisement display priorities.

[0082] When the computer program is executed by a processor, any one of the multifunctional unmanned sweeping robots and control methods thereof in the above method embodiments can be implemented.

[0083] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented: S11: Integrates LiDAR 3, camera 4, and ultrasonic sensor 5, and uses deep convolutional neural networks to deeply understand environmental information, especially for obstacle recognition and material perception in complex environments; S12: Through the support vector machine algorithm, combined with the machine learning model, it can intelligently distinguish between cleaning areas and non-cleaning areas, identify advertising display areas, and optimize cleaning strategies; S13: Detect and classify obstacles in real time, use the YOLOv4 deep learning model to accurately classify obstacles and predict obstacle behavior; S14: Based on the environmental information, a cleaning path and an advertisement display strategy are generated to optimize resource allocation and dynamically adjust cleaning and advertisement display priorities.

[0084] When the computer program is executed by a processor, any one of the multifunctional unmanned sweeping robots and control methods thereof in the above method embodiments can be implemented.

[0085] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0086] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0087] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application is described in detail in the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application.

Claims

1. A multifunctional unmanned sweeper, characterized in that: The invention comprises a sweeping machine body (1), wherein the sweeping machine body (1) is provided with an automatic navigation system and an advertising screen (2), wherein the automatic navigation system comprises a laser radar (3), a camera (4), an ultrasonic sensor (5), a GPS module and a navigation algorithm processor, wherein the laser radar (3) is located at the top of the sweeping machine body (1) and is used for 360-degree environmental scanning, the camera (4) is installed at the top of the sweeping machine and is located on one side of the laser radar (3) and is used for identifying obstacles and environmental features at a longer distance, the ultrasonic sensor (5) is distributed around the sweeping machine body (1) and is used for detecting obstacles at a short distance; the GPS module is located inside the sweeping machine body (1) and is used for determining the precise position of the unmanned sweeping machine, the navigation algorithm processor is built inside the sweeping machine body (1), and the collected data is transmitted to the navigation algorithm processor, the navigation algorithm processor processes the sensor data, executes the path planning algorithm, and generates navigation instructions, and the advertising screen (2) is installed at the front end of the sweeping machine body (1) and faces pedestrians or vehicles.

2. A control method for a multifunctional unmanned sweeper, applied to a multifunctional unmanned sweeper as claimed in claim 2, characterized in that: The following steps are involved: S1: Utilizes integrated multimodal sensors to perceive environmental changes in real time, including dynamic information of obstacles, pedestrian and vehicle flows, intelligently distinguish between cleaning and non-cleaning areas, and identify potential advertising display areas, thus achieving dynamic adjustment of cleaning paths and precise positioning of advertising displays; S2: Based on real-time environmental perception data, the cleaning path is dynamically planned to avoid obstacles. During non-cleaning periods, the system automatically switches to inspection mode: During intelligent inspections, abnormal situations are identified and reported in a timely manner. The intelligent scheduling system of the cloud platform automatically dispatches the unmanned sweeper to perform cleaning or inspection tasks based on real-time task requirements and equipment status. S3: By analyzing the flow of people and vehicles in the park and the time period, intelligently matching advertising content, dynamic display of advertisements is achieved, and the advertising display strategy is dynamically adjusted according to the real-time environment and crowd characteristics; S4: Real-time transmission of data such as equipment status, cleaning progress, and advertising display effects to the cloud platform. Through AI algorithms, the cloud platform can intelligently analyze data and achieve remote monitoring and maintenance of equipment; S5: When the battery is low, it automatically returns to the charging station to charge. At the same time, it intelligently adjusts the maintenance cycle and maintenance strategy to ensure long-term stable operation of the equipment.

3. The control method of a multifunctional unmanned sweeper according to claim 2, characterized in that: The integrated multimodal sensor is used to perceive environmental changes in real time, including dynamic information of obstacles, pedestrian and vehicle flows, intelligently distinguish between cleaning areas and non-cleaning areas, and identify potential advertising display areas, so as to achieve dynamic adjustment of the cleaning path and precise positioning of advertising display, including the following steps: S11: Integrates LiDAR (3), camera (4), and ultrasonic sensor (5), and uses deep convolutional neural network to deeply understand environmental information, especially for obstacle recognition and material perception in complex environments; S12: Through the support vector machine algorithm, combined with the machine learning model, it can intelligently distinguish between cleaning areas and non-cleaning areas, identify advertising display areas, and optimize cleaning strategies; S13: Detect and classify obstacles in real time, use the YOLOv4 deep learning model to accurately classify obstacles and predict obstacle behavior; S14: Based on the environmental information, a cleaning path and an advertisement display strategy are generated to optimize resource allocation and dynamically adjust cleaning and advertisement display priorities.

