Dynamic release system for wireless routing

Abstract

The present invention relates to a dynamic release system for wireless routing, the system comprising: a startup detection device connected to a wireless router device, used to issue a first control instruction when detecting that the wireless router device is started, and to issue a second control instruction when detecting that the wireless router device is in a shutdown state; a third extraction device, used to determine the number of dynamic IP addresses that the wireless router device needs to automatically release after startup based on the number of network terminals in a foreground image area obtained through intelligent analysis. The dynamic release system for wireless routing of the present invention has a compact structure and intelligent operation. Since the BP neural network that has completed multiple trainings can be used to intelligently analyze the number of network terminals on site, and the number of dynamic IP addresses that the wireless router device needs to automatically release after startup is determined based on the number of network terminals on site, the connection speed and connection efficiency between the wireless router device and each network terminal nearby are improved.

Description

Technical Field

[0001] The invention relates to the field of routing strategy management, and in particular to a dynamic release system for wireless routing. Background Art

[0002] Wireless routers are mainly used to execute routing policy management. With the gradual popularization of broadband networks, broadband routers have been used more and more widely, deriving and developing the broadband routing market. Router products are also of various types. It is necessary to select the corresponding type of wireless router suitable for the application scenario according to specific usage requirements, for example, the maximum transmit and receive power of the wireless router, and the maximum number of concurrent lines of the wireless router.

[0003] However, in the actual use of the wireless router, it is unable to distinguish in real time the number of network terminals with network usage needs in the current wireless network usage environment, resulting in that the network resources allocated in real time can only be allocated in as many directions as possible to avoid network scenarios with insufficient network resources, such as dynamically allocated network addresses. This relatively excessive allocation of network resources actually brings difficulties to the management of the wireless router and causes an unnecessary increase in the operating power consumption of the wireless router.

[0004] The invention application publication CN115278726A discloses a 5G smart router control system based on the Internet of Things, including a smart router control system, a router client control system, a data intelligent optimization system, and a diversion data intelligent optimization system. The smart router control system is connected to an Internet of Things information input terminal and an Internet of Things information output terminal, and the Internet of Things information input terminal and the Internet of Things information output terminal are receiving and transmitting modules for the router 5G network data. The smart router control system is connected to a router client control system. Through the design of systems such as the smart router control system and the diversion data intelligent optimization system, the problems of low control authority of existing routers, inability to perform reasonable control operations within the authority, and insufficient intelligence of existing router network allocation, and lack of the ability to identify and prioritize devices that currently require network traffic support are solved. Summary of the invention

[0005] In order to solve the technical problems in the prior art, the present invention proposes a dynamic release system for wireless routing, which can input the binary representation value of the standard pattern corresponding to the network terminal, the total number of pixels occupied by the foreground image area, and the red and green component values, black and white component values, and yellow and blue component values ​​of each pixel in the foreground image area in the LAB space into a BP neural network that has completed multiple trainings to run the BP neural network that has completed multiple trainings, obtain the number of network terminals in the foreground image area output by the BP neural network that has completed multiple trainings, and determine the number of dynamic IP addresses that need to be automatically released by the wireless router device after startup based on the number of network terminals in the foreground image area, thereby improving the connection speed and connection efficiency between the wireless router device and each network terminal nearby.

[0006] According to the technical solution of the present invention, the system comprises:

[0007] A startup detection device connected to the wireless router device, used to issue a first control instruction when detecting that the wireless router device is started, and to issue a second control instruction when detecting that the wireless router device is in a shutdown state;

[0008] A visual input device, arranged above the wireless router device and connected to the start detection device, for acquiring a visual imaging image of the environment where the wireless router device is located upon receiving the first control instruction;

[0009] A multi-layer optimization device, comprising a first-layer optimization device, a second-layer optimization device and a last-layer optimization device, wherein the second-layer optimization device is connected to the first-layer optimization device and the last-layer optimization device respectively, and the first-layer optimization device is also connected to the visual input device, and is used to perform spatial differential sharpening processing on the received visual imaging image to obtain and output a corresponding data-sharpened image, the second-layer optimization device is used to perform guided filtering processing on the received data-sharpened image to obtain and output a corresponding guided filtered image, and the last-layer optimization device is used to perform adaptive recursive filtering processing on the received guided filtered image to obtain and output a corresponding multi-layer optimized image;

