Data processing method and system and cleaning equipment

Abstract

The invention relates to a data processing method and system and cleaning equipment, the method and system are applied to a cleaning robot, the cleaning robot performs data communication with an intelligent base station based on a target communication protocol, the cleaning robot and the intelligent base station each comprise a data processing system, and the method comprises the steps of collecting sensing data; analyzing the action type of the sensing data; if the action type of the sensing data is a first type, executing a real-time analysis task according to the sensing data; and if the action type of the sensing data is a second type, sending the sensing data to the intelligent base station. After the cleaning robot collects the sensing data, the sensing data of which the action type is the second type is sent to the intelligent base station, and the data processing system arranged in the intelligent base station is utilized to analyze and train the sensing data, so that the data processing pressure of the cleaning robot can be greatly relieved, and the operation stability of the robot is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent electrical appliance technology, and in particular to a data processing method, system and cleaning equipment. Background Art

[0002] With the iteration and upgrade of cleaning robots, the AI ​​functions integrated in sweeping robots are becoming more and more abundant. However, with the increasing degree of intelligence, the hardware cost of cleaning robots is increasing. As AI data processing becomes more and more complicated, the data capacity required by cleaning robots is growing exponentially. A single data processing module integrated on the cleaning robot host will be difficult to meet the needs of concurrent data processing. Summary of the invention

[0003] Based on this, it is necessary to provide a data processing method, system and cleaning equipment that can effectively reduce the data processing pressure of cleaning robots and improve the AI ​​function integration capabilities of cleaning equipment in response to the above-mentioned technical problems.

[0004] In a first aspect, the present application provides a data processing method, which is applied to a cleaning robot, wherein the cleaning robot performs data communication with an intelligent base station based on a target communication protocol, and the cleaning robot and the intelligent base station both include a data processing system, including:

[0005] Collect sensor data;

[0006] Analyzing the sensor data for a type of effect;

[0007] If the action type of the sensor data is the first type, performing a real-time analysis task according to the sensor data;

[0008] If the effect type of the sensing data is the second type, the sensing data is sent to the intelligent base station, so that the intelligent base station trains the sensing data according to a preset data training model.

[0009] In one embodiment, the method further comprises:

[0010] If the effect type of the sensor data is the third type, a real-time analysis task is performed according to the sensor data, and the sensor data is sent to the intelligent base station.

[0011] In one embodiment, the type of action of analyzing the sensor data includes:

[0012] If the sensor data is used to perform a real-time analysis task, determining that the effect type of the sensor data is a first type;

[0013] If the sensor data is used to perform a static data training task, it is determined that the effect type of the sensor data is the second type.

[0014] In one embodiment, the type of action of analyzing the sensor data includes:

[0015] If the sensor data is used to perform a semantic map construction task and is not used to perform a real-time analysis task, determining that the role type of the sensor data is the second type;

[0016] If the sensor data is used to execute a semantic map construction task and is used to execute a real-time analysis task, it is determined that the function type of the sensor data is the third type.

[0017] In one embodiment, if the effect type of the sensor data is the third type, the method further includes:

[0018] Sending an analysis instruction to the intelligent base station according to the target communication protocol, so that the intelligent base station performs a static data training task according to the sensor data to obtain a target training result;

[0019] The target training result is received, which is sent by the intelligent base station according to the target communication protocol.

[0020] In a second aspect, the present application further provides a data processing method, which is applied to an intelligent base station, wherein the intelligent base station performs data communication with a cleaning robot based on a target communication protocol, and the cleaning robot and the intelligent base station both include a data processing system, including:

[0021] Receiving sensor data sent by the cleaning robot;

[0022] Training the sensor data according to a preset data training model in the data processing system to obtain a target training result;

[0023] The target training result is transmitted back to the cleaning robot.

[0024] In one embodiment, transmitting the target training result back to the cleaning robot includes:

[0025] If an analysis instruction sent by the cleaning robot is received, the target training result is sent to the intelligent base station according to the target communication protocol;

[0026] If the analysis instruction sent by the cleaning robot is not received, the target training result is transmitted to the cleaning robot when the cleaning robot returns to the intelligent base station for charging.

