CN110929754A - Method and device for processing equipment distribution network data, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a method and a device for processing equipment distribution network data, computer equipment and a storage medium. The method comprises the following steps: acquiring an image containing an equipment control panel; inputting an image to a trained image recognition model, extracting the characteristics of a control panel through the trained image recognition model, and outputting the recognition result of equipment according to the extracted characteristics of the control panel; acquiring distribution network data corresponding to the identification result of the equipment; and displaying the distribution network data. The device type is automatically identified through the trained image identification model, the distribution network data corresponding to the device type are obtained, the distribution network data are displayed, inconvenience caused by manual model searching is avoided, and user experience is improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for processing device distribution network data, a computer device, and a storage medium.
Background
Along with the development of computer technology and communication technology, the smart home starts to walk into people's life, and the equipment that contains in the smart home is various, and contains the equipment of different producers, and the net mode of joining in marriage of each equipment is all inequality, and along with the increase of household electrical appliances type, the network configuration becomes more complicated, and it is more troublesome that the user will find the household electrical appliances model that corresponds, and the configuration process is complicated, joins in marriage the net through looking over the instruction manual of equipment, and user experience is poor.
Disclosure of Invention
In order to solve the technical problem, the application provides a method and a device for processing equipment distribution network data, computer equipment and a storage medium.
In a first aspect, the present application provides a method for processing device distribution network data, including:
acquiring an image containing an equipment control panel;
inputting an image to a trained image recognition model, extracting the characteristics of a control panel through the trained image recognition model, and outputting the recognition result of equipment according to the extracted characteristics of the control panel;
acquiring distribution network data corresponding to the identification result of the equipment;
and displaying the distribution network data.
In a second aspect, the present application provides a device for processing device distribution network data, including:
the data acquisition module is used for acquiring an image containing an equipment control panel;
the recognition module is used for inputting images to the trained image recognition model, extracting the characteristics of the control panel through the trained image recognition model and outputting the recognition result of the equipment according to the extracted characteristics of the control panel;
the distribution network data acquisition module is used for acquiring distribution network data corresponding to the identification result of the equipment;
and the display module is used for displaying the distribution network data.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring an image containing an equipment control panel;
inputting an image to a trained image recognition model, extracting the characteristics of a control panel through the trained image recognition model, and outputting the recognition result of equipment according to the extracted characteristics of the control panel;
acquiring distribution network data corresponding to the identification result of the equipment;
and displaying the distribution network data.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring an image containing an equipment control panel;
inputting an image to a trained image recognition model, extracting the characteristics of a control panel through the trained image recognition model, and outputting the recognition result of equipment according to the extracted characteristics of the control panel;
acquiring distribution network data corresponding to the identification result of the equipment;
and displaying the distribution network data.
The method, the device, the computer equipment and the storage medium for processing the distribution network data of the equipment comprise the following steps: acquiring an image containing an equipment control panel; inputting an image to a trained image recognition model, extracting the characteristics of a control panel through the trained image recognition model, and outputting the recognition result of equipment according to the extracted characteristics of the control panel; acquiring distribution network data corresponding to the identification result of the equipment; and displaying the distribution network data. The device type is identified through the trained image identification model, the distribution network data corresponding to the device type are obtained, the distribution network data are displayed, inconvenience caused by manual model searching is avoided, and user experience is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a diagram of an application environment of a method for processing device distribution network data according to an embodiment;
fig. 2 is a schematic flow chart illustrating a method for processing device distribution network data according to an embodiment;
FIG. 3 is a block diagram of an apparatus for processing device distribution network data according to an embodiment;
FIG. 4 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is an application environment diagram of a processing method of device distribution network data in an embodiment. Referring to fig. 1, the method for processing the equipment distribution network data is applied to a system for processing the equipment distribution network data. The processing system of the device distribution network data comprises a shooting device 110 and a computer device 120. The computer device 120 acquires an image including a device control panel photographed by the photographing device 110, inputs the image to the trained image recognition model, extracts features of the control panel through the trained image recognition model, outputs a device recognition result according to the extracted features of the control panel, acquires distribution network data corresponding to the device recognition result, and displays the distribution network data.
