Laboratory safety early warning system

Abstract

The invention relates to the technical field of laboratory safety, in particular to a laboratory safety early warning system which comprises a registration module, a portrait tracking module, a digital twin module, a common monitoring module, a monitoring analysis module for determining a monitoring mode of a laboratory and a monitoring control module. The monitoring control module is used for determining a safety warning library according to the experiment content of each experimenter so as to carry out abnormal behavior identification on the experimenter, and determining the operation of the experiment equipment according to the experiment content and the experiment video so as to carry out real-time monitoring and updating on a part of digital twinborn models corresponding to the operation of the experiment equipment; and controlling the common monitoring module to perform common monitoring on the experimental area to determine the influence characterization trend of the non-experimental area on the experimental area so as to determine whether to perform safety early warning or not. The problems that in the prior art, in the aspect of laboratory safety management, monitoring is not comprehensive, early warning is not timely, accident reason tracing is difficult, and resource utilization is unreasonable are solved.

Description

Technical Field

[0001] The present invention relates to the field of laboratory safety technology, and in particular to a laboratory safety early warning system. Background Art

[0002] In various laboratories, such as chemical laboratories, biological laboratories, and physical laboratories, safety risks always exist because the experimental process often involves hazardous chemicals, complex experimental equipment, and special experimental environment requirements. Traditional laboratory safety management relies more on manual inspections and simple sensor monitoring, and there are many problems such as incomplete monitoring, untimely warnings, and difficulty in tracing the causes of accidents. For example, it is difficult to monitor the operating specifications of experimental personnel in real time, the overall and local changes in the laboratory environment cannot be accurately grasped, and it is difficult to quickly determine the starting link of the accident and the relevant responsible persons after the accident occurs. All of these urgently require a more intelligent, efficient, and comprehensive laboratory safety early warning system.

[0003] Chinese patent publication number CN116580350A discloses a laboratory safety monitoring and early warning method and system, which relates to the field of laboratory safety technology. First, the video to be detected in the laboratory is obtained, and several key frame images are collected for preprocessing to lay the foundation for contour extraction and equipment detection. Then, based on the preprocessed key frame images, the human body contour is extracted using the background difference method, and feature extraction and behavior classification are performed to identify the abnormal behavior of the personnel. At the same time, the key frame image is input into the preset equipment recognition model, and the abnormal equipment status is identified based on the established benchmark information about the laboratory equipment. Finally, when the abnormal behavior of the personnel / abnormal equipment status is detected, the corresponding alarm information is pushed to the management personnel. Thereby, the abnormal behavior of personnel and the abnormal status of equipment that do not meet the safety requirements of the scientific research laboratory are automatically monitored and reminded in time, reducing the workload of laboratory safety managers and avoiding the occurrence of major safety accidents in the laboratory. It can be seen that the invention has the following problems:

[0004] In terms of laboratory safety management, monitoring is incomplete, early warning is not timely, it is difficult to trace the causes of accidents, and resources are used irrationally. Summary of the invention

[0005] To this end, the present invention provides a laboratory safety early warning system to overcome the problems of incomplete monitoring, untimely early warning, difficulty in tracing the cause of accidents and unreasonable resource utilization in the prior art in laboratory safety management, so as to achieve comprehensive monitoring and intelligent early warning of personnel operations, equipment status and environmental conditions through the collaborative work of multiple modules, and provide complete data support for accident tracing, while optimizing resource utilization and improving management efficiency.

[0006] To achieve the above object, the present invention provides a laboratory safety early warning system, comprising:

[0007] The registration module is used to record the time when the experimenter enters the laboratory, the content of the experiment and the time when he leaves;

[0008] Portrait tracking module, used to identify, track and record the experimental videos of each experimenter in the laboratory;

[0009] Digital twin module, used to generate a digital twin model of the laboratory;

[0010] A general monitoring module is used to obtain the environmental monitoring data of the laboratory and the experimental area in real time, wherein the environmental monitoring data includes temperature data, humidity data and smoke data;

[0011] A monitoring and analysis module, which is connected to the registration module and is used to determine the monitoring method of the laboratory according to whether there are experimenters in the laboratory;

[0012] The monitoring method includes performing portrait recognition and tracking on each experimenter in the laboratory and shooting the experimental video of each experimenter through the portrait tracking module, and generating a digital twin model in the laboratory through the digital twin module;

[0013] A monitoring and control module is respectively connected to the registration module, the portrait tracking module, the digital twin module, the general monitoring module and the monitoring and analysis module, and is used to determine the safety warning library according to the experimental content of each experimenter to identify the abnormal behavior of the experimenter, determine the running experimental equipment according to the experimental content and the experimental video to perform real-time monitoring and update of the partial digital twin model corresponding to the running experimental equipment, and control the general monitoring module to perform general monitoring of the experimental area to determine the impact characterization trend of the non-experimental area on the experimental area to determine whether to issue a safety warning.

[0014] Furthermore, the common monitoring module includes:

[0015] The overall monitoring unit is set at a fixed position in the laboratory to obtain the laboratory's environmental monitoring data in real time;

[0016] The mobile monitoring unit is configured to be mobile and is used to obtain environmental monitoring data of the experimental area in real time.

[0017] Furthermore, the monitoring and analysis module determines the monitoring mode of the laboratory according to whether there is an experimenter in the laboratory to control the portrait tracking module to start working according to the monitoring mode, including:

[0018] If there are experimenters in the laboratory, the monitoring method is determined to be controlling the portrait tracking module to perform portrait recognition and tracking on each experimenter in the laboratory and to shoot an experiment video of each experimenter;

[0019] If there is no experimenter in the laboratory, it is determined that the monitoring method is to control the digital twin module to generate a digital twin model in the laboratory to monitor the laboratory, and the portrait tracking module is not controlled to start working.

