CN115456057A - User similarity calculation method and device based on sweeping robot and storage medium - Google Patents
User similarity calculation method and device based on sweeping robot and storage medium Download PDFInfo
- Publication number
- CN115456057A CN115456057A CN202211049711.9A CN202211049711A CN115456057A CN 115456057 A CN115456057 A CN 115456057A CN 202211049711 A CN202211049711 A CN 202211049711A CN 115456057 A CN115456057 A CN 115456057A
- Authority
- CN
- China
- Prior art keywords
- different users
- data
- sweeping robot
- track
- sweeping
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000010408 sweeping Methods 0.000 title claims abstract description 120
- 238000004364 calculation method Methods 0.000 title claims abstract description 28
- 238000003860 storage Methods 0.000 title claims abstract description 17
- 238000000034 method Methods 0.000 claims abstract description 66
- 238000005096 rolling process Methods 0.000 claims description 46
- 238000010586 diagram Methods 0.000 claims description 30
- 238000005070 sampling Methods 0.000 claims description 23
- 238000004590 computer program Methods 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 8
- 238000013519 translation Methods 0.000 claims description 4
- 238000013461 design Methods 0.000 description 9
- 238000004422 calculation algorithm Methods 0.000 description 8
- 230000006399 behavior Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000006855 networking Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
- G06V10/457—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by analysing connectivity, e.g. edge linking, connected component analysis or slices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/64—Three-dimensional objects
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Manipulator (AREA)
Abstract
The application discloses a user similarity calculation method and device based on a sweeping robot, a storage medium and an electronic device, and relates to the technical field of smart homes/smart families, wherein the user similarity calculation method based on the sweeping robot comprises the following steps: acquiring working track data of sweeping robots of different users in any historical time; generating map data of a ground area where the sweeping robot operates according to the working track data; and calculating the similarity of different users according to the map data of the ground area. According to the method and the device, the working track data of the sweeping robots of different users are obtained, the map data of the ground area where the sweeping robots operate are generated according to the working track data, the similarity of the different users is calculated according to the map data of the ground area, the result of the similarity can be used for identifying the users with similar living spaces, and the similar users are grouped to provide a basis for designing products and personalized services for the user groups of different groups subsequently.
Description
Technical Field
The application relates to the technical field of smart home, in particular to a user similarity calculation method and device based on a sweeping robot, a storage medium and an electronic device.
Background
With the popularization of smart homes, more and more smart home products such as sweeping robots support networking, remote control and personalized work modes, and a large amount of information related to daily life of users, such as preferred use modes, use time, clean areas and the like, is accumulated. In order to continuously improve intelligent home products and improve services such as personalized design and personalized recommendation of the intelligent home products, a service party needs to know living habits and characteristics of residence space of users, such as residence use areas and furniture placement modes, so as to group similar users, and accurate and personalized products and services can be provided according to characteristics of different user groups.
In the prior art, a method for measuring the similarity of the user is mainly based on user behavior data, such as browsing records, geographical positions and the like, but data of a physical space of the actual life of the user is relatively lacked, but the measurement of the similarity of the user from the perspective of a daily life space is very important for improving personalized design and personalized recommendation of smart home products. Therefore, how to analyze the living habits and the individual preferences of the users by using the data of the working process of the intelligent household products and identify similar users while ensuring the privacy of the users is a big challenge for the popularization and the use of the current intelligent household appliances.
Accordingly, there is a need in the art for a new user similarity calculation scheme based on a sweeping robot to solve the above problems.
Disclosure of Invention
The application aims to solve the technical problems, namely, the problems that data of physical space of actual life of a user is lack in the prior art, and similarity measurement of the user from the perspective of space of daily life becomes necessary for improving personalized design and personalized recommendation of smart home products are solved. According to the user similarity calculation method based on the sweeping robot, the work track data of the sweeping robot of different users can be obtained, the map data of the ground area of the sweeping robot operation can be generated according to the work track data, the similarity between different users can be calculated according to the map data of the ground area, the calculated similarity result can support subsequent user classification, clustering of similar user habits and the like, and accurate data are provided for design of smart home products, accurate advertisement putting and personalized recommendation.
In a first aspect, the present application provides a user similarity calculation method based on a sweeping robot, including:
acquiring working track data of sweeping robots of different users in any historical time;
generating map data of a ground area where the sweeping robot operates according to the working track data;
and calculating the similarity between different users according to the map data of the ground area.
