A method and apparatus for automatic network access of dummy resources in the field of intelligent networks.

Abstract

This invention discloses a method and apparatus for automatically adding dumb resources to the network in the field of intelligent self-reliant networks. Belonging to the technical field of dumb resource network addition, it constructs a general front-end and back-end model and the interrelationships between these models. Through data configuration of the models, automatic dumb resource addition is achieved. The general front-end and back-end models include a front-end interface model and a back-end business data model. The front-end interface model includes a device information model, a device image information model, and a device interaction model. The device information model is used to present specific devices, the device image information model is used to present images contained in the device, and the device interaction model is used for interactive management of specific images of specific devices. The back-end business data model includes a device management model, a device model, and a device image model. This invention can automatically add a new dumb resource device to the network, improving the level of the intelligent dumb resource network.

Description

Technical Field

[0001] This invention relates to the field of communication equipment and facilities technology, specifically to a method and apparatus for automatically adding dumb resources to the network in the field of intelligent networks. Background Technology

[0002] With the development of mobile communication, cloud computing, big data, and artificial intelligence, the services provided to individual users and corporate customers are becoming increasingly diverse, and the number of various terminal devices is also increasing dramatically. Among these terminal devices, there are a large number of dumb devices that cannot be managed by the network. Traditionally, the process of adding dumb devices to the network has been done by customizing each one individually. Dumb resources cannot be automatically added to the network, and the addition of each dumb resource to the network requires a lot of IT system development and upgrades. Summary of the Invention

[0003] The technical objective of this invention is to address the above-mentioned shortcomings by providing a method and apparatus for automatically adding dumb resources to the network in the field of intelligent networks. This method and apparatus can automatically enable a new dumb resource device to join the network, thereby improving the level of the intelligent network for dumb resources.

[0004] The technical solution adopted by this invention to solve its technical problem is:

[0005] A method for automatically adding dummy resources to the network in the field of intelligent networks is proposed. This method constructs a common front-end and back-end model and the interrelationships between these models. By configuring the data in these models, the automatic addition of dummy resources to the network is achieved. The common front-end and back-end model includes a front-end interface model and a back-end business data model.

[0006] The front-end interface model includes a device information model, a device image information model, and a device interaction model. The device information model is used to present a specific device, the device image information model is used to present the images contained in the device, and the device interaction model is used to manage the interaction of specific images of a specific device.

[0007] The backend business data model includes a device management model, a device model, and a device image model. The device management model is used to manage specific device types, the device model is used to manage the device information model for each specific device, and the device image model is used to manage the images contained in each type of device.

[0008] Dumb resources, being unmanageable by the network, cannot automatically collect various types of data. Therefore, it is crucial to clearly describe the device when it joins the network, requiring a large amount of image information to facilitate subsequent management and improve the data quality of dumb resources. Thus, dumb resources require two types of information when joining the network: basic device information and relevant image information. Different devices require different types and numbers of images, and each type of image must meet certain requirements to be considered acceptable. Only after the images are acceptable can the device join the network. This method primarily utilizes the interaction between front-end and back-end model devices and between models. When a new type of dumb resource needs to join the network, only the relevant models need to be configured to complete the automatic network joining of the device, thereby improving the level of intelligent networking for dumb resources.

[0009] This method addresses the problem of having to independently consider each new dummy resource when adding a large number of new ones to the network, requiring customized implementations for each. By constructing a common front-end and back-end model and establishing interrelationships between these models, and through data configuration, it enables automatic dummy resource integration. It provides a device for automatic dummy resource integration in situations with a rapid increase in end-user dummy devices, based on sound model design, data configuration, and inter-model interaction. This reduces the need for extensive customized development for dummy resource integration, focusing instead on extracting the inherent business characteristics of the dummy resources themselves, rather than large-scale customization, thus enhancing the level of self-sufficient dummy resource networking.

