CN111543898A - Garbage classification cleaning method and system, electronic equipment, storage medium and sweeper - Google Patents
Garbage classification cleaning method and system, electronic equipment, storage medium and sweeper Download PDFInfo
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
- A47L11/24—Floor-sweeping machines, motor-driven
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47L—DOMESTIC WASHING OR CLEANING; SUCTION CLEANERS IN GENERAL
- A47L11/00—Machines for cleaning floors, carpets, furniture, walls, or wall coverings
- A47L11/40—Parts or details of machines not provided for in groups A47L11/02 - A47L11/38, or not restricted to one of these groups, e.g. handles, arrangements of switches, skirts, buffers, levers
- A47L11/4011—Regulation of the cleaning machine by electric means; Control systems and remote control systems therefor
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Abstract
The invention provides a garbage classification cleaning method, a garbage classification cleaning system, electronic equipment, a storage medium and a sweeper, wherein the method comprises the following steps: carrying out garbage recognition in the sweeping process of the sweeper; determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category; determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box; controlling the sweeper to collect the garbage into the corresponding dust collecting box. Can realize that garbage classification cleans, need not the manual rubbish that cleans the sweeper and classify, save manpower and time, promote user experience.
Description
Technical Field
The invention relates to the technical field of sweeper, in particular to a garbage classification sweeping method and system, electronic equipment, a storage medium and a sweeper.
Background
At present, with the continuous development of intellectualization, the intellectualization is embodied in the life. The floor sweeping robot is used as a mobile robot and can realize indoor space division, complete sweeping, automatic recharging and other functions. The environment to be cleaned has various kinds of garbage, and the sweeper in the prior art usually collects various kinds of garbage into one dust collecting box in a centralized manner, then artificially classifies the garbage in the dust collecting box and pours the garbage into various classified garbage cans, so that the waste of manpower and time is caused, the classification error is easily caused, and the user experience is reduced.
Disclosure of Invention
The invention provides a garbage classification cleaning method and system, an electronic device, a storage medium and a sweeper, which can realize garbage classification cleaning without manually classifying garbage cleaned by the sweeper, save manpower and time and improve user experience.
In a first aspect, the present invention provides a method for sorting and cleaning garbage, which is applied to a sweeper, the sweeper including a plurality of dust collecting boxes corresponding to different garbage categories, the method including:
carrying out garbage recognition in the sweeping process of the sweeper;
determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category;
determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box;
controlling the sweeper to collect the garbage into the corresponding dust collecting box.
Furthermore, the garbage recognition during the sweeping process of the sweeper comprises the following steps:
acquiring shape data and image data in real time in the sweeping process of the sweeper;
and performing garbage recognition processing on the shape data and the image data to obtain a garbage recognition result.
Further, the performing garbage recognition processing on the shape data and the image data to obtain a garbage recognition result includes:
extracting the characteristics of the shape data and the image data by using a preset characteristic extraction algorithm;
and determining a garbage recognition result by using a preset garbage recognition algorithm and the extracted characteristic data.
Further, the performing garbage recognition processing on the shape data and the image data to obtain a garbage recognition result includes:
extracting the characteristics of the shape data and the image data by using a preset characteristic extraction algorithm;
comparing the extracted characteristic data with characteristic data of a pre-stored garbage learning model;
determining garbage corresponding to the shape data and the image data.
Furthermore, the pre-stored garbage learning model is obtained by training through a deep learning algorithm by utilizing feature data of different garbage.
In a second aspect, the present invention provides a garbage sorting and cleaning system for a sweeper, wherein the sweeper includes a plurality of dust collecting boxes respectively corresponding to different garbage categories, and the system includes:
the recognition module is used for recognizing garbage in the sweeping process of the sweeper;
the first determining module is used for determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category;
the second determining module is used for determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box;
and the control module is used for controlling the sweeper to collect the garbage to the corresponding dust collecting box.
In a third aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program implements the garbage classification cleaning method according to the first aspect when executed by the processor.
In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by one or more processors, implements the garbage classification cleaning method according to the first aspect.
