Method, apparatus and electronic device for generating a room layout

Abstract

The application discloses a method, device and electronic equipment for generating a room layout, and relates to the technical field of communication. The method comprises the following steps: acquiring network signal strength averages of each terminal in different time periods; performing clustering processing on the network signal strength averages to obtain N clustering clusters; determining K fixed terminals in the N clustering clusters; finally, labeling room identifiers for the K fixed terminals, and generating a first room layout based on all the room identifiers. Based on the method, the positioning of a room through a wireless AP is realized by performing network data analysis on each terminal in a home environment, and a home floor plan is generated.

Description

Technical Field

[0001] This application relates primarily to the field of communication technology, and in particular to a method, apparatus, and electronic device for generating room layouts. Background Technology

[0002] With the continuous development of WiFi technology, users have placed higher demands on the WiFi experience. WiFi is a wireless local area network technology that conforms to the IEEE 802.11 standard. Currently, wireless access points (APs) have become essential home devices for various smart terminals to access the Internet. However, when a wireless AP is affected by obstacles, such as walls, doors, or cabinets, the WiFi signal will be weakened. The wireless AP cannot adjust the WiFi signal to strengthen the signal in rooms with weak signals.

[0003] In existing technologies, to perform signal coverage analysis for wireless access points (APs), home floor plans are typically used in signal heat maps. However, the home floor plans used in this process... Figure 1 Generally, these are obtained through purchase or drawn by the user themselves. When a user adjusts the layout of their home, such as adding cabinets or doors that obstruct WiFi signal transmission, the wireless access point (AP) cannot detect the changes. The signal coverage analysis based on the purchased floor plan becomes less valuable, as the AP cannot perform signal coverage analysis according to the floor plan. Consequently, when WiFi signal anomalies occur, the AP cannot quickly locate the affected room, resulting in a poor user experience.

[0004] By identifying the rooms where terminal devices are frequently used, a more intuitive analytical basis can be provided when addressing user network environment issues in business operations. Existing methods cannot analyze home network environments and obtain home floor plans using a single wireless access point. Summary of the Invention

[0005] This application provides a method, apparatus, and electronic device for generating room layouts. By obtaining the WiFi radio frequency strength values ​​of each terminal in the room, terminals with similar signal strength are grouped into the same cluster using a clustering algorithm. The room where the fixed terminal is located is obtained by using the maximum complete subgraph among the fixed terminals, thereby completing the positioning of the room and generating a room layout map.

[0006] In a first aspect, this application provides a method for generating a room layout, the method comprising:

[0007] The average network signal strength of each terminal is obtained at different time periods; wherein, the average network signal strength is the average value when the network signal of each terminal remains stable.

[0008] The average signal strength of each network is clustered to obtain N clusters; where N is an integer greater than 1.

[0009] K fixed terminals are identified from the N clusters; wherein the K fixed terminals belong to different clusters, and K is an integer greater than or equal to 0;

[0010] Label the K fixed terminals with room identifiers, and generate the first room layout map based on all the room identifiers.

[0011] By utilizing the similarity of WiFi radio frequency strength values ​​among various terminals, the method described above can better distinguish between different rooms in a home with similar signals, thereby enabling the location of the rooms and the generation of room layout maps.

[0012] In one optional implementation, the average network signal strength of each terminal at different time periods is obtained, including:

[0013] Obtain the network signal strength values ​​of each terminal at different time periods; wherein, the network signal strength values ​​do not include network signal strength values ​​that change abruptly under stable conditions;

[0014] The average network signal strength of each terminal at different time periods is obtained based on the network signal strength value.

[0015] In one optional implementation, the step of clustering the average network signal strengths to obtain N clusters includes:

[0016] A corresponding feature vector is formed based on the average network strength of each terminal;

[0017] By using a clustering algorithm, each feature vector is clustered to obtain N clusters.

[0018] By using the above method, clustering algorithms can be used to group terminals with similar signals into the same cluster, thus achieving preliminary room location.

[0019] In one optional implementation, determining K fixed terminals in the N clusters includes:

[0020] J fixed terminals are identified from the N clusters;

[0021] The J fixed terminals are grouped into pairs, and a relationship matrix of the J fixed terminals is created based on the grouping results.