[0010] Preferably, the data configuration of the model includes adding a device type to the device management model and configuring the API for parsing and storing the device in the database, adding a corresponding device information model to the device model, and adding the image information that needs to be managed for the device type to the device image model.

[0011] Furthermore, when adding new devices, existing APIs are directly used. When existing APIs cannot support a new type of device, the device entry and image parsing APIs are encoded.

[0012] Furthermore, when adding a new device, device information is collected in the front-end device information model, and the corresponding API for parsing and storing the device information is called from the back-end device model to store the device information in the database. Then, the device type is passed to the device image information model.

[0013] The device image information model obtains the image management information corresponding to the device type from the device image model, initializes the device image information model of the device according to the obtained information, and then passes the image name and image parsing API to the device interaction model;

[0014] After the device interaction model takes a picture, it calls the corresponding API for quality inspection and returns the quality inspection result to the device image information model. The device image information model then returns the network access result to the device information model.

[0015] Preferably, if a new dummy resource of type A is added to the network, the process of automatically adding the dummy resource to the network using this method is as follows:

[0016] 1) Add a new device type A to the device type management model in the backend and match the entry API of this device. If the system does not currently match any other API for this device, API implementation coding is required. This API is mainly responsible for entering the device information from the frontend into the corresponding device model.

[0017] 2) Add a device type A and a device information model A to the device model, and manage the device through the device information model;

[0018] 3) Add image information for device type A to the device image model and match the API for parsing the image. If the system does not currently match the API for this type of image, the necessary API implementation encoding needs to be performed. This API is mainly responsible for image recognition of the image and outputting whether the image passes quality inspection.

[0019] 4) Input device information through the front-end device information model;

[0020] 5) Call the API for this device type to import the device model of the specific device into the database;

[0021] 6) In the device information model interface, click "Upload Image" to pass the device type A of this record to the device image information model;

[0022] 7) Obtain the image management information corresponding to the device type from the device image model using device type A;

[0023] 8) Initialize the device image information model of the device according to the obtained image management information, and present it on the interface;

[0024] 9) The device image information model is divided into multiple image areas. By clicking on each image area in the device image information model, the image name and image parsing API are passed to the device interaction model.

[0025] 10) Complete the input of specific image information for each image in the device interaction model, and perform quality inspection on the image by calling the quality inspection API of the image, and return whether the quality inspection passed or failed.

[0026] 11) Return the quality inspection result of the image to the device image information model and display it on the page;

[0027] 12) When all device images pass the quality inspection, the results are returned to the device information model to realize the device's network access. The device's image quality inspection status is "passed" and the status is "network access". At the same time, the device's network access time is entered in the device information model A of the device model.

[0028] Preferably, the device information at the front end is obtained and entered through scanning a code or manually.

[0029] Preferably, the image information of device type A added to the device image model refers to the images required for this type of device to correctly access the network. The number of images required corresponds to the number of information entries, and each information entry includes the image name and the API for parsing the image.

[0030] Preferably, the device image information model of the device is initialized according to the obtained image management information and the interface is presented. In step 7), the number of images is instantiated as many as there are images. The content of each instantiation includes the image name, the default image status and the image parsing API.

[0031] This invention also claims a device for automatically adding dumb resources to the network in the field of intelligent networks. The device has a common front-end and back-end model. Through the interrelation between the models and the data configuration of the models, the automatic addition of dumb resources to the network is realized.

[0032] The common models for both front-end and back-end include a front-end interface model and a back-end business data model.

[0033] The front-end interface model includes a device information model, a device image information model, and a device interaction model. The device information model is used to present a specific device, the device image information model is used to present the images contained in the device, and the device interaction model is used to manage the interaction of specific images of a specific device.

[0034] The backend business data model includes a device management model, a device model, and a device image model. The device management model is used to manage specific device types, the device model is used to manage the device information model for each specific device, and the device image model is used to manage the images contained in each type of device.

