Intelligent image segmentation identification method based on Internet of Things

Abstract

The invention discloses an intelligent image segmentation and identification method based on the Internet of Things, and the method employs a monitoring area grating type division module and an Internet of Things equipment monitoring module which is cooperatively used, and the monitoring area grating type division module comprises a virtual mark division unit and a base point installation part. The method comprises the following steps: firstly, carrying out longitude and latitude unit region division on a region through a virtual mark division unit, separating the region by the virtual mark division unit through a satellite image, manually marking each unit region, and erecting an Internet of Things equipment monitoring module through the installation of a base point; the Internet of Things equipment monitoring module comprises a plurality of third-level monitoring units, a second-level monitoring unit and a first-level monitoring unit, and the third-level monitoring units are distributed in the centers of all unit areas and transmit image data to the second-level monitoring unit through the Internet of Things. According to the image segmentation identification method designed by the invention, the efficiency of regional images and the data transmission rate can be greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image segmentation and recognition methods, and in particular to an image intelligent segmentation and recognition method based on the Internet of Things. Background Art

[0002] Image segmentation and recognition based on the Internet of Things refers to the process of segmenting and identifying the collected image data by combining the Internet of Things technology and image processing technology. In this method, Internet of Things devices (such as cameras, sensors, etc.) are used to collect image data in the environment and transmit the data to the cloud or server for processing through the Internet of Things connection. When it is necessary to monitor the ground environment of a certain area, the Internet of Things devices can be used to obtain image information in the area and identify and organize the collected data, which can realize intelligent perception and recognition of objects and targets in the environment, and improve the efficiency of managers in regional environmental management.

[0003] However, the existing image recognition methods in regional environments have the following problems: they mainly rely on image acquisition devices distributed in the region to identify and acquire environmental images at various locations in the region, and upload the collected data to the background, which processes the data centrally. However, when the regional scale is large, such an operation method consumes a lot of computing resources due to the transmission and processing of large-scale image data, resulting in high transmission delays and inability to meet real-time requirements. The image data volume is large and complex, and the requirements for data storage, processing and analysis are high, requiring powerful computing resources and algorithm support. Therefore, it is necessary to design corresponding technical solutions to solve the existing technical problems. Summary of the invention

[0004] The purpose of the present invention is to provide an image intelligent segmentation and recognition method based on the Internet of Things, which solves the problem of mainly using image acquisition devices distributed in the area to identify and acquire environmental images at various locations in the area, and centrally uploading the collected data to the background, and the background performs centralized data processing. However, when the area is large in scale, such an operation mode consumes a large amount of computing resources due to the transmission and processing of large-scale image data, resulting in high transmission delay, and the real-time requirements cannot be achieved. The image data volume is large and complex, and the requirements for data storage, processing and analysis are high, requiring powerful computing resources and algorithm support. This technical problem, the present invention designs a method for segmenting and identifying environmental images in the area, the image segmentation and recognition method uses a grid-type division method to divide the area into multiple unit areas, and establishes a three-level monitoring unit in each unit area for real-time monitoring of the unit area, and summarizes and transmits the image information of every four unit areas to the second-level monitoring unit, the image information is processed by the second-level monitoring unit, and the monitoring of image static data and image dynamic data is differentiated during the processing, so as to achieve the purpose of segmentation and recognition, so as to reduce the amount of data sorting and processing, improve the efficiency of processing regional environmental image data, and finally upload it to the first-level monitoring unit to complete the processing of the regional environmental image.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an image intelligent segmentation and recognition method based on the Internet of Things, comprising a monitoring area grid division module and an Internet of Things device monitoring module used in conjunction, the monitoring area grid division module comprising a virtual marking division unit and a base point installation. First, the area is divided into unit areas of latitude and longitude by the virtual marking division unit, the virtual marking division unit separates the area with the help of satellite images and manually marks each unit area, and the Internet of Things device monitoring module is installed through the base point. The Internet of Things device monitoring module comprises a three-level monitoring unit, a second-level monitoring unit and a first-level monitoring unit. The three-level monitoring unit is divided into several groups and distributed at the center of each unit area, and transmits image data to the second-level monitoring unit through the Internet of Things. The second-level monitoring unit is established in the center area of ​​each adjacent four groups of unit areas and is equipped with a data integration processing unit. The data sorting processing unit receives data, divides data and encrypts data for the image. The processed image data is transmitted to the first-level monitoring unit through the Internet of Things. The first-level monitoring unit is established at the center of the monitoring area.

[0006] As a preferred embodiment of the present invention, the virtual marking division unit acquires an overall image of the area through a satellite and evenly divides the area in the background, and the area of ​​each unit area after division is the same.

