Intelligent supervision system for laboratory

Abstract

The invention discloses an intelligent supervision system for a laboratory, and the system comprises an environment monitoring subsystem which is used for monitoring the environment information in the laboratory; the internal image acquisition subsystem is used for acquiring images in the laboratory; the identity verification subsystem is used for collecting and verifying identity information; the intelligent supervision system processes the environment information to generate laboratory environment warning; and the intelligent supervision system processes the image in the laboratory and generates an internal abnormity warning. According to the invention, the problems of insufficient environment monitoring, non-strict personnel management, lack of behavior monitoring, data dispersion, slow emergency response and the like in traditional laboratory management are solved, and intelligent, efficient and safe management of the laboratory is realized.

Description

Technical Field

[0001] The present invention relates to the field of supervision systems, and in particular to an intelligent supervision system for laboratories. Background Art

[0002] As the core place for scientific research and teaching, laboratories involve high-risk elements such as chemical reagents, high-voltage equipment, and biological samples. Their safety management faces multiple challenges. Traditional laboratory environmental monitoring methods are relatively limited and mainly rely on manual regular inspections of ventilation equipment. This method can only obtain basic environmental parameters such as temperature and humidity, and it is difficult to detect abnormal operating conditions of ventilation equipment in real time, such as reduced fan blade speed and motor overheating. At the same time, traditional monitoring methods are unable to detect the accumulation of pollutants in the environment, such as excessive concentrations of suspended particulate matter and volatilization of harmful gases in a timely manner; In terms of personnel management, traditional methods are relatively extensive and have obvious security risks. The identity verification process usually uses static methods such as work cards and passwords, which makes it difficult to prevent cheating methods such as photo and video impersonation. In addition, there is a lack of effective pre-screening mechanisms for personnel's health conditions, such as whether they have fever, respiratory symptoms, etc., and whether they carry pollutants such as external particles and microorganisms. This may cause some people with health risks or contamination to enter the laboratory, thereby causing problems such as experimental contamination. Traditional laboratories mainly rely on manual inspections to monitor the behavior of laboratory personnel, but this method cannot achieve real-time supervision. Some abnormal behaviors of laboratory personnel, such as fast movement and violent waving of laboratory equipment, are prone to cause problems such as reagent collisions, dust pollution or equipment vibration interference, but they are difficult to be discovered and stopped in time.

[0003] At the same time, there is a lack of dynamic monitoring methods for the density and aggregation of laboratory personnel. When the laboratory personnel are too dense, the ventilation efficiency will decrease, the harmful gas will diffuse unevenly, and the safety risk will increase. Therefore, an intelligent laboratory supervision system is proposed. Summary of the invention

[0004] The technical problem to be solved by the present invention is: how to solve the problem that the existing intelligent supervision system has a single supervision type and only monitors a single protection item, which has a poor overall supervision effect on the laboratory and has a certain impact on the use of the intelligent supervision system. A laboratory intelligent supervision system is provided.

[0005] The present invention solves the above technical problems through the following technical solutions, which include: Environmental monitoring subsystem, used to monitor environmental information in the laboratory; Internal image acquisition subsystem, used for image acquisition within the laboratory; Identity verification subsystem, used to collect and verify identity information; The intelligent supervision system processes environmental information to generate laboratory environmental warnings; The intelligent supervision system processes the images in the laboratory to generate internal anomaly warnings.

[0006] Furthermore, the environmental monitoring subsystem includes: A ventilation equipment monitoring module for continuously collecting information on ventilation equipment in the laboratory; A particulate detection module for collecting the concentration value of suspended particulate matter in the air in the laboratory; A humidity sensing module for real-time monitoring of the environmental humidity in the laboratory.

[0007] Furthermore, the specific processing process for processing environmental information to generate laboratory environmental warnings is as follows: Extract the ventilation equipment information, which includes the operating status of the ventilation equipment and the environmental information of the ventilation equipment; The operating status of the ventilation equipment includes the rotational speed of the fan blades of the ventilation equipment and the operating temperature information of the fan motor; The environmental information of the ventilation equipment includes the concentration value of suspended particulate matter in the environment where the ventilation equipment is located and the humidity of the environment where it is located; Perform calculation processing on the rotational speed of the fan blades to obtain a rotational speed evaluation parameter, and process the operating temperature information of the fan motor to obtain a ventilation equipment operating temperature parameter; Process the concentration value of suspended particulate matter in the environment where the ventilation equipment is located to obtain an environmental suspended particulate matter concentration parameter; Process the environmental humidity in the laboratory to obtain an environmental humidity parameter; When any one of the rotational speed evaluation parameter, operating temperature parameter, humidity parameter, and environmental suspended particulate matter concentration parameter is abnormal, a laboratory environmental warning is generated.

