Signal diagram generation method, related device and system, medium and cleaning robot

Abstract

The invention discloses a signal diagram generation method, a related device, a system, a medium and a cleaning robot, and the method comprises the steps: carrying out the planning of a motion path of the cleaning robot based on a target house type diagram of a to-be-detected space; wherein the cleaning robot is wirelessly connected to the target wireless information source, and the motion path comprises a plurality of collection points which are connected in sequence; in the process that the cleaning robot moves along the movement path, first signal intensity of wireless connection measured by the cleaning robot at each collection point is obtained; acquiring a signal distribution diagram generated for the to-be-measured space based on the target house type diagram and the first signal intensity of each acquisition point; wherein the signal distribution diagram represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be detected. According to the scheme, the efficiency and precision of drawing the wireless signal diagram can be improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a signal graph generation method and related devices, systems, media and cleaning robots. Background Art

[0002] The wireless signal map can present the signal coverage situation so that the wireless signal source equipment (such as routers, APs, etc.) can be adjusted based on personalized needs to better provide wireless network coverage.

[0003] At present, it is usually necessary for personnel to hold a terminal (such as a mobile phone, tablet computer, etc.) connected to a wireless signal source and move it to the corresponding position to generate a wireless signal map based on the signal strength measured at the corresponding position. However, this method is time-consuming and labor-intensive on the one hand, and on the other hand, considering the patience of personnel, signal measurements are usually only performed at a small number of points, so the accuracy of the wireless signal map is difficult to guarantee. In view of this, how to improve the efficiency and accuracy of drawing wireless signal maps has become an urgent problem to be solved. Summary of the invention

[0004] The main technical problem solved by the present application is to provide a signal graph generation method and related devices, systems, media and cleaning robots, which can improve the efficiency and accuracy of drawing wireless signal graphs.

[0005] In order to solve the above technical problems, the first aspect of the present application provides a signal graph generation method, including: based on a target floor plan of the space to be tested, planning a movement path of a cleaning robot; wherein the cleaning robot is wirelessly connected to a target wireless signal source, and the movement path includes a number of collection points connected in sequence; while the cleaning robot moves along the movement path, obtaining a first signal strength of the wireless connection measured by the cleaning robot at each collection point; obtaining a signal distribution graph generated for the space to be tested based on the target floor plan and the first signal strength of each collection point; wherein the signal distribution graph represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be tested.

[0006] In order to solve the above-mentioned technical problems, the second aspect of the present application provides a signal graph generating device, including: a path planning module, a strength acquisition module and a distribution acquisition module, the path planning module is used to plan the movement path of the cleaning robot based on the target floor plan of the space to be tested; wherein the cleaning robot is wirelessly connected to the target wireless signal source, and the movement path includes several collection points connected in sequence; the strength acquisition module is used to obtain the first signal strength of the wireless connection measured by the cleaning robot at each collection point during the movement of the cleaning robot along the movement path; the distribution acquisition module is used to obtain a signal distribution graph generated for the space to be tested based on the target floor plan and the first signal strength of each collection point; wherein the signal distribution graph represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be tested.

[0007] In order to solve the above-mentioned technical problems, the third aspect of the present application provides a cleaning robot, which at least includes a processor and a wireless communication circuit and a memory respectively coupled to the processor, the wireless communication circuit is used to wirelessly connect to a wireless signal source, the memory stores program instructions, and the processor is used to execute the program instructions to implement the signal graph generation method in the above-mentioned first aspect.

[0008] In order to solve the above technical problems, the fourth aspect of the present application provides a signal graph generation system, including a target wireless signal source, a server and the cleaning robot in the third aspect, the cleaning robot is wirelessly connected to the target wireless signal source, and the target wireless signal source is communicatively connected to the server.

[0009] In order to solve the above technical problems, the fifth aspect of the present application provides a computer-readable storage medium storing program instructions that can be executed by a processor, and the program instructions are used to implement the signal graph generation method of the first aspect.

[0010] The above scheme plans the movement path of the cleaning robot based on the target floor plan of the space to be tested, and the cleaning robot is wirelessly connected to the target wireless signal source. The movement path includes several collection points connected in sequence. Then, when the cleaning robot moves along the movement path, the first signal strength of the wireless connection measured by the cleaning robot at each collection point is obtained, and a signal distribution map generated for the space to be tested based on the target floor plan and the first signal strength of each collection point is obtained. The signal distribution map represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be tested. On the one hand, since the cleaning robot measures the wireless signal strength at each collection point in sequence according to the planned movement path without manual measurement, it helps to improve the efficiency of drawing the wireless signal map. On the other hand, signal measurement according to the planned movement path can increase the number of measurement points as much as possible compared to manual measurement, which helps to improve the accuracy of drawing the wireless signal map. Therefore, the efficiency and accuracy of drawing the wireless signal map can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a flow chart of an embodiment of a signal graph generating method of the present application;

[0012] Figure 2a It is to build a house type Figure 1 A schematic diagram of the process of the embodiment;

[0013] Figure 2b It is a process schematic diagram of an embodiment of a signal graph generating method of the present application;

[0014] Figure 3 It is a schematic diagram of the framework of an embodiment of a signal graph generating device of the present application;

[0015] Figure 4 This is a schematic diagram of the framework of an embodiment of the cleaning robot of the present application;

[0016] Figure 5 It is a schematic diagram of the framework of an embodiment of a signal graph generation system of the present application;

[0017] Figure 6 It is a schematic diagram of a framework of an embodiment of a computer-readable storage medium of the present application. DETAILED DESCRIPTION

[0018] The scheme of the embodiment of the present application is described in detail below in conjunction with the drawings of the specification.

