Mapping method and device based on Bluetooth positioning and storage medium

Abstract

The invention discloses a mapping method and device based on Bluetooth positioning and a storage medium, and the method comprises the steps: taking three Bluetooth devices in a building as base stations, and controlling a sweeping robot to receive Bluetooth beacon signals broadcasted by the base stations; positioning the sweeping robot according to the Bluetooth beacon signal to obtain sweeping coordinates; calculating the density of the sweeping coordinates in the building; if the density is greater than or equal to a preset first threshold value, constructing binarized image data according to the sweeping coordinates; calculating a matching degree between the image data and a preset house type image according to the plurality of Bluetooth devices; determining a house type image matched with the building in the plurality of house type images with the highest matching degree; constructing a three-dimensional digital twinborn model for the building according to the house type image matched with the building; and constructing an electronic map suitable for the sweeping robot to execute cleaning operation according to the digital twin model. The electronic map is highly adaptive to an actual building, and the accuracy of the electronic map is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Bluetooth, and in particular to a mapping method, device and storage medium based on Bluetooth positioning. Background Art

[0002] In family life, a sweeping robot is one of the commonly used household appliances. During the initial operation or when changing the environment, the sweeping robot senses the indoor environment, such as obstacles, through sensors such as lidar, cameras, infrared sensors, gyroscopes, and accelerometers, and uses the sensed data to achieve simultaneous positioning and mapping (SLAM), thereby using the map to plan the cleaning path.

[0003] However, the indoor environment is more complex and changes rapidly, resulting in omissions in the perceived data and poor timeliness. After a long period of operation, the parts of the sweeping robot (such as pulleys, tracks, etc.) may become entangled with hair and worn out, which can easily accumulate deviations during positioning, leading to low map accuracy and affecting path planning. Summary of the invention

[0004] In view of this, the present invention provides a mapping method, device and storage medium based on Bluetooth positioning, so as to improve the accuracy of building a map suitable for cleaning by a sweeping robot.

[0005] A first aspect of the present invention provides a mapping method based on Bluetooth positioning, comprising:

[0006] When the cleaning robot performs a cleaning operation in a building, the three Bluetooth devices in the building are used as base stations to control the cleaning robot to receive Bluetooth beacon signals broadcast by the base stations;

[0007] Positioning the sweeping robot according to the Bluetooth beacon signal to obtain sweeping coordinates;

[0008] Calculate the density of the sweeping coordinates in the building;

[0009] If the density is greater than or equal to a preset first threshold, constructing binary image data according to the scanning coordinates;

[0010] Calculating the matching degree between the image data and a preset floor plan based on the plurality of Bluetooth devices;

[0011] Determining the floor plan that matches the building among the multiple floor plans with the highest matching degree;

[0012] Constructing a three-dimensional digital twin model of the building according to the floor plan matching the building;

[0013] An electronic map suitable for the sweeping robot to perform cleaning operations is constructed based on the digital twin model.

[0014] A second aspect of the present invention provides a mapping device based on Bluetooth positioning, comprising:

[0015] A signal receiving module, used for controlling the sweeping robot to receive Bluetooth beacon signals broadcasted by the base stations when the sweeping robot performs a cleaning operation in the building, using the three Bluetooth devices in the building as base stations;

[0016] A robot positioning module, used to locate the sweeping robot according to the Bluetooth beacon signal to obtain sweeping coordinates;

[0017] A density calculation module, used for calculating the density of the sweeping coordinates in the building;

[0018] An image data construction module, configured to construct binary image data according to the sweeping coordinates if the density is greater than or equal to a preset first threshold;

[0019] A matching degree calculation module, used for calculating the matching degree between the image data and a preset floor plan based on the plurality of Bluetooth devices;

[0020] A floor plan determination module, used to determine the floor plan that matches the building among the multiple floor plans with the highest matching degree;

[0021] A digital twin model construction module, used to construct a three-dimensional digital twin model of the building according to the floor plan matching the building;

[0022] An electronic map construction module is used to construct an electronic map suitable for the sweeping robot to perform cleaning operations based on the digital twin model.

[0023] A third aspect of the present invention provides an electronic device, the electronic device comprising:

[0024] at least one processor; and

[0025] a memory communicatively connected to the at least one processor; wherein,

[0026] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the mapping method based on Bluetooth positioning as described in the first aspect above.

[0027] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the mapping method based on Bluetooth positioning as described in the first aspect above is implemented.

[0028] A fifth aspect of the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the mapping method based on Bluetooth positioning as described in the first aspect above.

