Cleaning strategy determination method and device, program product and electronic equipment

Abstract

The invention provides a cleaning strategy determination method and device, a program product and electronic equipment, and relates to the technical field of smart home. The method comprises the following steps: acquiring environment data of a working area of the cleaning equipment; determining a first cleaning strategy according to the environmental data; the first cleaning strategy comprises initial cleaning parameters; presenting the first cleaning strategy; adjusting guide information aiming at the initial cleaning parameters is provided, and first feedback information input by a user based on the adjusting guide information is obtained; and adjusting the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy. The second cleaning strategy can be matched with the environment characteristics of the working area and the requirements or preferences of the user, the limitation of a preset or fixed cleaning mode is broken through, high flexibility is achieved, and the diversified and personalized requirements of the user are met.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of smart home, and in particular, to a cleaning strategy determination method, device, program product and electronic device. BACKGROUND

[0002] With the rapid development and popularization of smart home, cleaning devices such as sweeping robots and floor washing machines have become common devices in many families. In the related art, the cleaning strategy of a cleaning device is usually fixed, for example, after a user selects from several preset modes (such as "normal mode" and "powerful mode") on a mobile phone application (application), the cleaning device fixedly adopts the mode selected by the user for cleaning. This is difficult to meet the diversified and personalized needs of users, resulting in poor cleaning effect. SUMMARY

[0003] The present disclosure provides a cleaning strategy determination method, device, program product and electronic device to at least partially solve the technical problem of a fixed cleaning strategy in the related art.

[0004] According to a first aspect of the present disclosure, a cleaning strategy determination method is provided, the method comprising: obtaining environmental data of a working area of a cleaning device; determining a first cleaning strategy according to the environmental data; the first cleaning strategy comprising initial cleaning parameters; presenting the first cleaning strategy; providing adjustment guide information for the initial cleaning parameters, and obtaining first feedback information input by a user based on the adjustment guide information; and adjusting the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy.

[0005] According to a second aspect of the present disclosure, a cleaning strategy determination device is provided, the device comprising: an environmental data obtaining module configured to obtain environmental data of a working area of a cleaning device; a first cleaning strategy determining module configured to determine a first cleaning strategy according to the environmental data; the first cleaning strategy comprising initial cleaning parameters; a first cleaning strategy presenting module configured to present the first cleaning strategy; an adjustment guide information interaction module configured to provide adjustment guide information for the initial cleaning parameters, and obtain first feedback information input by a user based on the adjustment guide information; and a second cleaning strategy determining module configured to adjust the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy.

[0006] According to a third aspect of this disclosure, a user equipment is provided, comprising: a communication unit configured to communicate with a cleaning device to acquire environmental data of a work area collected by the cleaning device; and a processing unit configured to: determine a first cleaning strategy based on the environmental data; the first cleaning strategy including initial cleaning parameters; present the first cleaning strategy; provide adjustment guidance information for the initial cleaning parameters and acquire first feedback information input by a user based on the adjustment guidance information; and adjust the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy.

[0007] According to a fourth aspect of this disclosure, a cleaning device is provided, comprising: a body; a sensing unit disposed within or on the body and configured to collect environmental data of the working area of ​​the cleaning device; and a control unit disposed within the body and configured to: determine a first cleaning strategy based on the environmental data; the first cleaning strategy including initial cleaning parameters; present the first cleaning strategy; provide adjustment guidance information for the initial cleaning parameters and obtain first feedback information input by a user based on the adjustment guidance information; and adjust the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy.

[0008] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method of the first aspect described above and possible implementations thereof.

[0009] According to a sixth aspect of this disclosure, an electronic device is provided, including a processor and a memory; wherein the memory is used to store executable instructions of the processor; the processor is configured to perform the method of the first aspect described above and possible implementations thereof via executing the executable instructions.

[0010] The technical solution disclosed herein has the following beneficial effects: On the one hand, a preliminary cleaning strategy is determined based on environmental data of the work area, and adjustment guidance information is provided accordingly. A second cleaning strategy is dynamically determined based on the user's initial feedback. This second cleaning strategy matches the environmental characteristics of the work area with the user's needs or preferences, breaking through the limitations of preset or fixed cleaning modes, offering greater flexibility, and meeting diverse and personalized user needs. On the other hand, since the second cleaning strategy is confirmed and adjusted by the user, it ensures that the cleaning equipment performs cleaning tasks in a way that best meets the user's expectations, effectively avoiding repeated cleaning or omissions caused by inappropriate cleaning strategies, improving the efficiency of the cleaning equipment and the utilization rate of system resources, and enhancing cleaning results. Furthermore, presenting the abstract first cleaning strategy to the user, along with understandable adjustment guidance information, allows the user to better understand how the cleaning equipment works and participate in the cleaning strategy formulation process. This breaks the limitations of traditional "one-way execution" by cleaning equipment, improving the transparency of cleaning decisions and user understanding, especially suitable for user groups unfamiliar with smart devices (such as the elderly), thus improving the user experience. Attached Figure Description

[0011] Figure 1 This diagram illustrates a system architecture of an operating environment according to an embodiment of the present disclosure. Figure 2 A flowchart illustrating a cleaning strategy determination method according to an embodiment of this disclosure is shown; Figure 3 A flowchart illustrating a cleaning strategy determination method according to an embodiment of this disclosure is shown; Figure 4 A flowchart illustrating a cleaning strategy determination method according to an embodiment of this disclosure is shown; Figure 5 A schematic diagram of a cleaning strategy determination device according to an embodiment of the present disclosure is shown; Figure 6 A schematic diagram of the structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0012] Exemplary embodiments of this disclosure will be described more fully below with reference to the accompanying drawings.

[0013] The accompanying drawings are schematic diagrams of the present disclosure and are not necessarily drawn to scale. Some of the block diagrams shown in the accompanying drawings may be functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software form, or in hardware modules or integrated circuits, or in networks, processors or microcontrollers. The embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein. The features, structures or characteristics described in the present disclosure can be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to give a full understanding of the embodiments of the present disclosure. However, those skilled in the art should realize that one or more specific details can be omitted when implementing the technical solutions of the present disclosure, or other methods, components, devices, steps, etc. can be used to replace one or more specific details.

[0014] In the related art, the cleaning strategy of cleaning devices is usually relatively fixed. For example, after the user selects from several preset modes (such as "normal mode", "power mode") on the mobile phone App, the cleaning device fixedly uses the mode selected by the user for cleaning. This makes it difficult to meet the diverse and personalized needs of users, resulting in poor cleaning effects.

