A method of constructing a dirt map, a cleaning method of a cleaning robot, a cleaning robot, a medium, and a program product

Abstract

The application relates to a dirty map construction method, a cleaning method of a cleaning robot, the cleaning robot, a medium and a program product. The method comprises the following steps: acquiring dirty information collected by the cleaning robot in a cleaning process; constructing a dirty map according to the dirty information; the dirty map comprises a dirty mark; and finally, the dirty map is sent to a user terminal to instruct the user terminal to display the dirty map after cleaning is completed. Further, the dirty mark is obtained by determining a mark area according to the dirty information and marking the mark area, wherein the dirty degree of the mark area is greater than a preset dirty threshold. In the above method, the dirty map containing the dirty mark is sent to the user terminal, so that the user can know the position and distribution range of the dirt, thereby performing targeted cleaning on the area corresponding to the dirty mark, and the cleaning effect on the dirty area is improved.

Description

Technical Field

[0001] This application relates to the field of cleaning robot technology, and in particular to a method for constructing a dirt map, a cleaning method for a cleaning robot, a cleaning robot, media, and program products. Background Technology

[0002] With the rapid development of artificial intelligence and home service robot technology, smart home devices, especially cleaning robots, have gradually become common terminals in modern home environments. Currently, when performing cleaning tasks, cleaning robots typically perform full-coverage cleaning or partial cleaning of preset room areas based on a room layout map.

[0003] However, the aforementioned cleaning robots have the problem of poor cleaning performance. Summary of the Invention

[0004] Therefore, it is necessary to provide a method for constructing a dirt map that can improve cleaning effectiveness, a cleaning method for a cleaning robot, a cleaning robot, media, and program products to address the above-mentioned technical problems.

[0005] Firstly, this application provides a method for constructing a dirt map, applied to a cleaning robot, the method comprising:

[0006] Obtain dirt information collected by the cleaning robot during the cleaning process;

[0007] A dirt map is constructed based on dirt information; the dirt map includes dirt markers;

[0008] Send the dirt map to the user terminal to instruct the user terminal to display the dirt map after cleaning is completed;

[0009] The process of constructing a dirt map based on dirt information includes:

[0010] The marked area is determined based on the dirt information; the dirt level of the marked area is greater than the preset dirt threshold.

[0011] Mark the marked areas to obtain a dirt map containing dirt markers.

[0012] In some embodiments, the marked areas are identified to obtain a dirt map, including:

[0013] Based on the degree of dirtiness in the marked areas, different shades of color are used as dirt indicators to obtain a dirt map.

[0014] In some embodiments, the method further includes, in the step of sending the dirt map to the user terminal to instruct the user terminal to display the dirt map after cleaning is completed:

[0015] Generate a prompt message and send it to the user's terminal; the prompt message is used to instruct the user whether the area corresponding to the dirt label needs to be cleaned more thoroughly.

[0016] In some embodiments, the prompt message is specifically used to instruct the user whether the area corresponding to the dirt indicator needs to be cleaned more thoroughly during the next cleaning session.

[0017] In some embodiments, the dirt map includes a first dirt identifier for each area and a second dirt identifier for at least one sub-area within each area. Constructing the dirt map based on the dirt information includes:

[0018] The overall level of dirtiness in each area is determined based on the dirtiness information of each area, and a first dirtiness label is generated for the corresponding area based on the overall level of dirtiness.

[0019] Within each region, at least one sub-region is determined based on the local level of dirt within the region, and a second dirt label is generated for the corresponding sub-region based on the local level of dirt.

[0020] In some embodiments, a first dirt label is generated for the corresponding area based on the overall level of dirtiness, including:

[0021] Obtain the preset mapping relationship; the preset mapping relationship includes the correspondence between the overall dirt level range and the dirt icon;

[0022] For each region, the first dirt identifier corresponding to the overall dirt level range of the region is matched from the preset mapping relationship.

[0023] In some embodiments, at least one sub-region is determined based on the degree of local contamination within the region, including:

[0024] The area within the region that has a local level of dirtiness exceeding a preset dirtiness threshold is identified as a sub-region.

[0025] In some embodiments, a second dirt label is generated for the corresponding sub-region based on the degree of dirt in each local area, including:

[0026] Based on the degree of local soiling in each sub-region, different shades of color are used as secondary soiling indicators; the higher the degree of local soiling, the darker the corresponding color.

[0027] In some embodiments, the method further includes:

[0028] Record the cleaning process of the area corresponding to the dirt label and generate a dirt cleaning video;

[0029] The video of the cleaning process is sent to the user's device for viewing.

[0030] In some embodiments, the acquisition of dirt information collected by the cleaning robot during the cleaning process includes:

[0031] The cleaning robot uses a camera to identify stains on the floor during the cleaning process and maps the location of the stains to the corresponding area to obtain dirt information for each area.

[0032] By using a turbidity sensor on the wastewater return pipe of the cleaning robot, the turbidity of the mop is detected during the cleaning process and the location of turbidity changes is recorded to obtain dirt information for each area.

[0033] After the cleaning robot returns to the base station to clean the mop, the turbidity data of the mop is analyzed by the turbidity detection device in the base station, and the turbidity data is associated with the corresponding location according to the cleaning path to obtain the dirt information of each area.

[0034] Secondly, this application also provides a method for constructing a dirty map, applied to a user terminal, the method comprising:

[0035] The system receives a dirt map sent by the cleaning robot and displays the dirt map after cleaning is completed. The dirt map is constructed by the cleaning robot based on the dirt information collected during the cleaning process. The dirt map includes dirt markers. The dirt map is obtained by the cleaning robot determining the marked areas based on the dirt information and marking the marked areas. The dirt level of the marked areas is greater than the preset dirt threshold.

[0036] In some embodiments, the dirt map is obtained by the cleaning robot using different shades of color to mark the dirt in the marked areas based on the degree of dirtiness.

[0037] In some embodiments, the method further includes:

[0038] Receive prompts from the cleaning robot; these prompts indicate whether the user needs to perform extra cleaning on the areas marked with dirt.

[0039] In some embodiments, the prompt message is specifically used to instruct the user whether the area corresponding to the dirt indicator needs to be cleaned more thoroughly during the next cleaning session.

[0040] In some embodiments, the dirt map is obtained by a cleaning robot determining the overall dirt level of each area based on the dirt information of each area, generating a first dirt label for the corresponding area based on the overall dirt level, and determining at least one sub-area within each area based on the local dirt level within the area, and generating a second dirt label for the corresponding sub-area based on the local dirt level; the dirt map includes the first dirt label of each area and the second dirt label of at least one sub-area within each area.

[0041] In some embodiments, the first dirt label is obtained by the cleaning robot acquiring a preset mapping relationship and matching the first dirt label corresponding to the overall dirt level range of the area from the preset mapping relationship for each area; the preset mapping relationship includes the correspondence between the overall dirt level range and the dirt label.

[0042] In some embodiments, the sub-region is the region within the region where the degree of local dirtiness is greater than a preset dirtiness threshold.

[0043] In some embodiments, the second dirt indicator is determined by the cleaning robot using different shades of color based on the degree of dirtiness in each sub-region; wherein, the higher the degree of dirtiness in a local area, the darker the corresponding color.

[0044] In some embodiments, the method further includes:

[0045] The system receives cleaning videos of dirt and grime from the cleaning robot for users to view; these videos are generated by the cleaning robot based on the cleaning process of the areas corresponding to the dirt and grime markers.

[0046] In some embodiments, the dirt information is obtained by the cleaning robot through at least one of the following methods:

[0047] The cleaning robot uses a camera to identify stains on the floor during the cleaning process and maps the location of the stains to the corresponding area to obtain dirt information for each area.

[0048] By using a turbidity sensor on the wastewater return pipe of the cleaning robot, the turbidity of the mop is detected during the cleaning process and the location of turbidity changes is recorded to obtain dirt information for each area.

[0049] After the cleaning robot returns to the base station to clean the mop, the turbidity data of the mop is analyzed by the turbidity detection device in the base station, and the turbidity data is associated with the corresponding location according to the cleaning path to obtain the dirt information of each area.

[0050] In some embodiments, the method further includes:

[0051] Display the cleaning path diagram after cleaning is completed.

[0052] Thirdly, this application also provides a cleaning method for a cleaning robot, applied to a cleaning robot, the method comprising:

[0053] Obtain the dirty map obtained by the method of constructing the dirty map of any instance in the first aspect;

[0054] When performing cleaning tasks, a heavy-duty cleaning strategy is used to clean the areas corresponding to the dirt indicators; the heavy-duty cleaning strategy includes at least one of the following: reducing the speed of the drive wheels, increasing the number of cleaning cycles, increasing the suction power of the vacuum cleaner, and increasing the amount of water on the rag.

[0055] In some embodiments, a heavy-duty cleaning strategy is used to clean the areas corresponding to the dirt indicators, including:

[0056] In response to the cleaning trigger signal, a heavy-duty cleaning strategy is used to clean the area corresponding to the dirt label;

[0057] Among them, the cleaning trigger signal includes the enhanced cleaning command triggered by the user based on the prompt information displayed on the user terminal, or the detection that the high dirt frequency of the area corresponding to the dirt label is greater than the preset frequency threshold; the high dirt frequency indicates the proportion of the number of times the dirt level is greater than the preset dirt threshold in the total number of historical cleaning tasks.

[0058] Fourthly, this application also provides an apparatus for constructing a dirt map, the apparatus comprising:

[0059] The information acquisition module is used to acquire dirt information collected by the cleaning robot during the cleaning process;

[0060] The map building module is used to construct a dirt map based on dirt information; the dirt map includes dirt markers.

[0061] The map sending module is used to send a dirty map to the user terminal, so as to instruct the user terminal to display the dirty map after cleaning is completed;

[0062] The map construction module includes: a determination unit, used to determine the marked area based on the dirt information; the dirt level of the marked area is greater than a preset dirt threshold; and a marking unit, used to mark the marked area to obtain a dirt map containing dirt markings.

