Processing method and apparatus, base station, and storage medium

Abstract

The present disclosure relates to a processing method, device, base station and storage medium. The method comprises: obtaining visual perception information and cleaning process information of a cleaning device within a target time length; the target time length refers to a time length from when the cleaning device leaves a base station to when the cleaning device returns to the base station, the visual perception information is used to represent object information identified by the cleaning device within the target time length, and the cleaning process information is used to represent a cleaning situation of the cleaning device within the target time length; determining base station task information of the base station based on a target model, the visual perception information and the cleaning process information; the target model is used to predict the base station task information, and the base station task information is used to represent a base station task to be performed by the base station; and performing the base station task represented by the base station task information. The method can determine a more accurate base station task, so that the base station can accurately perform a corresponding base station task according to a resource consumption situation of the cleaning device, avoid performing meaningless base station tasks, and improve the adaptability and energy efficiency of the base station.

Description

Technical Field

[0001] This disclosure relates to the field of base station technology, and in particular to a processing method, apparatus, base station, and storage medium. Background Technology

[0002] Cleaning equipment can be divided into two types: household and commercial. Household cleaning equipment includes robotic vacuum cleaners and air purifying robots, while commercial cleaning equipment includes building cleaning robots, general cleaning robots, and airport lounge cleaning robots. Cleaning equipment is usually equipped with one or more base stations.

[0003] Before the cleaning equipment begins operation, the base station pre-updates the equipment's resources (e.g., replenishing water for the cleaning robot). During operation, the cleaning equipment consumes resources (e.g., water volume, dustbin space, mop cleanliness). The basic function of the base station is to replenish these resources consumed by the cleaning equipment. For example, base station functions include, but are not limited to: dust collection, mop washing, mop drying, and base station self-cleaning. However, due to structural limitations of the cleaning equipment and the base station, the base station struggles to accurately assess the cleaning equipment's resource consumption. This results in the base station being unable to accurately execute corresponding base station tasks based on the cleaning equipment's resource consumption, leading to poor adaptability and the potential for the base station to execute meaningless tasks, resulting in poor energy efficiency. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides a processing method, apparatus, base station, and storage medium.

[0005] According to a first aspect of the present disclosure, a processing method is provided, the method comprising:

[0006] The system acquires visual perception information and cleaning process information of the cleaning equipment within a target duration. The target duration refers to the time from when the cleaning equipment leaves the base station to when it returns to the base station. The visual perception information is used to characterize the object information identified by the cleaning equipment within the target duration, and the cleaning process information is used to characterize the cleaning status of the cleaning equipment within the target duration.

[0007] Based on the target model, the visual perception information, and the cleaning process information, the base station task information of the base station is determined; the target model is used to predict the base station task information, and the base station task information is used to characterize the base station task to be performed by the base station, the base station task including the resource update task of the cleaning equipment and / or the self-cleaning task of the base station.

[0008] Execute the base station task represented by the base station task information.

[0009] In some embodiments, acquiring visual perception information and cleaning process information within a target time period includes:

[0010] In response to the detection that the cleaning device has returned to the base station, the visual perception information and the cleaning process information are obtained from the cleaning device.

[0011] In some embodiments, determining the base station task information of the base station based on the target model, the visual perception information, and the cleaning process information includes:

[0012] Feature extraction is performed on the visual perception information to obtain the first feature;

[0013] Feature extraction is performed on the cleaning process information to obtain a second feature;

[0014] The first feature and the second feature are fused to obtain a fused feature;

[0015] The fused features are input into the target model, and the base station task information is output.

[0016] The target model is used to predict base station task information based on the input features.

[0017] In some embodiments, the training process of the target model includes:

[0018] Acquire first training data; the first training data includes sample fusion features and first sample base station task information corresponding to the sample fusion features, the sample fusion features are features obtained by fusing the first sample features and the second sample features, the first sample features are features extracted from sample visual perception information, and the second sample features are features extracted from sample cleaning process information.

[0019] Based on the first training data, the first model to be trained is trained to obtain the target model.

[0020] In some embodiments, determining the base station task information of the base station based on the target model, the visual perception information, and the cleaning process information includes:

[0021] The visual perception information and the cleaning process information are input into the target model, and the base station task information is output.

[0022] The target model is used to predict base station task information based on the input visual perception information and cleaning process information.