[0019] In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures, interfaces, and technologies are provided to facilitate a thorough understanding of the present application.

[0020] The terms "system" and "network" are often used interchangeably in this article. The term "and / or" in this article is only a description of the association relationship of related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the fragment " / " in this article generally indicates that the related objects before and after are in an "or" relationship. In addition, "many" in this article means two or more than two.

[0021] See also Figure 1 , Figure 1 It is a flow chart of an embodiment of a signal graph generating method of the present application.

[0022] Specifically, the following steps may be included:

[0023] Step S11: Based on the target floor plan of the space to be tested, a motion path of the cleaning robot is planned.

[0024] In the disclosed embodiment, the cleaning robot is wirelessly connected to the target wireless signal source, and the motion path includes a number of sequentially connected acquisition points. It should be noted that the cleaning robot includes but is not limited to: a sweeping robot, a mopping robot, a sweeping and mopping robot, a window cleaning robot, etc., and the specific type of the cleaning robot is not limited here. In addition, the target wireless signal source may include but is not limited to: a wireless router, an AP (Access Point, hotspot), etc., and the specific type of the target wireless signal source is not limited here.

[0025] In an implementation scenario, taking a home scenario as an example, the space to be tested may include but is not limited to: designated areas such as the living room and bedroom, or the entire home space; taking an office scenario as an example, the space to be tested may include but is not limited to: designated areas such as public office areas and conference rooms, or the entire office space. Of course, the above examples are only a few possible examples of the space to be tested in actual applications. The specific connotations of the space to be tested in other scenarios can be deduced by analogy, and no examples are given here one by one.

[0026] In an implementation scenario, the target floor plan of the space to be tested may be a two-dimensional floor plan, or the target floor plan of the space to be tested may be a three-dimensional floor plan. The target floor plan of the space to be tested is not limited herein.

[0027] In one implementation scenario, the target floor plan of the space to be tested can be drawn by the user and imported into the cleaning robot. As a possible example, the user device (such as a mobile phone, tablet computer, etc.) can be communicatively connected to the target wireless signal source, so that the target floor plan drawn by the user can be transferred from the user device to the cleaning robot through the target wireless signal source. For example, the user device can run an application compatible with the cleaning robot and can support cleaning control (such as cleaning mode control, cleaning area control, cleaning start / stop, etc.).

[0028] In another implementation scenario, different from the aforementioned implementation, as another possible implementation, the target floor plan of the space to be tested can be reconstructed by the scanning results of the cleaning robot on the space to be tested. Specifically, the cleaning robot can be configured with scanning devices such as cameras and laser radars to scan the space to be tested, and reconstruct the target floor plan accordingly. Exemplarily, when the cleaning robot is configured with a laser radar, the scanning results of the cleaning robot on the space to be tested may include at least point cloud data, so that the target floor plan can be reconstructed based on the point cloud data; or, when the cleaning robot is configured with a camera, the scanning results of the cleaning robot on the space to be tested may include image data, so that the target floor plan can be reconstructed based on the image data. It should be noted that the specific process of reconstruction based on point cloud data or image data can refer to reconstruction technologies such as SLAM (Simultaneous Localization and Mapping, real-time positioning and mapping), SFM (Structure From Motion, motion structure recovery), etc., which will not be repeated here.

[0029] In another implementation scenario, when the target floor plan is reconstructed by the cleaning robot's scanning results of the space to be tested, in order to support users in editing the floor plan, the floor plan reconstructed by the cleaning robot based on the scanning results of the space to be tested can be used as a candidate floor plan, and then the candidate floor plan is synchronized to the user device through the target wireless signal source, and the scanning results also include the predicted type of each object in the space to be tested. The candidate floor plan is also marked with candidate points for signal measurement, and the candidate points can be predicted based on the predicted type of each object. On this basis, in response to detecting a confirmation instruction of the user device for the candidate floor plan, the selected floor plan can be selected as the target floor plan, and the candidate points located on the candidate floor plan are included in the motion path as collection points. The above method marks the candidate points for signal measurement on the candidate floor plan synchronized to the user device, and the candidate points are predicted based on the predicted types of each object in the space to be measured, so that after detecting the confirmation instruction of the user device for the candidate floor plan, the candidate floor plan is used as the target floor plan, and the candidate points on the candidate floor plan are included in the motion path as collection points, which can support the user device to interact and confirm with the floor plan, and help improve the usage perception.

