Robot-based indoor environment monitoring method and robot

Abstract

The embodiment of the invention relates to the technical field of robots, in particular to an indoor environment monitoring method based on a robot and the robot, and the method comprises the steps: collecting air quality data when the robot walks according to a global path, generating an air optimization index based on the air quality data, and if the air optimization index is smaller than a preset index threshold value, determining that the robot is in an indoor environment; if yes, a target local area is determined according to the air quality data, the target local area is the area needing to execute air optimization operation, and the robot is controlled to execute air optimization operation on the target local area. According to the embodiment of the invention, the robot moves to collect the air quality data, and when the air quality data meets the air optimization condition, the air optimization operation is executed on the area needing air optimization, so that the air quality can be monitored in all directions, the air optimization operation can be automatically executed, time and labor are saved, the air optimization efficiency is improved, and the user experience is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of robotics technology, and in particular to a robot-based indoor environment monitoring method and robot. Background Art

[0002] At present, indoor environment monitoring equipment on the market is usually static air quality detection, which is limited to monitoring at a specific location and cannot fully understand the air quality status of the entire indoor environment. In addition, most of these monitoring devices require manual adjustment, such as air purifiers and humidity control equipment. Users need to adjust them according to the data provided by the equipment, which is time-consuming and labor-intensive, with low air optimization efficiency and poor user experience. Summary of the invention

[0003] In view of this, one purpose of an embodiment of the present application is to provide a robot-based indoor environment monitoring method and robot, aiming to solve the technical problems in the prior art that the environmental monitoring location is incomplete, the optimization of air quality is time-consuming, labor-intensive and inefficient.

[0004] To solve the above technical problems, the present application provides the following technical solutions:

[0005] In a first aspect, an embodiment of the present application provides a robot-based indoor environment monitoring method, comprising:

[0006] The robot collects air quality data when walking according to the global path, and generates an air optimization index based on the air quality data;

[0007] If the air optimization index is less than a preset index threshold, determining a target local area according to the air quality data, wherein the target local area is an area where an air optimization operation needs to be performed;

[0008] The robot is controlled to perform an air optimization operation on the target local area.

[0009] In some embodiments, the global path includes a plurality of path points, the air quality data includes a plurality of air quality values, the air quality value is collected by the robot at each of the path points, and determining the target local area according to the air quality data includes:

[0010] Extracting the air quality value corresponding to each of the path points from the air quality data;

[0011] If the air quality value of the path point is greater than a preset quality threshold, determining the path point as a candidate path point;

[0012] A target local area is determined based on the plurality of candidate path points.

[0013] In some embodiments, determining the target local area based on the plurality of candidate path points includes:

[0014] Determine the candidate path points that meet the location density condition as the target path points;

[0015] A minimum bounding rectangle including all the target path points is generated, where the minimum bounding rectangle is the target local area.

[0016] In some embodiments, generating a minimum bounding rectangle containing all the target path points includes:

[0017] Determine the target path point with the smallest horizontal coordinate among all the target path points as the first path point, and the target path point with the largest horizontal coordinate as the second path point;

[0018] Determine the target path point with the smallest ordinate among all the target path points as the third path point, and the target path point with the largest ordinate as the fourth path point;

[0019] A rectangle that passes through the first path point, the second path point, the third path point, and the fourth path point, respectively, is determined to be a minimum circumscribed rectangle.

[0020] In some embodiments, determining a candidate path point that satisfies the location density condition as a target path point includes:

[0021] Selecting any one of the candidate path points as a reference path point;

[0022] Calculating the distances between the reference path point and each of the candidate path points, and recording each of the distances in a distance set belonging to the reference path point;

[0023] If at least one distance in the distance set is less than a preset distance threshold, the reference path point is determined to be a target path point.

[0024] In some embodiments, controlling the robot to perform an air optimization operation on the target local area includes:

[0025] Obtaining a unit optimization radius of the robot, where the unit optimization radius is the longest distance that can be covered by the air optimization operation performed by the robot at each position;

[0026] Based on the unit optimization radius, an air optimization path is planned in the target local area;

[0027] The robot is controlled to walk along the air optimization path and perform air optimization operations on the target local area.

[0028] In some embodiments, the air optimization path includes a plurality of optimization positions, and planning the air optimization path in the target local area based on the unit optimization radius includes:

[0029] Acquire a target circular area, wherein the target circular area is a circular area where an optimized position has been found, the center position of the target circular area is the optimized position, and the radius of the target circular area is equal to the unit optimized radius;

[0030] Sequentially collecting a plurality of pending positions on the boundary of the target circular area according to a preset step length;

[0031] Determine a reference circular area that is tangent to the target circular area at the to-be-determined position, wherein the position of the tangent point between the reference circular area and the target circular area is the to-be-determined position;

[0032] A reference circle area satisfying a preset condition is selected from the plurality of reference circle areas as a new target circle area, and the center position of the new target circle area is an optimized position;

[0033] An air optimized path is generated based on all the optimized positions.

[0034] In some embodiments, the step of selecting a reference circular area satisfying a preset condition from the plurality of reference circular areas as a new target circular area includes:

[0035] Calculating the overlapping area between each reference circle region and the target local region;

[0036] It is determined that the reference circle region with the largest overlapping area meets the preset conditions, and the reference circle region with the largest overlapping area is the new target circle region.

[0037] In some embodiments, the robot is equipped with an air purifier and / or a dehumidifier;

[0038] The controlling the robot to walk along the air optimization path and performing the air optimization operation on the target local area includes:

[0039] Controlling the robot to walk along the air optimization path, and starting the air purifier to work so as to purify the air in the target local area; and / or,

[0040] The robot is controlled to walk along the air optimization path, and the dehumidifier is started to work to reduce the air humidity in the target local area.

[0041] In a second aspect, an embodiment of the present application provides a robot comprising a memory and a processor, wherein the memory is connected to the processor, and the processor is used to execute one or more computer programs stored in the memory. When the processor executes the one or more computer programs, the robot implements any robot-based indoor environment monitoring method proposed in the first aspect.

[0042] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes any one of the robot-based indoor environment monitoring methods proposed in the first aspect.

[0043] The embodiments of the present application have the following technical effects: Different from the prior art, the robot-based indoor environment monitoring method provided by the embodiments of the present application includes: the robot collects air quality data while walking according to the global path, and generates an air optimization index based on the air quality data; if the air optimization index is less than a preset index threshold, the target local area is determined according to the air quality data, wherein the target local area is an area where air optimization operations need to be performed, and the robot is controlled to perform air optimization operations on the target local area.

