Automated security deployment system

Abstract

This disclosure provides an automated security deployment system. The automated security deployment system includes a front-end host device and a back-end server device. The front-end processing circuit acquires at least one monitoring data set through a monitoring device. The front-end processing circuit uses the acquired monitoring information as a training set to train a front-end artificial intelligence model, and deploys the training result of the front-end artificial intelligence model to the corresponding sensing device or monitoring device in the monitoring device. The back-end server device includes a back-end processing circuit. The front-end processing circuit uses the artificial intelligence model to perform security monitoring on the monitoring device to determine whether a trigger event has occurred, and the front-end processing circuit transmits the trigger event to the back-end processing circuit so that the back-end processing circuit can determine the trigger event and generate an intrusion closure report or a false trigger closure report.

Claims

1. An automated security deployment system, comprising: A front-end host device includes a front-end processing circuit. The front-end processing circuit acquires at least one monitoring information through a monitoring device. The front-end processing circuit uses the acquired monitoring information as a training set to train a front-end artificial intelligence model. The training result of the front-end artificial intelligence model is deployed to a corresponding sensing device or video monitoring device in the monitoring device, so that different sensing devices or monitoring devices deploy independent models. A back-end server device is connected to the front-end host device. The back-end server device includes a back-end processing circuit. The front-end processing circuit uses the artificial intelligence model to perform security monitoring on the monitoring device to determine whether a trigger event has occurred. The front-end processing circuit transmits the trigger event to the back-end processing circuit so that the back-end processing circuit can determine the trigger event and generate an intrusion closure report or a false trigger closure report.

2. The automated security deployment system as described in claim 1, wherein the monitoring device is either a non-AI-enabled or AI-enabled monitoring device.

3. The automated security deployment system as described in claim 2, wherein the monitoring device supporting artificial intelligence has an inference model and learns and trains by obtaining monitoring information through the inference model.

4. The automated security deployment system as described in claim 3, wherein when the front-end processing circuit monitors the monitoring device for the triggering event through the front-end artificial intelligence model, the front-end processing circuit transmits the process data of the triggering event to the back-end processing circuit when it determines that the triggering event is a valid alarm or an invalid alarm. When the triggering event is a valid alarm, the back-end processing circuit again uses a back-end artificial intelligence model to determine whether the process data belongs to the invalid alarm that is falsely reported. If the determination is yes, the back-end artificial intelligence model generates the false alarm closure report. If the determination is no, the back-end artificial intelligence model generates the intrusion closure report.

5. The automated security deployment system as described in claim 1, wherein the front-end host device further includes a front-end storage circuit, the back-end server device further includes a back-end storage circuit, and the front-end processing circuit selectively stores the monitoring information according to a storage space of the front-end storage circuit.

6. The automated security deployment system as described in claim 5, wherein when the storage space of the front-end storage circuit is lower than a preset space, the front-end processing circuit performs a feature conversion on the data stored in the front-end storage circuit to obtain feature data, releases the data from the storage space of the front-end storage circuit, and transmits the feature data to the back-end storage circuit of the back-end server device for storage, so that when the front-end host device needs to access the data, the back-end server device restores the display content corresponding to the data based on the feature data for the front-end host device to access.

7. The automated security deployment system as described in claim 1, wherein the training result is an inferential model.

8. The automated security deployment system as described in claim 1, wherein the sensing device includes one or more sensors.

9. The automated security deployment system as described in claim 1, wherein the video surveillance device includes one or more cameras.

10. The automated security deployment system as described in claim 1 or 6, wherein the front-end processing circuit selectively allocates the control information to a background artificial intelligence model of the back-end processing circuit for training based on a training load, and deploys the background artificial intelligence model to the front-end artificial intelligence model after the back-end processing circuit has completed training; when the training load required for the monitoring information obtained by the front-end processing circuit exceeds the training load, the front-end processing circuit transmits the monitoring information to the back-end processing circuit for training of the background artificial intelligence model.

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