CN114112221B - Detection method, detection device, electronic equipment, garbage can and storage medium - Google Patents
Detection method, detection device, electronic equipment, garbage can and storage medium Download PDFInfo
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- CN114112221B CN114112221B CN202111176294.XA CN202111176294A CN114112221B CN 114112221 B CN114112221 B CN 114112221B CN 202111176294 A CN202111176294 A CN 202111176294A CN 114112221 B CN114112221 B CN 114112221B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/02—Investigating fluid-tightness of structures by using fluid or vacuum
- G01M3/04—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point
- G01M3/16—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means
- G01M3/18—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means for pipes, cables or tubes; for pipe joints or seals; for valves; for welds; for containers, e.g. radiators
- G01M3/186—Investigating fluid-tightness of structures by using fluid or vacuum by detecting the presence of fluid at the leakage point using electric detection means for pipes, cables or tubes; for pipe joints or seals; for valves; for welds; for containers, e.g. radiators for containers, e.g. radiators
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W30/00—Technologies for solid waste management
- Y02W30/10—Waste collection, transportation, transfer or storage, e.g. segregated refuse collecting, electric or hybrid propulsion
Abstract
The application provides a detection method, a detection device, electronic equipment, a garbage can and a storage medium, wherein the detection method comprises the following steps: collecting first liquid leakage information in the dustbin; inputting a digital value corresponding to the first liquid leakage information into a pre-established prediction model to determine output information, wherein the prediction model is obtained by training based on a sample data set; and determining whether the garbage can has leakage based on the output information.
Description
Technical Field
The present disclosure relates to the field of detection technologies, and in particular, to a detection method, a detection device, an electronic device, a garbage can, and a storage medium.
Background
At present, a garbage can or a garbage collecting box which is being used by people is sleeved with a garbage bag when in use, when the garbage bag is full, the garbage bag is taken out, but aiming at wet garbage such as kitchen garbage, when leakage occurs, liquid of the kitchen garbage can drop into the garbage can, if the leakage amount is large, peculiar smell can be generated if the leakage amount is not treated in time, the problems of mosquito attraction and the like are solved, and therefore, whether the garbage can has liquid leakage is important to avoid the liquid leakage to generate peculiar smell and attract mosquitoes.
Disclosure of Invention
The present application provides a detection method, apparatus, electronic device, trash can, and storage medium for solving the above-mentioned problems in the related art, by constructing a model in advance, then converting the collected liquid leakage information into a digital value, inputting the digital value into a prediction model to determine output information, and determining whether leakage exists by outputting the information, it is possible to realize detection of whether leakage exists.
The application provides a detection method, which comprises the following steps:
collecting first liquid leakage information in the dustbin;
inputting a digital value corresponding to the first liquid leakage information into a pre-established prediction model to determine output information, wherein the prediction model is obtained by training based on a sample data set;
and determining whether the garbage can has leakage based on the output information.
In some embodiments, the determining whether the trash can has a leak based on the output information includes:
under the condition that the output information is the lighting information of the indicator lamp, determining that leakage exists in the dustbin;
and under the condition that the output information is the information that the indicator lamp is not on, determining that the garbage can is not leaked.
In some embodiments, the method further comprises:
when the output information is the indication lamp lighting information, the indication lamp is controlled to be lighted.
In some embodiments, the method further comprises:
and converting the first liquid leakage information into digital values corresponding to the first liquid leakage information, wherein each digital value corresponds to one liquid leakage information.
In some embodiments, the method further comprises:
acquiring an initial sample data set, wherein the initial sample data set is obtained by adopting a data acquisition device to perform a water drop experiment, and the initial sample data set comprises: the first digital value is different from the second digital value in the first digital value and the indication lamp lighting information and the second digital value is different from the indication lamp non-lighting information;
performing standardization processing on each sample data in the initial sample data set to obtain the sample data set;
and establishing a prediction model by adopting a deep learning algorithm based on the sample data set.
