Abnormality processing method and apparatus, base station, and medium

Abstract

The present disclosure relates to an abnormality processing method and device, a base station and a medium. The method comprises: when the base station is in an abnormal state, obtaining current base station information; determining a target abnormality processing type corresponding to the current base station information based on a target abnormality processing model and the current base station information; and performing an abnormality processing mode represented by the target abnormality processing type. The present disclosure can determine the target abnormality processing type corresponding to the current base station information based on the target abnormality processing model and the current base station information when the base station is in the abnormal state, and perform the abnormality processing mode represented by the target abnormality processing type, so that the abnormality processing mode corresponding to the abnormality can be determined efficiently and quickly when the base station is in the abnormal state, the influence of the abnormality of the base station on the base station and a host device corresponding to the base station can be avoided, and the user experience can be improved.

Description

Technical Field

[0001] This disclosure relates to the field of robotics technology, and in particular to an anomaly handling method, apparatus, base station, and medium. Background Technology

[0002] With economic development and increasing consumer demand, the use of robots has brought more convenience and benefits to society. For example, robots can improve production efficiency and processing speed, and effectively reduce labor costs. Robots include various types, such as cleaning robots, service robots, and material handling robots. Currently, robots typically consist of a main unit and a base station. The main unit may include a control center, sensors, actuators, and intelligent functional modules, etc., and is used to complete the robot's tasks, such as cleaning and material handling. The base station may include charging modules, maintenance modules, storage modules, water supply and drainage modules, power supply modules, communication modules, etc., and can provide various services to assist the main unit in completing its tasks.

[0003] However, due to the frequent operation of the robot and the complex internal wiring of the base station, the base station may malfunction. When the base station malfunctions, it will affect the normal operation of the base station and host equipment, thereby affecting the robot's performance and resulting in a poor user experience. Summary of the Invention

[0004] To overcome the problems existing in related technologies, this disclosure provides an anomaly handling method, apparatus, base station, and medium.

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

[0006] When a base station is in an abnormal state, the current base station information is obtained; the current base station information is used to characterize the current abnormality of the base station and the execution status of the base station's tasks.

[0007] Based on the target anomaly handling model and the current base station information, the target anomaly handling type corresponding to the current base station information is determined; the target anomaly handling model is used to predict the anomaly handling type when the base station is in an abnormal state, and the target anomaly handling type is used to characterize the anomaly handling method for the base station tasks being executed and the base station tasks to be executed by the base station.

[0008] Perform the exception handling method represented by the target exception handling type.

[0009] In some embodiments, determining the target anomaly handling type corresponding to the current base station information based on the target anomaly handling model and the current base station information includes:

[0010] Feature extraction is performed on the current base station information to obtain target anomaly features;

[0011] The target anomaly features are input into the target anomaly processing model, and the target anomaly processing type is output.

[0012] The target anomaly handling model is used to predict the anomaly handling type based on the input features.

[0013] In some embodiments, the training process of the target anomaly handling model includes:

[0014] Acquire first training data, which includes sample anomaly features and sample anomaly processing types corresponding to the sample anomaly features, wherein the sample anomaly features are features extracted from sample base station information.

[0015] Based on the first training data, a first initial anomaly handling model is trained to obtain the target anomaly handling model.

[0016] In some embodiments, determining the target anomaly handling type corresponding to the current base station information based on the target anomaly handling model and the current base station information includes:

[0017] The current base station information is input into the target anomaly handling model, and the target anomaly handling type is output.

[0018] The target anomaly handling model is used to predict the anomaly handling type based on the input base station information.

[0019] In some embodiments, the training process of the target anomaly handling model includes:

[0020] Acquire second training data, which includes sample base station information and sample anomaly handling types corresponding to the sample base station information;

[0021] Based on the second training data, a second initial anomaly handling model is trained to obtain the target anomaly handling model.

[0022] In some embodiments, the target anomaly handling type is further used to characterize the urgency of the target anomaly handling type, and the method further includes:

[0023] Issue a target prompt message; the target prompt message is used to inform the user of the urgency of the target exception handling type.