[0029] In this embodiment, when the sweeping robot performs cleaning operations in a building, three Bluetooth devices in the building are used as base stations to control the sweeping robot to receive Bluetooth beacon signals broadcast by the base stations; the sweeping robot is positioned according to the Bluetooth beacon signals to obtain sweeping coordinates; the density of the sweeping coordinates in the building is calculated; if the density is greater than or equal to a preset first threshold, binary image data is constructed based on the sweeping coordinates; the degree of matching between the image data and a preset floor plan is calculated based on multiple Bluetooth devices; a floor plan that matches the building is determined from multiple floor plans with the highest degree of matching; a three-dimensional digital twin model of the building is constructed based on the floor plan that matches the building; and an electronic map suitable for the sweeping robot to perform cleaning operations is constructed based on the digital twin model. This embodiment reuses the Bluetooth devices in the building as base stations to position the sweeping robot, which can reduce the accumulation of positioning deviations caused by hair entanglement, wear and tear of the internal parts of the sweeping robot. The appropriate house plan is matched based on the moving path of the sweeping robot to ensure the accuracy of the house plan. The digital twin model is improved based on the house plan and a standardized electronic map is constructed based on the digital twin model, which can increase the amount of information. The electronic map has a high degree of adaptability to the actual building, which can improve the timeliness of the electronic map, effectively improve the accuracy of the electronic map, and facilitate the sweeping robot to plan a high-precision path.

[0030] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0032] Figure 1 This is a flowchart of a mapping method based on Bluetooth positioning provided in Example 1 of the present invention.

[0033] Figure 2 This is an example diagram of a moving path of a sweeping robot provided in Embodiment 1 of the present invention.

[0034] Figure 3 It is a structural schematic diagram of a mapping device based on Bluetooth positioning provided in Embodiment 2 of the present invention.

[0035] Figure 4 It is a structural schematic diagram of an electronic device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0037] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can cover sequential implementations other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] Embodiment 1

[0039] See also Figure 1 , shows a flowchart of a method for building a map based on Bluetooth positioning provided by Embodiment 1 of the present invention, the method can be executed by a mapping device based on Bluetooth positioning, the mapping device based on Bluetooth positioning can be implemented in the form of hardware and / or software, and the mapping device based on Bluetooth positioning can be configured in an electronic device. Figure 1 As shown, the method includes:

[0040] Step 101 : When the sweeping robot performs a cleaning operation in a building, the three Bluetooth devices in the building are used as base stations to control the sweeping robot to receive Bluetooth beacon signals broadcast by the base stations.

[0041] Generally, multiple Bluetooth devices are installed in residential buildings, such as smart ceiling lamps, smart table lamps, smart refrigerators, smart speakers, smart refrigerators, smart air conditioners, etc. Most of these Bluetooth devices belong to the same smart home platform and can be controlled centrally.

[0042] If the sweeping robot initially performs cleaning operations in a building, the automatic cleaning mode can be started, and three Bluetooth devices in the building can be screened out based on operating status, distribution, and other dimensions. These three Bluetooth devices are temporarily set as Bluetooth base stations, and the sweeping robot is controlled to call the Bluetooth module to receive the Bluetooth beacon signals broadcast by these three base stations.

[0043] In automatic cleaning mode, the sweeping robot automatically plans the cleaning route to cover all areas within the building, and completes the cleaning by sucking the debris on the ground into the garbage storage box through brushing, vacuuming, etc.

[0044] In one embodiment of the present invention, step 101 may include the following steps:

[0045] Step 1011: Control various Bluetooth devices in the building to form a Bluetooth mesh network.

[0046] In this embodiment, Bluetooth devices that support the Bluetooth Mesh protocol are screened out in the building, and each Bluetooth device is added to the Bluetooth Mesh network through configuration work, and addresses and keys are assigned, and roles such as relay nodes and low-power nodes are assigned according to positioning requirements.

[0047] Step 1012: Position each Bluetooth device in the Bluetooth mesh network to obtain device coordinates.

[0048] In a Bluetooth Mesh network, the base station is negotiated automatically based on the status and other factors, and the Bluetooth beacon signal is broadcast, so that each Bluetooth device can be located based on Bluetooth to obtain the device coordinates of each Bluetooth device.

[0049] Step 1013: any three Bluetooth devices form a device set.

[0050] In this embodiment, three Bluetooth devices may be arbitrarily selected in a Bluetooth Mesh network to form a device set.

[0051] Step 1014: In the device set, calculate the dispersion degree of the Bluetooth devices in the building according to the device coordinates.

[0052] In each device set, the device coordinates of the three Bluetooth devices are statistically analyzed to calculate the dispersion of the Bluetooth devices in the building.

[0053] Exemplarily, in the device set, the difference between the device coordinates of each pair of Bluetooth devices is calculated to obtain the distribution distance between each pair of Bluetooth devices.

[0054] In the device set, the distribution distances are summed to obtain the total distance.

[0055] In the device set, the absolute value of the difference between the distribution distances of each pair is taken to obtain the distribution offset, and the distribution offsets are summed to obtain the total offset.

[0056] In the device set, the product between the total distance and the first weight is subtracted from the product between the total offset and the second weight to obtain the dispersion degree of the Bluetooth devices in the building.

[0057] In this example, assuming that the device set includes Bluetooth device A, Bluetooth device B, and Bluetooth device C, the dispersion degree of Bluetooth devices in the building can be expressed as:

[0058] Dispersion=w1×(D1+D2+D3)-w2×(|D1-D2|+|D2-D3|+|D3-D1|);

[0059] Wherein, Dispersion is the degree of dispersion, D1 is the distribution distance between Bluetooth device A and Bluetooth device B, D2 is the distribution distance between Bluetooth device B and Bluetooth device C, D3 is the distribution distance between Bluetooth device C and Bluetooth device A, w1 is the first weight, and w2 is the second weight.