[0010] A first extraction device is connected to the multi-layer optimization device, and is used to detect the mean square error of each brightness value corresponding to each pixel point of each image area in the received multi-layer optimization image, and when the mean square error is less than a set mean square error limit, the image area is judged to be a background image area, otherwise, the image area is judged to be a foreground image area;

[0011] A second extraction device, connected to the first extraction device, is used to input the binary representation value of the standard pattern corresponding to the network terminal, the total number of pixels occupied by the foreground image area, and the red and green component values, black and white component values, and yellow and blue component values ​​of each pixel in the foreground image area in the LAB space into the BP neural network that has completed multiple trainings to run the BP neural network that has completed multiple trainings, and obtain the number of network terminals in the foreground image area output by the BP neural network that has completed multiple trainings;

[0012] A third extraction device, connected to the second extraction device, is used to determine the number of dynamic IP addresses that need to be automatically released by the wireless router after startup based on the number of network terminals in the foreground image area;

[0013] The main controller of the wireless router device is connected to the third extraction device, and is used to automatically release a corresponding number of dynamic IP addresses after startup based on the received number of dynamic IP addresses that need to be automatically released;

[0014] Among them, in the BP neural network after completing multiple trainings, the number of trainings is proportional to the total number of pixels of the multi-layer optimized image. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The embodiments of the present invention will be described below with reference to the accompanying drawings.

[0016] Figure 1 FIG. 4 is a schematic diagram of the internal structure of a dynamic release system for wireless routing according to a first embodiment of the present invention.

[0017] Figure 2 FIG. 4 is a schematic diagram of the internal structure of a dynamic release system for wireless routing according to a second embodiment of the present invention.

[0018] Figure 3 FIG. 4 is a schematic diagram of the internal structure of a dynamic release system for wireless routing according to a third embodiment of the present invention. DETAILED DESCRIPTION

[0019] The embodiments of the dynamic release system for wireless routing of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] First embodiment

[0021] Figure 1 The internal structure diagram of the dynamic release system for wireless routing according to the first embodiment of the present invention is shown, and the system includes:

[0022] A startup detection device connected to the wireless router device, used to issue a first control instruction when detecting that the wireless router device is started, and to issue a second control instruction when detecting that the wireless router device is in a shutdown state;

[0023] A visual input device, arranged above the wireless router device and connected to the start detection device, for acquiring a visual imaging image of the environment where the wireless router device is located upon receiving the first control instruction;

[0024] A multi-layer optimization device, comprising a first-layer optimization device, a second-layer optimization device and a last-layer optimization device, wherein the second-layer optimization device is connected to the first-layer optimization device and the last-layer optimization device respectively, and the first-layer optimization device is also connected to the visual input device, and is used to perform spatial differential sharpening processing on the received visual imaging image to obtain and output a corresponding data-sharpened image, the second-layer optimization device is used to perform guided filtering processing on the received data-sharpened image to obtain and output a corresponding guided filtered image, and the last-layer optimization device is used to perform adaptive recursive filtering processing on the received guided filtered image to obtain and output a corresponding multi-layer optimized image;

[0025] A first extraction device is connected to the multi-layer optimization device, and is used to detect the mean square error of each brightness value corresponding to each pixel point of each image area in the received multi-layer optimization image, and when the mean square error is less than a set mean square error limit, the image area is judged to be a background image area, otherwise, the image area is judged to be a foreground image area;

[0026] A second extraction device, connected to the first extraction device, is used to input the binary representation value of the standard pattern corresponding to the network terminal, the total number of pixels occupied by the foreground image area, and the red and green component values, black and white component values, and yellow and blue component values ​​of each pixel in the foreground image area in the LAB space into the BP neural network that has completed multiple trainings to run the BP neural network that has completed multiple trainings, and obtain the number of network terminals in the foreground image area output by the BP neural network that has completed multiple trainings;

[0027] A third extraction device, connected to the second extraction device, is used to determine the number of dynamic IP addresses that need to be automatically released by the wireless router after startup based on the number of network terminals in the foreground image area;

[0028] The main controller of the wireless router device is connected to the third extraction device, and is used to automatically release a corresponding number of dynamic IP addresses after startup based on the received number of dynamic IP addresses that need to be automatically released;

[0029] Among them, in the BP neural network after completing multiple trainings, the number of trainings is proportional to the total number of pixels of the multi-layer optimized image;

[0030] Wherein, the visual recording device is further used to stop acquiring the visual imaging image of the environment where the wireless router device is located when receiving the second control instruction;

[0031] Among them, the binary representation value of the standard pattern corresponding to the network terminal, the total number of pixels occupied by the foreground image area, and the red and green component values, black and white component values, and yellow and blue component values ​​of each pixel in the foreground image area in the LAB space are input into the BP neural network that has completed multiple trainings to run the BP neural network that has completed multiple trainings, and the number of network terminals existing in the foreground image area output by the BP neural network that has completed multiple trainings includes: the standard pattern corresponding to the network terminal is in JPEG format.