[0044] The embodiment of the present application collects air quality data through robot movement, and when the air quality data meets the air optimization conditions, performs air optimization operations on the areas requiring air optimization. In this way, the air quality can be monitored in all directions and air optimization operations can be automatically performed, saving time and effort, improving air optimization efficiency, and enhancing user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0046] Figure 1 This is a schematic diagram of an application scenario of a robot performing indoor environment monitoring in some embodiments of the present application;

[0047] Figure 2 is a flowchart of a robot-based indoor environment monitoring method provided in some embodiments of the present application;

[0048] Figure 3 is a schematic diagram of a robot provided in some embodiments of the present application performing indoor environment monitoring according to a global path;

[0049] Figure 4 is a schematic diagram of a robot performing indoor environment monitoring according to a global path provided in other embodiments of the present application;

[0050] Figure 5 is a schematic diagram of a robot performing indoor environment monitoring according to a global path provided in other embodiments of the present application;

[0051] Figure 6 is a schematic diagram of determining a reference circle area in some embodiments of the present application;

[0052] Figure 7 is a schematic diagram of the structure of a robot-based indoor environment monitoring device provided in some embodiments of the present application;

[0053] Figure 8 It is a schematic diagram of the structure of the robot provided in some embodiments of the present application. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application.

[0055] It should be noted that, if there is no conflict, the various features in the embodiments of the present application can be combined with each other, all within the scope of protection of the present application. In addition, although the functional module division is performed in the device schematic diagram and the logical order is shown in the flow chart, in some cases, the steps shown or described can be performed in a sequence different from the module division in the device or the flow chart. Furthermore, the words "first", "second", "third", etc. used in this application do not limit the data and execution order, but only distinguish the same items or similar items with basically the same functions and effects.

[0056] See also Figure 1 , Figure 1 A schematic diagram of an application scenario in which a robot performs indoor environment monitoring in some embodiments of the present application is shown.

[0057] like Figure 1 As shown, the robot 100 is equipped with various high-precision sensors for environmental monitoring ( Figure 1 5 and PM10), carbon dioxide sensors, temperature and humidity sensors, and harmful gas sensors (such as formaldehyde), etc. These high-precision sensors for environmental monitoring can provide accurate environmental monitoring data (i.e., air quality data) in real time.

[0058] In order to realize mobile monitoring to obtain the air quality of the indoor environment, the robot 100 is also equipped with an autonomous navigation system, including, for example, laser radar (LIDAR), visual cameras and inertial measurement units, etc., and uses a path planning algorithm to plan a monitoring path that fully covers the indoor environment area, and combines it with an obstacle avoidance algorithm to drive along the monitoring path to monitor the air quality of the indoor environment, while ensuring accurate navigation and obstacle avoidance in various indoor environments, and ensuring the extensiveness, systematicness and accuracy of air quality data collection. Of course, in order to realize the mobile function, the robot 100 is factory-equipped with walking components (such as wheels, tracks, etc.), control components (such as microcontrollers, single-chip microcomputers, etc.), transmission components (such as belts, gears) and drive components (such as drive motors), etc. The control component controls the various components to work together to realize the movement of the robot 100.

[0059] In some embodiments, in order to achieve other business functions, the robot can also be equipped with other required devices or components. For example, to improve the efficiency of environmental monitoring, the robot can be equipped with a transport device. In the process of driving along the monitoring path to monitor the air quality of the indoor environment, if there are movable obstacles on the monitoring path, the robot can use the transport device to move the obstacle away from the monitoring path so as to continue driving along the monitoring path to monitor the air quality of the indoor environment, thereby improving the efficiency of environmental monitoring.

[0060] In some embodiments, after monitoring and obtaining the air quality data of the indoor environment, it is determined whether the current indoor environment needs to be optimized. For example, when the concentration of harmful gases in the current indoor environment air is too high, the robot 100 can start the air purification and filtration system equipped with itself, and absorb harmful gases and odors (such as formaldehyde, benzene, etc.) in the air through, for example, an activated carbon filter layer, thereby optimizing the indoor environment air, providing a good indoor air environment, and improving the user experience.

[0061] Please continue reading Figure 1 In a specific embodiment, after obtaining the map of the indoor environment area 200, the robot 100 plans a global path L1 for air quality monitoring of the indoor environment area 200 based on the map of the indoor environment area 200, drives along the global path L1, and monitors the air of the indoor environment area 200 through relevant air quality monitoring sensors (such as the aforementioned particulate matter sensor, carbon dioxide sensor, temperature and humidity sensor, and harmful gas sensor, etc.), thereby collecting and obtaining air quality data.

[0062] In some embodiments, according to a preset rule, the air quality data is used to generate a corresponding air optimization index, and when the air optimization index is less than a preset index threshold, a target local area is determined according to the air quality data, wherein the target local area is an area where the air quality is poor and an air optimization operation needs to be performed. For example, the target local area determined in one embodiment is as follows: Figure 1 The local area 201 and the local area 202 shown, that is, according to the air quality data, it is determined that the air quality of the local area 201 and the local area 202 is poor, and the air in the local area 201 and the local area 202 needs to be optimized, then the robot 100 performs air optimization operations on the target local areas (i.e., the local area 201 and the local area 202), thereby optimizing the air in the indoor environment, providing a good indoor air environment, and improving the user experience.

[0063] It should be understood that Figure 1 The application scenario shown is only a schematic illustration of a situation in which the robot 100 performs indoor environment monitoring and air optimization operations in some embodiments of the present application, and does not impose any limitation on the structure, type and number of robots in other application scenarios or embodiments, the planning and length of global paths, etc. For example, in some other application scenarios or embodiments, multiple robots may be used to simultaneously perform environmental monitoring and air optimization operations on the same indoor environment.

[0064] It is understandable that the implementation and execution subject of any robot-based indoor environment monitoring method provided in the embodiments of the present application can be any suitable type of robot with certain computing and control capabilities, for example, it can be implemented by the above-mentioned robot 100. In some embodiments, any robot-based indoor environment monitoring method provided in the embodiments of the present application can be implemented by calling the computer program instructions stored in the memory of the robot by the robot's processor.

[0065] See also Figure 2 , Figure 2 The flowchart of the robot-based indoor environment monitoring method provided in some embodiments of the present application is schematically shown.

[0066] like Figure 2 As shown, the robot-based indoor environment monitoring method includes but is not limited to the following steps S100-S300:

[0067] S100: The robot collects air quality data while walking according to the global path, and generates an air optimization index based on the air quality data.

[0068] In this step, the global path refers to the navigation path of the target area where indoor environment monitoring is required. The global path is used to instruct the robot to travel along the global path to monitor and collect air quality data of the indoor environment of the target area.

[0069] In some embodiments, after the robot determines the target area that needs to be monitored for indoor environment and obtains the map corresponding to the target area, it plans a global path based on the map of the target area using a path planning algorithm.