[0020] Furthermore, the monitoring control module determines whether to send a safety warning message based on the identity and number of the experimenters identified by the portrait tracking module and the experimenters recorded in the registration module, including:

[0021] If the comparison result shows that the identity and number of the identified experimenters are consistent with the recorded experimenters, it is determined that the safety warning information will not be sent;

[0022] If the comparison result is that the identity and / or number of the identified experimenters do not match the recorded experimenters, it is determined to send a safety warning message to the experimenters recorded in the registration module.

[0023] Furthermore, the monitoring and control module determines the corresponding safety warning library according to the experimental content recorded by each experimenter in the registration module and trains the classification model according to the safety warning library to obtain a safety warning classification model, and the monitoring and control module divides the experimental video into several frames and then determines several key frames, and extracts human images in each key frame to input each of the human images into the corresponding safety warning classification model to identify abnormal behaviors of the corresponding experimenters.

[0024] Further, the monitoring control module determines the similarity of each frame image according to the machine learning model to determine a number of identical frame groups and selects a frame with the highest definition in each identical frame group as a key frame;

[0025] The similarity between the frames in the same frame group should be greater than or equal to a preset similarity, and the number of key frames is equal to the number of the same frame group.

[0026] Furthermore, the monitoring and control module determines the running experimental equipment according to the experimental content and the experimental video and controls the digital twin module to monitor the running experimental equipment in real time to update the corresponding partial digital twin model in real time, and generates a digital twin model of the laboratory according to the partial digital twin model to determine the safety characterization status of the laboratory, including:

[0027] If all devices in the digital twin model updated in real time are normal, the impact characterization trend of the non-experimental area on the experimental area is determined based on the environmental monitoring data of the experimental area to determine the safety characterization status of the laboratory;

[0028] If there is an abnormal device in the digital twin model updated in real time, the safety representation state is determined to be a dangerous state and an alarm signal is issued.

[0029] Further, the digital twin model updated in real time by the monitoring and control module consists of a partial digital twin model and a fixed digital twin model;

[0030] Among them, the monitoring and control module monitors the running experimental equipment in real time to update the partial digital twin model in real time, and the monitoring and control module monitors and updates the fixed digital twin model of the non-running experimental equipment when the digital twin model is turned on.

[0031] Furthermore, the monitoring and control module determines the impact characterization trend of the non-experimental area on the experimental area according to the environmental monitoring data of the experimental area to determine the safety characterization status of the laboratory, including:

[0032] If the environmental monitoring data of the experimental area is not within the standard environmental data interval of the experimental area, the impact characterization trend of the non-experimental area on the experimental area is determined to be a negative impact trend and the safety characterization state is determined to be a dangerous state, and the monitoring control module controls the digital twin module to update part of the digital twin model and the fixed digital twin model and issue an alarm signal;

[0033] If the environmental monitoring data of the experimental area is within the standard environmental data range of the experimental area, it is determined that the impact characterization trend of the non-experimental area on the experimental area is a hidden impact trend and the safety characterization state is determined to be a safe state.

[0034] Furthermore, the monitoring and control module determines the storage location of the experimental preparation according to the experimental video and determines the storage location as the experimental area.

[0035] Compared with the prior art, the beneficial effect of the present invention lies in that the laboratory safety early warning system provided by the present invention accurately records the entry and exit time, experimental content and other information of the experimenter through the registration module, which is convenient for traceability management; the portrait tracking module effectively identifies and tracks the experimenter and records the experimental video, which is conducive to the subsequent viewing and analysis of the experimental process; the laboratory digital twin model generated by the digital twin module, combined with the monitoring method determined by the monitoring and analysis module, can intuitively present the operating status of the laboratory; the environmental monitoring data of the laboratory and the experimental area are obtained in real time through the general monitoring module to provide a basis for environmental safety assessment; the monitoring and control module determines the safety warning library according to the experimental content to accurately identify the abnormal behavior of the experimenter, and at the same time operates the experimental equipment based on the experimental content and the experimental video and updates part of the digital twin model content, and also controls the general monitoring module to analyze the impact of the non-experimental area on the experimental area. Characterization trend, so as to comprehensively and intelligently realize laboratory safety early warning, effectively prevent accidents, ensure the safety of experimental personnel, the smooth progress of experiments and the stability of the laboratory environment;

[0036] Furthermore, the overall monitoring unit of the general monitoring module is fixed at a specific location in the laboratory and is always turned on. It can continuously and stably collect the overall environmental monitoring data of the laboratory, providing a reliable basis for comprehensively grasping the overall environmental conditions of the laboratory, and helping to timely detect situations such as abnormal fluctuations in overall temperature, humidity imbalance or hidden dangers of smoke, so as to ensure the basic environmental safety of the laboratory; while the mobile monitoring unit is flexible. When the monitoring and control module determines that there is an experimental area, it can accurately move to the location of the experimental area to obtain its environmental monitoring data. This can not only perform accurate environmental monitoring on the core area of ​​the experiment, such as detecting special temperature and humidity requirements or tiny smoke changes in the local experimental area, but also effectively make up for the monitoring blind spots of fixed monitoring points, and realize more detailed and targeted environmental monitoring of the experimental area. The two work together to greatly improve the comprehensiveness, accuracy and flexibility of laboratory environmental monitoring, and build a solid data monitoring defense line for the safe operation of the laboratory.