[0032] Second embodiment

[0033] Figure 2 FIG. 4 is a schematic diagram of the internal structure of a dynamic release system for wireless routing according to a second embodiment of the present invention.

[0034] The dynamic release system for wireless routing shown in the second embodiment of the present invention may include:

[0035] A startup detection device connected to the wireless router device, used to issue a first control instruction when detecting that the wireless router device is started, and to issue a second control instruction when detecting that the wireless router device is in a shutdown state;

[0036] A visual input device, arranged above the wireless router device and connected to the start detection device, for acquiring a visual imaging image of the environment where the wireless router device is located upon receiving the first control instruction;

[0037] A multi-layer optimization device, comprising a first-layer optimization device, a second-layer optimization device and a last-layer optimization device, wherein the second-layer optimization device is connected to the first-layer optimization device and the last-layer optimization device respectively, and the first-layer optimization device is also connected to the visual input device, and is used to perform spatial differential sharpening processing on the received visual imaging image to obtain and output a corresponding data-sharpened image, the second-layer optimization device is used to perform guided filtering processing on the received data-sharpened image to obtain and output a corresponding guided filtered image, and the last-layer optimization device is used to perform adaptive recursive filtering processing on the received guided filtered image to obtain and output a corresponding multi-layer optimized image;

[0038] A first extraction device is connected to the multi-layer optimization device, and is used to detect the mean square error of each brightness value corresponding to each pixel point of each image area in the received multi-layer optimization image, and when the mean square error is less than a set mean square error limit, the image area is judged to be a background image area, otherwise, the image area is judged to be a foreground image area;

[0039] A second extraction device, connected to the first extraction device, is used to input the binary representation value of the standard pattern corresponding to the network terminal, the total number of pixels occupied by the foreground image area, and the red and green component values, black and white component values, and yellow and blue component values ​​of each pixel in the foreground image area in the LAB space into the BP neural network that has completed multiple trainings to run the BP neural network that has completed multiple trainings, and obtain the number of network terminals in the foreground image area output by the BP neural network that has completed multiple trainings;

[0040] A third extraction device, connected to the second extraction device, is used to determine the number of dynamic IP addresses that need to be automatically released by the wireless router after startup based on the number of network terminals in the foreground image area;

[0041] An on-site storage device, connected to the multi-layer optimization device, the first extraction device, the second extraction device, and the third extraction device, respectively, for providing data temporary storage services for the multi-layer optimization device, the first extraction device, the second extraction device, and the third extraction device in a time-sharing manner;

[0042] The on-site storage device is connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device respectively, and is used to provide data temporary storage services for the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device in a time-sharing manner, including: the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device are connected to the on-site storage device respectively using different data channels;

[0043] Among them, the field storage device is respectively connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and is used to provide data temporary storage services for the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device in a time-sharing manner, including: the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device use the same reference clock signal.

[0044] Third embodiment

[0045] Figure 3FIG. 4 is a schematic diagram of the internal structure of a dynamic release system for wireless routing according to a third embodiment of the present invention.

[0046] The dynamic release system for wireless routing shown in the third embodiment of the present invention may include:

[0047] A startup detection device connected to the wireless router device, used to issue a first control instruction when detecting that the wireless router device is started, and to issue a second control instruction when detecting that the wireless router device is in a shutdown state;

[0048] A visual input device, arranged above the wireless router device and connected to the start detection device, for acquiring a visual imaging image of the environment where the wireless router device is located upon receiving the first control instruction;

[0049] A multi-layer optimization device, comprising a first-layer optimization device, a second-layer optimization device and a last-layer optimization device, wherein the second-layer optimization device is connected to the first-layer optimization device and the last-layer optimization device respectively, and the first-layer optimization device is also connected to the visual input device, and is used to perform spatial differential sharpening processing on the received visual imaging image to obtain and output a corresponding data-sharpened image, the second-layer optimization device is used to perform guided filtering processing on the received data-sharpened image to obtain and output a corresponding guided filtered image, and the last-layer optimization device is used to perform adaptive recursive filtering processing on the received guided filtered image to obtain and output a corresponding multi-layer optimized image;