[0035] The device enables dumb resources to join the network through the aforementioned method for automatic dumb resource joining in the field of intelligent networks.

[0036] The present invention also claims a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for automatic dummy resource access in the field of intelligent networks.

[0037] The method and apparatus for automatic network access of dumb resources in the field of intelligent networks of the present invention have the following advantages compared with the prior art:

[0038] This method and device successfully solve the problem of dummy resources not being able to automatically join the network (i.e., joining the network for each dummy resource requires extensive IT system development and upgrades) through the design of front-end and back-end models. Tracking of dummy resources joining the network using this device reveals that most only require model data configuration, with a small number (approximately 20%) requiring encoding of image parsing APIs and device database entry APIs. About 80% of dummy resources achieve automatic network joining through model data configuration, while also ensuring the data quality of the joined dummy resources. As more and more dummy resources join the network through this device, more and more reusable image parsing APIs and device database entry APIs will be accumulated. This device provides an efficient method for joining a large number of diverse dummy resources in the future. Attached Figure Description

[0039] Figure 1 This is a diagram illustrating the front-end interface model of the method for automatic network access of dumb resources in the field of intelligent networks provided in this embodiment of the invention;

[0040] Figure 2 This is a diagram illustrating the backend service data model in the method for automatic network access of dumb resources in the field of intelligent networks provided in this embodiment of the invention;

[0041] Figure 3 This invention provides a data configuration example in the method for automatic network access of dumb resources in the field of intelligent networks. Figure 1 ;

[0042] Figure 4 This invention provides a data configuration example in the method for automatic network access of dumb resources in the field of intelligent networks. Figure 2 ;

[0043] Figure 5 This invention provides a data configuration example in the method for automatic network access of dumb resources in the field of intelligent networks. Figure 3 ;

[0044] Figure 6 This is a diagram illustrating the implementation process of the method for automatic network access of dumb resources in the field of intelligent networks provided in this embodiment of the invention;

[0045] Figure 7 This is an example diagram of device model entry into the database in the method for automatic entry of dumb resources into the network in the field of intelligent networks provided in this embodiment of the invention;

[0046] Figure 8It is an example diagram for presenting the device picture information model in the method of automatically connecting dumb resources to the network in the field of self-intelligent network provided by an embodiment of the present invention;

[0047] Figure 9 It is an example diagram for the device interaction model to return passing quality inspection in the method of automatically connecting dumb resources to the network in the field of self-intelligent network provided by an embodiment of the present invention;

[0048] Figure 10 It is an example diagram for returning the quality inspection result of the picture to the device picture information model and presenting it on the page in the method of automatically connecting dumb resources to the network in the field of self-intelligent network provided by an embodiment of the present invention. Detailed implementation manners

[0049] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0050] Since dumb resources cannot be managed by the network management, various types of data cannot be automatically collected. Therefore, when the device is connected to the network, it is necessary to describe the device as clearly as possible, so a large amount of picture information of the device is required to facilitate the subsequent management of the device and improve the data quality of dumb resources. Therefore, when dumb resources are connected to the network, two types of information are required, one is the basic information of the device, and the other is the relevant picture information of the device. Different devices require different types and numbers of pictures, and each type of picture needs to meet certain requirements to be considered qualified. Only when the pictures are qualified can the device be connected to the network. Based on this, an embodiment of the present invention provides a method for automatically connecting dumb resources to the network in the field of self-intelligent network, constructs a general model for the front and back ends and the mutual association between the models, and through the data configuration of the models, realizes the automatic connection of dumb resources through the management and coordination of various models.

[0051] Among them, the general models for the front and back ends include a front-end interface model and a back-end service data model,

[0052] As Figure 1 shown, the front-end interface model includes a device information model, a device picture information model and a device interaction model. The device information model is used for presenting a specific device, the device picture information model is used for presenting the pictures included in the device, and the device interaction model is used for the interactive management of specific pictures of a specific device;

[0053] As Figure 2 shown, the back-end service data model includes a device management model, a device model and a device picture model. The device management model is used for managing a specific device type, the device model is used for managing the device information model of each specific device, and the device picture model is used for managing the pictures included in each type of device.