[0070] In some embodiments, the robot can directly obtain the global path of the indoor environment of the target monitoring area. The global path can be stored in the robot's local storage, cloud or server, etc. The robot obtains the global path from the local storage, cloud or server through a wired communication connection or a wireless communication connection (such as CAN bus, Ethernet, Wi-Fi and Bluetooth, etc.).

[0071] Specifically, the robot drives according to the global path, and while driving along the global path, it monitors the air in the indoor environment of the target area in real time through air quality sensors (such as PM2.5, CO2 concentration, temperature and humidity, and harmful gas sensors) to collect air quality data. Based on the obtained air quality data (including PM2.5 concentration, PM10 concentration, CO2 concentration, temperature, humidity, and harmful gas concentration, etc.), the robot calculates the corresponding air optimization index, which combines multiple air quality parameters (such as PM2.5 concentration, PM10 concentration, CO2 concentration, ozone concentration, temperature and humidity, etc.) to reflect the overall quality of the air.

[0072] It is understandable that generating an air optimization index means converting a variety of air quality parameters (such as PM2.5 concentration, PM10 concentration, CO2 concentration, ozone concentration, temperature and humidity, etc.) into a unified index value. Each air quality parameter has its own specific calculation method and standard, and different air quality levels are defined according to the standard. Among them, PM2.5 (fine particulate matter) refers to particles in the air with a diameter less than or equal to 2.5 microns, and PM10 (inhalable particulate matter) refers to particles in the air with a diameter less than or equal to 10 microns.

[0073] In some embodiments, generating an air optimization index based on air quality data includes: mapping each air quality parameter in the acquired air quality data according to its concentration value and the corresponding standard range (e.g., U.S. EPA, Chinese national standard, or any other suitable standard), calculating a corresponding optimization score, thereby obtaining the optimization score of each air quality parameter, and substituting the optimization scores corresponding to all air quality parameters into a fusion calculation formula to calculate the air optimization index. Those skilled in the art can set a fusion calculation formula based on the impact of each air quality parameter on human health and other conditions. For example, the fusion calculation formula can be S=α*A+β*B+λ*C+δ*D, where A, B, C, and D are the optimization scores of each quality parameter, α, β, λ, and δ are known coefficients of each quality parameter, and are all negative values, and S is the air optimization index.

[0074] For example, the concentration values ​​of PM2.5, PM10, CO2 and ozone (in μg / m 3 ) are 15.6, 10.6, 54.3 and 30.5 respectively. The corresponding optimization scores after calculation are 60, 30, 70 and 90 respectively. The fusion calculation formula is S=(-0.24)*A+(-0.52)*B+(-0.33)*C+(-0.12)*D, where A is the optimization score of PM2.5, B is the optimization score of PM10, C is the optimization score of CO2, and D is the optimization score of ozone. The optimization scores of PM2.5, PM10, CO2 and ozone are substituted into the fusion calculation formula to calculate the air optimization index.

[0075] For example, assume that there is an optimized score calculation standard for PM2.5 as shown in Table 1 below:

[0076] Table 1:

[0077]

[0078]

[0079] Assuming PM2.5 concentration is 20 μg / m 3 According to the standards shown in Table 1, the optimized score of PM2.5 is in the range of 51-100. It can be understood that other air quality parameters, such as CO2 concentration, ozone concentration, etc., also have relevant calculation standards. To calculate the optimized scores of other air quality parameters, reference can be made to the schematic illustration of the optimized score for calculating the PM2.5 concentration mentioned above. Of course, there can be other ways to calculate the optimized scores of air quality parameters. Those skilled in the art can choose any suitable method for calculation according to actual needs, and the embodiments of the present application do not impose any limitation on this.

[0080] S200: If the air optimization index is less than a preset index threshold, determining a target local area according to the air quality data, wherein the target local area is an area where an air optimization operation needs to be performed.

[0081] In this step, a preset index threshold is pre-set, and the preset index threshold represents the minimum standard required for air optimization. Those skilled in the art can set a suitable preset index threshold based on empirical data and actual needs.

[0082] Specifically, the air optimization index is compared with a preset index threshold. If the air optimization index is less than the preset index threshold, it indicates that the air quality of the current indoor environment is poor and needs to be optimized to obtain an indoor environment with good air quality. The robot compares the concentration values ​​of each air quality parameter (such as PM2.5, PM10 and CO2) in the air quality data of each local area collected and obtained by the air quality sensor with the concentration value standards of each air quality parameter. If there is a concentration value of the air quality parameter greater than or equal to the corresponding concentration value standard, or if the concentration values ​​of each air quality parameter are greater than or equal to the corresponding concentration value standard, it means that the air quality data collected in the local area is poor, and the local area corresponding to the poor air quality data is determined as the target local area, wherein the target local area is an area where the air quality is poor and an air optimization operation needs to be performed. It can be understood that the target local area includes one or more, and the area, shape, etc. of different target local areas may be the same or different.

[0083] S300: Control the robot to perform air optimization operations on the target local area.

[0084] Specifically, after determining the target local area, the robot performs appropriate air optimization operations according to the specific air quality conditions of each target local area. For example, in some embodiments, the air quality data of a specific target local area shows that the air humidity of the target local area is lower than the preset humidity standard, indicating that the air humidity of the target local area is low and the air in the target local area needs to be humidified. The robot drives to the target local area and controls the humidifier equipped with itself to start working to spray water mist or other humidifiers to humidify the air in the target local area, thereby completing the air optimization operation for the target local area.

[0085] For another example, in some embodiments, the air quality data of a specific target local area shows that the solid particle concentration (including, for example, PM2.5 and PM10, etc.) of the target local area is higher than a preset concentration standard, indicating that the solid particle concentration in the target local area is relatively high and the solid particles in the target local area need to be removed. The robot drives to the target local area and controls the air purification and filtration system equipped with it to start working, and absorbs the solid particles in the air through, for example, a HEPA filter to effectively remove the fine solid particles in the air, thereby completing the air optimization operation for the target local area.

[0086] It is understandable that the air quality conditions in each target local area may be the same or different, and therefore the robot's air optimization operations on the target local areas may also be the same or different. The above embodiment is only a schematic illustration of the robot's air optimization operations on the target local areas, and does not impose any limitations on any conditions such as the air quality conditions and air optimization operations of other target local areas.

[0087] Through the above method, the embodiment of the present application collects air quality data through robot movement, and when the air quality data meets the air optimization conditions, it can perform air optimization operations on the local area where the air needs to be optimized. In this way, the air quality data of the indoor environment can be monitored in all directions, and the air optimization operations can be automatically performed, which saves time and effort, improves the efficiency of air optimization, provides a good indoor air environment, and enhances user experience.

[0088] In some embodiments, determining the target local area according to the air quality data includes but is not limited to the following steps S210-S230:

[0089] S210: Extracting the air quality value corresponding to each path point from the air quality data.