In a fifth aspect, the present invention provides a sweeper, comprising:
the image acquisition system is connected with the processing device and is used for acquiring shape data and image data in real time in the sweeping process of the sweeper;
a plurality of dust collecting boxes for collecting garbage of different garbage categories respectively;
the processing device is used for acquiring the shape data and the image data acquired by the image acquisition system and performing garbage recognition; determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category; determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box; controlling the sweeper to collect the garbage into the corresponding dust collecting box.
Still further, the image acquisition system comprises:
the structured light system is connected with the processing device and is used for acquiring shape data in real time in the sweeping process of the sweeper;
the camera is connected with the processing device and used for collecting image data in real time in the sweeping process of the sweeper.
Still further, the processing apparatus includes:
the GPU is connected with the image acquisition system and used for acquiring the shape data and the image data acquired by the image acquisition system, extracting the characteristics of the shape data and the image data by using a preset characteristic extraction algorithm, determining a garbage identification result by using a preset garbage identification algorithm and the extracted characteristic data, and determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category; determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box; controlling the sweeper to collect the garbage into the corresponding dust collecting box;
and the AI chip is connected with the GPU and is used for storing a preset feature extraction algorithm and a preset garbage recognition algorithm.
The invention has the beneficial effects that: the dust collecting box corresponding to the garbage identification result is determined by performing garbage identification in the sweeping process of the sweeper; the control machine of sweeping the floor collects corresponding dust-collecting box with rubbish, has avoided collecting rubbish and need artificially classify rubbish behind the same dust-collecting box, consumes the problem of manpower and time, has promoted user experience.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic view of a sweeper according to a first embodiment of the present invention;
fig. 2 is a flowchart of a garbage classification cleaning method according to an embodiment of the present invention;
FIG. 3 is a flowchart of another garbage classification cleaning method according to an embodiment of the present invention;
FIG. 4 is a flowchart of another garbage classification cleaning method according to an embodiment of the present invention;
FIG. 5 is a flowchart of another garbage classification cleaning method according to an embodiment of the present invention;
fig. 6 is a block diagram of a garbage sorting and cleaning system according to a second embodiment of the present invention;
fig. 7 is a block diagram of a sweeper according to a fifth embodiment of the present invention;
fig. 8 is a schematic diagram of a prior art image acquisition system.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Example one
A variety of waste is present in the cleaning environment of the sweeper, for example, dry waste, including such things as paper dust, plastic, dirt, paper, etc.; wet garbage including leftovers, pericarps, kernels, milk, liquids, etc.; if collect same dust-collecting box with all kinds of rubbish, throw away different rubbish when rubbish recovery unit (like the garbage bin), need artificially classify, consumed manpower and time, and influence user experience. Therefore, the present embodiment provides a method for sorting and cleaning garbage, which is applied to a sweeper, such as the sweeper shown in fig. 1, wherein the sweeper includes a plurality of dust collecting boxes corresponding to different garbage categories, as shown in fig. 2, and the method includes:
s100, identifying garbage in the sweeping process of the sweeper;
s200, determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category;
step S300, determining a dust collection box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collection box;
and S400, controlling the sweeper to collect the garbage into the corresponding dust collecting box.
Among them, the garbage categories may include, but are not limited to: dry trash, wet trash, dust bins may include, but are not limited to: a first dust collecting box for collecting dry garbage and a second dust collecting box for collecting wet garbage.
In the embodiment, the dust collecting box corresponding to the garbage identification result is determined by performing garbage identification in the sweeping process of the sweeper; the control machine of sweeping the floor collects corresponding dust-collecting box with rubbish, has avoided collecting rubbish and need artificially classify rubbish behind the same dust-collecting box, consumes the problem of manpower and time, has promoted user experience.
Preferably, the present embodiment further provides a garbage classification cleaning method as shown in fig. 3, and the step S100 may further include the following sub-steps:
step S110, acquiring shape data and image data in real time in the sweeping process of the sweeper;
and step S120, performing garbage recognition processing on the shape data and the image data to obtain a garbage recognition result.