[0060] Step 1015: Use three Bluetooth devices among the Bluetooth devices with the highest degree of dispersion as base stations to control the sweeping robot to receive Bluetooth beacon signals broadcast by the base stations.

[0061] The dispersion levels of the three Bluetooth devices in each device set in the building are compared, and the three Bluetooth devices in the device set with the highest dispersion level are selected as base stations. The base stations are controlled to temporarily broadcast Bluetooth beacon signals, and the sweeping robots are controlled to receive the Bluetooth beacon signals broadcast by the base stations.

[0062] In this way, base stations that are distributed as evenly as possible among the base stations can be screened out. When the sweeping robot moves in a building, the RSSI (Received Signal Strength Indication) of the Bluetooth beacon signal broadcast by the base station can be received at a higher level, thereby ensuring that the positioning is maintained at a certain degree of accuracy.

[0063] Step 102: Position the sweeping robot according to the Bluetooth beacon signal to obtain sweeping coordinates.

[0064] In a specific implementation, a relationship model can be obtained based on the RSSI and distance model of the Bluetooth beacon signal, and the RSSI indication values ​​of the Bluetooth beacon signals of the three base stations are substituted into the relationship model to obtain the distance between the sweeping robot and the base station.

[0065] Exemplarily, the relationship model is as follows:

[0066] P(d)=P(d0)-10nlog 10 (d / d0);

[0067] Wherein, P(d) is the RSSI when the distance from the base station is d, P(d0) is the RSSI when the distance from the base station is d0, and n is the path loss index.

[0068] Using algorithms such as three-sided positioning, multilateral positioning or weighted centroid positioning, the sweeping coordinates of the sweeping robot are calculated according to the distances between the three robots and each base station.

[0069] like Figure 2 As shown, by arranging the sweeping coordinates of the sweeping robot in chronological order, a moving path of the sweeping robot can be formed.

[0070] This embodiment temporarily reuses the Bluetooth device in the building as a base station to locate the sweeping robot, and observes the sweeping robot from the outside, which can reduce the accumulation of positioning deviations caused by problems such as hair entanglement and wear of the internal parts of the sweeping robot.

[0071] Step 103: Calculate the density of the sweeping coordinates in the building.

[0072] The sweeping robot moves and cleans the floor in the building, and continuously accumulates sweeping coordinates. At this time, the density of the sweeping coordinates in the building can be counted based on the distribution of the sweeping coordinates in the building, and the degree of the sweeping robot's detection of the building can be judged accordingly.

[0073] In a specific implementation, a minimum bounding rectangle may be added to all currently accumulated sweeping coordinates as a virtual building area, and the virtual building area may reflect the outline structure of the building to a certain extent.

[0074] Count the number of sweeping coordinates and the area of ​​the virtual building area.

[0075] The ratio between the number of sweeping coordinates and the area is calculated as the density of the sweeping coordinates in the building.

[0076] Step 104: If the density is greater than or equal to a preset first threshold, construct binary image data according to the scanning coordinates.

[0077] The density of the sweeping coordinates in the building is compared with a preset first threshold. If the density of the sweeping coordinates in the building is greater than or equal to the preset first threshold, it means that the density of the sweeping coordinates in the building is high, and the sweeping robot may have detected most areas of the building. The sweeping coordinates can be used to distinguish different pixel values, thereby constructing binary image data.

[0078] In a specific implementation, the virtual building area is converted into image data.

[0079] In the image data, there are multiple pixel points, and the coordinates of each pixel point are compared with the sweeping coordinates.

[0080] If the pixel point is at the sweeping coordinate (that is, the coordinate of the pixel point is the same as the sweeping coordinate), the pixel value of the pixel point is set to 1.

[0081] If the pixel point is not at the sweeping coordinates (ie, the coordinates of the pixel point are different from the sweeping coordinates), the pixel value of the pixel point is set to 0.

[0082] Step 105: Calculate the matching degree between the image data and the preset floor plan based on multiple Bluetooth devices.

[0083] In an offline environment, a housing type library can be constructed through different forms and channels, and a large number of two-dimensional floor plans of buildings are accumulated in the library. For example, a real estate agency authorizes the use of the floor plans it constructs, builds a generator based on the building specifications of the building, and modifies the parameters in the generator (such as the location, attributes, length and / or width of the room, the location and width of the door, etc.), and so on.

[0084] In practical applications, multiple Bluetooth devices act as anchor points. The matching degree between image data and floor plans can be calculated with the prior knowledge of Bluetooth devices, so that the matching degree between image data and floor plans has a higher accuracy.

[0085] Then, the floor plans in the floor plan library can be traversed according to the image data, so as to calculate the matching degree between the image data and the preset floor plans.

[0086] Furthermore, all the floor plans in the floor plan library can be traversed based on the image data, and possibly suitable floor plans can be screened and matched with the image data based on factors such as the area of ​​the virtual building area and the longitude and latitude of the sweeping robot, thereby reducing the amount of matching calculations.

[0087] In one embodiment of the present invention, step 105 may include the following steps:

[0088] Step 1051, load the preset floor plan.

