Smart community security and protection method based on Internet of Things

Abstract

The invention discloses a smart community security and protection method based on the Internet of Things, and relates to the technical field of the Internet. According to the method, the positions corresponding to the camera devices in the community are obtained, the position nodes are generated, the area of the community is subjected to unit division to obtain the community security and protection area, and then the camera device network corresponding to the community security and protection area is obtained according to the position nodes; collecting community behavior image data corresponding to each position node according to the camera device, setting an AI intelligent recognizer, carrying out AI recognition on the community behavior image data according to the AI intelligent recognizer, and obtaining an abnormal community behavior shot frame; acquiring a position node corresponding to the abnormal community behavior shot frame, acquiring an abnormal frequency corresponding to the position node, and further acquiring an abnormal community behavior key area; presetting a community resident basic information database, acquiring corresponding basic information data according to the abnormal community behavior shot frame, and performing early warning according to the basic information data; according to the invention, the comprehensiveness and real-time performance of security and protection are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of Internet technologies, and particularly to an intelligent community security and protection method based on the Internet of Things. Background Art

[0002] Security and protection can be understood as an abbreviation of "security prevention"; according to the explanation in the Chinese dictionary, the so-called security means no danger, no infringement, and no accident; the so-called prevention means preparation and guard, and preparation means making preparations to cope with attacks or avoid being harmed, and guard means prevention and protection; making preparations and protection to cope with attacks or avoid being harmed, so that the protected object is in a safe state of no danger, no infringement, and no accident; obviously, security is the goal, and prevention is the means. Achieving or realizing the goal of security through the means of prevention is the basic connotation of security prevention; Community security and protection is an important means to ensure the safety of the lives and property of community residents, mainly including three aspects: human prevention, physical prevention, and technical prevention; however, in the prior art, the community is usually secured through guards and monitoring systems. However, guards are unable to monitor the community all the time manually, lacking the real-time monitoring of the community; moreover, each subsystem in the monitoring system is usually independent, lacking linkage, and it is impossible to determine the key areas where abnormalities occur; therefore, in order to solve the above problems, the present invention provides an intelligent community security and protection method based on the Internet of Things. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides an intelligent community security and protection method based on the Internet of Things; The object of the present invention can be achieved through the following technical solutions: An intelligent community security and protection method based on the Internet of Things, the method comprising the following steps: Step 1: Obtain the positions corresponding to each camera device in the community and generate position nodes, divide the area of the community into unit areas to obtain community security and protection areas, and then obtain the camera device network corresponding to the community security and protection areas according to the position nodes; Step 2: Collect the community behavior image data corresponding to each position node of the camera device network according to the camera device, set an AI intelligent recognizer, perform AI recognition on the community behavior image data according to the AI intelligent recognizer, and obtain abnormal community behavior shot frames; Step 3: Obtain the position nodes corresponding to the abnormal community behavior shot frames, obtain the abnormal frequencies corresponding to the position nodes, and then obtain the key areas of abnormal community behavior; Step 4: Preset a database of basic information of community residents, obtain the corresponding basic information data according to the abnormal community behavior shot frames, and give an alarm according to the basic information data.

[0004] Further, the process of obtaining the community security and protection areas includes: Obtain all areas of the community, where the areas include access control areas, path areas, park areas, parking lot areas, and floor areas; obtain the unit numbers corresponding to all unit buildings in the community, connect the access control areas and floor areas with the corresponding unit buildings to generate unit building security areas, and then divide the unit building security areas corresponding to the same unit number into the same community unit; Connect the parking lot area with the corresponding community unit according to the unit number to generate a community unit security area; connect the park area and the path area to generate a community public security area; obtain the access control area corresponding to the entrance and exit of the community, and connect it with the community public security area; then connect each community unit security area with the community public security area to generate a community security area.

[0005] Furthermore, the process of obtaining the camera device network includes: Obtain the position nodes corresponding to all camera devices in the community security area, sequentially connect the position nodes corresponding to the unit building security area and the parking lot area respectively to generate the corresponding unit building camera device network and parking lot camera device network, and connect them to generate the community unit security area camera device network corresponding to each community unit security area; Sequentially connect the position nodes corresponding to the path area, park area, and access control area respectively to generate the corresponding path area camera device network, park area camera device network, and access control area camera device network, and connect them to generate the community public security area camera device network corresponding to the community public security area; Connect the community unit security area camera device network and the community public security area camera device network to generate a camera device network.