[0063] Fifthly, this application also provides an apparatus for constructing a dirt map, the apparatus comprising:

[0064] The map display module receives the dirt map sent by the cleaning robot and displays the dirt map after cleaning is completed. The dirt map is constructed by the cleaning robot based on the dirt information collected during the cleaning process, and includes dirt markers. Specifically, the dirt map is obtained by the cleaning robot determining and marking the marked areas based on the dirt information; the dirt level of the marked areas is greater than a preset dirt threshold.

[0065] Sixthly, this application also provides a cleaning device for a cleaning robot, the device comprising:

[0066] The map acquisition module is used to obtain the dirty map obtained by the construction method of the dirty map of any instance in the first aspect;

[0067] The cleaning module is used to clean the areas corresponding to the dirt indicators using a heavy-duty cleaning strategy when performing cleaning tasks. The heavy-duty cleaning strategy includes at least one of the following: reducing the speed of the drive wheels, increasing the number of cleaning cycles, increasing the suction power of the vacuum cleaner, and increasing the amount of water on the cloth.

[0068] In a seventh aspect, this application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in any of the embodiments of the first to third aspects described above.

[0069] Eighthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any of the embodiments of the first to third aspects described above.

[0070] Ninthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the method described in any of the embodiments of the first to third aspects described above.

[0071] The aforementioned method for constructing a dirt map, a cleaning method for a cleaning robot, the cleaning robot itself, and related media and software products involve acquiring dirt information collected by the cleaning robot during the cleaning process, then constructing a dirt map based on this information. The dirt map includes dirt markers, and is finally sent to a user terminal to instruct the user terminal to display the dirt map after cleaning is complete. Further, by determining and marking marked areas based on the dirt information, a dirt map containing dirt markers is obtained, where the dirt level in the marked areas exceeds a preset dirt threshold. In this method, by sending the dirt map containing dirt markers to the user terminal to display the dirt distribution, the user can accurately determine the location and distribution range of the dirt based on the displayed dirt markers, thus enabling targeted cleaning of the areas corresponding to the dirt markers. This avoids missed cleaning or repeated ineffective cleaning due to the inability to determine the dirt distribution, thereby improving the cleaning effect on dirty areas. Furthermore, by marking areas with a level of dirt exceeding a preset dirt threshold to create a dirt map, the dirt map only highlights areas with a high level of dirt while filtering out areas with a low level of dirt or no dirt. This avoids information overload on the dirt map, allowing users to quickly locate the dirty areas that need special attention and clean them accordingly, thereby improving the cleaning effect on areas with a high level of dirt. Attached Figure Description

[0072] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0073] Figure 1 These are internal structural diagrams of the cleaning robot in some embodiments;

[0074] Figure 2 This is one of the flowcharts illustrating the method for constructing a dirt map in some embodiments;

[0075] Figure 3 This is a second flowchart illustrating the method for constructing a dirt map in some embodiments;

[0076] Figure 4 This is one of the schematic diagrams of a dirt map in some embodiments;

[0077] Figure 5 This is a schematic diagram of cleaning task recording in some embodiments;

[0078] Figure 6 This is a second schematic diagram of a dirt map in some embodiments;

[0079] Figure 7 This is a cleaning path diagram in some embodiments;

[0080] Figure 8 This is the third flowchart illustrating the method for constructing a dirt map in some embodiments;

[0081] Figure 9 This is the fourth flowchart illustrating the method for constructing a dirt map in some embodiments;

[0082] Figure 10 This is the fifth flowchart illustrating the method for constructing a dirt map in some embodiments;

[0083] Figure 11 This is a flowchart illustrating the cleaning method of the cleaning robot in some embodiments;

[0084] Figure 12 This is a schematic diagram illustrating the cleaning of heavily soiled areas using a cleaning strategy in some embodiments;

[0085] Figure 13 This is a cleaning path diagram after the completion of area cleaning using a heavily soiled cleaning strategy in some embodiments;

[0086] Figure 14This is a structural block diagram of a device for constructing a dirt map in some embodiments;

[0087] Figure 15 This is a structural block diagram of the cleaning device of the cleaning robot in some embodiments. Detailed Implementation

[0088] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0089] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more, and other quantifiers are similar. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The term "and / or" used in this application refers to one of the solutions, or any combination of multiple solutions. In the embodiments of this application, the term "at least one" means one or more.

[0090] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0091] With the rapid development of artificial intelligence and home service robot technology, smart home devices, especially cleaning robots, have gradually become common terminals in modern home environments. Currently, when performing cleaning tasks, cleaning robots typically perform full-coverage cleaning based on a room layout map or partial cleaning of preset room areas. However, these cleaning robots suffer from poor cleaning performance.

[0092] In view of this, embodiments of this application propose a method for constructing a dirt map, a cleaning method for a cleaning robot, a cleaning robot, a medium, and a program product. By sending a dirt map containing dirt markers to a user terminal to display the distribution of dirt, the user can subsequently improve the cleaning effect of dirty areas based on the displayed dirt markers.

[0093] It should be noted that the beneficial effects or technical problems solved by the embodiments of this application are not limited to this one, but may also be other implicit or related problems. For details, please refer to the description of the embodiments below.

[0094] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0095] In some embodiments, the method for constructing a dirt map and the cleaning method for a cleaning robot provided in this application can be applied to, for example... Figure 1 The internal structure diagram of the cleaning robot shown can be as follows: Figure 1 As shown, the cleaning robot includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a method for constructing a dirt map and a cleaning method for the cleaning robot. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device for the cleaning robot can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the outer shell of the cleaning robot, or an external keyboard, touchpad, or mouse, etc.

[0096] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the cleaning robot to which the present application is applied. A specific cleaning robot may include more or fewer parts than shown in the figure, or combine certain parts, or have different part arrangements.

[0097] In some embodiments, such as Figure 2 As shown, a method for constructing a dirt map is provided, which can be applied to... Figure 1 Taking a cleaning robot as an example, the process includes the following steps:

[0098] S201, Obtain dirt information collected by the cleaning robot during the cleaning process.

[0099] The dirt information is used to characterize the degree of dirt at various locations on the ground, and may include at least one of the following: dirt area, dirt location, dirt type, dirt turbidity value, etc.

[0100] In this embodiment, the cleaning robot can activate the top wide-angle camera at a fixed frequency during its movement to capture ground images. Then, it performs grayscale processing and binarization on each frame of the image, extracts pixel areas whose color difference from the background exceeds a preset threshold as candidate dirty areas, calculates the area, shape, and position coordinates of the candidate areas, filters out noise points with small areas, and finally encodes the center point coordinates, area size, and grayscale value of each effective dirty area after filtering into dirty information, which is temporarily stored in the robot's local cache.

[0101] Optionally, the cleaning robot can communicate with external smart cameras or environmental monitoring stations to receive dirt information. Optionally, the cleaning robot can acquire ground images and identify stain locations using its onboard camera; or detect changes in mop turbidity and record locations using a turbidity sensor on the wastewater tank return pipe; or, after returning to the base station, the turbidity detection device inside the base station can analyze the mop turbidity and, combined with the cleaning path, infer the location of dirt. Optionally, the cleaning robot can also detect changes in the concentration of inhaled dust in real time using a dust sensor on its bottom, detect sudden changes in mechanical resistance using contact sensors on the side brush or roller brush, and identify stains using multispectral or ultraviolet sensors to obtain dirt information. Optionally, the cleaning robot can also periodically collect ground humidity data using a capacitive humidity sensor mounted on its chassis. Each sampling point is accompanied by the current robot pose coordinates. Then, the continuously sampled humidity values ​​are compared with the baseline humidity value of a clean floor. If the difference exceeds a preset value, the point is determined to be a wet dirt point. Then, the sampling points determined to be dirty are clustered and merged into a dirty area. Finally, a dirt information record is generated for each dirty area.

[0102] Optionally, the cleaning robot can fuse the data collected by one or more of the above methods and form dirt information in a structured form, such as location tags, dirt level quantification values, and collection timestamps.

[0103] S202, Construct a dirt map based on dirt information; the dirt map includes dirt labels.

[0104] The dirt map refers to an enhanced map created by overlaying dirt-related annotations onto the existing environmental map (such as a grid map or a zone map) of the cleaning robot. It displays which areas are dirty and the degree of dirt, allowing users to intuitively understand the cleanliness of the floor. The dirt map includes dirt icons, which are visually identifiable elements used to indicate the location and severity of dirt; examples include different shades of color blocks, different density fill patterns, and different sized stain icons.

[0105] In this embodiment, the cleaning robot determines the degree of dirtiness at each location or in each area based on the dirt information, and adds a dirt marker to the map for locations or areas where the degree of dirtiness meets preset conditions (e.g., exceeding a certain lower limit or needing to attract the user's attention).

[0106] Optionally, the cleaning robot can directly mark the dirt information of each location point as scatter points on the map without pre-setting a dirt threshold or binarizing or color-filling the area. This creates a scatter distribution map of dirt points, preserving the original dirt attributes of each detection point. Specifically, the size, shape, or icon type of each scatter point is dynamically determined based on the parameter type in the dirt information. For example, when the dirt information comes from camera recognition and the stain type is oil, an oil drop icon is used; when it comes from a turbidity sensor and the turbidity value is high, a water droplet with an exclamation mark icon is used; and when it comes from a dust sensor, a dust dot icon is used. The density or size of the scatter points reflects the relative level of dirt: the higher the level of dirt, the larger the diameter of the scatter points. Users can zoom in and out on the dirt map to view the distribution of dirt points at different granularities.

[0107] Optionally, such as Figure 3 As shown, "constructing a dirt map based on dirt information" in S202 above includes:

[0108] S2021, Determine the marked area based on dirt information.