[0022] The maximum complete subgraph corresponding to the J fixed terminals is calculated using the relation matrix, and the K fixed terminals in the maximum complete subgraph are determined.

[0023] Using the above method, the uncorrelated relationships between each terminal are obtained by employing the maximum complete subgraph, and the fixed terminal corresponding to each room can be obtained further.

[0024] In one optional implementation, after labeling the K fixed terminals with room identifiers and generating a first room layout diagram based on all the room identifiers, the method further includes:

[0025] Obtain the cluster identifiers of the K fixed terminals and the cluster identifiers of the mobile terminals;

[0026] Determine whether the cluster identifier of the mobile terminal is the same as any of the cluster identifiers of the K fixed terminals;

[0027] If not, then add room labels to the first room layout diagram to generate a second room layout diagram.

[0028] Using the above method, the room where the fixed terminal is located is obtained by using the maximum complete subgraph. Furthermore, based on the clustering relationship between the mobile terminal and the fixed terminal, the rooms that were not successfully located can be obtained, thus generating a complete room layout map of the entire environment.

[0029] Secondly, this application provides an apparatus for generating a room layout, the apparatus comprising:

[0030] The acquisition module is used to acquire the average network signal strength of each terminal at different time periods;

[0031] The processing module is used to cluster the average values ​​of the network signal strengths to obtain N clusters;

[0032] The determination module is used to identify K fixed terminals among the N clusters;

[0033] The generation module is used to label room identifiers on K fixed terminals and generate a first room layout diagram based on all room identifiers.

[0034] In one optional implementation, the acquisition module is specifically used for:

[0035] Obtain the network signal strength values ​​of each terminal at different time periods; wherein, the network signal strength values ​​do not include network signal strength values ​​that change abruptly under stable conditions;

[0036] The average network signal strength of each terminal at different time periods is obtained based on the network signal strength value.

[0037] In one optional implementation, the processing module is specifically used for:

[0038] A corresponding feature vector is formed based on the average network strength of each terminal;

[0039] By using a clustering algorithm, each feature vector is clustered to obtain N clusters.

[0040] In one optional implementation, the determining module is specifically used for:

[0041] J fixed terminals are identified from the N clusters;

[0042] The J fixed terminals are grouped into pairs, and a relationship matrix of the J fixed terminals is created based on the grouping results.

[0043] The maximum complete subgraph corresponding to the J fixed terminals is calculated using the relation matrix, and the K fixed terminals in the maximum complete subgraph are determined.

[0044] In an optional implementation, the generation module is further configured to:

[0045] Obtain the cluster identifiers of the K fixed terminals and the cluster identifiers of the mobile terminals;

[0046] Determine whether the cluster identifier of the mobile terminal is the same as any of the cluster identifiers of the K fixed terminals;

[0047] If not, then add room labels to the first room layout diagram to generate a second room layout diagram.

[0048] Thirdly, this application provides an electronic device, comprising:

[0049] Memory, used to store computer programs;

[0050] When a processor executes a computer program stored in the memory, it implements the steps of the method for generating a room layout described above.

[0051] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for generating a room layout.

[0052] For the various aspects of the second to fourth aspects mentioned above, and the technical effects that each aspect may achieve, please refer to the above description of the technical effects that can be achieved for the first aspect and the various possible solutions in the first aspect. They will not be repeated here. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of this application;

[0054] Figure 2A flowchart illustrating a method for generating a room layout, as provided in an embodiment of this application;

[0055] Figure 3 This is a schematic diagram of a clustering result provided in an embodiment of this application;

[0056] Figure 4 This is a schematic diagram illustrating the effect of generating a room layout, provided as an embodiment of this application.

[0057] Figure 5 A schematic diagram of an apparatus for generating a room layout provided in an embodiment of this application;

[0058] Figure 6 This is a schematic diagram of an electronic device structure provided in an embodiment of this application. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The specific operational methods in the method embodiments can also be applied to the device embodiments or system embodiments. It should be noted that in the description of this application, "multiple" is understood as "at least two". "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. A connected to B can represent: A and B directly connected, and A and B connected through C. Furthermore, in the description of this application, terms such as "first" and "second" are used only for distinguishing the purpose of description and should not be construed as indicating or implying relative importance or order.