[0090] S220: If the air quality value of the waypoint is greater than a preset quality threshold, the waypoint is determined to be a candidate waypoint.

[0091] Exemplarily, the global path has multiple path points, where a path point represents a specific location point where the robot needs to collect air quality data. The air quality data includes multiple air quality values. The robot travels to each path point along the global path, collects corresponding air quality values ​​(such as PM2.5, PM10, CO2 concentration, temperature and humidity, etc.) at each path point, and the collected air quality values ​​form the air quality data of each path point, thus obtaining the air quality data of the indoor environment covered by the global path. It can be understood that each air quality value corresponds to a path point one by one. For example, please refer to Figure 3 , Figure 3The gray dots 51 and black dots 52 shown in the figure are path points of the global path L2, and the robot collects and obtains the corresponding air quality value at each gray dot 51 or black dot 52.

[0092] After the robot drives along the global path and collects air quality data, it extracts the air quality value corresponding to each path point of the global path from the air quality data, and compares the air quality value of each path point with the preset quality threshold. If the air quality value of a specific path point is greater than the preset quality threshold, it means that the air quality in the area where the path point is located is poor, and the air quality in the area where the path point is located needs to be optimized, and the path point is determined as a candidate path point. For example, please refer to Figure 3 , Figure 3 The gray dots 51 and black dots 52 shown in the figure are the path points of the global path L2. The robot collects and obtains the corresponding air quality value at each gray dot 51 or black dot 52. The black dot 52 indicates that the air quality value collected at this path point is greater than the preset quality threshold, and the black dot 52 is determined as a candidate path point.

[0093] It is easy to understand that those skilled in the art can set the preset quality threshold according to empirical data and actual needs, and the embodiments of the present application do not impose any limitation on this.

[0094] S230: Determine a target local area based on multiple candidate path points.

[0095] In this step, the target local area is an area to be air optimized consisting of multiple path points.

[0096] After obtaining several candidate path points, the robot uses a clustering algorithm or other area division methods to define and divide the candidate path points into several target local areas.

[0097] In some embodiments, the area and shape of the local area can be preset, and the coverage of the local area can be obtained. With any candidate path point as the center of the local area, several candidate path points located within the coverage of the local area are screened out. If the number of candidate path points located within the coverage of the local area is greater than or equal to a preset number threshold, the local area centered on the candidate path point is determined as the target local area. For example, see Figure 4 , the gray dots 51 and the black dots 52 are path points of the global path L2, and the shape of the local area 60 is as follows Figure 4 The area of ​​the local area 60 is as shown in the rectangle. Figure 4As shown in the size, the preset number threshold is 2, and the candidate path point A is selected as the center of the local area 60. Obviously, there are 2 candidate path points within the coverage range of the local area 60, namely, candidate path point A and candidate path point B. That is, the number of candidate path points within the coverage range of the local area 60 is 2, which is equal to the preset number threshold 2. Then the local area 60 centered on the candidate path point A is determined as the target local area.

[0098] It should be understood that the area and shape of the local area can also be any other suitable area and shape. For example, the shape of the local area can be square, circle, etc. In addition, any suitable position in the indoor environment can be selected as the center of the local area. Technical personnel in this field can also set a preset quantity threshold based on actual needs and empirical data, and the embodiments of the present application do not impose any limitations on this.

[0099] In some embodiments, determining a target local area based on multiple candidate path points specifically includes but is not limited to the following steps S2310-S2320:

[0100] S2310: Determine the candidate path points that meet the location density condition as the target path points.

[0101] The dense location condition refers to that the distances between the candidate path points are relatively short or the candidate path points are concentrated in a specific area.

[0102] Specifically, after obtaining a number of candidate path points, the candidate path points are analyzed by a density analysis algorithm to determine the target path points that meet the location density condition. The density analysis algorithm includes but is not limited to: DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm, OPTICS (Ordering Points To Identify the Clustering Structure) algorithm and K-means / K-medoids algorithm.

[0103] In some embodiments, among all candidate path points, a number of candidate path points concentrated in a specific area are screened out through a density analysis algorithm, that is, candidate path points that meet the location density condition, and the candidate path points that meet the location density condition are determined as target path points. For example, see Figure 4 , Figure 4 The 12 candidate path points within the coverage of the local area 70 shown in the figure are candidate path points that meet the location density condition, and the 12 candidate path points within the local area 70 are all target path points.

[0104] Of course, any other suitable algorithm or any other suitable method may be used to screen out candidate path points that meet the location density condition to obtain the target path points, and the embodiments of the present application do not impose any limitation on this.

[0105] Exemplarily, in some embodiments, determining a candidate path point that meets the location density condition as a target path point specifically includes but is not limited to the following steps S2311-S2313:

[0106] S2311: Select any candidate path point from all candidate path points as a reference path point.

[0107] S2312: Calculate the distance between the reference path point and each candidate path point, and record each distance in a distance set belonging to the reference path point.

[0108] In some embodiments, among all the candidate path points, any one candidate path point is selected as a reference path point, and the distance between the reference path point and other candidate path points is calculated based on the reference path point using Euclidean distance or other appropriate distance measurement methods, and all calculated distance values ​​are recorded in a distance set, which is the distance set belonging to the reference path point.

[0109] S2313: If there is at least one distance in the distance set that is less than a preset distance threshold, the reference path point is determined to be the target path point.

[0110] Specifically, the distance set is traversed to determine the size relationship between the distance value in the distance set and the preset distance threshold. If there is at least one distance value in the distance set that is less than the preset distance threshold, it means that other candidate path points within the preset distance threshold range from the reference path point and the reference path point meet the position density condition, and the reference path point is determined to be the target path point. Among them, those skilled in the art can set the preset distance threshold according to actual needs and empirical data, etc., and the embodiments of the present application do not impose any limitation on this.

[0111] For example, see Figure 5 , Figure 5 A candidate path point C is selected as a reference path point, and a preset distance threshold is R. The distances between candidate path point C and other candidate path points are calculated as a distance set, wherein at least the distance d between candidate path point C and candidate path point D in the distance set is less than the preset distance threshold R, then candidate path point C (reference path point C) is determined to be the target path point.

[0112] S2320: Generate a minimum bounding rectangle that includes all target path points, where the minimum bounding rectangle is the target local area.

[0113] Specifically, after determining the target path points that meet the position density condition, the position information of each target path point is obtained, and based on the position information of each target path point, a geometric algorithm (for example, a convex hull or a bounding box algorithm, etc.) is used to generate a minimum enclosing rectangle covering all target path points. The minimum enclosing rectangle is the target local area.