In some embodiments, the data acquisition device comprises: the sensing plate is used for sensing whether liquid exists or not and outputting a level signal; the indicator light module is connected with the induction plate and used for controlling whether to light based on the level information; a detection module for detecting second liquid leakage information of the liquid; and the conversion module is used for converting the second liquid leakage information into a digital value.
In some embodiments, the method further comprises:
acquiring a verification data set; the data in the verification data set comprises a third digital value and indication lamp lighting information;
and verifying the prediction model based on the verification data set.
The embodiment of the application provides a detection device, including:
the collecting module is used for collecting first liquid leakage information in the garbage can;
the first determining module is used for inputting the digital value corresponding to the first liquid leakage information into a pre-established prediction model to determine output information, wherein the prediction model is obtained by training based on a sample data set;
and the second determining module is used for determining whether leakage exists in the garbage can or not based on the output information.
An embodiment of the present application provides an electronic device, including a memory and a processor, where the memory stores a computer program, and when the computer program is executed by the processor, the method performs any one of the foregoing detection methods.
The embodiment of the application provides a garbage can, which comprises the electronic equipment.
Embodiments of the present application provide a storage medium storing a computer program executable by one or more processors, which is capable of implementing any one of the above detection methods.
According to the detection method, the detection device, the electronic equipment and the storage medium, the model is built in advance, then the digital value corresponding to the acquired first liquid leakage information is input into the prediction model to determine the output information, whether leakage exists or not is determined through the output information, and whether leakage exists or not can be detected.
Drawings
The present application will be described in more detail hereinafter based on embodiments and with reference to the accompanying drawings.
Fig. 1 is a schematic implementation flow chart of a detection method according to an embodiment of the present application;
fig. 2 is a schematic implementation flow chart of another detection method according to an embodiment of the present application;
fig. 3 is a schematic implementation flow chart of another detection method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a detection device according to an embodiment of the present application;
fig. 5 is a schematic diagram of a composition structure of an electronic device according to an embodiment of the present application.
In the drawings, like parts are given like reference numerals, and the drawings are not drawn to scale.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
If a similar description of "first\second\third" appears in the application document, the following description is added, in which the terms "first\second\third" are merely distinguishing between similar objects and do not represent a particular ordering of the objects, it being understood that the "first\second\third" may be interchanged in a particular order or precedence, where allowed, so that the embodiments of the application described herein can be implemented in an order other than that illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.
Based on the problems existing in the related art, the embodiment of the application provides a detection method, which is applied to electronic equipment, wherein the electronic equipment can be a mobile terminal, a computer and the like. The functions implemented by the detection method provided by the embodiment of the application may be implemented by calling a program code by a processor of the electronic device, where the program code may be stored in a computer storage medium.
An embodiment of the present application provides a detection method, and fig. 1 is a schematic implementation flow diagram of the detection method provided in the embodiment of the present application, as shown in fig. 1, including:
step S1, collecting first liquid leakage information in the garbage can.
The collection module of electronic equipment can gather the first liquid leakage information in the garbage bin, collection module can be the raindrop detection sensor, first liquid leakage information can be the volume of liquid, and in this application embodiment, electronic equipment can with first liquid leakage information turns into the digital value that first liquid leakage information corresponds, and every digital value corresponds with a liquid leakage information. The first liquid leakage information may be converted into a digital value corresponding to the first liquid leakage information based on a stored correspondence table, where the correspondence table includes: correspondence between each fluid leak information and a digital value.
In the embodiment of the present application, the digital value may include: 0 to 255. The larger the digital value, the less the corresponding leak information for the liquid.
And S2, inputting the digital value corresponding to the first liquid leakage information into a pre-established prediction model to determine output information, wherein the prediction model is obtained by training based on a sample data set.
In the embodiment of the application, the prediction model can be obtained through training through the sample data set. When training is performed, an initial neural network model is firstly obtained, a digital value in a sample data set is taken as input, a corresponding indicator light is lighted or the indicator light is not lighted as output, and a prediction model is obtained by updating the coefficient of the initial neural network model. The sample data set comprises a first sample data set and a second sample data set, each sample data in the first sample data set comprising: a first digital value and indicator light illumination information, each sample data in the second sample data set comprising: the second digital value and the indication lamp non-lighting information, and the output information is the indication lamp lighting information or the indication lamp non-lighting information.