[0030] In a specific implementation scenario, as described above, the user device may run an application program compatible with the cleaning robot. For example, the application program may support the activation of the signal map generation function. When the signal map generation function is detected to be activated, a scanning instruction may be sent to the cleaning robot to drive the cleaning robot to scan in a specified area to reconstruct a floor plan as a candidate floor plan and synchronize it to the application program of the user device for interaction. Figure 2a , Figure 2a It is to build a house type Figure 1 The process diagram of the embodiment. Figure 2a As shown, after the wireless network configuration is completed, the cleaning robot can communicate and connect to the target wireless signal source to achieve networking. After receiving the relevant instructions representing the start of the signal map generation function, the cleaning robot can first perform a house type scan to complete the house type construction according to the scanning results.

[0031] In a specific implementation scenario, taking the cleaning robot equipped with a laser radar as an example, as mentioned above, the scanning result can at least include point cloud data. In addition, the scanning data can also include the intensity of the reflected signal. Since the reflection signal intensity of objects of different materials to the laser radar is also different, the position, contour, thickness, material and other information of the object can be determined by the reflected signal. For example, the attenuation of wireless signals by wooden boards is usually relatively small, while the attenuation of wireless signals by materials such as glass is usually slightly larger, and the attenuation of wireless signals by metal materials such as steel and iron is usually larger. For details, please refer to the technical details such as the attenuation value of common obstacle signals, which will not be repeated here. On this basis, the predicted type of the object can be obtained according to this information, such as the types of objects such as cabinets, beds, tables, sofas, etc. Alternatively, taking the cleaning robot equipped with a camera as an example, as mentioned above, the scanning result can at least include image data, then the image data can be directly subjected to target detection, and the predicted type of each object in the space to be tested can be obtained. For details, please refer to the technical details of target detection on image data, which will not be repeated here.

[0032] In a specific implementation scenario, after obtaining the predicted type of each object in the space to be tested, each object in the space to be tested can be mapped to the candidate floor plan. For example, taking the target floor plan as a two-dimensional plane floor plan as an example, the outline of each object can be displayed in the candidate floor plan. On this basis, for each object, it can be determined whether to add a candidate point at the object according to the predicted type of the object. Exemplarily, when it is determined according to the predicted type that the user often (or for a long time) moves at the location of the object, a candidate point can be added at the object. For example, when the predicted type of the object is "porch cabinet", since the user usually only passes by the porch cabinet briefly when entering and leaving the door, the candidate point can not be added at the object with the predicted type of "porch cabinet"; or, when the predicted type of the object is "bed", since the user usually stays in bed for a long time during the day and is likely to use wireless signals, the candidate point can be added at the object with the predicted type of "bed". Other situations can be deduced by analogy, and examples are not given one by one here.

[0033] In a specific implementation scenario, after the candidate points are predicted, the candidate points can be displayed on the candidate floor plan for user confirmation, and after detecting the confirmation instruction for the candidate floor plan, it can be considered that the user agrees with the candidate points, so that the candidate floor plan can be directly used as the target floor plan, and the candidate points retained in the candidate floor plan will also be included in the motion path as collection points.

[0034] In a specific implementation scenario, different from the aforementioned detection of the confirmation instruction for the candidate floor plan, as another possible example, in response to detecting the editing instruction of any candidate point by the user device, the corresponding candidate point in the candidate floor plan can be edited based on the editing instruction, and the editing instruction for any candidate point can include but is not limited to: position adjustment instruction for the candidate point, point deletion instruction, etc., and the specific content of the editing instruction is not limited here. The above method, according to the detected editing instruction, the candidate point is edited, which can further support the user to edit the candidate floor plan.

[0035] In a specific implementation scenario, the candidate floor plan may also display various objects and their predicted types to indicate the possibility of signal measurement. In this case, in response to detecting an add instruction from a user device on the candidate floor plan, a new candidate point may be added to the candidate floor plan based on the add instruction. For example, the candidate floor plan includes the outline of an object indicated as a "toilet", and the user adds a new candidate point at the object according to his actual needs to meet the personalized signal measurement requirements. Of course, the above example is only a possible example in the actual application process, and does not limit the specific location of the new candidate point. The above method can further meet the user's personalized needs for signal measurement by displaying various objects and their predicted types on the candidate floor plan, and adding new candidate points to the candidate floor plan based on the add instruction after detecting the add instruction on the candidate floor plan.

[0036] In one implementation scenario, after obtaining the target floor plan, the motion path during signal measurement can be planned. Specifically, a grid map can be constructed with the boundary line of the target floor plan as the baseline, and the grid map is synchronized to the user device through the target wireless signal, and the grid density is adjusted when a scaling instruction is detected. Thereafter, in response to detecting a first confirmation instruction of the user device to the grid map, a grid point is selected as a collection point based on the current grid density of the grid map, and a path is planned for the collection point to obtain a motion path. In the above method, the grid density is adjusted according to the scaling instruction of the user device to the gateway map, and after detecting the confirmation instruction of the grid map, a grid point is selected as a collection point based on the current density of the grid map, so as to plan the motion path accordingly. Therefore, collection points of different densities can be planned according to user needs to support the generation of signal distribution maps of different accuracies.