[0024] In some embodiments, the current base station information includes base station anomaly information, base station task information, and environmental information. The base station anomaly information includes at least one anomaly type when the base station experiences an anomaly. The base station task information includes base station tasks currently being executed by the base station, base station tasks to be executed by the base station, and base station tasks executed by the base station during the cleaning process of the cleaning equipment corresponding to the base station. The environmental information includes ambient temperature information, ambient humidity information, and cleaning area information. The cleaning area information is used to indicate relevant information about the cleaning area during the cleaning process of the cleaning equipment.

[0025] According to a second aspect of the present disclosure, an exception handling apparatus is provided, characterized in that it includes:

[0026] The acquisition module is configured to acquire current base station information when the base station is in an abnormal state; the current base station information is used to characterize the current abnormality of the base station and the execution status of the base station's tasks.

[0027] The determination module is configured to determine the target anomaly handling type corresponding to the current base station information based on the target anomaly handling model and the current base station information; the target anomaly handling model is used to predict the anomaly handling type when the base station is in an abnormal state, and the target anomaly handling type is used to characterize the anomaly handling method for the base station tasks being executed and the base station tasks to be executed by the base station;

[0028] The processing module is configured to execute the exception handling method represented by the target exception handling type.

[0029] According to a third aspect of the present disclosure, a base station is provided, comprising:

[0030] processor;

[0031] Memory used to store processor-executable instructions;

[0032] The processor is configured to execute the exception handling method as described in the first aspect of this disclosure.

[0033] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, which, when instructions in the storage medium are executed by a processor of a base station, enables the base station to perform the exception handling method as described in the first aspect of the present disclosure.

[0034] The method described above has the following advantages: When a base station is in an abnormal state, this disclosure can determine the target abnormal processing type corresponding to the current base station information based on the target abnormal processing model and the current base station information, and execute the abnormal processing method represented by the target abnormal processing type. Thus, when a base station is in an abnormal state, the abnormal processing method corresponding to the abnormality can be determined efficiently and quickly, avoiding the impact of base station abnormalities on the base station and the host equipment corresponding to the base station, and improving the user experience.

[0035] 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

[0036] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0037] Figure 1 This is a flowchart illustrating an exception handling method according to an exemplary embodiment.

[0038] Figure 2 This is a flowchart illustrating an exception handling method according to an exemplary embodiment.

[0039] Figure 3 This is a flowchart illustrating an exception handling method according to an exemplary embodiment.

[0040] Figure 4 This is a flowchart illustrating an exception handling method according to an exemplary embodiment.

[0041] Figure 5 This is a block diagram illustrating an exception handling apparatus according to an exemplary embodiment.

[0042] Figure 6 This is a block diagram of a base station according to an exemplary embodiment. Detailed Implementation

[0043] 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 this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0044] With economic development and increasing consumer demand, the use of robots has brought more convenience and benefits to society. For example, robots can improve production efficiency and processing speed, and effectively reduce labor costs. Robots include various types, such as cleaning robots, service robots, and material handling robots. Currently, robots typically consist of a main unit and a base station. The main unit may include a control center, sensors, actuators, and intelligent functional modules, etc., and is used to complete the robot's tasks, such as cleaning and material handling. The base station may include charging modules, maintenance modules, storage modules, water supply and drainage modules, power supply modules, communication modules, etc., and can provide various services to assist the main unit in completing its tasks.

[0045] However, due to the frequent operation of the robot and the complex internal wiring of the base station, the base station may malfunction. When the base station malfunctions, it will affect the normal operation of the base station and host equipment, thereby affecting the robot's performance and resulting in a poor user experience.

[0046] Taking cleaning robots as an example, these robots can automatically clean floors. Furthermore, with the continuous evolution of base station capabilities in recent years, cleaning robots have added several automated functions, such as automatic mop washing, automatic dust collection, and automatic water supply and drainage. Based on usage scenarios, cleaning robots can be divided into household cleaning robots and commercial cleaning robots. Household cleaning robots can include robotic vacuum cleaners and mobile smog removal robots, while commercial cleaning robots can include airport lounge cleaning robots and building cleaning robots.