In the embodiment of the application, the digital values in the first sample data set and the second sample data set are different. Each digital value in the first sample data set is smaller than each digital value in the second sample data set, and each digital value is determined based on the resistance value determined by the data acquisition device during data acquisition. .
In this embodiment of the present application, after inputting a digital value corresponding to the first liquid leakage information into a pre-established prediction model, output information may be obtained, where the output information is indication lamp lighting information or indication lamp non-lighting information.
And step S3, determining whether leakage exists in the garbage can or not based on the output information.
In the embodiment of the application, under the condition that the output information is the lighting information of the indicator lamp, the existence of leakage of the dustbin is determined; and under the condition that the output information is the information that the indicator lamp is not on, determining that the garbage can is not leaked.
In the embodiment of the application, the detection of whether leakage exists can be realized by constructing the model in advance, converting the acquired liquid leakage information into the digital value, inputting the digital value into the prediction model to determine the output information, and determining whether the leakage exists or not through the output information.
Based on the foregoing embodiments, the embodiments of the present application further provide a detection method, and fig. 2 is a schematic implementation flow diagram of another detection method provided in the embodiments of the present application, as shown in fig. 2, where the method includes:
step S21, collecting first liquid leakage information in the garbage can, and converting the first liquid leakage information into corresponding digital values, wherein each digital value corresponds to one liquid leakage information.
The collection module of electronic equipment can gather the first liquid leakage information in the garbage bin, collection module is with being the raindrop detection sensor, first liquid leakage information can be the volume of liquid, and in this application embodiment, can be based on the corresponding relation table of storage and first liquid leakage information conversion digital value that first liquid leakage information corresponds, include in the corresponding relation table: correspondence between each fluid leak information and a digital value.
In the embodiment of the present application, the digital value may include: 0 to 255. The larger the digital value, the less the corresponding leak information for the liquid.
Step S22, inputting the digital value corresponding to the first liquid leakage information into a pre-established prediction model to determine output information, wherein the prediction model is obtained by training based on a sample data set.
In the embodiment of the application, the prediction model can be obtained through training through the sample data set. When training is performed, an initial neural network model is firstly obtained, a digital value in a sample data set is taken as input, a corresponding indicator light is lighted or the indicator light is not lighted as output, and a prediction model is obtained by updating the coefficient of the initial neural network model. The sample data set comprises a first sample data set and a second sample data set, each sample data in the first sample data set comprising: a first digital value and indicator light illumination information, each sample data in the second sample data set comprising: the second digital value and the indication lamp non-lighting information, and the output information is the indication lamp lighting information or the indication lamp non-lighting information.
In the embodiment of the application, the digital values in the first sample data set and the second sample data set are different. Each digital value in the first sample data set is smaller than each digital value in the second sample data set.
In this embodiment of the present application, after inputting a digital value corresponding to the first liquid leakage information into a pre-established prediction model, output information may be obtained, where the output information is indication lamp lighting information or indication lamp non-lighting information.
And S23, determining that the garbage can has leakage under the condition that the output information is the lighting information of the indicator lamp.
In step S24, when the output information is the indication lamp lighting information, the indication lamp is controlled to be lighted.
In this application embodiment, through the model of constructing in advance, then through converting the liquid leakage information that gathers into the digital value to confirm output information in the predictive model with the digital value input, confirm under the condition that there is the seepage through output information, control pilot lamp lights, can remind the user to handle as early as possible, can avoid producing peculiar smell and bringing the mosquito because of the liquid of seepage.
Based on the foregoing embodiments, the embodiments of the present application further provide a detection method, and fig. 3 is a schematic implementation flow diagram of another detection method provided in the embodiments of the present application, as shown in fig. 3, where the method includes:
step S31, an initial sample data set is obtained, wherein the initial sample data set is obtained by performing a water droplet experiment by using a data acquisition device, and the initial sample data set includes: the first digital value and the indication lamp lighting information, and the second digital value and the indication lamp non-lighting information.