[0054] The data configuration of the model includes adding a device type in the device management model and configuring the API for parsing and storing the device (such as Figure 3As shown), add corresponding equipment information models (such as...) to the equipment model. Figure 4 As shown), add the image information that needs to be managed for this device type to the device image model (such as...). Figure 5 (As shown).

[0055] In this method, the coding work only involves two APIs: device entry and image parsing. Generally, when adding a new device, the existing APIs can be directly matched and used. Only when the existing APIs cannot support a new type of device is it necessary to code the device entry and image parsing APIs.

[0056] When a new device is added, device information is collected in the front-end device information model, the device information is entered into the database by calling the API corresponding to the device type from the back-end device model, and then the device type is passed to the device image information model.

[0057] The device image information model obtains the image management information corresponding to the device type from the device image model, initializes the device image information model of the device according to the obtained information, and then passes the image name and image parsing API to the device interaction model;

[0058] After the device interaction model takes a picture, it calls the corresponding API for quality inspection and returns the quality inspection result to the device image information model. The device image information model then returns the network access result to the device information model.

[0059] like Figure 6 The diagram shows the process of a new A-type dummy resource automatically joining the network.

[0060] If a new dummy resource of type A is added to the network, the process of automatically adding the dummy resource to the network using this method is as follows:

[0061] 1) Add a new device type A to the device type management model in the backend and match the entry API of this device. If the system does not currently match any other API for this device, API implementation coding is required. This API is mainly responsible for entering the device information from the frontend into the corresponding device model.

[0062] 2) Add a device type A and a device information model A to the device model, and manage the device through the device information model;

[0063] 3) Add image information for device type A to the device image model and match the API for parsing the image. If the system does not currently match the API for this type of image, the necessary API implementation encoding needs to be performed. This API is mainly responsible for image recognition of the image and outputting whether the image passes quality inspection.

[0064] 4) Input device information through the front-end device information model;

[0065] 5) Call the API for this device type to import the device model of the specific device into the database, such as... Figure 7 Example shown;

[0066] 6) In the device information model interface, click "Upload Image" to pass the device type A of this record to the device image information model;

[0067] 7) Obtain the image management information corresponding to device type A from the device image model, such as... Figure 5 Example shown;

[0068] 8) Initialize the device image information model for the device according to the acquired image management information, and present it on the interface, such as... Figure 8 As shown in the example; by obtaining the number of images in step 7), instantiate as many images as possible. Each instantiation includes the image name, the default image state, and the image parsing API.

[0069] 9) The device image information model is divided into multiple image areas. By clicking on each image area in the device image information model, the image name and image parsing API are passed to the device interaction model.

[0070] 10) Complete the input of specific image information for each image in the device interaction model, and perform quality inspection on the image by calling the image's quality inspection API, returning a pass or fail result. Figure 9 Example shown;

[0071] 11) Return the quality inspection result of the image to the device image information model and display it on the page, such as... Figure 10 Example shown;

[0072] 12) When all device images pass the quality inspection, the results are returned to the device information model to realize the device's network access. The device's "image quality inspection" status is "passed" and "status" is "network access". At the same time, the device's network access time is entered in the "A device information model" in the device model.

[0073] The device information at the front end is obtained and entered through scanning a code or manually.

[0074] The image information for device type A added to the device image model refers to the images required for this type of device to correctly access the network. The number of images required corresponds to the number of information entries, and each information entry includes the image name and the API for parsing the image.