[0114] For example, see Figure 4 , Figure 4 The 12 candidate path points within the coverage of the local area 70 shown in FIG. 7 are the target path points, and the generated minimum bounding rectangle is as follows: Figure 4 The rectangle 80 shown in FIG. 8 is a rectangle that corresponds to the local area (the minimum circumscribed rectangle) and is the target local area.

[0115] In some embodiments, generating a minimum bounding rectangle containing all target path points includes, but is not limited to, the following steps S2321-S2323:

[0116] S2321: Determine the target path point with the smallest horizontal coordinate among all target path points as the first path point, and the target path point with the largest horizontal coordinate as the second path point.

[0117] S2322: Determine the target path point with the smallest vertical coordinate among all target path points as the third path point, and the target path point with the largest vertical coordinate as the fourth path point.

[0118] Specifically, after determining several target path points, the robot obtains the coordinate position information of each target path point, and according to the coordinate position information of each target path point, finds the target path point with the smallest horizontal coordinate and the target path point with the largest horizontal coordinate among all the target path points, and determines the target path point with the smallest horizontal coordinate and the target path point with the largest horizontal coordinate as the first path point and the second path point, respectively.

[0119] Furthermore, based on the coordinate position information of each target path point, the target path point with the smallest vertical coordinate and the target path point with the largest vertical coordinate are found among all the target path points, and the target path point with the smallest vertical coordinate and the target path point with the largest vertical coordinate are respectively determined as the third path point and the fourth path point.

[0120] S2323: Determine that the rectangles passing through the first path point, the second path point, the third path point and the fourth path point respectively are the minimum circumscribed rectangles.

[0121] Specifically, taking the first path point and the second path point as the left boundary and the right boundary respectively, determine the first straight line and the second straight line passing through the first path point and the second path point respectively, and taking the third path point and the fourth path point as the upper boundary and the lower boundary respectively, determine the third straight line and the fourth straight line passing through the third path point and the fourth path point respectively, the rectangle formed by the intersection of the first straight line, the second straight line, the third straight line and the fourth straight line is the minimum circumscribed rectangle, the minimum circumscribed rectangle passes through the first path point, the second path point, the third path point and the fourth path point respectively, and the minimum rectangular area is the target local area.

[0122] For example, see again Figure 3 , Figure 3 The local area 90 shown in the figure includes 12 target path points, the target path point E with the smallest horizontal coordinate is the first path point, the target path point F with the largest horizontal coordinate is the second path point, the target path point G with the smallest vertical coordinate is the third path point, the target path point H with the largest vertical coordinate is the fourth path point, the first straight line passing through the first path point E is EL, the second straight line passing through the second path point F is FL, the third straight line passing through the third path point G is GL, and the fourth straight line passing through the fourth path point H is HL. Among them, the first straight line EL and the third straight line GL intersect at point O1, the first straight line EL and the fourth straight line HL intersect at point O4, the second straight line FL and the third straight line GL intersect at point O2, the second straight line FL and the fourth straight line HL intersect at point O3, and the first straight line EL, the second straight line FL, the third straight line GL and the fourth straight line HL intersect at point O3, and the rectangle O formed by the intersection of the first straight line EL, the second straight line FL, the third straight line GL and the fourth straight line HL 1 O 2 O 3 O 4 This is the minimum enclosing rectangle.

[0123] In some embodiments, the robot is controlled to perform air optimization operations on the target local area, including but not limited to the following steps S310-S330:

[0124] S310: Obtain the unit optimization radius of the robot, where the unit optimization radius is the longest distance that can be covered by the air optimization operation performed by the robot at each position.

[0125] In this step, the unit optimization radius refers to the longest distance that the robot can cover when performing air optimization operations at each position. The unit optimization radius can be set according to the characteristics of the air optimization device configured on the robot and the performance of the robot. For example, when performing air optimization operations, if the air optimization device is fixed to the robot, that is, the robot does not need to lift, extend, and rotate the air optimization device through action components (such as a robotic arm, etc.), then the unit optimization radius is the effective coverage distance of the air optimization device. If the air optimization device is not fixed to the robot, that is, the robot needs to lift, extend, and rotate the air optimization device through action components (such as a robotic arm, etc.), then the unit optimization radius is the sum of the effective coverage distance of the air optimization device and the operating distance of the robotic arm.

[0126] In some embodiments, the unit optimization radius is stored in the robot's local storage, and the robot can obtain the unit optimization radius from the local storage.

[0127] In some embodiments, the unit optimization radius is stored in the cloud or server, and the robot can communicate with the cloud or server through wired communication or wireless communication (such as Ethernet, CAN bus, Wi-Fi and Bluetooth, etc.), and obtain the unit optimization radius from the cloud or server through wired communication or wireless communication.

[0128] S320: Planning an air optimization path in the target local area based on the unit optimization radius.

[0129] In some embodiments, the robot obtains the shape and area size of the target local area, and plans an air optimization path in the target local area based on the unit optimization radius using a path planning algorithm (such as an A* algorithm or a Dijkstra algorithm, etc.). The coverage range of the robot's air optimization operations at all path points (positions) in the air optimization path can fully cover the target local area, so that after the robot performs air optimization operations according to the air optimization path, it can fully optimize the air in the target local area.

[0130] In some embodiments, based on the unit optimization radius, an air optimization path is planned in the target local area, specifically including but not limited to the following steps S3210-S3250:

[0131] S3210: Obtain a target circular area, where the target circular area is a circular area where an optimized position has been found, the center position of the target circular area is the optimized position, and the radius of the target circular area is equal to the unit optimization radius.

[0132] Exemplarily, the robot uses the navigation and positioning system configured on itself to locate the current position of the robot, finds the position closest to the current position of the robot on the boundary of the target local area (minimum circumscribed rectangle) as the first optimized position, and makes a circular area with the unit optimization radius as the radius and the first optimized position found as the center. The circular area is the target circular area, the center position of the target circular area is the optimized position, and the radius of the target circular area is equal to the unit optimization radius.

[0133] In some embodiments, a position can be arbitrarily determined within the target local area (i.e., the minimum circumscribed rectangle) as the first optimized position found, and a circular area can be drawn with the unit optimization radius as the radius and the first optimized position as the center. The circular area is the target circular area.

[0134] Of course, there may be other methods to determine the first optimal position, and those skilled in the art may select any appropriate method according to actual needs, and the embodiments of the present application do not impose any limitation on this.

[0135] S3220: Collect multiple pending positions in sequence according to a preset step length on the boundary of the target circular area.

[0136] Among them, those skilled in the art can set the preset step length according to actual needs and empirical data, and the preset step length can be expressed by length, radian or angle, etc., and the embodiments of the present application do not impose any limitations on this.

[0137] Specifically, the robot collects multiple pending positions on the boundary of the target circular area in sequence according to a preset step length, wherein the pending positions are located on the circumference of the target circular area, and the preset step length represents the spacing between each pending position.