[0037] Furthermore, the monitoring and analysis module can intelligently determine the monitoring method based on whether there are experimenters in the laboratory. When there are experimenters, the portrait tracking module is quickly started to shoot experimental videos with the help of portrait recognition and tracking technology, and the number of shots can be determined based on the number of people. This not only helps to monitor the operating behavior of experimenters in real time and promptly discover possible illegal operations or abnormal conditions, but also provides intuitive and detailed image data for subsequent experimental analysis and accident tracing. When there are no experimenters in the laboratory, it is reasonable to switch to the digital twin module to generate a laboratory model for monitoring, avoiding unnecessary waste of portrait tracking resources. The digital twin model effectively monitors equipment safety to ensure that the operating status of the laboratory is still under control when no one is on duty, greatly improving the intelligence level of laboratory monitoring, resource utilization efficiency, and overall safety and reliability, and realizing accurate, efficient, and adaptive monitoring and management of the laboratory under different personnel status.

[0038] Furthermore, the monitoring and control module cleverly integrates the data information of the registration module and the portrait tracking module, and accurately judges the personnel situation in the laboratory by rigorously comparing the identity and number of the identified experimenters with the recorded experimenter information; when the two are consistent, the normal operation order of the laboratory is maintained, no warning information is sent, and unnecessary interference is avoided; and once a discrepancy occurs, a safety warning information is immediately sent to the experimenter recorded in the registration module. This mechanism effectively prevents illegal intrusion by non-experimental personnel, greatly improves the safety and confidentiality of the laboratory, and protects the experimental process from external interference and potential threats. At the same time, it also provides experimenters with a safe and focused experimental environment, making laboratory management more standardized, orderly, and intelligent, effectively reducing the risk of safety accidents caused by poor personnel management, and effectively promoting the efficient operation and improvement of the laboratory safety management system;

[0039] Furthermore, the monitoring and control module accurately determines the corresponding safety warning library based on the experimental content recorded by each experimenter in the registration module and uses it to train the classification model to obtain the safety warning classification model. This measure enables the system to conduct in-depth cognition and learning of the potential risks of different experimental types and build a highly adaptive safety assessment system; then, the experimental video is divided into several frames and the key frames are determined, and the human body images are extracted and input into the safety warning classification model, so as to achieve effective identification of abnormal behaviors of the corresponding experimenters; this intelligent and refined processing method can keenly capture operating behaviors that do not meet experimental specifications in complex experimental scenarios, such as violations of titration operating procedures, grinding method errors, etc., and issue alarms in time to effectively prevent safety accidents caused by improper human operations, greatly improving the standardization and safety of laboratory operations and the ability to control various experimental risks, and providing solid and powerful technical support and guarantee for the safe and stable operation of the laboratory;

[0040] Furthermore, the monitoring and control module uses the machine learning model to determine the similarity of each frame image to accurately divide the same frame group, and cleverly selects the frame with the highest clarity in each group as the key frame. This operation not only effectively reduces data redundancy and improves data processing efficiency, but also provides high-quality and representative image samples for subsequent abnormal behavior identification. In addition, by reasonably setting the preset similarity, the accuracy of abnormal behavior identification results is guaranteed while optimizing the configuration of computing resources and avoiding excessive resource consumption.

[0041] Furthermore, the monitoring and control module divides the digital twin model into a partial digital twin model and a fixed digital twin model, and performs real-time monitoring on the running experimental equipment and updates the partial digital twin model, so that the dynamic changes in the equipment operation can be reflected in the model immediately, providing a reliable basis for accurately grasping the real-time status of the equipment; the fixed digital twin model of the non-operating experimental equipment is monitored and updated when the digital twin module is just turned on, effectively ensuring the accuracy and timeliness of the status information of the non-operating equipment, and then updated according to the preset update frequency, while ensuring that the model data is not outdated, and reasonably balancing the consumption of system resources; this comprehensive, intelligent and flexible digital twin model update strategy has greatly improved the integrity, timeliness and accuracy of laboratory equipment status monitoring, and provided strong technical support and guarantee for laboratory safety management, equipment maintenance and improvement of overall operational efficiency;

[0042] Furthermore, the monitoring and control module can judge the impact characterization trend of the non-experimental area on the experimental area based on the environmental monitoring data of the experimental area, and then determine the safety characterization status of the laboratory; when the environmental monitoring data of the experimental area exceeds the standard environmental data range, it is judged as a negative impact trend and the safety characterization status is determined as a dangerous state, and then the digital twin module is controlled to update some digital twin models and fixed digital twin models, and an alarm signal is issued at the same time. This series of measures allows laboratory managers to know the abnormal situation in time, take countermeasures quickly, effectively avoid further expansion of danger, and maximize laboratory safety; and when the environmental monitoring data is within the standard range, it is judged as a hidden impact trend and a safe state, realizing meticulous control of the laboratory environmental state; at the same time, the storage location of the experimental preparation is accurately determined through the experimental video and set as the experimental area, and the environmental data is analyzed and judged on this basis, so that the entire monitoring and judgment mechanism is more in line with the actual experimental situation, greatly improving the scientificity and accuracy of laboratory environmental safety monitoring and the ability to warn of potential risks, and building a solid line of defense for the safe and stable conduct of various experiments in the laboratory. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A connection diagram of a laboratory safety early warning system according to an embodiment of the present invention;