[0109] The dirt information includes dirt level, which quantifies the degree of contamination at a specific location or area on the ground. This can be expressed as turbidity value, dust concentration, stain area, color depth, or a fusion of data from multiple sensors. Dirt level can be a one-dimensional continuous value (e.g., a score between 0 and 100) or a discrete level (e.g., light, moderate, heavy). A marked area refers to a continuous or discontinuous local area on the dirt map that needs to be specially marked or highlighted. The dirt level of the marked area exceeds a preset dirt threshold. The preset dirt threshold is a pre-set critical value used to determine whether the dirt level meets the standard for marking. The preset dirt threshold can be a fixed value or an adaptive threshold dynamically adjusted based on historical cleaning data.

[0110] In this embodiment, the cleaning robot acquires ground images using its onboard camera, identifies stained areas using an image recognition model (such as a semantic segmentation network or classifier), and outputs a dirtiness score for the area based on the area ratio, color depth, or texture features of the stain. For example, a larger area ratio or a darker color results in a higher score. Optionally, the cleaning robot detects changes in turbidity after mop washing using a turbidity sensor on the wastewater return pipe of the wastewater tank. A higher turbidity value indicates a dirtier floor that the mop has been in contact with, and the cleaning robot directly uses this turbidity value as the dirtiness level of the corresponding location. Optionally, after the cleaning robot returns to the base station, a turbidity detection device in the base station analyzes the turbidity data of the mop. The cleaning robot then maps the turbidity value back to each location according to the cleaning path, and the turbidity value associated with each location represents its dirtiness level. Optionally, the cleaning robot detects the concentration or number of inhaled dust particles using a dust sensor on its bottom. A higher concentration or greater number of particles indicates a greater degree of dirtiness. Optionally, the cleaning robot detects changes in mechanical resistance during the cleaning process using contact sensors on its side brushes or roller brushes. A sudden increase in resistance usually indicates the encounter with sticky stains or large particles, and the magnitude of the resistance change can serve as a reference for the degree of dirtiness. Optionally, the cleaning robot can also fuse the outputs of the above-mentioned multiple sensors, such as by weighted averaging of image recognition scores and turbidity values ​​at the same location to obtain a comprehensive degree of dirtiness.

[0111] After acquiring dirt information, including quantified dirt levels at each location, the cleaning robot iterates through these values, comparing each location's dirt level to a preset dirt threshold. If a location's dirt level exceeds the threshold, it is marked as a dirty point. The robot then uses a connected component analysis algorithm to cluster adjacent dirty points into one or more connected regions, each serving as a labeled region. Optionally, the robot can smooth edge points based on the dirt information to avoid isolated false-labeled regions caused by sensor noise. For example, for labeled regions that are too small, the robot can ignore them or merge them into neighboring larger labeled regions to reduce redundant labels on the map.

[0112] S2022, Mark the marked areas to obtain a dirt map containing dirt markers.

[0113] In this embodiment, after the cleaning robot acquires the marked areas, it can select a fixed color (e.g., gray, red, or blue) as the annotation color. Then, this fixed color is filled into the area covered by all marked areas in the dirt map. Non-marked areas are not filled or retain their original background color. Finally, the annotated map is output as the dirt map, resulting in a dirt map containing dirt markers. It can be understood that dirt markers are the marked areas after visual element (color, icon, pattern, etc.) marking; that is, dirt markers include marked areas and visual elements. The marked area can be an area where the degree of dirt exceeds a preset dirt threshold. The marked area can also be a large area containing areas where the degree of dirt exceeds the preset dirt threshold. For example, if there is an area in the living room where the degree of dirt exceeds the preset dirt threshold, then the entire living room is marked as the marked area. In this embodiment, regardless of how much the degree of dirt in the marked area exceeds the threshold, the same fixed color is used for marking, without distinguishing between light and dark colors. For example, as... Figure 4 As shown, the living room is marked as an area with a dirt level exceeding a preset threshold. The cleaning robot marks this entire area in dark gray; this dark gray area represents the dirt markers on the dirt map (i.e., the dirt markers are a combination of the marked areas and visual elements). Areas in the living room with a dirt level below the threshold are marked in light gray. Other obstacles (such as the bed in the master bedroom and other obstacles in the living room) are marked in white or left unmarked. It should be noted that the colors in the diagram are only illustrative; in actual applications, the colors used are not limited, as long as the image is clearly visible.

[0114] Optionally, after acquiring each marked area, the cleaning robot can fill the marked area with a single pattern to obtain a dirt map. Specifically, the cleaning robot can overlay dirt icons onto each marked area on its stored cleaning area map (such as a grid map or topology map). These dirt icons can use uniform pattern fills (such as diagonal lines, grids, or dotted textures), special icons (such as stain icons or exclamation marks), or highlighted borders. Finally, the cleaning robot saves the map data with added dirt icons as a dirt map containing the dirt icons.

[0115] Optionally, after acquiring each marked area, the cleaning robot can mark it using outlines and annotations to obtain a dirt map. The specific implementation process is as follows: The cleaning robot extracts the outer boundary outline of each marked area, then draws the outline on the environmental map in the form of dashed or solid lines. Next, it adds text annotations near or inside each marked area, such as "Heavily Contaminated Area" or "Requires Special Attention." Finally, the cleaning robot merges the outlines and annotations with the original environmental map to generate a dirt map containing dirt markings.

[0116] S203, send the dirt map to the user terminal to instruct the user terminal to display the dirt map after cleaning is completed.

[0117] The user terminal refers to an electronic device used by the user that has display and interactive functions, including but not limited to various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. The user terminal has an application (App) or mini-program installed to accompany the cleaning robot.

[0118] In this embodiment, after constructing the dirt map, the cleaning robot sends the dirt map to the user terminal via a wireless communication module (such as Wi-Fi, Bluetooth, or cellular network) in the form of an image, vector graphic, or an interactive electronic map. Upon receiving the dirt map, the user terminal displays it in the application interface, allowing the user to view details of dirt in different areas through zooming, clicking, and other operations. For example, it displays which areas are clean, which areas are dirty, the gradual trend of dirt levels from light to heavy, and whether the dirt is concentrated in specific sub-areas (such as corners or under tables). Optionally, the cleaning robot can automatically send the dirt map after each cleaning task, or it can respond to user queries initiated through the terminal by pushing the latest constructed dirt map to the user terminal. Figure 5 The diagram shown illustrates the cleaning task record. In addition to sending a dirt map, the cleaning robot can also include auxiliary information such as cleaning time, dirt area statistics, and dirt level classification prompts, so that users can fully understand the cleaning effect.

[0119] The method for constructing a dirt map provided in this application involves acquiring dirt information collected by a cleaning robot during the cleaning process, and then constructing a dirt map based on this information. The dirt map includes dirt markers, and is finally sent to a user terminal to instruct the user terminal to display the dirt map after cleaning is completed. Further, by determining and marking marked areas based on the dirt information, a dirt map containing dirt markers is obtained, wherein the dirt level in the marked areas is greater than a preset dirt threshold. In the above method, by sending the dirt map containing dirt markers to the user terminal to display the dirt distribution, the user can accurately determine the location and distribution range of the dirt based on the displayed dirt markers, thereby enabling targeted cleaning of the areas corresponding to the dirt markers. This avoids missed cleaning or repeated ineffective cleaning due to the inability to determine the dirt distribution, thus helping to improve the cleaning effect of dirty areas. Furthermore, by marking areas with a level of dirt exceeding a preset dirt threshold to create a dirt map, the dirt map only highlights areas with a high level of dirt while filtering out areas with a low level of dirt or no dirt. This avoids information overload on the dirt map, allowing users to quickly locate the dirty areas that need special attention and clean them accordingly, thereby improving the cleaning effect on areas with a high level of dirt.

[0120] In some embodiments, a specific implementation method for marking the marked area is also provided, including: using different shades of color as dirt markers for the marked area according to the degree of dirtiness of the marked area, to obtain a dirt map.

[0121] The higher the degree of dirtiness, the darker or lighter the corresponding color. Taking the darker color for higher dirtiness as an example, the specific implementation process is as follows: When marking areas, the cleaning robot dynamically selects the corresponding color from a preset color map table based on the average or maximum dirtiness level within each marked area. For example, light dirtiness (slightly above a threshold) corresponds to light yellow, moderate dirtiness to orange, and heavy dirtiness to dark red or dark brown. The cleaning robot fills each marked area entirely with the selected color, or covers the area with a semi-transparent color block, while preserving the underlying map outline information. Non-marked areas remain unchanged or are displayed as semi-transparent gray. For cases where the degree of dirtiness is unevenly distributed within the same marked area, the cleaning robot can also use gradient color filling, transitioning from a lighter color in less dirty areas to a darker color in more dirty areas. After completing the color marking, the cleaning robot generates a colored dirt map. Figure 6The displayed dirt map, featuring dirt markers and a visual rendering effect, intuitively reflects the distribution and pollution levels of dirt in various areas of the house. The map retains basic topographical information such as the original floor plan, room layouts, wall boundaries, balconies, and hallways. The underlying map structure is clearly discernible, ensuring the accuracy of spatial location and allowing users to precisely identify the dirt levels in different areas of their home. The dirt color markings follow a mapping rule of "the higher the level of dirt, the darker the color," achieving a tiered visual display. Figure 6 In this diagram, the distribution of dirt can be displayed on the room layout (each room corresponding to a different color). For example, a gradient from dark blue to light blue can be used in the living room to represent the distribution of dirt from high to low levels. Similarly, a gradient can be used in the kitchen and hallway. It should be noted that the colors in the diagram are only illustrative. In actual applications, the colors used in the diagram are not limited, as long as they can clearly show the dirt.

