CN116070133A - Detection method, detection device, electronic equipment and storage medium - Google Patents
Detection method, detection device, electronic equipment and storage medium Download PDFInfo
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Abstract
According to the detection method, the detection device, the electronic equipment and the storage medium, a load curve in a scene implementation process is obtained; matching the load curve with a pre-established time sequence model to obtain a matching result; and detecting whether the load is abnormal or not in the scene implementation process based on the matching result, so that whether the load of the scene is abnormal or not can be detected.
Description
Technical Field
The present disclosure relates to the field of floor washing machines, and in particular, to a detection method, a detection device, an electronic device, and a storage medium.
Background
With the development of the age and the progress of technology, the living standard of people is becoming better, and more intelligent home devices are also installed by more and more families. The difference between the traditional home and the intelligent home is greatly reflected in the scene design of the intelligent home. The application of the Internet of things provides the capabilities of logic processing, information transmission and man-machine interaction for household equipment. The perfect combination of scene modes gives smart home life and intelligence.
However, the scene recommends that a plurality of household appliances are started simultaneously by virtue of close fit, a household power supply system can generate extremely large instant current, the load is overlarge, potential safety hazards exist, and problems such as tripping and fire disaster are caused, so that whether the load is abnormal or not is very important.
Disclosure of Invention
In view of the above problems, the present application provides a detection method, a detection device, an electronic apparatus, and a storage medium, which can detect whether a load is abnormal.
The embodiment of the application provides a detection method, which comprises the following steps:
acquiring a load curve in the scene implementation process;
matching the load curve with a pre-established time sequence model to obtain a matching result;
and detecting whether the load is abnormal or not in the scene implementation process based on the matching result.
In some embodiments, the matching the load curve with a pre-established time series model to obtain a matching result includes:
calculating the similarity between a load curve and a sample curve in a pre-established time sequence model by adopting a dynamic time warping algorithm;
the detecting whether the load is abnormal or not in the scene implementation process based on the matching result comprises the following steps:
and detecting whether the load is abnormal in the scene implementation process based on the similarity.
In some embodiments, the detecting whether the load is abnormal in the implementation process of the scene based on the similarity includes:
determining whether each similarity is greater than a similarity threshold;
and determining the label of the sample curve with the similarity larger than a similarity threshold as a detection result of the load in the scene implementation process, wherein the load abnormality is determined under the condition that the label is an abnormal load label.
In some embodiments, the method further comprises:
acquiring sample data of a load;
marking the sample data to obtain labels of the sample data;
training based on the marked sample data to obtain a time sequence model.
In some embodiments, the method further comprises:
and sending out alarm information under the condition that the detection result of the load is abnormal.
In some embodiments, the method further comprises:
acquiring parameter information of each electric appliance in a scene; the parameter information includes: a plurality of parameters;
optimizing the starting sequence of each electric appliance based on the genetic algorithm optimization model to obtain an optimization scheme;
and starting each electric appliance based on the optimized scheme so as to ensure that the load is not abnormal when the scene is started.
The embodiment of the application provides a detection device, including:
the acquisition module is used for acquiring a load curve in the scene implementation process;
the matching module is used for matching the load curve with a pre-established time sequence model to obtain a matching result;
and the detection module is used for detecting whether the load is abnormal or not in the scene implementation process based on the matching result.
In some embodiments, the matching the load curve with a pre-established time series model to obtain a matching result includes:
calculating the similarity between a load curve and a sample curve in a pre-established time sequence model by adopting a dynamic time warping algorithm;
the detecting whether the load is abnormal or not in the scene implementation process based on the matching result comprises the following steps:
and detecting whether the load is abnormal in the scene implementation process based on the similarity.
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.
Embodiments of the present application provide a computer readable storage medium storing a computer program executable by one or more processors for implementing the above-described detection method.
According to the detection method, the detection device, the electronic equipment and the storage medium, a load curve in a scene implementation process is obtained; matching the load curve with a pre-established time sequence model to obtain a matching result; and detecting whether the load is abnormal or not in the scene implementation process based on the matching result, so that whether the load of the scene is abnormal 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 structural diagram of a detection device according to an embodiment of the present application;
fig. 3 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, such as a computer, a mobile terminal and the like, wherein the mobile terminal can comprise a mobile phone, a tablet computer, an intelligent panel and the like. In some embodiments, the electronic device may be a controller of an electric home appliance, and the functions implemented by the detection method provided in the embodiments of the present application may be implemented by invoking program codes by a processor of the electronic device, where the program codes may be stored in a computer storage medium.
Example 1
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 S101, obtaining a load curve in the scene implementation process.
In the embodiment of the application, the user can load the scene through the APP, so that each device in the scene is controlled to start. When the electronic equipment is started, the electronic equipment can be in communication connection with the monitoring device, and the load curve of the scene implementation process is acquired through the monitoring device.
In the embodiment of the application, the load curve in the scene real-time process can be detected, so that the load curve in the scene implementation process can be obtained, and the load curve can be the curve of the current.