[0044] Figure 2 is a schematic diagram of a common monitoring module according to an embodiment of the present invention;

[0045] Figure 3 This is a working step diagram of the laboratory safety early warning system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0048] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0049] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] See also Figure 1 As shown, it is a connection diagram of a laboratory safety early warning system according to an embodiment of the present invention. An embodiment of the present invention provides a laboratory safety early warning system, including:

[0051] The registration module is used to record the time when the experimenter enters the laboratory, the content of the experiment and the time when he leaves;

[0052] The portrait tracking module is used to identify, track and record the experimental videos of each experimenter in the laboratory; in implementation, at least two cameras are set up, and at least one can monitor the laboratory door to determine the people entering and leaving;

[0053] A digital twin module, used to generate a digital twin model of the laboratory; in implementation, the digital twin module includes a number of binocular cameras to determine a complete digital twin model of the laboratory;

[0054] A general monitoring module is used to obtain the environmental monitoring data of the laboratory and the experimental area in real time, wherein the environmental monitoring data includes temperature data, humidity data and smoke data;

[0055] A monitoring and analysis module, which is connected to the registration module and is used to determine the monitoring method of the laboratory according to whether there are experimenters in the laboratory;

[0056] The monitoring method includes using a portrait tracking module to identify and track each experimenter in the laboratory and shoot the experiment video of each experimenter, and generating a digital twin model in the laboratory through a digital twin module; in implementation, it also includes obtaining environmental monitoring data of the experimental area and / or laboratory through a common monitoring module;

[0057] A monitoring control module, which is respectively connected to the registration module, the portrait tracking module, the digital twin module, the general monitoring module and the monitoring analysis module, and is used to determine the safety warning library according to the experimental content of each experimenter to identify abnormal behavior of the experimenter, determine the operating experimental equipment according to the experimental content and the experimental video to perform real-time monitoring and update of part of the digital twin model corresponding to the operating experimental equipment, and control the general monitoring module to perform general monitoring on the experimental area to determine the impact characterization trend of the non-experimental area on the experimental area to determine whether to issue a safety warning;

[0058] The safety warning library includes precautions and error demonstrations corresponding to the experimental content.

[0059] It can be understood that the laboratory safety early warning system provided by the present invention accurately records the experimenter's entry and exit time, experimental content and other information through the registration module, which is convenient for traceability management; the portrait tracking module effectively identifies and tracks the experimenter and records the experimental video, which is conducive to the subsequent viewing and analysis of the experimental process; the laboratory digital twin model generated by the digital twin module, combined with the monitoring method determined by the monitoring and analysis module, can intuitively present the operating status of the laboratory; the environmental monitoring data of the laboratory and the experimental area are obtained in real time through the ordinary monitoring module to provide a basis for environmental safety assessment; the monitoring and control module determines the safety warning library according to the experimental content to accurately identify the abnormal behavior of the experimenter, and at the same time operates the experimental equipment based on the experimental content and experimental video and updates part of the digital twin model content, and also controls the ordinary monitoring module to analyze the impact of the non-experimental area on the experimental area. Characterization trend, thereby comprehensively and intelligently realizing laboratory safety early warning, effectively preventing accidents, ensuring the safety of experimental personnel, the smooth progress of experiments and the stability of the laboratory environment.

[0060] See also Figure 2 As shown, it is a schematic diagram of a common monitoring module according to an embodiment of the present invention. Specifically, the common monitoring module includes:

[0061] The overall monitoring unit is arranged at a fixed position in the laboratory to obtain the environmental monitoring data of the laboratory in real time; it can be understood that the sensors for obtaining the environmental monitoring data are any of the existing technologies;

[0062] The mobile monitoring unit is configured to be mobile and is used to obtain the environmental monitoring data of the experimental area in real time. In implementation, it can be configured as a patrol car, on which sensors for obtaining environmental monitoring data are installed. It can also be configured to be any movable shape, as long as it can be moved to the experimental area and obtain the environmental monitoring data of the experimental area.

[0063] It is understandable that the overall monitoring unit in the ordinary monitoring module remains in an open state to obtain real-time environmental monitoring data of the laboratory; when the mobile monitoring unit determines that there is an experimental area, the monitoring control module controls the mobile monitoring unit to move to the location of the experimental area.

[0064] It is understandable that the overall monitoring unit of the ordinary monitoring module is fixed at a specific location in the laboratory and always remains turned on. It can continuously and stably collect the overall environmental monitoring data of the laboratory, providing a reliable basis for comprehensively grasping the overall environmental conditions of the laboratory, and helping to promptly detect situations such as abnormal overall temperature fluctuations, humidity imbalance or smoke hazards, to ensure the basic environmental safety of the laboratory; while the mobile monitoring unit is flexible. When the monitoring and control module determines that there is an experimental area, it can be accurately moved to the location of the experimental area to obtain its environmental monitoring data, which can not only perform accurate environmental monitoring of the core area of ​​the experiment, such as detecting special temperature and humidity requirements or tiny smoke changes in the local experimental area, but also effectively make up for the monitoring blind spots of fixed monitoring points, and realize more detailed and targeted environmental monitoring of the experimental area. The two work together to greatly improve the comprehensiveness, accuracy and flexibility of laboratory environmental monitoring, and build a solid data monitoring line of defense for the safe operation of the laboratory.