[0007] As a preferred embodiment of the present invention, the base point installation includes a main base point located at the center of the unit area and auxiliary base points distributed at the corners of the unit area, and the main base point and the auxiliary base point are both installed with three-level monitoring units.

[0008] As a preferred embodiment of the present invention, the three-level monitoring unit uses a camera and an image acquisition sensor as carriers and is installed in the top area of ​​the main base point and the auxiliary base point, so that the monitoring angle of view includes the entire unit area.

[0009] As a preferred embodiment of the present invention, the data segmentation module contained in the secondary monitoring unit monitors the image static data and image dynamic data of the monitoring area, wherein the image static data refers to the static objects in the monitoring area, including buildings, plants and surface forms in the environment, and the image dynamic data refers to the dynamic objects in the monitoring area, including weather conditions, temperature and humidity, and animals.

[0010] As a preferred embodiment of the present invention, a differentiated design is performed for the monitoring frequencies of image static data and image dynamic data in the above-mentioned data segmentation module, wherein the image static data in the area is monitored intermittently, while the image dynamic data in the area is monitored in real time, so as to reduce the amount of data to be sorted and processed.

[0011] As a preferred embodiment of the present invention, the primary monitoring unit aggregates the environmental image information in the area, encrypts the aggregated image data and uploads it to the cloud platform, and also provides feedback on different data in the monitoring area environment.

[0012] Compared with the prior art, the present invention has the following beneficial effects:

[0013] 1. The present invention designs a method for segmenting and identifying environmental images in a region. The image segmentation and identification method adopts a grid-type division method to divide the region into multiple unit areas, and establishes a three-level monitoring unit in each unit area for real-time monitoring of the unit area, and summarizes the image information of every four unit areas and transmits it to the second-level monitoring unit. The image information is processed by the second-level monitoring unit, and the monitoring of image static data and image dynamic data is differentiated during the processing process to achieve the purpose of segmentation and identification, thereby reducing the amount of data sorting and processing, and improving the efficiency of processing regional environmental image data. Finally, it is uploaded to the first-level monitoring unit to complete the processing of the regional environmental image.

[0014] 2. The image segmentation and recognition method designed in the present invention can greatly improve the efficiency of regional images and the data transmission rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1It is the overall distribution structure diagram of the present invention;

[0016] Figure 2 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1-2 The present invention provides a technical solution: an image intelligent segmentation and recognition method based on the Internet of Things, including a monitoring area grid division module and an Internet of Things device monitoring module used in conjunction, the monitoring area grid division module includes two parts: a virtual marking division unit and a base point installation, firstly, the area is divided into unit areas of latitude and longitude by the virtual marking division unit, the virtual marking division unit separates the area with the help of satellite images and manually marks each unit area, and the Internet of Things device monitoring module is set up through the installation of the base point, the Internet of Things device monitoring module includes a three-level monitoring unit, a second-level monitoring unit and a first-level monitoring unit, the three-level monitoring unit is divided into several groups and respectively distributed at the center of each unit area and transmits image data to the second-level monitoring unit through the Internet of Things, the second-level monitoring unit is established in the center area of ​​each adjacent four groups of unit areas and is equipped with a data integration processing unit, the data sorting processing unit receives data, divides data and encrypts data for the image, and the processed image data is transmitted to the first-level monitoring unit through the Internet of Things, and the first-level monitoring unit is established at the center of the monitoring area.

[0019] Further improvement, such as Figure 1 As shown, the virtual marking division unit acquires the overall image of the area through the satellite and evenly divides the area in the background, and the area of ​​each unit area after division is the same.

[0020] Further improvement, such as Figure 1 As shown, the base point installation includes a main base point located at the center of the unit area and auxiliary base points distributed at the corners of the unit area. Both the main base point and the auxiliary base point are installed with three-level monitoring units.

[0021] Further improvement, such as Figure 1 As shown, the three-level monitoring unit uses cameras and image acquisition sensors as carriers and is installed in the top area of ​​the main base point and the auxiliary base point, so that the monitoring angle of view includes the entire unit area.

[0022] Further improvement, such as Figure 2As shown, the data segmentation module contained in the secondary monitoring unit monitors the static image data and dynamic image data of the monitoring area, wherein the static image data refers to the static objects in the monitoring area, including buildings, plants and surface forms in the environment, while the dynamic image data refers to the dynamic objects in the monitoring area, including weather conditions, temperature and humidity, and animals.

[0023] Further improvement, such as Figure 2 As shown, a differentiated design is performed for the monitoring frequencies of image static data and image dynamic data in the above-mentioned data segmentation module, wherein the image static data in the area is monitored intermittently, while the image dynamic data in the area is monitored in real time, so as to reduce the amount of data to be sorted and processed.