In one technical solution of the method for calculating user similarity based on a sweeping robot, the acquiring of the work trajectory data of sweeping robots of different users in a period of historical time includes:
marking the route points and the obstacle points of the sweeping robot of different users in the working process within a period of historical time, and generating a track scatter diagram.
In an embodiment of the method for calculating user similarity based on a sweeping robot, the generating map data of a ground area where the sweeping robot operates according to the work trajectory data includes:
carrying out graphic binaryzation on the track scatter diagram;
setting an initial rolling ball radius value, and extracting an initial contour map of a track scatter diagram after graphic binarization based on a rolling ball method according to the initial rolling ball radius;
acquiring the number of connected domains of an enclosed area of the initial contour map;
and extracting the contour map of the track scatter diagram after graphic binarization as map data of the ground area of the sweeping robot operation based on the number of the connected domains.
In one technical solution of the method for calculating user similarity based on a sweeping robot, the extracting a contour map of a trajectory scatter diagram binarized by a graph as map data of a ground area where the sweeping robot operates based on the number of connected domains includes:
under the condition that the number of the connected domains is larger than a first preset threshold value, updating the radius value of the rolling ball, extracting a new contour map of the track scatter diagram after graphic binarization again based on a rolling ball method according to the updated radius of the rolling ball, and calculating the number of the connected domains of the area surrounded by the new contour map;
and under the condition that the number of the connected domains is smaller than or equal to a first preset threshold value, the rolling ball radius value is not updated any more, the rolling ball radius value updated last is used as a final rolling ball radius value, and a final contour map of the track scatter diagram after graphic binarization is extracted based on a rolling ball method according to the final rolling ball radius value and is used as map data of the ground area of the sweeping robot operation.
In one technical solution of the method for calculating user similarity based on a sweeping robot, the calculating similarity between different users according to the map data of the ground area includes:
acquiring track point clouds of ground areas of sweeping robot operation of different users, wherein the track point clouds represent a plurality of track scattered points;
respectively carrying out random consistent sampling on the acquired track point clouds of different users to acquire point cloud data of sampling points of different users;
matching and calculating the point cloud data of the sampling points of different users to obtain a translation loss parameter Rti and a rotation loss parameter Rri of the point cloud data of the sampling points of different users, and calculating the distance Li of the point cloud of the sweeping robot of different users to be Li = Rti Rri
Comparing the magnitude of the Li with a second preset threshold, and when the magnitude of the Li is larger than the second preset threshold, continuing to sample and calculating the value of the Li;
and when the Li is smaller than a second preset threshold value, taking the average value L _ avg of all the sampled Li as the ground area track point cloud distance of the sweeping robots of the two users to obtain that the similarity of the two sweeping robots corresponding to the two users is 1/L _ avg.
In one technical solution of the user similarity calculation method based on the sweeping robot, the initial rolling ball radius value is determined by a maximum euclidean distance between two points in the trajectory scatter diagram.
In one technical solution of the user similarity calculation method based on the sweeping robot, the acquiring the number of connected domains of the initial contour map enclosing area includes:
scanning the initial contour map twice, and finding and marking all connected domains existing in the initial contour map;
and obtaining the number of connected domains of the area enclosed by the initial contour map according to the number of the marks.
In a second aspect, the present application provides a user similarity calculation device based on a sweeping robot, including:
the acquisition module is used for acquiring the working track data of the sweeping robots of different users within a period of historical time;
the generating module is used for generating map data of a ground area where the sweeping robot operates according to the working track data;
and the similarity calculation module is used for calculating the similarity between different users according to the map data of the ground area.
In a third aspect, the present application provides a computer readable storage medium comprising a stored program, wherein the program when executed performs the method of the first aspect of the present application.
In a fourth aspect, the present application provides an electronic device comprising a memory having a computer program stored therein and a processor arranged to perform the method of the first aspect of the present application by means of the computer program.
