CN110929754B - Processing method and device of equipment distribution network data, computer equipment and storage medium - Google Patents
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Abstract
The application relates to a processing method and device of equipment distribution network data, computer equipment and a storage medium. The method comprises the following steps: acquiring an image containing a device control panel; inputting an 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 an identification result of equipment; and displaying the distribution network data. The equipment type is automatically identified through the trained image identification model, the distribution network data corresponding to the equipment type is obtained, the distribution network data is displayed, inconvenience caused by manually searching the model 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 data of a device distribution network, a computer device, and a storage medium.
Background
Along with development of computer technology and communication technology, intelligent house starts to walk into people's life, and the equipment that contains in the intelligent house is various, and contains the equipment of different producers, and the distribution network mode of each equipment is all different, along with the increase of household electrical appliances type, network configuration becomes more complicated, and the user needs to find corresponding household electrical appliances model more troublesome, and the configuration process is complicated, distributes the net through the use instruction of looking over equipment, and user experience is poor.
Disclosure of Invention
In order to solve the technical problems, the application provides a processing method and device of equipment distribution network data, computer equipment and a storage medium.
In a first aspect, the present application provides a method for processing data of a device distribution network, including:
acquiring an image containing a device control panel;
Inputting an 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 an identification result of equipment;
And displaying the distribution network data.
In a second aspect, the present application provides a device for processing data of a device distribution network, including:
The data acquisition module is used for acquiring an image containing the 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 recognition results 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 steps of:
acquiring an image containing a device control panel;
Inputting an 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 an identification result of equipment;
And displaying the distribution network data.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring an image containing a device control panel;
Inputting an 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 an identification result of equipment;
And displaying the distribution network data.
The method, the device, the computer equipment and the storage medium for processing the equipment distribution network data comprise the following steps: acquiring an image containing a device control panel; inputting an 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 an identification result of equipment; and displaying the distribution network data. The equipment type is identified through the trained image identification model, the distribution network data corresponding to the equipment type is obtained, the distribution network data is displayed, inconvenience caused by manually searching the model 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 invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is an application environment diagram of a method for processing device distribution network data in one embodiment;
FIG. 2 is a flow chart of a method for processing data of a device distribution network in one embodiment;
FIG. 3 is a block diagram of a device for processing data in a device distribution network in one embodiment;
Fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Fig. 1 is an application environment diagram of a method for processing device configuration network data in one embodiment. Referring to fig. 1, the method for processing data of a device distribution network is applied to a system for processing data of a device distribution network. The processing system of the device distribution network data comprises a shooting device 110 and a computer device 120. The computer device 120 acquires the image including the device control panel captured by the capturing device 110, inputs the image to the trained image recognition model, extracts the features of the control panel through the trained image recognition model, outputs the recognition result of the device according to the extracted features of the control panel, acquires the distribution network data corresponding to the recognition result of the device, 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 can be a desktop terminal or a mobile terminal, and the mobile terminal can 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 composed of a plurality of servers.
As shown in fig. 2, in one embodiment, a method for processing device distribution network data is provided. The present embodiment is mainly exemplified by the application of the method to the photographing apparatus 110 (or the computer apparatus 120) in fig. 1 described above. Referring to fig. 2, the method for processing the equipment distribution network data specifically includes the following steps:
Step S201, an image including a device control panel is acquired.
Step S202, inputting an 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 device according to the extracted characteristics of the control panel.
Step S203, acquiring distribution network data corresponding to the identification result of the device.
Step S204, displaying the distribution network data.