[0015] In view of the above problems, embodiments of the present disclosure provide a method for determining a cleaning strategy.

[0016] Figure 1 The system architecture diagram of the operating environment of the present disclosure is shown. The system architecture includes a user device 110, a cleaning device 120, and a computing device 130. Among them, the user device 110 can be a mobile phone, a tablet computer, a personal computer, a smart wearable device, etc., and the user can use the user device 110 to interact with the cleaning device 120. For example, the user device 110 runs the App supporting the cleaning device 120, and the user can input instructions for controlling the cleaning device 120 in the App, or view the first cleaning strategy, the second cleaning strategy, etc. The cleaning device 120 can be a floor sweeping robot, a floor washing machine, etc. The computing device 130 can be a server, an edge device, a personal computer, etc. Compared with the user device 110 or the cleaning device 120, the computing device 130 has stronger computing power and can deploy and run large-scale machine learning models, such as large language models (LLMs), vision language models (VLMs), etc.

[0017] The user equipment 110, cleaning equipment 120, and computing device 130 can establish a communication connection via wired or wireless means to transmit data. This disclosure does not limit the specific topology or connection method. For example, the user equipment 110 and cleaning equipment 120 can be connected via Bluetooth, with the user equipment 110 sending control commands to the cleaning equipment 120 and the cleaning equipment 120 sending environmental data of the working area to the user equipment 110. Alternatively, the user equipment 110 and computing device 130 can be connected via a mobile network, with the user equipment 110 sending environmental data, first feedback information, etc., to the computing device, and the computing device sending a first cleaning strategy, a second cleaning strategy, etc., to the user equipment 110.

[0018] In one implementation, the cleaning strategy determination method can be executed by user equipment 110. For example, user equipment 110 acquires environmental data collected and transmitted by cleaning equipment 120, and determines a first cleaning strategy based on the environmental data. This can be achieved by processing the environmental data using a locally deployed lightweight machine learning model (such as a lightweight LLM, VLM, etc.) to obtain the first cleaning strategy, or by transmitting the environmental data to a computing device 130 with a deployed machine learning model and receiving the first cleaning strategy returned by the computing device 130. User equipment 110 presents the first cleaning strategy through voice, video, text, or actual demonstrations, provides guidance information for adjusting initial cleaning parameters, and acquires first feedback information input by the user based on the adjustment guidance information. Based on the first feedback information, user equipment 110 adjusts the first cleaning strategy to obtain a second cleaning strategy. This can be achieved by processing the first feedback information using a locally deployed lightweight machine learning model to obtain the second cleaning strategy, or by transmitting the first feedback information to the computing device 130 with a deployed machine learning model and receiving the second cleaning strategy returned by the computing device 130.

[0019] In one implementation, the cleaning strategy determination method can be performed by a cleaning device 120. For example, the cleaning device 120 collects environmental data of the work area and determines a first cleaning strategy based on the environmental data. This can be achieved by processing the environmental data using a locally deployed lightweight machine learning model, or by transmitting the environmental data to a user device 110 or computing device 130 that has a machine learning model deployed thereon, and receiving the first cleaning strategy returned by the user device 110 or computing device 130. The cleaning device 120 presents the first cleaning strategy, such as by displaying text or video of the first cleaning strategy through its own display unit, by playing the first cleaning strategy via voice, or by demonstrating the first cleaning strategy through a physical demonstration. Users can interact with the cleaning device 120 by operating on the display unit or physical buttons, or by voice, to input first feedback information. After obtaining the first feedback information, the cleaning device 120 can process the first feedback information through a locally deployed lightweight machine learning model to obtain a second cleaning strategy, or transmit the first feedback information to the user device 110 or computing device 130 with a machine learning model deployed, and receive the second cleaning strategy returned by the user device 110 or computing device 130.

[0020] It should be noted that, Figure 1 The system architecture shown is merely an example, and this disclosure is not limited to it. It can be adapted to meet specific needs. Figure 1 The system architecture allows for the addition, removal, or adjustment of devices. For example, cleaning device 120 can execute the cleaning strategy determination method independently without user device 110 or computing device 130. Alternatively, either user device 110 or cleaning device 120 can execute the cleaning strategy determination method without computing device 130. Or, cleaning device 120 can execute the cleaning strategy determination method through interaction with computing device 130 without user device 110. Alternatively, any number and type of cleaning devices 120 can be configured to determine cleaning strategies for different cleaning devices 120. Alternatively, to improve communication quality between different devices, gateways, routers, or other devices can be added to the system. Alternatively, computing device 130 can be configured as a distributed cluster, with different units or machine learning models deployed on multiple nodes in the cluster, such as node 1 deploying a video or image data processing unit, node 2 deploying a voice data processing unit, node 3 deploying a text data processing unit, and node 4 deploying a multimodal fusion processing unit.

[0021] Figure 2 An exemplary flow of a cleaning strategy determination method is shown, including the following steps S210 to S250: Step S210: Obtain environmental data of the working area of ​​the cleaning equipment; Step S220: Determine a first cleaning strategy based on environmental data; the first cleaning strategy includes initial cleaning parameters; Step S230, presenting the first cleaning strategy; Step S240: Provide adjustment guidance information for the initial cleaning parameters and obtain first feedback information from the user based on the adjustment guidance information; Step S250: Adjust the first cleaning strategy based on the first feedback information to obtain the second cleaning strategy.

[0022] based on Figure 2 The method has two aspects. First, it initially determines a first cleaning strategy based on environmental data of the work area, and provides adjustment guidance information on this basis. Then, it dynamically determines a second cleaning strategy based on the user's initial feedback. This allows the second cleaning strategy to match the environmental characteristics of the work area with the user's needs or preferences, breaking through the limitations of preset or fixed cleaning modes, offering greater flexibility, and meeting diverse and personalized user needs. Second, because the second cleaning strategy is confirmed and adjusted by the user, it ensures that the cleaning equipment performs cleaning tasks in a way that best meets the user's expectations. This effectively avoids repeated cleaning or cleaning omissions caused by inappropriate cleaning strategies, improving the working efficiency of the cleaning equipment and the utilization rate of system resources, and enhancing cleaning results. Third, presenting the abstract first cleaning strategy to the user, along with understandable adjustment guidance information, allows the user to better understand how the cleaning equipment works and participate in the cleaning strategy formulation process. This breaks the limitations of traditional "one-way execution" of cleaning equipment, improves the transparency of cleaning decisions and user understanding, and is particularly suitable for user groups unfamiliar with smart devices (such as the elderly), thus improving the user experience.