One or more technical solutions described above in the present application have at least one or more of the following beneficial effects:
according to the method, the working track data of the sweeping robots of different users are obtained, the map data of the ground area where the sweeping robots operate are generated according to the working track data, the similarity among the different users is calculated according to the map data of the ground area, the calculated similarity results can support subsequent user classification, clustering of habits of similar users and the like, and accurate data are provided for design of smart home products, accurate advertisement putting and personalized recommendation.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a schematic diagram of a hardware environment of a method for calculating user similarity based on a sweeping robot according to an embodiment of the present application;
fig. 2 is a schematic flow chart illustrating main steps of a user similarity calculation method based on a sweeping robot according to an embodiment of the present application;
FIG. 3 is a flow chart illustrating the main steps of step S102 according to an embodiment of the present application;
FIG. 4 is a flowchart illustrating the main steps of step S1024 according to an embodiment of the present application;
FIG. 5 is a flow chart illustrating the main steps of step S103 according to an embodiment of the present application;
fig. 6 is a flowchart illustrating main steps of a user similarity calculation device based on a sweeping robot according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the description of the present application, "module", "processor" may include hardware, software, or a combination of both. A module may comprise hardware circuitry, various suitable sensors, communication ports, memory, may comprise software components such as program code, or may be a combination of software and hardware. The processor may be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and/or signal processing functionality. The processor may be implemented in software, hardware, or a combination thereof. Non-transitory computer-readable storage media include any suitable medium that can store program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random-access memory, and so forth. The term "A and/or B" denotes all possible combinations of A and B, such as only A, only B or both A and B. The term "at least one A or B" or "at least one of A and B" means similar to "A and/or B" and may include only A, only B, or both A and B. The singular forms "a", "an" and "the" may include the plural forms as well.
With the popularization of intelligent household appliances, more and more intelligent household products such as sweeping robots support networking, remote control and personalized work modes, and a large amount of information related to daily life of users, such as preferred use modes, use time, clean areas and the like, is accumulated. The data reflect the behavior habits, personal preferences and family conditions of the user to a certain extent. In order to continuously improve intelligent home products and improve services such as personalized design and personalized recommendation of the intelligent home products, a service party needs to know living habits and characteristics of residence space of users, such as residence use areas and furniture placement modes, so as to group similar users, and accurate and personalized products and services can be provided according to characteristics of different user groups.
In the prior art, a method for measuring the similarity of the user is mainly based on user behavior data, such as browsing records, geographical positions and the like, but data of a physical space of the actual life of the user is relatively lack, but the measurement of the similarity of the user from the perspective of a daily life space is very important for improving personalized design and personalized recommendation of smart home products. Therefore, how to analyze the living habits and the individual preferences of the users by using the data of the working process of the intelligent household products and identify similar users while ensuring the privacy of the users is a great challenge for the popularization and the use of the current intelligent household appliances.
Therefore, the method comprises the steps of obtaining working track data of sweeping robots of different users, generating map data of ground areas where the sweeping robots operate according to the working track data, calculating the similarity between the different users according to the map data of the ground areas, enabling the calculated similarity results to be used for identifying the users with similar living spaces, and providing a basis for designing products and personalized services for user groups of different groups subsequently by grouping the similar users.
According to one aspect of the embodiment of the application, a user similarity calculation method based on a sweeping robot is provided. The user similarity calculation method based on the sweeping robot is widely applied to full-House intelligent digital control application scenes such as intelligent homes (Smart Home), intelligent homes, intelligent Home equipment ecology, intelligent House (Intelligent House) ecology and the like. Alternatively, in the present embodiment, the user similarity calculation method based on the sweeping robot may be applied to a hardware environment formed by the terminal device 102 and the server 104 as shown in fig. 1. As shown in fig. 1, the server 104 is connected to the terminal device 102 through a network, and may be configured to provide a service (e.g., an application service) for the terminal or a client installed on the terminal, where the client installed on the terminal may be an intelligent APP of the sweeping robot, work trajectory data of the sweeping robot during a period of accumulated work, for example, 20 hours, may be acquired on the intelligent APP of the sweeping robot, the terminal device 102 uploads the acquired work trajectory data to the server 104, the server 104 receives the work trajectory data, and generates map data of a ground area where the sweeping robot operates according to the work trajectory data, and the server 104 calculates similarity between different users according to the acquired map data of the ground area where the sweeping robot operates of different users.
The network may include, but is not limited to, at least one of: wired network, wireless network. The wired network may include, but is not limited to, at least one of: wide area networks, metropolitan area networks, local area networks, which may include, but are not limited to, at least one of the following: WIFI (Wireless Fidelity), bluetooth. The terminal device 102 may not be limited to a PC, a mobile phone, a tablet computer, an intelligent sweeping robot, and the like. The server 104 includes, but is not limited to, various personal computers, laptops, tablets, portable wearable devices, and the like.
Referring to fig. 2-5, fig. 2 is a flowchart illustrating main steps of a user similarity calculation method based on a sweeping robot according to an embodiment of the present application. As shown in fig. 2, the method for calculating the user similarity based on the sweeping robot in the embodiment of the present application mainly includes the following steps S101 to S103.
Step S101: and acquiring the working track data of the sweeping robots of different users in any historical time.