[0075] This method addresses the problem of having to independently consider each new dummy resource when adding a large number of new ones to the network, requiring customized implementations for each. By constructing a common front-end and back-end model and establishing interrelationships between these models, and through data configuration, it enables automatic dummy resource integration. It provides a device for automatic dummy resource integration in situations with a rapid increase in end-user dummy devices, based on sound model design, data configuration, and inter-model interaction. This reduces the need for extensive customized development for dummy resource integration, focusing instead on extracting the inherent business characteristics of the dummy resources themselves, rather than large-scale customization, thus enhancing the level of self-sufficient dummy resource networking.

[0076] This invention also provides a device for automatically adding dumb resources to the network in the field of intelligent networks. The device has a common front-end and back-end model. Through the interrelation between the models and the data configuration of the models, the automatic addition of dumb resources to the network is realized.

[0077] The common models for both front-end and back-end include a front-end interface model and a back-end business data model.

[0078] The front-end interface model includes a device information model, a device image information model, and a device interaction model. The device information model is used to present a specific device, the device image information model is used to present the images contained in the device, and the device interaction model is used to manage the interaction of specific images of a specific device.

[0079] The backend business data model includes a device management model, a device model, and a device image model. The device management model is used to manage specific device types, the device model is used to manage the device information model for each specific device, and the device image model is used to manage the images contained in each type of device.

[0080] The data configuration of the model includes adding device types to the device management model and configuring the API for parsing and storing the device in the database, adding corresponding device information models to the device model, and adding image information that needs to be managed for the device type to the device image model.

[0081] In this method, the coding work only involves two APIs: device entry and image parsing. Generally, when adding a new device, the existing APIs can be directly matched and used. Only when the existing APIs cannot support a new type of device is it necessary to code the device entry and image parsing APIs.

[0082] When a new device is added, device information is collected in the front-end device information model, the device information is entered into the database by calling the API corresponding to the device type from the back-end device model, and then the device type is passed to the device image information model.

[0083] The device image information model obtains the image management information corresponding to the device type from the device image model, initializes the device image information model of the device according to the obtained information, and then passes the image name and image parsing API to the device interaction model;

[0084] After the device interaction model takes a picture, it calls the corresponding API for quality inspection and returns the quality inspection result to the device image information model. The device image information model then returns the network access result to the device information model.

[0085] This device achieves the automatic network access of dumb resources through the method for automatic network access of dumb resources in the field of intelligent networks described in the above embodiments.

[0086] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the automatic network access method for dumb resources in the field of intelligent networks described in the above embodiments.

[0087] The steps and methods involved in the apparatus of the above embodiments correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this disclosure.

[0088] Those skilled in the art will understand that the modules or steps described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. This disclosure is not limited to any particular combination of hardware and software.

[0089] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

[0090] While the specific embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this disclosure. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this disclosure are still within the scope of protection of this disclosure.

Claims

1. A method for automatic network entry of a dumb resource in a smart network domain, characterized in that, The general models of the front end and the back end and the interrelation between the models are constructed, and through data configuration of the models, the dumb resource is automatically networked; the general models of the front end and the back end include a front end interface model and a back end business data model, The front end interface model includes a device information model, a device picture information model and a device interaction model, the device information model is used for presenting a specific device, the device picture information model is used for presenting a picture contained by the device, and the device interaction model is used for interactive management of the specific device and the specific picture; The back end business data model includes a device management model, a device model and a device picture model, the device management model is used for managing a specific device type, the device model is used for managing the device information model of each specific device, and the device picture model is used for managing pictures contained by each device type; If a new A type dumb resource is networked, the method implements the process of automatically networked dumb resource as follows: 1) a new A device is added in the device management model of the back end, and an entry API of the device is matched, when no other API of the device is matched in the current system, API implementation coding is needed; the API is mainly responsible for entering the device information of the front end into the corresponding device model; 2) an A device type and an A device information model are added in the device model, and the device is managed through the device information model; 3) picture information of the A device type is added in the device picture model, and an API for analyzing the picture is matched, when no API of the picture of the device type is matched in the current system, necessary API implementation coding is needed; the API is mainly responsible for image recognition of the picture, and outputs whether the picture is passed in quality inspection; 4) device information is entered through the front end device information model; 5) the entry API of the device type is called to enter the device model of the specific device into the warehouse; 6) the device type A of the uploaded record is transmitted to the device picture information model by clicking on the picture in the interface of the device information model; 7) the picture management information corresponding to the device type A is obtained from the device picture model through the device type A; 8) the device picture information model of the device is initialized according to the obtained picture management information, and the interface is presented; 9) a plurality of picture areas are divided in the device picture information model, and the picture name and image analysis API are transmitted to the device interaction model by clicking on each picture area in the device picture information model; 10) the specific picture information of each picture is entered in the device interaction model, and the quality inspection API of the picture is called to realize the quality inspection of the picture, and the quality inspection pass or fail is returned; 11) the quality inspection result of the picture is returned to the device picture information model, and the page is presented; 12) when all the device picture quality inspections are passed, the result is returned to the device information model, the device is networked, the picture quality inspection state of the device is passed, the state is networked, and the device entry time is recorded in the A device information model of the device model.