[0047] The emergence of cleaning robots has made the entire cleaning process largely self-sufficient, effectively saving users' cleaning time and improving cleaning efficiency. In summary, cleaning robots, with their automation, intelligence, and high-efficiency cleaning capabilities, are gradually becoming an important tool for home and commercial cleaning. However, the frequent operation of cleaning robots often leads to abnormal operating conditions at base stations, such as overflowing wastewater tanks and empty clean water tanks. Furthermore, the water and air circuits within the base station also increase the probability of these abnormalities. Base station abnormalities can affect the normal operation of the base station and its corresponding cleaning equipment, as well as the cleaning effectiveness of the cleaning robots.

[0048] To address the aforementioned issues, this disclosure provides an anomaly handling method. When a base station is in an abnormal state, this disclosure can determine the target anomaly handling type corresponding to the current base station information based on the target anomaly handling model and the current base station information, and execute the anomaly handling method represented by the target anomaly handling type. This allows for efficient and rapid determination of the anomaly handling method corresponding to the anomaly when the base station is in an abnormal state, avoiding the impact of base station anomalies on the base station and the corresponding host equipment, and improving the user experience.

[0049] The exception handling method provided in this embodiment is executed by a base station, which can be a base station of a smart device such as a cleaning robot. The cleaning robot includes cleaning equipment and a base station.

[0050] Figure 1 This is a flowchart illustrating an exception handling method according to an exemplary embodiment, executed by a base station. See also... Figure 1 The method includes the following steps:

[0051] Step S101: When the base station is in an abnormal state, obtain the current base station information; the current base station information is used to characterize the current abnormality of the base station and the execution status of the base station's tasks.

[0052] The anomalies currently occurring at the base station can include issues such as the wastewater tank being out of place, the wastewater tank being full, the cleaning rack being out of place, the cleaning tray being full of water, the dust bag being out of place, the cleaning fluid pump being out of place, and the water supply and drainage modules being out of place. Furthermore, the base station may be experiencing one anomaly or two or more anomalies.

[0053] The execution status of base station tasks is used to characterize the base station tasks that the base station is currently executing and those that are yet to be executed. In one example, since the base station tasks to be executed may include multiple tasks, they can be defined as a queue of base station tasks to be executed. Optionally, the queue of base station tasks to be executed can be pre-set, for example, at a certain time, the base station will execute base station task A; or, after base station task A is completed, the base station will execute base station task B, that is, base station tasks A and B are executed sequentially.

[0054] Additionally, it should be noted that when a base station is not performing base station tasks, the base station task information can be defined as preset task information. The preset task information is used to indicate that the base station is in an idle state under the current circumstances.

[0055] In some embodiments, the current base station information includes base station anomaly information, base station task information, and environmental information.

[0056] Base station anomaly information includes at least one anomaly type when a base station malfunctions, such as sewage tank anomaly and water supply and drainage module anomaly.

[0057] Base station task information includes base station tasks that the base station is currently executing, such as mop drying tasks, base station tasks that the base station is waiting to execute, and base station tasks that the base station is executing during the cleaning process of the cleaning equipment corresponding to the base station, such as base station tasks before the cleaning equipment starts cleaning, base station tasks that return to the base station midway through the cleaning process (such as the host returning to the base station due to insufficient power), and base station tasks that return to the base station after the cleaning equipment has finished cleaning.

[0058] Environmental information includes ambient temperature, ambient humidity, and cleaning area information. The cleaning area information indicates the relevant details of the cleaning area during cleaning. Specifically, ambient temperature information can be obtained from a temperature sensor installed on the cleaning equipment, ambient humidity information can be obtained from a humidity sensor installed on the cleaning equipment, and the cleaning area information can include the cleaning type of the cleaning equipment, such as sweeping before mopping or sweeping only, and the degree of dirtiness in the work area, i.e., the dust content per square mile, the amount of dirty liquid per square mile, etc.

[0059] In some embodiments, before step S101, the status of the base station can be determined first, and if the base station is in an abnormal state, step S101 and subsequent steps are executed. When the base station is in a normal state, it continues to operate normally according to the set base station task queue.

[0060] Step S102: Based on the target anomaly handling model and the current base station information, determine the target anomaly handling type corresponding to the current base station information; the target anomaly handling model is used to predict the anomaly handling type when the base station is in an abnormal state, and the target anomaly handling type is used to characterize the anomaly handling method for the base station tasks being executed and the base station tasks to be executed.