[0037] In a specific implementation scenario, grid lines can be drawn on the baseline toward the opposite end at intervals of a preset length, so that a grid diagram with a unit size of "preset length * preset length" can be formed on the target floor plan. It should be noted that the specific size of the preset length can be set according to the area of ​​the space to be measured. For example, the larger the area of ​​the space to be measured, the larger the preset length can be set. Conversely, the smaller the area of ​​the space to be measured, the smaller the preset length can be set. The specific value of the preset length is not limited here.

[0038] In a specific implementation scenario, as described above, the grid map can be synchronously displayed on an application running on a user device, and the application can be provided with related controls such as buttons for adjusting the grid density, so as to determine to increase or decrease the grid density when a trigger instruction for the related control is detected. Of course, the above method is only a possible example of adjusting the grid density, and the specific method of adjusting the grid density is not limited here. For example, the grid density can also be increased or decreased through gesture instructions such as pinching or opening two fingers, and examples are not given one by one here.

[0039] In a specific implementation scenario, after the collection points are determined, as a possible example, the shortest path passing through each collection point can be obtained as the motion path. Of course, the above method is only a possible example of path planning, and does not limit the specific method of path planning.

[0040] Step S12: when the cleaning robot moves along the motion path, obtaining the first signal strength of the wireless connection measured by the cleaning robot at each collection point.

[0041] Specifically, after the motion path is planned, the cleaning robot can move along the motion path, and when passing through the collection point, the first signal strength for measuring the wireless connection can be obtained. For example, the first signal strength can include but is not limited to RSSI (Received Signal Strength Indicator), etc., and the specific indicator of the first signal strength is not limited here.

[0042] Step S13: obtaining a signal distribution diagram generated for the space to be measured based on the target floor plan and the first signal strength of each collection point.

[0043] In the disclosed embodiment, the signal distribution diagram represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be tested. Exemplarily, based on the first signal strength at each collection point, rendering can be performed at the corresponding collection point. For example, the greater the signal strength, the cooler the rendering (such as green, etc.), and the smaller the signal strength, the warmer the rendering (such as red, etc.), so that the intensity distribution of the wireless signal in the space to be tested can be intuitively displayed. Of course, the above example is only a possible example of a signal distribution diagram, and does not limit the display method of the signal strength. For example, it can also be displayed directly with the strength value, and no examples are given here one by one.

[0044] In one implementation scenario, as a possible example, the first signal strength of the collection point can be directly reflected at the position of the corresponding collection point in the target floor plan, such as directly reflected by intensity value, or rendered by color, etc., as a signal distribution map of the space to be tested.

[0045] In one implementation scenario, different from the aforementioned implementation, as another possible example, the target wireless signal source can also be connected to the server for communication, so that the target floor plan and the first signal strength of each collection point can be sent to the server through the target wireless signal source. In addition, as mentioned above, the target floor plan can also be marked with the predicted type of each object in the space to be measured. For any collection point, the server can predict the obstruction result of the cleaning robot at the collection point based on the predicted type and location of each object, and predict the second signal strength at the target height above the collection point based on the obstruction result and the first signal strength, and generate and return the signal distribution map based on the second signal strength of each collection point. It should be noted that the target height above the collection point is the predicted height of the user using the wireless signal at the collection point. In the above method, according to the predicted type and location of each object, the obstruction result of the cleaning robot when measuring the signal at the collection point is predicted, so as to be combined with the first signal strength to deduce the second signal strength at the target height above the collection point, and the target height is the predicted height of the user using the wireless signal at the collection point, which helps to further enhance the reference value of the signal distribution map.

[0046] In a specific implementation scenario, taking the target floor plan as a two-dimensional floor plan as an example, after obtaining the predicted type and location of each object, for each collection point, it can be determined whether the collection point is covered by any object on the two-dimensional plane. If so, the occlusion result of the collection point can be further determined based on the predicted type of the object covering the collection point. Exemplarily, if the predicted type characterizes the object as a ground-based object (such as a bed, a cabinet, a sofa, etc.), it can be determined that the occlusion result includes that the cleaning robot is occluded at the collection point, or if the predicted type characterizes the object as a suspended object (such as a wall cabinet, etc.) or can be regarded as a suspended object (such as a dining table, a desk, etc.), it can be determined that the occlusion result includes that the cleaning robot can be regarded as unobstructed at the collection point. In addition, if the collection point is not covered by any object on the two-dimensional plane, it can be determined that the occlusion result of the cleaning robot at the collection point includes unobstructed.

[0047] In a specific implementation scenario, after obtaining the obstruction result of the collection point, in response to the obstruction result of the collection point including being obstructed, a target height is predicted based on at least the predicted type of the target object, and the target object is an object that obstructs the cleaning robot at the collection point in the measured space. Exemplarily, taking the predicted type of the target object as "bed" as an example, a certain height can be added to the general height of the bed (such as the height from the bed when the wireless signal is usually used on the bed) as the target height; or, taking the predicted type of the target object as "dining table" as an example, a certain height can be added to the general height of the dining table (such as the height from the table when the wireless signal is usually used when sitting at the dining table) as the target height. Other scenarios can be deduced by analogy, and examples are not given one by one here.