The sweeping robot can be a laser navigation sweeping robot, a visual navigation sweeping robot and a gyroscope navigation sweeping robot, wherein the laser navigation sweeping robot emits laser to an obstacle to form a light spot, the distance is measured according to the pixel sequence number of the light spot, and programs such as path planning and house construction are implemented by combining with an SLAM algorithm system. The visual navigation sweeping robot observes and detects the surrounding environment through a panoramic camera on the robot body and an internal sensor, constructs and draws a room sweeping map by using sensed environmental information, and positions the robot body through an algorithm system so as to design a cleaning route and a scheme. The gyroscope navigation sweeping robot is commonly called inertial navigation, and obtains environment information through a gyroscope and an accelerator, and calculates position information of the sweeping robot.
It should be noted that, the application does not limit the navigation type of whether the sweeping robot is laser navigation, visual navigation or gyroscope navigation, and the next step can be performed according to the working track data as long as the working track data of the sweeping robot within a period of historical time can be obtained.
In one embodiment, different users have different working tracks of the sweeping robots placed in homes of different users due to different living habits, home layouts and other conditions, the working track data of the sweeping robots of different users within a period of historical time can be obtained through the intelligent sweeping robot APP, wherein the selection length of any period of historical time can be set according to the duration of the sweeping robot, route errors, a number reporting mechanism and other conditions, for example, the working track data of 20 hours of accumulated working time within seven days of the current time is selected within a period of historical time.
In one embodiment, the step S101 includes:
marking the passing route points and obstacle points of the sweeping robot of different users in the working process in any historical time, and generating a track scatter diagram.
In one embodiment, the sweeping robot establishes a coordinate system of the sweeping robot, marks coordinates of approach points and coordinates of obstacle points automatically in the sweeping process of the sweeping robot, generates track scatter diagrams, displays the track scatter diagrams on the intelligent sweeping robot APP, and uploads the track scatter diagrams to a server after the intelligent sweeping robot APP acquires the track scatter diagrams.
Step S102: and generating map data of the ground area operated by the sweeping robot according to the working track data.
In one embodiment, fig. 3 is a schematic flow chart of main steps of step S102 according to an embodiment of the present application, and as shown in fig. 3, the step S102 includes:
step S1021: and carrying out graphic binarization on the trajectory scatter diagram.
In one embodiment, the track scatter diagram generated according to the working track data of the sweeping robot is subjected to graphic binarization, the background pixel gray value is set to be 0, and the gray value of each scatter pixel on the track scatter diagram is set to be 255.
Step S1022: and setting an initial rolling ball radius value, and extracting an initial contour map of the track scatter diagram after graphic binarization according to the initial rolling ball radius based on a rolling ball method.
In one embodiment, the initial contour map of the track scattergram after graphic binarization is extracted based on a rolling sphere method, firstly, an initial rolling sphere radius value is determined according to the maximum Euclidean distance between two points in the track scattergram, then, the boundary points of the track scattergram after graphic binarization are extracted based on the rolling sphere method according to the initial rolling sphere radius, and the area surrounded by the boundary points is obtained and used as the initial contour map of the track scattergram.
Step S1023: and acquiring the number of connected domains of the area enclosed by the initial contour map.
In one embodiment, the number of connected components in the area enclosed by the initial contour map can be obtained by using a Two-Pass scanning method (Two-Pass) or a Seed-Filling method (Seed-Filling) through the way of connected component marking.
Scanning the initial contour map twice, and finding and marking all connected domains existing in the initial contour map; and obtaining the number of connected domains of the area enclosed by the initial contour map according to the number of the marks. The general flow of the two-pass scanning method is as follows: during the first scanning, traversing pixel points of the initial contour map from the upper left corner, keeping background pixels unchanged at 0, finding a point with a first pixel of 255, and enabling label =1; when the left adjacent pixel and the upper adjacent pixel of the pixel are invalid values, setting a new label value, label + +, for the pixel, and recording a set; when the left adjacent pixel or the upper adjacent pixel of the pixel has a valid value, the label of the valid value pixel is assigned to the label value of the pixel; when the left adjacent pixel and the upper adjacent pixel of the pixel are both effective values, selecting a smaller label value from the effective values and assigning the smaller label value to the label value of the pixel; and during the second scanning, updating the label of each point to be the smallest label in the set, and after the two scanning is completed, forming the same connected region by the pixels with the same label value in the initial contour map.
