Flow totalizer integrating camera snapshot analysis and snapshot identification method

Abstract

The invention discloses a traffic totalizer integrating camera snapshot analysis and a snapshot recognition method, and belongs to the field of intelligent monitoring. The flow totalizer comprises a shell, a main control module, a flow data processing module, an image acquisition module, an image analysis module, a communication module and a human-computer interaction interface, the main control module, the flow data processing module, the image acquisition module, the image analysis module and the communication module are all packaged in the shell. The system is high in integration level, image acquisition, analysis and flow measurement functions are integrated, the system structure is simplified, and the wiring and installation difficulty is reduced. And the safety is high, illegal operation is actively recognized and recorded through the image recognition technology, and equipment tampering and misoperation are effectively prevented. And the response speed is high: edge AI calculation is adopted, and real-time identification and alarm can be realized without depending on the cloud. All captured images and operation records can be locally stored and remotely called, so that post auditing and responsibility tracking are facilitated.

Description

Technical Field

[0001] The present invention relates to intelligent monitoring, and in particular to a flow integrator integrated with camera capture analysis and a capture recognition method. Background Art

[0002] In industrial automation systems, flow totalizers, as crucial devices for fluid metering and process control, are widely used in energy management, petrochemicals, water and gas supply, and other fields. As industrial sites increasingly demand data security, operational compliance, and remote monitoring capabilities, traditional flow totalizers, relying solely on sensor acquisition and local data display, are no longer able to meet the demands of complex applications.

[0003] Currently, most flow totalizers primarily focus on collecting, calculating, and outputting fluid parameters, lacking effective monitoring capabilities for external operational behavior. In critical environments (such as gas stations, natural gas pressure regulating stations, and chemical plants), totalizers may face security risks such as illegal operation, tampering with settings, and malicious damage. While some existing systems are equipped with independent video surveillance devices, these devices are typically not directly connected to the totalizer itself, making it impossible to achieve real-time recognition and coordinated response to operational behavior. This results in delayed monitoring, difficulty in obtaining evidence, and weak risk prevention and control capabilities.

[0004] Furthermore, traditional video surveillance systems rely heavily on manual monitoring or post-event playback analysis, lacking intelligent image recognition capabilities and making it difficult to proactively detect potential security threats. Furthermore, the separate deployment of cameras, traffic flow measurement modules, and alarm systems also leads to complex wiring, low integration, and high maintenance costs.

[0005] Therefore, there is an urgent need for a new type of flow integrator that can integrate camera image acquisition and intelligent analysis modules while completing conventional flow measurement and calculation functions. In particular, it can automatically trigger image capture and behavior recognition when someone approaches or operates the equipment, thereby achieving timely early warning, recording and remote notification of abnormal operations, and improving the security and intelligent management level of the system. Summary of the Invention

[0006] Purpose of the invention: The purpose of the present invention is to provide a flow meter and a capture recognition method with integrated camera capture analysis; to solve the problem that existing flow meters lack effective monitoring means for operating behaviors in industrial field applications, especially the problem that they cannot be identified and recorded in time when there are people approaching or abnormal operations.

[0007] Technical solution: A flow totalizer with integrated camera capture and analysis, including: a housing, a main control module, a flow data processing module, an image acquisition module, an image analysis module, a communication module, and a human-computer interaction interface; the main control module, the flow data processing module, the image acquisition module, the image analysis module, and the communication module are all encapsulated in the housing;

[0008] Image acquisition module: used to continuously monitor the surrounding environment during equipment operation, and trigger image acquisition when a moving object is detected entering the preset range;

[0009] Image analysis module: connected to the image acquisition module, used to perform face recognition, posture analysis or gesture recognition on the collected images to determine whether the operator is a legitimate operator or whether there is any abnormal behavior;

[0010] Main control module: used to decide whether to start capturing, recording or issuing an alarm based on the image analysis results, and upload the relevant data to the remote monitoring platform;

[0011] Flow data processing module: used to perform flow measurement tasks normally, and suspend non-essential functions when abnormal operations occur, giving priority to image analysis and alarm response;

[0012] Communication module: used to send captured images, timestamps, and device status information to a backend server or mobile terminal for subsequent review and evidence collection;

[0013] Human-computer interaction interface: LCD display with touch panel or knob is used for parameter setting and status viewing.