[0122] Optionally, in addition to directly displaying the dirt map, the cleaning robot records its own trajectory during the cleaning process, including its position coordinates, direction of movement, dwell time, and dirt information collected at each location. This trajectory is then linked to timestamps to form complete cleaning path data. Finally, a path map is generated based on this data and compared with the dirt map. Figure 1 And send it to the user terminal, see as follows: Figure 7 The cleaning path diagram shown can be displayed on the user terminal after cleaning is completed, once received. The specific implementation process is as follows: During the cleaning process, the cleaning robot records cleaning operation information for each location. This information includes: the number of cleaning cycles (i.e., the number of times the same location is repeatedly swept or mopped by the cleaning robot), the type of cleaning tool used (e.g., mop mode, side brush + vacuum mode, or mop + vacuum combination mode), and the degree of dirt detected each time. Based on the recorded data, the cleaning robot generates a corresponding path trajectory for each room or area. Furthermore, the cleaning robot marks the number of cleaning cycles and cleaning tool information on the path diagram, which can be represented by different types of markers or lines on the path. For the cleaning tool type, a prompt box can pop up when the user clicks on a segment of the path, displaying the tool used for that segment (e.g., "mop + vacuum"), or it can be directly represented by different icons in the legend. After generating the complete dirt path diagram, the cleaning robot sends the diagram to the user terminal. When displayed on the user terminal, the bottom layer is a room layout map, and the top layer is a colored path layer, accompanied by legends explaining what different colors and line types represent—the rooms, the degree of dirtiness, or the number of times they have been cleaned. Users can zoom in and out and click on the path to see how many times each specific area has been cleaned and what types of cleaning tools were used. Furthermore, the process of generating the path map from the cleaning path data can be made into a video for demonstration, allowing users to view dynamic cleaning path information.

[0123] The method described in this application uses different shades of color as dirt indicators, with the higher the degree of dirt, the darker the corresponding color. This allows users to intuitively identify the severity of dirt in different marked areas based on the shade of color when viewing a dirt map, without needing to view specific values ​​or make additional comparisons. This prioritizes cleaning areas with darker colors, thereby improving the information recognition efficiency and ease of use of the dirt map.

[0124] In some embodiments, in the step of sending a dirt map to a user terminal to instruct the user terminal to display the dirt map after cleaning is completed, a method for prompting the user to perform intensive cleaning is also provided, including:

[0125] Generate a prompt message and send it to the user's terminal; the prompt message is used to instruct the user whether the area corresponding to the dirt label needs to be cleaned more thoroughly.

[0126] The prompts are interactive notifications generated by the cleaning robot based on a dirt map, asking the user whether to take further cleaning measures for the areas corresponding to the dirt markers. Enhanced cleaning refers to a more thorough treatment of the dirty areas than regular cleaning, which may include increasing the number of cleaning cycles, slowing down the cleaning speed, increasing suction power, increasing the amount of water on the cloth, or extending the cleaning time.

[0127] In this embodiment, after sending a dirt map to the user terminal, the cleaning robot generates a prompt message based on the dirt markers in the dirt map, or at the same time as sending the dirt map. Alternatively, it can generate a prompt message for marked areas where the dirt level exceeds a preset reminder threshold, for example, only generating a prompt for heavily soiled areas and not generating a prompt for lightly soiled areas. The prompt message includes a description of the location of the area corresponding to the dirt marker (e.g., "near the coffee table in the living room" or "left side of the bed in the bedroom"), the level of dirt level, and an option to ask the user whether they need intensive cleaning (e.g., "Yes," "No," or "Will remind you later"). The cleaning robot sends the prompt message to the user terminal via a wireless communication module in the form of push notifications, in-app messages, or SMS. After receiving the prompt message, the user terminal displays a dialog box or notification message on the screen, and the user can click the corresponding option to provide feedback. After receiving the user's feedback, if the user selects "Yes," the cleaning robot can immediately perform intensive cleaning in the current cleaning task, or automatically adopt an intensive cleaning strategy for the area in the next cleaning task. The cleaning robot can also automatically perform intensive cleaning after a certain period of time if the user does not provide feedback, or only perform it after the user confirms.

[0128] Furthermore, the prompt message is specifically used to instruct the user whether the area corresponding to the dirt label needs to be cleaned more thoroughly during the next cleaning session.

[0129] In this embodiment, the prompt message generated by the cleaning robot explicitly asks the user whether they need intensive cleaning of the areas marked with dirt during the next cleaning task. After the cleaning robot completes the dirt map construction in the current cleaning task and sends the prompt message to the user's terminal, the user can choose "yes" or "no". If the user chooses "yes", the cleaning robot stores the location information of the area marked with dirt and the intensive cleaning requirement in local or cloud storage, and automatically reads the stored information when starting the next cleaning task, and adopts an intensive cleaning strategy when cleaning the corresponding area. If the user chooses "no", the cleaning robot does not record the intensive cleaning requirement. The prompt message can also allow the user to choose "only next time" or "every time". Furthermore, the cleaning robot can send a reminder to the user's terminal before or at the start of the next cleaning task, informing the user that intensive cleaning of the previously marked dirt areas is about to begin.

[0130] The method described in this application generates prompts indicating to the user whether intensive cleaning is needed for areas marked with dirt. This allows users to promptly identify which areas require extra cleaning and decide whether to perform intensive cleaning based on their needs. This avoids the cleaning robot over-cleaning unnecessary areas or under-cleaning heavily soiled areas, thus improving the cleaning effect on dirty areas while reducing unnecessary energy consumption and time waste. It also enhances the flexibility of cleaning tasks and user satisfaction. Furthermore, by limiting the timing of intensive cleaning to the "next cleaning session," rather than immediate execution or permanent repetition, users can preset future intensive cleaning needs without interrupting the current cleaning task. This ensures the continuity of the current task and avoids the waste of energy, time, and consumables caused by repeatedly performing intensive cleaning on the same area in all subsequent cleaning tasks. Therefore, it effectively saves cleaning resources while meeting the needs for targeted treatment of stubborn dirt.

[0131] In some embodiments, the aforementioned dirt map includes a first dirt identifier for each region and a second dirt identifier for at least one sub-region within each region. Based on this, a specific implementation method for constructing a dirt map based on dirt information is also provided, such as... Figure 8 As shown, "constructing a dirt map based on dirt information" in S202 above includes:

[0132] S301, determine the overall dirt level of each area based on the dirt information of each area, and generate a first dirt label for the corresponding area based on the overall dirt level.

[0133] In this context, a "region" refers to a larger spatial unit pre-defined or in real-time segmented by the cleaning robot on an environmental map, such as a master bedroom, secondary bedroom, living room, kitchen, or bathroom. The overall dirt level refers to the overall dirt level of the region after comprehensive statistical analysis of all dirt information, such as the average, median, or maximum value. The first dirt indicator is a visual element used to identify dirt at the entire region level. This first dirt indicator is distinct from subsequent second dirt indicators at the sub-region level and reflects the overall dirt condition of the region.

[0134] In this embodiment, after acquiring dirt information collected during the cleaning process, the cleaning robot assigns the dirt information to various areas according to their location coordinates. For each area, the cleaning robot collects the quantified dirt level values ​​of all locations within that area, and then determines the overall dirt level of the area using statistical methods (such as calculating the arithmetic mean, taking the median, taking the maximum value, or using an area-weighted average). For example, if a living room area contains 100 dirt information points with dirt level values ​​of ..., and the cleaning robot calculates the average value to be 65, then the overall dirt level is 65. After determining the overall dirt level, the cleaning robot divides each area into different dirt levels, such as lightly dirty, moderately dirty, and heavily dirty. Finally, the cleaning robot generates a first dirt label for each area corresponding to its dirt level; for example, lightly dirty areas use a light-colored label, and heavily dirty areas use a dark-colored label.

[0135] Optionally, such as Figure 9 As shown, the "generating a first dirt label for the corresponding area based on the overall dirt level" in S301 above includes:

[0136] S3011, Obtain the preset mapping relationship; the preset mapping relationship includes the correspondence between the overall dirt level range and the dirt label.

[0137] The preset mapping relationship refers to the pre-defined and stored rules used to map the overall dirt level range to specific dirt labels. This preset mapping relationship can exist in the form of a table, function, or configuration file. The overall dirt level range refers to the numerical intervals (e.g., 0-30, 31-60, 61-100) or level names (e.g., light, moderate, heavy) obtained by dividing the overall dirt level into segments, with each interval corresponding to a dirt level. The dirt label can be at least one of the following: color, pattern, icon, or text label.

[0138] In this embodiment, the cleaning robot pre-configures a mapping table in its local memory. This table contains multiple overall dirt levels, each corresponding to a dirt indicator. For example, an overall dirt level of 0-30 corresponds to a light yellow indicator, 31-60 to an orange indicator, and 61-100 to a dark red indicator. When the cleaning robot performs the step of constructing a dirt map, it reads this mapping table from its local memory and loads it into memory for subsequent matching.

[0139] Optionally, before the cleaning robot starts building the dirt map, it can obtain a user-defined mapping relationship through an application on the user's terminal, that is, the correspondence between the overall dirt level range and the dirt icon, such as setting 0~20 as green, 21~50 as yellow, and 51~100 as red.

[0140] S3012, for each area, match the first dirt identifier corresponding to the overall dirt level range of the area from the preset mapping relationship.

[0141] In this embodiment, after determining the overall dirt level of each area, the cleaning robot traverses all areas and compares the overall dirt level value of each area with various dirt level ranges in a preset mapping relationship. It then finds the range to which the value belongs and reads the dirt icon corresponding to that range as the first dirt icon for that area. For example, if the overall dirt level of a living room area is 75, and 61-100 corresponds to dark red in the preset mapping relationship, the cleaning robot determines the first dirt icon for that area as dark red. If multiple areas fall into the same dirt level range, they use the same first dirt icon. The cleaning robot records the first dirt icons of all areas in a dirt map.

[0142] S302, within each area, at least one sub-area is determined based on the local level of dirt within the area, and a second dirt label is generated for the corresponding sub-area based on the local level of dirt.