[0048] In a specific implementation scenario, after obtaining the obstruction result of the collection point, in response to the obstruction result of the collection point including being unobstructed, the target height can be predicted based on the height of the user device above the ground. For example, the user device can be integrated with a device such as a barometer to measure the height of the user using the user device above the ground, which can be used as the target height.

[0049] In a specific implementation scenario, when the blocked result includes unblocked, the second signal strength at the target height above the acquisition point can be predicted based on the first signal strength. Exemplarily, the strength prediction can be obtained based on a strength prediction model, and the input parameters of the strength prediction model can include the first signal strength, the signal propagation medium and the target height, so as to predict the second signal strength based on the above input parameters. It should be noted that when the blocked result includes unblocked, the signal propagation medium can be set to air. In addition, the strength prediction model can be set according to a wireless signal propagation model, or can be set based on a deep learning neural network model. The specific type of the strength prediction model is not limited here.

[0050] In a specific implementation scenario, when the blocked result includes being blocked, the attribute information of the target object can be predicted based on the scanning result of the cleaning robot on the target object, and the target object is an object that blocks the cleaning robot at the collection point in the measured space, and the attribute information includes at least one of material and thickness, and then based on the first signal strength and the attribute information of the target object, the second signal strength at the target height above the collection point is predicted. For the specific method of obtaining the attribute information, please refer to the above-mentioned related description, which will not be repeated here. Similar to the above situation, the intensity prediction can be obtained based on the intensity prediction model, and the input parameters of the intensity prediction model may include the first signal strength, the signal propagation medium and the target height, so as to predict the second signal strength according to the above input parameters. It should be noted that when the blocked result includes being blocked, the signal propagation medium can be set to the predicted material. In addition, the intensity prediction model can be set according to the wireless signal propagation model, or it can be set based on the neural network model of deep learning. The specific type of the intensity prediction model is not limited here. The above method predicts the attribute information of the target object based on the scanning result of the target object by the cleaning robot, and then predicts the second signal strength at the target height above the collection point based on the first signal strength and the attribute information of the target object. The second signal strength can be predicted as accurately as possible in combination with the attribute information when it is blocked.

[0051] In an implementation scenario, please refer to Figure 2b , Figure 2b 1 is a schematic diagram of the process of an embodiment of the signal graph generation method of the present application. Figure 2b As shown, the user can select the function module that triggers the signal map generation through the application adapted to the cleaning robot on the user device, so as to drive the cleaning robot to scan the space to be tested to obtain the floor plan of the space to be tested, and then build a grid map based on the boundary line in the floor plan as the baseline, and then set the grid point as the collection point of the wireless signal, so as to obtain the motion path of the cleaning robot for subsequent signal measurement according to the collection point planning, and the cleaning robot can measure the first signal strength of the target wireless signal source at the collection point during the movement along the motion path, and then upload the first signal strength to the server until it reaches the end of the motion path. The server can generate a signal distribution map based on the first signal strength of each collection point. In addition, the signal distribution map can also be synchronized to the user device to display the signal distribution map on the application running on the user device.

[0052] The above scheme plans the movement path of the cleaning robot based on the target floor plan of the space to be tested, and the cleaning robot is wirelessly connected to the target wireless signal source. The movement path includes several collection points connected in sequence. Then, when the cleaning robot moves along the movement path, the first signal strength of the wireless connection measured by the cleaning robot at each collection point is obtained, and a signal distribution map generated for the space to be tested based on the target floor plan and the first signal strength of each collection point is obtained. The signal distribution map represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be tested. On the one hand, since the cleaning robot measures the wireless signal strength at each collection point in sequence according to the planned movement path without manual measurement, it helps to improve the efficiency of drawing the wireless signal map. On the other hand, signal measurement according to the planned movement path can increase the number of measurement points as much as possible compared to manual measurement, which helps to improve the accuracy of drawing the wireless signal map. Therefore, the efficiency and accuracy of drawing the wireless signal map can be improved.

[0053] See also Figure 3 , Figure 3 It is a schematic diagram of the framework of an embodiment of a signal graph generating device 30 of the present application. The signal graph generating device 30 includes: a path planning module 31, a strength acquisition module 32 and a distribution acquisition module 33. The path planning module 31 is used to plan the movement path of the cleaning robot based on the target floor plan of the space to be tested; wherein the cleaning robot is wirelessly connected to the target wireless signal source, and the movement path includes a number of collection points connected in sequence; the strength acquisition module 32 is used to obtain the first signal strength of the wireless connection measured by the cleaning robot at each collection point during the movement of the cleaning robot along the movement path; the distribution acquisition module 33 is used to obtain a signal distribution map generated for the space to be tested based on the target floor plan and the first signal strength of each collection point; wherein the signal distribution map represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be tested.