[0027] In a third aspect, the present application also provides a data processing system, which is applied to a cleaning robot, comprising:

[0028] A collection module, used for collecting sensor data;

[0029] An analysis module, used for analyzing the effect type of the sensor data;

[0030] A first execution module, configured to execute a real-time analysis task according to the sensor data if the action type of the sensor data is the first type;

[0031] The second execution module is used to send the sensor data to the intelligent base station if the action type of the sensor data is the second type, so that the intelligent base station trains the sensor data according to a preset data training model.

[0032] In a fourth aspect, the present application also provides a data processing system, which is applied to an intelligent base station, including:

[0033] A receiving module, used for receiving sensor data sent by the cleaning robot;

[0034] A training module, used to train the sensor data according to a preset data training model in the data processing system to obtain a target training result;

[0035] A feedback module is used to transmit the target training result back to the cleaning robot.

[0036] In a fifth aspect, the present application also provides a cleaning device, including a cleaning robot and an intelligent base station, wherein the cleaning robot is used for the data processing method described in the first aspect, and the intelligent base station is used for executing the data processing method described in the second aspect.

[0037] In a sixth aspect, the present application further provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the data processing method described in the first aspect when executing the computer program.

[0038] In a seventh aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the data processing method described in the first aspect are implemented.

[0039] In an eighth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the data processing method described in the first aspect are implemented.

[0040] In summary, the present application proposes a data processing method, system and cleaning equipment, which are applied to a cleaning robot. The cleaning robot communicates data with an intelligent base station based on a target communication protocol. Both the cleaning robot and the intelligent base station include a data processing system, including: collecting sensor data; analyzing the action type of the sensor data; if the action type of the sensor data is the first type, performing a real-time analysis task according to the sensor data; if the action type of the sensor data is the second type, sending the sensor data to the intelligent base station. In the present application, after collecting the sensor data, the cleaning robot will send the sensor data of the second action type to the intelligent base station, and use the data processing system set in the intelligent base station to analyze and train the sensor data, which can greatly alleviate the data processing pressure of the cleaning robot and improve the robot's operating stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is a structural block diagram of a cleaning device in one embodiment;

[0042] Figure 2 is a flow chart of a data processing method in one embodiment;

[0043] Figure 3 A schematic diagram of a flow chart of steps in which a cleaning robot communicates with an intelligent base station based on an RPC protocol in an embodiment;

[0044] Figure 4 is a flow chart of a data processing method in another embodiment;

[0045] Figure 5 is a structural block diagram of a data processing system in one embodiment;

[0046] Figure 6 is a structural block diagram of a data processing system in another embodiment;

[0047] Figure 7 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0049] The data processing method provided in the embodiment of the present application can be applied to Figure 1In the cleaning device shown, the cleaning device includes a cleaning robot 102 and an intelligent base station 104, wherein the cleaning robot 102 and the intelligent base station 104 perform data communication based on a target communication protocol, and both the cleaning robot 102 and the intelligent base station 104 include a data processing system.

[0050] In this embodiment, the target communication protocol may be a Remote Procedure Call (RPC) protocol or other wireless communication protocols. The target communication protocol may be customized according to the communication requirements between the cleaning robot 102 and the intelligent base station 104 .

[0051] As a computer communication protocol, the RPC communication protocol allows a computer program to call a subroutine (i.e., a remote procedure) in another computer program through a network and obtain a return value. The RPC communication protocol includes a client and a server. The client can pass parameters to the remote procedure in the server, and the remote procedure returns the result to the client after execution. In this embodiment, the cleaning robot 102 can be defined as the client in the RPC communication protocol, and the smart base station 104 can be defined as the server in the RPC communication protocol, so that the cleaning robot 102 can pass local parameters to the smart base station 104, call the preset program in the smart base station 104 to analyze the parameters, and then receive the analysis results returned by the smart base station 104.

[0052] In actual application, this embodiment configures a data processing system for data training and data analysis in the smart base station 104 by adding hardware such as a hard disk and an ARM architecture processor in the smart base station 104. It should be noted that configuring a data processing system in the smart base station 104 can be used to analyze and train a large number of data sets, and can implement the construction of various big data models based on the training model pre-configured in the memory, such as the construction of an AI semantic function map.