[0089] In this embodiment, a floor plan in a floor plan diagram may be loaded into a memory and wait for matching, wherein the floor plan diagram includes indoor areas with multiple labeled attributes, such as a living room, a bedroom, a toilet, a kitchen, a balcony, and the like.

[0090] Step 1052: Cluster the sweeping coordinates in the image data according to the virtual building area to obtain a plurality of sweeping clusters.

[0091] In this embodiment, since the virtual building area can reflect the contour structure of the building to a certain extent, clustering algorithms such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and K-means (K means) can be used to cluster the sweeping coordinates in the image data according to the parameters of the virtual building area to obtain multiple sweeping clusters, so that the sweeping clusters converge to the indoor area, that is, the clustering goal is to gather one or more sweeping clusters in the same indoor area, and the sweeping clusters do not cross the indoor area at the same time.

[0092] Taking K-means as an example, a mapping table can be determined; the mapping table contains mapping relationships between multiple building ranges and cluster numbers; among them, the building range is positively correlated with the cluster number, that is, the larger the value of the building range (represented by the midpoint), the larger the cluster number, and conversely, the smaller the value of the building range (represented by the midpoint), the smaller the cluster number.

[0093] For example, for buildings below 30 square meters (building range), most of them are designed with one bedroom and one living room, and the number of clusters can be set to about 4. For buildings between 40 square meters and 70 square meters (building range), most of them are designed with two bedrooms and one living room, and the number of clusters can be set to about 7, and so on.

[0094] The building range where the area of ​​the virtual building area is located is queried in the mapping table as the target range, the number of clusters mapped by the target range is taken as the K value, and K-means clustering is performed on the sweeping coordinates in the image data to obtain multiple sweeping clusters.

[0095] Furthermore, the process of K-means clustering is as follows:

[0096] S1. Initialize K sweeping clusters; the sweeping cluster has a center point.

[0097] S2. For each sweeping coordinate, calculate the distance between the sweeping coordinate and the center point of each sweeping cluster, such as the Euclidean distance.

[0098] S3. For each sweeping coordinate, divide the sweeping coordinate into a sweeping cluster with the smallest distance.

[0099] S4. For each sweeping cluster, calculate the average value of all sweeping coordinates in the sweeping cluster, and assign the average value to the center point of the sweeping cluster to update the center point.

[0100] S5. Calculate clustering indices for the K sweeping clusters, such as SSE (Sum of Squared Errors), CH index (Calinski-Harabasz Index), DB index (Davies-Bouldin Index), etc.

[0101] S5. Determine whether the clustering index meets the clustering condition, for example, the clustering index is less than a certain threshold, the fluctuation range of the clustering index is less than a certain threshold, etc.; if so, determine that K sweeping clusters complete the clustering; if not, return to S2.

[0102] Step 1053: Classify the sweeping clusters to obtain the attributes of the sweeping clusters in the house type.

[0103] In this embodiment, each sweeping cluster is traversed, and a multi-classification operation is performed on the sweeping cluster using the parameters of the sweeping cluster itself to obtain the attributes of the sweeping cluster in the apartment type.

[0104] In the specific implementation, the gradient boosting decision tree (GBDT) trained on the sweeping cluster in an offline environment can be determined. GBDT is an additive model based on the idea of ​​boosting (enhanced ensemble method) ensemble learning. The forward distribution algorithm is used for greedy learning during training. Each iteration learns a CART tree (decision tree) to fit the residual of the prediction results of the previous t-1 (t is a positive integer) trees and the true value of the training sample. GBDT has strong interpretability and better multi-classification effect for sweeping clusters with relatively simple parameters.

[0105] For each sweeping cluster, cluster features can be extracted for the sweeping cluster (including sweeping coordinates).

[0106] Exemplarily, cluster features include the coordinates of the center point of the sweeping cluster relative to the center point of the virtual building area (i.e., the coordinates of the center point of the sweeping cluster in the coordinate system when the center point of the virtual building area is the origin of the coordinate system), the length and width of the minimum circumscribed rectangle of the sweeping cluster, the number of sweeping coordinates in the sweeping cluster and the area of ​​the sweeping cluster, etc.

[0107] The cluster features are input into the gradient boosting decision tree for classification, and the attributes of the sweeping cluster in the house type are obtained.

[0108] Step 1054: Convert the sweeping cluster into a virtual sweeping area.

[0109] In this embodiment, each sweeping cluster may be traversed and normalized, so as to fit a virtual sweeping area to the sweeping cluster.

[0110] In the specific implementation, considering that the accuracy of Bluetooth positioning is mostly at the meter level, especially the positioning error of non-professional base stations is relatively large, therefore, the sweeping cluster can be corroded.

[0111] If the erosion operation is completed, a minimum circumscribed rectangle is added to the sweeping cluster as the full sweeping area, and the edge of the full sweeping area is converged toward the center by a specified length to obtain a virtual sweeping area.

[0112] In this way, the impact of errors can be reduced through corrosion operations and convergence operations, and, in conjunction with subsequent matching algorithms, the degree of matching between image data and floor plans can be less affected.