[0060] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0061] With the continuous development of WiFi technology, users have placed higher demands on their WiFi experience. WiFi is a wireless local area network technology that conforms to the IEEE 802.11 standard. Currently, wireless access points (APs) have become essential home devices for various smart terminals to access the Internet. However, when a wireless AP is affected by obstacles, such as walls, doors, or cabinets, the WiFi signal will be weakened. The wireless AP cannot adjust the WiFi signal to strengthen the signal in rooms with weaker signals.

[0062] In existing technologies, to perform signal coverage analysis for wireless access points (APs), home floor plans are typically used in signal heat maps. However, the home floor plans used in this process... Figure 1Generally, these are obtained through purchase or drawn by the user themselves. When a user adjusts the layout of their home, such as adding cabinets or doors that obstruct WiFi signal transmission, the wireless access point (AP) cannot detect the changes. The signal coverage analysis based on the purchased floor plan becomes less valuable, as the AP cannot perform signal coverage analysis according to the floor plan. Consequently, when WiFi signal anomalies occur, the AP cannot quickly locate the affected room, resulting in a poor user experience.

[0063] By identifying the rooms where terminal devices are frequently used, operators can provide a more intuitive basis for analysis when locating user network environment issues. Existing methods cannot analyze home network environments and obtain home floor plans using a single wireless access point.

[0064] In view of this, such as Figure 1 As shown, in a typical home environment, a wireless access point (AP) provides a wireless network for various terminals. The network usage data generated by each terminal is used to locate the room where each terminal is located, thereby generating a room layout map. This application provides a method for generating a room layout map, specifically including: first, obtaining the average network signal strength of each terminal at different time periods; then, clustering the average network signal strength to obtain N clusters; further, identifying K fixed terminals within each of the N clusters; labeling the K fixed terminals with room identifiers; and generating a first room layout map based on all room identifiers. Through the above method, the average network signal strength of each terminal is divided into multiple clusters according to similarity, and the fixed terminals within each cluster can be identified to locate each room. Thus, a single wireless AP can be used to locate each room in a home environment and generate a room layout map.

[0065] The methods and apparatus described and used in the embodiments of this application are based on the same technical concept. Since the principles by which the methods and apparatus solve the problems are similar, the embodiments of the apparatus and methods can be referred to each other, and repeated parts will not be described again.

[0066] like Figure 2 The diagram shown is a flowchart of a method for generating a room layout according to an embodiment of this application, which specifically includes the following steps:

[0067] S1, obtain the average network signal strength of each terminal at different time periods;

[0068] In this embodiment, determining the room where each terminal is located requires a large amount of data. Therefore, it is first necessary to obtain the average network signal strength of each terminal at different time periods. Specifically, different time periods refer to periods where the network signal strength of each terminal remains stable without sudden changes. For example, if the network signal strength of a mobile phone is stable at around -60dB from 10:00 to 10:30, the average value of the network signal strength during this period can be taken. However, if the network signal strength of the mobile phone fluctuates drastically at 10:31, reaching -80dB, the network signal strength value during this drastic fluctuation cannot be included in the average value. If the network signal strength of the mobile phone stabilizes after a period of time, for example, stabilizing at around -62dB from 10:35 to 11:00, then the network signal strength of the mobile phone is considered to be stable, and the average value of the network signal strength during this period can be taken. Specifically, devices that can use WiFi in a home environment include various types, including fixed terminals and mobile terminals. Examples include fixed terminals such as cameras and televisions set up in the room, and mobile terminals such as mobile phones and tablets. When various devices are in use, the quality of the network is related to the WiFi radio frequency signal strength value (English: Received Signal Strength Indication, abbreviated as RSSI). The distance between the terminal and the wireless AP, as well as obstacles in the room, will affect the RSSI value.

[0069] The number of RSSI values ​​that each terminal can obtain is not fixed, and the length of different time periods is also not fixed. It is determined by actual usage and specifically by whether there are drastic changes in the current network signal.