[0023] In some embodiments, the training process of the target model includes:

[0024] Acquire second training data; the second training data includes sample visual perception information, sample cleaning process information, and second sample base station task information corresponding to the sample visual perception information and the sample cleaning process information.

[0025] Based on the second training data, a second model to be trained is trained to obtain the target model.

[0026] In some embodiments, the base station task information further includes task parameters of the base station task; executing the base station task represented by the base station task information includes:

[0027] According to the task parameters, the base station task represented by the base station task information is executed.

[0028] In some embodiments, the visual perception information includes at least one of the object type of the identified target object, the size information of the target object, and the location information of the target object, and the cleaning process information includes at least one of the cleaning area of ​​the cleaning equipment, the number of rooms to be cleaned, the type of rooms to be cleaned, the cleaning time for each room, and the total cleaning time for all rooms.

[0029] According to a second aspect of the present disclosure, a processing apparatus is provided, the apparatus comprising:

[0030] The information acquisition module is configured to acquire visual perception information and cleaning process information of the cleaning equipment within a target duration; the target duration refers to the time from when the cleaning equipment leaves the base station to when it returns to the base station, the visual perception information is used to characterize the object information identified by the cleaning equipment within the target duration, and the cleaning process information is used to characterize the cleaning status of the cleaning equipment within the target duration.

[0031] The base station task determination module is configured to determine the base station task information of the base station based on the target model, the visual perception information, and the cleaning process information; the target model is used to predict the base station task information, and the base station task information is used to characterize the base station task to be performed by the base station, the base station task including the resource update task of the cleaning equipment and / or the self-cleaning task of the base station;

[0032] The base station task execution module is configured to execute the base station task represented by the base station task information.

[0033] According to a third aspect of the present disclosure, a base station is provided, comprising:

[0034] processor;

[0035] Memory used to store processor-executable instructions;

[0036] The processor is configured to perform the processing method as described in the first aspect of the embodiments of this disclosure.

[0037] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein when instructions in the storage medium are executed by a processor of a base station, the base station is enabled to perform the processing method as described in the first aspect of the present disclosure.

[0038] The method described above, as disclosed in this invention, has the following beneficial effects:

[0039] The method provided in this disclosure uses visual perception information and cleaning process information of cleaning equipment to characterize the working environment and cleaning process of the cleaning equipment. Based on this, a target model for predicting base station task information is adopted. Based on the visual perception information and cleaning process information, a more accurate base station task can be determined, so that the base station can accurately execute the corresponding base station task according to the resource consumption of the cleaning equipment, avoid executing meaningless base station tasks, and improve the adaptability and energy efficiency of the base station.

[0040] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0042] Figure 1 This is a flowchart illustrating a processing method according to an exemplary embodiment.

[0043] Figure 2 This is a flowchart illustrating a processing method according to an exemplary embodiment.

[0044] Figure 3 This is a schematic diagram of a fully connected layer structure according to an exemplary embodiment.

[0045] Figure 4 This is a flowchart illustrating a processing method according to an exemplary embodiment.

[0046] Figure 5 This is a block diagram illustrating a processing apparatus according to an exemplary embodiment.

[0047] Figure 6 This is a block diagram of a base station according to an exemplary embodiment. Detailed Implementation

[0048] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the invention as detailed in the appended claims.

[0049] In related technologies, taking a cleaning robot as an example, after the cleaning robot finishes mopping a preset cleaning area, it returns to the base station. The base station, after maintaining the mopping components, determines the degree of dirt in the preset cleaning area. Based on the degree of dirt, it determines whether at least a portion of the preset cleaning area needs to be mopped again, and performs repeated mopping accordingly. Specifically, the degree of dirt is determined based on the detected wastewater level, the time and / or volume of water used to clean the mopping components. However, the robustness of this dirt determination is poor. The lifespan and detection progress of the wastewater detection sensors both affect the accuracy of the determination. This results in the base station being unable to accurately execute corresponding base station tasks based on the resource consumption of the cleaning robot, leading to poor adaptability and the potential for the base station to execute meaningless tasks, resulting in poor energy efficiency.