[0065] See also Figure 3 As shown, it is a working step diagram of the laboratory safety early warning system of an embodiment of the present invention. Specifically, the monitoring and analysis module determines the monitoring mode of the laboratory according to whether there is an experimenter in the laboratory to control the portrait tracking module to start working according to the monitoring mode, including:

[0066] If there are experimenters in the laboratory, the monitoring method is determined to control the portrait tracking module to start working to perform portrait recognition and tracking on each experimenter in the laboratory and shoot the experiment video of each experimenter; it can be understood that the portrait recognition and tracking technology is a prior art and will not be described in detail; in implementation, the portrait tracking module determines how many experiment videos need to be shot according to the number of experimenters in the laboratory;

[0067] If there is no experimenter in the laboratory, it is determined that the monitoring method is to control the digital twin module to generate a digital twin model in the laboratory to monitor the laboratory, and the portrait tracking module is not controlled to start working.

[0068] It is understandable that when there are people in the laboratory, the experimenter in the experiment can check whether the running experimental equipment is normal, so there is no need to turn on the digital twin module and determine whether the running experimental equipment is running normally through the generated digital twin model; when there are no experimenters in the laboratory, the safety of the equipment in the laboratory can only be determined by turning on the digital twin module.

[0069] It is understandable that the monitoring and analysis module can intelligently determine the monitoring method based on whether there are experimenters in the laboratory. When there are experimenters, the portrait tracking module is quickly started to shoot experimental videos with the help of portrait recognition and tracking technology, and the number of shots can be determined based on the number of people. This not only helps to monitor the experimenters' operating behaviors in real time and promptly discover possible illegal operations or abnormal conditions, but also provides intuitive and detailed image data for subsequent experimental analysis and accident tracing; and when there are no experimenters in the laboratory, it is reasonable to switch to the digital twin module to generate a laboratory model for monitoring, avoiding unnecessary waste of portrait tracking resources, and effectively monitoring equipment safety through the digital twin model to ensure that the operation status of the laboratory is still under control when unattended, greatly improving the intelligence of laboratory monitoring, resource utilization efficiency, overall safety and reliability, and realizing accurate, efficient and adaptive monitoring and management of the laboratory under different personnel status.

[0070] Specifically, the monitoring control module determines whether to send a safety warning message based on the identity and number of the experimenters identified by the portrait tracking module and the experimenters recorded in the registration module, including:

[0071] If the comparison result is that the identified identity and number of experimenters are consistent with the recorded experimenters, it is determined that no safety warning information will be sent; it is understandable that the number of people in the laboratory and their respective identities at any time can be determined through the name registration module (fingerprint registration, face recognition, etc. can be used to enter the door, and fingerprint and face recognition are required again when leaving), and the number of people in the laboratory and their respective identities can also be identified in real time through the portrait tracking module (face recognition can be used), so by comparing the number of people and identities, it can be determined whether there are non-experimental personnel entering, which can improve the safety of the experiment; in addition, when the name registration module or the portrait tracking module finds that the number of people has changed, it will trigger the monitoring and control module to compare the number and identities of the identified and recorded experimenters;

[0072] If the comparison result is that the identity and / or number of the identified experimenters do not match the recorded experimenters, it is determined that a safety warning message is sent to the experimenters recorded in the name registration module; in implementation, if there is a mismatch, a safety warning message is sent based on the currently registered experimenters entering the laboratory in the name registration module to remind the correct experimenter to confirm the identity of the people in the laboratory. In one implementation, the name registration module records that there should be 3 experimenters at the current moment, and the portrait tracking module finds that the number is not equal to 3 experimenters, then it is necessary to send information to the 3 experimenters recorded in the name registration module; in one implementation, the name registration module records that there should be a and b in the laboratory at the current moment, but the portrait tracking module identifies a and c, then it is necessary to send information to a and b.

[0073] It is understandable that the monitoring and control module cleverly integrates the data information of the name registration module and the portrait tracking module, and accurately judges the laboratory personnel situation by rigorously comparing the identity and number of identified experimenters and the recorded experimenter information; when the two are consistent, the normal operation order of the laboratory is maintained, no warning information is sent, and unnecessary interference is avoided; and once a discrepancy occurs, a safety warning information is immediately sent to the experimenter recorded in the name registration module. This mechanism effectively prevents illegal intrusion by non-experimental personnel, greatly improves the safety and confidentiality of the laboratory, and protects the experimental process from external interference and potential threats. At the same time, it also provides experimenters with a safe and focused experimental environment, making laboratory management more standardized, orderly, and intelligent, effectively reducing the risk of safety accidents caused by poor personnel management, and effectively promoting the efficient operation and improvement of the laboratory safety management system.

[0074] Specifically, the monitoring and control module determines the corresponding safety warning library according to the experimental content recorded by each experimenter in the registration module and trains the classification model according to the safety warning library to obtain a safety warning classification model. In addition, the monitoring and control module divides the experimental video into several frames, determines several key frames, and extracts human images in each key frame to input each human image into the corresponding safety warning classification model to identify abnormal behavior of the corresponding experimenter.

[0075] It is understandable that each experiment has its corresponding precautions and error demonstrations, and the precautions and error demonstrations corresponding to each experiment are set as a safety warning library for the experimental content. For example, for titration, grinding, coating and other experimental contents, only the specific raw materials are different, while the overall process, steps, precautions and error demonstrations are the same.