The shape data can reflect the shape characteristics of different types of garbage, the image data can reflect the plane characteristics and the color characteristics of different types of garbage, the shape data and the image data are used as garbage identification data, the garbage characteristics can be accurately reflected, the garbage identification accuracy is improved, the problems that the garbage identification accuracy is low and the result is easy to misjudge or not identified by only using the image data are avoided, and the accurate garbage category is determined in the subsequent process, so that the sweeper is controlled to collect the garbage into the dust collection boxes corresponding to the garbage category.
In order to implement the garbage recognition processing, the present embodiment provides two following preferred modes:
first preferred mode: the present embodiment further provides a garbage classification cleaning method as shown in fig. 4, and step S120 may further include:
step S121, extracting the characteristics of the shape data and the image data by using a preset characteristic extraction algorithm;
and S122, determining a garbage recognition result by using a preset garbage recognition algorithm and the extracted feature data.
Preferably, the preset feature extraction algorithm may be, but not limited to, one of Scale-invariant feature transform (SIFT), Histogram of Oriented Gradients (HOG) or Local Binary Patterns (LBP). The pre-set spam identification algorithm may be, but is not limited to, one of Faster R-CNN, R-FCN, and SSD.
In the preferred mode, a preset feature extraction algorithm is used for extracting feature data in the shape data and the image data, a preset garbage recognition algorithm and the extracted feature data are used for recognizing garbage corresponding to the currently acquired shape data and the image data, classification corresponding to the garbage is further determined, and the sweeper is controlled to collect the garbage into a dust box corresponding to the garbage classification. For example, shape data and image data of the garbage are obtained, feature data are extracted by using a preset feature extraction algorithm, the current garbage is determined to be paper scraps by using a preset garbage recognition algorithm, the paper scraps belong to dry garbage according to a preset corresponding relation between the garbage category and a dust collection box, the dust collection box corresponding to the dry garbage is a first dust collection box, and at the moment, the sweeper controls a dust collection opening to collect the garbage into the first dust collection box.
Second preferred mode: the present embodiment further provides a garbage classification cleaning method as shown in fig. 5, and step S120 may further include:
s123, extracting the characteristics of the shape data and the image data by using a preset characteristic extraction algorithm;
step S124, comparing the extracted feature data with feature data of a pre-stored garbage learning model;
and step S125, determining the garbage corresponding to the shape data and the image data.
Preferably, the pre-stored garbage learning model is obtained by training through a deep learning algorithm by using feature data of different garbage. After the extracted feature data is compared with the feature data of the pre-stored garbage learning model in the garbage identification process, the garbage corresponding to the currently acquired shape data and image data can be determined from the plurality of pre-stored garbage learning models. The deep learning algorithm may be, but is not limited to, Convolutional Neural Networks (CNN). The pre-stored garbage learning model may include, but is not limited to: paper scraps, plastics, dregs, paper, leftovers, fruit peels, fruit pits, milk, liquid and the like.
In the embodiment, the preset feature extraction algorithm is used for extracting the features of the shape data and the image data which are acquired in real time, the garbage corresponding to the shape data and the image data is determined, the garbage identification accuracy rate can be improved, the garbage category is determined, classified cleaning can be achieved according to the garbage category cleaned by the sweeper, the sweeper can finish garbage classified collection while sweeping, and the cleaning efficiency is improved.
Example two
In correspondence with the embodiment, the embodiment provides a garbage sorting and cleaning system, which is applied to a sweeper, the sweeper includes a plurality of dust collecting boxes corresponding to different garbage categories, as shown in fig. 6, the system includes:
the recognition module 100 is used for recognizing garbage in the sweeping process of the sweeper;
a first determining module 200, configured to determine a garbage category corresponding to the garbage recognition result according to a preset corresponding relationship between the garbage and the garbage category;
a second determining module 300, configured to determine a dust box corresponding to the garbage recognition result according to a preset corresponding relationship between the garbage category and the dust box;
and the control module 400 is used for controlling the sweeper to collect the garbage to the corresponding dust collecting box.