[0023] The following describes, in conjunction with one or more embodiments and related accompanying drawings, Figure 2 Each step is explained in detail.

[0024] In step S210, environmental data of the working area of ​​the cleaning equipment is acquired.

[0025] The work area is the area where a cleaning strategy needs to be determined; it can be the entire home area or a room within it. For example, the cleaning equipment supports setting different cleaning strategies for different rooms. Each room can be used as a work area, and the cleaning strategy determination method can be executed to obtain a second cleaning strategy for each room.

[0026] Environmental data refers to relevant information affecting the cleaning process within the work area. This data may include the spatial structure of the work area, its type (e.g., whether it belongs to a living room, bedroom, or kitchen), floor conditions (e.g., floor type and material), and furniture distribution. In one embodiment, the cleaning equipment includes one or more sensing units (e.g., sensors). These sensing units collect data from the work area according to a preset collection frequency and range. After collection, the environmental data can be preliminarily processed and stored to ensure data integrity and usability.

[0027] For example, after the cleaning equipment is started, its sensing unit automatically enters working mode, capturing information about the work area from all directions. The cleaning equipment uses a LiDAR sensor to collect scene sensing data and constructs a map of the work area based on this data. It also uses a camera module to collect image or video data of the work area and determines the ground conditions based on this data. Specifically, the LiDAR emits laser beams into the work area at a preset scanning angle and frequency. When the laser beam encounters an object, it reflects and is received by the LiDAR. Based on the propagation time and angle of the laser beam, the distance and orientation of objects within the work area are calculated to form scene sensing data. The cleaning equipment can then filter, stitch, and transform the scene sensing data to remove redundant and interfering data, integrating the effective data to construct a map of the work area. The camera module can include components such as a camera, image sensor, and graphics signal processor. It can capture image or video data of the work area, identify ground types and materials in the images or videos to determine ground conditions, and identify objects (such as furniture) within the work area. The object recognition results are combined with scene sensor data to improve the accuracy of map data. Furthermore, it can determine the area type based on object recognition results and label objects in the map data, such as identifying the specific type of an object. Combining the map data of the work area with ground condition information forms relatively complete environmental data.

[0028] Continue to refer to Figure 2 In step S220, a first cleaning strategy is determined based on environmental data; the first cleaning strategy includes initial cleaning parameters.

[0029] In this embodiment of the disclosure, the cleaning strategy can be a set of various cleaning parameters. By combining different cleaning parameters, a complete cleaning strategy is formed. The cleaning parameters include, but are not limited to: cleaning mode (such as vacuuming, mopping), cleaning intensity, cleaning frequency, water output, roller brush speed, whether to clean low spaces, key cleaning areas (such as corners, under beds), areas to avoid (such as pet areas, toy areas), cleaning route pattern (such as "Z" route pattern, "U" route pattern, etc.), whether the mop is raised, whether to sterilize, etc.

[0030] The first cleaning strategy is a preliminary cleaning strategy determined for the work area, including initial cleaning parameters, i.e., initial values ​​set under different cleaning parameters. In one implementation, cleaning rules can be preset, such as the correspondence between environmental categories and cleaning parameters (e.g., cleaning parameter values ​​or ranges corresponding to each environmental category). After obtaining environmental data, the first cleaning strategy is determined based on the cleaning rules. For example, the environmental category of the work area is determined based on the environmental data, and then the cleaning parameters corresponding to that environmental category are determined, i.e., the initial cleaning parameters are obtained. The first cleaning strategy is formed by combining the initial cleaning parameters.

[0031] In one implementation, determining the first cleaning strategy based on environmental data includes the following steps: Environmental data is input into a first machine learning model, and a first cleaning strategy is determined based on the first inference information output by the first machine learning model.

[0032] The first machine learning model is a machine learning model capable of environmental data analysis and cleaning strategy generation, and can be deployed on any one or more of cleaning equipment, user equipment, and computing devices. This disclosure does not limit the specific type and structure of the first machine learning model; for example, CNN (Convolutional Neural Network), Transformer, LLM, etc., can be used as the first machine learning model. Furthermore, the first machine learning model can be a pre-trained model or a model fine-tuned on a specific dataset. For example, a cleaning strategy dataset can be obtained, including sample environmental data (such as environmental data from different areas obtained from historical cleaning data) and cleaning strategy annotation data (such as cleaning strategies manually annotated based on sample environmental data). With a pre-trained machine learning model obtained, the cleaning strategy dataset can be used to fine-tune the machine learning model. In addition, a cleaning knowledge base (such as including cleaning rules) can be set up, allowing the machine learning model to learn the knowledge in the cleaning knowledge base. After training, a practically usable first machine learning model is obtained.

[0033] Environmental data can be directly input into the first machine learning model, or it can be preprocessed before being input into the model. Preprocessing methods include, but are not limited to, format standardization (e.g., converting non-numerical environmental data to numerical values, normalizing numerical values, etc.) and noise reduction (e.g., filtering sensor interference data). The first machine learning model extracts features from the environmental data and performs inference calculations based on these features, outputting first inference information. In one implementation, a prompt can be generated based on the environmental data. This prompt prompts the first machine learning model to output a cleaning strategy based on the environmental data. For example, the prompt can be obtained by combining environmental data, cleaning strategy, and task information. The prompt is input into the first machine learning model, which outputs first inference information. The first inference information may include initial cleaning parameters. The first inference information is further parsed and integrated into a structured first cleaning strategy.

[0034] The first machine learning model can accurately mine the features of environmental data and generate a first cleaning strategy that is highly adaptable to the work area, thus ensuring the quality of the cleaning strategy.

[0035] In one implementation, reference Figure 3 As shown, the above-described determination of the first cleaning strategy based on environmental data includes the following steps S310 to S330: Step S310: Determine the first cleaning time applicable to the work area; Step S320: Obtain the user's second feedback information regarding the first cleaning time; Step S330: Determine a first cleaning strategy based on environmental data and second feedback information; the cleaning time in the first cleaning strategy is a first cleaning time, or a second cleaning time obtained by adjusting the first cleaning time according to the second feedback information.