[0054] In the above scheme, the signal map generating device 30 plans the movement path of the cleaning robot based on the target floor plan of the space to be tested, and the cleaning robot is wirelessly connected to the target wireless signal source. The movement path includes a number of collection points connected in sequence. Then, when the cleaning robot moves along the movement path, the first signal strength of the wireless connection measured by the cleaning robot at each collection point is obtained, and a signal distribution map generated for the space to be tested based on the target floor plan and the first signal strength of each collection point is obtained. The signal distribution map represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be tested. On the one hand, since the cleaning robot measures the wireless signal strength at each collection point in sequence according to the planned movement path without manual measurement, it helps to improve the efficiency of drawing the wireless signal map. On the other hand, signal measurement according to the planned movement path can increase the number of measurement points as much as possible compared to manual measurement, which helps to improve the accuracy of drawing the wireless signal map. Therefore, the efficiency and accuracy of drawing the wireless signal map can be improved.

[0055] In some disclosed embodiments, the path planning module 31 includes a grid construction submodule for constructing a grid map with the boundary line of the target floor plan as a baseline; wherein the grid map is synchronously displayed on the user device through the target wireless signal source, and the grid density is adjusted when a zoom instruction is detected; the path planning module 31 includes a path acquisition submodule for selecting a grid point as a collection point based on the current grid density of the grid map in response to detecting a first confirmation instruction of the user device for the grid map, and performing path planning on the collection point to obtain a motion path.

[0056] In some disclosed embodiments, the signal graph generating device 30 includes a scanning and reconstruction module, which is used to reconstruct a candidate floor plan based on the scanning results of the space to be measured; the signal graph generating device 30 includes an information synchronization module, which is used to synchronize the candidate floor plan to the user device through the target wireless signal source; wherein the scanning results include the predicted type of each object in the space to be measured, and the candidate floor plan is also marked with candidate points for signal measurement, and the candidate points are predicted based on the predicted type of each object; the signal graph generating device 30 includes a floor plan confirmation module, which is used to select the candidate floor plan as the target floor plan in response to detecting a second confirmation instruction of the user device for the candidate floor plan; wherein the candidate points located on the candidate floor plan are included in the motion path as collection points.

[0057] In some disclosed embodiments, the signal diagram generating device 30 includes a point editing module for responding to detecting an editing instruction from a user device for any candidate point, and based on the editing instruction, performing point editing on the corresponding candidate point in the candidate floor plan; wherein the editing instruction for any candidate point includes any one of an instruction to adjust the position of the candidate point and a point deletion instruction.

[0058] In some disclosed embodiments, the candidate floor plan also displays various objects and their predicted types to indicate the possibility of the need for signal measurement. The signal map generating device 30 includes a point adding module for adding new candidate points to the candidate floor plan in response to detecting an add instruction from a user device on the candidate floor plan based on the add instruction.

[0059] In some disclosed embodiments, the target wireless signal source is also connected to the server for communication, and the distribution acquisition module 33 is specifically used to send the target floor plan and the first signal strength of each collection point to the server through the target wireless signal source; wherein the target floor plan icon has the predicted type of each object in the space to be measured, and for any collection point, the server predicts the obstruction result of the cleaning robot at the collection point based on the predicted type and location of each object, and predicts the second signal strength at the target height above the collection point based on the obstruction result and the first signal strength, and generates and transmits back the signal distribution map based on the second signal strength of each collection point, wherein the target height above the collection point is the predicted height of the user using the wireless signal at the collection point.

[0060] In some disclosed embodiments, the signal graph generating device 30 includes a height prediction module for predicting a target height in response to an occlusion result of the collection point including occlusion, at least based on a predicted type of the target object, and the target object is an object that obscures the cleaning robot at the collection point in the measured space; and in response to an occlusion result of the collection point including unocclusion, predicting a target height based on the height of a user device above the ground.

[0061] In some disclosed embodiments, the signal graph generating device 30 includes an intensity prediction module, which is used to predict the attribute information of the target object based on the scanning result of the target object by the cleaning robot when the occlusion result includes occlusion; wherein the target object is an object that obscures the cleaning robot at the collection point in the measured space, and the attribute information includes at least one of material and thickness; based on the first signal strength and the attribute information of the target object, predict the second signal strength at the target height above the collection point.

[0062] See also Figure 4 , Figure 4It is a schematic diagram of a framework of an embodiment of the cleaning robot 40 of the present application. The cleaning robot 40 includes at least a processor 42 and a wireless communication circuit 41 and a memory 43 respectively coupled to the processor 42, the wireless communication circuit 41 is used to wirelessly connect to a wireless signal source, the memory 43 stores program instructions, and the processor 42 is used to execute the program instructions to implement the steps in any of the above-mentioned signal graph generation method embodiments. For details, please refer to the aforementioned disclosed embodiments, which will not be repeated here. As a possible case, the cleaning robot 40 may also include a motion device (not shown), a cleaning device (not shown), a scanning device (not shown), etc., which are not limited here. Among them, the motion device is used to drive the cleaning robot 40 itself to move; in addition, the cleaning device is used to clean stains, and the cleaning device may include but is not limited to a mop, a side brush, a dust collection device, etc.; in addition, the scanning device is used to scan the cleaning space, and the scanning result is at least used to construct a floor plan of the cleaning space, and the scanning device may include but is not limited to a laser radar, a camera, etc. Of course, the above examples are only a few possible examples of devices that the cleaning robot 40 may include, and the specific structure of the cleaning robot 40 is not limited thereto.