In the embodiment of the application, when a water drop experiment is carried out, leakage data of wet garbage liquid are collected through simulation in a real environment. The experimental data are collected by the data collecting device. The data acquisition device comprises: the sensing plate is used for sensing whether liquid exists or not and outputting a level signal; the indicator light module is connected with the induction plate and used for controlling whether to light based on the level information; a detection module for detecting second liquid leakage information of the liquid; and the conversion module is used for converting the second liquid leakage information into a digital value.
The level signal includes: a high level signal and a low level signal. The data acquisition device may be a raindrop detection sensor.
The process of carrying out the experiment through data acquisition device is as follows, connects 5V power, and the power pilot lamp is bright, and when there is not water droplet on the induction plate, the output is high level on the induction plate, and the switch pilot lamp goes out this moment, drops a drip, and the induction plate output is low level, and the switch pilot lamp is bright, brushes off the water droplet above, resumes to output high level state again. The detection module detects the water amount dropped on the water tank, and the conversion module converts the water amount into water.
Data collected through a large number of experiments are combined with data such as batch standardized deep learning cycle records. The AO analog output is connected with an analog input AIN0 port, and the size of the water drop on the AO analog output can be detected by comparing the size of the digital value converted from the analog value, and the larger the water drop is, the smaller the resistance value is, and the smaller the digital value converted from the analog value is. The range of the digital value is 0-255, and different values correspond to the millimeter of leaked liquid, and the data of the raindrop detection sensor which is triggered and activated to light and not light by a large amount of experimental data are acquired.
And step S32, carrying out standardization processing on each sample data in the initial sample data set to obtain the sample data set.
In the present embodiment, these gradients are adjusted from outliers to normal values by batch normalization and are converged to a common target in a small batch range (by normalization).
And step S33, establishing a prediction model by adopting a deep learning algorithm based on the sample data set.
Step S34, acquiring a verification data set; the data in the verification data set comprises a third digital value and indication lamp lighting information;
and step S35, verifying the prediction model based on the verification data set.
In the embodiment of the application, the accuracy of the prediction model can be ensured by verifying the data set. In the case where the verification satisfies the accuracy, step S36 is performed, and if the verification does not satisfy the accuracy, step S31 is performed.
Step S36, collecting first liquid leakage information in the garbage can, and converting the first liquid leakage information into digital values corresponding to the first liquid leakage information, wherein each digital value corresponds to one liquid leakage information.
The collection module of electronic equipment can gather the first liquid leakage information in the garbage bin, collection module is with being the raindrop detection sensor, first liquid leakage information can be the volume of liquid, and in this application embodiment, can be based on the corresponding relation table of storage and first liquid leakage information conversion digital value that first liquid leakage information corresponds, include in the corresponding relation table: correspondence between each fluid leak information and a digital value.
In the embodiment of the present application, the digital value may include: 0 to 255. The larger the digital value, the less the corresponding leak information for the liquid.
Step S37, inputting the digital value corresponding to the first liquid leakage information into a pre-established prediction model to determine output information, where the prediction model is obtained by training based on a sample data set, the sample data set includes a first sample data set and a second sample data set, and each sample data in the first sample data set includes: a first digital value and indicator light illumination information, each sample data in the second sample data set comprising: the output information is the indication lamp lighting information or the indication lamp non-lighting information, and the first digital value is different from the second digital value.
In the embodiment of the application, the prediction model can be obtained through training through the sample data set. When training is performed, an initial neural network model is firstly obtained, a digital value in a sample data set is taken as input, a corresponding indicator light is lighted or the indicator light is not lighted as output, and a prediction model is obtained by updating the coefficient of the initial neural network model.
In the embodiment of the application, the digital values in the first sample data set and the second sample data set are different. Each digital value in the first sample data set is smaller than each digital value in the second sample data set.
In this embodiment of the present application, after inputting a digital value corresponding to the first liquid leakage information into a pre-established prediction model, output information may be obtained, where the output information is indication lamp lighting information or indication lamp non-lighting information.
