Personnel erroneous rushing recognition method and erroneous rushing alarm system for automatic sample preparation system

Abstract

The invention discloses a personnel mistaken running recognition method and mistaken running alarm system for an automatic sample preparation system, and belongs to the technical field of coal sample preparation. The personnel intruding identification method for the automatic sample preparation system comprises the following steps: setting a monitoring area and personnel intruding judgment conditions; data signals from a monitoring area are collected and processed; identifying personnel in the monitoring area; carrying out behavior analysis on the intrusion behavior and the stay behavior of the detected personnel, and judging whether personnel intrusion or personnel stay exists or not; and according to an analysis result, an alarm signal is sent to inform personnel to process. Objects in front can be detected in all directions, and an alarm can be immediately given when a person intrudes into the working area of the automatic sample preparation system by mistake.

Description

Technical Field

[0001] This invention relates to the field of coal sample preparation technology, and in particular to a method for identifying personnel trespassing in an automated sample preparation system and a trespassing alarm system. Background Technology

[0002] In coal-based industries such as power generation, steel, and cement, automated sample preparation technology has become a trend. It has completely transformed the traditional manual sample preparation method, automating the entire process from sample collection to preparation and significantly reducing the need for human intervention. This technology not only improves the accuracy and repeatability of sample preparation but also reduces the possibility of human error, thereby enhancing the quality control level of the entire industrial production process.

[0003] In particular, with the integration of robotics technology, the development of automated sample preparation systems has entered a new stage. Robotic sample preparation systems, with their high transparency, intelligence, and efficiency, have become a major highlight in the field of industrial sample preparation. This system can prepare individual samples independently or continuously as needed, greatly improving sample preparation efficiency and reducing labor costs.

[0004] During the coal sample preparation process, if personnel accidentally enter the automated sample preparation system while the robot is handling coal samples in multiple stages, they will face various potential safety risks, especially mechanical collision injuries, due to the complexity of the equipment and the limited space.

[0005] Therefore, there is an urgent need to provide a method for identifying unauthorized personnel in an automated sample preparation system and an alarm system for such intrusion to solve the above problems. Summary of the Invention

[0006] The purpose of this invention is to provide a method for identifying unauthorized personnel in an automated sample preparation system and an alarm system for such intrusion. This system can detect objects in front of the system from all angles and immediately trigger an alarm when personnel accidentally enter the working area of ​​the automated sample preparation system.

[0007] To achieve the above objectives, the following technical solution is provided:

[0008] Methods for identifying unauthorized personnel entry into automated sample preparation systems include:

[0009] S1. Set the monitoring area and the criteria for determining personnel intrusion;

[0010] S2. Acquire and process data signals from the monitored area;

[0011] S3. Identify personnel within the monitored area;

[0012] S4. Conduct behavioral analysis on the detected personnel to determine whether there is any intrusion or loitering.

[0013] S5. Based on the analysis results, issue an alarm signal to notify personnel to handle the situation.

[0014] As an alternative method for identifying unauthorized personnel in an automated sample preparation system, in step S1, the criteria for determining unauthorized entry include:

[0015] An alarm is triggered when personnel enter the monitored area. The alarm signal lasts for a first set time and the robot of the automated sample preparation system stops working. Within the first set time, the alarm stops when personnel leave the monitored area, and the robot of the automated sample preparation system automatically restarts.

[0016] As an alternative method for identifying unauthorized personnel in an automated sample preparation system, in step S1, the criteria for determining unauthorized entry further include:

[0017] When personnel remain in the monitored area for more than the first set time, a continuous alarm is triggered and the automatic sample preparation system stops working; when personnel leave the monitored area, the alarm continues until maintenance personnel turn off the alarm and restart the automatic sample preparation system to resume operation.

[0018] As an optional solution for the method of identifying unauthorized personnel in an automated sample preparation system, in step S1, the setting of the monitoring area includes:

[0019] Define the monitoring area: Define the working area of ​​the automated sample preparation system as a prohibited monitoring area on the monitoring screen;

[0020] Drawing Area: Using the drawing tools in the monitoring software interface, draw the boundary of the monitored area on the video screen.

[0021] As an optional solution for the method of identifying unauthorized personnel in an automated sample preparation system, in step S2: video images of the monitored area are captured by a camera device, and the captured video images are preprocessed.

[0022] As an alternative method for identifying unauthorized personnel in an automated sample preparation system, in step S3, a target detection algorithm is used to detect and identify the movement trajectory and behavior of personnel in the video footage.

[0023] As an alternative method for identifying unauthorized personnel in an automated sample preparation system, the target detection algorithm includes a Haar feature classifier or a YOLOv8 deep learning model.