[0076] In implementation, the classification models that can be used include: (1) Computer vision-based models: ① YOLO series models, such as YOLOv5. YOLO series models are widely used for their fast and accurate target detection capabilities. They can identify whether the experimenter performs tasks according to the established operating procedures, whether the correct tools are used, and whether safety regulations are followed. Through training with the safety warning library, the model can learn to identify correct and incorrect action patterns in various experimental operations, thereby detecting and warning incorrect actions in real time; for example, in chemical experiments, it can detect whether the dropper is used correctly, whether the reagent is poured in the specified area, etc.; the image and video data in the safety warning library can be used as additional training Data can enrich the model's understanding of incorrect actions; for example, after annotating images or video clips of different types of incorrect actions in the safety warning library and adding them to the training set, the YOLO model can learn these specific error patterns, thereby improving the ability to detect similar incorrect actions; ② CNN can automatically extract features from images. By learning and training a large amount of annotated correct and incorrect experimental action image data, CNN can learn the feature representations of different actions, and then classify new experimental action images to determine whether they are incorrect actions; for example, whether the operating gestures and positions of instruments in physical experiments are correct; CNN can be pre-trained or fine-tuned using image data in the safety warning library. By allowing CNN to learn the features of incorrect action images in the safety warning library during the pre-training stage, the model can better capture abnormal features in experimental actions, and then more accurately identify incorrect actions in practical applications; (2) Models based on video analysis: ① Temporal Action Localization ion (temporal action localization) model, which focuses on locating and classifying actions in videos, and can understand the sequence and time span of actions in video content; through training, it can identify whether the actions at different stages of the experiment meet the specifications and locate the time segments where incorrect actions occur; for example, in biological experiments, it can identify whether the operations of different steps in the cell culture process are correct and the time when the incorrect actions occur; ②C3D is a video classification model based on 3D convolutional neural network, which can simultaneously capture the spatiotemporal information of the video and model and classify the action sequences in the experiment; by inputting continuous experimental operation video frames, the C3D model can learn the patterns of different actions in time and space, so as to judge whether the actions are correct; the video data in the safety warning library can provide more training samples for the C3D model, so that it can learn richer spatiotemporal characteristics of incorrect actions. By learning and analyzing these data, the C3D model can establish a more accurate classification model to improve the recognition accuracy and recall rate of incorrect actions in the experiment.

[0077] It can be understood that the monitoring and control module accurately determines the corresponding safety warning library based on the experimental content recorded by each experimenter in the registration module and uses it to train the classification model to obtain the safety warning classification model. This measure enables the system to conduct in-depth cognition and learning of the potential risks of different experimental types and build a highly adaptive safety assessment system; then, the experimental video is divided into several frames and the key frames are determined, and the human body images are extracted and input into the safety warning classification model, so as to achieve effective identification of abnormal behaviors of the corresponding experimenters; this intelligent and refined processing method can keenly capture operational behaviors that do not meet experimental specifications in complex experimental scenarios, such as violations of titration operation procedures, grinding technique errors, etc., and issue alarms in time to effectively prevent safety accidents caused by improper human operations, greatly improving the standardization and safety of laboratory operations and the ability to control various experimental risks, and providing solid and strong technical support and guarantee for the safe and stable operation of the laboratory.

[0078] Specifically, the monitoring control module determines the similarity of each frame image according to the machine learning model to determine a number of identical frame groups and selects a frame with the highest definition in each identical frame group as a key frame;

[0079] The similarity between the frames in the same frame group should be greater than or equal to a preset similarity, and the number of key frames is equal to the number of the same frame group.

[0080] It is understandable that the method of determining the similarity of images through a machine learning model is an existing technology, so it will not be elaborated on; in implementation, the preset similarity affects the number of key frames. The greater the preset similarity, the more key frames there are and the more accurate the results of abnormal behavior recognition are. In order to balance computing resources and the results of abnormal behavior recognition, the preset similarity ∈ [90%, 100%) is preferably set to 95%.

[0081] In the implementation, (1) if the abnormal behavior of the experimenter is identified through the YOLO series model or the convolutional neural network, this step is performed to extract several key frames in each experimental video; this method can reduce the waste of computer resources; (2) if the abnormal behavior of the experimenter is identified through the Temporal Act Localization model or the C3D model, this step is not performed to directly determine the abnormal behavior identification through the experimental video.

[0082] It is understandable that the monitoring and control module uses the machine learning model to determine the similarity of each frame image to accurately divide the same frame group, and cleverly selects the frame with the highest clarity in each group as the key frame. This operation not only effectively reduces data redundancy and improves data processing efficiency, but also provides high-quality and representative image samples for subsequent abnormal behavior identification; in addition, by reasonably setting the preset similarity, it not only ensures the accuracy of the abnormal behavior identification results, but also achieves the optimal allocation of computing resources and avoids excessive resource consumption.

[0083] When using the YOLO series models or convolutional neural networks for abnormal behavior identification, the step of extracting key frames can significantly reduce the waste of computer resources and make the system run more efficiently and smoothly; for the Temporal Action Localization model or the C3D model, it can flexibly choose not to execute this step and directly determine the abnormal behavior identification through the experimental video, which reflects the adaptability and intelligence of the monitoring and control module in different model application scenarios, and comprehensively enhances the laboratory safety early warning system's accuracy and efficiency in identifying abnormal behaviors of experimenters, as well as the stability and reliability of the overall operation, building a solid technical defense line for laboratory safety assurance.

[0084] Specifically, the monitoring and control module determines the running experimental equipment according to the experimental content and the experimental video and controls the digital twin module to monitor the running experimental equipment in real time to update the corresponding partial digital twin model in real time, and generates a digital twin model of the laboratory according to the partial digital twin model to determine the safety characterization status of the laboratory, including:

[0085] If all devices in the digital twin model updated in real time are normal, the impact characterization trend of the non-experimental area on the experimental area is determined based on the environmental monitoring data of the experimental area to determine the safety characterization status of the laboratory;

[0086] If there is an abnormal device in the digital twin model updated in real time, the safety representation state is determined to be a dangerous state and an alarm signal is issued.