[0006] Furthermore, the process of setting the AI intelligent recognizer includes: Set a number of recognition models and feature partition data pools according to the camera device network, and set corresponding abnormal community behavior feature data pools in the recognition models. The number of recognition models is wirelessly connected to the feature partition data pools to generate an AI intelligent recognizer; The feature partition data pool includes a number of feature data nodes, and different feature identifiers are set on the feature data nodes, which are used to extract and store community behavior feature data from community behavior image data according to the feature identifiers on different feature data nodes. The abnormal community behavior feature data pool is used to identify abnormal community behavior feature data.

[0007] Furthermore, the process of obtaining the abnormal community behavior lens frames includes: Mark the feature data nodes storing community behavior feature data in the feature partition data pool as stored feature data nodes; Set the transmission period of the feature data node, obtain the corresponding recognition model according to the community behavior feature data, and send the community behavior feature data on the corresponding feature data node to the corresponding recognition model according to the transmission period of the feature data node. The recognition model recognizes the community behavior feature data according to the pre-stored abnormal community behavior feature database. If the recognition is successful, mark the corresponding community behavior image data as abnormal community behavior image data, and stop the feature data node from sending the community behavior feature data; otherwise, do nothing.

[0008] Further, obtain the acquisition time t corresponding to the abnormal community behavior image data, and set the acquisition time interval value , and obtain the abnormal community behavior video acquisition interval value corresponding to several position nodes of the camera device network according to the acquisition time and the acquisition time interval value, that is, the formula is: ; Obtain several community behavior image frames corresponding to the abnormal community behavior video acquisition interval value, and compare them with the abnormal community behavior image data respectively. If the comparison is successful, mark the community behavior image frame as an abnormal community behavior image frame; otherwise, do nothing. Integrate the community behavior image frames according to the abnormal community behavior video acquisition interval value to generate an abnormal community behavior shot frame.

[0009] Further, the process of obtaining the key area of the abnormal community behavior includes: Obtain the position nodes corresponding to all abnormal community behavior image frames in each abnormal community behavior shot frame, and obtain the number and acquisition time of the abnormal community behavior image frames corresponding to the same position node. Then, obtain the earliest acquisition time and the latest acquisition time of the acquisition time corresponding to the same position node, obtain the total time span according to the earliest acquisition time and the latest acquisition time, and further obtain the abnormal frequency of the abnormal community behavior shot frame occurring at the position node. That is, the specific formula is: ; Among them, represents the abnormal frequency corresponding to the position node D i , and i = 1, 2,..., j, where j takes positive integers; C 总 represents the number of abnormal community behavior image frames corresponding to the position node; T 总 represents the total time span, represents that the number of abnormal community behavior shot frames corresponding to the position node D i is n, that is, n = 1, 2,..., m, and m takes positive integers; Set the abnormal frequency threshold corresponding to the position node , and compare it with the abnormal frequency. If , the corresponding position node is marked as the key area of abnormal community behavior. Otherwise, no processing is performed.

[0010] Furthermore, compare the basic information data corresponding to the abnormal community behavior image data with the basic information database of community residents to obtain the contact information of residents, and conduct real-time warnings for the corresponding residents according to the contact information of residents.

[0011] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention generates position nodes by obtaining the positions corresponding to each camera device in the community, divides the area of the community into unit areas to obtain the community security areas, and then obtains the camera device network corresponding to the community security areas according to the position nodes; collects the community behavior image data corresponding to each position node by the camera device, sets an AI intelligent recognizer, performs AI recognition on the community behavior image data by the AI intelligent recognizer to obtain the abnormal community behavior lens frames; obtains the position nodes corresponding to the abnormal community behavior lens frames, obtains the abnormal frequencies corresponding to the position nodes, and further obtains the key areas of abnormal community behavior; preset the basic information database of community residents, obtain the corresponding basic information data according to the abnormal community behavior lens frames, and conduct warnings according to the basic information data; the present invention effectively improves the comprehensiveness and real-time performance of security. Description of the Drawings

[0012] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0013] Figure 1 It is a flowchart of the present invention. Detailed Embodiments