And step S102, matching the load curve with a pre-established time sequence model to obtain a matching result.
In this embodiment of the present application, the time sequence model may be a shapelets model, and the electronic device may pre-establish the shapelets model, and in case that a load curve is obtained, may match the load curve with the pre-established time sequence model to obtain a matching result.
In this embodiment, before step S102, the method further includes:
step S1, acquiring sample data of a load.
In embodiments of the present application, the sample data may be abnormal sample data, and in some embodiments, the sample data may further include: abnormal sample data, non-abnormal sample data.
And S2, marking the sample data to obtain labels of the sample data.
In this embodiment of the present application, the labels of the abnormal sample data may be abnormal, normal, abnormal types, and the like.
And step S3, training based on the marked sample data to obtain a time sequence model.
In the embodiment of the application, the time sequence model may be stored in an electronic device, and the electronic device may not be connected to a network device, so that matching between the load curve and a time sequence model established in advance can be achieved, and a matching result is obtained.
In some embodiments, step S102 may be implemented by:
in step S1021, a dynamic time warping algorithm is used to calculate the similarity between the load curve and the sample curve in the pre-established time series model.
The embodiment of the application can be based on the DTW distance matching classification of the dynamic time warping algorithm.
And step S103, detecting whether the load is abnormal or not in the scene implementation process based on the matching result.
In this embodiment of the present application, the matching result may include: similarity between the load curve and the sample data in the time series model.
In the embodiment of the present application, step S103 may be implemented by the following steps:
whether the load is abnormal in the scene implementation process can be detected based on the similarity.
In this embodiment of the present application, a label of a sample curve corresponding to the similarity greater than a similarity threshold is determined as a detection result of a load in the implementation process of the scenario, where, in a case that the label is an abnormal load label, the load is determined to be abnormal.
If the tag is abnormal, the load curve is characterized as abnormal, and in some embodiments, if the tag is normal, the load curve is characterized as normal.
In some embodiments, after step S103, the method further comprises:
step S104, when the detection result of the load is abnormal, alarm information is sent out.
In this embodiment of the application, the alarm information may be pushed through the APP.
According to the detection method, the load curve in the scene implementation process is obtained; matching the load curve with a pre-established time sequence model to obtain a matching result; and detecting whether the load is abnormal or not in the scene implementation process based on the matching result, so that whether the load of the scene is abnormal or not can be detected.
In some embodiments, the method further comprises:
step S105, acquiring parameter information of each electric appliance in a scene; the parameter information includes: a plurality of parameters.
In this embodiment, the electrical apparatus may include: washing machine, air conditioner, computer, robot, etc. The parameter information may include: the type of electrical appliance, power and current.
And S106, optimizing the starting sequence of each electric appliance based on the genetic algorithm optimization model to obtain an optimization scheme.
In this embodiment of the present application, the genetic algorithm optimization model may be pre-established, and the starting sequence of each electrical appliance may be calculated by using a one-bed algorithm optimization model.
And step S107, starting each electric appliance based on the optimized scheme so as to ensure that the load at the scene starting is not abnormal.
Example two
Based on the foregoing embodiments, embodiments of the present application further provide a detection method, where the method includes: the loads are marked by data training and learning shape models, load real-time curves are matched, load abnormality is found in a self-adaptive mode, labor cost is reduced, load data generated in the scene recommendation implementation process are obtained, and then a DTW-shape learning model is trained. The record of abnormally loaded shapelets, on the one hand, may also provide offline data matching for direct home use where uploading of data is not desired.
The model is loaded in an intelligent home central control system, a home circuit load monitoring device is installed or load information is applied to be shared in real time, when an app loading scene is applied by a user, DTW distance matching classification of shapelets is carried out on the load at the moment, the DTW distance is a dynamic time warping distance, and compared with Euclidean distance, the method has strong robustness on complex curves. If an abnormal load, an alarm is provided on the app and the data is uploaded.
According to the scene recommended scheme selected by a user or customized, uploading parameters such as the type of the electric appliance to be started, the power, the current and the like, optimizing the starting sequence of the electric appliance by using a genetic algorithm optimizing model, and feeding back the scheme, wherein the power and the circuit load of the electric appliances of different families are different, so that the self-adaptive electric appliance starting optimizing model is very necessary.
If the family selection information is not uploaded and shared, the household appliances can apply scenes according to several preset scene starting modes in the app respectively, and the optimum scheme is found by using shape matching.
And feeding back the structure obtained by the optimization algorithm to the user, so that the user can realize safer and more stable intelligent home scene application.
Example III
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. 2 is a schematic structural diagram of the detection device provided in the embodiment of the present application, as shown in fig. 2, a detection device 200 includes:
an obtaining module 201, configured to obtain a load curve in a scenario implementation process;
the matching module 202 is configured to match the load curve with a pre-established time sequence model to obtain a matching result;
and the detection module 203 is configured to detect whether the load is abnormal in the implementation process of the scene based on the matching result.