It is understood that the identification module 100 may be configured to execute the step S100 in the first embodiment, the first determination module 200 may be configured to execute the step S200 in the first embodiment, the second determination module 300 may be configured to execute the step S300 in the first embodiment, and the control module 400 may be configured to execute the step S400 in the first embodiment.
In the embodiment, the dust collecting box corresponding to the garbage identification result is determined by performing garbage identification in the sweeping process of the sweeper; the control machine of sweeping the floor collects corresponding dust-collecting box with rubbish, has avoided collecting rubbish and need artificially classify rubbish behind the same dust-collecting box, consumes the problem of manpower and time, has promoted user experience.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, or they may be separately fabricated into various integrated circuit modules, or multiple modules or steps thereof may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
EXAMPLE III
The embodiment provides an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the computer program is executed by the processor to implement the garbage classification cleaning method according to the first embodiment.
The Processor may be an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components. For the specific contents of the garbage classification cleaning method, please refer to embodiment one, which is not described herein again.
Example four
The embodiment provides a storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by one or more processors, the garbage classification cleaning method of the first embodiment is realized.
The storage medium may be a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application mall, etc. For the specific contents of the garbage classification cleaning method, please refer to embodiment one, which is not described herein again.
EXAMPLE five
The present embodiment provides a sweeper, as shown in fig. 7, including:
the image acquisition system is connected with the processing device and is used for acquiring shape data and image data in real time in the sweeping process of the sweeper;
a plurality of dust collecting boxes for collecting garbage of different garbage categories respectively;
the processing device is used for acquiring the shape data and the image data acquired by the image acquisition system and performing garbage recognition; determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category; determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box; controlling the sweeper to collect the garbage into the corresponding dust collecting box.
In the embodiment, the shape data and the image data are collected in real time in the sweeping process of the sweeper through the image collecting system, garbage recognition is carried out according to the shape data and the image data, and the dust collecting box corresponding to the garbage recognition result is determined; the control machine of sweeping the floor collects corresponding dust-collecting box with rubbish, has avoided collecting rubbish and need artificially classify rubbish behind the same dust-collecting box, consumes the problem of manpower and time, has promoted user experience.
Preferably, the image acquisition system comprises:
the structured light system is connected with the processing device and is used for acquiring shape data in real time in the sweeping process of the sweeper;
the camera is connected with the processing device and used for collecting image data in real time in the sweeping process of the sweeper.
For example, the image capturing system may be an image capturing system shown in fig. 8 in the prior art, but is not limited thereto, a camera may be installed in front of or on both sides of the structured light system to capture image data and shape data simultaneously, and the installation position of the camera does not affect the structured light system to capture shape data, and is not limited to the front of or on both sides of the structured light system.
Preferably, the processing means may comprise:
the GPU is connected with the image acquisition system and used for acquiring the shape data and the image data acquired by the image acquisition system, extracting the characteristics of the shape data and the image data by using a preset characteristic extraction algorithm, determining a garbage identification result by using a preset garbage identification algorithm and the extracted characteristic data, and determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category; determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box; controlling the sweeper to collect the garbage into the corresponding dust collecting box.
And the AI chip is connected with the GPU and is used for storing a preset feature extraction algorithm and a preset garbage recognition algorithm.
Since a GPU (Graphics Processing Unit) needs to perform feature extraction on shape data and image data by using a preset feature extraction algorithm, determine a garbage recognition result by using a preset garbage recognition algorithm and the extracted feature data, and the preset feature extraction algorithm and the preset garbage recognition algorithm need to occupy a large amount of storage space, in order to improve the speed and efficiency of garbage recognition Processing by the GPU and ensure the real-time performance of garbage classification cleaning, the preset feature extraction algorithm and the preset garbage recognition algorithm are stored by an AI chip to reduce the storage pressure of the GPU and improve the Processing capability.