[0113] Step 1055: Under the constraint of minimizing the overlap between the virtual sweeping area and the walls of the floor plan, align the image data with the floor plan so that the attributes of the virtual sweeping area are the same as those of the indoor area.

[0114] In the same two-dimensional vector space, the positions of the image data and the floor plan are continuously adjusted to align the image data with the floor plan. There are two goals of the alignment: one is to minimize the overlapping range between the virtual sweeping area and the wall of the floor plan, and the other is to make the attributes of the virtual sweeping area the same as those of the indoor area.

[0115] Step 1056: If the alignment is completed, the device coordinates of the Bluetooth device are projected into the image data.

[0116] When the image data is aligned with the floor plan, the device coordinates of the Bluetooth device are projected into the image data to become an anchor point.

[0117] Step 1057: If the projection is completed, query the probability that the Bluetooth device appears in the indoor area where the device coordinates are located, and calculate the proportion of the virtual sweeping area in the indoor area.

[0118] In an offline environment, the probability of various Bluetooth devices appearing in various areas can be recorded based on prior knowledge and the probability can be recorded in a configuration file. For example, when the Bluetooth device is a large smart ceiling lamp, the probability of it appearing in the living room is 70%, the probability of appearing in the bedroom is 30%, the probability of appearing in the kitchen is 5%, the probability of appearing in the toilet is 5%, and so on.

[0119] In the same two-dimensional vector space, the probability of a Bluetooth device appearing in the indoor area where the device coordinates are located is queried in the configuration file.

[0120] In the same indoor area, the ratio between the area of ​​all sweeping areas and the area of ​​the indoor area can be calculated as the proportion of the virtual sweeping area in the indoor area.

[0121] Step 1058: If the proportion is greater than or equal to a preset second threshold, the indoor area is marked as a target area.

[0122] Generally, users will clear away obstacles in the building and use the sweeping robot to move as smoothly as possible to improve the effect of cleaning the building. At this time, the sweeping robot can usually traverse a larger part of the indoor area.

[0123] In this case, the proportion of the virtual sweeping area in the indoor area is compared with a preset second threshold.

[0124] If the proportion of the virtual sweeping area in the indoor area is greater than or equal to the preset second threshold, it means that the sweeping robot has completed cleaning in the indoor area, and the sweeping area matches the indoor area reliably, then the indoor area can be marked as the target area.

[0125] On the contrary, if the proportion of the virtual sweeping area in the indoor area is less than the preset second threshold, it means that the sweeping robot has not completed cleaning in the indoor area, and the match between the sweeping area and the indoor area is untrustworthy, and the indoor area mark can be ignored.

[0126] Step 1059: Calculate the matching degree between the image data and the floor plan based on the probability and the target area.

[0127] In this embodiment, the probability of a Bluetooth device appearing in the indoor area where the device coordinates are located and the target area where the sweeping robot completes cleaning can be combined to calculate the degree of matching between the image data and the floor plan using a statistical algorithm.

[0128] Exemplarily, the ratio between the area of ​​all target areas and the area of ​​the floor plan is calculated to obtain the cleaning ratio, and the product between the average value of the probability and the third weight is added to the product between the cleaning ratio and the fourth weight to obtain the degree of matching between the image data and the floor plan.

[0129] In this example, assuming that the device set includes Bluetooth device A, Bluetooth device B, and Bluetooth device C, the matching degree between the image data and the floor plan can be expressed as:

[0130] Score=w3×((P1+P2+P3) / 3)+w4×((S1+S2+……+S n ) / S0);

[0131] Among them, Score is the matching degree, P1 is the probability corresponding to Bluetooth device A, P2 is the probability corresponding to Bluetooth device B, P3 is the probability corresponding to Bluetooth device C, n is the number of target areas, S1 is the area of ​​the first target area, S2 is the area of ​​the second target area, and so on. n is the area of ​​the nth target area, S0 is the area of ​​the floor plan, w3 is the third weight, and w4 is the fourth weight.

[0132] Step 106: Determine a floor plan that matches the building among multiple floor plans with the highest matching degree.

[0133] In this embodiment, the floor plans can be sorted from high to low according to the degree of matching, and multiple floor plans with the highest sorting (i.e., multiple floor plans with the highest degree of matching) are selected, and these floor plans are pushed to the client for rendering and display for users to browse and compare.

[0134] If the user selects a floor plan in the client and triggers a confirmation operation in the client, it can be determined that the floor plan selected by the user is a floor plan that matches the current building.

[0135] Step 107: construct a three-dimensional digital twin model of the building based on the floor plan that matches the building.

[0136] In this embodiment, a three-dimensional digital twin model of the building is constructed with the floor plan matching the building as the base map, and the digital twin model is pushed to the client for rendering and display. The client provides an editing component. At this time, the user can call the editing component to edit the digital twin model according to the distribution of furniture, home appliances and other objects in the building, add common objects, and / or generate custom objects, thereby improving the digital twin model.

[0137] In actual applications, the digital twin model can vividly display the status of each smart device in the same smart home platform, making it easier for users to monitor and manage each smart device in the same smart home platform.

[0138] Step 108: construct an electronic map suitable for the sweeping robot to perform cleaning operations based on the digital twin model.