4. The control method of a multifunctional unmanned sweeper according to claim 2, characterized in that: Based on real-time environmental perception data, the cleaning path is dynamically planned to avoid obstacles, and the robot automatically switches to the inspection mode during non-cleaning periods: when performing intelligent inspections, abnormal situations are identified and reported in a timely manner, and the robot automatically dispatches the robot to perform cleaning or inspection tasks according to real-time task requirements and equipment status through the intelligent scheduling system of the cloud platform, including the following steps: S21: Use genetic algorithm to optimize the path and dynamically plan the cleaning path; S22: Based on the urgency of the task and the status of the equipment, the cleaning tasks are intelligently sorted by combining algorithms to predict equipment health; S23: Automatically switch to inspection mode during non-cleaning period; S24: Through the cloud platform, combined with the time series analysis model, the unmanned sweeper is automatically scheduled to perform tasks according to real-time demand and equipment status, optimizing resource allocation.

5. The control method of a multifunctional unmanned sweeper according to claim 2, characterized in that: The method analyzes the data of the flow of people and vehicles in the park and the time period, intelligently matches the advertising content, realizes the dynamic display of advertisements, and dynamically adjusts the advertising display strategy according to the real-time environment and crowd characteristics, including the following steps: S31: Real-time analysis of park pedestrian and vehicle flow data, combined with social network analysis, to identify high-exposure time periods and hot spots to improve advertising accuracy; S32: intelligently matching and generating advertising content according to the analysis results; S33: Predict user behavior based on real-time environment and crowd characteristics, dynamically adjust ad display strategies, and optimize ad display frequency and duration; S34: Monitor advertising display effects, analyze advertising conversion rates, and dynamically adjust advertising strategies.

6. The control method of a multifunctional unmanned sweeper according to claim 2, characterized in that: The real-time transmission of data such as equipment status, cleaning progress, and advertising display effects to the cloud platform, through the AI ​​algorithm, the cloud platform can intelligently analyze the data to achieve remote monitoring and maintenance of the equipment, including the following steps: S41: Real-time transmission of device status to the cloud platform to achieve low-latency data transmission and preliminary data processing; S42: Through AI algorithms, data is analyzed to achieve remote monitoring of equipment and predict equipment performance trends; S43: Automatically detect equipment failures, send early warning information, reduce downtime, and predict potential failures; S44: Intelligently adjust maintenance cycles and strategies based on equipment operating status.

7. The control method of a multifunctional unmanned sweeper according to claim 2, characterized in that: When the battery is low, the device automatically returns to the charging station for charging. At the same time, the maintenance cycle and maintenance strategy are intelligently adjusted to ensure long-term stable operation of the device, including the following steps: S51: When the battery is low, the route is optimized by combining the Dijkstra algorithm and the vehicle automatically returns to the charging station for charging; S52: Built-in self-check system, combined with DBN, regular self-check; S53: Send early warning information, combine remote diagnosis technology, and intelligently handle potential faults; S54: Intelligently adjust maintenance cycles based on equipment usage.

8. A control system for a multifunctional unmanned sweeper, applied to the multifunctional unmanned sweeper according to claim 1, characterized in that: Includes the following modules: Environmental perception and decision-making module, which integrates multimodal sensors, uses deep learning technology to identify the environment, and intelligently generates cleaning and advertising display strategies; Path planning and dynamic scheduling module, used to optimize cleaning paths, intelligently schedule tasks, and ensure efficient operation of the equipment; Advertisement content dynamic matching and intelligent display module, used to analyze traffic data, intelligently match advertisement content, and dynamically adjust display strategies; Remote monitoring and intelligent maintenance module, used to monitor equipment status in real time, predict maintenance needs, and intelligently adjust maintenance plans; Autonomous charging and intelligent self-checking module is used for automatic charging, regular self-checking, and intelligent adjustment of maintenance cycles to ensure long-term stable operation of the equipment.

9. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of a control method for a multifunctional unmanned sweeping robot as described in any one of claims 2 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the control method of a multifunctional unmanned sweeping robot as described in any one of claims 2 to 7 are implemented.

Images