Specifically, the image of the device control panel is an image obtained by photographing the device by the photographing device, and the image includes the control panel of the device. The control panel refers to a panel including operations of controlling the operation of the apparatus, networking, and the like. The trained image recognition model is trained from a number of images carrying device type tags and containing a device control surface. 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 model may be a machine learning model and a deep learning model, such as a deep learning model including, but not limited to Inception models, VGG models, and the like, wherein Inception models include InceptionV1, inceptionV2, inceptionV3, inceptionV, and the like. The features of the control panel extracted by the trained image recognition model determine the recognition result of the device. The identification result is determined according to the similarity between the characteristics of the control panel and the preset characteristics in the trained image identification model, and the maximum similarity is the determined result. The identification result can be the type, the 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 a method for guiding a user to carry out distribution network, the distribution network data is displayed, and the distribution network data comprises at least one of video, text, language and the like. The equipment type is identified through the trained image identification model, the distribution network data corresponding to the equipment type is obtained, the distribution network data is displayed, inconvenience caused by manually searching the model is avoided, and user experience is improved.
In one embodiment, device reset data corresponding to a device identification result is obtained; device reset data is displayed, the device reset data being used to guide the resetting of the device.
Specifically, the device reset data is data for guiding a user to perform a reset operation on the device. Reset means that the device enters a network-capable state, and the device can be discovered and connected by networking terminals such as application terminals. After the reset operation is executed, the network distribution data corresponding to the identification result of the equipment is acquired. The network allocation data corresponding to the identification result of the device may be acquired after the reset is successful, or may be acquired by a colleague who acquires the reset data.
The control panel needs to execute a reset operation to finish the network distribution, for example, two keys in the control panel are pressed for 5 seconds for finishing the reset of the equipment. The appearance of every household electrical appliances is different, and different household electrical appliances guide text (join in marriage net data and equipment reset data) is different, because the household electrical appliances are of a wide variety, and the manual identification degree of difficulty is big, directly adopts trained image recognition model to discern the image, finds corresponding guide text fast according to the recognition result, promotes user experience.
In one embodiment, generating a trained image recognition model includes: acquiring a training image, wherein the training image carries equipment labels; 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 converges or not according to the labels of the training images and the corresponding prediction results; and when the initial image recognition model converges, obtaining a trained image recognition model.
Specifically, the training image is an image for training the initial image recognition model, the training image carries a device tag, the device tag is data for uniquely identifying a device, different recognition tags represent different types of devices, and the devices of the same control panel have the same device tag. Inputting the training images into an initial image recognition model, extracting the characteristics of a control panel of the training images by adopting the initial image recognition model, determining the prediction results of the training images according to the characteristics of the control panel of each training image, matching the prediction results of each training image with corresponding equipment labels to obtain corresponding matching results, and judging whether the initial image recognition model is converged according to the matching results, wherein convergence conditions can be customized according to requirements, model convergence conditions of conventional machine learning or deep learning can be adopted, and the customized model convergence conditions can also be adopted, such as error thresholds including mean square error, root mean square error, average absolute error, exponential error and the like of the model convergence conditions of conventional machine learning or deep learning. And when the preset convergence condition is met, the representation model converges to obtain a trained image recognition model.
In one embodiment, when the initial image recognition model is not converged, updating parameters of the initial image recognition model according to labels of the training images and corresponding prediction results until the initial image recognition model is updated to converge, and obtaining the trained image recognition model.
Specifically, when the preset convergence condition is not met, the model is not converged, the parameters of the initial image recognition model are updated, the training image is retrained by adopting the initial image recognition model with updated parameters, the prediction result is obtained again, whether the initial image model with updated parameters is converged or not is judged according to the obtained prediction result and the corresponding label, if the initial image model with updated parameters is not converged, the parameters of the model are continuously updated until the initial image model with updated parameters meets the preset convergence condition, and the trained image recognition model is obtained. Wherein the updating of the model parameters can be performed by adopting a conventional updating method of model machine learning models, 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 adoption of the transfer learning method can accelerate the training of the model, thereby improving the training efficiency.