[0139] After being improved by users, the three-dimensional digital twin model is a standardized model. The three-dimensional digital twin model (including objects on the ground in the digital twin model) can be projected onto a two-dimensional plane, thereby constructing an electronic map suitable for the sweeping robot to perform cleaning operations (especially path planning).

[0140] Furthermore, users add furniture, home appliances and other objects to the digital twin model. For some large objects, such as sofas, sideboards, bookcases, air-conditioning cabinets, etc., they can be marked as fixed obstacles on the electronic map. For some small objects, such as dining tables and chairs, laundry baskets, etc., they can be marked as movable obstacles on the electronic map. For obstacles with different properties (fixed, movable), corresponding algorithms can be designed to plan paths, thereby achieving automatic obstacle avoidance and improving cleaning efficiency.

[0141] Since the walls and other structures in buildings basically do not change, the electronic map is valid for a long time. The sweeping robot can dynamically adjust the obstacles in the electronic map based on the situations detected during cleaning to maintain the accuracy of the electronic map.

[0142] In this embodiment, when the sweeping robot performs cleaning operations in a building, three Bluetooth devices in the building are used as base stations to control the sweeping robot to receive Bluetooth beacon signals broadcast by the base stations; the sweeping robot is positioned according to the Bluetooth beacon signals to obtain sweeping coordinates; the density of the sweeping coordinates in the building is calculated; if the density is greater than or equal to a preset first threshold, binary image data is constructed based on the sweeping coordinates; the degree of matching between the image data and a preset floor plan is calculated based on multiple Bluetooth devices; a floor plan that matches the building is determined from multiple floor plans with the highest degree of matching; a three-dimensional digital twin model of the building is constructed based on the floor plan that matches the building; and an electronic map suitable for the sweeping robot to perform cleaning operations is constructed based on the digital twin model. This embodiment reuses the Bluetooth devices in the building as base stations to position the sweeping robot, which can reduce the accumulation of positioning deviations caused by hair entanglement, wear and tear of the internal parts of the sweeping robot. The appropriate house plan is matched based on the moving path of the sweeping robot to ensure the accuracy of the house plan. The digital twin model is improved based on the house plan and a standardized electronic map is constructed based on the digital twin model, which can increase the amount of information. The electronic map has a high degree of adaptability to the actual building, which can improve the timeliness of the electronic map, effectively improve the accuracy of the electronic map, and facilitate the sweeping robot to plan a high-precision path.

[0143] Embodiment 2

[0144] See also Figure 3 , shows a schematic diagram of the structure of a mapping device based on Bluetooth positioning provided by Embodiment 2 of the present invention. Figure 3 As shown, the device comprises:

[0145] The signal receiving module 301 is used to control the cleaning robot to receive the Bluetooth beacon signal broadcasted by the base station when the cleaning robot performs a cleaning operation in the building, using the three Bluetooth devices in the building as base stations;

[0146] A robot positioning module 302 is used to position the cleaning robot according to the Bluetooth beacon signal to obtain cleaning coordinates;

[0147] A density calculation module 303, used to calculate the density of the sweeping coordinates in the building;

[0148] An image data construction module 304 is used to construct binary image data according to the sweeping coordinates if the density is greater than or equal to a preset first threshold;

[0149] A matching degree calculation module 305, used for calculating the matching degree between the image data and a preset floor plan based on the plurality of Bluetooth devices;

[0150] A floor plan determination module 306, configured to determine the floor plan that matches the building among the plurality of floor plans with the highest matching degree;

[0151] A digital twin model construction module 307 is used to construct a three-dimensional digital twin model of the building according to the floor plan matching the building;

[0152] The electronic map construction module 308 is used to construct an electronic map suitable for the sweeping robot to perform cleaning operations based on the digital twin model.

[0153] In one embodiment of the present invention, the signal receiving module 301 includes:

[0154] A mesh network forming module, used for controlling various Bluetooth devices in the building to form a Bluetooth mesh network;

[0155] A Bluetooth device positioning module, used to locate each Bluetooth device in the Bluetooth mesh network to obtain device coordinates;

[0156] A device set forming module, used for forming a device set by forming any three of the Bluetooth devices;

[0157] A dispersion degree calculation module, used for calculating the dispersion degree of the Bluetooth devices in the building according to the device coordinates in the device set;

[0158] The robot control module is used to control the sweeping robot to receive the Bluetooth beacon signal broadcast by the base station by using the three Bluetooth devices in the device set with the highest degree of dispersion as base stations.

[0159] In one embodiment of the present invention, the dispersion degree calculation module is further used for:

[0160] In the device set, the difference between the device coordinates of two Bluetooth devices is calculated to obtain the distribution distance between two Bluetooth devices;

[0161] In the device set, summing the distribution distances to obtain a total distance;

[0162] In the device set, taking absolute values ​​of differences between the distribution distances of each pair to obtain distribution offsets, and summing the distribution offsets to obtain a total offset;

[0163] In the device set, the product between the total distance and the first weight is subtracted from the product between the total offset and the second weight to obtain the dispersion degree of the Bluetooth devices in the building.