[0087] In practice, the alarm signals include sounding an alarm bell, lighting up an alarm light, and sending alarm information to laboratory administrators and laboratory building security personnel.

[0088] It is understandable that the monitoring and control module determines the operating experimental equipment based on the experimental content and experimental video, and drives the digital twin module to monitor it in real time and update the corresponding part of the digital twin model. This not only makes the operating status of the experimental equipment intuitively presented, but also provides the possibility of timely discovery of potential equipment problems; through in-depth analysis of the digital twin model, once abnormal equipment is found, it can quickly determine that the safety characterization state is a dangerous state and immediately issue a multiple alarm signal including an alarm bell, a constantly on alarm light, and information to laboratory administrators and security personnel to ensure that dangerous situations can be known and handled in a timely manner; and when the equipment is normal, it can be combined with the environmental monitoring data of the experimental area to deeply analyze the impact characterization trend of the non-experimental area on the experimental area, so as to comprehensively and accurately determine the overall safety characterization state of the laboratory, and realize all-round and intelligent safety monitoring of the laboratory from equipment operation to environmental impact, which greatly improves the timeliness, accuracy and efficiency of laboratory safety management, effectively reduces the risk of safety accidents, and provides a solid and reliable guarantee for the stable operation of the laboratory and personnel safety.

[0089] Specifically, the digital twin model updated in real time by the monitoring and control module consists of a partial digital twin model and a fixed digital twin model;

[0090] Among them, the monitoring and control module monitors the running experimental equipment in real time to update the partial digital twin model of the running experimental equipment in real time, and the monitoring and control module monitors and updates the fixed digital twin model of the non-running experimental equipment every time the digital twin model is turned on (that is, when all the experimenters leave the laboratory).

[0091] During implementation, if there is no running experimental equipment / for a fixed digital twin model, the digital twin model is updated according to a preset update frequency, and the preset update frequency is once every hour to once every two hours.

[0092] It is understandable that the monitoring and control module divides the digital twin model into partial digital twin models and fixed digital twin models, and performs real-time monitoring of the running experimental equipment and updates the partial digital twin model, so that the dynamic changes in the equipment operation can be reflected in the model immediately, providing a reliable basis for accurately grasping the real-time status of the equipment; the fixed digital twin model of the non-operating experimental equipment is monitored and updated when the digital twin module is just turned on, which effectively ensures the accuracy and timeliness of the status information of the non-operating equipment, and then updates it according to the preset update frequency, while ensuring that the model data is not outdated, and reasonably balancing the consumption of system resources; this comprehensive, intelligent and flexible digital twin model update strategy has greatly improved the integrity, timeliness and accuracy of laboratory equipment status monitoring, and provided strong technical support and guarantee for laboratory safety management, equipment maintenance and improvement of overall operational efficiency.

[0093] Specifically, the monitoring and control module determines the impact characterization trend of the non-experimental area on the experimental area according to the environmental monitoring data of the experimental area to determine the safety characterization status of the laboratory, including:

[0094] If the environmental monitoring data of the experimental area is not within the standard environmental data interval of the experimental area, the impact characterization trend of the non-experimental area on the experimental area is determined to be a negative impact trend and the safety characterization state is determined to be a dangerous state, and the monitoring control module controls the digital twin module to update part of the digital twin model and the fixed digital twin model and issue an alarm signal;

[0095] If the environmental monitoring data of the experimental area is within the standard environmental data range of the experimental area, it is determined that the impact characterization trend of the non-experimental area on the experimental area is a hidden impact trend and the safety characterization state is determined to be a safe state.

[0096] It can be understood that the standard environmental data range of the experimental area is input by the experimenter after preparing the experimental preparation.

[0097] Specifically, the monitoring and control module determines the storage location of the experimental preparation (ie, the experimental product prepared by the experimenter after each experiment) according to the experimental video and determines the storage location as the experimental area.

[0098] It can be understood that the monitoring and control module can judge the impact characterization trend of the non-experimental area on the experimental area based on the environmental monitoring data of the experimental area, and then determine the safety characterization status of the laboratory; when the environmental monitoring data of the experimental area exceeds the standard environmental data range, it is judged as a negative impact trend and the safety characterization status is determined as a dangerous state, and then the digital twin module is controlled to update some digital twin models and fixed digital twin models, and an alarm signal is issued at the same time. This series of measures allows laboratory managers to know the abnormal situation in time, take countermeasures quickly, effectively avoid further expansion of danger, and maximize laboratory safety; and when the environmental monitoring data is within the standard range, it is judged as a hidden impact trend and a safe state, realizing meticulous control of the laboratory environment status; at the same time, the storage location of the experimental preparation is accurately determined through the experimental video and set as the experimental area, and the environmental data is analyzed and judged on this basis, so that the entire monitoring and judgment mechanism is more in line with the actual experimental situation, which greatly improves the scientificity and accuracy of laboratory environmental safety monitoring and the ability to warn of potential risks, and builds a solid line of defense for the safe and stable conduct of various experiments in the laboratory.