The seed filling method is to assume that at least one pixel inside a polygon or region is known, and then try to find all other pixels inside the region and fill them in. Regions may be defined with internal definitions or boundaries; if the boundary is defined, all pixels on the boundary of the region have a specific value or color, and all pixels inside the region do not take the specific value, however, pixels outside the boundary may have the same value as the boundary; if internally defined, then all pixels inside the region have the same color or value, while all pixels outside the region have another color or value. Accordingly, the Algorithm for filling the internally defined area is called a Flood Fill Algorithm (Flood Fill Algorithm), and the Algorithm for filling the boundary defined area is called a boundary Fill Algorithm.
It should be noted that, the method for obtaining the number of connected domains of the initial contour map bounding area is not limited in the present application, and no matter the two-pass scanning method, the seed filling method, or other algorithms are adopted, as long as the number of connected domains of the initial contour map bounding area can be obtained.
Step S1024: and extracting the contour map of the track scatter diagram after graphic binarization as map data of the ground area of the sweeping robot operation based on the number of the connected domains.
In an implementation manner, fig. 4 is a schematic flowchart of a main step of step S1024 according to an embodiment of the present application, and as shown in fig. 4, the step S1024 includes:
step S10241: under the condition that the number of the connected domains is larger than a first preset threshold value, updating the radius value of the rolling ball, extracting a new contour map of the track scatter diagram after graphic binarization again based on a rolling ball method according to the updated radius of the rolling ball, and calculating the number of the connected domains of the area surrounded by the new contour map;
step S10242: and under the condition that the number of the connected domains is smaller than or equal to a first preset threshold value, the radius value of the rolling ball is not updated any more, the radius value of the rolling ball updated last is used as a final radius value of the rolling ball, and a final contour map of a track scatter diagram after graphic binarization is extracted based on a rolling ball method according to the final radius value of the rolling ball and is used as map data of a ground area of the sweeping robot operation.
In one embodiment, after obtaining the number of connected domains, comparing whether the number of connected domains meets the number of connected domains required in the embodiment, in order to obtain the ground area of the sweeping robot operation clearly, in the embodiment, the number of connected domains is set to 1, namely, the first preset threshold value is 1, when the number of connected domains in the area surrounded by the obtained initial contour map is greater than 1, the radius of the rolling ball is updated, a new contour map of the trajectory scatter map after graphic binarization is extracted based on the rolling ball method again by using the radius of the updated rolling ball, the number of connected domains in the area surrounded by the new contour map is obtained by using the two-pass scanning method or the seed filling method again, if the number of connected domains is still greater than 1 at the moment, the radius of the rolling ball is updated until the corresponding contour map of the trajectory scatter map after graphic binarization is extracted based on the rolling ball method by using the radius of the last updated rolling ball, and the number of connected domains in the area surrounded by the corresponding contour map area of the contour map is obtained by using the two-pass scanning method or the seed filling method again as 1, and the final contour map of the ground area of the sweeping robot operation based on the trajectory scatter map after the radius of the rolling ball is extracted based on the rolling ball method.
Step S103: and calculating the similarity between different users according to the map data of the ground area.
In one embodiment, after determining the map data of the floor area where the sweeping robot works of different users, the floor area where the sweeping robot works is the daily activity area of the users, and when the similarity between the users is desired, the similarity between the different users can be calculated according to the map data of the floor area of the different users, and then, the calculation of the similarity between the two users is taken as an example for explanation.
In one embodiment, fig. 5 is a schematic flow chart of main steps of step S103 according to an embodiment of the present application, and as shown in fig. 5, the step S103 includes:
step S1031: acquiring track point clouds of ground areas of sweeping robot operation of different users, wherein the track point clouds represent a plurality of track scattered points;
step S1032: respectively performing random consistent sampling on the acquired track point clouds of different users to acquire point cloud data of sampling points of different users;
step S1033: matching and calculating the point cloud data of the sampling points of different users to obtain a translation loss parameter Rti and a rotation loss parameter Rri of the point cloud data of the sampling points of different users, and calculating the distance Li of the point cloud of the sweeping robot of different users to be Li = Rti Rri;
step S1034: comparing the magnitude of the Li with a second preset threshold, and when the Li is larger than the second preset threshold, continuing to sample and calculating the value of the Li;
step S1035: and when the Li is smaller than a second preset threshold value, taking the average value L _ avg of all the sampled Li as the ground area track point cloud distance of the sweeping robots of the two users to obtain that the similarity of the two sweeping robots corresponding to the two users is 1/L _ avg.