[0014] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0015] As Figure 1 shown, a smart community security method based on the Internet of Things, the method includes the following steps: Step 1: Obtain the positions corresponding to each camera device in the community and generate position nodes, divide the area of the community into unit areas to obtain the community security areas, and then obtain the camera device network corresponding to the community security areas according to the position nodes; The process of obtaining the community security area includes: Obtain all areas of the community, where the areas include access control areas, path areas, park areas, parking lot areas, and floor areas; obtain the unit numbers corresponding to all unit buildings in the community, connect the access control areas and floor areas with the corresponding unit buildings to generate unit building security areas, and then divide the unit building security areas corresponding to the same unit number into the same community unit; Connect the parking lot area with the corresponding community unit according to the unit number to generate a community unit security area; connect the park area and the path area to generate a community public security area; obtain the access control area corresponding to the entrance and exit of the community, and connect it with the community public security area; then connect each community unit security area with the community public security area to generate a community security area; In the above embodiment, it should be further noted that the community is divided into units and public areas, which improves the comprehensiveness of the community security scope; The process of obtaining the camera device network includes: Obtain the position nodes corresponding to all camera devices in the community security area, connect the position nodes corresponding to the unit building security area and the parking lot area in sequence to generate the corresponding unit building camera device network and parking lot camera device network, and connect them to generate the community unit security area camera device network corresponding to each community unit security area; Connect the position nodes corresponding to the path area, park area, and access control area in sequence to generate the corresponding path area camera device network, park area camera device network, and access control area camera device network, and connect them to generate the community public security area camera device network corresponding to the community public security area; Connect the community unit security area camera device network and the community public security area camera device network to generate a camera device network; In the above embodiment, it should be further noted that one or more camera devices are installed in different areas of the community. Among them, the camera devices include, but are not limited to, face recognition camera devices, high-definition camera devices, etc.; in this embodiment, the camera devices installed in different areas are sufficient in quantity; Step 2: Collect the community behavior image data corresponding to each position node of the camera device network according to the camera device, set an AI intelligent recognizer, and perform AI recognition on the community behavior image data according to the AI intelligent recognizer to obtain abnormal community behavior shot frames; The setting process of the AI intelligent recognizer includes: Set up a number of recognition models and feature partition data pools according to the camera device network, and set corresponding abnormal community behavior feature data pools in the recognition models. The number of recognition models is wirelessly connected to the feature partition data pool to generate an AI intelligent recognizer; The feature partition data pool includes a number of feature data nodes, and different feature identifiers are set on the feature data nodes, which are used to extract community behavior feature data from community behavior image data according to the feature identifiers and store them on different feature data nodes. The abnormal community behavior feature data pool is used to identify abnormal community behavior feature data; The process of obtaining the abnormal community behavior lens frames includes: Mark the feature data nodes storing community behavior feature data in the feature partition data pool as storage feature data nodes; Set the transmission period of the feature data nodes, obtain the corresponding recognition model according to the community behavior feature data, and send the community behavior feature data on the corresponding feature data nodes to the corresponding recognition model according to the transmission period of the feature data nodes. The recognition model identifies the community behavior feature data according to the pre-stored abnormal community behavior feature database. If the identification is successful, mark the corresponding community behavior image data as abnormal community behavior image data, and stop the feature data node from sending the community behavior feature data; otherwise, do nothing; It should be further noted that in the specific implementation process, the recognition models include but are not limited to models for face recognition, vehicle recognition, behavior recognition, etc.; the feature identifiers corresponding to the feature data nodes include but are not limited to face, vehicle, behavior, etc.; exemplarily, the feature data nodes and the recognition models are trained in advance and are obtained through learning a large amount of training data, and can identify various different objects and behaviors and