[0053] In this embodiment, the intelligent base station 104 and the cleaning robot 102 are both internally configured with corresponding data processing systems, which can be used to analyze and train the collected data. During operation, the cleaning robot 102 transfers some data analysis tasks and data training tasks to the intelligent base station 104 for processing based on the RPC communication protocol, which can effectively reduce the algorithm operation pressure and hardware pressure of the cleaning robot 102, and can reduce the cost pressure of the cleaning robot 102 requiring high-performance chips, and can achieve more accurate and efficient completion of various AI model building functions.

[0054] In one embodiment, Figure 2 As shown, a data processing method is provided, which is applied to Figure 1 The cleaning robot in the example is used to illustrate, including the following steps:

[0055] S201, collecting sensor data.

[0056] In this embodiment, the cleaning robot is equipped with multiple types of sensor devices, including but not limited to image sensors, distance sensors, temperature sensors, and humidity sensors.

[0057] The sensor data in this embodiment can be any one or more of image sensor data, distance sensor data, temperature sensor data or humidity sensor data. It should be noted that the specific type of sensor data can be determined based on the real-time operating state of the cleaning robot and the startup status of the functional module.

[0058] S202, analyzing the effect type of the sensing data.

[0059] In this embodiment, after collecting sensor data each time, the cleaning robot will analyze the actual effect of the sensor data to identify and determine the specific purpose of the sensor data, so as to determine the type of effect of the sensor data according to the purpose of the real-time sensor.

[0060] In this embodiment, the action type of the sensor data can be determined by the parameter type of the sensor data and the function module of the sensor device corresponding to the cleaning robot. For example, if the sensor data is image data, and the function module of the cleaning robot image sensor is a map construction module, the action type of the sensor data can be determined to be a map construction type. If the sensor data is image data and distance data, and the function module of the cleaning robot image sensor and distance sensor is an obstacle avoidance module, the action type of the sensor data can be determined to be a real-time obstacle avoidance type. If the sensor data is image data and distance data, and the function module of the cleaning robot image sensor and distance sensor is a path navigation module, the action type of the sensor data can be determined to be a route planning type.

[0061] It should be noted that the specific names of the functional modules and the specific names of the types of sensor data can be customized according to the needs of the actual application scenario. The sensor data types that the functional modules need to collect during actual operation should also be configured according to the needs of the actual application scenario, and are not specifically limited here.

[0062] In this embodiment, the role types of sensor data can be divided into three types, namely, the first type, the second type and the third type. Among them, the first type is used to indicate that the sensor data needs to be used to perform real-time analysis tasks of the cleaning robot, such as mobile path planning, real-time positioning, motion control and other tasks. The second type is used to indicate that the sensor data needs to be used to perform model training tasks of static data, such as semantic map construction and other tasks. The third type is used to indicate that the sensor data needs to be used to perform both real-time analysis tasks of the cleaning robot and model training tasks of static data.

[0063] It should be noted that the present embodiment may also identify the type of function of the sensing data collected by the sensor device based on the current task or program being executed by the cleaning robot.

[0064] In this embodiment, after identifying the action type of the sensor data, the cleaning robot will further determine whether to perform the corresponding task locally or on other service ends based on the action type of the sensor data.

[0065] S203: If the action type of the sensor data is the first type, execute a real-time analysis task according to the sensor data.

[0066] In this embodiment, when the action type of the sensor data is the first type, it is determined that the cleaning robot needs to analyze the sensor data in the local data processing system. Then, according to the needs of the cleaning robot's real-time analysis task, the analysis and processing of the sensor data will be completed in the local data processing system.

[0067] S204: If the function type of the sensing data is the second type, the sensing data is sent to the intelligent base station, so that the intelligent base station trains the sensing data according to a preset data training model.

[0068] In this embodiment, when the action type of the sensor data is the second type, it is determined that the cleaning robot needs to analyze and train the sensor data at the service end, that is, the intelligent base station. At this time, the sensor data will first be sent to the intelligent base station so that the data processing system in the intelligent base station can analyze and train the sensor data according to a preset data training model.