The data acquisition, device identification, and policy acquisition and policy display processes described above may all be performed on the capture device 110.
The computer device 120 includes a terminal and/or a server. The terminal may specifically be a desktop terminal or a mobile terminal, and the mobile terminal may specifically be at least one of a mobile phone, a tablet computer, a notebook computer, and the like. The server may be implemented as a stand-alone server or as a server cluster consisting of a plurality of servers.
As shown in fig. 2, in an embodiment, a method for processing device distribution network data is provided. The present embodiment is mainly illustrated by applying the method to the shooting device 110 (or the computer device 120) in fig. 1. Referring to fig. 2, the method for processing the distribution network data of the device specifically includes the following steps:
in step S201, an image including a device control panel is acquired.
Step S202, inputting images to the trained image recognition model, extracting the characteristics of the control panel through the trained image recognition model, and outputting the recognition result of the equipment according to the extracted characteristics of the control panel.
Step S203, acquiring distribution network data corresponding to the identification result of the device.
And step S204, displaying distribution network data.
Specifically, the image of the device control panel is an image obtained by shooting the device by the shooting device, and the control panel of the device is included in the image. The control panel is a panel that controls operations of the device such as operation, networking, and the like. The trained image recognition model is obtained by training a large number of images which carry device type labels and contain the control surface of the device. The features of the control panel refer to features extracted according to feature extraction parameters in the trained image recognition model. The trained image recognition models can be machine learning models and deep learning models, such as deep learning models including, but not limited to, inclusion models, VGG models, and the like, wherein the inclusion models include inclusion v1, inclusion v2, inclusion v3, inclusion v4, and the like. The recognition result of the device is determined by the features of the control panel extracted by the trained image recognition model. The recognition result is determined according to the similarity between the features of the control panel and the preset features in the trained image recognition model, and the maximum similarity is the determined result. The identification result can be the type, model and the like of the equipment, and corresponding distribution network data is obtained according to the identification result of the equipment, wherein the distribution network data is used for guiding a user to carry out a distribution network method and displaying the distribution network data, and the distribution network data comprises at least one of videos, texts, languages and the like. The device type is identified through the trained image identification model, the distribution network data corresponding to the device type are obtained, the distribution network data are displayed, inconvenience caused by manual model searching is avoided, and user experience is improved.
In one embodiment, device reset data corresponding to a recognition result of a device is obtained; display device reset data, the device reset data to direct a reset of the device.
Specifically, the device reset data is data for guiding a user to perform a reset operation on the device. The reset means that the device enters a state of being capable of distributing the network, and the device can be discovered and connected by the networking terminals such as the application terminal and the like. And after the reset operation is executed, acquiring distribution network data corresponding to the identification result of the equipment. The distribution network data corresponding to the identification result of the device may be acquired after the reset is successful, or the distribution network data may be acquired by a colleague acquiring the reset data.
The control panel needs to perform a reset operation to complete the distribution network, for example, two keys in the control panel are pressed for 5 seconds to complete the reset of the equipment. The appearances of all household appliances are different, different household appliance guidance documentations (distribution network data and equipment reset data) are different, and due to the fact that household appliances are various in types and large in difficulty in manual identification, images are directly identified by the trained image identification model, the corresponding guidance documentations are quickly found according to identification results, and user experience is improved.
In one embodiment, generating a trained image recognition model comprises: acquiring a training image carrying an equipment label; inputting each training image to the initial image recognition model, and outputting a prediction result corresponding to each training image; judging whether the initial image recognition model converges according to the label of each training image and the corresponding prediction result; and when the initial image recognition model converges, obtaining the trained image recognition model.