[0138] S3230: Determine a reference circular region that is tangent to the target circular region at a to-be-determined position, wherein the position of the tangent point between the reference circular region and the target circular region is the to-be-determined position.

[0139] Specifically, for each pending position, the pending position is used as the tangent point to determine a reference circular area that is tangent to the target circular area at the pending position. Obviously, the position where the tangent point (i.e., the tangent point) of the reference circular area and the target circular area is located is the pending position. It can be understood that the radius of the reference circular area can be a unit optimization radius or other values.

[0140] For example, see Figure 6 , Figure 6The rectangular area 91 shown in is the target local area (minimum circumscribed rectangle). On the boundary of the rectangular area 91, the position closest to the robot is the position of point Q1, which is the first optimized position. With point Q1 as the center and the unit optimization radius as the radius, a circular area is drawn. The circular area is the target circular area. The target circular area is Figure 6 The circular area N is shown. Figure 6 In the figure, the pending positions of the target circular area N include 9 (i.e., the positions of the circular points on the target circular area N, such as K1 and K2), and the reference circular area tangent to the target circular area N at the pending positions includes the circular area M1 and the circular area M2, wherein the reference circular area M1 is tangent to the target circular area N at the pending position K1, the center of the reference circular area M1 is point Q2, and the position of point Q2 is the center position of the reference circular area M1, the reference circular area M2 is tangent to the target circular area N at the pending position K2, the center of the reference circular area M2 is point Q3, and the position of point Q3 is the center position of the reference circular area M2. It can be understood that Figure 6 The radii of the reference circular area M1 and the reference circular area M2 shown in FIG. 5 are the same as the radius of the target circular area N, and are both unit optimization radii.

[0141] S3240: Filter out a reference circle area that meets a preset condition from among multiple reference circle areas as a new target circle area, and the center position of the new target circle area is the optimized position.

[0142] Among them, the preset conditions include the size, position, overlapping area of ​​the reference circle area and other reference circle areas, etc. Of course, technical personnel in this field can also add, delete or modify the preset conditions according to actual needs, and the embodiments of the present application do not impose any limitations on this.

[0143] Specifically, after determining several reference circular areas, the robot screens out reference circular areas that meet the preset conditions from among the several reference circular areas according to preset conditions, and uses the reference circular areas that meet the preset conditions as new target circular areas. Obviously, the center position of the new target circular area is the optimized position.

[0144] In some embodiments, selecting a reference circle area that meets a preset condition from multiple reference circle areas as a new target circle area specifically includes but is not limited to the following steps S3241-S3242:

[0145] S3241: Calculate the overlapping area between each reference circle region and the target local region.

[0146] Specifically, after determining a number of reference circular regions that are circumscribed to the target circular region at the to-be-determined position, for each reference circular region, the overlapping area between the reference circular region and the target local region (ie, the minimum circumscribed rectangle) is calculated.

[0147] In some embodiments, if the reference circular area completely contains the target local area (ie, the diameter of the reference circular area is greater than or equal to the diagonal length of the rectangle), the overlapping area of ​​the reference circular area and the target local area is the area of ​​the target local area (minimum circumscribed rectangle).

[0148] In some embodiments, if the reference circular area partially overlaps with the target local area, numerical integration or intersection calculation can be performed using a geometric library (such as CGAL, Shapely, etc.). For example, the Monte Carlo integration method is used to calculate the overlapping area of ​​the reference circular area and the target local area, that is, the overlapping area of ​​the reference circular area and the target local area is estimated by randomly sampling the overlapping area of ​​the reference circular area and the target local area.

[0149] Alternatively, an analytical geometry method may be used to obtain the position of the intersection of the reference circular region and the target local region boundary, and the overlapping area of ​​the reference circular region and the target local region may be calculated based on the position of the intersection and the center position of the reference circular region.

[0150] S3242: Determine whether the reference circular region with the largest overlapping area meets a preset condition, and the reference circular region with the largest overlapping area is a new target circular region.

[0151] Specifically, after calculating the overlapping areas of several reference circular areas and the target local area, the maximum overlapping area among the overlapping areas is obtained, and it is determined that the reference circular area corresponding to the maximum overlapping area meets the preset conditions, that is, the reference circular area corresponding to the maximum overlapping area is the new target circular area, thereby screening out the reference circular area that meets the preset conditions as the new target circular area.

[0152] S3250: Generate an air optimization path based on all optimized positions.

[0153] Specifically, a path planning algorithm (such as an A* algorithm or a Dijkstra algorithm, etc.) is used to connect all determined optimization positions (i.e., the center positions of the target circular areas) to generate an optimal air optimization path, wherein the air optimization path includes multiple optimization positions, and the optimization positions are the center positions of each target circular area.

[0154] S330: Control the robot to walk along the air optimization path and perform air optimization operations on the target local area.

[0155] In some embodiments, the robot determines the air optimization device required to be used when performing air optimization operations on the target local area based on the air quality data of the target local area. For example, the air quality data of the target local area shows that the air humidity in the target local area is higher than a preset threshold, indicating that the air in the target local area needs to be dehumidified. In this way, it can be determined that the air optimization device required to be used when performing air optimization operations (dehumidification operations) on the target local area is a dehumidifier.

[0156] Of course, the air optimization operation may also be any other suitable operation or operations, and therefore the air optimization device required to be used when performing the air optimization operation may also be any other suitable operation or operations, and the embodiments of the present application do not impose any limitations on this.

[0157] Specifically, after planning the air optimization path, the robot walks along the air optimization path, and controls the target air optimization device configured on itself (that is, the air optimization device required to perform corresponding air optimization operations on the target local area) to start working, and performs corresponding air optimization operations on the target local area. In this way, the air in the target local area can be optimized, providing an indoor environment with good air quality, and improving the user experience.

[0158] In some embodiments, the robot is controlled to walk along the air optimization path and perform air optimization operations on the target local area, specifically including but not limited to the following steps S3310:

[0159] S3310: Control the robot to walk along the air optimization path, and start the air purifier to purify the air in the target local area.

[0160] In this step, the robot is equipped with an air purifier and / or a dehumidifier. The air purifier is used to purify the air, and the dehumidifier is used to dehumidify the air.

[0161] Specifically, after planning the air optimization path, the robot walks along the air optimization path and starts the air purifier to work, staying at each optimization position of the air optimization path long enough so that the air purifier can fully purify the air at each optimization position and remove pollutants in the air (such as PM2.5, PM10, ozone and volatile organic compounds, etc.), thereby completing the purification of the air in the target local area, improving the air quality of the indoor environment, and ensuring a better purification effect.