In some embodiments, the matching the load curve with a pre-established time series model to obtain a matching result includes:
calculating the similarity between a load curve and a sample curve in a pre-established time sequence model by adopting a dynamic time warping algorithm;
the detecting whether the load is abnormal or not in the scene implementation process based on the matching result comprises the following steps:
and detecting whether the load is abnormal in the scene implementation process based on the similarity.
In some embodiments, the detecting whether the load is abnormal in the implementation process of the scene based on the similarity includes:
determining whether each similarity is greater than a similarity threshold;
and determining the label of the sample curve with the similarity larger than a similarity threshold as a detection result of the load in the scene implementation process, wherein the load abnormality is determined under the condition that the label is an abnormal load label.
In some embodiments, the detection apparatus 200 is further configured to:
acquiring sample data of a load;
marking the sample data to obtain labels of the sample data;
training based on the marked sample data to obtain a time sequence model.
In some embodiments, the method further comprises:
and sending out alarm information under the condition that the detection result of the load is abnormal.
In some embodiments, the detection apparatus 200 is further configured to:
acquiring parameter information of each electric appliance in a scene; the parameter information includes: a plurality of parameters;
optimizing the starting sequence of each electric appliance based on the genetic algorithm optimization model to obtain an optimization scheme;
and starting each electric appliance based on the optimized scheme so as to ensure that the load is not abnormal when the scene is started.
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 computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the detection method provided in the above embodiment.
Example IV
The embodiment of the application provides electronic equipment; fig. 3 is a schematic diagram of a composition structure of an electronic device according to an embodiment of the present application, as shown in fig. 3, the electronic device 700 includes: a processor 701, at least one communication bus 702, a user interface 703, at least one external communication interface 704, a memory 705. Wherein the communication bus 702 is configured to enable connected communication between these components. The user interface 703 may include a display screen, and the external communication interface 704 may include a standard wired interface and a wireless interface, among others. The processor 701 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 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 description of the storage medium and apparatus embodiments above is similar to that of the method embodiments described above, with similar benefits 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 comprising:
acquiring a load curve in the scene implementation process;
matching the load curve with a pre-established time sequence model to obtain a matching result;
and detecting whether the load is abnormal or not in the scene implementation process based on the matching result.
2. The method of claim 1, wherein matching the load curve with a pre-established time series model to obtain a matching result comprises:
calculating the similarity between a load curve and a sample curve in a pre-established time sequence model by adopting a dynamic time warping algorithm;
the detecting whether the load is abnormal or not in the scene implementation process based on the matching result comprises the following steps:
and detecting whether the load is abnormal in the scene implementation process based on the similarity.
3. The method according to claim 2, wherein the detecting whether the load is abnormal during the implementation of the scene based on the similarity comprises:
determining whether each similarity is greater than a similarity threshold;
and determining the label of the sample curve with the similarity larger than a similarity threshold as a detection result of the load in the scene implementation process, wherein the load abnormality is determined under the condition that the label is an abnormal load label.
4. A method according to claim 3, characterized in that the method further comprises:
acquiring sample data of a load;
marking the sample data to obtain labels of the sample data;
training based on the marked sample data to obtain a time sequence model.
5. A method according to claim 3, characterized in that the method further comprises:
and sending out alarm information under the condition that the detection result of the load is abnormal.
6. The method according to claim 1, wherein the method further comprises:
acquiring parameter information of each electric appliance in a scene; the parameter information includes: a plurality of parameters;
optimizing the starting sequence of each electric appliance based on the genetic algorithm optimization model to obtain an optimization scheme;
and starting each electric appliance based on the optimized scheme so as to ensure that the load is not abnormal when the scene is started.
7. A detection apparatus, characterized by comprising:
the acquisition module is used for acquiring a load curve in the scene implementation process;
the matching module is used for matching the load curve with a pre-established time sequence model to obtain a matching result;
and the detection module is used for detecting whether the load is abnormal or not in the scene implementation process based on the matching result.
8. The apparatus according to claim 7, wherein the matching the load curve with a pre-established time series model to obtain a matching result comprises:
calculating the similarity between a load curve and a sample curve in a pre-established time sequence model by adopting a dynamic time warping algorithm;
the detecting whether the load is abnormal or not in the scene implementation process based on the matching result comprises the following steps:
and detecting whether the load is abnormal in the scene implementation process based on the similarity.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program which, when executed by the processor, performs the method of any of claims 1 to 6.
10. A storage medium storing a computer program executable by one or more processors for implementing a method as claimed in any one of claims 1 to 6.
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| CN202211667565.6A CN116070133A (en) | 2022-12-23 | 2022-12-23 | Detection method, detection device, electronic equipment and storage medium |
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| CN202211667565.6A CN116070133A (en) | 2022-12-23 | 2022-12-23 | Detection method, detection device, electronic equipment and storage medium |
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| CN202211667565.6A Pending CN116070133A (en) | 2022-12-23 | 2022-12-23 | Detection method, detection device, electronic equipment and storage medium |
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