[0061] The target anomaly handling model is a pre-trained model, which can be directly invoked in step S103. The anomaly handling method for base station tasks currently being executed and base station tasks to be executed can be determined based on the actual situation of the target anomaly handling type. For example, the base station task currently being executed can be stopped, and the base station task to be executed can be continued after a preset time; or, all base station tasks of the base station can be stopped, that is, the base station task currently being executed and the base station task to be executed can be stopped.

[0062] In some embodiments, the training data and training process of the target anomaly handling model can be set and selected based on actual conditions. For details of the training process, please refer to the following embodiments, which will not be repeated here.

[0063] Optionally, the current base station information can be input into the target anomaly handling model to determine the target anomaly handling type corresponding to the current base station information. Alternatively, information to be input into the target anomaly handling model can be determined based on the current base station information, and then this determined information can be input into the target anomaly handling model to determine the target anomaly handling type corresponding to the current base station information.

[0064] It should be noted that anomalies occurring at the base station may affect ongoing and pending base station tasks. For example, if the water supply and drainage module is missing, the base station will be unable to successfully perform the mop cleaning task or the subsequent dirty water collection task. Alternatively, anomalies may not affect ongoing or pending base station tasks; for instance, if the water supply and drainage module is missing, the base station can still perform the dust collection task normally. Therefore, different anomalies may correspond to different anomaly handling types.

[0065] In some embodiments, exception handling types can be classified into the following types:

[0066] The first type involves suspending all base station tasks. This means stopping all currently executing and pending base station tasks, and monitoring the base station status in real time. If the base station status recovers from an abnormal state to a normal state within a preset time, all base station tasks resume execution; conversely, if the base station status remains abnormal within the preset time, all base station tasks terminate. The preset time can be set and selected based on actual needs. It should be noted that the base station status can recover from an abnormal state to a normal state through human intervention, such as users manually handling problems with the base station, or through automatic machine correction, such as the base station checking and correcting itself.

[0067] The second type involves the base station performing a restart task and then continuing to execute all base station tasks after the restart. That is, the base station will automatically restart and continue executing both currently running and pending base station tasks after the restart. The process of the base station performing the restart task will not be detailed here.

[0068] In the third type, the base station continues to execute its ongoing tasks. It's important to note that in this third type, the anomaly occurring at the base station does not affect the currently executing tasks, but may affect tasks yet to be executed. Therefore, in this situation, the base station does not need to take any action on the currently executing tasks. However, if it is determined that the current anomaly will affect tasks yet to be executed, then appropriate actions will be taken on those tasks, which will not be elaborated upon here.

[0069] The fourth type involves the base station ending its current task and executing a pending task. In other words, the anomaly affecting the base station will impact the currently executing task but not the pending task; therefore, in this situation, the base station will end its current task.

[0070] The fifth type is when the base station terminates all base station tasks. That is, the base station stops executing both currently running and pending base station tasks. In the current situation, the anomaly occurring at the base station will have a significant impact on both currently running and pending base station tasks.

[0071] In some embodiments, the target anomaly handling type corresponding to the current base station information can be determined from the above five types based on the current base station information and the target anomaly handling model.

[0072] Step S103: Execute the exception handling method represented by the target exception handling type.

[0073] In some embodiments, based on the content shown in step S102 above, the exception handling method represented by each different type of exception handling can be determined, and the exception handling method represented by the exception handling type can be executed. For example, when the target exception handling type is the third type, the currently executing base station task continues to be executed. Or, for example, when the target exception handling type is the fifth type, all base station tasks are terminated.

[0074] In some embodiments, the base station can automatically execute the exception handling method characterized by the target exception handling type. For exception handling methods that the base station cannot execute automatically, the base station can notify the user to handle them manually. In one example, the base station can connect to the user's electronic device through a communication model and send the exception handling method to the user's electronic device to notify the user to handle base station exceptions manually.