[0099] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. 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. A laboratory safety early warning system, characterized in that: include: The registration module is used to record the time when the experimenter enters the laboratory, the content of the experiment and the time when he leaves; Portrait tracking module, used to identify, track and record the experimental videos of each experimenter in the laboratory; Digital twin module, used to generate a digital twin model of the laboratory; A general monitoring module is used to obtain the environmental monitoring data of the laboratory and the experimental area in real time, wherein the environmental monitoring data includes temperature data, humidity data and smoke data; A monitoring and analysis module, which is connected to the registration module and is used to determine the monitoring method of the laboratory according to whether there are experimenters in the laboratory; The monitoring method includes performing portrait recognition and tracking on each experimenter in the laboratory and shooting the experimental video of each experimenter through the portrait tracking module, and generating a digital twin model in the laboratory through the digital twin module; A monitoring and control module is respectively connected to the registration module, the portrait tracking module, the digital twin module, the general monitoring module and the monitoring and analysis module, and is used to determine the safety warning library according to the experimental content of each experimenter to identify the abnormal behavior of the experimenter, determine the running experimental equipment according to the experimental content and the experimental video to perform real-time monitoring and update of the partial digital twin model corresponding to the running experimental equipment, and control the general monitoring module to perform general monitoring of the experimental area to determine the impact characterization trend of the non-experimental area on the experimental area to determine whether to issue a safety warning.

2. The laboratory safety early warning system according to claim 1, characterized in that: The common monitoring module includes: The overall monitoring unit is set at a fixed position in the laboratory to obtain the laboratory's environmental monitoring data in real time; The mobile monitoring unit is configured to be mobile and is used to obtain environmental monitoring data of the experimental area in real time.

3. The laboratory safety early warning system according to claim 1, characterized in that: The monitoring and analysis module determines the monitoring mode of the laboratory according to whether there is an experimenter in the laboratory to control the portrait tracking module to start working according to the monitoring mode, including: If there are experimenters in the laboratory, the monitoring method is determined to be controlling the portrait tracking module to perform portrait recognition and tracking on each experimenter in the laboratory and to shoot an experiment video of each experimenter; If there is no experimenter in the laboratory, it is determined that the monitoring method is to control the digital twin module to generate a digital twin model in the laboratory to monitor the laboratory, and the portrait tracking module is not controlled to start working.

4. The laboratory safety early warning system according to claim 1, characterized in that: The monitoring control module determines whether to send a safety warning message based on the identity and number of the experimenters identified by the portrait tracking module and the experimenters recorded in the registration module, including: If the comparison result shows that the identity and number of the identified experimenters are consistent with the recorded experimenters, it is determined that the safety warning information will not be sent; If the comparison result is that the identity and / or number of the identified experimenters do not match the recorded experimenters, it is determined to send a safety warning message to the experimenters recorded in the registration module.

5. The laboratory safety early warning system according to claim 1, characterized in that: The monitoring and control module determines the corresponding safety warning library according to the experimental content recorded by each experimenter in the registration module and trains the classification model according to the safety warning library to obtain the safety warning classification model. In addition, the monitoring and control module divides the experimental video into several frames and then determines several key frames, and extracts human images in each key frame to input each of the human images into the corresponding safety warning classification model to identify abnormal behaviors of the corresponding experimenters.

6. The laboratory safety early warning system according to claim 5, characterized in that: The monitoring control module determines the similarity of each frame image according to the machine learning model to determine a number of identical frame groups and selects a frame with the highest definition in each identical frame group as a key frame; The similarity between the frames in the same frame group should be greater than or equal to a preset similarity, and the number of key frames is equal to the number of the same frame group.

7. The laboratory safety early warning system according to claim 1, characterized in that: The monitoring and control module determines the operating experimental equipment according to the experimental content and the experimental video and controls the digital twin module to monitor the operating experimental equipment in real time to update the corresponding partial digital twin model in real time, and generates a digital twin model of the laboratory according to the partial digital twin model to determine the safety characterization status of the laboratory, including: If all devices in the digital twin model updated in real time are normal, the impact characterization trend of the non-experimental area on the experimental area is determined based on the environmental monitoring data of the experimental area to determine the safety characterization status of the laboratory; If there is an abnormal device in the digital twin model updated in real time, the safety representation state is determined to be a dangerous state and an alarm signal is issued.

8. The laboratory safety early warning system according to claim 7, characterized in that: The digital twin model updated in real time by the monitoring and control module consists of a partial digital twin model and a fixed digital twin model; Among them, the monitoring and control module monitors the running experimental equipment in real time to update the partial digital twin model in real time, and the monitoring and control module monitors and updates the fixed digital twin model of the non-running experimental equipment when the digital twin model is turned on.

9. The laboratory safety early warning system according to claim 7, characterized in that: The monitoring and control module determines the impact characterization trend of the non-experimental area on the experimental area according to the environmental monitoring data of the experimental area to determine the safety characterization status of the laboratory, including: If the environmental monitoring data of the experimental area is not within the standard environmental data interval of the experimental area, the impact characterization trend of the non-experimental area on the experimental area is determined to be a negative impact trend and the safety characterization state is determined to be a dangerous state, and the monitoring control module controls the digital twin module to update part of the digital twin model and the fixed digital twin model and issue an alarm signal; If the environmental monitoring data of the experimental area is within the standard environmental data range of the experimental area, it is determined that the impact characterization trend of the non-experimental area on the experimental area is a hidden impact trend and the safety characterization state is determined to be a safe state.

10. The laboratory safety early warning system according to claim 1, characterized in that: The monitoring and control module determines the storage location of the experimental preparation according to the experimental video and determines the storage location as the experimental area.

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