[0014] Preferably, the image acquisition module uses a high-definition wide-angle camera, supports imaging in low-light environments, and has autofocus and infrared night vision functions.

[0015] Preferably, the image analysis module is composed of an embedded AI chip, running a deep learning-based target detection algorithm, and is capable of detecting whether someone is approaching or operating the device.

[0016] Preferably, the main control module adopts a high-performance ARM CortexM7 microcontroller, which is responsible for coordinating the work of each module and controlling the triggering capture and alarm logic.

[0017] Preferably, the flow data processing module receives analog or digital signals from the sensor and completes the calculation and storage of instantaneous flow and accumulated flow.

[0018] Preferably, the communication module supports multiple communication modes such as RS485, Modbus TCP, and 4G / 5G to realize remote data reporting and alarm information push.

[0019] Preferably, the main control module, the flow data processing module, the image acquisition module, the image analysis module, and the communication module are connected via a high-speed bus to ensure efficient collaboration between image acquisition, analysis, and flow data processing.

[0020] A method for capturing and identifying images by integrating camera capture analysis includes the following steps:

[0021] S1. After the device is powered on, the image acquisition module is initialized and enters the standby state;

[0022] S2. The main control module sets the monitoring area and starts the motion detection algorithm;

[0023] S3. When a person enters the monitoring area, the image acquisition module captures the first frame of image;

[0024] S4, the image analysis module performs target recognition on the image to determine whether the operator is an authorized operator;

[0025] S5. If it is identified as illegal approach or operation behavior, then: start continuous capture or video recording, trigger local sound and light alarm, and send alarm notification to the designated terminal through the communication module;

[0026] S6. All images and event logs are stored locally and uploaded to the cloud database simultaneously;

[0027] S7. After the alarm ends, the system automatically returns to standby mode and continues monitoring.

[0028] Beneficial effects: The present invention has a high degree of integration: it integrates image acquisition, analysis and flow measurement functions into one, simplifies the system structure, and reduces the difficulty of wiring and installation. Strong security: through image recognition technology, it can actively identify and record illegal operations, effectively preventing equipment tampering and misoperation. Fast response speed: using edge AI computing, real-time identification and alarm can be achieved without relying on the cloud. Strong traceability: all captured images and operation records can be saved locally and retrieved remotely, which is convenient for post-audit and responsibility tracking. Wide adaptability: supports a variety of communication methods and installation environments, and is suitable for multiple industry scenarios such as oil, natural gas, water supply, and energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a system flow chart. DETAILED DESCRIPTION

[0030] In order to make the technical solution of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] Example

[0032] like Figure 1As shown, a flow totalizer with integrated camera capture and analysis includes a main control module, flow data processing module, image acquisition module, image analysis module, and communication module, all integrated within the housing. The high level of integration allows for easy on-site installation and maintenance. It also features a human-machine interface consisting of an LCD display and touch buttons or knobs for viewing device parameters and status. The main control module, flow data processing module, image acquisition module, image analysis module, and communication module are connected via a high-speed bus, ensuring efficient coordination between image acquisition, analysis, and flow data processing.

[0033] The image acquisition module is installed on the surface of the shell or the built-in light-transmitting window. It uses the OV5640 series high-definition wide-angle camera with autofocus and infrared night vision functions that supports imaging in low-light environments. It continuously monitors the surrounding environment during equipment operation and triggers image acquisition when a moving object is detected entering the preset range.

[0034] The captured images are sent to an image analysis module comprised of an embedded AI chip (such as the Rockchip RK3328). This module runs a lightweight deep learning model based on YOLOv5 or MobileNetV3. After training, it identifies the following behaviors in the captured images: whether someone is approaching the device, whether there is any obstruction or sabotage, whether the user is a known user (with optional facial recognition), and whether there are any unusual gestures or actions. This allows the user to determine if the user is legitimate or if there are any unusual behaviors. The model is deployed on the edge AI chip, enabling real-time inference without relying on the cloud, ensuring fast response times and data security.

[0035] The main control module uses an ARM CortexM7 microcontroller to coordinate the work of each module, and decide whether to start capturing, recording or alarming based on the image analysis results, and upload the relevant data to the remote monitoring platform.

[0036] The flow data processing module receives analog or digital signals from sensors, performs flow measurement tasks normally, completes the calculation and storage of instantaneous flow and cumulative flow, and suspends non-essential work when abnormal operations occur, giving priority to image analysis and alarm response.