In this embodiment, carry out rubbish identification processing to shape data and image data through GPU, relative CPU, the processing speed promotes by a wide margin, to the machine of sweeping the floor, accurately acquire shape data and image data in the environment through structured light system and camera, GPU carries out rubbish identification processing to shape data and image data, the processing speed has effectively been improved, control machine of sweeping the floor is with rubbish automatic collection to the dust collection box that corresponds, the problem that needs the people to classify rubbish behind the same dust collection box with rubbish collection of current machine of sweeping the floor, consume manpower and time has promoted user experience. Can reach and clear the dust-collecting box who confirms simultaneously according to rubbish, recognition speed is fast, avoids the time delay, cleans efficiency and obviously improves.
It can be understood that, in another preferred mode, the GPU may be further configured to perform feature extraction on the shape data and the image data by using a preset feature extraction algorithm; comparing the extracted characteristic data with characteristic data of a pre-stored garbage learning model; determining garbage corresponding to the shape data and the image data.
Therefore, preferably, the sweeper further includes a storage system for storing the pre-stored garbage learning model required for garbage recognition, and the pre-stored garbage learning model in this embodiment is stored in the storage system, so that the storage space of the GPU and the AI chip is not occupied, and the operation speed of the GPU is not affected. The pre-stored garbage learning model is obtained by training feature data of different garbage by adopting a deep learning algorithm. After the extracted feature data is compared with the feature data of the pre-stored garbage learning model in the garbage identification process, the garbage corresponding to the currently acquired shape data and image data can be determined from the plurality of pre-stored garbage learning models. The deep learning algorithm may be, but is not limited to, Convolutional Neural Networks (CNN). The pre-stored garbage learning model may include, but is not limited to: paper scraps, plastics, dregs, paper, leftovers, fruit peels, fruit pits, milk, liquid and the like.
The storage system may further store a computer program, and the computer program, when executed by the GPU, implements the garbage classification cleaning method according to the first embodiment.
The application environment of the sweeper is likely to change, so that the pre-stored garbage learning model can be updated at any time according to the application environment of the sweeper to adapt to garbage recognition of the sweeper in different environments, and the pre-stored garbage learning model is obtained through off-line training, so that real-time processing time is not required to be occupied.
It can be understood that, the sweeper in this embodiment may further include: the device comprises a traveling system, a driving system, a sensor system and a cleaning system.
Wherein, traveling system includes: left and right traveling wheels and universal wheels; the drive system includes: the left and right travelling wheel driving motors and the universal wheel motor; the driving system can drive the walking system to realize the walking mode switching, for example, the walking mode can comprise a normal walking mode, an acceleration walking mode and a deceleration walking mode.
Further, the above cleaning system includes: the side brush and the rolling brush collect the garbage into the dust collection port through the side brush and the rolling brush in the process of classifying and cleaning the sweeper, and the garbage is sucked into the corresponding dust collection box to finish the classified cleaning of the garbage.
Further, the sensor system comprises: the infrared sensor, cliff sensor, keep away barrier sensor etc. realize the different signal acquisition of machine of sweeping the in-process through these sensors.
The sweeper in this embodiment can also realize control and display functions through the APP, and the APP can be realized in the PC end or the mobile phone end APP. For example, APP provides a dust box selection interface, and a user can autonomously select which dust box to clean garbage, and remotely control the sweeper to perform classified cleaning. The display interface that APP provided can show the rubbish of sweeping the floor machine and the dust collection box that its corresponding classification, use that discern at present.
In summary, according to the garbage classification cleaning method, the garbage classification cleaning system, the electronic device, the storage medium and the sweeper provided by the invention, the garbage recognition is carried out in the cleaning process of the sweeper, and the dust collection box corresponding to the garbage recognition result is determined; the control machine of sweeping the floor collects corresponding dust-collecting box with rubbish, has avoided collecting rubbish and need artificially classify rubbish behind the same dust-collecting box, consumes the problem of manpower and time, has promoted user experience. Furthermore, the shape data and the image data are used as data for garbage identification, so that the garbage characteristics can be accurately reflected, the garbage identification accuracy is improved, the problems that the garbage identification accuracy is not high and the result is easy to misjudge or not identified by only using the image data are solved, and the accurate garbage category is determined in the following process, so that the sweeper is controlled to collect the garbage into the dust collecting box corresponding to the garbage category.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It should be noted that, in the present invention, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term "comprising" is used to specify the presence of stated features, integers, steps, operations, elements, components, and/or groups thereof, but does not exclude the presence of other similar features, integers, steps, operations, components, or groups thereof.