[0050] This disclosure provides a processing method in which a base station acquires visual perception information and cleaning process information of a cleaning device within a target duration. Based on a target model, the visual perception information, and the cleaning process information, the base station determines its base station task information and executes the base station task represented by the task information. The visual perception information and cleaning process information of the cleaning device can characterize the operating environment and cleaning process of the cleaning device. Based on this, a target model for predicting base station task information is used. Based on the visual perception information and the cleaning process information, a more accurate base station task can be determined, enabling the base station to accurately execute corresponding base station tasks according to the resource consumption of the cleaning device, avoiding the execution of meaningless base station tasks, and improving the adaptability and energy efficiency of the base station.

[0051] The method provided in this embodiment is executed by a base station, which refers to a base station equipped for cleaning equipment. The cleaning equipment and the base station are connected for communication. The cleaning equipment can be a robot used for cleaning, such as a sweeping robot, an air purifying robot, or a cleaning robot.

[0052] Figure 1 This is a flowchart illustrating a processing method according to an exemplary embodiment, executed by a base station. See also... Figure 1 The method includes the following steps:

[0053] Step S101: Obtain visual perception information and cleaning process information of the cleaning equipment within the target duration; the target duration refers to the time from when the cleaning equipment leaves the base station to when it returns to the base station, the visual perception information is used to characterize the object information identified by the cleaning equipment within the target duration, and the cleaning process information is used to characterize the cleaning status of the cleaning equipment within the target duration.

[0054] During the entire cleaning process from leaving the base station to returning to the base station, the cleaning equipment collects visual perception information and cleaning process information. That is, the cleaning equipment collects visual perception information and cleaning process information within a target duration. After the cleaning equipment returns to the base station, the base station obtains the visual perception information and cleaning process information within the target duration from the cleaning equipment.

[0055] The visual perception information is used to characterize the object information identified by the cleaning equipment within the target time period. The visual perception information includes at least one of the following: object type, size information, and location information of the identified target object. The object type includes, but is not limited to: obstacles, cables, shoes, power strips, dynamic obstacles, dirt, carpets, pet information, etc. The size information characterizes the size of the target object, and the location information characterizes the position of the target object within the cleaning area.

[0056] Optionally, the cleaning equipment is equipped with an image sensor. The cleaning equipment collects visual perception information through the image sensor; for example, the cleaning equipment captures images in real time using the image sensor, and then identifies objects in the images to obtain information such as object type, size, and location. The image sensor can be a binocular sensor, a monocular sensor, or other image sensors.

[0057] The cleaning process information is used to characterize the cleaning status of the cleaning equipment within the target time period. This information includes at least one of the following: the cleaning area of ​​the cleaning equipment, the number of rooms cleaned, the type of rooms cleaned, the cleaning time for each room, and the total cleaning time for all rooms. The cleaned rooms are the cleaning areas of the cleaning equipment.

[0058] Optionally, the cleaning equipment records the cleaning status of each room during the cleaning process. After the cleaning process is completed, the cleaning equipment can determine the cleaning process information based on the recorded cleaning status of each room.

[0059] Step S102: Based on the target model, visual perception information, and cleaning process information, determine the base station task information of the base station; the target model is used to predict the base station task information, and the base station task information is used to characterize the base station tasks to be performed by the base station. The base station tasks include resource update tasks for cleaning equipment and / or self-cleaning tasks for the base station.

[0060] The target model is a trained model used to predict base station task information. In this embodiment, visual perception information and cleaning process information have already indicated the cleaning status of the cleaning equipment in this round (one round is the cleaning equipment leaving the base station and returning to the base station). Based on this, the base station can use the target model to predict the base station task information based on the visual perception information and cleaning process information. The base station task information is used to characterize the base station tasks to be performed by the base station. Base station tasks include resource update tasks for the cleaning equipment and / or self-cleaning tasks for the base station. For example, resource update tasks for the cleaning equipment include tasks related to the cleaning equipment such as dust collection, mop washing, and mop drying. Self-cleaning tasks for the base station include tasks related to base station self-cleaning such as water changing and adding base station automated cleaning fluid.

[0061] In some embodiments, the base station task information also includes task parameters for the base station task, which are used to indicate the extent of performing the base station task. For example, if the base station task is to wash a mop, the task parameter can be the amount of water used to wash the mop.

[0062] In some embodiments, when the base station task information represents multiple base station tasks to be executed, the base station task information also includes an execution order, which is used to instruct the base station to execute the multiple base station tasks sequentially.

[0063] Step S103: Execute the base station task represented by the base station task information.

[0064] After determining the base station task information, the base station executes the base station task represented by the base station task information to achieve resource updates for cleaning equipment and / or self-cleaning of the base station.