And step S38, determining that the garbage can has leakage under the condition that the output information is the lighting information of the indicator lamp.
In step S39, when the output information is the indication lamp lighting information, the indication lamp is controlled to be lighted.
In the embodiment of the application, the initial sample data set is acquired, the initial sample data set is subjected to standardized processing to obtain the sample data set, the prediction model is built based on the sample data set, the accuracy of the prediction model is verified, in the actual application process, the acquired liquid leakage information is converted into a digital value, the digital value is input into the prediction model to determine output information, whether leakage exists or not is determined through the output information, and whether leakage exists or not can be detected. Different leakage amounts can be detected, and due to different leakage amounts, the leakage amounts can be omitted, for example, under the condition of very small leakage amount, and reminding can be carried out under the condition of large leakage amount, so that peculiar smell and mosquito can be prevented from being generated due to leaked liquid.
Based on the foregoing embodiments, the embodiments of the present application provide a detection apparatus, where each module included in the detection apparatus, and each unit included in each module may be implemented by a processor in a computer device; of course, the method can also be realized by a specific logic circuit; in practice, the processor may be a central processing unit (CPU, central Processing Unit), a microprocessor (MPU, microprocessor Unit), a digital signal processor (DSP, digital Signal Processing), or a field programmable gate array (FPGA, field Programmable Gate Array), or the like.
An embodiment of the present application provides a detection device, fig. 4 is a schematic structural diagram of the detection device provided in the embodiment of the present application, as shown in fig. 4, a detection device 400 includes:
the collection module 401 is configured to collect first liquid leakage information in the dustbin;
a first determining module 402, configured to input a digital value corresponding to the first liquid leakage information into a pre-established prediction model to determine output information, where the prediction model is obtained by training based on a sample data set;
a second determining module 403, configured to determine whether there is a leak in the trash can based on the output information.
In some embodiments, the second determining module 403 includes:
the first determining unit is used for determining that leakage exists in the garbage can under the condition that the output information is the lighting information of the indicator lamp;
and the second determining unit is used for determining that the garbage can has no leakage under the condition that the output information is the information that the indicator lamp is not on.
In some embodiments, the detection apparatus 400 includes:
and the control unit is used for controlling the indication lamp to be lighted when the output information is the indication lamp lighting information.
In some embodiments, the detection apparatus 400 further comprises:
the first acquisition module is used for acquiring an initial sample data set, wherein the initial sample data set is obtained by adopting a data acquisition device to perform water drop experiments, and the initial sample data set comprises: the first digital value and the indication lamp lighting information, and the second digital value and the indication lamp non-lighting information;
the standardized processing module is used for carrying out standardized processing on each sample data in the initial sample data set to obtain the sample data set;
and the establishing module is used for establishing a prediction model by adopting a deep learning algorithm based on the sample data set.
In some embodiments, the data acquisition device comprises: the sensing plate is used for sensing whether liquid exists or not and outputting a level signal; the indicator light module is connected with the induction plate and used for controlling whether to light based on the level information; a detection module for detecting second liquid leakage information of the liquid; and the conversion module is used for converting the second liquid leakage information into a digital value.
In some embodiments, the detection apparatus 400 further comprises:
a second acquisition module for acquiring a verification data set; the data in the verification data set comprises a third digital value and indication lamp lighting information;
and the verification module is used for verifying the prediction model based on the verification data set.
It should be noted that, in the embodiment of the present application, if the above-mentioned detection method is implemented in the form of a software functional module, and is sold or used as a separate product, the detection method may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partly contributing to the prior art, and the computer software product may be stored in a storage medium, and include several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, an embodiment of the present application provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the detection method provided in the above embodiment.
The embodiment of the application provides electronic equipment; fig. 5 is a schematic diagram of a composition structure of an electronic device according to an embodiment of the present application, as shown in fig. 5, the electronic device 500 includes: a processor 501, at least one communication bus 502, a user interface 503, at least one external communication interface 504, a memory 505. Wherein the communication bus 502 is configured to enable connected communication between these components. The user interface 503 may include a display screen, and the external communication interface 504 may include a standard wired interface and a wireless interface, among others. The processor 501 is configured to execute a program of the detection method stored in the memory to realize the steps in the detection method provided in the above-described embodiment.