[0070] Furthermore, the average RSSI value of different terminals over different time periods was obtained. Specifically, when a terminal is using WiFi, its RSSI value changes with time and location.

[0071] Therefore, the average RSSI value for each terminal is obtained under the condition that the network signal of each terminal remains stable. For example, when a user uses a mobile phone in the master bedroom, the phone's RSSI value may be around -60dB. During use, the RSSI value will not remain constant; small signal fluctuations can be considered as the RSSI value remaining stable. WiFi signals are essentially electromagnetic waves. When the door to the master bedroom is closed, the WiFi signal will be interfered with, as the WiFi signal emitted from the wireless access point will be reflected by the door. At this time, the RSSI value will decrease; for example, the mobile phone's RSSI value will decrease from -60dB to around -65dB. It should be noted that the RSSI value is negative; a higher value indicates a better WiFi signal.

[0072] Furthermore, the average RSSI of each terminal over different time periods is obtained. Specifically, the RSSI values ​​of each terminal in each time period are obtained, and then the average RSSI of each terminal over different time periods is obtained based on the obtained RSSI values. When obtaining the RSSI values ​​of the terminals, the RSSI values ​​during periods of drastic fluctuations should not be included.

[0073] For example, when a tablet is used in a room, the RSSI value fluctuates around -70 dB. At 10:10, the RSSI value is -69 dB; at 10:15, the RSSI value is -70 dB; and at 10:20, the RSSI value is -71 dB. Therefore, the average RSSI value of the tablet during the period from 10:10 to 10:20 is -70 dB.

[0074] When the room where the tablet is located is closed, the WiFi signal will be blocked by the door or other obstacles. After the WiFi signal is reflected, the RSSI value will decrease. For example, if the room is closed at 10:30 PM, the RSSI value is -75 dB; at 10:35 PM, it is -76 dB; and at 10:40 PM, it is -74 dB. If the RSSI value jumps drastically at 10:42 PM, abruptly dropping to -85 dB, then the RSSI value at the point of the jump cannot be used for averaging. Therefore, the average RSSI value after the door is closed or the RSSI value is reduced due to other obstacles is -75 dB. If the RSSI value does not fluctuate drastically over a period of time, the WiFi signal received by the terminal is considered to be in a stable state.

[0075] Similarly, obtain the average RSSI of other devices in the room, such as cameras, televisions, mobile phones, and audio equipment.

[0076] S2, cluster the average signal strength of each network to obtain N clusters;

[0077] After obtaining the mean RSSI values ​​of each terminal at different time periods, the mean RSSI values ​​are clustered to obtain N clusters.

[0078] In one optional implementation, the mean RSSI values ​​are clustered to obtain N clusters, including the following steps:

[0079] S201, form a corresponding feature vector based on the average RSSI value of each terminal;

[0080] S202 uses a clustering algorithm to cluster each feature vector to obtain N clusters.

[0081] Specifically, after obtaining the mean RSSI values ​​of different terminals at different time periods, each mean RSSI value is transformed into a corresponding feature vector through upsampling and downsampling. The feature vector describes the main transformation direction of the mean RSSI value.

[0082] Furthermore, a clustering algorithm is used to cluster all feature vectors from different time periods to obtain N clusters. Specifically, in this embodiment, the K-means clustering algorithm is used to divide the feature vectors obtained by RSSI mean transformation into N classes.

[0083] First, the Canopy algorithm is used to obtain the number of clusters. Specifically, the Canopy algorithm uses a distance metric to find the centroids of the clusters, identifying multiple centroids of the feature vector formed by the RSSI mean of the corresponding fixed terminal within the feature vector formed by all RSSI means. Here, the number of clusters obtained by the Canopy algorithm can be considered the number of rooms with fixed terminals. For example, the number of clusters can be divided into 3. In this embodiment, the feature vector can be divided multiple times using the K-means algorithm until 3 clusters are obtained. The number of clusters N is the same as the minimum number of rooms to be obtained.