[0069] It should be noted that the specific processing method for the cleaning robot to send the sensor data to the intelligent base station in this embodiment can be that when the cleaning robot returns to the base station to charge or perform self-cleaning, the cleaning robot transmits the sensor data through the port interconnected with the base station. The specific processing method for the cleaning robot to send the sensor data to the intelligent base station in this embodiment can also be that the cleaning robot sends the sensor data to the intelligent base station through the network based on the target communication protocol.

[0070] Preferably, after acquiring the second type of sensor data, the cleaning robot will send the sensor data to the intelligent base station based on the RPC communication protocol, so that the intelligent base station has sufficient time to train the sensor data according to the preset data training model.

[0071] To summarize, this embodiment provides a data processing method. After the cleaning robot obtains sensor data, it will determine whether to use the sensor data locally for analysis based on the actual function type of the sensor data. When the function type of the sensor data is to perform a model training task, the cleaning robot can transfer the sensor data to the intelligent base station based on the RPC communication protocol, so as to reduce the computing pressure of the cleaning robot through the data processing system set up in the intelligent base station, thereby reducing the cleaning robot's demand for smart chips and reducing the hardware cost of the cleaning robot.

[0072] In one embodiment, the data processing method further includes:

[0073] If the action type of the sensor data is the third type, a real-time analysis task is performed according to the sensor data, and the sensor data is sent to the intelligent base station.

[0074] In this embodiment, when the action type of the sensor data is the third type, it is determined that the cleaning robot needs to use the sensor data to execute the corresponding program on both the client and the server. At this time, while the local data processing system performs real-time analysis tasks based on the sensor data, it will also send the sensor data to the intelligent base station, so that the data processing system of the intelligent base station can train the sensor data according to the preset data training model to obtain the target training result.

[0075] For example, if the sensor data is image data, and the function type of the sensor data is map construction type, the cleaning robot needs to complete real-time map construction in the local data processing system based on the image data, and also needs to complete AI semantic map construction. The cleaning robot will send the image data to the smart base station, so that the smart base station can analyze the image data according to the preset image processing algorithm to obtain relevant information of the target object in the image data, and then use machine learning or deep learning algorithms to convert the image data into a semantic map.

[0076] It should be noted that the map information in the AI ​​semantic map includes not only simple geometric location information, but also rich semantic information, such as place names, POI (point of interest) attributes, etc. The actual construction algorithm of the AI ​​semantic map can be adaptively selected according to the needs of the actual application scenario.

[0077] In one embodiment, analyzing the sensor data for the type of effect includes:

[0078] If the sensor data is used to perform a real-time analysis task, determining that the role type of the sensor data is a first type;

[0079] If the sensor data is used to perform a static data training task, the role type of the sensor data is determined to be the second type.

[0080] In this embodiment, when analyzing the action type of the sensor data, the type of the sensor data can be directly determined based on the real-time task or real-time program executed by the cleaning robot. During daily operation, the cleaning robot will complete the corresponding task or program based on the instructions issued by the user terminal or the instructions automatically issued by the intelligent base station.

[0081] For example, a user can control a cleaning robot through a user terminal to complete a cleaning task in a designated area. At this time, the cleaning robot needs to execute a series of programs including path planning, motion control, and adaptive cruising. During the execution of each program, the cleaning robot will collect a large amount of sensor data through sensor equipment. The cleaning robot can determine the type of function of the sensor data based on the real-time tasks or programs executed under the real-time operating state.

[0082] In one embodiment, analyzing the sensor data for the type of effect includes:

[0083] If the sensor data is used to perform the semantic map construction task and is not used to perform the real-time analysis task, the role type of the sensor data is determined to be the second type;

[0084] If the sensor data is used to perform a semantic map construction task and is used to perform a real-time analysis task, the role type of the sensor data is determined to be the third type.

[0085] In this embodiment, when the cleaning robot performs the semantic map construction task, it will collect sensor data including image data, distance data, humidity data, etc. through sensor equipment. If this part of the sensor data is not used for real-time analysis tasks, the role type of the sensor data can be determined as the second type, so that all the sensor data can be transmitted to the intelligent base station, and data processing and AI semantic function map construction are performed in the data processing system of the intelligent base station.