[0162] And / or, in some embodiments, the robot is controlled to walk along the air optimization path and perform air optimization operations on the target local area, specifically including but not limited to the following steps S3320:

[0163] S3320: Control the robot to walk along the air optimization path, and start the dehumidifier to work to reduce the air humidity in the target local area.

[0164] Specifically, after planning the air optimization path, the robot walks along the air optimization path and starts the dehumidifier to work, staying at each optimization position of the air optimization path long enough to allow the dehumidifier to fully dehumidify the air at each optimization position and reduce the air humidity in the target local area, thereby completing the work of dehumidifying the air in the target local area and reducing the deterioration of air quality or mold growth caused by humidity problems.

[0165] Of course, the robot can also be equipped with any other suitable air optimization device, and when the robot walks along the air optimization path, the corresponding air optimization device is activated to work to optimize the air in the target local area, and the embodiments of the present application do not make any limitation on this. For example, in some embodiments, the robot can also be equipped with a humidifier, and when the air in the target local area needs to be humidified, the robot walks along the air optimization path and activates the humidifier to work to humidify the air in the target local area.

[0166] It should be noted that, in each of the above-mentioned embodiments, there is not necessarily a certain order between the above-mentioned steps. A person of ordinary skill in the art can understand, based on the description of the embodiments of the present application, that in different embodiments, the above-mentioned steps may have a different execution order, that is, the steps may be executed in parallel, may be executed interchangeably, and so on.

[0167] To summarize, the robot-based indoor environment monitoring method provided in the embodiment of the present application includes: the robot collects air quality data while walking according to the global path, and generates an air optimization index based on the air quality data; if the air optimization index is less than a preset index threshold, the target local area is determined according to the air quality data, wherein the target local area is an area where air optimization operations need to be performed, and the robot is controlled to perform air optimization operations on the target local area.

[0168] The embodiment of the present application collects air quality data through robot movement, and when the air quality data meets the air optimization conditions, performs air optimization operations on the areas requiring air optimization. In this way, the air quality can be monitored in all directions and air optimization operations can be automatically performed, saving time and effort, improving air optimization efficiency, and enhancing user experience.

[0169] As another aspect of the embodiment of the present application, the embodiment of the present application provides a robot-based indoor environment monitoring device. The robot-based indoor environment monitoring device can be a software module, which includes a number of instructions stored in a memory, and the processor can access the memory and call the instructions for execution to complete the robot-based indoor environment monitoring method described in the above various embodiments.

[0170] In some implementations, the robot-based indoor environment monitoring device can also be built with hardware devices. For example, the robot-based indoor environment monitoring device can be built with one or more chips, and each chip can work in coordination with each other to complete the robot-based indoor environment monitoring method described in each of the above embodiments. For another example, the robot-based indoor environment monitoring device can also be built with various logic devices, such as a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine) or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, or any combination of these components.

[0171] See also Figure 7 , Figure 7 The schematic diagram of the structure of the robot-based indoor environment monitoring device provided in some embodiments of the present application is shown schematically. It is easy to understand that the robot-based indoor environment monitoring device can be configured on the robot.

[0172] For example, Figure 7 As shown, the robot-based indoor environment monitoring device 300 includes: a generating module 310 , a determining module 320 and an executing module 330 .

[0173] The generating module 310 is used for collecting air quality data when the robot walks according to the global path, and generating an air optimization index based on the air quality data. The determining module 320 is used for determining the target local area according to the air quality data if the air optimization index is less than a preset index threshold, wherein the target local area is the area where the air optimization operation needs to be performed. The executing module 330 is used for controlling the robot to perform the air optimization operation on the target local area.

[0174] In some embodiments, the global path includes multiple path points, the air quality data includes multiple air quality values, the air quality values ​​are collected by the robot at each path point, and the determination module 320 is specifically used to: extract the air quality value corresponding to each path point in the air quality data, if the air quality value of the path point is greater than a preset quality threshold, then determine the path point as a candidate path point, and determine the target local area based on multiple candidate path points.

[0175] In some embodiments, the determination module 320 is further specifically used to: determine the candidate path points that meet the location density condition as the target path points, generate a minimum bounding rectangle containing all the target path points, and the minimum bounding rectangle is the target local area.

[0176] In some embodiments, the determination module 320 is also specifically used to: determine the target path point with the smallest horizontal coordinate among all target path points as the first path point, and the target path point with the largest horizontal coordinate as the second path point, determine the target path point with the smallest vertical coordinate among all target path points as the third path point, and the target path point with the largest vertical coordinate as the fourth path point, and determine the rectangle that passes through the first path point, the second path point, the third path point and the fourth path point respectively as the minimum circumscribed rectangle.

[0177] In some embodiments, the determination module 320 is also specifically used to: select any candidate path point from all candidate path points as a reference path point, calculate the distance between the reference path point and each candidate path point, and record each distance in a distance set belonging to the reference path point; if there is at least one distance in the distance set that is less than a preset distance threshold, the reference path point is determined to be the target path point.

[0178] In some embodiments, the execution module 330 is specifically used to: obtain the unit optimization radius of the robot, the unit optimization radius is the longest distance that the air optimization operation performed by the robot at each position can cover, based on the unit optimization radius, plan an air optimization path in the target local area, control the robot to walk along the air optimization path, and perform air optimization operations on the target local area.

[0179] In some embodiments, the air optimization path includes multiple optimization positions, and the execution module 330 is also specifically used to: obtain a target circular area, the target circular area is a circular area where the optimization position has been found, the center position of the target circular area is the optimization position, the radius of the target circular area is equal to the unit optimization radius, and multiple pending positions are collected in sequence according to a preset step size on the boundary of the target circular area, and a reference circular area that is tangent to the target circular area at the pending position is determined, wherein the position of the tangent point of the reference circular area and the target circular area is the pending position, and a reference circular area that meets the preset conditions is screened out from multiple reference circular areas as a new target circular area, and the center position of the new target circular area is the optimization position, and an air optimization path is generated based on all the optimization positions.

[0180] In some embodiments, the execution module 330 is further specifically used to: calculate the overlapping area between each reference circular area and the target local area, determine that the reference circular area with the largest overlapping area meets the preset conditions, and the reference circular area with the largest overlapping area is the new target circular area.

[0181] In some embodiments, the robot is equipped with an air purifier and / or a dehumidifier, and the execution module 330 is also specifically used to: control the robot to walk along the air optimization path, and start the air purifier to work to purify the air in the target local area, and / or control the robot to walk along the air optimization path, and start the dehumidifier to work to reduce the air humidity in the target local area.

[0182] It should be noted that the robot-based indoor environment monitoring device 300 can execute the robot-based indoor environment monitoring method provided in the embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method. For technical details not fully described in the embodiment of the robot-based indoor environment monitoring device, please refer to the robot-based indoor environment monitoring method provided in the embodiment of the present application.