[0063] Specifically, the processor 42 is used to control itself and the wireless communication circuit 41, the memory 43 to implement the steps in any of the above-mentioned signal graph generation method embodiments. The processor 42 can also be called a CPU (Central Processing Unit). The processor 42 may be an integrated circuit chip with signal processing capabilities. The processor 42 can also be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field-programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. In addition, the processor 42 can be implemented by an integrated circuit chip.

[0064] In the above scheme, the cleaning robot 40 implements the steps in any of the above signal map generation method embodiments. Therefore, on the one hand, the cleaning robot measures the wireless signal strength at each collection point in turn according to the planned motion path without manual measurement, which helps to improve the efficiency of drawing the wireless signal map. On the other hand, signal measurement according to the planned motion path can increase the number of measurement points as much as possible compared to manual measurement, which helps to improve the accuracy of drawing the wireless signal map. Therefore, the efficiency and accuracy of drawing the wireless signal map can be improved.

[0065] See also Figure 5 , Figure 5 It is a schematic diagram of a framework of an embodiment of a signal graph generation system 50 of the present application. The signal graph generation system 50 includes a target wireless signal source 51, a server 52 and a cleaning robot 53, wherein the cleaning robot 53 is wirelessly connected to the target wireless signal source 51, and the target wireless signal source 51 is communicatively connected to the server 52. The cleaning robot 53 may specifically be the cleaning robot in the aforementioned cleaning robot embodiment, which may be specifically referred to in the aforementioned cleaning robot embodiment, and will not be described in detail here. In addition, the specific interaction process between the target wireless signal source 51, the server 52 and the cleaning robot 53 may refer to the relevant description in the aforementioned signal graph generation method embodiment, which will not be described in detail here. As a possible example, the signal graph generation system 50 may also include a user device 54, which may include but is not limited to a mobile phone, a tablet computer, etc., and the specific type of the user device 54 is not limited here. The user device 54 may be communicatively connected to the target wireless signal source 51, and / or, communicatively connected to the server 52, to obtain and display the floor plan and the signal distribution map, which may be specifically referred to in the relevant description in the signal graph generation method embodiment, and will not be described in detail here.

[0066] In the above scheme, the signal map generation system 50 includes a target wireless signal source 51, a server 52 and a cleaning robot 53. The cleaning robot 53 is wirelessly connected to the target wireless signal source 51, and the cleaning robot 53 is specifically the cleaning robot in the aforementioned cleaning robot embodiment. Therefore, on the one hand, the cleaning robot measures the wireless signal strength at each collection point in turn according to the planned motion path without manual measurement, which helps to improve the efficiency of drawing the wireless signal map. On the other hand, signal measurement according to the planned motion path can increase the number of measurement points as much as possible compared to manual measurement, which helps to improve the accuracy of drawing the wireless signal map. Therefore, the efficiency and accuracy of drawing the wireless signal map can be improved.

[0067] See also Figure 6 , Figure 6 The schematic diagram is a schematic diagram of a framework of an embodiment of a computer-readable storage medium 60 of the present application. The computer-readable storage medium 60 stores program instructions 61 that can be executed by a processor, and the program instructions 61 are used to implement the steps in any of the above signal graph generation method embodiments.

[0068] In the above scheme, the computer-readable storage medium 60 plans the movement path of the cleaning robot based on the target floor plan of the space to be tested, and the cleaning robot is wirelessly connected to the target wireless signal source. The movement path includes several collection points connected in sequence. Then, when the cleaning robot moves along the movement path, the first signal strength of the wireless connection measured by the cleaning robot at each collection point is obtained, and a signal distribution map generated for the space to be tested based on the target floor plan and the first signal strength of each collection point is obtained. The signal distribution map represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be tested. On the one hand, since the cleaning robot measures the wireless signal strength at each collection point in sequence according to the planned movement path without manual measurement, it helps to improve the efficiency of drawing the wireless signal map. On the other hand, signal measurement according to the planned movement path can increase the number of measurement points as much as possible compared to manual measurement, which helps to improve the accuracy of drawing the wireless signal map. Therefore, the efficiency and accuracy of drawing the wireless signal map can be improved.

[0069] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0070] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0071] In the several embodiments provided in the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device implementation described above is only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0072] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0073] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0074] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code.

[0075] If the technical solution of this application involves personal information, the product using the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using the technical solution of this application has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, the personal information processing rules are notified by obvious signs / information, and the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A signal graph generation method, characterized in that: include: Based on the target floor plan of the space to be tested, a motion path of the cleaning robot is planned; wherein the cleaning robot is wirelessly connected to a target wireless signal source, and the motion path includes a plurality of sequentially connected collection points; During the movement of the cleaning robot along the motion path, obtaining first signal strengths of the wireless connections measured by the cleaning robot at each of the collection points; A signal distribution diagram generated for the space to be measured based on the target floor plan and the first signal strength of each of the collection points is obtained; wherein the signal distribution diagram represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be measured.