[0075] This embodiment of the present disclosure can determine the target anomaly handling type corresponding to the current base station information based on the target anomaly handling model and the current base station information when the base station is in an abnormal state, and execute the anomaly handling method represented by the target anomaly handling type. In this way, when the base station is in an abnormal state, the anomaly handling method corresponding to the anomaly can be determined efficiently and quickly, avoiding the impact of base station anomalies on the base station and the host equipment corresponding to the base station, and improving the user experience.

[0076] Furthermore, this disclosure can consider information such as base station anomaly information, base station task information, and environmental information when determining the anomaly handling type and method for a base station, thereby improving the accuracy and versatility of the anomaly handling type and method. Moreover, based on the target anomaly handling type, the base station can prioritize executing base station tasks unaffected by base station anomalies, thereby improving the timeliness of base station task execution.

[0077] In some embodiments, the target anomaly handling model may include two training methods, which are described below. Figure 2 and Figure 3 The illustrated examples explain the training process of two training methods for the target anomaly handling model.

[0078] Figure 2 This is a flowchart illustrating an exception handling method according to an exemplary embodiment, executed by a base station. See also... Figure 2 The method includes the following steps:

[0079] Step S201: Obtain first training data. The first training data includes sample anomaly features and sample anomaly processing types corresponding to the sample anomaly features. The sample anomaly features are features extracted from sample base station information.

[0080] In some embodiments, after obtaining the original sample base station information and the sample anomaly handling type corresponding to the sample base station information, feature extraction can be performed on the sample base station information to obtain the sample anomaly features corresponding to the sample base station information. The sample anomaly features can be represented in vector form; for example, a certain sample anomaly feature can be represented as (a, b, c, d), where a, b, c, and d refer to the features extracted based on the sample base station information.

[0081] Of course, it is understood that this embodiment does not limit the method of feature extraction for base station information. Optionally, the bag-of-words method can be used to extract features from the sample base station information; alternatively, statistical feature extraction methods can be used to extract features from the sample base station information; alternatively, principal component analysis, convolutional neural networks, encoders, etc., can also be used to extract features from the sample base station information, which will not be elaborated here.

[0082] In some embodiments, based on the discussion of the current base station information in step S101 above, it can be determined that the base station information includes multiple types of information. Optionally, in the process of extracting features from the sample base station information to obtain sample anomaly features, a feature extraction method can be used to extract features from each piece of information in the sample base station information separately to obtain multiple intermediate anomaly features, and then the multiple intermediate anomaly features can be combined to obtain the sample anomaly features. Optionally, the sample base station information can also be directly subjected to a feature extraction process based on the feature extraction method to obtain the sample anomaly features.

[0083] Step S202: Based on the first training data, train the first initial anomaly handling model to obtain the target anomaly handling model.

[0084] In some embodiments, the first initial anomaly handling model can be a support vector machine model. Accordingly, when it is a support vector machine model, the kernel function corresponding to the support vector machine model can be determined, such as a linear kernel function, a polynomial kernel function, a Gaussian kernel function, etc., and the optimal hyperplane can be determined based on the sample anomaly features and the sample anomaly handling type, and different sample anomaly features can be divided based on the hyperplane.

[0085] It should be noted that, in order to improve the performance of the target anomaly handling model, the parameters of the first initial anomaly handling model can be adjusted by methods such as cross-validation to obtain better classification results.

[0086] Furthermore, after obtaining the target anomaly handling model, test data can be used to test its classification performance. Specifically, when acquiring the first training data, the first test data (including test anomaly features and the corresponding test anomaly handling types) can be acquired simultaneously, and the target anomaly handling model can be validated based on the first test data. For example, the test anomaly features from the first test data can be input into the target anomaly handling model to determine the anomaly handling type. Finally, based on the obtained anomaly handling type and the actual test anomaly handling type, validation parameters such as accuracy and recall of the target anomaly handling model can be determined. In one example, when the validation parameters meet the preset adjustment, it is determined that the classification performance of the target anomaly handling model is good, and no adjustment of the target anomaly handling model's parameters is needed; it can be applied to anomaly handling methods. For example, when the accuracy of the target anomaly handling model is greater than the preset value, no adjustment of the target anomaly handling model's parameters is needed. However, when the validation parameters do not meet the preset conditions, it is determined that the classification performance of the target anomaly handling model does not meet the expected results, and the parameters of the target anomaly handling model need to be adjusted until the validation parameters meet the preset conditions.