[0143] In this context, a sub-region refers to a smaller area within a larger region, defined based on local dirt information. It represents a dirt hotspot or key area within that region, such as a corner of a living room or a stained area. Local dirt level refers to the degree of dirt at a specific location or within a small area of ​​the region. It is more refined and localized than the overall dirt level and can be the average, maximum, or median of the dirt level at various points within that area. A second dirt marker is used to indicate the sub-region and can be the same as or different from the first dirt marker. Preferably, the second dirt marker has higher visual prominence than the first dirt marker, and together they form a hierarchical dirt map.

[0144] In this embodiment, after generating a first dirt label for each region, the cleaning robot further identifies areas with significantly higher dirt levels as sub-regions within each region based on the distribution of dirt levels at various locations within that region. The cleaning robot can use sliding window, peak detection, connected component analysis, or density-based clustering methods to determine the sub-regions. After determining the sub-regions, the cleaning robot generates a corresponding second dirt label for each sub-region based on the local dirt levels (such as average or maximum values) within each sub-region. The second dirt label can be independent of the first dirt label. For example, within a region with a moderate overall dirt level, there may be a heavily dirty sub-region. This sub-region uses a dark red second dirt label, while the rest of the region retains the moderate first dirt label. The cleaning robot overlays the second dirt label onto the corresponding sub-region location on the dirt map.

[0145] Optionally, the cleaning robot can identify areas within a region where the level of local dirt exceeds a preset dirt threshold as sub-regions. Specifically, the cleaning robot iterates through all locations within each region to assess the level of local dirt. It then clusters consecutive locations where the level of local dirt exceeds the preset dirt threshold (which may be the same as or different from the threshold used to identify the region) to form sub-regions. For example, if the preset dirt threshold is 60, adjacent locations within a region with local dirt levels of 85, 82, and 90 constitute a continuous area; this continuous area is then identified as a sub-region.

[0146] Optionally, different shades of color are used as second dirt indicators based on the local dirt level of each sub-region; the higher the local dirt level, the darker the corresponding color. The specific implementation process is as follows: When generating second dirt indicators for a sub-region, the cleaning robot can select the corresponding shade of color from a color map table based on the local dirt level of that sub-region (such as average or maximum value). For example, a local dirt level of 65 corresponds to light red, 80 to red, and 95 to dark red. The cleaning robot fills or outlines the sub-region with the selected color, thus creating a color overlay display of the region level (first dirt indicator) and the sub-region level (second dirt indicator) in the dirt map. Users can intuitively see which areas are generally dirtier and which specific locations within that region are the core dirt areas. Furthermore, the cleaning robot generates a continuous color transition from light to dark (or from dark to light) within the sub-region according to the distribution of dirt level at location points. The cleaning robot can also set multi-color gradients, such as a gradient transition from light yellow, orange, and dark red corresponding to dirt levels from low to high. Finally, the cleaning robot plots the pre-defined gradient sub-areas on the dirt map. When users view it, they can intuitively distinguish which areas are dirtier and which are relatively clean by observing the variations in color intensity within the sub-areas, rather than simply seeing a uniform color block.

[0147] The method described in this application generates a first dirt label for each area based on the overall level of dirt, and simultaneously determines sub-areas based on the local level of dirt within each area and generates a second dirt label. This allows the dirt map to simultaneously provide macro-level dirt information at the area level and detailed dirt information at the sub-area level. Users can gain an overall understanding of the dirt status of each room, and can also intuitively view the specific location and severity of dirt within each room through the different shades of the second dirt labels. This enhances the information hierarchy and practicality of the dirt map, enabling targeted cleaning of sub-areas with higher levels of dirt, avoiding the omission of heavily soiled areas based solely on overall area information, thereby improving the cleaning effect on dirty areas.

[0148] In some embodiments, such as Figure 10 As shown, a video-based visualization method is also provided, including:

[0149] S401 records the cleaning process of the area corresponding to the dirt label and generates a dirt cleaning video.

[0150] Among them, dirt cleaning videos refer to video files generated by integrating and editing recorded images or video clips of the cleaning process. These videos demonstrate how cleaning robots clean dirty areas.

[0151] In this embodiment, the cleaning robot can record real-time video of cleaning dirty areas. The specific implementation process is as follows: After constructing a dirt map, the cleaning robot obtains the location information of the areas corresponding to the dirt markers. When the cleaning robot begins cleaning these areas, it activates its onboard camera to record video. During recording, the cleaning robot points the camera at the dirty area and records the entire process from the start to the completion of cleaning, including the contact between cleaning tools (such as mops and side brushes) and the ground, and the changes in the removal of stains. After cleaning is completed, recording stops, generating a complete dirt cleaning video file.

[0152] Optionally, the cleaning robot can stitch together multiple clips to generate a dirt-cleaning video. The specific implementation process is as follows: The cleaning robot continuously records throughout the cleaning process, but only marks and saves video clips when passing through marked dirt areas; each dirty area corresponds to a short video clip. After the cleaning task is completed, the cleaning robot stitches together all the marked short video clips in chronological order of cleaning, adding transition effects between clips to generate a unified dirt-cleaning video. Text annotations can also be overlaid in this video, indicating the name of the area corresponding to each clip and the degree of dirt.

[0153] Optionally, the cleaning robot can also generate videos showing the cleaning effect in comparison to the dirt. The specific implementation process is as follows: Before cleaning the marked dirty area, images of the area before cleaning can be captured to record the original state of the dirt. Then, the cleaning process is started and video is recorded. After cleaning, images of the area after cleaning are captured. Finally, the images before cleaning, the cleaning process video, and the clean image are combined into a single video file. For example, the dirty state before cleaning can be shown first, then the cleaning process can be played, and finally the clean effect after cleaning can be shown, allowing users to intuitively compare the cleaning results.

[0154] S402 sends a video of the cleaning process to the user's terminal for viewing.

[0155] In this embodiment, after generating a video of cleaning dirt, the cleaning robot can send the video file to the user terminal via a wireless communication module (such as Wi-Fi, Bluetooth, or cellular network). The cleaning robot can send the video immediately after each recording, or in batches after all cleaning tasks are completed, or in response to a query request initiated by the user through the terminal. After receiving the video, the user terminal provides a video player in the application interface, allowing the user to watch the video content. The cleaning robot can also generate video thumbnails or keyframe screenshots (such as before-and-after comparison images) and send them along with the video for quick preview by the user. If the video file is large, the cleaning robot can first upload the video to a cloud server and then send a viewing link or push notification to the user terminal, allowing the user to play it online without waiting for a full download. When sending the video, the cleaning robot can also include information such as the location description of the dirty area, cleaning time, and changes in the degree of dirt to help the user understand the video content.

[0156] The method described in this application, by recording the cleaning process of the area corresponding to the dirt marker and generating a dirt cleaning video, and then sending the video to the user's terminal for viewing, allows the user to intuitively see the complete cleaning process and effect from dirty to clean, verifying whether the cleaning is thorough. This enables timely re-cleaning when insufficient cleaning is detected in the video, further improving the cleaning effect on dirty areas. In addition, this method avoids the problem of incomplete information that may result from relying solely on text or static maps, thereby enhancing user trust and satisfaction with the cleaning robot's work, and also providing users with traceable cleaning evidence.

[0157] In some embodiments, a specific implementation method for obtaining dirt information of each area is also provided, including at least one of the following:

[0158] Method 1: Using a camera mounted on a cleaning robot, identify stains on the floor during the cleaning process and map the location of the stains to the corresponding area to obtain dirt information for each area.

[0159] The camera can be an AI camera or a depth camera. Ground stains include visible contaminants such as dust, oil stains, water stains, footprints, and liquid residue.

[0160] In this embodiment, the cleaning robot can acquire real-time ground images using its onboard camera during cleaning tasks. Specifically, the cleaning robot can analyze the acquired images using computer vision algorithms (such as deep learning-based object detection models or semantic segmentation models) to identify the location, type, area, and shape of stains in the images. Simultaneously, the cleaning robot acquires its own pose information (including position coordinates and orientation), and, combined with the camera's intrinsic and extrinsic parameters, converts the pixel coordinates of the stains in the image into world coordinates on the robot's environment map. Then, the cleaning robot matches these world coordinates with pre-defined areas (such as rooms or grids) to determine the area to which the stain belongs and records the dirt information of that area (such as stain type, area, and degree of dirt). Optionally, the cleaning robot can continuously acquire multiple frames of images and fuse multiple identification results for the same location to improve the accuracy of dirt information.

[0161] Method 2: By using the turbidity sensor on the wastewater return pipe of the cleaning robot, the turbidity of the mop is detected during the cleaning process and the location of turbidity changes is recorded to obtain dirt information for each area.

[0162] The wastewater return pipe refers to the channel through which wastewater from the mop cleaning process flows back to the wastewater tank. A turbidity sensor is installed on the return pipe to measure the turbidity of the flowing wastewater in real time; a higher turbidity value indicates more dirt in the water. Mop turbidity refers to the turbidity value of the water returning after mop cleaning, indirectly reflecting the amount of dirt absorbed by the mop when it contacts the floor. The location of the turbidity change indicates the location where the cleaning robot experiences a sudden change in turbidity.

[0163] In this embodiment, when the cleaning robot performs a mopping task, the mop comes into contact with the ground and absorbs dirt. The cleaning robot periodically or continuously delivers clean water to the mop via a water pump, and collects the wastewater generated after washing the mop into a wastewater tank via a return water pipe. A turbidity sensor installed on the return water pipe detects the turbidity value of the wastewater in real time. The cleaning robot associates and stores each detected turbidity value with its current location information. When the turbidity value increases significantly (e.g., exceeding a set multiple of the previous turbidity value or exceeding a fixed threshold), the cleaning robot marks that location as a dirty area and uses that turbidity value as the degree of dirtiness in that area. Finally, the cleaning robot can estimate the extent of the dirty area by combining the turbidity change trends of multiple consecutive locations.

[0164] Method 3: After the cleaning robot returns to the base station to clean the mop, the turbidity data of the mop is analyzed by the turbidity detection device in the base station, and the turbidity data is associated with the corresponding location according to the cleaning path to obtain the dirt information of each area.