[0084] Specifically, after obtaining the feature vectors corresponding to each RSSI mean, the number of clusters can first be determined using the Canopy algorithm. Further, for example, if the number of clusters N is 3, then in the K-means algorithm, K = 3. The K-means algorithm is used to divide the feature vectors formed by the RSSI means into 3 clusters. In this embodiment, we will use 3 clusters as an example. The centroid of each cluster is found, and the clusters obtained through the K-means algorithm are designed to be as close as possible to the centroid of the original cluster.

[0085] In the K-means algorithm's partitioning process, N data points are first selected in the dimension of each feature vector, resulting in three centroids. After selecting the three centroids, the Euclidean distance between each feature vector and the three centroids is calculated. The Euclidean distance represents the true distance between two points in multidimensional space.

[0086] After calculating the Euclidean distance between each feature vector and its centroid, the Euclidean distances between each feature vector and its centroid are compared. Furthermore, the feature vectors are assigned to the cluster with the smallest corresponding centroid, resulting in three clusters. Next, the centroids of these three clusters are determined. Specifically, the mean of all data points along the dimension containing each feature vector in each cluster is calculated, and the mean of each feature vector is used as the new centroid of each cluster.

[0087] After obtaining the new centroids of each cluster, calculate the Euclidean distance from each feature vector to the new centroid, and re-divide each cluster. Repeat the above operation until the partitioning results of the clusters containing all feature vectors no longer change, thus obtaining the final N clusters. Figure 3 The diagram shown is a schematic representation of the clustering results provided in an embodiment of this application.

[0088] Specifically, in actual usage environments, the average network signal strength of the terminal will not change significantly. For example, when using a Bluetooth speaker in a study, its RSSI value may fluctuate within the range of -60dB, -61dB, or -62dB, but will quickly return to a stable level. Similarly, the RSSI value of a camera in the study will also fluctuate within this range. Since the WiFi signal propagation paths of the Bluetooth speaker and camera are roughly the same in a study environment, their RSSI values ​​will also be roughly the same. Therefore, it can be assumed that the Bluetooth speaker and camera are in the same room.

[0089] The strength of the network signal a terminal can receive varies depending on whether the door is open or closed. When the door is open, the network signal is stronger, and the average RSSI value is higher; when the door is closed, the network signal is weaker, and the average RSSI value decreases accordingly. Therefore, for the same fixed terminal, the average RSSI value at different times may be assigned to different clusters. The room in which the fixed terminal is located, determined by the cluster identifier, remains constant and will not change due to occasional signal fluctuations that place it in different clusters.

[0090] For mobile terminals, when the mean RSSI of a mobile terminal is divided into different clusters at different time periods, the corresponding room will change, but the number of rooms obtained by clustering through the mean RSSI will not change.

[0091] Using the above method, the RSSI mean is used to form a feature vector. Then, a clustering algorithm is used to group terminals with similar RSSI means into the same cluster, with each cluster representing a room.

[0092] S3, identify K fixed terminals from N clusters;

[0093] In one alternative implementation, identifying K fixed terminals in N clusters includes the following steps:

[0094] S301, Identify J fixed terminals from N clusters;

[0095] S302, group the J fixed terminals into pairs, and create a relationship matrix of the J fixed terminals based on the grouping results;

[0096] S303 calculates the maximum complete subgraph corresponding to J fixed terminals through the relation matrix, and determines K fixed terminals in the maximum complete subgraph.

[0097] Specifically, fixed terminals and mobile terminals in the home environment are divided into N clusters based on their RSSI mean. Further, J fixed terminals are identified from these N clusters. For example, the N clusters may include four fixed terminals. In this embodiment, when J fixed terminals are identified from the N clusters, a terminal identification service is introduced to determine which terminals are fixed and which are mobile terminals.

[0098] Furthermore, these four fixed terminals are grouped into pairs. For example, fixed terminal 1 and fixed terminal 2 are grouped into group A, and fixed terminal 3 and fixed terminal 4 are grouped into group B. After grouping, a J×J relation matrix is ​​created. Initially, all values ​​in the matrix are 0. For example, a 4×4 relation matrix is ​​created, with all values ​​initially set to 0. If fixed terminal 1 and fixed terminal 2 are in different clusters, they are considered not to be in the same cluster. Furthermore, the positions corresponding to [1, 2] and [2, 1] in the relation matrix are filled with 1. If fixed terminal 1 and fixed terminal 2 are in the same cluster, they are considered to be in the same cluster. Furthermore, the positions corresponding to [1, 2] and [2, 1] in the relation matrix are kept at 0.