2. The method for automatic network entry of dumb resources in the field of self- intelligent networks according to claim 1, characterized in that, The data configuration of the model includes adding a device type in the device management model and configuring the API for device parsing and warehousing, adding a corresponding device information model in the device model, and adding picture information required for management of the device type in the device picture model.

3. The method for automatic network entry of a dumb resource in the field of self- intelligent network according to claim 2, characterized in that, When a new device is added, the existing API is directly matched for use, and when the existing API cannot support a new type of device, device warehousing and image parsing API coding are performed.

4. The method for automatic network entry of a dumb resource in the field of self- intelligent networks according to claim 2 or 3, characterized in that, When a new device is added, device information is collected in the front-end device information model, the corresponding warehousing and parsing API of the device type is called from the back-end device model to perform device information warehousing, and then the device type is passed to the device picture information model; The device picture information model obtains the picture management information corresponding to the device type from the device picture model, initializes the device picture information model according to the obtained information, and then passes the picture name and picture parsing API to the device interaction model; After the device interaction model takes a picture, the API corresponding to the picture is called to perform quality inspection, and the quality inspection result is returned to the device picture information model, and the device picture information model returns the network entry result to the device information model.

5. The method for automatic network entry of dumb resources in the field of self- intelligent networks according to claim 1, characterized in that, The front-end device information is obtained by scanning code or manual input.

6. The method for automatic network entry of dumb resources in the field of self- intelligent networks according to claim 1, characterized in that, The picture information of the A device type added in the device picture model, i.e., the pictures required for correct network entry of this type of device, requires several pieces of information to be input, and each piece of information includes a picture name and an API for parsing the picture.

7. The method for automatic network entry of a dumb resource in the field of self- intelligent network according to claim 1, characterized in that, The device picture information model of the device is initialized according to the obtained picture management information, and the interface is presented, and several pictures are instantiated through step 7), and each instantiated content includes a picture name, a default picture state, and a picture parsing API.

8. A device for automatic network entry of a dumb resource in a smart network domain, characterized in that, The device has front-end and back-end general models, and through the mutual association between the models and the data configuration of the models, the dumb resource is automatically connected to the network; The front-end and back-end general models include a front-end interface model and a back-end business data model, The front-end interface model includes a device information model, a device picture information model, and a device interaction model, the device information model is used for presenting a specific device, the device picture information model is used for presenting pictures contained by the device, and the device interaction model is used for interactive management of a specific device and a specific picture; The back-end business data model includes a device management model, a device model, and a device picture model, the device management model is used for managing a specific device type, the device model is used for managing the device information model of each specific device, and the device picture model is used for managing pictures contained by each type of device; The device realizes dumb resource network entry through the method for dumb resource automatic network entry in the intelligent network field in any one of claims 1 to 7.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to realize the dumb resource automatic network entry method in the intelligent network field in any one of claims 1 to 7.

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