The embodiment of the application provides a garbage can, which comprises the electronic equipment provided by the embodiment.
The description of the electronic device and the storage medium embodiments above is similar to that of the method embodiments described above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the computer apparatus and the storage medium of the present application, please refer to the description of the method embodiments of the present application.
It should be noted here that: the above description of the storage medium and the electronic device, the trash can embodiments is similar to the description of the method embodiments described above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the storage medium and the apparatus of the present application, please refer to the description of the method embodiments of the present application for understanding.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application. The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments.
It should be noted that, in this document, 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. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read Only Memory (ROM), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the integrated units described above may be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partly contributing to the prior art, embodied in the form of a software product stored in a storage medium, comprising several instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.
The foregoing is merely an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of detection, the method comprising:
collecting first liquid leakage information in the dustbin, wherein the first liquid leakage information is the volume of liquid;
determining a digital value corresponding to the first liquid leakage information based on a stored corresponding relation table, wherein the corresponding relation table comprises corresponding relations between each liquid leakage information and the digital value, and the larger the digital value is, the smaller the corresponding liquid leakage information is;
inputting a digital value corresponding to the first liquid leakage information into a pre-established prediction model to determine output information, wherein the prediction model is obtained by training based on a sample data set;
and determining whether the garbage can has leakage based on the output information.
2. The method of claim 1, wherein the determining whether the trash can has a leak based on the output information comprises:
under the condition that the output information is the lighting information of the indicator lamp, determining that leakage exists in the dustbin;
and under the condition that the output information is the information that the indicator lamp is not on, determining that the garbage can is not leaked.
3. The method according to claim 2, wherein the method further comprises:
when the output information is the indication lamp lighting information, the indication lamp is controlled to be lighted.
4. The method according to claim 1, wherein the method further comprises:
acquiring an initial sample data set, wherein the initial sample data set is obtained by adopting a data acquisition device to perform a water drop experiment, and the initial sample data set comprises: the first digital value is different from the second digital value in the first digital value and the indication lamp lighting information and the second digital value is different from the indication lamp non-lighting information;
performing standardization processing on each sample data in the initial sample data set to obtain the sample data set;
and establishing a prediction model by adopting a deep learning algorithm based on the sample data set.
5. The method of claim 4, wherein the data acquisition device comprises: the sensing plate is used for sensing whether liquid exists or not and outputting a level signal; the indicator light module is connected with the induction plate and used for controlling whether to light based on the level signal; a detection module for detecting second liquid leakage information of the liquid; and the conversion module is used for converting the second liquid leakage information into a digital value.
6. The method according to claim 4, wherein the method further comprises:
acquiring a verification data set; the data in the verification data set comprises a third digital value and indication lamp lighting information;
and verifying the prediction model based on the verification data set.
7. A detection apparatus, characterized by comprising:
the collecting module is used for collecting first liquid leakage information in the garbage can, wherein the first liquid leakage information is the volume of liquid;
a module for determining a digital value corresponding to the first liquid leakage information based on a stored correspondence table, wherein the correspondence table comprises correspondence between each liquid leakage information and the digital value, and the larger the digital value is, the smaller the corresponding liquid leakage information is;
the first determining module is used for inputting the digital value corresponding to the first liquid leakage information into a pre-established prediction model to determine output information, wherein the prediction model is obtained by training based on a sample data set;
and the second determining module is used for determining whether leakage exists in the garbage can or not based on the output information.
8. An electronic device, comprising:
a memory and a processor, said memory having stored thereon a computer program which, when executed by said processor, performs the detection method according to any of claims 1 to 6.
9. A trash can comprising the electronic device of claim 8.
10. A storage medium storing a computer program executable by one or more processors for implementing the detection method according to any one of claims 1 to 6.
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| CN107054932A (en) * | 2017-06-07 | 2017-08-18 | 钱宝珠 | One kind leak detection dustbin |
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