[0099] The essence of calculating the maximum complete subgraph among fixed terminals using the Hungarian algorithm is to find the uncorrelated relationships between the terminals. For example, if fixed terminal 1 and fixed terminal 2 are not in the same room, fixed terminal 2 and fixed terminal 3 are not in the same room, fixed terminal 3 and fixed terminal 4 are not in the same room, and fixed terminal 3 and fixed terminal 1 are not in the same room, then fixed terminal 1, fixed terminal 2, and fixed terminal 3 are in different rooms.

[0100] Therefore, it can be concluded that fixed terminal 1, fixed terminal 2, and fixed terminal 3 are each located in a separate room. Finally, the three rooms in the home environment containing the fixed terminals can be identified.

[0101] The number of clusters N is not necessarily related to the number of fixed terminals J. When the number of fixed terminals is large, J may be greater than N. When the number of clusters is exactly equal to the number of fixed terminals, J equals N. Or, when the number of fixed terminals is small, or some rooms do not have fixed terminals, J may be less than N. Ultimately, the number of K fixed terminals obtained is less than or equal to the number of clusters N.

[0102] The maximum complete subgraph is obtained from the relation matrix of the J fixed terminals, and then the number of K fixed terminals that are unrelated to each other is obtained from the maximum complete subgraph.

[0103] S4: Label the K fixed terminals with room identifiers and generate a room layout diagram based on all the room identifiers.

[0104] First, the number of rooms corresponding to each fixed terminal is obtained by calculating the uncorrelated relationships between all fixed terminals. After determining the room where each fixed terminal is located, each room is labeled with a room identifier. For example, the room corresponding to fixed terminal 1 is labeled as room1, and the room corresponding to fixed terminal 2 is labeled as room2. If four rooms are located, the maximum room identifier is room4.

[0105] K fixed terminals are identified by using the maximum complete subgraph, and each fixed terminal can represent a room. Room identifiers are then added to the K fixed terminals, and a first room layout diagram is generated based on these identifiers.

[0106] In one optional implementation, after labeling the K fixed terminals with room identifiers and generating a first room layout diagram based on all the room identifiers, the following steps are also included:

[0107] S401, Obtain the cluster identifiers of K terminals and the cluster identifier of the mobile terminal;

[0108] S402, determine whether the cluster identifier of the mobile terminal is the same as any of the cluster identifiers of the K fixed terminals;

[0109] S403, if not, add room labels to the first room layout diagram to generate a second room layout diagram.

[0110] Cluster identifiers serve as identity markers to distinguish different clusters; all terminals within the same cluster share the same cluster identifier.

[0111] Specifically, fixed and mobile terminals may be in the same room, or only the mobile terminal may be present in the room. Therefore, the cluster identifiers of the fixed and mobile terminals are compared to determine if there are other rooms that have not been successfully located. For example, if both camera 1 and phone 1 are clustered in group A, then camera 1 and phone 1 are considered to be in the same room. Furthermore, phone 1 is labeled with the same room identifier as camera 1. If phone 2 is clustered in group H, and the cluster identifiers for each fixed terminal in the clustering results are group B, C, and D respectively, then phone 2's cluster identifier is different from all the fixed terminals. Therefore, phone 2 is not in the same room as any of the fixed terminals.

[0112] Furthermore, new room labels are added to the existing first room layout diagram, and an updated second room layout diagram is generated based on the updated room labels.

[0113] Specifically, three rooms are located using fixed terminals, labeled room1, room2, and room3. Rooms located using mobile terminals are further identified by adding additional room labels. For example, a newly located room via mobile terminal will be labeled room4.

[0114] Finally, all rooms are located using both fixed and mobile terminals, and an updated room layout map is generated based on the updated room identifiers. The final effect of the room layout generation method provided in this embodiment is shown in the following diagram. Figure 4 As shown.