Although the embodiments of the present invention have been described above, the above description is only for the convenience of understanding the present invention, and is not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (11)
1. A garbage classification cleaning method is applied to a sweeper, the sweeper comprises a plurality of dust collecting boxes respectively corresponding to different garbage categories, and the method comprises the following steps:
carrying out garbage recognition in the sweeping process of the sweeper;
determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category;
determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box;
controlling the sweeper to collect the garbage into the corresponding dust collecting box.
2. The garbage classification and cleaning method according to claim 1, wherein the garbage recognition during the cleaning process of the sweeper comprises:
acquiring shape data and image data in real time in the sweeping process of the sweeper;
and performing garbage recognition processing on the shape data and the image data to obtain a garbage recognition result.
3. The garbage classification cleaning method according to claim 2, wherein the performing of the garbage recognition processing on the shape data and the image data to obtain a garbage recognition result includes:
extracting the characteristics of the shape data and the image data by using a preset characteristic extraction algorithm;
and determining a garbage recognition result by using a preset garbage recognition algorithm and the extracted characteristic data.
4. The garbage classification cleaning method according to claim 2, wherein the performing of the garbage recognition processing on the shape data and the image data to obtain a garbage recognition result includes:
extracting the characteristics of the shape data and the image data by using a preset characteristic extraction algorithm;
comparing the extracted characteristic data with characteristic data of a pre-stored garbage learning model;
determining garbage corresponding to the shape data and the image data.
5. The garbage classification cleaning method according to claim 4, wherein the pre-stored garbage learning model is obtained by training with a deep learning algorithm by using feature data of different garbage.
6. The utility model provides a waste classification cleaning system which characterized in that is applied to the machine of sweeping the floor, the machine of sweeping the floor includes a plurality of dust-collecting box that correspond respectively with different rubbish categories, the system includes:
the recognition module is used for recognizing garbage in the sweeping process of the sweeper;
the first determining module is used for determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category;
the second determining module is used for determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box;
and the control module is used for controlling the sweeper to collect the garbage to the corresponding dust collecting box.
7. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, implements the garbage classification cleaning method according to any one of claims 1 to 5.
8. A storage medium having stored thereon a computer program which, when executed by one or more processors, implements a method of garbage classified cleaning as claimed in any one of claims 1 to 5.
9. A sweeper is characterized by comprising:
the image acquisition system is connected with the processing device and is used for acquiring shape data and image data in real time in the sweeping process of the sweeper;
a plurality of dust collecting boxes for collecting garbage of different garbage categories respectively;
the processing device is used for acquiring the shape data and the image data acquired by the image acquisition system and performing garbage recognition; determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category; determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box; controlling the sweeper to collect the garbage into the corresponding dust collecting box.
10. The sweeper of claim 9, wherein the image acquisition system comprises:
the structured light system is connected with the processing device and is used for acquiring shape data in real time in the sweeping process of the sweeper;
the camera is connected with the processing device and used for collecting image data in real time in the sweeping process of the sweeper.
11. The sweeper of claim 9, wherein the processing device comprises:
the GPU is connected with the image acquisition system and used for acquiring the shape data and the image data acquired by the image acquisition system, extracting the characteristics of the shape data and the image data by using a preset characteristic extraction algorithm, determining a garbage identification result by using a preset garbage identification algorithm and the extracted characteristic data, and determining the garbage category corresponding to the garbage identification result according to the preset corresponding relation between the garbage and the garbage category; determining the dust collecting box corresponding to the garbage identification result according to the preset corresponding relation between the garbage category and the dust collecting box; controlling the sweeper to collect the garbage into the corresponding dust collecting box;
and the AI chip is connected with the GPU and is used for storing a preset feature extraction algorithm and a preset garbage recognition algorithm.
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