[0086] If some sensor data need to perform real-time analysis tasks, for example, the cleaning robot needs to use AI semantic map-related content immediately, the cleaning robot will process the data and build the AI ​​semantic function map in the local data processing system.

[0087] Preferably, in this embodiment, the cleaning robot will set the priority of the real-time analysis task to a higher priority. In the tasks that require the cleaning robot to complete the analysis and processing within a preset time, the cleaning robot can complete the analysis and processing of the sensor data in the local data processing system.

[0088] Preferably, in the present embodiment, when the data processing performance of the local data processing system of the cleaning robot is less than a preset ratio threshold, the sensor data involved in the semantic map construction task will be sent to the intelligent base station for processing, and the processing results of the intelligent base station will be received based on the target communication protocol, so as to obtain the map information in the AI ​​semantic map within a preset time.

[0089] In one embodiment, if Figure 3 As shown, if the action type of the sensing data is the third type, the data processing method further includes:

[0090] S301, sending an analysis instruction to the intelligent base station according to the target communication protocol, so that the intelligent base station performs a static data training task according to the sensor data to obtain a target training result;

[0091] S302, receiving a target training result sent by the intelligent base station according to a target communication protocol.

[0092] In this embodiment, due to the changes in the complexity of the environment, the cleaning robot needs the training results of the static data training task as support while performing the real-time task. At this time, the cleaning robot will send the serialized sensor data and analysis instructions to the intelligent base station at the same time according to the target communication protocol. After receiving the sensor data, the intelligent base station will perform the static data training task according to the preset data training model. After receiving the analysis instruction, the intelligent base station will feed back the training results of the static data training task as a response to the cleaning robot after the static data training task has been executed for a period of time, so that the cleaning robot can complete the real-time task according to the target training results.

[0093] When the sensor data is serialized, the data set may be converted into a format such as JSON, ProtocolBuffers, etc. The analysis instruction may also be referred to as a request response, and the actual type of the analysis instruction may be determined according to the type of the target communication protocol.

[0094] To sum up, this embodiment provides a data processing method that can effectively reduce the hardware pressure of the cleaning robot. It can transfer a large number of static data training tasks, such as AI semantic function map construction tasks, to the intelligent base station for execution. While releasing the computing power of the cleaning robot, it provides sufficient time for the execution of AI semantic function map construction tasks, and can achieve higher precision and greater complexity map model construction, effectively improving the application depth of the AI ​​function of the cleaning equipment.

[0095] In one embodiment, Figure 4 As shown, a data processing method is provided, which is applied to Figure 1 The intelligent base station in the example is used to illustrate, including the following steps:

[0096] S401, receiving sensor data sent by the cleaning robot;

[0097] S402, training the sensor data according to a preset data training model in the data processing system to obtain a target training result;

[0098] S403, transmitting the target training result back to the cleaning robot.

[0099] In this embodiment, the preset data training model and the target training result in the intelligent base station can be customized based on the needs of the actual application scenario. For example, the preset data training model is an AI semantic function map construction model, and the target training result is an AI semantic function map.

[0100] In this embodiment, the specific data interaction process between the intelligent base station and the cleaning robot can refer to the specific implementation methods in the aforementioned embodiments, which will not be repeated here.

[0101] The intelligent base station provided in this embodiment is equipped with a data processing system capable of performing data analysis and training, which can be used to complete model training of various types of static data to share the hardware pressure of the cleaning robot, improve the accuracy of the cleaning equipment in various model building tasks, and enable the cleaning equipment to apply more AI functions and build a larger AI semantic function map.

[0102] In one embodiment, transmitting the target training result back to the cleaning robot includes:

[0103] If an analysis instruction is received from the cleaning robot, the target training result is sent to the intelligent base station according to the target communication protocol;

[0104] If the analysis instruction sent by the cleaning robot is not received, the target training result is transmitted to the cleaning robot when the cleaning robot returns to the intelligent base station for charging.

[0105] In this embodiment, when the intelligent base station does not receive the analysis instructions sent by the cleaning robot, it means that the cleaning robot is in real-time operation and does not need to use the target training results to perform related tasks or programs. At this time, the intelligent base station can only complete the analysis and processing of the sensor data in the local data processing system, and store the target training results in the local memory.