[0008] Furthermore, the process for obtaining the rotational speed evaluation parameter is as follows: Continuously collect the rotational speed Vi of the fan blades of the ventilation equipment, where i is the number of collections, i≥10, and the collection interval is a preset duration. After removing the maximum and minimum values in Vi, calculate the mean value of the remaining Vi to obtain the rotational speed evaluation parameter. When the rotational speed evaluation parameter exceeds the preset range, it indicates that there is an abnormality, and at this time, a laboratory environmental warning is generated; The process for obtaining the ventilation equipment operating temperature parameter is as follows: Continuously collect the operating temperature Wi, where i is the number of collections, i≥10, and the collection interval is a preset duration. When any one of Wi is greater than the warning value, directly export the temperature greater than the warning value as the ventilation equipment operating temperature parameter, and determine that the ventilation equipment operating temperature parameter is abnormal, and generate a laboratory environmental warning; When all the operating temperatures Wi are less than the warning value, after removing the maximum and minimum values from the operating temperatures Wi, calculate the mean value of the remaining Wi, that is, obtain the operating temperature parameter of the ventilation equipment. When the operating temperature parameter of the ventilation equipment is greater than the preset value, it is determined that the operating temperature parameter of the ventilation equipment is abnormal, and a laboratory environment warning is generated. The process of obtaining the environmental suspended particulate matter concentration parameter is as follows: Continuously collect the environmental suspended particulate matter concentration parameter Mi, where i is the number of collections, i ≥ 10, and the collection interval is a preset time period each time. Calculate the mean value of the environmental suspended particulate matter concentration Mi, that is, obtain the environmental suspended particulate matter concentration parameter. When the environmental suspended particulate matter concentration parameter is greater than f1, it indicates that there is an abnormality, and a laboratory environment warning is issued. The process of obtaining the environmental humidity parameter is as follows: Continuously collect the environmental humidity Si, where i is the number of collections, i ≥ 10, and the collection interval is a preset time period each time. Calculate the mean value of the environmental humidity Si, that is, obtain the environmental humidity parameter. When the environmental humidity is greater than the preset humidity u1, it indicates that there is an abnormality, and a laboratory environment warning is generated.

[0009] Furthermore, when the number of the fan blade rotation speeds Vi of the ventilation equipment that are less than the currently set rotation speed exceeds i / 3 or the number of the operating temperatures Wi that are greater than the standard temperature exceeds i / 3, extract the environmental suspended particulate matter concentration Mi and the environmental humidity Si. The duration Mt when the environmental suspended particulate matter concentration Mi is greater than the preset value f2 and the duration St when the environmental humidity Si is greater than the preset value u2. When both Mt and St are greater than the preset value, a laboratory environment warning is generated. f2 < f1, u2 < u1.

[0010] Furthermore, the specific process of the identity verification subsystem for collecting and verifying identity information is as follows: Collect the real-time face image of the experimental personnel, and retrieve the face information of the experimental personnel corresponding to the identity card from the database, that is, the standard face information. Compare the real-time face image collection with the retrieved standard face information. After the comparison passes, the person is released to enter the preset detection area for personnel liveness verification and status verification. When both the personnel liveness verification and the status verification pass, it means that the verification is passed and the experimental personnel are allowed to enter the laboratory.

[0011] Furthermore, the process of personnel liveness verification is as follows: After the entering personnel enter the preset detection area, send a notification message to notify the verified experimental personnel to shake their faces in a clockwise direction. At this time, collect the face features again and compare them with the standard face information. If the comparison fails, it means that the personnel liveness verification fails. The comparison is passed, which means the preliminary live body verification of the personnel is passed. At this time, simple dust removal of the human body is carried out, and notification information is sent in real time. The experimental personnel being verified are notified to move according to the content of the notification information. At this time, the image acquisition device set in the preset detection area collects the human body image of the experimental personnel, processes the human body image of the experimental personnel to extract the secondary verification feature points, processes the secondary verification feature points, obtains the step distance evaluation parameter and the step number evaluation parameter, and compares the step distance evaluation parameter and the step number evaluation parameter with the standard step distance and the standard step number of the corresponding experimental personnel in the database to obtain the step distance and step number comparison result; A detector is also set to detect the experimental personnel. During the acquisition process of the step distance evaluation parameter and the step number evaluation parameter, the detector collects the body temperature information of the experimental personnel through an infrared temperature measurement device and records the number of sneezes and coughs of the experimental personnel; When the step distance and step number comparison result is passed, the body temperature information of the experimental personnel meets the standard, and the number of sneezes and coughs of the experimental personnel within the preset duration are both less than the preset values, it means the identity verification is passed. After completing the simple dust removal, they are allowed to enter the laboratory.