[0065] The method provided in this disclosure uses visual perception information and cleaning process information of cleaning equipment to characterize the working environment and cleaning process of the cleaning equipment. Based on this, a target model for predicting base station task information is adopted. Based on the visual perception information and cleaning process information, a more accurate base station task can be determined, so that the base station can accurately execute the corresponding base station task according to the resource consumption of the cleaning equipment, avoid executing meaningless base station tasks, and improve the adaptability and energy efficiency of the base station.

[0066] In some embodiments, the input to the target model can be visual perception information and cleaning process information, or it can be features extracted based on visual perception information and features extracted based on cleaning process information. The following describes... Figure 2 and Figure 4 The examples shown illustrate these two cases.

[0067] Figure 2 This is a flowchart illustrating a processing method according to an exemplary embodiment, executed by a base station. See also... Figure 2 The method includes the following steps:

[0068] Step S201: In response to the detection that the cleaning equipment has returned to the base station, visual perception information and cleaning process information are obtained from the cleaning equipment.

[0069] In some embodiments, since the cleaning device and the base station are connected, when the base station detects that the cleaning device has returned to the base station, the base station can send an information acquisition request to the cleaning device. After receiving the information acquisition request, the cleaning device sends the visual perception information and cleaning process information of the cleaning device in this round to the base station. Alternatively, when the cleaning device determines that it has returned to the base station, it can actively send the visual perception information and cleaning process information of the cleaning device in this round to the base station.

[0070] Step S202: Extract features from the visual perception information to obtain the first feature.

[0071] The first feature is used to characterize visual perception information, and this first feature can be a vector or other form.

[0072] In some embodiments, since the cleaning device collects visual perception information in a temporal order, the first feature can be obtained by following the temporal order of the visual perception information collection when extracting features.

[0073] In some embodiments, a first feature extraction network is used to extract features from the visually perceived information to obtain a first feature. The first feature extraction network is used to extract features from the visually perceived information. Of course, other methods can also be used to extract the first feature, and this disclosure does not limit this approach.

[0074] Step S203: Extract features from the cleaning process information to obtain the second feature.

[0075] The second feature is used to characterize cleaning process information, and this second feature can be a vector or other form.

[0076] In some embodiments, a second feature extraction network is used to extract features from the cleaning process information to obtain second features. The second feature extraction network is used to extract features from the cleaning process information. Of course, other methods can also be used to extract the second features, and this disclosure does not limit this approach.

[0077] Step S204: The first feature and the second feature are fused to obtain the fused feature.

[0078] The fusion of the first feature and the second feature includes: splicing the first feature and the second feature together, or using other methods to fuse the first feature and the second feature. This disclosure does not limit the feature fusion method.

[0079] Step S205: Input the fused features into the target model and output the base station task information.

[0080] The target model is used to predict base station task information based on the input features.

[0081] In some embodiments, base station task information obtained from different combinations of base station tasks can be used as different base station task categories. In this case, the target model outputs the base station task category, and then the base station determines the base station task information corresponding to that base station task category based on the base station task category. Optionally, the base station determines the base station task information and task parameters corresponding to that base station task category based on the base station task category.

[0082] In some embodiments, for a target model whose input is a feature, the training process of the target model includes: acquiring first training data; and training a first model to be trained based on the first training data to obtain the target model. The first training data includes sample fusion features and first sample base station task information corresponding to the sample fusion features. The sample fusion features are features obtained by fusing the first sample features and the second sample features. The first sample features are features extracted from sample visual perception information, and the second sample features are features extracted from sample cleaning process information.

[0083] Optionally, based on the first training data, a first model to be trained is trained to obtain a target model, including: inputting sample fusion features into the first model to be trained, outputting first predicted base station task information, and adjusting the model parameters of the first model based on the difference between the first predicted base station task information and the first sample base station task information to obtain the target model.

[0084] Optionally, the first training data also includes the first sample task parameters corresponding to the first sample base station task information. In this case, the sample fusion features are input into the first model to be trained, and the first predicted base station task information and the first predicted task parameters are output. Based on the differences between the first predicted base station task information and the first sample base station task information, as well as the differences between the first predicted task parameters and the first sample task parameters, the model parameters of the first model are adjusted to obtain the target model.