[0036] The cleaning time can include the cleaning cycle (i.e., how often to clean), the specific time of each cleaning session (e.g., cleaning at 10:00 AM), and the cleaning duration. The first cleaning time is a preliminary determination based on environmental data (e.g., area) of the work area. For example, if the user device determines the area type of the work area based on the environmental data, it retrieves the commonly used cleaning time corresponding to that area type from the cleaning rules and uses it as the first cleaning time. The first cleaning time is communicated to the user via voice, text, or other means. The user can input second feedback information regarding the first cleaning time. The second feedback information can be a confirmation message, indicating that the user confirms the use of the first cleaning time, or it can be an adjustment message, i.e., adjusting the cleaning cycle, cleaning duration, etc., within the first cleaning time. If the second feedback information is a confirmation message, the cleaning time in the first cleaning strategy becomes the first cleaning time; if the second feedback information is an adjustment message, the first cleaning time is adjusted accordingly to obtain the second cleaning time. For example, once the cleaning equipment determines a first cleaning time, it announces the time via voice, such as, "We have set your bedroom to be cleaned at 10:00 AM every day, with each cleaning session lasting approximately 20 minutes." The user can provide feedback via voice, such as by inputting "Confirm this time" or "Please change to cleaning the bedroom every half day." Based on this feedback, the cleaning equipment confirms the adoption of the first cleaning time or adjusts it to obtain a second cleaning time. Simultaneously, it determines initial cleaning parameters based on environmental data. These initial cleaning parameters are then combined with the previously determined first or second cleaning time to obtain a first cleaning strategy.

[0037] based on Figure 3 This method allows users to conveniently and flexibly adjust cleaning time, improving the flexibility and practicality of the first cleaning strategy and avoiding negative impacts on user experience due to inappropriate cleaning time.

[0038] This disclosure does not limit the specific form of the first cleaning strategy. For example, the first cleaning strategy can be represented in the form of text or a set of parameters. Alternatively, the first cleaning strategy can be represented as a video, such as a demonstration video of the first cleaning strategy included in the first inference information output by the first machine learning model.

[0039] In one implementation, initial guidance information may be provided before executing step S230. This initial guidance information guides the user to the work area and / or guides the user to begin developing a cleaning strategy for the work area. The initial guidance information may be provided by the user device or cleaning device in any form, such as text or voice, and its purpose is to guide the user to the work area and / or begin developing a cleaning strategy, ensuring that the user can participate in strategy confirmation and adjustment on-site.

[0040] For example, after the cleaning equipment completes a scan of the home environment for the first time, or when it is time to update the cleaning strategy, the cleaning equipment can automatically start the cleaning strategy formulation task. It can play a voice message saying, "Please follow me to the bedroom to formulate a cleaning strategy for the bedroom." After the user follows to the bedroom, the cleaning equipment will prompt with a voice message, "We have arrived at the bedroom. Please prepare to start formulating a cleaning strategy. I will present you with a preliminary plan," and then present the first cleaning strategy.

[0041] Continue to refer to Figure 2 In step S230, the first cleaning strategy is presented.

[0042] The first cleaning strategy can be presented in a user-perceptible way, so that users can understand the specific content of the first cleaning strategy.

[0043] In one implementation, the above-described first cleaning strategy includes one or more of the following methods: The first cleaning strategy is played via voice. For example, the audio unit (such as a speaker) of the cleaning device or user device plays the content of the first cleaning strategy.

[0044] The first cleaning strategy is presented via video. This can be achieved by obtaining a demonstration video of the first cleaning strategy from the first inference information output by a first machine learning model. Alternatively, a demonstration video can be generated based on the first cleaning strategy, such as by inputting the first cleaning strategy into a video generation model (which could be a first machine learning model, a second machine learning model, or another machine learning model), or by adjusting a preset video based on the first cleaning strategy (e.g., adjusting the cleaning intensity of the equipment in the preset video). The demonstration video is then played through a cleaning device or a user device.

[0045] The first cleaning strategy is displayed in text format. For example, the display unit (such as a screen) of the cleaning equipment or user equipment displays text information about the first cleaning strategy.

[0046] A cleaning demonstration instruction is sent to the cleaning equipment so that the equipment can demonstrate the first cleaning strategy by executing the instruction. The cleaning demonstration instruction includes information about the first cleaning strategy. When executing the instruction, the cleaning equipment can either perform actual cleaning of the work area according to the first cleaning strategy or perform simplified cleaning (i.e., instead of performing a complete cleaning process, each step can be simplified), allowing the user to intuitively see the actual execution of the first cleaning strategy.

[0047] Continue to refer to Figure 2 In step S240, adjustment guidance information for the initial cleaning parameters is provided, and first feedback information is obtained from the user based on the adjustment guidance information.

[0048] The adjustment guidance information is used to enable users to quickly adjust the initial cleaning parameters, and the adjustment information entered by the user serves as the first feedback information. For example, corresponding adjustment guidance information can be generated for each initial cleaning parameter, and corresponding adjustment controls (such as a numerical slider) or optional values ​​can be provided.

[0049] In one implementation, the above-described guidance information for adjusting initial cleaning parameters includes one or more of the following methods: Adjustment guidance information is played via voice playback. For example, after the cleaning equipment or user device presents the initial cleaning strategy, it prompts, "If you are not satisfied with the cleaning effect, you can adjust the cleaning parameters." Then, it plays adjustment guidance information corresponding to each initial cleaning parameter in sequence, such as "Adjust cleaning mode?", "Increase cleaning intensity?", "Increase cleaning under the bed area?", etc. The user can respond with voice in sequence, thereby obtaining the user's first feedback information input by voice.

[0050] Adjustment guidance information is displayed on the display unit. For example, cleaning equipment or user equipment displays adjustment guidance information in the form of text, graphics, images or videos on its display unit. Users can make adjustments on the display unit (such as a touch screen), or by using physical buttons, or by using voice control, thereby obtaining initial feedback information.

[0051] In one implementation, the above-described guidance information for adjusting initial cleaning parameters includes the following steps: The prompt message is determined based on the initial cleaning parameters; The prompts are input into a second machine learning model, which then provides guidance on adjusting the initial cleaning parameters.

[0052] The second machine learning model is a machine learning model capable of generating adjustment guidance information, and can be deployed on one or more of cleaning equipment, user equipment, and computing equipment. This disclosure does not limit the specific type and structure of the second machine learning model; for example, an LLM (Limited Least Mesh) model can be used. Furthermore, the second machine learning model can be a pre-trained model or a model fine-tuned on a specific dataset. For example, an adjustment guidance information dataset can be obtained, including sample cleaning parameters (such as initial cleaning parameters for different areas obtained from historical cleaning data) and adjustment guidance information annotation data (such as manually annotated adjustment guidance information for sample cleaning parameters). With a pre-trained machine learning model obtained, the adjustment guidance information dataset is used to fine-tune the machine learning model. In addition, a cleaning knowledge base (such as knowledge related to cleaning parameters) can be set up, allowing the machine learning model to learn the knowledge in the cleaning knowledge base. After training, a practically usable second machine learning model is obtained.

[0053] The initial cleaning parameters of the first cleaning strategy generate prompt information, which is descriptive information used to guide the second machine learning model in generating adjustment guidance information. This information may include parameter types, current values, associated environmental factors, and a task description for generating the adjustment guidance information. The prompt information is input into the second machine learning model, which outputs adjustment guidance information. This output may include text guiding the user to adjust the cleaning parameters and explanations of associated environmental factors.

[0054] For example, the second machine learning model is a multimodal large model. In the first cleaning strategy of the cleaning equipment, the initial cleaning parameters include "mop wiping frequency near curtains: 2 times / m". 2 After inputting the prompt information into the second machine learning model, the second machine learning model can output adjustment guidance information for each cleaning parameter, such as "Current mop wiping frequency near the curtains: 2 times / m". 2 "Do we need to reduce the frequency of mopping near the curtains?" This message is presented to the user via voice playback.

[0055] Through the above methods, the adjustment guidance information is generated by the second machine learning model based on the prompts, which is highly targeted, reduces irrelevant and redundant options, improves user adjustment efficiency, and adapts to the environmental characteristics of the work area, which is conducive to improving the cleaning effect.

[0056] In one implementation, when providing guidance information for adjusting initial cleaning parameters, the cleaning strategy determination method further includes the following steps: Provides a preview of the adjustment effect based on the current cleaning parameters to be adjusted in the adjustment guidance information.

[0057] The adjustment guidance information can be presented to the user one by one, such as one adjustment guidance message for each cleaning parameter. If the user selects a particular adjustment guidance message, the cleaning parameter in that message becomes the current cleaning parameter to be adjusted. Alternatively, the user device or cleaning device can provide each adjustment guidance message sequentially, such as displaying adjustment guidance messages for cleaning mode, cleaning intensity, cleaning frequency, etc., in a preset order according to the initial cleaning parameters. The cleaning parameter in the currently displayed adjustment guidance message becomes the current cleaning parameter to be adjusted. A preview of the adjustment effect is provided based on the current cleaning parameter to be adjusted, using visualization or voice description to show the user the corresponding cleaning effect after adjusting the parameter. This provides the user with intuitive adjustment parameters, helping them make accurate judgments and reducing blind adjustments.

[0058] In one implementation, reference Figure 4As shown, the above-mentioned preview of the adjustment effect based on the current cleaning parameters to be adjusted in the adjustment guidance information includes the following steps S410 and S420: Step S410: Based on the pre-configured correspondence between cleaning parameters and display parameters, determine the target display parameter corresponding to the current cleaning parameter to be adjusted; Step S420: Display the effect of changing the target display parameters in the simulated cleaning screen of the work area to indicate the preview of the adjustment effect.

[0059] The user equipment or cleaning equipment can display a simulated cleaning screen of the work area via a display unit. This screen may include a simulated map of the work area and simulated cleaning effect screens of different sub-areas. For example, based on the cleaning intensity and water output in the cleaning strategy, the cleanliness and humidity of the floor in different sub-areas are determined and displayed using different colors, textures, and filling effects. The simulated cleaning screen can be static or dynamic, and this disclosure does not limit this. For example, environmental data and cleaning parameters of the work area (such as initial cleaning parameters or cleaning parameters set by the user during the adjustment process) can be input into a second machine learning model to output a simulated cleaning screen.

[0060] Display parameters are used to display the simulated cleaning screen. These parameters can include the effect area (the area affected by changes in cleaning parameters), color parameters, texture parameters, fill effects, and dynamic effects. The correspondence between cleaning parameters and display parameters can include which display parameters correspond to different cleaning parameters, and the display parameter values ​​corresponding to different values ​​of the cleaning parameters. Based on the cleaning parameter to be adjusted, the corresponding display parameter, i.e., the target display parameter, is determined by querying the correspondence. Then, the effect of changing the target display parameter is displayed in the simulated cleaning screen of the work area. For example, if the user does not adjust the current cleaning parameter, the target display parameter value can be automatically determined according to the result of increasing or decreasing the current cleaning parameter, and the corresponding screen effect is displayed (e.g., if the current cleaning parameter to be adjusted is cleaning intensity, and the user has not actually adjusted the cleaning intensity, the screen effect corresponding to cleaning intensity plus one is automatically displayed). If the user adjusts the current cleaning parameter, such as by dragging the sliding value axis, the corresponding target display parameter value is synchronously determined according to the adjusted value, and the corresponding screen effect is displayed. For example, if the parameter to be adjusted is the water output, the corresponding display parameters include the filling effect of the floor area. When the user drags the water output slider to adjust the water output value, the filling effect of the floor area in the simulated cleaning screen changes synchronously (e.g., when the water output is increased, the floor area displays a denser water droplet filling effect). This allows the user to visually preview the adjustment effect during the adjustment process, making it easier to adjust to the optimal value.

[0061] based onFigure 4 The method presents the adjustment effect in a visual simulation of cleaning, and ensures that the changes in the screen effect match the adjustment effect of the cleaning parameters by matching the cleaning parameters with the corresponding relationship between the cleaning parameters and the display parameters. This improves the intuitiveness of the adjustment effect preview, reduces the difficulty for users to understand the cleaning parameters, improves users' ability to make accurate adjustments, and reduces the situation where the adjustment does not meet expectations.

[0062] Continue to refer to Figure 2 In step S250, the first cleaning strategy is adjusted according to the first feedback information to obtain the second cleaning strategy.

[0063] The process involves adjusting the initial cleaning parameters based on the first feedback information to obtain the adjusted cleaning parameters, and then integrating these adjusted parameters to form a structured second cleaning strategy. This second cleaning strategy is the final cleaning strategy.

[0064] In one implementation, adjusting the first cleaning strategy based on the first feedback information to obtain the second cleaning strategy includes the following steps: The first feedback information is input into the second machine learning model, and the second cleaning strategy is determined based on the second inference information output by the second machine learning model.

[0065] Specifically, a second machine learning model can be trained or fine-tuned using a cleaning strategy dataset, enabling it to adjust cleaning parameters or generate cleaning strategies. Hints can be generated based on the first feedback information and input into the second machine learning model to guide it in adjusting the first cleaning strategy. The second inference information output by the second machine learning model can include the adjusted cleaning parameters. This second inference information can be used as the second cleaning strategy, or it can be further analyzed to form a structured second cleaning strategy.

[0066] By analyzing the first feedback information through a second machine learning model, the cleaning parameters can be precisely adjusted, ensuring the rationality and feasibility of the second cleaning strategy.

[0067] This disclosure also provides a cleaning strategy determination apparatus. (See reference...) Figure 5 As shown, the cleaning strategy determination device 500 includes the following modules: The environmental data acquisition module 510 is configured to acquire environmental data of the working area of ​​the cleaning equipment. The first cleaning strategy determination module 520 is configured to determine a first cleaning strategy based on the environmental data; the first cleaning strategy includes initial cleaning parameters. The first cleaning strategy presentation module 530 is configured to present the first cleaning strategy. The adjustment guidance information interaction module 540 is configured to provide adjustment guidance information for the initial cleaning parameters and to obtain first feedback information input by the user based on the adjustment guidance information; The second cleaning strategy determination module 550 is configured to adjust the first cleaning strategy based on the first feedback information to obtain a second cleaning strategy.

[0068] In one implementation, presenting the first cleaning strategy includes one or more of the following methods: playing the first cleaning strategy by voice; presenting the first cleaning strategy by video; displaying the first cleaning strategy by text; sending a cleaning demonstration instruction to the cleaning device so that the cleaning device demonstrates the first cleaning strategy by executing the cleaning demonstration instruction.

[0069] In one embodiment, determining the first cleaning strategy based on the environmental data includes: determining a first cleaning time applicable to the work area; obtaining second feedback information from the user regarding the first cleaning time; determining the first cleaning strategy based on the environmental data and the second feedback information; wherein the cleaning time in the first cleaning strategy is the first cleaning time, or a second cleaning time obtained by adjusting the first cleaning time according to the second feedback information.

[0070] In one implementation, determining the first cleaning strategy based on the environmental data includes: inputting the environmental data into a first machine learning model, and determining the first cleaning strategy based on first inference information output by the first machine learning model.

[0071] In one implementation, providing adjustment guidance information for the initial cleaning parameters includes: determining prompt information based on the initial cleaning parameters; inputting the prompt information into a second machine learning model, and providing adjustment guidance information for the initial cleaning parameters through the second machine learning model.

[0072] In one implementation, adjusting the first cleaning strategy based on the first feedback information to obtain a second cleaning strategy includes: inputting the first feedback information into the second machine learning model, and determining the second cleaning strategy based on the second inference information output by the second machine learning model.

[0073] In one implementation, providing adjustment guidance information for the initial cleaning parameters includes one or more of the following methods: playing the adjustment guidance information via voice; or displaying the adjustment guidance information via a display unit.

[0074] In one embodiment, the first cleaning strategy presentation module 530 is further configured to: provide initial guidance information before presenting the first cleaning strategy; the initial guidance information is used to guide the user to move to the work area and / or guide the user to start formulating a cleaning strategy for the work area.

[0075] In one embodiment, the adjustment guidance information interaction module 540 is further configured to: when providing adjustment guidance information for the initial cleaning parameters, provide a preview of the adjustment effect based on the current cleaning parameters to be adjusted in the adjustment guidance information.

[0076] In one implementation, providing an adjustment effect preview based on the current cleaning parameter to be adjusted in the adjustment guidance information includes: determining the target display parameter corresponding to the current cleaning parameter to be adjusted based on a pre-configured correspondence between cleaning parameters and display parameters; and displaying the effect of changing the target display parameter in the simulated cleaning screen of the work area to represent the adjustment effect preview.

[0077] This disclosure also provides a user equipment, such as... Figure 1 The user equipment 110 shown is described. This user equipment includes: The communication unit is configured to communicate with the cleaning equipment to acquire environmental data of the work area collected by the cleaning equipment. The processing unit is configured to: determine a first cleaning strategy based on the environmental data; the first cleaning strategy includes initial cleaning parameters; present the first cleaning strategy; provide adjustment guidance information for the initial cleaning parameters, and obtain first feedback information input by the user based on the adjustment guidance information; adjust the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy.

[0078] In addition, the processing unit is also configured to perform other method steps of various embodiments of this disclosure.

[0079] This disclosure also provides a cleaning device, such as... Figure 1 The cleaning device 120 shown. The cleaning device includes: Organism; A sensing unit, disposed within or on the body, is configured to collect environmental data of the working area of ​​the cleaning equipment. The control unit, disposed within the machine body, is configured to: determine a first cleaning strategy based on the environmental data; the first cleaning strategy includes initial cleaning parameters; present the first cleaning strategy; provide adjustment guidance information for the initial cleaning parameters, and obtain first feedback information input by the user based on the adjustment guidance information; adjust the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy.

[0080] In addition, the control unit is also configured to perform other method steps of various embodiments of this disclosure.

[0081] This disclosure also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the above-described method.

[0082] In one implementation, the computer program product can be a tangible product, such as a computer-readable storage medium storing a computer program. The readable storage medium can be based on electrical, magnetic, optical, electromagnetic, infrared, or other signals, and includes, but is not limited to: RAM, ROM, magnetic tape, floppy disk, flash memory, hard disk drive (HDD), solid-state drive (SSD), etc. For example, the computer program product can be a non-volatile storage medium storing a computer program, such as a read-only memory (ROM) or NAND flash memory.

[0083] In one implementation, the computer program product can be an intangible product. For example, the computer program product can be a virtual digital product, such as an executable file or installation package containing a computer program.

[0084] Computer program code can be written in one or more programming languages. Examples of programming languages ​​include C, Java, and C++. Program code can execute entirely on the user's computing device, partially on the user's computing device, or as a standalone software package. It can also execute partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, such as a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via an internet connection provided by a mobile network operator).

[0085] Computer programs can be carried or transmitted via signals such as electrical, magnetic, optical, electromagnetic, and infrared rays. Electronic devices can convert the signals carrying computer programs into digital signals, thereby running the computer programs. When a computer program runs on an electronic device, its code is used to cause the electronic device to execute (more specifically, to be executed by the processor of the electronic device) the method steps of various embodiments of this disclosure, such as...Figure 2 The method shown.

[0086] Implementing the above method steps through a computer program achieves the following technical effects: Firstly, a first cleaning strategy is initially determined based on environmental data of the work area, and adjustment guidance information is provided accordingly. A second cleaning strategy is dynamically determined based on the user's initial feedback. This second cleaning strategy matches the environmental characteristics of the work area with the user's needs or preferences, breaking through the limitations of preset or fixed cleaning modes, offering greater flexibility, and meeting diverse and personalized user needs. Secondly, since the second cleaning strategy is confirmed and adjusted by the user, it ensures that the cleaning equipment performs cleaning tasks in a way that best meets the user's expectations, effectively avoiding repeated cleaning or omissions due to inappropriate cleaning strategies, improving the working efficiency of the cleaning equipment and the utilization rate of system resources, and enhancing cleaning effectiveness. Thirdly, presenting the abstract first cleaning strategy to the user, along with understandable adjustment guidance information, allows the user to better understand the working method of the cleaning equipment and participate in the cleaning strategy formulation process. This breaks the limitations of traditional "one-way execution" of cleaning equipment, improves the transparency of cleaning decisions and user understanding, and is particularly suitable for user groups unfamiliar with smart devices (such as the elderly), improving the user experience.

[0087] Exemplary embodiments of this disclosure also provide an electronic device. This electronic device can be any one of the user equipment, cleaning equipment, and computing devices described above. The electronic device includes a processor and a memory. The memory stores executable instructions for the processor, such as computer programs. The processor executes these executable instructions to perform the method steps of various exemplary embodiments of this disclosure.

[0088] The following is for reference. Figure 6 The electronic device is illustrated by way of a general-purpose computing device. It should be understood that... Figure 6 The electronic device 600 shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments disclosed herein.

[0089] like Figure 6 As shown, the electronic device 600 may include: a processor 610, a memory 620, a bus 630, an I / O (input / output) interface 640, and a network adapter 650.

[0090] Memory 620 may include volatile memory, such as RAM 621 and cache unit 622, and may also include non-volatile memory, such as ROM 623. Memory 620 may also include one or more program modules 624, such program modules 624 including, but not limited to: operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. For example, program module 624 may include the modules in the above-described device.

[0091] The processor 610 may include one or more processing units, such as an AP (Application Processor), a modem processor, a GPU (Graphics Processing Unit), an ISP (Image Signal Processor), a controller, an encoder, a decoder, a DSP (Digital Signal Processor), a baseband processor, and / or an NPU (Neural-Network Processing Unit).

[0092] The processor 610 can be used to execute executable instructions stored in the memory 620 to perform method steps of various embodiments of this disclosure, such as... Figure 2 The method.

[0093] By executing the above method steps through processor 610, the following technical effects are achieved: Firstly, a first cleaning strategy is initially determined based on environmental data of the work area, and adjustment guidance information is provided on this basis. A second cleaning strategy is dynamically determined based on the first feedback information input by the user. This second cleaning strategy can match the environmental characteristics of the work area with the user's needs or preferences, breaking through the limitations of preset or fixed cleaning modes, possessing strong flexibility, and meeting the diverse and personalized needs of users. Secondly, since the second cleaning strategy is confirmed and adjusted by the user, it ensures that the cleaning equipment performs cleaning tasks in a way that best meets the user's expectations, thereby effectively avoiding repeated cleaning or cleaning omissions caused by inappropriate cleaning strategies, improving the working efficiency of the cleaning equipment and the utilization rate of system resources, and enhancing the cleaning effect. Thirdly, presenting the abstract first cleaning strategy to the user and providing user-understandable adjustment guidance information allows the user to better understand the working method of the cleaning equipment and participate in the cleaning strategy formulation process, breaking the limitations of the traditional "one-way execution" of cleaning equipment, improving the transparency of cleaning decisions and user understanding, especially suitable for user groups unfamiliar with smart devices (such as the elderly), thus improving the user experience.

[0094] Bus 630 is used to connect different components of electronic device 600 and may include a data bus, an address bus and a control bus.

[0095] Electronic device 600 can communicate with one or more external devices 700 (such as keyboard, mouse, external controller, etc.) through I / O interface 640.

[0096] Electronic device 600 can communicate with one or more networks via network adapter 650. For example, network adapter 650 can provide mobile communication solutions such as 3G / 4G / 5G, or wireless communication solutions such as wireless LAN, Bluetooth, and near-field communication. Network adapter 650 can communicate with other modules of electronic device 600 via bus 630.

[0097] although Figure 6 As not shown in the diagram, other hardware and / or software modules may also be configured in the electronic device 600, including but not limited to: a display, microcode, device driver, redundant processor, external disk drive array, RAID (Redundant Arrays of Independent Disks) system, tape drive, and data backup storage system.

[0098] As can be seen from the above, the technical solutions disclosed herein can be implemented as methods, apparatus, systems, computer program products, storage media, electronic devices, etc. Those skilled in the art will understand that various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software aspects, which may be referred to as "circuit," "module," or "system," respectively.

[0099] It should be understood that this disclosure is not limited to the specific methods, steps, or structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. Those skilled in the art will readily conceive of other embodiments based on the specific implementations provided in this disclosure. Therefore, the specific implementations provided in this disclosure are merely exemplary, and the scope and spirit of this disclosure are indicated by the claims, and should cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary technical means in the art not disclosed in this disclosure.

Claims

1. A cleaning policy determination method characterized by comprising: The method comprises: obtaining environmental data of a working area of a cleaning device; determining a first cleaning strategy according to the environmental data; the first cleaning strategy comprises initial cleaning parameters; presenting the first cleaning strategy; providing adjustment guide information for the initial cleaning parameters, and obtaining first feedback information input by a user based on the adjustment guide information; adjusting the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy.

2. The method of claim 1, wherein, The presentation of the first cleaning strategy comprises one or more of the following modes: playing the first cleaning strategy through voice; presenting the first cleaning strategy through video; displaying the first cleaning strategy in text mode; sending a cleaning demonstration instruction to the cleaning device, so that the cleaning device demonstrates the first cleaning strategy by executing the cleaning demonstration instruction.

3. The method of claim 1, wherein, The determination of the first cleaning strategy according to the environmental data comprises: determining a first cleaning time suitable for the working area; obtaining second feedback information of the user for the first cleaning time; determining the first cleaning strategy according to the environmental data and the second feedback information; the cleaning time in the first cleaning strategy is the first cleaning time, or a second cleaning time obtained by adjusting the first cleaning time according to the second feedback information.

4. The method of claim 1, wherein, The determination of the first cleaning strategy according to the environmental data comprises: inputting the environmental data into a first machine learning model, and determining the first cleaning strategy according to first inference information output by the first machine learning model.

5. The method of claim 1, wherein, The provision of the adjustment guide information for the initial cleaning parameters comprises: determining prompt information according to the initial cleaning parameters; inputting the prompt information into a second machine learning model, and providing the adjustment guide information for the initial cleaning parameters through the second machine learning model.

6. The method of claim 5, wherein, The adjustment of the first cleaning strategy according to the first feedback information to obtain the second cleaning strategy comprises: inputting the first feedback information into the second machine learning model, and determining the second cleaning strategy according to second inference information output by the second machine learning model.

7. The method of claim 1, wherein, The provision of the adjustment guide information for the initial cleaning parameters comprises one or more of the following modes: playing the adjustment guide information through voice; displaying the adjustment guide information through a display unit.

8. The method of claim 1, wherein, Before presenting the first cleaning strategy, the method further comprises: providing initial guide information; the initial guide information is used to guide the user to move to the working area, and / or guide the user to start formulating a cleaning strategy for the working area.

9. The method according to any one of claims 1 to 8, characterized in that, When providing the adjustment guide information for the initial cleaning parameters, the method further comprises: providing an adjustment effect preview based on a current to-be-adjusted cleaning parameter in the adjustment guide information.

10. The method of claim 9, wherein, The provision of the adjustment effect preview based on the current to-be-adjusted cleaning parameter in the adjustment guide information comprises: determining a target display parameter corresponding to the current to-be-adjusted cleaning parameter based on a preconfigured correspondence between cleaning parameters and display parameters; displaying a picture effect of changing the target display parameter in a simulated cleaning picture of the working area to represent the adjustment effect preview.

11. A cleaning policy determination apparatus characterized by comprising: The device comprises: An environment data obtaining module configured to obtain environment data of a working area of a cleaning device; A first cleaning strategy determining module configured to determine a first cleaning strategy according to the environment data; the first cleaning strategy comprises initial cleaning parameters; A first cleaning strategy presenting module configured to present the first cleaning strategy; An adjustment guidance information interacting module configured to provide adjustment guidance information for the initial cleaning parameters, and obtain first feedback information input by a user based on the adjustment guidance information; A second cleaning strategy determining module configured to adjust the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy.

12. The apparatus of claim 11, wherein, The presenting of the first cleaning strategy comprises one or more of the following manners: playing the first cleaning strategy through voice; presenting the first cleaning strategy through video; displaying the first cleaning strategy in text mode; sending a cleaning demonstration instruction to the cleaning device, so that the cleaning device demonstrates the first cleaning strategy by executing the cleaning demonstration instruction.

13. The apparatus of claim 11, wherein, The determining of the first cleaning strategy according to the environment data comprises: determining a first cleaning time suitable for the working area; obtaining second feedback information of a user for the first cleaning time; determining the first cleaning strategy according to the environment data and the second feedback information; the cleaning time in the first cleaning strategy is the first cleaning time, or a second cleaning time obtained by adjusting the first cleaning time according to the second feedback information.

14. The apparatus of claim 11, wherein, The determining of the first cleaning strategy according to the environment data comprises: inputting the environment data into a first machine learning model, and determining the first cleaning strategy according to first inference information output by the first machine learning model.

15. The apparatus of claim 11, wherein, The providing of the adjustment guidance information for the initial cleaning parameters comprises: determining prompt information according to the initial cleaning parameters; inputting the prompt information into a second machine learning model, and providing the adjustment guidance information for the initial cleaning parameters through the second machine learning model.

16. The apparatus of claim 15, wherein, The adjusting of the first cleaning strategy according to the first feedback information to obtain the second cleaning strategy comprises: inputting the first feedback information into the second machine learning model, and determining the second cleaning strategy according to second inference information output by the second machine learning model.

17. The apparatus of claim 11, wherein, The providing of the adjustment guidance information for the initial cleaning parameters comprises one or more of the following manners: playing the adjustment guidance information through voice; displaying the adjustment guidance information through a display unit.

18. The apparatus of claim 11, wherein, The first cleaning strategy presenting module is further configured to: before presenting the first cleaning strategy, provide initial guidance information; the initial guidance information is used to guide the user to move to the working area, and / or guide the user to start formulating a cleaning strategy for the working area.

19. The apparatus of any one of claims 11 to 18, wherein, The adjustment guidance information interacting module is further configured to: when providing the adjustment guidance information for the initial cleaning parameters, provide an adjustment effect preview based on a current cleaning parameter to be adjusted in the adjustment guidance information.

20. The apparatus of claim 19, wherein, The providing of the adjustment effect preview based on the current cleaning parameter to be adjusted in the adjustment guidance information comprises: Determine a target display parameter corresponding to the current cleaning parameter to be adjusted based on a pre-configured correspondence between cleaning parameters and display parameters. Display a picture effect of changing the target display parameter in the simulated cleaning picture of the working area to represent an adjustment effect preview.

21. A user equipment, comprising: Comprise: A communication unit configured to be communicatively connected with the cleaning device and acquire environmental data of a working area collected by the cleaning device; A processing unit configured to determine a first cleaning strategy according to the environmental data; The first cleaning strategy comprises an initial cleaning parameter; Present the first cleaning strategy; Provide adjustment guide information for the initial cleaning parameter and acquire first feedback information input by a user based on the adjustment guide information; and adjust the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy.

22. A cleaning apparatus, characterized by Comprise: A machine body; A sensing unit arranged in or on the machine body and configured to collect environmental data of a working area of the cleaning device; A control unit arranged in the machine body and configured to determine a first cleaning strategy according to the environmental data; The first cleaning strategy comprises an initial cleaning parameter; Present the first cleaning strategy; provide adjustment guide information for the initial cleaning parameter and acquire first feedback information input by a user based on the adjustment guide information; and adjust the first cleaning strategy according to the first feedback information to obtain a second cleaning strategy.

23. A computer program product comprising a computer program, characterized in that, The computer program, when executed by a processor, implements the method of any one of claims 1 to 10.

24. An electronic device, comprising: Comprise a processor and a memory; Wherein the memory is used to store executable instructions of the processor; and the processor is configured to execute the method of any one of claims 1 to 10 via executing the executable instructions.

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