correspond one by one; Obtain the acquisition time t corresponding to the abnormal community behavior image data, and set the acquisition time interval value , and obtain the abnormal community behavior video acquisition interval value corresponding to several position nodes of the camera device network according to the acquisition time and the acquisition time interval value, that is, the formula is: ; Obtain several community behavior image frames corresponding to the abnormal community behavior video acquisition interval value, and compare them with the abnormal community behavior image data respectively. If the comparison is successful, mark the community behavior image frames as abnormal community behavior image frames; otherwise, do nothing; Integrate the community behavior image frames according to the abnormal community behavior video acquisition interval value to generate abnormal community behavior lens frames; It should be further noted that in the specific implementation process, the abnormal community behavior video acquisition interval value is obtained according to the acquisition time interval value and the abnormal community behavior image data. The abnormal community behavior video acquisition interval value is the video interval in which the abnormal community behavior image data occurs, and this video interval can be changed according to the assignment of the acquisition time interval value. This method obtains the video interval value where the abnormal community behavior image data occurs, and then obtains a number of image data in the video interval. The image data is compared with the abnormal community behavior image data. This method avoids repeated recognition of the same impact feature image data based on the AI intelligent recognizer and improves the recognition speed; Step 3: Obtain the position nodes corresponding to the abnormal community behavior shot frames, and merge the abnormal community behavior shot frames according to the position nodes to generate the key area of abnormal community behavior; The process of obtaining the key area of abnormal community behavior includes: Obtain all the position nodes corresponding to the abnormal community behavior image frames in each abnormal community behavior shot frame, and obtain the number and acquisition time of the abnormal community behavior image frames corresponding to the same position node. Then obtain the earliest acquisition time and the latest acquisition time of the acquisition time corresponding to the same position node, obtain the total time span according to the earliest acquisition time and the latest acquisition time, and then obtain the abnormal frequency of the position node where the abnormal community behavior shot frame occurs; That is, the specific formula is: ; Among them, represents the abnormal frequency corresponding to the position node D i , and i = 1, 2,..., j, where j takes positive integers; C 总 represents the number of abnormal community behavior image frames corresponding to the position node; T 总 represents the total time span, represents that the number of abnormal community behavior shot frames corresponding to the position node D i is n, that is, n = 1, 2,..., m, and m takes positive integers; Set the abnormal frequency threshold corresponding to the position node , and compare it with the abnormal frequency. If , then mark the corresponding position node as the key area of abnormal community behavior, otherwise, do not perform any processing; It should be further noted that in the specific implementation process, each location node can collect community behavior image data, identify abnormal community behaviors according to the AI intelligent recognizer, and obtain the corresponding abnormal community behavior shot frames. Then, based on the abnormal community behavior shot frames, the location nodes where the same abnormal community behavior characteristics occur at other location nodes can be obtained. Furthermore, each location node may have one or more abnormal community behavior image frames corresponding to the abnormal community behavior shot frames. Finally, the abnormal frequency corresponding to the location node is obtained to determine whether the location node is a key area where abnormal community behavior image frames often occur; Step 4: Preset a basic information database of community residents, obtain the corresponding basic information data according to the abnormal community behavior shot frames, and issue a warning based on the basic information data; Compare the basic information data corresponding to the abnormal community behavior image data with the basic information database of community residents to obtain the contact information of the residents, and issue a real-time warning to the corresponding residents according to the contact information of the residents; It should be further noted that in the specific implementation process, the basic information data of the community residents includes, but is not limited to, resident portraits, resident names, resident contact information, resident addresses, and resident vehicle information, etc.; The features and exemplary embodiments of various aspects of the present application will be described in detail above. In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in combination with the accompanying drawings and specific embodiments; it should be understood that the specific embodiments described herein are only intended to explain the present application, rather than limiting the present application; for those skilled in the art, the present application can be implemented without some of these specific details; the above description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