[0012] Furthermore, the specific process of the step distance evaluation parameter and the step number evaluation parameter is as follows: Extract the foot image from the human body image of the experimental personnel, mark the rearmost end of one foot as point a1, and mark the foremost end of the other foot as point a2; Continuously collect the distances between point a1 and point a2 for m times, and calculate the average value of the distances between point a1 and point a2 for m times, that is, obtain the step distance evaluation parameter; At the same time, collect the number of times point a1 and a2 coincide within the preset distance, that is, obtain the step number evaluation parameter; Calculate the difference between the step distance evaluation parameter and the standard step distance to obtain the step distance difference; Calculate the difference between the step number evaluation parameter and the standard step number to obtain the step number difference; When both the step distance difference and the step number difference are within the preset range, the step distance and step number comparison result is passed. If either the step distance difference or the step number difference exceeds the preset range, the step distance and step number comparison result is not passed.

[0013] Furthermore, the generation process of the internal anomaly warning is as follows: Extract the image inside the laboratory and perform personnel image recognition on the image inside the laboratory; After identifying the personnel image, record the number of personnel images to obtain the number of personnel inside the laboratory, and then collect the area inside the laboratory; Calculate the ratio of the area inside the laboratory to the number of personnel inside the laboratory to obtain the unit personnel parameter. When the unit personnel parameter is greater than the preset value, an internal anomaly warning is generated; Collect the distances between individuals simultaneously. When the distances between more than a preset number of individuals are less than the preset value and greater than the preset duration, an internal anomaly warning is generated. After identifying the human body image, monitor the moving speed of the human body in real time. When the moving speed of any human body is greater than the preset value, an internal anomaly warning is generated. Process the human body image simultaneously and collect the swinging amplitude of the arms and the swinging amplitude of the legs. When the swinging amplitude of any arm or the swinging amplitude of any leg is abnormal, an internal anomaly warning is generated.

[0014] Furthermore, the process of obtaining the swinging amplitude of the arms and the process of anomaly determination are as follows: Perform human body image recognition on the images in the laboratory to obtain the human body image including the left human body image and the right human body image. Process the left human body image and extract the key points of the left upper limb of the human body. The key points of the left upper limb include the shoulder joint point, the elbow joint point, and the wrist joint point. Taking the shoulder joint point as the reference point, establish an upper body coordinate system, mark the positions of the elbow joint point and the wrist joint point on the upper body coordinate system, and connect them to obtain the left arm line. Calculate the swinging angle of the arm line in the horizontal direction, and calculate the angle between the maximum swinging angle of the arm in the horizontal direction and the x-axis of the upper body coordinate system, that is, obtain the swinging amplitude of the left arm. Record the number of times L1 that the swinging amplitude of the left arm is greater than the preset value within the preset duration. Perform the same processing on the right human body image as on the left human body image to obtain the swinging amplitude of the right arm. Record the number of times R1 that the swinging amplitude of the right arm is greater than the preset value within the preset duration. The process of obtaining the swinging amplitude of the legs and the process of anomaly determination are as follows: Process the left human body image and extract the key points of the left lower limb of the human body. The key points of the left lower limb include the hip joint point, the knee joint point, and the ankle joint point. Taking the position of the hip joint point as the reference point, establish a lower limb coordinate system, mark the positions of the knee joint point and the ankle joint point on the lower limb coordinate system, obtain the leg line, calculate the angle between the leg line and the x-axis in the horizontal direction, obtain the swinging angle of the lower limb, and record the maximum swinging angle of the leg line in the horizontal direction, that is, the swinging amplitude of the left leg. Record the number of times L2 that the swinging amplitude of the left leg is greater than the preset value within the preset duration. Perform the same processing on the right human body image as on the left human body image to obtain the swinging amplitude of the right leg. Record the number of times R2 that the swinging amplitude of the right leg is greater than the preset value within the preset duration. Extract R1 and L1. When either R1 or L1 is greater than the preset value, it indicates that the swing amplitude of the arm is abnormal; Extract R2 and L2. When either R2 or L2 is greater than the preset value, it indicates that the swing amplitude of the leg is abnormal.