[0087] Of course, it is understandable that the parameters of the target anomaly handling model can be set based on the type of the target anomaly handling model. For example, when the target anomaly handling model is a support vector machine model, the parameters of the target anomaly handling model can include the kernel function type, kernel function parameters, penalty parameters, etc.

[0088] Accordingly, referring to the training method of the target anomaly processing model, the target anomaly processing model is used to predict the anomaly processing type based on the input features. Based on the target anomaly processing model and the current base station information, the target anomaly processing type corresponding to the current base station information is determined, including: extracting features from the current base station information to obtain target anomaly features; inputting the target anomaly features into the target anomaly processing model and outputting the target anomaly processing type. The method for extracting features from the current base station information can refer to the method for extracting features from sample base station information, and will not be elaborated here.

[0089] This disclosure provides a first training method for the target anomaly handling model. By training the target anomaly handling model, the target anomaly handling type can be output based on the target anomaly handling model. Thus, when the base station is in an abnormal state, the corresponding anomaly handling method can be determined, effectively solving the problem of base station anomalies.

[0090] Figure 3This is a flowchart illustrating an exception handling method according to an exemplary embodiment, executed by a base station. See also... Figure 3 The method includes the following steps:

[0091] Step S301: Obtain second training data, which includes sample base station information and sample anomaly handling types corresponding to the sample base station information.

[0092] In some embodiments, the information included in the sample base station information may refer to the content included in the current base station information in step S101, and will not be repeated here.

[0093] Step S302: Based on the second training data, train the second initial anomaly handling model to obtain the target anomaly handling model.

[0094] In some embodiments, after obtaining the sample base station information and its corresponding sample anomaly handling type, there is no need to process the sample base station information. Instead, a second initial anomaly handling model is directly trained based on the sample base station information and its corresponding sample anomaly handling type to obtain the target anomaly handling model. It should be noted that the second initial anomaly handling model is similar to the first initial anomaly model, and the training and testing processes of both are similar. For details, please refer to steps S201-S202 in the above embodiments, which will not be repeated here.

[0095] Accordingly, referring to the training method of the target anomaly processing model, the target anomaly processing model is used to predict the anomaly processing type based on the input base station information. Therefore, based on the target anomaly processing model and the current base station information, determining the target anomaly processing type corresponding to the current base station information includes: inputting the current base station information into the target anomaly processing model and outputting the target anomaly processing type.

[0096] This disclosure provides a second training method for the target anomaly handling model. By training the target anomaly handling model, the target anomaly handling type can be output based on the target anomaly handling model. Thus, when the base station is in an abnormal state, the corresponding anomaly handling method can be determined, effectively solving the problem of base station anomalies.

[0097] In some embodiments, in order to improve the interactivity between the base station and the user, the target anomaly handling type is also used to characterize the urgency of the target anomaly handling type. Furthermore, when determining the target anomaly handling type corresponding to the current base station information, a target prompt message can also be issued to prompt the user about the urgency of the target anomaly handling type.

[0098] In one example, the target prompt information can be voice, text, or image information. Optionally, target prompt information with varying levels of urgency can be issued. For example, the base station can output a voice message such as, "The base station's water pipe has burst, an extremely urgent alarm has been triggered. Please handle it immediately," to prompt the user to address the base station malfunction. Additionally, alternatively, corresponding target prompt information can be set for different levels of urgency. For instance, when the urgency level is an extremely urgent alarm, the voice information can be designated as the first voice information, and when the urgency level is a general alarm, the voice information can be designated as the second voice information. Thus, the urgency level can be determined based on the type of received voice information.

[0099] In some embodiments, the user who receives the target prompt information can determine whether to manually intervene in handling the base station anomaly based on the urgency of the target anomaly handling type. For example, if the urgency level indicates an extremely urgent alarm, manual intervention is possible; or, if the urgency level indicates a general alarm, but it is determined that the base station cannot handle it automatically, manual intervention is possible.