[0024] As an optional solution for the method of identifying unauthorized personnel in an automated sample preparation system, in step S1, the setting of the monitoring area includes:

[0025] Define the monitoring area: Define the working area of ​​the automated sample preparation system as a prohibited monitoring area on the monitoring screen;

[0026] Deploy a sensor network: Deploy detection units within the monitoring area, and the detection area of ​​the detection units is not smaller than the monitoring area.

[0027] As an alternative method for identifying unauthorized personnel in an automated sample preparation system, the detection unit includes an infrared sensor, a microwave sensor, and / or a vibration sensor.

[0028] The trespass alarm system adopts the personnel trespass identification method for the automated sample preparation system as described in any of the above claims. The trespass alarm system includes a control module and an alarm release module, and the control module is electrically connected to the alarm release module.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] The present invention provides a method for identifying unauthorized personnel in an automated sample preparation system. This method sets up a monitoring area and intrusion criteria, collects and processes data signals from the monitoring area, identifies personnel within the monitoring area, analyzes the intrusion and loitering behaviors of detected personnel, determines whether unauthorized entry or loitering has occurred, and issues an alarm signal based on the analysis results to notify personnel to take appropriate action. It can monitor the operational status of the automated sample preparation system and unauthorized personnel in real time. When unauthorized entry into the working area of ​​the automated sample preparation system or loitering within the monitoring area is detected, the system will immediately issue an alarm signal.

[0031] The trespass alarm system provided by this invention adopts the personnel trespass identification method for automated sample preparation systems mentioned above. The trespass alarm system includes a control module and an alarm release module. The control module and the alarm release module are electrically connected and can detect objects in front from all directions. When personnel mistakenly enter the working area of ​​the automated sample preparation system, an alarm can be sounded immediately. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the content of the embodiments of the present invention and these drawings without creative effort.

[0033] Figure 1This is a flowchart of a method for identifying unauthorized personnel entering an automated sample preparation system, as described in an embodiment of the present invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0035] In the description of this invention, it should be noted that the terms "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of this invention is in use. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0036] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0037] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0038] To enable all-around detection of objects ahead and to immediately trigger an alarm when personnel mistakenly enter the working area of ​​the automated sample preparation system, this embodiment provides a method for identifying and alarming personnel trespassing in an automated sample preparation system. The following describes a method for identifying such trespassing and an alarm system for such a system. Figure 1 The specific content of this embodiment will be described in detail.

[0039] Example 1

[0040] like Figure 1As shown, the personnel intrusion identification method for the automated sample preparation system in this embodiment includes: S1, setting the monitoring area and personnel intrusion judgment conditions; S2, collecting and processing data signals from the monitoring area; S3, identifying personnel within the monitoring area; S4, performing behavioral analysis on the detected personnel's intrusion and loitering behaviors to determine whether personnel have intruded or loitered; S5, based on the analysis results, if abnormal or intrusive behavior is detected, promptly issuing an alarm signal to notify maintenance personnel for handling. This personnel intrusion identification method for the automated sample preparation system in this embodiment sets the monitoring area and personnel intrusion judgment conditions, collects and processes data signals from the monitoring area, identifies personnel within the monitoring area, performs behavioral analysis on the detected personnel's intrusion and loitering behaviors to determine whether personnel have intruded or loitered, and issues an alarm signal based on the analysis results to notify personnel for handling. It can monitor the operating status of the automated sample preparation system and personnel intrusion in real time. When personnel are detected mistakenly entering the working area of ​​the automated sample preparation system or loitering in the monitoring area, the system will immediately issue an alarm signal.

[0041] Furthermore, in step S1, the personnel intrusion determination conditions include: when personnel enter the monitored area, an alarm is triggered, the alarm signal lasts for a first set time, and the robot of the automated sample preparation system stops working; within the first set time, when personnel leave the monitored area, the alarm stops, and the robot of the automated sample preparation system automatically restarts. Even further, in step S1, the personnel intrusion determination conditions also include: when personnel stay in the monitored area for more than the first set time, a continuous alarm is triggered, and the automated sample preparation system stops working; when personnel leave the monitored area, the alarm continues until maintenance personnel turn off the alarm and restart the automated sample preparation system, after which it resumes operation. It should be noted that the first set time is M seconds, and M can be, but is not limited to, a value selected between 5s and 10s. The specific value will be determined after system testing and will not be subject to further restrictions here.

[0042] The personnel trespassing identification method for automated sample preparation systems provided by this invention can monitor the operating status of the automated sample preparation system and personnel trespassing in real time. When personnel are detected trespassing into the working area of ​​the automated sample preparation system or lingering in the monitored area, the system will immediately issue an alarm signal, the robot of the automated sample preparation system will immediately stop working, and the trespassing personnel will be reminded to leave the working area of ​​the automated sample preparation system, thereby ensuring personnel safety. If personnel are detected lingering in the monitored area for more than a certain period of time, it will be judged as unauthorized trespassing, triggering the alarm to sound continuously, and the automated sample preparation system will stop working until maintenance personnel turn off the alarm and restart the automated sample preparation system to resume operation.