[0085] In some embodiments, the target model includes at least one fully connected network, each fully connected network including a fully connected layer and an activation function for the fully connected layer, wherein the fully connected layer includes an input layer, a hidden layer, and an output layer. In one example, the network structure of the fully connected layer is as follows: Figure 3 As shown, the fully connected layer comprises an input layer with m neurons, a hidden layer with p neurons, and an output layer with n neurons. Of course, the target model can also have other structures; this embodiment does not limit the model structure of the target model.

[0086] Step S206: Execute the base station task represented by the base station task information according to the task parameters.

[0087] In some embodiments, when there are multiple base station tasks to be executed, the multiple base station tasks are executed sequentially according to the execution order and the task parameters of each base station task.

[0088] The method provided in this disclosure utilizes visual perception information and cleaning process information of cleaning equipment to characterize the operating environment and cleaning process of the cleaning equipment. Based on this, a target model for predicting base station task information is employed. Using the visual perception information and cleaning process information, a relatively accurate base station task can be determined. This allows the base station to accurately execute corresponding base station tasks based on the resource consumption of the cleaning equipment, avoiding the execution of meaningless base station tasks. In other words, it enables precise control of various functions of the base station, reduces meaningless base station operations under specific environments and operating conditions, and improves the energy efficiency of the base station. Furthermore, based on the generalization capability of the target model, it can adapt to and handle more complex and cumbersome situations, improving the adaptability of the base station.

[0089] Figure 4 This is a flowchart illustrating a processing method according to an exemplary embodiment, executed by a base station. See also... Figure 4 The method includes the following steps:

[0090] Step S401: In response to the detection that the cleaning equipment has returned to the base station, visual perception information and cleaning process information are obtained from the cleaning equipment.

[0091] The implementation method of step S401 is similar to that of step S201 described above, and will not be repeated here.

[0092] Step S402: Input visual perception information and cleaning process information into the target model and output base station task information.

[0093] The target model is used to predict base station task information based on the input visual perception information and cleaning process information.

[0094] In some embodiments, for a target model whose input is information, the training process of the target model includes: acquiring second training data; training a second model to be trained based on the second training data to obtain the target model; the second training data includes sample visual perception information, sample cleaning process information, and second sample base station task information corresponding to the sample visual perception information and sample cleaning process information.

[0095] Optionally, based on the second training data, a second model to be trained is trained to obtain a target model, including: inputting sample visual perception information and sample cleaning process information into the second model to be trained, outputting second predicted base station task information, and adjusting the model parameters of the second model based on the difference between the second predicted base station task information and the second sample base station task information to obtain the target model.

[0096] Optionally, the second training data also includes the second sample task parameters corresponding to the second sample base station task information. In this case, the sample visual perception information and sample cleaning process information are input into the second model to be trained, and the second predicted base station task information and the second predicted task parameters are output. Based on the difference between the second predicted base station task information and the second sample base station task information, as well as the difference between the second predicted task parameters and the second sample task parameters, the model parameters of the second model are adjusted to obtain the target model.

[0097] In this case, the target model can be a CNN (Convolutional Neural Network) or other neural network models, and this disclosure does not limit this.

[0098] Step S403: Execute the base station task represented by the base station task information according to the task parameters.

[0099] The implementation method of step S403 is similar to that of step S206 described above, and will not be repeated here.

[0100] The method provided in this disclosure utilizes visual perception information and cleaning process information of cleaning equipment to characterize the operating environment and cleaning process of the cleaning equipment. Based on this, a target model for predicting base station task information is employed. Using the visual perception information and cleaning process information, a relatively accurate base station task can be determined. This allows the base station to accurately execute corresponding base station tasks based on the resource consumption of the cleaning equipment, avoiding the execution of meaningless base station tasks. In other words, it enables precise control of various functions of the base station, reduces meaningless base station operations under specific environments and operating conditions, and improves the energy efficiency of the base station. Furthermore, based on the generalization capability of the target model, it can adapt to and handle more complex and cumbersome situations, improving the adaptability of the base station.

[0101] Figure 5 This is a block diagram illustrating a processing apparatus configured in a base station according to an exemplary embodiment. See also... Figure 5 The device includes:

[0102] The information acquisition module 501 is configured to acquire visual perception information and cleaning process information of the cleaning equipment within a target duration. The target duration refers to the time from when the cleaning equipment leaves the base station to when it returns to the base station. The visual perception information is used to characterize the object information identified by the cleaning equipment within the target duration, and the cleaning process information is used to characterize the cleaning status of the cleaning equipment within the target duration.