[0100] The urgency level corresponding to different types of target anomaly handling can be determined. Optionally, the first type corresponds to an extremely urgent alarm, the second type to a warning, the third type to a general alarm, the fourth type to a general alarm, and the fifth type to an extremely urgent alarm. The urgency level indicated by a warning is lower than that indicated by a general alarm, and the urgency level indicated by a general alarm is lower than that indicated by an extremely urgent alarm. Furthermore, when an extremely urgent alarm is issued, to reduce potential problems caused by base station anomalies, the base station anomaly can be handled as quickly as possible after receiving the extremely urgent alarm.

[0101] It should be noted that the urgency levels for each type mentioned above are for illustrative purposes only. The urgency level of the target notification information for each type can be set and selected based on actual needs.

[0102] In this embodiment of the disclosure, by issuing target prompt information, the user can be informed of the urgency of the target anomaly handling type, thereby facilitating the user to handle base station anomalies in a timely manner, improving the efficiency of base station anomaly handling and the user experience.

[0103] Figure 4 This is a flowchart illustrating an exception handling method according to an exemplary embodiment, executed by a base station. See also... Figure 4 The method includes the following steps:

[0104] Step S401: When the base station is in an abnormal state, obtain the current base station information; the current base station information includes base station abnormal information, base station task information and environmental information.

[0105] Step S402: Based on the target anomaly handling model and the current base station information, determine the target anomaly handling type corresponding to the current base station information, and issue a target prompt message.

[0106] Step S403: Execute the exception handling method represented by the target exception handling type.

[0107] Furthermore, in some embodiments, the methods shown in the above embodiments can be applied to base stations of other robotic devices, such as base stations for handling robots or service robots. However, it should be noted that when applied to base stations of other robotic devices, certain methods may be adapted based on actual circumstances, which will not be elaborated upon here.

[0108] Figure 5 This is a block diagram illustrating an exception handling apparatus according to an exemplary embodiment, see [link to example]. Figure 5 The device includes:

[0109] The acquisition module 501 is configured to acquire current base station information when the base station is in an abnormal state; the current base station information is used to characterize the abnormality currently occurring in the base station and the execution status of the base station's tasks.

[0110] The determination module 502 is configured to determine the target anomaly handling type corresponding to the current base station information based on the target anomaly handling model and the current base station information; the target anomaly handling model is used to predict the anomaly handling type when the base station is in an abnormal state, and the target anomaly handling type is used to characterize the anomaly handling method for the base station tasks being executed and the base station tasks to be executed.

[0111] Processing module 503 is configured to execute the exception handling method represented by the target exception handling type.

[0112] In some embodiments, the determining module 502 is configured to:

[0113] Feature extraction is performed on the current base station information to obtain the target anomaly features;

[0114] Input the target anomaly features into the target anomaly processing model and output the target anomaly processing type;

[0115] Among them, the target anomaly handling model is used to predict the anomaly handling type based on the input features.

[0116] In some embodiments, the determining module 502 is configured to:

[0117] Acquire first training data, which includes sample anomaly features and sample anomaly processing types corresponding to the sample anomaly features. The sample anomaly features are features extracted from sample base station information.

[0118] Based on the first training data, a first initial anomaly handling model is trained to obtain the target anomaly handling model.

[0119] In some embodiments, the determining module 502 is configured to:

[0120] Input the current base station information into the target anomaly handling model and output the target anomaly handling type;

[0121] Among them, the target anomaly handling model is used to predict the anomaly handling type based on the input base station information.

[0122] In some embodiments, the determining module 502 is configured to:

[0123] Acquire second training data, which includes sample base station information and sample anomaly handling types corresponding to the sample base station information;

[0124] Based on the second training data, a second initial anomaly handling model is trained to obtain the target anomaly handling model.

[0125] In some embodiments, the target exception handling type is also used to characterize the urgency of the target exception handling type, and the determining module 502 is configured to:

[0126] Issue a target alert message; the target alert message is used to indicate to the user the urgency of the target exception handling type.

[0127] In some embodiments, the current base station information includes base station anomaly information, base station task information, and environmental information. The base station anomaly information includes at least one anomaly type when the base station malfunctions. The base station task information includes base station tasks that the base station is currently executing, base station tasks that the base station is about to execute, and base station tasks that the base station is executing during the cleaning process of the cleaning equipment corresponding to the base station. The environmental information includes ambient temperature information, ambient humidity information, and cleaning area information. The cleaning area information is used to indicate relevant information about the cleaning area when the cleaning equipment is cleaning.