[0050] A first extraction device is connected to the multi-layer optimization device, and is used to detect the mean square error of each brightness value corresponding to each pixel point of each image area in the received multi-layer optimization image, and when the mean square error is less than a set mean square error limit, the image area is judged to be a background image area, otherwise, the image area is judged to be a foreground image area;

[0051] A second extraction device, connected to the first extraction device, is used to input the binary representation value of the standard pattern corresponding to the network terminal, the total number of pixels occupied by the foreground image area, and the red and green component values, black and white component values, and yellow and blue component values ​​of each pixel in the foreground image area in the LAB space into the BP neural network that has completed multiple trainings to run the BP neural network that has completed multiple trainings, and obtain the number of network terminals in the foreground image area output by the BP neural network that has completed multiple trainings;

[0052] A third extraction device, connected to the second extraction device, is used to determine the number of dynamic IP addresses that need to be automatically released by the wireless router after startup based on the number of network terminals in the foreground image area;

[0053] a content display device, connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, respectively, and used to simultaneously display various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device;

[0054] Wherein, the content display device is connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device respectively, and is used to simultaneously display various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, including: the various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device displayed simultaneously includes the sleep state or working state of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device;

[0055] Wherein, the content display device is connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device respectively, and is used to simultaneously display various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and further includes: the content display device is a liquid crystal display screen or an LED display array;

[0056] Wherein, the content display device is connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device respectively, and is used to simultaneously display various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and further includes: the LED display array is composed of a plurality of LED display units arranged in a rectangular matrix mode;

[0057] Among them, the content display device is respectively connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and is used to simultaneously display various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and also includes: the structure of the multiple LED display units arranged in a rectangular matrix pattern is the same.

[0058] In addition, in the dynamic release system for wireless routing, the startup detection device is connected to the wireless router device and is used to issue a first control instruction when detecting that the wireless router device is started, and to issue a second control instruction when detecting that the wireless router device is in a shutdown state, including: the startup detection device determines whether the wireless router device is started based on the operating state of the main controller of the wireless router device;

[0059] Among them, the startup detection device is connected to the wireless router device and is used to issue a first control instruction when it is detected that the wireless router device is started, and to issue a second control instruction when it is detected that the wireless router device is in a shutdown state. It also includes: the startup detection device has a built-in instruction trigger unit, which is used to issue a first control instruction when it is detected that the wireless router device is started, and to issue a second control instruction when it is detected that the wireless router device is in a shutdown state.

[0060] It can be seen that the present invention mainly has the following significant technical effects:

[0061] Firstly: detecting the mean square error of each brightness value corresponding to each pixel point of each image area in the received multi-layer optimized image, and when the mean square error is less than the set mean square error limit, judging the image area as a background image area, otherwise, judging the image area as a foreground image area;

[0062] Secondly: inputting the binary representation value of the standard pattern corresponding to the network terminal, the total number of pixels occupied by the foreground image area, and the red and green component values, black and white component values, and yellow and blue component values ​​of each pixel in the foreground image area in the LAB space into the BP neural network after multiple trainings to run the BP neural network after multiple trainings, and obtaining the number of network terminals in the foreground image area output by the BP neural network after multiple trainings;

[0063] Thirdly: determining the number of dynamic IP addresses that the wireless router device needs to automatically release after startup based on the number of network terminals in the foreground image area, thereby improving the connection speed and connection efficiency between the wireless router device and each network terminal nearby;

[0064] Finally: After completing multiple trainings, the number of trainings in the BP neural network is proportional to the total number of pixels in the multi-layer optimized image, so as to realize the customized construction of the BP neural network.

[0065] The dynamic release system for wireless routing of the present invention has a compact structure and intelligent operation. Since the BP neural network after multiple trainings can be used to intelligently analyze the number of network terminals on site, and the number of dynamic IP addresses that need to be automatically released by the wireless router after startup is determined based on the number of network terminals on site, the connection speed and efficiency between the wireless router and each network terminal nearby are improved.

[0066] Various embodiments of the display system according to the present invention have been described above, and those skilled in the art can easily make modifications and changes based on the above teachings. Therefore, it should be understood that within the scope of the claims, the present invention can be implemented in a manner different from that specifically described herein.