[0043] Step S1 also includes a testing and adjustment phase: During the testing process, real-time monitoring is conducted by observing the real-time monitoring screen to ensure that the personnel intrusion judgment conditions in the monitored area are tested as expected. The simulation test includes test condition one and test condition two.

[0044] Specifically, Test Condition 1: A person repeatedly enters the monitored area and exits within M seconds (e.g., M = 1s, 2s, 3s, 4s, or 5s) to test whether the alarm signal is triggered as set, and whether the robot automatically starts after the person exits. Test Condition 2: A person stays in the monitored area for more than 5 seconds (or M seconds) and walks within the working area of ​​the automated sample preparation system to test whether the alarm is triggered as set, whether the alarm remains effective after exiting, and whether the alarm ends as set after maintenance personnel turn it off. Parameter Adjustment: Based on the test results, adjust the judgment conditions and the value of M until satisfactory monitoring performance requirements are met. Record Settings: Record all setting parameters for future reference or restoration. Periodic Checks: Periodically check whether the area intrusion judgment conditions are still effective and adjust them as needed.

[0045] In some application scenarios, camera devices are used to monitor the working area of ​​the automated sample preparation system. In step S1, setting the monitoring area includes: defining the monitoring area: defining the working area of ​​the automated sample preparation system as a prohibited monitoring area on the monitoring screen; drawing the area: using drawing tools on the video screen through the monitoring software interface to draw the boundary of the monitoring area.

[0046] Further, in step S2: video footage of the monitored area is captured using a camera device, and the captured video footage is preprocessed. Preprocessing includes noise reduction, enhancement, and jitter reduction operations to improve image quality and facilitate subsequent processing.

[0047] Furthermore, in step S3, an object detection algorithm is used to detect and identify the movement trajectories and behaviors of people in the video footage. For example, the object detection algorithm includes a Haar feature classifier or a YOLOv8 deep learning model.

[0048] In some application scenarios, this embodiment provides an intrusion alarm system. It employs the personnel intrusion identification method for automated sample preparation systems mentioned above. The intrusion alarm system includes a control module and an alarm dissemination module, with the control module and alarm dissemination module electrically connected. The intrusion alarm system can detect objects in all directions and immediately trigger an alarm when personnel accidentally enter the working area of ​​the automated sample preparation system.

[0049] For example, the control module includes a video surveillance system, a data transmission network, a data processing center, an image recognition and analysis system, an alarm logic judgment unit, and a user interface. The video surveillance system includes cameras, thermal imaging cameras, etc., for real-time monitoring and image capture. The data transmission network includes wired (e.g., Ethernet) and wireless (e.g., Wi-Fi, 4G / 5G networks) transmission devices for transmitting data collected by the perception layer to the processing center. The data processing center includes servers and computer systems for receiving, processing, and storing monitoring data. The image recognition and analysis system includes applications of artificial intelligence algorithms, such as deep learning, to identify, classify, and analyze the behavior of people in the monitored images. The alarm logic judgment unit includes judging detected events based on preset rules and algorithms to determine whether an alarm should be triggered. The user interface is used for operators to monitor, control, and configure the system. When the system determines that a false intrusion has occurred, the alarm issuing module sends an alarm to relevant personnel through various means such as audible and visual alarms, SMS, or telephone.

[0050] Example 2

[0051] This embodiment provides a method for identifying unauthorized personnel in an automated sample preparation system and an alarm system for such intrusion. Compared with Embodiment 1, the basic principle of the unauthorized personnel identification method provided in this embodiment is the same as that in Embodiment 1. The only difference is the monitoring equipment and the setting of the method. This embodiment will not repeat the content that is the same as that in Embodiment 1.

[0052] In other application scenarios, detection units are used to monitor the working area of ​​the automated sample preparation system. Detection units include infrared sensors, microwave sensors, and / or vibration sensors. In step S1, setting the monitoring area includes: defining the monitoring area: defining the working area of ​​the automated sample preparation system as a prohibited monitoring area on the monitoring screen; deploying a sensor network: deploying detection units such as infrared sensors, microwave sensors, and vibration sensors within the monitoring area, selecting sensors with appropriate sensitivity, and ensuring that the detection area of ​​the detection units coincides with the defined monitoring area as much as possible, and that the detection area of ​​the detection units is slightly larger than or equal to the defined monitoring area.