[0164] In one embodiment of the present invention, the density calculation module is further used for:

[0165] Adding a minimum bounding rectangle to all the sweeping coordinates as a virtual building area;

[0166] Counting the number of the sweeping coordinates and the area of ​​the virtual building area;

[0167] Calculating a ratio between the number of the sweeping coordinates and the area as the density of the sweeping coordinates in the building;

[0168] The image data construction module 304 is also used for:

[0169] converting the virtual building area into image data;

[0170] In the image data, if a pixel point is located at the sweeping coordinate, the pixel value of the pixel point is set to 1; if the pixel point is not located at the sweeping coordinate, the pixel value of the pixel point is set to 0.

[0171] In one embodiment of the present invention, the matching degree calculation module 305 includes:

[0172] A floor plan loading module, used to load a preset floor plan; the floor plan has indoor areas with multiple annotated attributes;

[0173] A sweeping cluster clustering module, used for clustering the sweeping coordinates in the image data according to the virtual building area to obtain a plurality of sweeping clusters;

[0174] An attribute classification module, used for classifying the sweeping clusters to obtain the attributes of the sweeping clusters in terms of the apartment type;

[0175] A virtual sweeping area conversion module, used for converting the sweeping cluster into a virtual sweeping area;

[0176] A floor plan alignment module, configured to align the image data with the floor plan under the constraint of minimizing the overlap range between the virtual floor sweeping area and the wall of the floor plan, so that the attributes of the virtual floor sweeping area are the same as the attributes of the indoor area;

[0177] A coordinate projection module, configured to project the device coordinates of the Bluetooth device into the image data if the alignment is completed;

[0178] A parameter calculation module, for querying the probability of the Bluetooth device appearing in the indoor area where the device coordinates are located if the projection is completed, and calculating the proportion of the virtual sweeping area in the indoor area;

[0179] A target area marking module, configured to mark the indoor area as a target area if the proportion is greater than or equal to a preset second threshold;

[0180] A joint calculation module is used to calculate the matching degree between the image data and the floor plan according to the probability and the target area.

[0181] In one embodiment of the present invention, the sweeping cluster clustering module is further used for:

[0182] Determine a mapping table; the mapping table contains mapping relationships between multiple building ranges and cluster numbers; the building range is positively correlated with the cluster number;

[0183] Searching the mapping table for the building range where the area of ​​the virtual building area is located as the target range;

[0184] Taking the number of clusters mapped by the target range as a K value, performing K-means clustering on the sweeping coordinates in the image data to obtain a plurality of sweeping clusters;

[0185] The attribute classification module is also used for:

[0186] Determine the gradient boosting decision tree;

[0187] Extracting cluster features from the sweeping cluster; the cluster features include coordinates of the center point of the sweeping cluster relative to the center point of the virtual building area, the length and width of the minimum circumscribed rectangle of the sweeping cluster, the number of sweeping coordinates in the sweeping cluster and the area of ​​the sweeping cluster;

[0188] The cluster features are input into the gradient boosting decision tree for classification, so as to obtain the attributes of the sweeping cluster in terms of the house type.

[0189] In one embodiment of the present invention, the virtual sweeping area conversion module is further used for:

[0190] performing an erosion operation on the sweeping cluster;

[0191] If the erosion operation is completed, a minimum bounding rectangle is added to the sweeping cluster as the full sweeping area;

[0192] The edge of the full sweeping area is converged toward the center by a specified length to obtain a virtual sweeping area.

[0193] In one embodiment of the present invention, the joint calculation module is further used for:

[0194] Calculating the ratio between the area of ​​all the target areas and the area of ​​the floor plan to obtain a cleaning ratio;

[0195] The product of the average value of the probability and the third weight is added to the product of the cleaning ratio and the fourth weight to obtain the matching degree between the image data and the floor plan.

[0196] The mapping device based on Bluetooth positioning provided in the embodiment of the present invention can execute the mapping method based on Bluetooth positioning provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the mapping method based on Bluetooth positioning.

[0197] Embodiment 3

[0198] See also Figure 4 , shows a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0199] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0200] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0201] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a mapping method based on Bluetooth positioning.

[0202] In some embodiments, the mapping method based on Bluetooth positioning can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the mapping method based on Bluetooth positioning described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the mapping method based on Bluetooth positioning in any other appropriate manner (for example, by means of firmware).

[0203] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0204] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0205] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0206] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0207] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0208] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0209] Embodiment 4

[0210] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the mapping method based on Bluetooth positioning as provided in any embodiment of the present invention.

[0211] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0212] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0213] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A mapping method based on Bluetooth positioning, characterized in that: include: When the cleaning robot performs a cleaning operation in a building, the three Bluetooth devices in the building are used as base stations to control the cleaning robot to receive Bluetooth beacon signals broadcast by the base stations; Positioning the sweeping robot according to the Bluetooth beacon signal to obtain sweeping coordinates; Calculate the density of the sweeping coordinates in the building; If the density is greater than or equal to a preset first threshold, constructing binary image data according to the scanning coordinates; Calculating the matching degree between the image data and a preset floor plan based on the plurality of Bluetooth devices; Determining the floor plan that matches the building among the multiple floor plans with the highest matching degree; Constructing a three-dimensional digital twin model of the building according to the floor plan matching the building; An electronic map suitable for the sweeping robot to perform cleaning operations is constructed based on the digital twin model.