Specifically, the training image is an image used for training an initial image recognition model, the training image carries an equipment tag, the equipment tag is data used for uniquely identifying equipment, different recognition tags represent different types of equipment, and the equipment of the same control panel has the same equipment tag. Inputting a training image into an initial image recognition model, extracting the characteristics of a control panel of the training image by using the initial image recognition model, determining the prediction result of the training image according to the characteristics of the control panel of each training image, matching the prediction result of each training image with a corresponding equipment label to obtain a corresponding matching result, and judging whether the initial image recognition model converges according to the matching result, wherein the convergence condition can be customized according to requirements, and the convergence condition of a conventional machine learning or deep learning model can be adopted, namely the customized model convergence condition can be adopted, for example, the convergence condition of the conventional machine learning or deep learning model comprises error thresholds such as mean square error, root mean square error, mean absolute error, exponential error and the like. And when the preset convergence condition is met, the model is represented to be converged, and the trained image recognition model is obtained.
In one embodiment, when the initial image recognition model is not converged, the parameters of the initial image recognition model are updated according to the labels of the training images and the corresponding prediction results until the initial image recognition model is updated to be converged, and the trained image recognition model is obtained.
Specifically, when the preset convergence condition is not met, the model is represented to be not converged, the parameters of the initial image recognition model are updated, the training image is retrained again by using the initial image recognition model with the updated parameters, the prediction result is obtained again, whether the initial image model with the updated parameters is converged is judged according to the obtained prediction result and the corresponding label, if not, the parameters of the model are continuously updated until the initial image model with the updated parameters meets the preset convergence condition, and the trained image recognition model is obtained. The updating of the model parameters can adopt a conventional updating method of the model machine learning model, such as a gradient descent method, an end-to-end training method and the like.
In one embodiment, the initial image recognition model is a migration model, i.e., the initial image recognition model is a recognition model trained in other scenarios. The model training can be accelerated by adopting the transfer learning method, so that the training efficiency is improved.
In a specific embodiment, a method for processing device distribution network data includes:
the method comprises the steps of collecting an image data set, photographing household appliances in different light environments according to different types to obtain training images under various illumination conditions, wherein models obtained by adopting the training images under different illumination conditions have better environmental adaptability. The images are classified according to the models of the home appliances, and corresponding category labels (device labels) are printed. The image is pre-processed, wherein the pre-processing includes cropping, rotating, interpolating, dessicating, noise adding, color conversion, and the like. The method comprises the steps of preprocessing data to obtain an image, arranging the image into batch as input data of a neural network, and using the batch as input data of an inclusion-V3 model, wherein the inclusion-V3 is divided into 46 layers and consists of 11 inclusion modules, and the inclusion uses filters with different sizes to splice matrixes obtained by the filters with different sizes. Training the Incep-V3 through a training image to obtain a trained Incep-V3 model. The trained model is adopted as an initial image recognition model, other models are migrated, a small amount of data can be used, and a neural network model with good effect can be trained in a short time.
When a user is in a network distribution, prompting the user to open a camera, shooting the household appliance to obtain an image of a control panel containing household appliance equipment, transmitting the image to a trained image recognition model, outputting a recognition result of the image, acquiring corresponding equipment reset data according to the recognition result, executing reset operation according to the reset data, completing reset operation, jumping to a network distribution interface of the household appliance, acquiring corresponding network distribution data according to the recognition result, and knowing that the user completes network distribution operation by adopting the acquired network distribution data. The distribution network data comprises one or more of distribution network prompting words, distribution network images or distribution network video data. The user does not need to read a stiff instruction for operation any more, the operation is guided through voice and electronic instruction documents, the interaction is friendly, and the user experience is improved.
Fig. 2 is a schematic flow chart of a processing method of device distribution network data in an embodiment. It should be understood that, although the steps in the flowchart of fig. 2 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 3, there is provided an apparatus 200 for processing device distribution network data, including:
a data acquiring module 201, configured to acquire an image including a device control panel.