[0037] The communication module supports multiple communication methods such as RS485, Modbus TCP, 4G / 5G, etc., to realize remote data upload and alarm information push, and can send captured images, timestamps, device status and other information to the background server or mobile terminal for subsequent review and evidence collection.

[0038] The specific implementation method of the image capture and behavior recognition of the flow integrator is: starting from the startup of the device until the completion of a complete image capture and analysis process: 1. After the device is powered on, the image acquisition module is initialized and enters the standby state; 2. The main control module sets the monitoring area range and starts the motion detection algorithm; 3. When a person enters the monitoring area, the image acquisition module captures the first frame of the image; 4. The image analysis module performs target recognition on the image to determine whether it is an authorized operator; 5. If it is identified as an illegal approach or operation behavior, then: start continuous capture or recording, trigger a local sound and light alarm, and send an alarm notification to the designated terminal through the communication module; 6. All images and event logs are stored locally and uploaded to the cloud database synchronously; 7. After the alarm ends, the system automatically returns to the standby state and continues monitoring.

[0039] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A flow totalizer with integrated camera capture and analysis, characterized in that: include: Housing, main control module, flow data processing module, image acquisition module, image analysis module, communication module and human-computer interaction interface; The main control module, flow data processing module, image acquisition module, image analysis module, and communication module are all encapsulated in the housing; Image acquisition module: used to continuously monitor the surrounding environment during equipment operation, and trigger image acquisition when a moving object is detected entering the preset range; Image analysis module: connected to the image acquisition module, used to perform face recognition, posture analysis or gesture recognition on the collected images to determine whether the operator is a legitimate operator or whether there is any abnormal behavior; Main control module: used to decide whether to start capturing, recording or issuing an alarm based on the image analysis results, and upload the relevant data to the remote monitoring platform; Flow data processing module: used to perform flow measurement tasks normally, and suspend non-essential functions when abnormal operations occur, giving priority to image analysis and alarm response; Communication module: used to send captured images, timestamps, and device status information to a backend server or mobile terminal for subsequent review and evidence collection; Human-computer interaction interface: LCD display with touch panel or knob is used for parameter setting and status viewing.

2. The flow integrator with integrated camera capture and analysis according to claim 1 is characterized in that: The image acquisition module uses a high-definition wide-angle camera, supports imaging in low-light environments, and has autofocus and infrared night vision functions.

3. The flow integrator with integrated camera capture and analysis according to claim 1 is characterized in that: The image analysis module consists of an embedded AI chip that runs a deep learning-based target detection algorithm and can detect whether someone is approaching or operating the device.

4. The flow integrator with integrated camera capture and analysis according to claim 1 is characterized in that: The main control module adopts a high-performance ARM CortexM7 microcontroller, which is responsible for coordinating the work of each module and controlling the triggering, capturing and alarming logic.

5. The flow totalizer with integrated camera capture and analysis according to claim 1 is characterized in that: The flow data processing module receives analog or digital signals from sensors and completes the calculation and storage of instantaneous flow and accumulated flow.

6. The flow integrator with integrated camera capture and analysis according to claim 1, characterized in that: The communication module supports multiple communication methods such as RS485, Modbus TCP, and 4G / 5G, enabling remote data reporting and alarm information push.

7. The flow totalizer with integrated camera capture and analysis according to claim 1 is characterized in that: The main control module, traffic data processing module, image acquisition module, image analysis module, and communication module are connected through a high-speed bus to ensure efficient collaboration between image acquisition, analysis, and traffic data processing.

8. A method for capturing and identifying a flow integrator using the integrated camera capture analysis method according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. After the device is powered on, the image acquisition module is initialized and enters the standby state; S2. The main control module sets the monitoring area and starts the motion detection algorithm; S3. When a person enters the monitoring area, the image acquisition module captures the first frame of image; S4, the image analysis module performs target recognition on the image to determine whether the operator is an authorized operator; S5. If it is identified as illegal approach or operation behavior, then: start continuous capture or recording, trigger local sound and light alarm, and send alarm notification to the designated terminal through the communication module; S6. All images and event logs are stored locally and uploaded to the cloud database simultaneously; S7. After the alarm ends, the system automatically returns to standby mode and continues monitoring.

Images