[0115] Based on the same inventive concept, embodiments of this application also provide an apparatus for generating room layouts, such as... Figure 5 As shown, the device includes:

[0116] The acquisition module 501 is used to acquire the average network signal strength of each terminal at different time periods;

[0117] Processing module 502 is used to cluster the average values ​​of the network signal strengths to obtain N clusters;

[0118] The determination module 503 is used to determine K fixed terminals in the N clusters;

[0119] The generation module 504 is used to label room identifiers on K fixed terminals and generate a first room layout diagram based on all room identifiers.

[0120] In one optional implementation, the acquisition module is specifically used for:

[0121] Obtain the network signal strength values ​​of each terminal at different time periods; wherein, the network signal strength values ​​do not include network signal strength values ​​that change abruptly under stable conditions;

[0122] The average network signal strength of each terminal at different time periods is obtained based on the network signal strength value.

[0123] In one optional implementation, the processing module is specifically used for:

[0124] A corresponding feature vector is formed based on the average network strength of each terminal;

[0125] By using a clustering algorithm, each feature vector is clustered to obtain N clusters.

[0126] In one optional implementation, the determining module is specifically used for:

[0127] J fixed terminals are identified from the N clusters;

[0128] The J fixed terminals are grouped into pairs, and a relationship matrix of the J fixed terminals is created based on the grouping results.

[0129] The maximum complete subgraph corresponding to the J fixed terminals is calculated using the relation matrix, and the K fixed terminals in the maximum complete subgraph are determined.

[0130] In an optional implementation, the generation module is further configured to:

[0131] Obtain the cluster identifiers of the K fixed terminals and the cluster identifiers of the mobile terminals;

[0132] Determine whether the cluster identifier of the mobile terminal is the same as any of the cluster identifiers of the K fixed terminals;

[0133] If not, then add room labels to the first room layout diagram to generate a second room layout diagram.

[0134] It should be noted that the apparatus provided in this application embodiment can implement all the method steps in the above-described method embodiment for generating room layout, and can achieve the same technical effect. Therefore, the parts and beneficial effects that are the same as those in the method embodiment will not be described in detail here.

[0135] Based on the same inventive concept, this application also provides an electronic device that can realize the function of the aforementioned method for generating room layouts, see reference. Figure 6 As shown, the electronic device includes:

[0136] At least one processor 601 and a memory 602 connected to at least one processor 601. This embodiment does not limit the specific connection medium between the processor 61 and the memory 62. Figure 6 Taking the connection between processor 601 and memory 602 via bus 600 as an example. Bus 600 in... Figure 6 The connections between other components are indicated by thick lines and are for illustrative purposes only, not as limiting information. The 600 bus can be divided into address bus, data bus, control bus, etc., for ease of representation. Figure 6 The term is represented by a single thick line, but this does not imply that there is only one bus or one type of bus. Alternatively, the processor 601 can also be called a controller; there is no restriction on the name.

[0137] In this embodiment, memory 602 stores instructions executable by at least one processor 601. By executing the instructions stored in memory 602, at least one processor 601 can perform the method for generating room layouts described above. Processor 601 can implement... Figure 5 The functions of each module in the device shown.

[0138] The processor 601 is the control center of the device. It can connect to various parts of the control device through various interfaces and lines. By running or executing instructions stored in memory 602 and calling data stored in memory 602, the processor can perform various functions and process data, thereby monitoring the device as a whole.

[0139] In one possible design, processor 601 may include one or more processing units. Processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into processor 601. In some embodiments, processor 601 and memory 602 may be implemented on the same chip; in some embodiments, they may also be implemented on separate chips.

[0140] The processor 601 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor, application-specific integrated circuit, field-programmable gate array or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method for generating room layouts disclosed in the embodiments of this application can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0141] Memory 602, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. Memory 602 may include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage, magnetic disk, optical disk, etc. Memory 602 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, memory 602 may also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0142] By designing and programming the processor 601, the code corresponding to the method for generating room layouts described in the foregoing embodiments can be embedded into the chip, thereby enabling the chip to execute the code during runtime. Figure 2 The steps of the method for generating a room layout in the illustrated embodiment are as follows. How to design and program the processor 601 is a technique well-known to those skilled in the art and will not be described further here.

[0143] Based on the same inventive concept, embodiments of this application also provide a storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the method for generating room layouts described above.