[0106] When the intelligent base station receives the analysis instruction sent by the cleaning robot, it means that the cleaning robot needs to use the target training result to perform related tasks or programs in real-time operation. At this time, the intelligent base station can wait for the preset data training model to complete the training task of the sensor data and obtain the target training result, or directly extract the completed target training result from the local storage, and then transmit the target training result back to the cleaning robot based on the target communication protocol, so that the cleaning robot can complete the relevant tasks or programs in time.

[0107] To sum up, this embodiment provides a data processing system that can add data analysis and processing capabilities to the smart base station based on the original functions of the smart base station, so that it can perform a larger range of map model training tasks or deeper AI functions, greatly improving the comprehensive computing power of the cleaning equipment.

[0108] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0109] Based on the same inventive concept, the embodiment of the present application also provides a data processing system for implementing the data processing method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more data processing system embodiments provided below can refer to the limitations on the data processing method above, and will not be repeated here.

[0110] In one embodiment, Figure 5 As shown, a data processing system 500 is provided, which is applied to Figure 1 The cleaning robot of the cleaning device shown includes: a collection module 510, an analysis module 520, a first execution module 530 and a second execution module 540, wherein:

[0111] A collection module 510, for collecting sensor data;

[0112] An analysis module 520, for analyzing the action type of the sensor data;

[0113] A first execution module 530, configured to execute a real-time analysis task according to the sensor data if the action type of the sensor data is the first type;

[0114] The second execution module 540 is used to send the sensor data to the intelligent base station if the action type of the sensor data is the second type, so that the intelligent base station trains the sensor data according to a preset data training model.

[0115] In one embodiment, the data processing system 500 further includes:

[0116] The third execution module is used to execute the real-time analysis task according to the sensor data if the action type of the sensor data is the third type, and send the sensor data to the intelligent base station.

[0117] In one embodiment, the analysis module 520 is specifically used to determine that the action type of the sensor data is the first type if the sensor data is used to perform a real-time analysis task; if the sensor data is used to perform a static data training task, determine that the action type of the sensor data is the second type.

[0118] In one embodiment, the analysis module 520 is specifically used to determine that the role type of the sensor data is the second type if the sensor data is used to perform a semantic map construction task and is not used to perform a real-time analysis task; if the sensor data is used to perform a semantic map construction task and is used to perform a real-time analysis task, determine that the role type of the sensor data is the third type.

[0119] In one embodiment, the third execution module is specifically used to send an analysis instruction to the intelligent base station according to the target communication protocol, so that the intelligent base station performs a static data training task according to the sensor data to obtain a target training result; and receive the target training result sent by the intelligent base station according to the target communication protocol.

[0120] To sum up, this embodiment provides a data processing system applied to a cleaning robot, which can effectively reduce the hardware pressure of the cleaning robot, and can transfer a large number of static data training tasks, such as AI semantic function map construction tasks, to the intelligent base station for execution. While releasing the computing power of the cleaning robot, it provides sufficient time for the execution of the AI ​​semantic function map construction task, and can achieve higher precision and greater complexity map model construction, effectively improving the application depth of the AI ​​function of the cleaning equipment.

[0121] In one embodiment, Figure 6 As shown, a data processing system 600 is provided, which is applied to Figure 1 The smart base station of the cleaning equipment shown includes: a receiving module 610, a training module 620 and a return module 630, wherein:

[0122] A receiving module 610 is used to receive sensor data sent by the cleaning robot;

[0123] The training module 620 is used to train the sensor data according to the preset data training model in the data processing system to obtain the target training result;

[0124] The feedback module 630 is used to transmit the target training results back to the cleaning robot.

[0125] In one embodiment, the return module 630 is specifically used to send the target training result to the intelligent base station according to the target communication protocol if the analysis instruction sent by the cleaning robot is received;

[0126] If the analysis instruction sent by the cleaning robot is not received, the target training result is transmitted to the cleaning robot when the cleaning robot returns to the intelligent base station for charging.