In a specific embodiment, a method for processing equipment distribution network data includes:
The method comprises the steps of collecting an image data set, photographing home appliances according to different types under different light environments to obtain training images under various illumination conditions, and obtaining a model with better environmental adaptability by adopting the training images under different illumination conditions. The images are classified according to the model of the household appliance, and corresponding class labels (equipment labels) are marked. The image is preprocessed, wherein the preprocessing includes cropping, rotation, interpolation, drying, agitation, color conversion, and the like. The data is preprocessed to obtain an image which is processed into a batch to serve as input data of a neural network, such as input data of Inception-V3 model, wherein Inception-V3 is divided into 46 layers and consists of 11 Inception modules, inception filters with different sizes are used, and matrixes obtained by the filters with different sizes are spliced together. And training Inception-V3 through the training images to obtain a trained Inception-V3 model. The trained model is adopted as an initial image recognition model, namely, other models are migrated, so that a neural network model with good effect can be trained in a short time while a smaller amount of data is used.
When a user joins in marriage the network, prompting the user to open a camera, aiming at the household appliance to take a picture, obtaining an image containing a control panel of the household appliance, transmitting the image to a trained image recognition model, outputting a recognition result of the image, obtaining 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, obtaining corresponding network distribution data according to the recognition result, and adopting the obtained network distribution data to know that the user completes network distribution operation. The distribution network data comprises one or more of distribution network prompt, distribution network image or distribution network video data. The user does not need to read rigid instruction operation any more, and the user experience is improved through voice and electronic instruction document guiding operation and interaction friendliness.
Fig. 2 is a flow chart of a method for processing data of a device distribution network in an embodiment. It should be understood that, although the steps in the flowchart of fig. 2 are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2 may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
In one embodiment, as shown in fig. 3, there is provided a processing apparatus 200 for device distribution network data, including:
The data acquisition module 201 is configured to acquire an image including a device control panel.
The recognition module 202 is configured to input an image to a trained image recognition model, 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.
And the distribution network data acquisition module 203 is configured to acquire distribution network data corresponding to the identification result of the device.
And the display module 204 is used for displaying the distribution network data.
In one embodiment, the device configuration network data processing apparatus 200 further includes:
and the reset data acquisition module is used for acquiring the device reset data corresponding to the identification result of the device.
And the reset data display module is used for displaying device reset data, and the device reset data is used for guiding the reset of the device.
In one embodiment, the device configuration network data processing apparatus 200 further includes:
a model generation module for generating a trained image recognition model, wherein the model generation module comprises:
the training data acquisition unit is used for acquiring a training image containing the equipment control panel, wherein the training image carries equipment labels.
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 or not according to the labels of the training images and the corresponding prediction results.
And the model generating unit is used for obtaining a trained image recognition model when the initial image recognition model converges.
In one embodiment, the model generating unit is further configured to update parameters of the initial image recognition model according to the labels of the respective training images and the corresponding prediction results when the initial image recognition model is not converged, until the initial image recognition model is updated to converge, and obtain the trained image recognition model.
FIG. 4 illustrates an internal block diagram 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 device is connected to the processor, memory, network interface, input device and display screen via a system bus. The memory includes a nonvolatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system, and may also store a computer program that, when executed by a processor, causes the processor to implement a method for processing device distribution network data. The internal memory may also store a computer program that, when executed by the processor, causes the processor to perform a method for processing device configuration data. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, the device for processing device configuration network data provided by the present application may be implemented as a computer program, and the computer program may run on a computer device as shown in fig. 4. The memory of the computer device may store various program modules constituting the processing means of the network data of the device, such as the data acquisition module 201, the identification module 202, the network data acquisition module 203 and the display module 204 shown in fig. 3. The computer program constituted by the respective program modules causes the processor to execute the steps in the processing method of the equipment distribution network data of the respective embodiments of the present application described in the present specification.