2. The method according to claim 1, characterized in that The method of using the three Bluetooth devices in the building as base stations and controlling the sweeping robot to receive Bluetooth beacon signals broadcast by the base stations includes: Controlling various Bluetooth devices in the building to form a Bluetooth mesh network; Positioning each of the Bluetooth devices in the Bluetooth mesh network to obtain device coordinates; Any three of the Bluetooth devices form a device set; In the device set, calculating the dispersion degree of the Bluetooth devices in the building according to the device coordinates; The three Bluetooth devices in the device set with the highest degree of dispersion are used as base stations, and the cleaning robot is controlled to receive the Bluetooth beacon signals broadcast by the base stations.

3. The method according to claim 2, characterized in that The step of calculating the dispersion degree of the Bluetooth devices in the building according to the device coordinates in the device set includes: In the device set, the difference between the device coordinates of two Bluetooth devices is calculated to obtain the distribution distance between two Bluetooth devices; In the device set, summing the distribution distances to obtain a total distance; In the device set, taking absolute values ​​of differences between the distribution distances of each pair to obtain distribution offsets, and summing the distribution offsets to obtain a total offset; In the device set, the product between the total distance and the first weight is subtracted from the product between the total offset and the second weight to obtain the dispersion degree of the Bluetooth devices in the building.

4. The method according to any one of claims 1 to 3, characterized in that The calculating the density of the sweeping coordinates in the building comprises: Adding a minimum bounding rectangle to all the sweeping coordinates as a virtual building area; Counting the number of the sweeping coordinates and the area of ​​the virtual building area; Calculating a ratio between the number of the sweeping coordinates and the area as the density of the sweeping coordinates in the building; The step of constructing binary image data according to the sweeping coordinates includes: converting the virtual building area into image data; In the image data, if a pixel point is located at the sweeping coordinate, the pixel value of the pixel point is set to 1; if the pixel point is not located at the sweeping coordinate, the pixel value of the pixel point is set to 0.

5. The method according to claim 4, characterized in that The calculating the matching degree between the image data and the preset floor plan based on the plurality of Bluetooth devices includes: Loading a preset floor plan; the floor plan has an indoor area with multiple annotated attributes; Clustering the sweeping coordinates in the image data according to the virtual building area to obtain a plurality of sweeping clusters; Classifying the sweeping clusters to obtain attributes of the sweeping clusters based on the apartment type; Converting the sweeping cluster into a virtual sweeping area; Under the constraint of minimizing the overlapping range between the virtual sweeping area and the wall of the floor plan, aligning the image data with the floor plan so that the attributes of the virtual sweeping area are the same as the attributes of the indoor area; If the alignment is completed, projecting the device coordinates of the Bluetooth device into the image data; If the projection is completed, query the probability that the Bluetooth device appears in the indoor area where the device coordinates are located, and calculate the proportion of the virtual sweeping area in the indoor area; If the proportion is greater than or equal to a preset second threshold, marking the indoor area as a target area; The matching degree between the image data and the floor plan is calculated according to the probability and the target area.

6. The method according to claim 5, characterized in that The clustering of the sweeping coordinates in the image data according to the virtual building area to obtain a plurality of sweeping clusters includes: Determine a mapping table; the mapping table contains mapping relationships between multiple building ranges and cluster numbers; the building range is positively correlated with the cluster number; Searching the mapping table for the building range where the area of ​​the virtual building area is located as the target range; Taking the number of clusters mapped by the target range as a K value, performing K-means clustering on the sweeping coordinates in the image data to obtain a plurality of sweeping clusters; The classifying the sweeping clusters to obtain the attributes of the sweeping clusters in terms of the apartment type includes: Determine the gradient boosting decision tree; Extracting cluster features from the sweeping cluster; the cluster features include coordinates of a center point of the sweeping cluster relative to a center point of the virtual building area, a length and a width of a minimum circumscribed rectangle of the sweeping cluster, the number of sweeping coordinates in the sweeping cluster, and an area of ​​the sweeping cluster; The cluster features are input into the gradient boosting decision tree for classification, so as to obtain the attributes of the sweeping cluster in terms of the house type.

7. The method according to claim 6, characterized in that The converting the sweeping cluster into a virtual sweeping area comprises: performing an erosion operation on the sweeping cluster; If the erosion operation is completed, a minimum bounding rectangle is added to the sweeping cluster as the full sweeping area; The edge of the full sweeping area is converged toward the center by a specified length to obtain a virtual sweeping area.

8. The method according to claim 5, characterized in that The calculating the matching degree between the image data and the floor plan according to the probability and the target area includes: Calculating the ratio between the area of ​​all the target areas and the area of ​​the floor plan to obtain a cleaning ratio; The product of the average value of the probability and the third weight is added to the product of the cleaning ratio and the fourth weight to obtain the matching degree between the image data and the floor plan.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the mapping method based on Bluetooth positioning as described in any one of claims 1 to 8 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the mapping method based on Bluetooth positioning as described in any one of claims 1 to 8 is implemented.

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