[0183] See also Figure 8 , Figure 8 The schematic diagram of the structure of the robot provided by some embodiments of the present application is shown schematically. The robot 100 includes one or more processors 110 and a processor 120. The processor 120 is connected to the one or more processors 110, for example, connected to the processor 110 via a bus.

[0184] The processor 110 is configured to support the robot 100 to perform the corresponding functions in the method in the above method embodiment. The processor 110 can be a central processing unit (CPU), a network processor (NP), a hardware chip or any combination thereof. The above hardware chip can be an application specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof.

[0185] The processor 120 is used to store program codes, etc. The processor 120 may include a volatile memory (VM), such as a random access memory (RAM); the processor 120 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD); the processor 120 may also include a combination of the above-mentioned types of memory.

[0186] The processor 120 can be used to store non-volatile software programs, non-volatile computer executable programs and modules, such as program instructions / modules corresponding to the robot-based indoor environment monitoring method in the embodiment of the present application. The processor 110 executes various functional applications and data processing of the robot-based indoor environment monitoring method and the robot-based indoor environment monitoring device by running the non-volatile software programs, instructions and modules stored in the processor 120, that is, realizes the functions of the various modules or units of the robot-based indoor environment monitoring method and the robot-based indoor environment monitoring device provided in the above method embodiment.

[0187] The processor 120 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function. The data storage area may store data created based on the use of the processor 110 and the robot-based indoor environment monitoring device, etc. In some embodiments, the processor 120 may optionally include a memory remotely disposed relative to the processor 110, and these remotely disposed memories may be connected to the processor 110 and the robot-based indoor environment monitoring device via a network. It is understood that examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0188] One or more modules are stored in the processor 120. When executed by the one or more processors 110, the robot-based indoor environment monitoring method in any of the above method embodiments is executed, for example, the method steps described in the above method embodiments are executed to realize the functions of the modules described in the above device embodiments.

[0189] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. The computer program includes program instructions. When the program instructions are executed by a processor, the robot executes any one of the robot-based indoor environment monitoring methods described in the aforementioned embodiments.

[0190] Through the description of the above implementation methods, those skilled in the art can understand that all or part of the processes in the above embodiment methods can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a disk, an optical disk, a register, a read-only memory (ROM), a flash memory, a hard disk, a magnetic surface storage, a removable disk, a CD-ROM, or a random access memory (RAM), etc.

[0191] The above disclosure is only the preferred embodiment of the present application, and it certainly cannot be used to limit the scope of rights of the present application. Equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A robot-based indoor environment monitoring method, characterized in that: include: The robot collects air quality data when walking according to the global path, and generates an air optimization index based on the air quality data; If the air optimization index is less than a preset index threshold, determining a target local area according to the air quality data, wherein the target local area is an area where an air optimization operation needs to be performed; The robot is controlled to perform an air optimization operation on the target local area.

2. The indoor environment monitoring method according to claim 1, characterized in that: The global path includes a plurality of path points, the air quality data includes a plurality of air quality values, the air quality value is collected by the robot at each of the path points, and determining the target local area according to the air quality data includes: Extracting the air quality value corresponding to each of the path points from the air quality data; If the air quality value of the path point is greater than a preset quality threshold, determining the path point as a candidate path point; A target local area is determined based on the plurality of candidate path points.

3. The indoor environment monitoring method according to claim 2, characterized in that: The determining of the target local area based on the plurality of candidate path points comprises: Determine the candidate path points that meet the location density condition as the target path points; A minimum bounding rectangle including all the target path points is generated, where the minimum bounding rectangle is the target local area.

4. The indoor environment monitoring method according to claim 3, characterized in that: The generating of the minimum bounding rectangle containing all the target path points comprises: Determine the target path point with the smallest horizontal coordinate among all the target path points as the first path point, and the target path point with the largest horizontal coordinate as the second path point; Determine the target path point with the smallest ordinate among all the target path points as the third path point, and the target path point with the largest ordinate as the fourth path point; A rectangle that passes through the first path point, the second path point, the third path point, and the fourth path point, respectively, is determined to be a minimum circumscribed rectangle.

5. The indoor environment monitoring method according to claim 3, characterized in that: The step of determining a candidate path point that meets the location density condition as a target path point includes: Selecting any one of the candidate path points as a reference path point; Calculating the distances between the reference path point and each of the candidate path points, and recording each of the distances in a distance set belonging to the reference path point; If at least one distance in the distance set is less than a preset distance threshold, the reference path point is determined to be a target path point.

6. The indoor environment monitoring method according to any one of claims 1 to 5, characterized in that: The controlling the robot to perform an air optimization operation on the target local area includes: Obtaining a unit optimization radius of the robot, where the unit optimization radius is the longest distance that can be covered by the air optimization operation performed by the robot at each position; Based on the unit optimization radius, an air optimization path is planned in the target local area; The robot is controlled to walk along the air optimization path and perform air optimization operations on the target local area.

7. The indoor environment monitoring method according to claim 6, characterized in that: The air optimization path includes a plurality of optimization positions, and the air optimization path is planned in the target local area based on the unit optimization radius, including: Acquire a target circular area, wherein the target circular area is a circular area where an optimized position has been found, the center position of the target circular area is the optimized position, and the radius of the target circular area is equal to the unit optimized radius; Sequentially collecting a plurality of pending positions on the boundary of the target circular area according to a preset step length; Determine a reference circular area that is tangent to the target circular area at the to-be-determined position, wherein the position of the tangent point between the reference circular area and the target circular area is the to-be-determined position; A reference circle area satisfying a preset condition is selected from the plurality of reference circle areas as a new target circle area, and the center position of the new target circle area is an optimized position; An air optimized path is generated based on all the optimized positions.

8. The indoor environment monitoring method according to claim 7, characterized in that: The step of selecting a reference circle area satisfying a preset condition from the plurality of reference circle areas as a new target circle area comprises: Calculating the overlapping area between each reference circle region and the target local region; It is determined that the reference circle region with the largest overlapping area meets the preset conditions, and the reference circle region with the largest overlapping area is the new target circle region.

9. The indoor environment monitoring method according to claim 6, characterized in that: The robot is equipped with an air purifier and / or a dehumidifier; The controlling the robot to walk along the air optimization path and performing the air optimization operation on the target local area includes: Controlling the robot to walk along the air optimization path, and starting the air purifier to work so as to purify the air in the target local area; and / or, The robot is controlled to walk along the air optimization path, and the dehumidifier is started to work to reduce the air humidity in the target local area.

10. A robot, characterized in that: It includes a memory and a processor, the memory is connected to the processor, the processor is used to execute one or more computer programs stored in the memory, and when the processor executes the one or more computer programs, the robot implements the robot-based indoor environment monitoring method as described in any one of claims 1-9.

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