[0144] In some possible implementations, various aspects of the scene restoration method provided in this application can also be implemented in the form of a program product, which includes program code that, when the program product is run on a device, causes the control device to perform the steps in the method for generating room layouts according to various exemplary embodiments of this application described above.

[0145] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0146] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0147] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0148] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0149] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method of generating a room layout, characterized by, The method comprises: obtaining network signal strength averages of each terminal in different time periods; wherein the network signal strength averages are averages when the network signals of the terminals are stable; performing clustering processing on each network signal strength average to obtain N clustering clusters; wherein N is an integer greater than 1; determining K fixed terminals in the N clustering clusters; wherein the K fixed terminals belong to different clustering clusters, and K is an integer greater than 0; annotating room identifiers for the K fixed terminals, and generating a first room layout based on all room identifiers.

2. The method of claim 1, wherein, Obtaining network signal strength averages of each terminal in different time periods comprises: obtaining network signal strength values of the terminals in different time periods; wherein the network signal strength values do not include network signal strength values that jump in a stable state; obtaining network signal strength averages of each terminal in different time periods according to the network signal strength values.

3. The method of claim 1, wherein, The clustering processing on each network signal strength average to obtain N clustering clusters comprises: forming corresponding feature vectors according to the network strength averages of the terminals; performing clustering on each feature vector by a clustering algorithm to obtain N clustering clusters.

4. The method of claim 1, wherein, The determination of K fixed terminals in the N clustering clusters comprises: determining J fixed terminals in the N clustering clusters; grouping the J fixed terminals in pairs, creating a relationship matrix of the J fixed terminals according to the grouping result; calculating maximum complete subgraphs corresponding to the J fixed terminals through the relationship matrix, and determining K fixed terminals in the maximum complete subgraphs.

5. The method of claim 1, wherein, After the annotation of room identifiers for the K fixed terminals and the generation of a first room layout based on all room identifiers, the method further comprises: obtaining clustering identifiers of the K fixed terminals and a clustering identifier of a mobile terminal; determining whether the clustering identifier of the mobile terminal is the same as any of the clustering identifiers of the K fixed terminals; if not, adding a room identifier to the first room layout to generate a second room layout.

6. An apparatus for generating a room layout, the apparatus comprising: The device comprises: an obtaining module for obtaining network signal strength averages of each terminal in different time periods; a processing module for performing clustering processing on each network signal strength average to obtain N clustering clusters; a determination module for determining K fixed terminals in the N clustering clusters; a generation module for annotating room identifiers for the K fixed terminals, and generating a first room layout based on all room identifiers.

7. The apparatus of claim 6, wherein, The obtaining module is specifically configured to: obtain network signal strength values of each terminal in each time period; wherein the network signal strength values do not include network signal strength values that jump in a stable state; obtain network signal strength averages of each terminal in different time periods according to the network signal strength values.

8. The apparatus of claim 6, wherein, The processing module is specifically configured to: form corresponding feature vectors according to the network strength averages of the terminals; perform clustering on each feature vector by a clustering algorithm to obtain N clustering clusters.

9. The apparatus of claim 6, wherein, The determination module is specifically configured to: determine J fixed terminals in the N clustering clusters; grouping the J fixed terminals two by two, creating a relation matrix of the J fixed terminals according to the grouping result; calculating a maximum complete subgraph corresponding to the J fixed terminals through the relation matrix, and determining K fixed terminals in the maximum complete subgraph.

10. The apparatus of claim 6, wherein, The generating module is further configured to: obtain a cluster identifier of the K fixed terminals and a cluster identifier of the mobile terminal; determine whether the cluster identifier of the mobile terminal is the same as any of the cluster identifiers of the K fixed terminals; if not, adding a room identifier on the basis of the first room layout map to generate a second room layout map.

11. An electronic device, comprising: comprise: a memory for storing a computer program; a processor for executing the computer program stored in the memory to implement the method steps in any one of claims 1-5.

12. A computer-readable storage medium, characterized in that, The computer program is stored in the computer readable storage medium, and the computer program is executed by the processor to implement the method steps in any one of claims 1-5.

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