In one embodiment, track point clouds of ground areas where sweeping robots of a user a and a user B operate are obtained, random consistent sampling is performed on the track point clouds of the ground areas where the sweeping robots of the user a and the user B operate, N points are randomly selected from the track point clouds of the ground areas where the sweeping robots of the user a and the user B operate, matching calculation is performed on the randomly selected N points from the track point clouds of the ground areas where the sweeping robots of the user a and the user B operate, sampling is performed on the randomly selected N points from the track point clouds of the ground areas where the sweeping robots of the user a and the user B operate, translation loss parameters Li and rotation loss parameters Rri of point cloud data of sampling points of the robots of the user a and the user B are calculated from the first sampling, the distance Li = i rrri of the point clouds of the robots of the user a and the user B is calculated from the first sampling, the size of the Li is compared with the second preset threshold, when the size is larger than the second preset threshold, sampling is continued until the second sampling is equal to the second sampling distance Li — av, and the average value is calculated as the second sampling distance L — av, and the average value of the two sampling points is equal to 1-av, and the second sampling distance.
Based on the steps S101 to S103, by acquiring the working track data of the sweeping robots of different users, generating map data of a ground area where the sweeping robot operates according to the working track data, and calculating the similarity between different users according to the map data of the ground area, the calculated similarity result can support subsequent user classification, clustering and the like, and accurate data is provided for design, accurate advertisement delivery and personalized recommendation of smart home products.
It should be noted that, although the foregoing embodiments describe each step in a specific sequential order, those skilled in the art can understand that, in order to achieve the effect of the present application, different steps do not necessarily need to be executed in such an order, and they may be executed simultaneously (in parallel) or in other orders, and these changes are all within the scope of the present application.
Further, the application also provides a user similarity calculation device based on the sweeping robot.
Referring to fig. 6, fig. 6 is a main structural block diagram of a user similarity calculation device based on a sweeping robot according to an embodiment of the present application. As shown in fig. 6, the user similarity calculation apparatus based on the sweeping robot in the embodiment of the present application mainly includes an obtaining module 11, a generating module 12, and a similarity calculation module 13. In some embodiments, one or more of the acquisition module 11, the generation module 12, and the similarity calculation module 13 may be combined together into one module. For example, the obtaining module 11 and the generating module 12 may be two separate modules, or may be combined, and the combined module is referred to as a ground inference module. In some embodiments, the obtaining module 11 may be configured to obtain the work trajectory data of the sweeping robots of different users in a historical time period. The generating module 12 may be configured to generate map data of a ground area of the sweeping robot operation from the work trajectory data. The similarity calculation module 13 may be configured to calculate the similarity between different users from the map data of the ground area. The combined ground inference module is configured to obtain work trajectory data of the sweeping robots of different users over a historical period of time, and generate map data of a ground area where the sweeping robots operate according to the work trajectory data.
In one embodiment, the description of the specific implementation function may be referred to in steps S101 to S103.
It will be understood by those skilled in the art that all or part of the flow of the method implemented by the present application may also be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the above-mentioned method embodiments when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable storage medium may include: any entity or device capable of carrying said computer program code, media, usb disk, removable hard disk, magnetic diskette, optical disk, computer memory, read-only memory, random access memory, electrical carrier wave signals, telecommunication signals, software distribution media, etc. It should be noted that the computer readable storage medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable storage media that does not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
Further, the application also provides a computer readable storage medium. In one computer-readable storage medium embodiment according to the present application, the computer-readable storage medium may be configured to store a program for executing the method of acquiring vehicle travel information of the above-described method embodiment, and the program may be loaded and executed by a processor to implement the above-described method of acquiring vehicle travel information. For convenience of explanation, only the portions related to the embodiments of the present application are shown, and specific technical details are not disclosed, please refer to the method portion of the embodiments of the present application. The computer readable storage medium may be a memory device formed by various electronic devices, and optionally, the computer readable storage medium in the embodiments of the present application is a non-transitory computer readable storage medium.
Further, the application also provides an electronic device. In one embodiment of the electronic device according to the present application, as shown in fig. 7, the electronic device includes a processor and a memory, the memory may be configured to store a program for executing the method of acquiring vehicle trip information of the above method embodiment, and the processor may be configured to execute a program in the memory, the program including but not limited to a program for executing the method of acquiring vehicle trip information of the above method embodiment. For convenience of explanation, only the parts related to the embodiments of the present application are shown, and details of the specific technology are not disclosed. The electronic device may be a control device apparatus formed including various electronic apparatuses.