[0053] Furthermore, in step S2, sensors deployed in the monitored area, such as infrared sensors, microwave sensors, and vibration sensors, are used to collect data on personnel intrusion and activity within the monitored area. The collected sensor signals are preprocessed, including noise reduction and smoothing operations, to improve signal quality and facilitate subsequent processing.

[0054] In other application scenarios, this embodiment provides an intrusion alarm system, employing the personnel intrusion identification method for automated sample preparation systems mentioned above. The intrusion alarm system includes a control module and an alarm dissemination module, with the control module and alarm dissemination module electrically connected. The intrusion alarm system can detect objects in all directions and immediately trigger an alarm when personnel accidentally enter the working area of ​​the automated sample preparation system.

[0055] For example, the control module includes a sensor network, a data acquisition unit, a data processing unit, a communication network, a data processing and decision-making module, and a signal preprocessing module. The sensor network uses sensors such as infrared sensors, microwave sensors, and vibration sensors installed at entrances, exits, and critical areas to detect personnel activity and abnormal behavior. The data acquisition unit collects sensor data. The data processing unit preprocesses, analyzes, and makes decisions based on the data. The communication network ensures stable and reliable data transmission between the sensors and the control center. The signal preprocessing module of the data processing and decision-making module performs filtering, noise reduction, and normalization; the personnel detection algorithm in the data processing and decision-making module uses machine learning or pattern recognition algorithms to distinguish people from other objects. The decision logic of the data processing and decision-making module determines whether an intrusion is accidental based on preset rules. When an accidental intrusion is detected, the alarm system of the alarm release module triggers an audible and visual alarm or sends a notification. The emergency response system of the alarm release module automatically notifies security personnel or management personnel.

[0056] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for identifying unauthorized personnel entry into an automated sample preparation system, characterized in that, include: S1. Set the monitoring area and the criteria for determining personnel intrusion; S2. Acquire and process data signals from the monitored area; S3. Identify personnel within the monitored area; S4. Conduct behavioral analysis on the detected personnel to determine whether there is any intrusion or loitering. S5. Based on the analysis results, issue an alarm signal to notify personnel to handle the situation.

2. The method for identifying unauthorized personnel entry into an automated sample preparation system according to claim 1, characterized in that, In step S1, the conditions for determining intrusion include: An alarm is triggered when personnel enter the monitored area. The alarm signal lasts for a first set time and the robot of the automated sample preparation system stops working. Within the first set time, the alarm stops when personnel leave the monitored area, and the robot of the automated sample preparation system automatically restarts.

3. The method for identifying unauthorized personnel entry into an automated sample preparation system according to claim 2, characterized in that, In step S1, the criteria for determining intrusion also include: When personnel remain in the monitored area for more than the first set time, a continuous alarm is triggered and the automatic sample preparation system stops working; when personnel leave the monitored area, the alarm continues until maintenance personnel turn off the alarm and restart the automatic sample preparation system to resume operation.

4. The method for identifying unauthorized personnel entry into an automated sample preparation system according to any one of claims 1-3, characterized in that, In step S1, setting the monitoring area includes: Define the monitoring area: Define the working area of ​​the automated sample preparation system as a prohibited monitoring area on the monitoring screen; Drawing Area: Using the drawing tools in the monitoring software interface, draw the boundary of the monitored area on the video screen.

5. The method for identifying unauthorized personnel entry into an automated sample preparation system according to claim 4, characterized in that, In step S2: video images of the monitored area are captured by a camera device, and the captured video images are preprocessed.

6. The method for identifying unauthorized personnel entry into an automated sample preparation system according to claim 5, characterized in that, In step S3, a target detection algorithm is used to detect and identify the movement trajectory and behavior of people in the video footage.

7. The method for identifying unauthorized personnel entry into an automated sample preparation system according to claim 6, characterized in that, The target detection algorithm includes the Haar feature classifier or the YOLOv8 deep learning model.

8. The method for identifying unauthorized personnel entry into an automated sample preparation system according to any one of claims 1-3, characterized in that, In step S1, setting the monitoring area includes: Define the monitoring area: Define the working area of ​​the automated sample preparation system as a prohibited monitoring area on the monitoring screen; Deploy a sensor network: Deploy detection units within the monitoring area, and the detection area of ​​the detection units is not smaller than the monitoring area.

9. The method for identifying unauthorized personnel entry into an automated sample preparation system according to claim 8, characterized in that, The detection unit includes an infrared sensor, a microwave sensor, and / or a vibration sensor.

10. An trespass alarm system, characterized in that, The method for identifying unauthorized personnel entry into an automated sample preparation system as described in any one of claims 1-9 is used, wherein the unauthorized entry alarm system includes a control module and an alarm release module, and the control module is electrically connected to the alarm release module.

Images