2. The method according to claim 1, characterized in that The method of planning a motion path of the cleaning robot based on the target floor plan of the space to be tested includes: A grid diagram is constructed with the boundary line of the target floor plan as a baseline; wherein the grid diagram is synchronously displayed on the user equipment through the target wireless signal source, and the grid density is adjusted when a zoom instruction is detected; In response to detecting a first confirmation instruction of the user equipment to the grid map, a grid point is selected as the collection point based on a current grid density of the grid map, and path planning is performed on the collection point to obtain the motion path.

3. The method according to claim 1, characterized in that Before planning and obtaining the motion path of the cleaning robot based on the target floor plan of the space to be tested, the method further includes: Reconstructing a candidate floor plan based on a scan result of the space to be tested; The candidate floor plan is synchronized to the user device through the target wireless signal source; wherein the scanning result includes the predicted type of each object in the space to be measured, and the candidate floor plan is also marked with candidate points for signal measurement, and the candidate points are predicted based on the predicted type of each object; In response to detecting a second confirmation instruction of the user equipment to the candidate floor plan, the candidate floor plan is selected as the target floor plan; wherein the candidate point located on the candidate floor plan is included in the motion path as the collection point.

4. The method according to claim 3, characterized in that The method further comprises: In response to detecting an editing instruction from the user device for any of the candidate points, point editing is performed on the corresponding candidate point in the candidate floor plan based on the editing instruction; wherein the editing instruction for any of the candidate points includes any one of an instruction to adjust the position of the candidate point and an instruction to delete the point.

5. The method according to claim 3, characterized in that: The candidate floor plan also displays the various objects and their predicted types, which are used to indicate the possibility of needing signal measurement. The method further includes: In response to detecting an adding instruction from the user equipment on the candidate floor plan, a new candidate point is added to the candidate floor plan based on the adding instruction.

6. The method according to claim 1, characterized in that The target wireless signal source is also connected to the server for communication, and the acquiring of a signal distribution diagram generated for the space to be measured based on the target floor plan and the first signal strength of each of the collection points includes: Sending the target floor plan and the first signal strength of each of the collection points to the server through the target wireless signal source; Among them, the target apartment icon has the predicted type of each object in the space to be tested, and for any of the collection points, the server predicts the obstruction result of the cleaning robot at the collection point based on the predicted type and location of each object, and predicts the second signal strength at the target height above the collection point based on the obstruction result and the first signal strength, and generates and transmits back the signal distribution map based on the second signal strength of each of the collection points, and the target height above the collection point is the predicted height of the user using the wireless signal at the collection point.

7. The method according to claim 6, characterized in that Before predicting the second signal strength at the target height above the acquisition point based on the obstruction result and the first signal strength, the method further includes at least one of the following: In response to the obstruction result of the collection point including obstruction, the target height is predicted based at least on the predicted type of the target object, and the target object is an object that obstructs the cleaning robot at the collection point in the measured space; In response to the obstruction result of the collection point including being not obstructed, the target height is predicted based on the height of the user equipment above the ground.

8. The method according to claim 6, characterized in that In a case where the obstruction result includes obstruction, predicting a second signal strength at a target height above the acquisition point based on the obstruction result and the first signal strength includes: Based on the scanning result of the target object by the cleaning robot, predict the attribute information of the target object; wherein the target object is an object that blocks the cleaning robot at the collection point in the measured space, and the attribute information includes at least one of material and thickness; Based on the first signal strength and the attribute information of the target object, a second signal strength at the target height above the collection point is predicted.

9. A signal graph generating device, characterized in that: include: A path planning module is used to plan a motion path of the cleaning robot based on a target floor plan of the space to be tested; wherein the cleaning robot is wirelessly connected to a target wireless signal source, and the motion path includes a plurality of sequentially connected collection points; A strength acquisition module, used for acquiring the first signal strength of the wireless connection measured by the cleaning robot at each of the collection points during the movement of the cleaning robot along the motion path; A distribution acquisition module is used to obtain a signal distribution diagram generated for the space to be tested based on the target floor plan and the first signal strength of each collection point; wherein the signal distribution diagram represents the intensity distribution of the wireless signal of the target wireless signal source in the space to be tested.

10. A cleaning robot, characterized in that: It at least includes a processor and a wireless communication circuit and a memory respectively coupled to the processor, the wireless communication circuit is used to wirelessly connect to a wireless signal source, the memory stores program instructions, and the processor is used to execute the program instructions to implement the signal graph generation method described in any one of claims 1 to 8.

11. A signal graph generation system, characterized in that: It comprises a target wireless signal source, a server and the cleaning robot as claimed in claim 10, wherein the cleaning robot is wirelessly connected to the target wireless signal source, and the target wireless signal source is communicatively connected to the server.

12. A computer-readable storage medium, characterized in that: Program instructions that can be executed by a processor are stored, and the program instructions are used to implement the signal graph generation method according to any one of claims 1 to 8.

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