[0165] The base station is a docking and charging device for the cleaning robot. It has a built-in mop cleaning mechanism and a turbidity detection device to detect the turbidity of the cleaning water when cleaning the mop.

[0166] In this embodiment, the cleaning robot returns to the base station to wash the mop after completing all or part of the cleaning task. During the mop washing process, the turbidity detection device in the base station analyzes the turbidity of the wastewater generated, obtaining a comprehensive turbidity value or turbidity change curve. Since this turbidity is the result of the accumulation of all dirt throughout the entire cleaning task, it cannot directly distinguish the degree of dirt at each location. Therefore, the cleaning robot uses the following method to associate the turbidity data with specific locations: the cleaning robot establishes a dirt accumulation model based on the cleaning sequence of each location point in the cleaning path and the contact time between the mop and the ground. For example, assuming that the degree of dirt on the mop increases linearly with the amount of dirt on the ground it passes during cleaning, the cleaning robot can use this model to revert the final turbidity data to each location point on the cleaning path, so that the sum of the dirt levels assigned to each location point matches the total turbidity detected by the base station. The cleaning robot can also return to the base station multiple times during the cleaning task to wash the mop. After each wash, the turbidity detected by the base station corresponds to the cleaning path segment between the previous wash and the current wash, thereby improving the accuracy of the association. Ultimately, the cleaning robot maps the turbidity data associated with each location point to the corresponding area, thus obtaining dirt information for each area.

[0167] The method described in this application acquires dirt information for each area through at least one of three methods: camera visual recognition, online detection by a turbidity sensor in the sewage tank return water pipeline, and offline analysis by a turbidity detection device in the base station. This allows the cleaning robot to flexibly collect dirt data according to different hardware configurations, taking into account the advantages of visual recognition (high intuitiveness), sensor detection (high real-time performance), and base station analysis (low cost). This provides an accurate and reliable dirt information foundation for the subsequent construction of a dirt map, which helps to improve the cleaning effect on dirty areas.

[0168] In some embodiments, a cleaning method for a cleaning robot is also provided, such as... Figure 11 As shown, the method includes:

[0169] S501, Obtain the dirty map obtained by the dirty map construction method of any of the above instances.

[0170] The dirt map includes dirt markers to indicate locations or areas with a high degree of dirt in the clean area.

[0171] In this embodiment, before initiating a cleaning task or during its execution, the cleaning robot reads the most recent or a specified historical dirt map from its local non-volatile memory via an internal communication interface. If the cleaning robot supports cloud synchronization, it can send a request to a cloud server via a wireless network, carrying a user account or device identifier, to download the corresponding dirt map file. The dirt map data format can be a structured file or a binary raster map. The cleaning robot can also receive a dirt map output by its own dirt map construction module in real time during the execution of the current cleaning task. After acquiring the dirt map, the cleaning robot loads it into memory and parses out information such as the location coordinates and dirt level of the areas corresponding to the dirt markers, preparing for subsequent heavy-duty cleaning strategies.

[0172] S502, when performing cleaning tasks, a heavy-duty cleaning strategy is used to clean the areas corresponding to the dirt indicators.

[0173] Heavy-duty cleaning strategies refer to using a more intensive cleaning approach than regular cleaning for areas with high levels of dirt. These strategies include at least one of the following: reducing the speed of the drive wheels, increasing the number of cleaning passes, increasing suction power, and increasing the amount of water on the mop. Reducing the speed of the drive wheels allows the cleaning robot to stay in dirty areas for a longer time, increasing the contact time between the cleaning brushes and the suction port and the floor; increasing the number of cleaning passes means the cleaning robot cleans the area repeatedly; increasing suction power improves the robot's ability to capture dust and particles; and increasing the amount of water on the mop enhances the dissolving and wiping effect on sticky or dried stains.

[0174] In this embodiment, after acquiring a dirt map and identifying the areas corresponding to the dirt markers, the cleaning robot automatically switches from the regular cleaning mode to a heavy-duty cleaning strategy when its planned path enters or approaches the area during the cleaning task. The cleaning robot can employ one or more combinations of the aforementioned strategies. For example, the cleaning robot can simultaneously reduce its travel speed, increase its suction power from the standard setting to the strong setting, and increase the number of cleaning passes in that area. To control the water content of the mop, the cleaning robot can adjust the water pump's duty cycle or extend the water supply time to achieve a higher water content in the mop. After completing the heavy-duty cleaning strategy, the cleaning robot can automatically revert to the regular cleaning mode and continue cleaning other areas.

[0175] Optionally, in response to a cleaning trigger signal, a heavy-duty cleaning strategy is employed to clean the area corresponding to the dirt label.

[0176] Among them, the cleaning trigger signal includes the enhanced cleaning command triggered by the user based on the prompt information displayed on the user terminal, or the detection that the high dirt frequency of the area corresponding to the dirt label is greater than the preset frequency threshold; the high dirt frequency indicates the proportion of the number of times the dirt level is greater than the preset dirt threshold in the total number of historical cleaning tasks.

[0177] In this embodiment, the cleaning robot does not automatically employ a heavy-duty cleaning strategy every time it passes through a soiled area; instead, it executes the strategy only after receiving a cleaning trigger signal. The cleaning trigger signal includes the following two types:

[0178] The first method involves enhanced cleaning commands triggered by prompts displayed on the user's terminal. Specifically, after constructing a dirt map, the cleaning robot sends the map to the user's terminal and displays a prompt (such as asking if the user wants enhanced cleaning of the dirty areas). When the user clicks the "Yes" or "Enhanced Cleaning" button, the user's terminal generates an enhanced cleaning command and sends it to the cleaning robot via wireless network. Upon receiving the command, the cleaning robot uses it as a cleaning trigger signal.

[0179] The second method involves the cleaning robot detecting areas with a high frequency of dirtiness corresponding to a dirt marker, exceeding a preset frequency threshold. Specifically, in multiple historical cleaning tasks (e.g., the most recent 10), the cleaning robot records whether the area was identified as a dirty area (i.e., the degree of dirtiness exceeds the preset dirtiness threshold) in each task. The cleaning robot calculates the proportion of times the degree of dirtiness exceeds the threshold out of the total number of historical cleaning tasks to obtain the high dirtiness frequency. If this proportion is greater than the preset frequency threshold (e.g., 50% or 70%), it indicates that the area is frequently dirty, and the cleaning robot automatically uses the detection result as a cleaning trigger signal without user intervention.

[0180] When a cleaning trigger signal is generated, the cleaning robot will use a heavy-duty cleaning strategy to clean the area corresponding to the dirt marker during the next cleaning task (or if the area has not yet been cleaned in the current task). If both trigger signals are present simultaneously, the cleaning robot may prioritize responding to the user's command. The cleaning process described above can be found in [reference needed]. Figure 12 The diagram shown illustrates the cleaning of areas using a heavily soiled cleaning strategy, and can be seen as follows: Figure 13 The diagram shows the cleaning path after the completion of the cleaning of the heavily soiled area using the cleaning strategy.

[0181] The method described in this application, by acquiring a dirt map and applying a heavy-duty cleaning strategy to areas corresponding to dirt markers during cleaning tasks, can perform intensive cleaning on highly soiled areas, effectively improving the dirt removal rate. Furthermore, by conditionally activating the heavy-duty cleaning strategy in response to cleaning trigger signals (user-intensified cleaning commands or automatic triggering due to high dirt frequency), it respects the user's subjective cleaning intentions while automatically identifying frequently soiled areas for proactive intensive cleaning, avoiding energy waste caused by indiscriminate heavy-duty cleaning, thereby improving cleaning effectiveness and efficiency.

[0182] In some embodiments, a method for constructing a dirt map is also provided, applied to a user terminal. The method includes: receiving a dirt map sent by a cleaning robot and displaying the dirt map after cleaning is completed; the dirt map is constructed by the cleaning robot based on dirt information collected during the cleaning process, and the dirt map includes dirt markers.

[0183] The dirt map is generated by the cleaning robot identifying and marking designated areas based on dirt information; the dirt level in these areas exceeds a preset dirt threshold. Furthermore, the dirt map is created by the cleaning robot using different shades of color to indicate the dirt level in each designated area.

[0184] Optionally, the method further includes receiving a prompt message sent by the cleaning robot; the prompt message is used to instruct the user whether the area corresponding to the dirt label needs to be cleaned more thoroughly. Further, the prompt message specifically instructs the user whether the area corresponding to the dirt label needs to be cleaned more thoroughly during the next cleaning cycle.

[0185] Optionally, the dirt map is obtained by the cleaning robot determining the overall dirt level of each area based on the dirt information of each area, generating a first dirt label for the corresponding area based on the overall dirt level, and determining at least one sub-area within each area based on the local dirt level, and generating a second dirt label for the corresponding sub-area based on the local dirt level; the dirt map includes the first dirt label of each area and the second dirt label of at least one sub-area within each area.

[0186] Optionally, the first dirt label is obtained by the cleaning robot acquiring a preset mapping relationship and matching the first dirt label corresponding to the overall dirt level range of the area from the preset mapping relationship for each area; the preset mapping relationship includes the correspondence between the overall dirt level range and the dirt label.

[0187] The sub-regions are those areas within the main area where the level of dirt exceeds a preset dirt threshold. The second dirt indicator is determined by the cleaning robot using different shades of color based on the level of dirt in each sub-region; the higher the level of dirt, the darker the color.

[0188] Optionally, the method further includes: receiving a dirty cleaning video sent by a cleaning robot for the user to view; the dirty cleaning video is generated by the cleaning robot based on the cleaning process of the area corresponding to the dirty label.