[0024] As a preferred embodiment of the present invention, the primary monitoring unit aggregates the environmental image information in the area, encrypts the aggregated image data and uploads it to the cloud platform, and also provides feedback on different data in the monitoring area environment.

[0025] When in use: The image segmentation and recognition method designed in this scheme adopts a grid-type division method to divide the area into multiple unit areas, and establishes a three-level monitoring unit in each unit area for real-time monitoring of the unit area, and summarizes the image information of every four unit areas and transmits it to the second-level monitoring unit. The image information is processed by the second-level monitoring unit, and the static data and dynamic data of the image are monitored separately during the processing to achieve the purpose of segmentation and recognition, so as to reduce the amount of data sorting and processing, and improve the efficiency of regional environmental image data processing. Finally, it is uploaded to the first-level monitoring unit to complete the processing of the regional environmental image, which can greatly improve the efficiency of the regional image and the data transmission rate.

[0026] In the description of the present invention, it is necessary to understand that the terms "coaxial", "bottom", "one end", "top", "middle", "the other end", "upper", "one side", "top", "inside", "front", "center", "both ends" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0027] In addition, the terms "first", "second", "third" and "fourth" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first", "second", "third" and "fourth" may explicitly or implicitly include at least one of such features.

[0028] In the present invention, unless otherwise clearly stipulated and limited, the terms such as "installation", "setting", "connection", "fixation" and "screw-on" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal connection of two elements or the interaction relationship between two elements. Unless otherwise clearly defined, ordinary technicians in this field can understand the specific meanings of the above terms in the present invention according to the specific circumstances.

[0029] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An image intelligent segmentation and recognition method based on the Internet of Things, characterized by: It includes a monitoring area grid division module and an Internet of Things device monitoring module used in conjunction with it. The monitoring area grid division module includes two parts: a virtual marking division unit and a base point installation. First, the area is divided into unit areas of latitude and longitude through the virtual marking division unit. The virtual marking division unit separates the area with the help of satellite images and manually marks each unit area, and the Internet of Things device monitoring module is set up through the installation of the base point. The Internet of Things device monitoring module includes a three-level monitoring unit, a second-level monitoring unit and a first-level monitoring unit. The three-level monitoring unit is divided into several groups and distributed at the center of each unit area, and transmits image data to the second-level monitoring unit through the Internet of Things. The second-level monitoring unit is established in the center area of ​​each adjacent four groups of unit areas and is equipped with a data integration processing unit. The data sorting processing unit receives data, divides data and encrypts data. The processed image data is transmitted to the first-level monitoring unit through the Internet of Things. The first-level monitoring unit is established at the center of the monitoring area.

2. According to the method of intelligent image segmentation and recognition based on the Internet of Things in claim 1, it is characterized by: The virtual marking division unit acquires the overall image of the area through a satellite and evenly divides the area in the background, and the area of ​​each unit area after division is the same.

3. According to the method of intelligent image segmentation and recognition based on the Internet of Things in claim 1, it is characterized by: The base point installation includes a main base point located at the center of the unit area and auxiliary base points distributed at the corners of the unit area. Both the main base point and the auxiliary base point are installed with three-level monitoring units.

4. The method for intelligent image segmentation and recognition based on the Internet of Things according to claim 1 is characterized in that: The three-level monitoring unit uses a camera and an image acquisition sensor as carriers and is installed in the top area of ​​the main base point and the auxiliary base point, so that the monitoring angle of view includes the entire unit area.

5. The method for intelligent image segmentation and recognition based on the Internet of Things according to claim 1 is characterized in that: The data segmentation module contained in the secondary monitoring unit monitors the static image data and dynamic image data of the monitoring area, wherein the static image data refers to the static objects in the monitoring area, including buildings, plants and surface forms in the environment, while the dynamic image data refers to the dynamic objects in the monitoring area, including weather conditions, temperature and humidity, and animals.

6. The method for intelligent image segmentation and recognition based on the Internet of Things according to claim 1 is characterized in that: A differentiated design is performed for the monitoring frequencies of image static data and image dynamic data in the above-mentioned data segmentation module, wherein the image static data in the area is monitored intermittently, while the image dynamic data in the area is monitored in real time, so as to reduce the amount of data to be sorted and processed.

7. The method for intelligent image segmentation and recognition based on the Internet of Things according to claim 1 is characterized in that: The first-level monitoring unit aggregates the environmental image information in the area, encrypts the aggregated image data and uploads it to the cloud platform, and also provides feedback on different data in the monitoring area environment.

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