Further, it should be understood that, since the setting of each module is only for explaining the functional units of the apparatus of the present application, the corresponding physical devices of the modules may be the processor itself, or a part of software, a part of hardware, or a part of a combination of software and hardware in the processor. Thus, the number of individual modules in the figures is merely illustrative.
Those skilled in the art will appreciate that the various modules in the apparatus may be adaptively split or combined. Such splitting or merging of specific modules does not cause the technical solutions to deviate from the principle of the present application, and therefore, the technical solutions after splitting or merging will fall within the protection scope of the present application.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A user similarity calculation method based on a sweeping robot is characterized by comprising the following steps:
acquiring working track data of sweeping robots of different users in any historical time;
generating map data of a ground area where the sweeping robot operates according to the working track data;
and calculating the similarity between different users according to the map data of the ground area.
2. The method of claim 1, wherein the obtaining of the work trajectory data of the sweeping robots of different users over a historical period of time comprises:
marking the path points and the obstacle points of the sweeping robots of different users in the working process within a period of historical time, and generating a track scatter diagram.
3. The method of claim 2, wherein generating map data of a ground area over which the sweeping robot operates from the work trajectory data comprises:
carrying out graphic binarization on the trajectory scatter diagram;
setting an initial rolling ball radius value, and extracting an initial contour map of a track scatter diagram after graphic binarization based on a rolling ball method according to the initial rolling ball radius;
acquiring the number of connected domains of an enclosed area of the initial contour map;
and extracting the contour map of the track scatter diagram after graphic binarization as map data of the ground area of the sweeping robot operation based on the number of the connected domains.
4. The method according to claim 3, wherein the extracting the contour map of the trajectory scatter diagram after the graphic binarization as the map data of the ground area of the sweeping robot operation based on the number of the connected domains comprises:
under the condition that the number of the connected domains is larger than a first preset threshold value, updating the radius value of the rolling ball, extracting a new contour map of the track scatter diagram after graphic binarization again based on a rolling ball method according to the updated radius of the rolling ball, and calculating the number of the connected domains of the area surrounded by the new contour map;
and under the condition that the number of the connected domains is smaller than or equal to a first preset threshold value, the rolling ball radius value is not updated any more, the rolling ball radius value updated last is used as a final rolling ball radius value, and a final contour map of the track scatter diagram after graphic binarization is extracted based on a rolling ball method according to the final rolling ball radius value and is used as map data of the ground area of the sweeping robot operation.
5. The method of claim 4, wherein calculating the similarity between different users from the map data of the ground area comprises:
acquiring track point clouds of ground areas of sweeping robot operation of different users, wherein the track point clouds represent a plurality of track scattered points;
respectively performing random consistent sampling on the acquired track point clouds of different users to acquire point cloud data of sampling points of different users;
matching and calculating the point cloud data of the sampling points of different users to obtain a translation loss parameter Rti and a rotation loss parameter Rri of the point cloud data of the sampling points of different users, and calculating the distance Li of the point cloud of the sweeping robot of different users to be Li = Rti Rri
Comparing the magnitude of the Li with a second preset threshold, and when the Li is larger than the second preset threshold, continuing to sample and calculating the value of the Li;
and when the Li is smaller than a second preset threshold value, taking the average value L _ avg of all the sampled Li as the ground area track point cloud distance of the sweeping robots of the two users to obtain that the similarity of the two sweeping robots corresponding to the two users is 1/L _ avg.
6. A method according to claim 3 or 4, wherein the initial ball radius value is determined by the maximum Euclidean distance between two points in the trajectory scatter plot.
7. The method according to claim 3 or 4, wherein the obtaining the number of connected components of the initial contour map bounding region comprises:
scanning the initial contour map twice, and finding and marking all connected domains existing in the initial contour map;
and obtaining the number of connected domains of the area enclosed by the initial contour map according to the number of the marks.
8. The utility model provides a user similarity computing device based on robot of sweeping floor which characterized in that includes:
the acquisition module is used for acquiring the working track data of the sweeping robots of different users within a period of historical time;
the generating module is used for generating map data of a ground area where the sweeping robot operates according to the working track data;
and the similarity calculation module is used for calculating the similarity between different users according to the map data of the ground area.
9. A computer-readable storage medium, comprising a stored program, wherein the program when executed performs the method of any of claims 1 to 7.