[0189] Optionally, the dirt information is obtained by the cleaning robot through at least one of the following methods: using a camera mounted on the cleaning robot to identify stains on the ground during the cleaning process and mapping the location of the stains to the corresponding area to obtain dirt information for each area; using a turbidity sensor on the wastewater return pipe of the cleaning robot to detect the turbidity of the mop during the cleaning process and record the location of turbidity changes to obtain dirt information for each area; after the cleaning robot returns to the base station to clean the mop, the turbidity data of the mop is analyzed by a turbidity detection device in the base station, and the turbidity data is associated with the corresponding location according to the cleaning path to obtain dirt information for each area.

[0190] Optionally, the method further includes: displaying a cleaning path diagram after cleaning is completed.

[0191] The methods described in each of the above steps and the beneficial effects of each step have been explained in the foregoing embodiments. For details, please refer to the foregoing descriptions, which will not be repeated here.

[0192] In summary, based on all the above embodiments, a cleaning method for a cleaning robot based on a dirt map is also provided, applied to a cleaning robot, the method comprising:

[0193] S601, the cleaning robot acquires dirt information collected during the cleaning process through at least one of the following methods: using a camera mounted on the cleaning robot to identify stains on the ground during the cleaning process and mapping the stain locations to corresponding areas to obtain dirt information for each area; using a turbidity sensor on the wastewater return pipe of the cleaning robot to detect the turbidity of the mop during the cleaning process and record the location of turbidity changes to obtain dirt information for each area; after the cleaning robot returns to the base station to clean the mop, the turbidity data of the mop is analyzed by a turbidity detection device in the base station, and the turbidity data is associated with the corresponding location according to the cleaning path to obtain dirt information for each area.

[0194] S602, determine the marked areas based on the dirt information, and use different shades of color as dirt markers for the marked areas according to the degree of dirt, thus obtaining a dirt map containing dirt markers. The dirt map includes dirt markers; the dirt level of the marked area is greater than a preset dirt threshold, and the higher the degree of dirt, the darker the corresponding color.

[0195] S603, generate a prompt message and send the prompt message and a dirt map to the user terminal, instructing the user terminal to display the dirt map after cleaning is completed. Specifically, the prompt message indicates to the user whether to perform intensive cleaning on the areas corresponding to the dirt markers during the next cleaning cycle.

[0196] S604, Obtain a preset mapping relationship, and for each region, match the first dirt identifier corresponding to the overall dirt level range of the region from the preset mapping relationship. The preset mapping relationship includes the correspondence between the overall dirt level range and the dirt identifier.

[0197] S605, within each region, areas where the degree of local dirtiness exceeds a preset dirtiness threshold are defined as sub-regions, and different shades of color are used as second dirtiness indicators based on the degree of local dirtiness in each sub-region. The dirt map includes a first dirtiness indicator for each region and a second dirtiness indicator for at least one sub-region within each region; the higher the degree of local dirtiness, the darker the corresponding color.

[0198] S606 records the cleaning process of the area corresponding to the dirt label, generates a dirt cleaning video, and sends the dirt cleaning video to the user terminal for the user to view.

[0199] S607, Obtain the dirty map obtained by the dirty map construction method of S601-S606.

[0200] S608, when performing a cleaning task, directly employs a heavy-duty cleaning strategy to clean the area corresponding to the dirt indicator, or responds to a cleaning trigger signal by employing a heavy-duty cleaning strategy to clean the area corresponding to the dirt indicator. The heavy-duty cleaning strategy includes at least one of reducing the speed of the drive wheels, increasing the number of cleaning cycles, increasing suction power, and increasing the amount of water on the mop. The cleaning trigger signal includes an enhanced cleaning command triggered by the user based on a prompt displayed on the user terminal, or the detection that the high dirt frequency of the area corresponding to the dirt indicator is greater than a preset frequency threshold; the high dirt frequency represents the proportion of times the dirt level exceeds the preset dirt threshold in a historical cleaning task out of the total number of historical cleaning tasks.

[0201] The methods described in each of the above steps have been described in the foregoing embodiments. For details, please refer to the foregoing descriptions. They will not be repeated here.

[0202] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0203] Based on the same inventive concept, this application also provides a dirty map construction apparatus for implementing the dirty map construction method described above. The solution provided by this apparatus is similar to the solution described in the above method; therefore, the specific limitations of one or more dirty map construction apparatus embodiments provided below can be found in the limitations of the dirty map construction method described above, and will not be repeated here.

[0204] In some embodiments, such as Figure 14 As shown, a device for constructing a dirty map is provided, comprising:

[0205] The information acquisition module 141 is used to acquire dirt information collected by the cleaning robot during the cleaning process.

[0206] Map construction module 142 is used to construct a dirt map based on dirt information; the dirt map includes dirt markers. This map construction module includes: a determination unit, used to determine marker areas based on dirt information; the dirt level of the marker areas is greater than a preset dirt threshold; and a marker unit, used to mark the marker areas to obtain a dirt map containing dirt markers.

[0207] The map sending module 143 is used to send a dirty map to the user terminal to instruct the user terminal to display the dirty map after cleaning is completed.

[0208] In some embodiments, the above-mentioned marking unit is specifically used to use different shades of color as dirt markers for the marking area according to the degree of dirt in the marking area, so as to obtain a dirt map.

[0209] In some embodiments, the apparatus for constructing the dirt map further includes:

[0210] The first generation module is used to generate prompt information and send the prompt information to the user terminal; the prompt information is used to indicate whether the user needs to perform intensive cleaning on the area corresponding to the dirt label.

[0211] In some embodiments, the above-mentioned prompt information is specifically used to instruct the user whether the area corresponding to the dirt mark needs to be cleaned more thoroughly during the next cleaning.

[0212] In some embodiments, the map building module described above includes:

[0213] The first generation unit is used to determine the overall dirt level of each area based on the dirt information of each area, and generate a first dirt label for the corresponding area based on the overall dirt level.

[0214] The second generation unit is used to determine at least one sub-region within each region based on the local level of dirt within the region, and to generate a second dirt label for the corresponding sub-region based on the local level of dirt.

[0215] In some embodiments, the first generating unit includes:

[0216] The sub-unit is used to obtain a preset mapping relationship; the preset mapping relationship includes the correspondence between the overall dirt level range and the dirt label.

[0217] The matching subunit is used to match the first dirt identifier corresponding to the overall dirt level range of the region from a preset mapping relationship for each region.

[0218] In some embodiments, the second generation unit is specifically used to determine the area within the region where the degree of local dirtiness is greater than a preset dirtiness threshold as a sub-region.

[0219] In some embodiments, the second generation unit is specifically used to use different shades of color as a second dirt identifier according to the degree of local dirt in each sub-region; wherein, the higher the degree of local dirt, the darker the corresponding color.

[0220] In some embodiments, the apparatus for constructing the aforementioned dirt map includes:

[0221] The second generation module is used to record the cleaning process of the area corresponding to the dirt label and generate a dirt cleaning video.

[0222] The video sending module is used to send videos of cleaning dirt and grime to the user's terminal for viewing.

[0223] In some embodiments, the information acquisition module described above includes at least one of the following units:

[0224] The identification unit is used to identify stains on the ground during the cleaning process using a camera mounted on the cleaning robot, and to map the location of the stains to the corresponding area to obtain dirt information for each area.

[0225] The first detection unit is used to detect the turbidity of the mop during the cleaning process and record the location of turbidity changes through the turbidity sensor on the wastewater return pipe of the cleaning robot, so as to obtain the dirt information of each area.

[0226] The second detection unit is used to analyze the turbidity data of the mop through the turbidity detection device in the base station after the cleaning robot returns to the base station to clean the mop, and associate the turbidity data with the corresponding location according to the cleaning path to obtain the dirt information of each area.

[0227] In some embodiments, an apparatus for constructing a dirt map is provided, comprising:

[0228] The map display module is used to receive the dirt map sent by the cleaning robot and display the dirt map after cleaning is completed. The dirt map is constructed by the cleaning robot based on the dirt information collected during the cleaning process, and the dirt map includes dirt markers.

[0229] The dirt map is generated by the cleaning robot identifying and marking designated areas based on dirt information; the dirt level in these areas exceeds a preset dirt threshold. The dirt map is created by the cleaning robot using different shades of color to mark the dirt levels in the designated areas; the higher the dirt level, the darker the color.

[0230] The dirt map is generated by the cleaning robot based on the dirt information of each area to determine the overall dirt level of each area, and generating a first dirt label for the corresponding area based on the overall dirt level. Within each area, at least one sub-area is determined based on the local dirt level, and a second dirt label is generated for the corresponding sub-area based on the local dirt level. The dirt map includes the first dirt label for each area and the second dirt label for at least one sub-area within each area. The first dirt label is obtained by the cleaning robot from a preset mapping relationship, matching the first dirt label corresponding to the overall dirt level range of each area from the preset mapping relationship. The preset mapping relationship includes the correspondence between the overall dirt level range and the dirt labels. A sub-area is a region within the area where the local dirt level is greater than a preset dirt threshold. The second dirt label is determined by the cleaning robot using different shades of color based on the local dirt level of each sub-area; the higher the local dirt level, the darker the color.

[0231] The dirt information is obtained by the cleaning robot through at least one of the following methods: using a camera mounted on the cleaning robot to identify stains on the ground during the cleaning process and mapping the stain locations to corresponding areas to obtain dirt information for each area; using a turbidity sensor on the wastewater return pipe of the cleaning robot to detect the turbidity of the mop during the cleaning process and record the location of turbidity changes to obtain dirt information for each area; after the cleaning robot returns to the base station to clean the mop, the turbidity data of the mop is analyzed by a turbidity detection device in the base station, and the turbidity data is associated with the corresponding location according to the cleaning path to obtain dirt information for each area.

[0232] In some embodiments, the apparatus for constructing the dirt map further includes:

[0233] The first receiving module is used to receive prompts sent by the cleaning robot; the prompts are used to instruct the user whether the area corresponding to the dirt indicator needs to be cleaned more thoroughly. Specifically, the prompts are used to instruct the user whether the area corresponding to the dirt indicator needs to be cleaned more thoroughly during the next cleaning cycle.