[0128] 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.

[0129] 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 exception handling method in the above embodiments.

[0130] Figure 6 This is a block diagram of a base station 600 according to an exemplary embodiment.

[0131] Reference Figure 6The 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] A non-transitory computer-readable storage medium, when instructions in the storage medium are executed by a base station's processor, enables the base station to perform the exception handling method provided in exemplary embodiments of this disclosure.

[0143] Other embodiments of this disclosure 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 this disclosure that follow the general principles of this disclosure 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 this disclosure are indicated by the following claims.

[0144] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. An exception handling method, characterized in that, include: When the base station is in an abnormal state, obtain the current base station information; The current base station information is used to characterize the anomalies currently occurring at the base station and the execution status of the base station's tasks; Based on the target anomaly handling model and the current base station information, determine the target anomaly handling type corresponding to the current base station information; The target anomaly handling model is used to predict the anomaly handling type when the base station is in an abnormal state. The target anomaly handling type is used to characterize the anomaly handling method for the base station tasks that the base station is currently executing and the base station tasks that are to be executed. Perform the exception handling method represented by the target exception handling type.

2. The anomaly handling method according to claim 1, characterized in that, The step of determining the target anomaly handling type corresponding to the current base station information based on the target anomaly handling model and the current base station information includes: Feature extraction is performed on the current base station information to obtain target anomaly features; The target anomaly features are input into the target anomaly processing model, and the target anomaly processing type is output. The target anomaly handling model is used to predict the anomaly handling type based on the input features.

3. The anomaly handling method according to claim 2, characterized in that, The training process of the target anomaly handling model includes: Acquire first training data, which includes sample anomaly features and sample anomaly processing types corresponding to the sample anomaly features, wherein the sample anomaly features are features extracted from sample base station information. Based on the first training data, a first initial anomaly handling model is trained to obtain the target anomaly handling model.

4. The anomaly handling method according to claim 1, characterized in that, The step of determining the target anomaly handling type corresponding to the current base station information based on the target anomaly handling model and the current base station information includes: The current base station information is input into the target anomaly handling model, and the target anomaly handling type is output. The target anomaly handling model is used to predict the anomaly handling type based on the input base station information.

5. The anomaly handling method according to claim 4, characterized in that, The training process of the target anomaly handling model includes: Acquire second training data, which includes sample base station information and sample anomaly handling types corresponding to the sample base station information; Based on the second training data, a second initial anomaly handling model is trained to obtain the target anomaly handling model.

6. The anomaly handling method according to claim 1, characterized in that, The target anomaly handling type is also used to characterize the urgency of the target anomaly handling type, and the method further includes: Issue a target prompt message; the target prompt message is used to inform the user of the urgency of the target exception handling type.

7. The anomaly handling method according to claim 1, characterized in that, The current base station information includes base station anomaly information, base station task information, and environmental information. The base station anomaly information includes at least one anomaly type when the base station malfunctions. The base station task information includes the base station task currently being executed by the base station, the base station task to be executed by the base station, and the base station task executed by the base station during the cleaning process of the cleaning equipment corresponding to the base station. The environmental information includes ambient temperature information, ambient humidity information, and cleaning area information. The cleaning area information is used to indicate relevant information about the cleaning area during the cleaning process of the cleaning equipment.

8. An anomaly handling device, characterized in that, include: The acquisition module is configured to acquire current base station information when the base station is in an abnormal state. The current base station information is used to characterize the anomalies currently occurring at the base station and the execution status of the base station's tasks; The determination module is configured to determine the target anomaly handling type corresponding to the current base station information based on the target anomaly handling model and the current base station information; The target anomaly handling model is used to predict the anomaly handling type when the base station is in an abnormal state. The target anomaly handling type is used to characterize the anomaly handling method for the base station tasks that the base station is currently executing and the base station tasks that are to be executed. The processing module is configured to execute the exception handling method represented by the target exception handling type.

9. A base station, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute the exception handling method as described in any one of claims 1-7.

10. 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 exception handling method as described in any one of claims 1-7.

Images