[0015] The present invention has the following advantages compared with the prior art: The intelligent supervision system of this laboratory constructs a precise control system for the access of campus laboratory personnel through a triple mechanism of face comparison, live detection and status verification.

[0016] It not only verifies the authenticity of the identity, but also effectively prevents the situation of forging identities using photos, videos, etc. through live detection; in the preset detection area, by collecting step distance evaluation parameters and step number evaluation parameters, and combining with the infrared temperature measurement device to record body temperature, sneezing and coughing times, it comprehensively evaluates the health status and behavior norms of personnel. This is particularly important for campus laboratories, which can accurately intercept students who may contaminate the experimental environment due to frequent coughing and sneezing, such as those with colds and fevers, and avoid the risk of biological contamination from the source, ensuring the accuracy of experimental data and the cleanliness of the experimental environment.

[0017] The ventilation equipment monitoring module continuously collects the rotational speed of the fan blades and the operating temperature of the motor, and combines the concentration value of suspended particulate matter obtained by the particulate detection module and the data of the humidity sensing module. Through multi-parameter fusion analysis, it can accurately identify problems such as ventilation equipment failures or excessive environmental pollutants; The system monitors the behavior dynamics of experimental personnel in real time by identifying the swing amplitudes of the arms and legs of personnel. In campus laboratories, improper operations by students (such as walking quickly and waving experimental equipment violently) are likely to generate dust, resulting in an increase in the concentration of suspended particulate matter.

[0018] When the swing amplitude of the arm or leg is detected to be abnormal, on the one hand, the system generates an internal abnormal warning to remind the experimental personnel to operate in a standardized manner; on the other hand, it is linked with the data of the particulate detection module to form a behavior warning. A closed-loop management of pollution monitoring and linkage disposal. This preventive supervision mode can more effectively control the source of dust and reduce the environmental fluctuations caused by human actions during the experiment, ensuring the stability of precision experiments compared with simple post-event environmental governance.

[0019] By calculating the unit personnel parameters of the laboratory and monitoring the distance between personnel, the system can give a real-time warning of the situation of excessive personnel density or close aggregation. In the scenario of student group experiments, if the unit personnel parameters exceed the preset value, or multiple groups of students have long-term close contact, the system immediately issues an internal abnormal warning to prompt the teacher to intervene and adjust, avoiding problems such as reagent collisions and uneven gas diffusion caused by overcrowding.

[0020] Monitor the human body movement speed in real time. When abnormal behaviors such as running or fast moving of personnel are detected, the system automatically generates a warning. In a campus laboratory, students may panic due to operation errors or emergencies, and fast movement is likely to cause accidents such as collisions and knocking over reagent bottles. This function can help management personnel discover and stop dangerous behaviors in time. Combined with the detection of movement amplitude, it forms a three-dimensional supervision of personnel behaviors, creating a standardized and orderly experimental operation environment.

[0021] Through in-depth processing and analysis of environmental information, personnel images, and identity verification data, the system provides a scientific basis for laboratory management.

[0022] Aiming at the personnel characteristics and usage scenarios of campus laboratories, this intelligent supervision system constructs a safety protection system integrating prevention, monitoring, and disposal through the organic integration of functions such as identity verification, environmental monitoring, behavior control, and abnormal warning. It has significant advantages in ensuring experimental safety, maintaining environmental quality, and standardizing operation behaviors, providing all-round technical support for the efficient and safe operation of campus laboratories. Brief Description of the Drawings

[0023] Figure 1 It is the system structure block diagram of the present invention. Detailed Embodiment

[0024] The embodiments of the present invention will be described in detail below. These embodiments are implemented on the premise of the technical solution of the present invention, and detailed implementation manners and specific operation processes are given. However, the protection scope of the present invention is not limited to the following embodiments.

[0025] As Figure 1 shown, this embodiment provides a technical solution: an intelligent supervision system for a laboratory, including: An environmental monitoring subsystem for monitoring environmental information in the laboratory; An internal image acquisition subsystem for acquiring images in the laboratory; An identity verification subsystem for collecting and verifying identity information; The intelligent supervision system processes environmental information to generate laboratory environmental warnings; The intelligent supervision system processes images in the laboratory to generate internal abnormal warnings.

[0026] The environmental monitoring subsystem includes: A ventilation equipment monitoring module for continuously collecting ventilation equipment information in the laboratory; A particulate detection module for collecting the concentration value of suspended particulate matter in the air in the laboratory; A humidity sensing module for real-time monitoring of the environmental humidity in the laboratory.