[0103] The base station task determination module 502 is configured to determine the base station task information of the base station based on the target model, visual perception information and cleaning process information; the target model is used to predict the base station task information, and the base station task information is used to characterize the base station task to be performed by the base station, including the resource update task for the cleaning equipment and / or the self-cleaning task for the base station.

[0104] The base station task execution module 503 is configured to execute the base station task represented by the base station task information.

[0105] In some embodiments, the information acquisition module 501 is configured to:

[0106] In response to the detection of the cleaning equipment returning to the base station, visual perception information and cleaning process information are obtained from the cleaning equipment.

[0107] In some embodiments, the base station task determination module 502 is configured to:

[0108] Feature extraction is performed on visual perception information to obtain the first feature;

[0109] Feature extraction is performed on the cleaning process information to obtain the second feature;

[0110] The first feature and the second feature are fused to obtain the fused feature;

[0111] The fused features are input into the target model, and the base station task information is output.

[0112] The target model is used to predict base station task information based on the input features.

[0113] In some embodiments, the training process of the target model includes:

[0114] Acquire first training data; the first training data includes sample fusion features and first sample base station task information corresponding to the sample fusion features. The sample fusion features are features obtained by fusing the first sample features and the second sample features. The first sample features are features extracted from sample visual perception information, and the second sample features are features extracted from sample cleaning process information.

[0115] Based on the first training data, train the first model to be trained to obtain the target model.

[0116] In some embodiments, the base station task determination module 502 is configured to:

[0117] The visual perception information and cleaning process information are input into the target model, and the base station task information is output.

[0118] The target model is used to predict base station task information based on the input visual perception information and cleaning process information.

[0119] In some embodiments, the training process of the target model includes:

[0120] Acquire second training data; the second training data includes sample visual perception information, sample cleaning process information, and second sample base station task information corresponding to the sample visual perception information and sample cleaning process information.

[0121] Based on the second training data, a second model is trained to obtain the target model.

[0122] In some embodiments, the base station task execution module 503 is configured to:

[0123] According to the task parameters, execute the base station task represented by the base station task information.

[0124] In some embodiments, the visual perception information includes at least one of the object type of the identified target object, the size information of the target object, and the location information of the target object, and the cleaning process information includes at least one of the cleaning area of ​​the cleaning equipment, the number of rooms to be cleaned, the type of rooms to be cleaned, the cleaning time for each room, and the total cleaning time for all rooms.

[0125] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0126] This disclosure also provides a base station, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the processing method described in the above embodiments.

[0127] Figure 6 This is a block diagram of a base station 600 according to an exemplary embodiment.

[0128] Reference Figure 6 The base station 600 may include one or more of the following components: a processing component 602, a memory 604, a power supply component 606, a multimedia component 608, an audio component 610, an input / output (I / O) interface 612, a sensor component 614, and a communication component 616.

[0129] Processing component 602 typically controls the overall operation of base station 600, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 602 may include one or more processors 620 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 602 may include one or more modules to facilitate interaction between processing component 602 and other components. For example, processing component 602 may include a multimedia module to facilitate interaction between multimedia component 608 and processing component 602.

[0130] Memory 604 is configured to store various types of data to support operation on base station 600. Examples of this data include instructions for any application or method operating on base station 600, contact data, phonebook data, messages, pictures, videos, etc. Memory 604 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0131] The power supply component 606 provides power to various components of the base station 600. The power supply component 606 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the base station 600.

[0132] Multimedia component 608 includes a screen that provides an output interface between the base station 600 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 608 includes a front-facing camera and / or a rear-facing camera. When the base station 600 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0133] Audio component 610 is configured to output and / or input audio signals. For example, audio component 610 includes a microphone (MIC) configured to receive external audio signals when base station 600 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 604 or transmitted via communication component 616. In some embodiments, audio component 610 also includes a speaker for outputting audio signals.