10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the method of any of claims 1 to 7 by means of the computer program.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211049711.9A CN115456057A (en) | 2022-08-30 | 2022-08-30 | User similarity calculation method and device based on sweeping robot and storage medium |
| PCT/CN2023/073863 WO2024045489A1 (en) | 2022-08-30 | 2023-01-30 | User similarity calculation method and device based on sweeping robot, and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211049711.9A CN115456057A (en) | 2022-08-30 | 2022-08-30 | User similarity calculation method and device based on sweeping robot and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115456057A true CN115456057A (en) | 2022-12-09 |
Family
ID=84301879
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211049711.9A Pending CN115456057A (en) | 2022-08-30 | 2022-08-30 | User similarity calculation method and device based on sweeping robot and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN115456057A (en) |
| WO (1) | WO2024045489A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024045489A1 (en) * | 2022-08-30 | 2024-03-07 | 海尔智家股份有限公司 | User similarity calculation method and device based on sweeping robot, and storage medium |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110362099B (en) * | 2018-03-26 | 2022-08-09 | 科沃斯机器人股份有限公司 | Robot cleaning method, device, robot and storage medium |
| CN109000655B (en) * | 2018-06-11 | 2021-11-26 | 东北师范大学 | Bionic indoor positioning and navigation method for robot |
| JP2023540304A (en) * | 2020-09-01 | 2023-09-22 | シーバ・テクノロジーズ・インコーポレイテッド | Dead Reckoning of Planar Robots with Optical Flow, Wheel Encoders and Inertial Measurement Devices |
| CN112487234B (en) * | 2020-11-25 | 2024-03-29 | 北京集光通达科技股份有限公司 | Track analysis method, device and storage medium based on split hierarchical clustering |
| CN112562029B (en) * | 2020-12-07 | 2024-05-07 | 深圳市愚公科技有限公司 | Image reconstruction method, system, electronic device and computer readable storage medium |
| CN113520233B (en) * | 2021-09-16 | 2023-04-07 | 深圳市元鼎智能创新有限公司 | Control method and device of sweeping robot with multiple sweeping modes and sweeping robot |
| CN115456057A (en) * | 2022-08-30 | 2022-12-09 | 海尔优家智能科技（北京）有限公司 | User similarity calculation method and device based on sweeping robot and storage medium |
-
2022
- 2022-08-30 CN CN202211049711.9A patent/CN115456057A/en active Pending
-
2023
- 2023-01-30 WO PCT/CN2023/073863 patent/WO2024045489A1/en unknown
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024045489A1 (en) * | 2022-08-30 | 2024-03-07 | 海尔智家股份有限公司 | User similarity calculation method and device based on sweeping robot, and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2024045489A1 (en) | 2024-03-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9436695B2 (en) | Dynamically expiring crowd-sourced content | |
| Getz et al. | A local nearest‐neighbor convex‐hull construction of home ranges and utilization distributions | |
| CN111526119B (en) | Abnormal flow detection method and device, electronic equipment and computer readable medium | |
| CN111212383B (en) | Method, device, server and medium for determining number of regional permanent population | |
| CN109272543B (en) | Method and apparatus for generating a model | |
| CN103839290A (en) | Information processing apparatus, terminal apparatus, information processing method, and program | |
| CN109360028A (en) | Method and apparatus for pushed information | |
| Doyle et al. | Population mobility dynamics estimated from mobile telephony data | |
| CN113190757A (en) | Multimedia resource recommendation method and device, electronic equipment and storage medium | |
| CN106658653A (en) | Method and device used for providing wireless access point information | |
| CN113268641B (en) | User data processing method based on big data and big data server | |
| CN108805174A (en) | clustering method and device | |
| KR102362470B1 (en) | Mehtod and apparatus for processing foot information | |
| CN110020144A (en) | A kind of recommended models method for building up and its equipment, storage medium, server | |
| CN115456057A (en) | User similarity calculation method and device based on sweeping robot and storage medium | |
| CN111475746B (en) | Point-of-interest mining method, device, computer equipment and storage medium | |
| CN110609864B (en) | Chemical supply chain-oriented data visualization management method and device | |
| CN105940435A (en) | Measuring and diagnosing noise in urban environment | |
| CN113869599A (en) | Fish epidemic disease development prediction method, system, equipment and medium | |
| CN110895543A (en) | Population migration tracking display method and device and storage medium | |
| CN112686690A (en) | Data processing method and device, electronic equipment and computer readable storage medium | |
| CN110619090B (en) | Regional attraction assessment method and device | |
| CN110197459B (en) | Image stylization generation method and device and electronic equipment | |
| CN112667924A (en) | Robot map acquisition method and device, processor and electronic device | |
| CN115098792B (en) | Exhibit recommendation method and device, computer-readable storage medium and electronic device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |