Method, apparatus, device and product for cleaning a twin cleaner

Abstract

The application provides a cleaning method, device, equipment and product of a twin cleaning machine, the method comprising: in response to a work instruction, obtaining a work area corresponding to the work instruction; a first cleaning machine enters the work area to perform a cleaning work, extracts region terrain data and obstacle appearance data of the work area, and generates cleaning big data features corresponding to the work area according to the region terrain data and the obstacle appearance data; if it is determined that the work area needs a second cleaning machine to perform cleaning, the second cleaning machine cleans the work area according to the cleaning big data features. The application sets two independent twin cleaning machines, and the functions of the two twin cleaning machines are different, so that the efficiency of the function components of each cleaning machine is maximized, the cleaning ability of each cleaning machine is enhanced, and the indoor region terrain data and obstacle data are obtained through the cooperation of the two twin cleaning machines, the indoor region is cleaned, and the work efficiency is improved.

Description

Technical Field

[0001] This invention relates to the field of smart home technology, and in particular to a cleaning method, apparatus, equipment and product of a twin cleaning machine. Background Technology

[0002] With the development of technology, smart homes are playing an increasingly important role in people's lives. Smart devices such as cleaning robots have added a lot of convenience to people's lives. Existing cleaning robots mainly include sweeping robots and floor scrubbers. However, due to structural and size limitations, the functions of each component of the cleaning robot are limited by structural and size constraints, resulting in insufficient battery life when cleaning indoors. After cleaning for a period of time, it is necessary to change the water, dry, disinfect, and recharge, which seriously affects the cleaning efficiency. Summary of the Invention

[0003] This invention provides a cleaning method, apparatus, equipment, and product for a twin cleaning robot, which solves the problem that the functions of various components of the existing cleaning robot are limited by structure and size, resulting in insufficient battery life when the cleaning robot is performing indoor cleaning.

[0004] According to a first aspect of the present invention, a cleaning method using a twin cleaning machine is applied to a server, wherein the twin cleaning machine comprises: a first cleaning machine and a second cleaning machine that are communicatively connected to each other;

[0005] The method includes:

[0006] In response to a work instruction, obtain the work area corresponding to the work instruction;

[0007] The first cleaning machine enters the work area to perform cleaning operations, extracts regional terrain data and obstacle shape data of the work area, and generates cleaning big data features corresponding to the work area based on the regional terrain data and obstacle shape data;

[0008] If it is determined that the work area needs to be cleaned by the second cleaning machine, then the second cleaning machine cleans the work area according to the characteristics of the cleaning big data.

[0009] According to one embodiment of the present invention, the step of obtaining the work area corresponding to the work instruction in response to the work instruction specifically includes:

[0010] Obtain the job feature information contained in the job instruction, and make a judgment based on the job feature information;

[0011] If the operation feature information is determined to include regional location features, then the operation area is determined based on the regional location features.

[0012] If the operation feature information is determined to include a first type of dirt feature, then all indoor areas are acquired, and all indoor areas are inspected according to the first type of dirt feature.

[0013] According to one embodiment of the present invention, if the step of determining that the operation feature information includes regional location features, then the step of determining the operation area based on the regional location features specifically includes:

[0014] Obtain the identification information of the area's location features, determine the suspected indoor area where the dirt is located based on the identification information, and generate a work area based on the suspected indoor area.

[0015] According to one embodiment of the present invention, the identification information is at least any one or a combination of coordinate parameters identifying the location characteristics of the area, a preset name, and a remark name;

[0016] The coordinate parameters indicate the location of the regional location feature within the indoor area;

[0017] The preset name is a code for the preset location characteristics of the area in the indoor area;

[0018] The remarks name is the code set by the user for the location feature of the area in the indoor area.

[0019] According to one embodiment of the present invention, the step of determining that the operation feature information includes a first dirt type feature, then acquiring all indoor areas, and inspecting all indoor areas according to the first dirt type feature, specifically includes:

[0020] Extract the dirt image and suspected dirt image from the first dirt type features. The dirt image is a preset dirt category, and the suspected dirt image is a derived form of dirt in the preset dirt category.

[0021] Based on the dirty image and the suspected dirty image, construct the inspection target objects;

[0022] The first cleaning machine performs inspections on the preset inspection path of the target object in the indoor area.

[0023] According to one embodiment of the present invention, the step of the first cleaning machine entering the work area to perform cleaning operations, extracting regional terrain data and obstacle shape data of the work area, and generating cleaning big data features corresponding to the work area based on the regional terrain data and obstacle shape data specifically includes:

[0024] The first cleaning path and the second cleaning path of the first cleaning machine in the work area are obtained. The first cleaning path points to the movement path of the first cleaning machine in the work area, and the second cleaning path points to the cleaning trajectory of the first cleaning machine in the work area during the cleaning process.

[0025] The system acquires pre-stored obstacle features and real-time obstacle features within the work area. The pre-stored obstacle features are obstacle data pre-stored in the terrain data of the area, and the real-time obstacle features are obstacle data scanned by the first cleaning machine during the cleaning process within the work area.

[0026] Cleaning big data features are generated based on the first cleaning path, the second cleaning path, the pre-stored obstacle features, and the real-time obstacle features.

[0027] According to one embodiment of the present invention, the step of determining that the work area needs to be cleaned by the second cleaning machine specifically includes:

[0028] Obtain the dirty cleaning area corresponding to the first cleaning machine within the work area, and extract the cleanliness index of the dirty cleaning area;

[0029] If the cleanliness index does not meet the preset cleaning threshold or the second dirt type characteristic pointed to by the cleanliness index is the dirt cleaning type of the second cleaning machine, then the second cleaning machine is controlled to clean the work area.

[0030] According to one embodiment of the present invention, the step of the second cleaning machine cleaning the work area based on the cleaning big data characteristics specifically includes:

[0031] The movement path of the second cleaning machine to the work area is determined based on the first cleaning path;

[0032] The second cleaning machine is controlled to clean the work area based on the second cleaning path, the pre-stored obstacle features, and the real-time obstacle features.

[0033] A cleaning apparatus for a twin cleaning machine according to a second aspect of the present invention includes: an instruction response module, a data generation module, and a job execution module;

[0034] The instruction response module is used to respond to a work instruction and obtain the work area corresponding to the work instruction;

[0035] The data generation module is used for the first cleaning machine to enter the work area to carry out cleaning operations, extract the regional terrain data and obstacle shape data of the work area, and generate cleaning big data features corresponding to the work area based on the regional terrain data and obstacle shape data;

[0036] The job execution module is used to determine that the job area needs to be cleaned by the second cleaning machine, and then the second cleaning machine cleans the job area according to the cleaning big data characteristics.

[0037] An electronic device according to a third aspect of the present invention includes: a memory and a processor;

[0038] The memory and the processor communicate with each other via a bus;

[0039] The memory stores computer instructions that can be executed on the processor;

[0040] When the processor invokes the computer instructions, it can execute the cleaning method of the twin cleaning machine described above.

[0041] According to a fourth aspect of the present invention, a computer program product includes a non-transitory machine-readable medium storing a computer program, which, when executed by a processor, implements the steps of the cleaning method of the twin cleaning machines described above.

[0042] The above-mentioned one or more technical solutions of the present invention have at least one of the following technical effects: The cleaning method, device, equipment and product of the twin cleaning machine provided by the present invention maximizes the efficiency of the functional components of each cleaning machine by setting up separate twin cleaning machines with different functions, thereby enhancing the cleaning ability of each cleaning machine. At the same time, the twin cleaning machines cooperate with each other to acquire indoor area terrain data and obstacle data, thereby cleaning the indoor area and improving work efficiency. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0044] Figure 1 This is one of the schematic diagrams showing the arrangement of the twin cleaning machine and the server provided by the present invention;

[0045] Figure 2This is the second schematic diagram showing the arrangement of the twin cleaning machine and the server provided by the present invention;

[0046] Figure 3 This is a schematic flowchart of the cleaning method of the twin cleaning machine provided by the present invention;

[0047] Figure 4 This is a schematic diagram of the cleaning device of the twin cleaning machine provided by the present invention;

[0048] Figure 5 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0049] Figure label:

[0050] 10. Server; 20. First cleaning machine; 30. Second cleaning machine;

[0051] 40. Command Response Module; 50. Data Generation Module; 60. Job Execution Module;

[0052] 810, Processor; 820, Communication interface; 830, Memory; 840, Communication bus. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] The present invention will now be described in detail with reference to the accompanying drawings. The specific operation methods in the method embodiments can also be applied to the device embodiments or system embodiments. In the description of the present invention, unless otherwise stated, "at least one" includes one or more. "Multiple" refers to two or more. For example, at least one of A, B, and C includes: A existing alone, B existing alone, A and B existing simultaneously, A and C existing simultaneously, B and C existing simultaneously, and A, B, and C existing simultaneously. In the present invention, " / " means "or". For example, A / B can mean A or B. "And / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone.

[0055] The present invention will now be described in detail with reference to specific embodiments.

[0056] In some specific embodiments of the present invention, such as Figures 1 to 3As shown, this solution provides a cleaning method for a twin cleaning machine, applied to server 10. The twin cleaning machine includes: a first cleaning machine 20 and a second cleaning machine 30 that are communicatively connected to each other.

[0057] The methods include:

[0058] In response to a work instruction, obtain the work area corresponding to the work instruction;

[0059] The first cleaning machine 20 enters the work area to carry out cleaning operations, extracts regional terrain data and obstacle shape data of the work area, and generates corresponding cleaning big data features of the work area based on the regional terrain data and obstacle shape data;

[0060] If it is determined that the work area needs to be cleaned by the second cleaning machine 30, then the second cleaning machine 30 will clean the work area based on the characteristics of cleaning big data.

[0061] It should be noted that by setting up twin cleaning machines, the functions of the same cleaning machine can be fully or partially distributed to the two cleaning machines, which enhances the functionality of each cleaning machine and also solves the problem of weakening the functions of each part when all functions are integrated into one machine.

[0062] In a possible implementation, server 10 is a service device in the cloud.

[0063] In a possible implementation, server 10 is a chip located within the first cleaning machine 20 and / or the second cleaning machine 30 and / or the home IoT device.

[0064] In one application scenario, the twin cleaning machine includes a first cleaning machine 20 and a second cleaning machine 30, which are two cleaning robots with different functions.

[0065] In one application scenario, the twin cleaning machine includes a first cleaning machine 20 and a second cleaning machine 30, which are two cleaning robots with completely different functions.

[0066] In one application scenario, the twin cleaning machine includes a first cleaning machine 20 and a second cleaning machine 30, which are two cleaning robots with different functional parts.

[0067] In one application scenario, the first cleaning machine 20 is a sweeper or a floor scrubber, and the second cleaning machine 30 is a floor scrubber or a sweeper.

[0068] In some possible embodiments of the present invention, the step of obtaining the work area corresponding to the work instruction in response to the work instruction specifically includes:

[0069] Obtain the job feature information contained in the job instruction, and make a judgment based on the job feature information;

[0070] If the operation feature information includes regional location features, then the operation area is determined based on the regional location features.

[0071] If the operation feature information is determined to include the first type of dirt feature, then all indoor areas are acquired, and all indoor areas are inspected according to the first type of dirt feature.

[0072] Specifically, this embodiment provides an implementation method for obtaining the work area corresponding to the work instruction. By obtaining the work feature information of the work instruction, the first cleaning machine 20 can perform corresponding operations according to the work feature information. Based on different work feature information, the first cleaning machine 20 performs different actions.

[0073] Furthermore, the operation feature information includes the location of the operation area and / or the type of dirt that needs to be cleaned. The first cleaning machine 20 identifies the operation feature information, makes a corresponding judgment, and performs corresponding actions based on the judgment result.

[0074] In a possible implementation, both the first cleaning machine 20 and the second cleaning machine 30 are equipped with a sensor module, a voice recognition module, an image acquisition module, an AI recognition module, and a WIFI module, so as to facilitate communication between the first cleaning machine 20 and the second cleaning machine 30 and communication with the home IoT, and also enable the first cleaning machine 20 and the second cleaning machine 30 to acquire regional terrain data and dirt feature data of the work area.

[0075] In some possible embodiments of the present invention, if the job feature information is determined to include regional location features, then the step of determining the job area based on the regional location features specifically includes:

[0076] Obtain the identification information of the area's location features, determine the suspected indoor area where the dirt is located based on the identification information, and generate the work area based on the suspected indoor area.

[0077] Specifically, this embodiment provides an implementation method for determining the work area based on the regional location features. By extracting the identification information in the regional location features, the first cleaning machine 20 can accurately locate the suspected indoor area where the dirt is located, and generate the work area for the suspected indoor area, thereby realizing the cleaning of the work area.

[0078] In some possible embodiments of the present invention, the identification information is at least any one or a combination of coordinate parameters of the location characteristics of the identification area, a preset name, and a remark name;

[0079] Among them, the coordinate parameters identify the location features of the area within the indoor area;

[0080] The preset name is the code for the preset area location characteristics in the indoor area;

[0081] The remarks name is the code for the user-defined regional location characteristics in the indoor area.

[0082] Specifically, this embodiment provides an implementation method for identification information. The identification information has multiple forms. By setting multiple forms, the first cleaning machine 20 can more accurately locate the suspected indoor area where the dirt is located, thereby improving the accuracy of the judgment.

[0083] In a possible implementation, the identification information is a coordinate parameter identifier. By marking the indoor area on a virtual map according to the coordinates, the first cleaning machine 20 can determine the location of the dirt specified by the user through the coordinates, and thus determine the suspected indoor area.

[0084] In a possible implementation, the coordinate parameter identifier can be a three-dimensional coordinate in a Cartesian coordinate system, or a relative coordinate with reference to some internal home facilities.

[0085] In one application scenario, the user sends coordinate parameter identifier information to the first cleaning machine 20 through the controller. The coordinate parameter identifier content is (102, 163, 188), and the specific value indicates the relative position of the user's location on the virtual map. The first cleaning machine 20 confirms the work area based on the coordinate parameter identifier.

[0086] Understandably, the coordinate parameter identifier sent by the user to the first cleaning machine 20 is implemented through the corresponding operating device, which is equipped with a controller to control the actions of the first cleaning machine 20.

[0087] In one application scenario, a user sends a voice message to the first cleaning machine 20, which includes coordinate parameter identifiers, such as "First cleaning machine 20, clean around the chair" or "First cleaning machine 20, clean around the sofa". The first cleaning machine 20 confirms the work area based on the coordinate parameter identifiers.

[0088] In a possible implementation, the identification information is a preset name. By setting a preset name for the indoor area in the system, the first cleaning machine 20 can determine the location of the dirt specified by the user through the preset name, and thus determine the suspected indoor area.

[0089] In one application scenario, a user sends a voice message to the first cleaning machine 20, which includes a preset name for the indoor area, such as "First cleaning machine 20, clean the living room", "First cleaning machine 20, clean the study", "First cleaning machine 20, clean the dressing room", etc. The first cleaning machine 20 confirms the work area based on the preset name.

[0090] In a possible implementation, the identification information is a note name. By setting a note name for the indoor area in the system, personalized customization is provided to the user, improving the user experience. At the same time, it is also convenient for the first cleaning machine 20 to determine the location of the dirt specified by the user through the note name, and thus determine the suspected indoor area.

[0091] In one application scenario, a user sends a voice message to the first cleaning machine 20, which includes a note for the indoor area, such as "First cleaning machine 20, clean the baby room", "First cleaning machine 20, clean the sunroom", "First cleaning machine 20, clean area A", etc. Among them, "baby room", "sunroom" and "area A" are note names set by the user. The first cleaning machine 20 confirms the work area based on the note name.

[0092] In some possible embodiments of the present invention, the step of determining that the work characteristic information includes a first dirt type characteristic, then acquiring all indoor areas, and inspecting all indoor areas according to the first dirt type characteristic, specifically includes:

[0093] Extract the dirt image and suspected dirt image from the features of the first dirt type. The dirt image is a preset dirt category, and the suspected dirt image is a derived form of dirt in the preset dirt category.

[0094] Based on the images of dirt and suspected dirt, construct the target objects for inspection;

[0095] The first cleaning machine 20 performs inspections on the preset inspection path of the target object in the indoor area.

[0096] Specifically, this embodiment provides an implementation method for inspecting all indoor areas based on the characteristics of a first type of dirt. The first cleaning machine 20 inspects the indoor areas according to the type of dirt and finally determines the work area based on the inspection results.

[0097] It should be noted that the dirt image is a preset dirt category in the system, such as paper scraps, fruit peels, hair, sand, etc., while the suspected dirt image is a derivative form of the preset dirt category in the system. For example, the system pre-stores the size, shape and other judgment features of paper scraps, but paper scraps may also exist in unconventional shapes or may exist together with other dirt. Therefore, the system sets up a suspected dirt image derived from the preset dirt category.

[0098] Furthermore, the first cleaning machine 20 inspects target objects constructed by analyzing dirt images and suspected dirt images along a preset inspection path in the indoor area, thereby determining the work area.

[0099] In some possible embodiments of the present invention, the step of the first cleaning machine 20 entering the work area to perform cleaning operations, extracting regional terrain data and obstacle shape data of the work area, and generating corresponding big data features of the cleaning area based on the regional terrain data and obstacle shape data specifically includes:

[0100] The first cleaning path and the second cleaning path of the first cleaning machine 20 in the working area are obtained. The first cleaning path points to the movement path of the first cleaning machine 20 in the working area, and the second cleaning path points to the cleaning trajectory of the first cleaning machine 20 in the working area during the process of cleaning dirt.

[0101] Acquire pre-stored obstacle features and real-time obstacle features within the work area. The pre-stored obstacle features are obstacle data pre-stored in the regional terrain data, while the real-time obstacle features are obstacle data scanned by the first cleaning machine 20 during the cleaning process within the work area.

[0102] Cleaning big data features are generated based on the first cleaning path, the second cleaning path, pre-stored obstacle features, and real-time obstacle features.

[0103] Specifically, this embodiment provides an implementation method for generating cleaning big data features of the corresponding work area based on regional terrain data and obstacle shape data. By acquiring the first cleaning path, the second cleaning path, pre-stored obstacle features, and real-time obstacle features, the cleaning big data features are generated, enabling the second cleaning machine 30 to clean the dirt in the work area that has not yet been cleaned or that the first cleaning machine 20 could not clean, thereby improving the cleaning effect.

[0104] In a possible implementation, the pre-stored obstacle features are obstacles identified and stored in server 10 during the virtual map construction process of the twin cleaning machines in the indoor area.

[0105] In a possible implementation, the immediate obstacle feature is an obstacle identified by the first cleaning machine 20 during the cleaning operation, such as when the first cleaning machine 20 identifies temporary obstacles such as slippers or boxes in the work area while cleaning.

[0106] In some possible embodiments of the present invention, the step of determining that the work area needs to be cleaned by the second cleaning machine 30 specifically includes:

[0107] Obtain the dirty cleaning area corresponding to the first cleaning machine 20 within the working area, and extract the cleanliness index of the dirty cleaning area;

[0108] If the cleanliness index does not meet the preset cleaning threshold or the second type of dirt indicated by the cleanliness index is the dirt cleaning type of the second cleaning machine 30, then the second cleaning machine 30 is controlled to clean the work area.

[0109] Specifically, this embodiment provides an implementation method for determining that a work area needs to be cleaned by a second cleaning machine 30. When the work area after cleaning the dirt has a cleanliness index that does not meet the preset cleaning threshold or the second dirt type characteristic pointed to by the cleanliness index is the dirt cleaning type of the second cleaning machine 30, the second cleaning machine 30 needs to enter the work area to clean the dirt again.

[0110] In some possible embodiments of the present invention, the step of the second cleaning machine 30 cleaning the work area based on the characteristics of big data on cleaning specifically includes:

[0111] The movement path of the second cleaning machine 30 to the work area is determined based on the first cleaning path;

[0112] The second cleaning machine 30 is controlled to clean within the work area based on the second cleaning path, pre-stored obstacle characteristics, and real-time obstacle characteristics.

[0113] Specifically, this embodiment provides an implementation method in which a second cleaning machine 30 cleans the work area based on the characteristics of cleaning big data. The second cleaning machine 30 cleans the work area again based on the second cleaning path, pre-stored obstacle characteristics and real-time obstacle characteristics, thereby improving the cleaning effect.

[0114] In some possible embodiments of the present invention, the clean big data features also include dirt and grime feature data, specifically including:

[0115] The first cleaning path and the second cleaning path of the first cleaning machine 20 in the working area are obtained. The first cleaning path points to the movement path of the first cleaning machine 20 in the working area, and the second cleaning path points to the cleaning trajectory of the first cleaning machine 20 in the working area during the process of cleaning dirt.

[0116] The system acquires the dirt update image, suspected dirt update image, and cleanliness index of the work area after cleaning. The dirt update image is the dirt image after the work area is cleaned. The suspected dirt update image is the suspected area of ​​the dirt update image within a preset range. The cleanliness index is the rating level of the dirt update image and suspected dirt update image after the work area is cleaned.

[0117] Cleaning big data features are generated based on the first cleaning path, the second cleaning path, the dirt update profile, the suspected dirt update profile, and the cleanliness index.

[0118] In some possible embodiments of the present invention, the step of controlling the second cleaning machine 30 to clean the work area specifically includes:

[0119] The movement path of the second cleaning machine 30 to the work area is determined based on the first cleaning path;

[0120] Based on the second cleaning path, the dirt update image, and the suspected dirt update image, the second cleaning machine 30 is controlled to clean within the work area.

[0121] Specifically, this embodiment provides an implementation method for controlling a second cleaning machine 30 to clean a work area. The second cleaning machine 30 performs a second cleaning of the work area based on a second cleaning path, a dirt update image, and a suspected dirt update image, thereby improving the cleaning effect.

[0122] In some specific embodiments of the present invention, such as Figure 4 As shown, this solution provides a cleaning device for a twin cleaning machine, including: an instruction response module 40, a data generation module 50, and a job execution module 60;

[0123] The instruction response module 40 is used to respond to the work instruction and obtain the work area corresponding to the work instruction;

[0124] The data generation module 50 is used when the first cleaning machine 20 enters the work area to carry out cleaning operations, extracts the regional terrain data and obstacle shape data of the work area, and generates corresponding cleaning big data features of the work area based on the regional terrain data and obstacle shape data.

[0125] The job execution module 60 is used to determine that the work area needs to be cleaned by the second cleaning machine 30, and then the second cleaning machine 30 cleans the work area according to the characteristics of cleaning big data.

[0126] Optionally, the step of obtaining the work area corresponding to the work instruction in response to the work instruction specifically includes:

[0127] Obtain the job feature information contained in the job instruction, and make a judgment based on the job feature information;

[0128] If the operation feature information includes regional location features, then the operation area is determined based on the regional location features.

[0129] If the operation feature information is determined to include the first type of dirt feature, then all indoor areas are acquired, and all indoor areas are inspected according to the first type of dirt feature.

[0130] Optionally, if the operation feature information is determined to include regional location features, then the step of determining the operation area based on the regional location features specifically includes:

[0131] Obtain the identification information of the area's location features, determine the suspected indoor area where the dirt is located based on the identification information, and generate the work area based on the suspected indoor area.

[0132] Optionally, the identification information is at least one or a combination of coordinate parameters of the location characteristics of the identification area, a preset name, and a remark name;

[0133] Among them, the coordinate parameters identify the location features of the area within the indoor area;

[0134] The preset name is the code for the preset area location characteristics in the indoor area;

[0135] The remarks name is the code for the user-defined regional location characteristics in the indoor area.

[0136] Optionally, if the operational characteristic information is determined to include a first type of dirtiness characteristic, then the step of acquiring all indoor areas and inspecting all indoor areas according to the first type of dirtiness characteristic specifically includes:

[0137] Extract the dirt image and suspected dirt image from the features of the first dirt type. The dirt image is a preset dirt category, and the suspected dirt image is a derived form of dirt in the preset dirt category.

[0138] Based on the images of dirt and suspected dirt, construct the target objects for inspection;

[0139] The first cleaning machine 20 performs inspections on the preset inspection path of the target object in the indoor area.

[0140] Optionally, the step of the first cleaning machine 20 entering the work area to perform cleaning operations, extracting regional terrain data and obstacle shape data of the work area, and generating corresponding big data features of the work area based on the regional terrain data and obstacle shape data specifically includes:

[0141] The first cleaning path and the second cleaning path of the first cleaning machine 20 in the working area are obtained. The first cleaning path points to the movement path of the first cleaning machine 20 in the working area, and the second cleaning path points to the cleaning trajectory of the first cleaning machine 20 in the working area during the process of cleaning dirt.

[0142] Acquire pre-stored obstacle features and real-time obstacle features within the work area. The pre-stored obstacle features are obstacle data pre-stored in the regional terrain data, while the real-time obstacle features are obstacle data scanned by the first cleaning machine 20 during the cleaning process within the work area.

[0143] Cleaning big data features are generated based on the first cleaning path, the second cleaning path, pre-stored obstacle features, and real-time obstacle features.

[0144] Optionally, the step of determining that the work area needs to be cleaned by the second cleaning machine 30 specifically includes:

[0145] Obtain the dirty cleaning area corresponding to the first cleaning machine 20 within the working area, and extract the cleanliness index of the dirty cleaning area;

[0146] If the cleanliness index does not meet the preset cleaning threshold or the second type of dirt indicated by the cleanliness index is the dirt cleaning type of the second cleaning machine 30, then the second cleaning machine 30 is controlled to clean the work area.

[0147] Optionally, the step of the second cleaning machine 30 cleaning the work area based on the characteristics of cleaning big data specifically includes:

[0148] The movement path of the second cleaning machine 30 to the work area is determined based on the first cleaning path;

[0149] The second cleaning machine 30 is controlled to clean within the work area based on the second cleaning path, pre-stored obstacle characteristics, and real-time obstacle characteristics.

[0150] Figure 5 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 5 As shown, the electronic device may include a processor 810, a communications interface 820, a memory 830, and a communication bus 840. The processor 810, communications interface 820, and memory 830 communicate with each other via the communication bus 840. The processor 810 can call logical instructions from the memory 830 to execute the cleaning method of the twin cleaning machine.

[0151] It should be noted that the electronic device in this embodiment can be a server, a PC, or other devices, as long as its structure includes the following: Figure 5 The processor 810, communication interface 820, memory 830, and communication bus 840 shown are interconnected via the communication bus 840. The processor 810 can call logical instructions stored in the memory 830 to execute the aforementioned method. This embodiment does not limit the specific implementation of the electronic device.

[0152] The server can be a single server or a group of servers. The server group can be centralized or distributed (e.g., the servers can be a distributed system). In some embodiments, the server can be local or remote relative to the terminal. For example, the server can access information stored in a user terminal, a database, or any combination thereof via a network. As another example, the server can directly connect to at least one of the user terminal and a database to access the information and / or data stored therein. In some embodiments, the server can be implemented on a cloud platform; by way of example only, the cloud platform can include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud, multi-cloud, etc., or any combination thereof. In some embodiments, the server and user terminal can be implemented on an electronic device having one or more components as described in the embodiments of the present invention.

[0153] Furthermore, the network can be used for the exchange of information and / or data. In some embodiments, one or more components in the interaction scenario (e.g., servers, user terminals, and databases) can send information and / or data to other components. In some embodiments, the network can be any type of wired or wireless network, or a combination thereof. By way of example only, the network can include wired networks, wireless networks, fiber optic networks, telecommunications networks, intranets, the Internet, local area networks (LANs), wide area networks (WANs), wireless local area networks (WLANs), metropolitan area networks (MANs), wide area networks (WANs), public switched telephone networks (PSTNs), Bluetooth networks, ZigBee networks, or near field communication (NFC) networks, etc., or any combination thereof. In some embodiments, the network can include one or more network access points. For example, the network can include wired or wireless network access points, such as base stations and / or network switching nodes, through which one or more components in the interaction scenario can connect to the network to exchange data and / or information.

[0154] Furthermore, the logical instructions in the aforementioned memory 830 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0155] In a possible implementation, embodiments of the present invention also provide a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the cleaning method of the twin cleaning machine provided in the above embodiments.

[0156] In a possible implementation, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer is able to perform the methods provided in the above-described method embodiments.

[0157] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0158] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0159] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cleaning method for a twin cleaning machine, characterized in that, Applied to a server (10), the twin cleaning machine includes: a first cleaning machine (20) and a second cleaning machine (30) that are communicatively connected to each other, wherein the first cleaning machine (20) is a sweeper or a floor scrubber, and the second cleaning machine (30) is a floor scrubber or a sweeper; The method includes: In response to a work instruction, obtain the work area corresponding to the work instruction; The first cleaning machine (20) enters the work area to perform cleaning operations, extracts the regional terrain data and obstacle shape data of the work area, and generates cleaning big data features corresponding to the work area based on the regional terrain data and obstacle shape data; If it is determined that the work area needs to be cleaned by the second cleaning machine (30), then the second cleaning machine (30) cleans the work area according to the cleaning big data characteristics; The step of obtaining the work area corresponding to the work instruction in response to the work instruction specifically includes: Obtain the job feature information contained in the job instruction, and make a judgment based on the job feature information; If the operation feature information is determined to include regional location features, then the operation area is determined based on the regional location features. If the operation feature information is determined to include a first type of dirt feature, then all indoor areas are acquired, and all indoor areas are inspected according to the first type of dirt feature.

2. The cleaning method of the twin cleaning machine according to claim 1, characterized in that, The step of determining the work area based on the regional location features, after determining that the work feature information includes regional location features, specifically includes: Obtain the identification information of the area's location features, determine the suspected indoor area where the dirt is located based on the identification information, and generate a work area based on the suspected indoor area.

3. The cleaning method of the twin cleaning machine according to claim 2, characterized in that, The identification information is at least one or a combination of coordinate parameters that identify the location characteristics of the area, a preset name, and a remark name; The coordinate parameters indicate the location of the regional location feature within the indoor area; The preset name is a code for the preset location characteristics of the area in the indoor area; The remarks name is the code set by the user for the location feature of the area in the indoor area.

4. The cleaning method of the twin cleaning machine according to claim 1, characterized in that, The step of determining that the operation feature information includes a first dirt type feature, then acquiring all indoor areas, and inspecting all indoor areas according to the first dirt type feature, specifically includes: Extract the dirt image and suspected dirt image from the first dirt type features. The dirt image is a preset dirt category, and the suspected dirt image is a derived form of dirt in the preset dirt category. Based on the dirty image and the suspected dirty image, construct the inspection target objects; The first cleaning machine (20) performs inspections on the preset inspection path of the inspection target in the indoor area.

5. The cleaning method of the twin cleaning machine according to any one of claims 1 to 4, characterized in that, The steps of the first cleaning machine (20) entering the work area to perform cleaning operations, extracting regional terrain data and obstacle shape data of the work area, and generating corresponding cleaning big data features of the work area based on the regional terrain data and obstacle shape data specifically include: Obtain the first cleaning path and the second cleaning path of the first cleaning machine (20) in the work area. The first cleaning path points to the movement path of the first cleaning machine (20) in the work area, and the second cleaning path points to the cleaning trajectory of the first cleaning machine (20) during the cleaning process in the work area. Obtain pre-stored obstacle features and real-time obstacle features in the work area. The pre-stored obstacle features are obstacle data pre-stored in the terrain data of the area. The real-time obstacle features are obstacle data scanned by the first cleaning machine (20) during the cleaning process in the work area. Cleaning big data features are generated based on the first cleaning path, the second cleaning path, the pre-stored obstacle features, and the real-time obstacle features.

6. The cleaning method of the twin cleaning machine according to claim 5, characterized in that, The step of determining that the work area needs to be cleaned by the second cleaning machine (30) specifically includes: Obtain the dirty cleaning area corresponding to the first cleaning machine (20) within the working area, and extract the cleanliness index of the dirty cleaning area; If the cleanliness index does not meet the preset cleaning threshold or the second dirt type characteristic pointed to by the cleanliness index is the dirt cleaning type of the second cleaning machine (30), then the second cleaning machine (30) is controlled to clean the work area.

7. The cleaning method of the twin cleaning machine according to claim 6, characterized in that, The second cleaning machine (30) cleans the work area based on the cleaning big data characteristics, specifically including: The movement path of the second cleaning machine (30) to the work area is determined according to the first cleaning path; The second cleaning machine (30) is controlled to clean the work area according to the second cleaning path, the pre-stored obstacle features and the real-time obstacle features.

8. A cleaning device for a twin cleaning machine, characterized in that, include: The system includes an instruction response module (40), a data generation module (50), and a job execution module (60). The instruction response module (40) is used to respond to the work instruction and obtain the work area corresponding to the work instruction; The data generation module (50) is used to extract regional terrain data and obstacle shape data of the work area when the first cleaning machine (20) enters the work area to carry out cleaning operations, and to generate cleaning big data features corresponding to the work area based on the regional terrain data and obstacle shape data; The operation execution module (60) is used to determine that the operation area needs to be cleaned by the second cleaning machine (30), and then the second cleaning machine (30) cleans the operation area according to the cleaning big data characteristics; The step of obtaining the work area corresponding to the work instruction in response to the work instruction specifically includes: Obtain the job feature information contained in the job instruction, and make a judgment based on the job feature information; If the operation feature information is determined to include regional location features, then the operation area is determined based on the regional location features. If the operation feature information is determined to include a first type of dirt feature, then all indoor areas are acquired, and all indoor areas are inspected according to the first type of dirt feature.

9. An electronic device, characterized in that, include: Memory (830) and processor (810); The memory (830) and the processor (810) communicate with each other via a bus; The memory (830) stores computer instructions that can run on the processor (810); When the processor (810) invokes the computer instructions, it is able to execute the cleaning method of the twin cleaning machine according to any one of claims 1 to 7.

10. A computer program product comprising a non-transitory machine-readable medium storing a computer program, characterized in that, When the computer program is executed by the processor (810), it implements the steps of the cleaning method of the twin cleaning machine according to any one of claims 1 to 7.

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