[0016] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A smart community security method based on the Internet of Things, characterized in that: The method comprises the following steps: Step 1: Obtain the location corresponding to each camera device in the community and generate a location node, divide the community area into units to obtain a community security area, and then obtain the camera device network corresponding to the community security area according to the location node; Step 2: Collect community behavior image data corresponding to each location node of the camera network according to the camera device, set an AI intelligent identifier, perform AI recognition on the community behavior image data according to the AI ​​intelligent identifier, and obtain abnormal community behavior lens frames; Step 3: Obtain the location node corresponding to the abnormal community behavior shot frame, obtain the abnormal frequency corresponding to the location node, and then obtain the key area of ​​abnormal community behavior; Step 4: Preset a basic information database of community residents, obtain corresponding basic information data based on abnormal community behavior footage frames, and issue early warnings based on the basic information data.

2. According to the method of claim 1, the smart community security method based on the Internet of Things is characterized in that: The process of obtaining the community security area includes: Obtain all areas of the community, including access control areas, path areas, park areas, parking areas, and floor areas; obtain unit numbers corresponding to all unit buildings in the community, connect the access path areas and floor areas with the corresponding unit buildings to generate unit building security areas, and then divide the unit building security areas corresponding to the same unit number into the same community unit; According to the unit number, the parking lot area is regionally connected with the corresponding community unit to generate the community unit security area; the park area and the path area are regionally connected to generate the community public security area; the access control area of ​​the corresponding entrance and exit of the community is obtained, and it is regionally connected with the community public security area; and then each community unit security area is regionally connected with the community public security area to generate the community security area.

3. According to claim 2, a smart community security method based on the Internet of Things is characterized in that: The acquisition process of the camera device network includes: Obtain the location nodes corresponding to all the cameras corresponding to the community security area, connect the location nodes corresponding to the unit building security area and the parking lot area in sequence to generate the corresponding unit building camera device network and parking lot camera device network, and connect them to generate the community unit security area camera device network corresponding to each community unit security area; The location nodes corresponding to the path area, the park area and the access control area are sequentially connected to generate the corresponding path area camera device network, the park area camera device network and the access control area camera device network, and the community public security area camera device network corresponding to the community public security area is connected; The community unit security area camera network and the community public security area camera network are connected to generate a camera network.

4. The method for smart community security based on the Internet of Things according to claim 3 is characterized in that: The setting process of the AI ​​intelligent identifier includes: According to the camera device network, a plurality of recognition models and feature partition data pools are set, and a corresponding abnormal community behavior feature data pool is set in the recognition model, and the plurality of recognition models are wirelessly connected to the feature partition data pool to generate an AI intelligent identifier; The feature partition data pool includes several feature data nodes, and different feature identifiers are set on the feature data nodes, which are used to extract community behavior feature data from community behavior image data according to the feature identifiers and store them on different feature data nodes. The abnormal community behavior feature data pool is used to identify abnormal community behavior feature data.

5. According to claim 4, a smart community security method based on the Internet of Things is characterized in that: The process of acquiring the abnormal community behavior footage frame includes: Marking a feature data node storing community behavior feature data in a feature partition data pool as a storage feature data node; A feature data node transmission period is set, a corresponding recognition model is obtained according to the community behavior feature data, and the community behavior feature data on the corresponding feature data node is sent to the corresponding recognition model according to the feature data node transmission period. The recognition model recognizes the community behavior feature data according to a pre-stored abnormal community behavior feature database. If the recognition is successful, the corresponding community behavior image data is marked as abnormal community behavior image data, and the feature data node is stopped from sending the community behavior feature data; otherwise, no processing is performed.

6. The method for smart community security based on the Internet of Things according to claim 5, characterized in that: Obtain the collection time t corresponding to the abnormal community behavior image data, set the collection time interval value, and obtain the abnormal community behavior video collection interval values ​​corresponding to several position nodes of the camera device network according to the collection time and the collection time interval value; Obtain several community behavior image frames corresponding to the abnormal community behavior video collection interval value, and compare them with the abnormal community behavior image data respectively. If the comparison is successful, the community behavior image frame is marked as an abnormal community behavior image frame, otherwise, no processing is performed; According to the abnormal community behavior video acquisition interval value, the community behavior image frames are integrated to generate abnormal community behavior lens frames.

7. The method for smart community security based on the Internet of Things according to claim 6 is characterized in that: The process of obtaining the abnormal community behavior key area includes: Obtain the location nodes corresponding to all abnormal community behavior image frames in each abnormal community behavior lens frame, and obtain the number and acquisition time of the abnormal community behavior image frames corresponding to the same location node, and then obtain the earliest acquisition time and the latest acquisition time corresponding to the same location node, and obtain the total time span according to the earliest acquisition time and the latest acquisition time, and then obtain the abnormal frequency of abnormal community behavior lens frames occurring at the location node; Set the abnormal frequency threshold corresponding to the location node , and compared with the abnormal frequency, if , the corresponding location node is marked as a critical area of ​​abnormal community behavior, otherwise, no processing is done.

8. The method for smart community security based on the Internet of Things according to claim 1, characterized in that: The basic information data corresponding to the abnormal community behavior image data is compared with the basic information database of community residents to obtain the residents' contact information, and real-time warnings are issued to the corresponding residents based on the residents' contact information.

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