And the recognition module 202 is used for inputting images to the trained image recognition model, extracting the characteristics of the control panel through the trained image recognition model, and outputting the recognition result of the equipment according to the extracted characteristics of the control panel.
And a distribution network data obtaining module 203, configured to obtain distribution network data corresponding to the identification result of the device.
And the display module 204 is configured to display the distribution network data.
In an embodiment, the apparatus 200 for processing device distribution network data further includes:
and the reset data acquisition module is used for acquiring the equipment reset data corresponding to the identification result of the equipment.
And the reset data display module is used for displaying the equipment reset data, and the equipment reset data is used for guiding the equipment reset.
In an embodiment, the apparatus 200 for processing device distribution network data further includes:
a model generation module for generating a trained image recognition model, wherein the model generation module comprises:
and the training data acquisition unit is used for acquiring a training image containing the equipment control panel, and the training image carries an equipment label.
And the training prediction unit is used for inputting each training image to the initial image recognition model and outputting a prediction result corresponding to each training image.
And the convergence judging unit is used for judging whether the initial image recognition model converges according to the label of each training image and the corresponding prediction result.
And the model generation unit is used for obtaining the trained image recognition model when the initial image recognition model converges.
In one embodiment, the model generating unit is further configured to, when the initial image recognition model is not converged, update parameters of the initial image recognition model according to the labels of the training images and the corresponding prediction results until the initial image recognition model is updated to be converged, and obtain the trained image recognition model.
FIG. 4 is a diagram illustrating an internal structure of a computer device in one embodiment. The computer device may specifically be the photographing device 110 (or the computer device 120) in fig. 1. As shown in fig. 4, the computer apparatus includes a processor, a memory, a network interface, an input device, and a display screen connected via a system bus. Wherein the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and also stores a computer program, and when the computer program is executed by a processor, the computer program can enable the processor to realize the processing method of the device distribution network data. The internal memory may also store a computer program, and the computer program, when executed by the processor, may cause the processor to perform a method for processing device distribution network data. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, the device distribution network data processing apparatus provided in the present application may be implemented in the form of a computer program, and the computer program may be executed on a computer device as shown in fig. 4. The memory of the computer device can store various program modules constituting the processing device of the distribution network data of the device, such as a data acquisition module 201, an identification module 202, a distribution network data acquisition module 203 and a display module 204 shown in fig. 3. The computer program formed by the program modules enables the processor to execute the steps of the method for processing the equipment distribution network data of the embodiments of the application described in the specification.
For example, the computer device shown in fig. 4 may perform the acquisition of the image containing the device control panel by the data acquisition module 201 in the processing apparatus of the device distribution network data shown in fig. 3. The computer device may perform inputting an image to the trained image recognition model through the recognition module 202, extract features of the control panel through the trained image recognition model, and output a recognition result of the device according to the extracted features of the control panel. The computer device may perform the acquisition of the distribution network data corresponding to the identification result of the device through the distribution network data acquisition module 203. The computer device may perform displaying the distribution network data through the display module 204.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program: acquiring an image containing an equipment control panel; inputting an image to a trained image recognition model, extracting the characteristics of a control panel through the trained image recognition model, and outputting the recognition result of equipment according to the extracted characteristics of the control panel; acquiring distribution network data corresponding to the identification result of the equipment; and displaying the distribution network data.
In one embodiment, the processor, when executing the computer program, further performs the steps of: acquiring equipment reset data corresponding to the identification result of the equipment; display device reset data, the device reset data to direct a reset of the device.
In one embodiment, the processor, when executing the computer program, further performs the steps of: generating a trained image recognition model, comprising: acquiring a training image carrying an equipment label; inputting each training image to the initial image recognition model, and outputting a prediction result corresponding to each training image; judging whether the initial image recognition model converges according to the label of each training image and the corresponding prediction result; and when the initial image recognition model converges, obtaining the trained image recognition model.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and when the initial image recognition model is not converged, updating the parameters of the initial image recognition model according to the labels of the training images and the corresponding prediction results until the initial image recognition model is updated to be converged, and obtaining the trained image recognition model.