[0134] I / O interface 612 provides an interface between processing component 602 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0135] Sensor assembly 614 includes one or more sensors for providing status assessments of various aspects of base station 600. For example, sensor assembly 614 can detect the on / off state of base station 600, the relative positioning of components such as the display and keypad of base station 600, changes in the position of base station 600 or one of its components, the presence or absence of user contact with base station 600, the orientation or acceleration / deceleration of base station 600, and temperature changes of base station 600. Sensor assembly 614 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 614 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 614 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0136] Communication component 616 is configured to facilitate wired or wireless communication between base station 600 and other devices. Base station 600 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 616 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 616 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0137] In an exemplary embodiment, base station 600 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0138] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 604 including instructions, which can be executed by a processor 620 of a base station 600 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0139] This disclosure also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of a base station, enables the base station to perform the processing methods described in the above embodiments.

[0140] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the following claims.

[0141] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A processing method, characterized in that, The method includes: The system acquires visual perception information and cleaning process information of the cleaning equipment within a target duration. The target duration refers to the time from when the cleaning equipment leaves the base station to when it returns to the base station. The visual perception information is used to characterize the object information identified by the cleaning equipment within the target duration, and the cleaning process information is used to characterize the cleaning status of the cleaning equipment within the target duration. Based on the target model, the visual perception information, and the cleaning process information, the base station task information of the base station is determined; the target model is used to predict the base station task information, and the base station task information is used to characterize the base station task to be performed by the base station, the base station task including the resource update task of the cleaning equipment and / or the self-cleaning task of the base station. Execute the base station task represented by the base station task information.

2. The processing method according to claim 1, characterized in that, The acquisition of visual perception information and cleaning process information within the target time period includes: In response to the detection that the cleaning device has returned to the base station, the visual perception information and the cleaning process information are obtained from the cleaning device.

3. The processing method according to claim 1, characterized in that, The process of determining the base station task information based on the target model, the visual perception information, and the cleaning process information includes: Feature extraction is performed on the visual perception information to obtain the first feature; Feature extraction is performed on the cleaning process information to obtain a second feature; The first feature and the second feature are fused to obtain a fused feature; The fused features are input into the target model, and the base station task information is output. The target model is used to predict base station task information based on the input features.

4. The processing method according to claim 3, characterized in that, The training process of the target model includes: Acquire first training data; the first training data includes sample fusion features and first sample base station task information corresponding to the sample fusion features, the sample fusion features are features obtained by fusing the first sample features and the second sample features, the first sample features are features extracted from sample visual perception information, and the second sample features are features extracted from sample cleaning process information. Based on the first training data, the first model to be trained is trained to obtain the target model.

5. The processing method according to claim 1, characterized in that, The process of determining the base station task information based on the target model, the visual perception information, and the cleaning process information includes: The visual perception information and the cleaning process information are input into the target model, and the base station task information is output. The target model is used to predict base station task information based on the input visual perception information and cleaning process information.

6. The processing method according to claim 5, characterized in that, The training process of the target model includes: Acquire second training data; the second training data includes sample visual perception information, sample cleaning process information, and second sample base station task information corresponding to the sample visual perception information and the sample cleaning process information. Based on the second training data, a second model to be trained is trained to obtain the target model.

7. The processing method according to claim 1, characterized in that, The base station task information also includes the task parameters of the base station task; The execution of the base station task represented by the base station task information includes: According to the task parameters, the base station task represented by the base station task information is executed.

8. The processing method according to claim 1, characterized in that, The visual perception information includes at least one of the object type of the identified target object, the size information of the target object, and the location information of the target object. The cleaning process information includes at least one of the cleaning area of ​​the cleaning equipment, the number of rooms to be cleaned, the type of rooms to be cleaned, the cleaning time for each room, and the total cleaning time for all rooms.

9. A processing apparatus, characterized in that, The device includes: The information acquisition module is configured to acquire visual perception information and cleaning process information of the cleaning equipment within a target duration; the target duration refers to the time from when the cleaning equipment leaves the base station to when it returns to the base station, the visual perception information is used to characterize the object information identified by the cleaning equipment within the target duration, and the cleaning process information is used to characterize the cleaning status of the cleaning equipment within the target duration. The base station task determination module is configured to determine the base station task information of the base station based on the target model, the visual perception information, and the cleaning process information; the target model is used to predict the base station task information, and the base station task information is used to characterize the base station task to be performed by the base station, the base station task including the resource update task of the cleaning equipment and / or the self-cleaning task of the base station; The base station task execution module is configured to execute the base station task represented by the base station task information.

10. A base station, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to perform the processing method as described in any one of claims 1-8.

11. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the base station, the base station is able to perform the processing method as described in any one of claims 1-8.

Images