Claims

1. A dynamic release system for wireless routing, characterized in that: The system comprises: A startup detection device connected to the wireless router device, used to issue a first control instruction when detecting that the wireless router device is started, and to issue a second control instruction when detecting that the wireless router device is in a shutdown state; A visual input device, arranged above the wireless router device and connected to the start detection device, for acquiring a visual imaging image of the environment where the wireless router device is located upon receiving the first control instruction; A multi-layer optimization device, comprising a first-layer optimization device, a second-layer optimization device and a last-layer optimization device, wherein the second-layer optimization device is connected to the first-layer optimization device and the last-layer optimization device respectively, and the first-layer optimization device is also connected to the visual input device, and is used to perform spatial differential sharpening processing on the received visual imaging image to obtain and output a corresponding data-sharpened image, the second-layer optimization device is used to perform guided filtering processing on the received data-sharpened image to obtain and output a corresponding guided filtered image, and the last-layer optimization device is used to perform adaptive recursive filtering processing on the received guided filtered image to obtain and output a corresponding multi-layer optimized image; A first extraction device is connected to the multi-layer optimization device, and is used to detect the mean square error of each brightness value corresponding to each pixel point of each image area in the received multi-layer optimization image, and when the mean square error is less than a set mean square error limit, the image area is judged to be a background image area, otherwise, the image area is judged to be a foreground image area; A second extraction device, connected to the first extraction device, is used to input the binary representation value of the standard pattern corresponding to the network terminal, the total number of pixels occupied by the foreground image area, and the red and green component values, black and white component values, and yellow and blue component values ​​of each pixel in the foreground image area in the LAB space into the BP neural network that has completed multiple trainings to run the BP neural network that has completed multiple trainings, and obtain the number of network terminals in the foreground image area output by the BP neural network that has completed multiple trainings; A third extraction device, connected to the second extraction device, is used to determine the number of dynamic IP addresses that need to be automatically released by the wireless router after startup based on the number of network terminals in the foreground image area; The main controller of the wireless router device is connected to the third extraction device, and is used to automatically release a corresponding number of dynamic IP addresses after startup based on the received number of dynamic IP addresses that need to be automatically released; Among them, in the BP neural network after completing multiple trainings, the number of trainings is proportional to the total number of pixels of the multi-layer optimized image; The visual recording device is also used to stop acquiring the visual imaging image of the environment where the wireless router device is located when receiving the second control instruction; The binary representation value of the standard pattern corresponding to the network terminal, the total number of pixels occupied by the foreground image area, and the red and green component values, black and white component values, and yellow and blue component values ​​of each pixel in the foreground image area in the LAB space are input into the BP neural network after multiple trainings to run the BP neural network after multiple trainings, and the number of network terminals in the foreground image area output by the BP neural network after multiple trainings is obtained, including: the standard pattern corresponding to the network terminal is in JPEG format; An on-site storage device, connected to the multi-layer optimization device, the first extraction device, the second extraction device, and the third extraction device, respectively, for providing data temporary storage services for the multi-layer optimization device, the first extraction device, the second extraction device, and the third extraction device in a time-sharing manner; The on-site storage device is connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device respectively, and is used to provide data temporary storage services for the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device in a time-sharing manner, including: the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device are connected to the on-site storage device respectively using different data channels; The field storage device is connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device respectively, and is used to provide data temporary storage services for the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device in a time-sharing manner, including: the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device use the same reference clock signal; The content display device is respectively connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and is used to simultaneously display various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device.

2. The dynamic release system for wireless routing as claimed in claim 1, characterized in that: The content display device is respectively connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and is used to simultaneously display various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, including: the various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device displayed simultaneously includes the sleep state or working state of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device.

3. The dynamic release system for wireless routing as claimed in claim 2, characterized in that: A content display device is respectively connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and is used to simultaneously display various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and also includes: the content display device is a liquid crystal display screen or an LED display array.

4. The dynamic release system for wireless routing as claimed in claim 3, characterized in that: The content display device is respectively connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and is used to simultaneously display various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and also includes: the LED display array is composed of multiple LED display units arranged in a rectangular matrix pattern.

5. The dynamic release system for wireless routing as claimed in claim 4, characterized in that: The content display device is respectively connected to the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and is used to simultaneously display various status information of the multi-layer optimization device, the first extraction device, the second extraction device and the third extraction device, and also includes: the structure of the multiple LED display units arranged in a rectangular matrix pattern is the same.

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