[0234] In some embodiments, the apparatus for constructing the dirt map further includes:

[0235] The second receiving module is used to receive the dirt cleaning video sent by the cleaning robot for the user to view; the dirt cleaning video is generated by the cleaning robot according to the cleaning process of the area corresponding to the dirt label.

[0236] In some embodiments, the above-mentioned apparatus for constructing a dirt map further includes:

[0237] The path map display module shows the cleaning path map after cleaning is completed.

[0238] Based on the same inventive concept, this application also provides a cleaning device for a cleaning robot to implement the cleaning method of the cleaning robot described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more cleaning device embodiments of cleaning robots provided below can be found in the limitations of the cleaning method of cleaning robots above, and will not be repeated here.

[0239] In some embodiments, such as Figure 15 As shown, a cleaning device for a cleaning robot is provided, comprising:

[0240] The map acquisition module 151 is used to acquire the dirty map obtained by the above-described dirty map construction method.

[0241] The cleaning module 152 is used to clean the area corresponding to the dirt mark by adopting a heavy dirt cleaning strategy when performing a cleaning task; the heavy dirt cleaning strategy includes at least one of reducing the speed of the drive wheel, increasing the number of cleaning cycles, increasing the suction power of the vacuum cleaner, and increasing the amount of water on the cloth.

[0242] In some embodiments, the cleaning module specifically responds to a cleaning trigger signal and employs a heavy-duty cleaning strategy to clean the area corresponding to the dirt label; wherein, the cleaning trigger signal includes an enhanced cleaning command triggered by the user based on the prompt information displayed on the user terminal, or the detection that the high dirt frequency of the area corresponding to the dirt label is greater than a preset frequency threshold; the high dirt frequency represents the proportion of the number of times the dirt level is greater than the preset dirt threshold in a series of historical cleaning tasks to the total number of historical cleaning tasks.

[0243] The various modules in the aforementioned dirt map construction device and cleaning robot cleaning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0244] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for constructing a dirt map as described in any of the above embodiments or the steps of the cleaning method of a cleaning robot.

[0245] In some embodiments, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the method for constructing a dirt map as described in any of the above embodiments or the steps of the cleaning method of a cleaning robot.

[0246] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0247] In some embodiments, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method for constructing a dirt map as described in any of the above embodiments or the steps of a cleaning method of a cleaning robot.

[0248] The computer program product provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0249] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0250] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0251] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0252] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for constructing a dirty map, characterized in that, Applied to cleaning robots, the method includes: Acquiring dirt information collected by the cleaning robot during the cleaning process includes: after the cleaning robot returns to the base station to clean the mop, performing turbidity analysis on the wastewater generated during the mop cleaning process using a turbidity detection device in the base station to obtain turbidity data of the mop; establishing a dirt accumulation model based on the cleaning sequence of each location point in the cleaning path and the contact time between the mop and the ground; and using the dirt accumulation model to distribute the turbidity data in reverse to each location point on the cleaning path, so that the sum of the dirt levels distributed at each location point matches the total turbidity detected by the base station, thereby obtaining dirt information for each area. The marked area is determined based on the dirt information; the degree of dirt in the marked area is greater than a preset dirt threshold. The process of marking the marked areas to obtain a dirt map containing dirt markers includes: determining the overall dirt level of each area based on the dirt information of each area; matching a first dirt marker corresponding to the overall dirt level range of the area from a preset mapping relationship, wherein the preset mapping relationship includes the correspondence between the overall dirt level range and the dirt markers; within each area, identifying the area where the local dirt level is greater than a preset dirt threshold as a sub-area, and using different shades of color as a second dirt marker based on the local dirt level of each sub-area, with a darker color corresponding to a higher local dirt level; wherein the area is a large spatial unit divided based on an environmental map, the sub-area is a dirt hotspot or key area within the large spatial unit, the second dirt marker is superimposed on the area marked by the first dirt marker, and the first dirt marker and the second dirt marker together constitute a hierarchical dirt map; The dirt map is sent to the user terminal to instruct the user terminal to display the dirt map after cleaning is completed.

2. The method according to claim 1, characterized in that, The process of marking the marked areas to obtain the dirt map includes: Based on the degree of dirtiness of the marked area, different shades of color are used as dirt indicators for the marked area to obtain the dirt map.

3. The method according to claim 1, characterized in that, In the step of sending the dirt map to the user terminal to instruct the user terminal to display the dirt map after cleaning is completed, the method further includes: A prompt message is generated and sent to the user terminal; the prompt message is used to indicate whether the user needs to perform intensive cleaning on the area corresponding to the dirt label.

4. The method according to claim 3, characterized in that, The prompt message is specifically used to instruct the user whether they need to perform extra cleaning on the area corresponding to the dirt indicator during the next cleaning session.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: Record the cleaning process of the area corresponding to the dirt label and generate a dirt cleaning video; The video of the cleaning process is sent to the user's terminal for viewing.

6. A method for constructing a dirty map, characterized in that, Applied to a user terminal, the method includes: Receive a dirt map sent by the cleaning robot and display the dirt map after cleaning is completed; The dirt map is constructed by the cleaning robot based on dirt information collected from various areas during the cleaning process. After the cleaning robot returns to the base station to clean the mop, the dirt information for each area is analyzed by a turbidity detection device in the base station during the mop cleaning process to obtain turbidity data for the mop. A dirt accumulation model is established based on the cleaning sequence of each location point in the cleaning path and the contact time between the mop and the ground. The turbidity data is then back-allocated to each location point on the cleaning path using the dirt accumulation model, so that the sum of the dirt levels allocated to each location point matches the total turbidity detected by the base station. The dirt map is obtained by the cleaning robot identifying and marking designated areas based on the dirt information. The dirt level of the designated areas is greater than a preset dirt threshold. The dirt map is also obtained by the cleaning robot determining the overall dirt level of each area based on the dirt information of each area, matching the first dirt icon corresponding to the overall dirt level range of the area from a preset mapping relationship, and within each area, identifying the area where the local dirt level is greater than the preset dirt threshold as a sub-area, and using different shades of color as a second dirt icon based on the local dirt level of each sub-area. The preset mapping relationship includes the correspondence between the overall dirt level range and the dirt icon, with a darker color corresponding to a higher local dirt level. The area is a large spatial unit divided based on the environmental map, and the sub-area is a dirt hotspot or key area within the large spatial unit. The second dirt icon is superimposed on the area marked by the first dirt icon, and the first and second dirt icons together constitute a hierarchical dirt map.

7. The method according to claim 6, characterized in that, The dirt map is generated by the cleaning robot using different shades of color to indicate the dirt level of the marked areas.

8. The method according to claim 6, characterized in that, The method further includes: The system receives a prompt message from the cleaning robot; the prompt message is used to indicate whether the user needs to perform intensive cleaning on the area corresponding to the dirt label.

9. The method according to claim 8, characterized in that, The prompt message is specifically used to instruct the user whether they need to perform extra cleaning on the area corresponding to the dirt indicator during the next cleaning session.

10. The method according to any one of claims 6-9, characterized in that, The method further includes: The system receives cleaning videos of dirt and grime sent by the cleaning robot for the user to view; the cleaning videos are generated by the cleaning robot based on the cleaning process of the areas corresponding to the dirt and grime markers.

11. The method according to any one of claims 6-9, characterized in that, The method further includes: Display the cleaning path diagram after cleaning is completed.

12. A cleaning method for a cleaning robot, characterized in that, Applied to cleaning robots, the method includes: A dirt map is obtained; the dirt map is constructed by the cleaning robot based on dirt information collected from various areas during the cleaning process; after the cleaning robot returns to the base station to clean the mop, the dirt information of each area is analyzed by the turbidity detection device in the base station during the mop cleaning process to obtain the turbidity data of the mop; a dirt accumulation model is established based on the cleaning sequence of each location point in the cleaning path and the contact time between the mop and the ground; the turbidity data is then back-allocated to each location point on the cleaning path using the dirt accumulation model, so that the sum of the dirt levels allocated to each location point matches the total turbidity detected by the base station; the dirt map is obtained by the cleaning robot determining and marking the marked areas based on the dirt information; the dirt level of the marked areas is greater than a preset dirt threshold; the dirt map is... The cleaning robot determines the overall dirt level of each area based on the dirt information of each area, and matches the first dirt icon corresponding to the overall dirt level range of the area from a preset mapping relationship. Within each area, areas where the local dirt level exceeds a preset dirt threshold are identified as sub-areas, and second dirt icons are obtained by using different shades of color based on the local dirt level of each sub-area. The preset mapping relationship includes the correspondence between the overall dirt level range and the dirt icon; the higher the local dirt level, the darker the corresponding color. The area is a large spatial unit divided based on an environmental map, and the sub-area is a dirt hotspot or key area within the large spatial unit. The second dirt icon is superimposed on the area identified by the first dirt icon, and the first and second dirt icons together constitute a hierarchical dirt map. When performing cleaning tasks, a heavy-duty cleaning strategy is used to clean the areas corresponding to the dirt indicators; the heavy-duty cleaning strategy includes at least one of reducing the speed of the drive wheels, increasing the number of cleaning cycles, increasing the suction power of the vacuum cleaner, and increasing the amount of water on the rag.

13. The method according to claim 12, characterized in that, The method of using a heavy-duty cleaning strategy to clean the area corresponding to the dirt label includes: In response to the cleaning trigger signal, a heavy-duty cleaning strategy is used to clean the area corresponding to the dirt label; The cleaning trigger signal includes an enhanced cleaning command triggered by the user based on the prompt information displayed on the user terminal, or the detection that the high dirt frequency of the area corresponding to the dirt icon is greater than a preset frequency threshold; the high dirt frequency represents the proportion of the number of times the dirt level is greater than the preset dirt threshold in the total number of historical cleaning tasks.

14. A cleaning robot, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1-5 and 12-13.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 13.

16. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 13.

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