[0127] To sum up, this embodiment provides a data processing system applied to an intelligent base station, which can add data analysis and processing capabilities to the intelligent base station on the basis of the original functions of the intelligent base station, so that a larger range of map model training tasks or deeper AI functions can be executed, greatly improving the comprehensive computing power of the cleaning equipment.

[0128] Each module in the above data processing system can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0129] In one embodiment, a computer device is provided, which may be a terminal. The computer device is applied to the cleaning robot or the intelligent base station in the aforementioned embodiment, and its internal structure diagram may be as shown in FIG. Figure 7As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a data processing method is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0130] Those skilled in the art will understand that Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0131] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the data processing method in the implementation of the aforementioned method is implemented.

[0132] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the data processing method in the aforementioned method implementation is implemented.

[0133] In one embodiment, a computer program product is provided, including a computer program, which implements the data processing method in the aforementioned method implementation when executed by a processor.

[0134] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0135] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0136] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A data processing method, characterized in that: Applied to a cleaning robot, the cleaning robot performs data communication with an intelligent base station based on a target communication protocol, and the cleaning robot and the intelligent base station both include a data processing system, including: Collect sensor data; Analyzing the sensor data for a type of effect; If the action type of the sensor data is the first type, performing a real-time analysis task according to the sensor data; If the effect type of the sensing data is the second type, the sensing data is sent to the intelligent base station, so that the intelligent base station trains the sensing data according to a preset data training model.

2. The method according to claim 1, characterized in that The method further comprises: If the effect type of the sensor data is the third type, a real-time analysis task is performed according to the sensor data, and the sensor data is sent to the intelligent base station.

3. The method according to claim 1, characterized in that The type of action of analyzing the sensor data includes: If the sensor data is used to perform a real-time analysis task, determining that the effect type of the sensor data is a first type; If the sensor data is used to perform a static data training task, it is determined that the effect type of the sensor data is the second type.

4. The method according to claim 2, characterized in that: The type of action of analyzing the sensor data includes: If the sensor data is used to perform a semantic map construction task and is not used to perform a real-time analysis task, determining that the role type of the sensor data is the second type; If the sensor data is used to execute a semantic map construction task and is used to execute a real-time analysis task, it is determined that the function type of the sensor data is the third type.

5. The method according to claim 2, characterized in that: If the effect type of the sensing data is the third type, the method further includes: Sending an analysis instruction to the intelligent base station according to the target communication protocol, so that the intelligent base station performs a static data training task according to the sensor data to obtain a target training result; The target training result is received, which is sent by the intelligent base station according to the target communication protocol.

6. A data processing method, characterized in that: Applied to an intelligent base station, the intelligent base station performs data communication with a cleaning robot based on a target communication protocol, and the cleaning robot and the intelligent base station both include a data processing system, including: Receiving sensor data sent by the cleaning robot; Training the sensor data according to a preset data training model in the data processing system to obtain a target training result; The target training result is transmitted back to the cleaning robot.

7. The method according to claim 6, characterized in that The step of transmitting the target training result back to the cleaning robot includes: If an analysis instruction sent by the cleaning robot is received, the target training result is sent to the intelligent base station according to the target communication protocol; If the analysis instruction sent by the cleaning robot is not received, the target training result is transmitted to the cleaning robot when the cleaning robot returns to the intelligent base station for charging.

8. A data processing system, characterized in that: Applied to cleaning robots, including: A collection module, used for collecting sensor data; An analysis module, used for analyzing the effect type of the sensor data; A first execution module, configured to execute a real-time analysis task according to the sensor data if the action type of the sensor data is the first type; The second execution module is used to send the sensor data to the intelligent base station if the action type of the sensor data is the second type, so that the intelligent base station trains the sensor data according to a preset data training model.

9. A data processing system, characterized in that: Applied to intelligent base stations, including: A receiving module, used for receiving sensor data sent by the cleaning robot; A training module, used to train the sensor data according to a preset data training model in the data processing system to obtain a target training result; A feedback module is used to transmit the target training result back to the cleaning robot.

10. A cleaning device, characterized in that: It comprises a cleaning robot and an intelligent base station, wherein the cleaning robot is used to execute the data processing method described in any one of claims 1 to 5, and the intelligent base station is used to execute the data processing method described in any one of claims 6 or 7.

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