In one embodiment, the initial image recognition model is a migration model.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: acquiring an image containing an equipment control panel; inputting an image to a trained image recognition model, extracting the characteristics of a control panel through the trained image recognition model, and outputting the recognition result of equipment according to the extracted characteristics of the control panel; acquiring distribution network data corresponding to the identification result of the equipment; and displaying the distribution network data.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring equipment reset data corresponding to the identification result of the equipment; display device reset data, the device reset data to direct a reset of the device.
In one embodiment, the computer program when executed by the processor further performs the steps of: generating a trained image recognition model, comprising: acquiring a training image carrying an equipment label; inputting each training image to the initial image recognition model, and outputting a prediction result corresponding to each training image; judging whether the initial image recognition model converges according to the label of each training image and the corresponding prediction result; and when the initial image recognition model converges, obtaining the trained image recognition model.
In one embodiment, the computer program when executed by the processor further performs the steps of: and when the initial image recognition model is not converged, updating the parameters of the initial image recognition model according to the labels of the training images and the corresponding prediction results until the initial image recognition model is updated to be converged, and obtaining the trained image recognition model.
In one embodiment, the initial image recognition model is a migration model.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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 an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A method for processing equipment distribution network data is characterized by comprising the following steps:
acquiring an image containing an equipment control panel;
inputting the image to a trained image recognition model, extracting the characteristics of the control panel through the trained image recognition model, and outputting the recognition result of the equipment according to the extracted characteristics of the control panel;
acquiring distribution network data corresponding to the identification result of the equipment;
and displaying the distribution network data.
2. The method of claim 1, further comprising:
acquiring equipment reset data corresponding to the identification result of the equipment;
displaying the device reset data, the device reset data for directing a reset of the device.
3. The method of claim 1, wherein generating the trained image recognition model comprises:
acquiring a training image, wherein the training image carries an equipment label;
inputting each training image to an initial image recognition model, and outputting a prediction result corresponding to each training image;
judging whether the initial image recognition model is converged or not according to the label of each training image and the corresponding prediction result;
and when the initial image recognition model converges, obtaining the trained image recognition model.
4. The method of claim 1, further comprising:
and when the initial image recognition model is not converged, updating the parameters of the initial image recognition model according to the labels of the training images and the corresponding prediction results until the initial image recognition model with the updated parameters is converged, and obtaining the trained image recognition model.
5. The method of claim 3, wherein the initial image recognition model is a migration model.
6. An apparatus for processing device distribution network data, the apparatus comprising:
the data acquisition module is used for acquiring an image containing an equipment control panel;
the recognition module is used for inputting the images to a trained image recognition model, extracting the characteristics of the control panel through the trained image recognition model, and outputting the recognition result of the equipment according to the extracted characteristics of the control panel;
the distribution network data acquisition module is used for acquiring distribution network data corresponding to the identification result of the equipment;
and the display module is used for displaying the distribution network data.
7. The apparatus of claim 6, further comprising:
the reset data acquisition module is used for acquiring equipment reset data corresponding to the identification result of the equipment;
a reset data display module for displaying the device reset data, the device reset data being used to guide the reset of the device;
the distribution network data acquisition module is further used for executing the acquisition of the distribution network data corresponding to the identification result of the equipment when receiving a signal that the equipment is successfully reset.
8. The apparatus of claim 6, further comprising:
a model generation module for generating the trained image recognition model, wherein the model generation module comprises:
the training data acquisition unit is used for acquiring a training image containing an equipment control panel, and the training image carries an equipment label;
the training prediction unit is used for inputting each training image to an initial image recognition model and outputting a prediction result corresponding to each training image;
a convergence judging unit, configured to judge whether the initial image recognition model converges according to the label of each training image and the corresponding prediction result;
and the model generation unit is used for obtaining the trained image recognition model when the initial image recognition model converges.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 5 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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