For example, the computer device shown in fig. 4 may perform acquisition of an image containing a device control panel through the data acquisition module 201 in the processing apparatus of the device distribution network data shown in fig. 3. The computer device may execute the input image to the trained image recognition model through the recognition module 202, extract the features of the control panel through the trained image recognition model, and output the recognition result of the device according to the extracted features of the control panel. The computer device may perform 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 execute the display of the distribution network data via 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 steps of when executing the computer program: acquiring an image containing a device control panel; inputting an 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 an identification result of equipment; and displaying the distribution network data.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring device reset data corresponding to a device identification result; device reset data is displayed, the device reset data being used to guide the resetting 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, wherein the training image carries equipment labels; 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 converges or not according to the labels of the training images and the corresponding prediction results; and when the initial image recognition model converges, obtaining a 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 parameters of the initial image recognition model according to labels of all training images and 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 a device control panel; inputting an 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 an identification result of equipment; and displaying the distribution network data.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring device reset data corresponding to a device identification result; device reset data is displayed, the device reset data being used to guide the resetting 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, wherein the training image carries equipment labels; 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 converges or not according to the labels of the training images and the corresponding prediction results; and when the initial image recognition model converges, obtaining a 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 parameters of the initial image recognition model according to labels of all training images and 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.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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 (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It should be noted that in this document, relational terms such as "first" and "second" and the like are 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. Moreover, 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.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the 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 (8)
1. A method for processing data of a device distribution network, the method comprising:
When a user performs network distribution, prompting the user to open a camera of shooting equipment to aim at equipment to be network distributed for shooting, and obtaining an image of an equipment control panel containing the equipment to be network distributed;
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;
Displaying the distribution network data;
wherein generating the trained image recognition model comprises:
Acquiring a training image, wherein the training image carries equipment labels;
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 converges or not according to the labels of the training images and the corresponding prediction results;
And when the initial image recognition model converges, obtaining a trained image recognition model, wherein the trained image is a training image under various illumination conditions obtained by photographing under different light environments, the trained image recognition model is a model obtained by adopting the training image under different illumination conditions, and the trained image recognition model is obtained by training a large number of images which carry equipment type labels and contain equipment control panels.
2. The method according to claim 1, wherein the method further comprises:
acquiring equipment reset data corresponding to the identification result of the equipment;
And displaying the equipment reset data, wherein the equipment reset data is used for guiding the reset of the equipment.
3. The method according to claim 1, wherein the method further comprises:
and when the initial image recognition model is not converged, updating parameters of the initial image recognition model according to labels of the training images and corresponding prediction results until the initial image recognition model with the updated parameters converges, and obtaining the trained image recognition model.
4. The method of claim 1, wherein the initial image recognition model is a migration model.
5. A device for processing data of a device distribution network, the device comprising:
The data acquisition module is used for prompting a user to open a camera of shooting equipment to shoot aiming at equipment to be distributed when the user distributes the network, and acquiring an image of an equipment control panel containing the equipment to be distributed;
the recognition module is used for inputting the image into 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;
the display module is used for displaying the distribution network data;
Wherein the apparatus further comprises:
The model generation module is used for generating the trained image recognition model;
wherein, the model generation module includes:
The training data acquisition unit is used for acquiring training images, and the training images carry equipment labels;
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;
the convergence judging unit is used for judging whether the initial image recognition model converges or not according to the labels of the training images and the corresponding prediction results;
The model generation unit is used for obtaining the trained image recognition model when the initial image recognition model converges, wherein the training image is a training image under various illumination conditions obtained by photographing under different light environments, the trained image recognition model is a model obtained by adopting training images under different illumination conditions, and the trained image recognition model is obtained by training a large number of images which carry equipment type labels and contain equipment control panels.
6. The apparatus of claim 5, wherein the apparatus further comprises:
the reset data acquisition module is used for acquiring equipment reset data corresponding to the identification result of the equipment;
the reset data display module is used for displaying the equipment reset data, and the equipment reset data is used for guiding the reset of the equipment;
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 the signal of successful equipment reset is received.
7. 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 processor implements the steps of the method according to any one of claims 1 to 4 when the computer program is executed.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910995612.1A CN110929754B (en) | 2019-10-18 | 2019-10-18 | Processing method and device of equipment distribution network data, computer equipment and storage medium |
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