[0027] The specific processing procedure for processing environmental information to generate laboratory environmental warnings is as follows: Extract ventilation equipment information, which includes the operating status of the ventilation equipment and the environmental information of the ventilation equipment; The operating status of the ventilation equipment includes the rotational speed of the fan blades of the ventilation equipment and the operating temperature information of the fan motor; The environmental information of the ventilation equipment includes the concentration value of suspended particulate matter in the environment where the ventilation equipment is located and the humidity of the environment; Perform calculation processing on the rotational speed of the fan blades to obtain a rotational speed evaluation parameter, and process the operating temperature information of the fan motor to obtain a ventilation equipment operating temperature parameter; Process the concentration value of suspended particulate matter in the environment where the ventilation equipment is located to obtain an environmental suspended particulate matter concentration parameter; Process the environmental humidity in the laboratory to obtain an environmental humidity parameter; When any one of the rotational speed evaluation parameter, operating temperature parameter, humidity parameter, and environmental suspended particulate matter concentration parameter is abnormal, a laboratory environmental warning is generated; Laboratory ventilation equipment (such as fume hoods, exhaust fans) operates at high load for a long time, and its performance is likely to decline due to dust accumulation and component aging. This processing procedure accurately identifies mechanical failures such as impeller jamming and belt slack by real-time collecting the rotational speed of the fan blades and the operating temperature of the motor, avoiding the accumulation of harmful gases (such as volatile gases of organic reagents, retention of dust pollutants) caused by a sudden drop in ventilation efficiency; The operating temperature parameter gives a real-time warning of the risk of motor overheating, preventing local high temperatures caused by equipment failures and reducing potential safety hazards such as circuit short circuits and equipment burnout. Compared with traditional manual inspections, the system realizes the full-cycle monitoring of ventilation equipment, transforming equipment maintenance from passive response to active prevention, which is especially suitable for scenarios with a large number of laboratory equipment and high usage frequencies in universities;

[0028] College experiments often involve acid-base volatilization and dust generation (such as grinding operations, chemical synthesis). Monitoring a single indicator is easily interfered with (such as an instantaneous increase in particulate matter concentration caused by local air flow fluctuations). This process forms a dual pollution warning mechanism through the combined analysis of the concentration of suspended particulate matter and environmental humidity: When the suspended particulate matter concentration parameter is abnormal, the humidity parameter can be combined to distinguish the nature of the pollution. In a high-humidity environment, particulate matter is prone to condensation and sedimentation, while in a low-humidity environment, dust may spread due to electrostatic adsorption, providing a basis for administrators to judge the source of pollution (such as reagent spills, improper operations); Real-time humidity monitoring of the environment where the ventilation equipment is located can prevent circuit short - circuits and equipment corrosion caused by humidity. At the same time, it can avoid the risk of explosion and fire caused by static electricity accumulation in a dry environment (such as in an environment of magnesium powder or ethanol vapor). This multi - parameter cross - verification mode effectively filters out environmental noise interference and improves the accuracy of early warning, especially suitable for complex pollution scenarios in chemical and materials laboratories.

[0029] In college laboratories, the personnel flow is strong (students come in and out frequently) and the operation standardization varies. It is difficult for traditional manual supervision to detect hidden risks in a timely manner. This processing process triggers a warning through the linkage of multi - parameter thresholds; Aiming at the common problems of being used and having light maintenance in college laboratories, through digital modeling and abnormal determination logic, based on the long - term operation data of rotational speed and temperature, the system can predict the equipment life cycle (such as trend analysis of speed reduction caused by fan blade wear), helping the logistics department formulate a precise maintenance plan and reducing the impact of sudden shutdowns on the teaching progress; when the humidity parameter is abnormal, the system synchronously pushes adjustment suggestions (such as turning on the dehumidifier, checking the tightness of doors and windows), assisting experimental personnel to respond quickly and solving the pain point of traditional supervision that only alarms without guidance, especially suitable for teaching scenarios where students first come into contact with high - precision equipment.

[0030] The process of obtaining the rotational speed evaluation parameter is as follows: Continuously collect the rotational speed Vi of the fan blades of the ventilation equipment, where i is the number of collections, i ≥ 10, and the preset time interval is set for each collection. After removing the maximum and minimum values from Vi, calculate the mean value of the remaining Vi to obtain the rotational speed evaluation parameter. When the rotational speed evaluation parameter exceeds the preset range, it indicates that there is an abnormality, and at this time, a laboratory environment warning is generated; The process of obtaining the operating temperature parameter of the ventilation equipment is as follows: Continuously collect the operating temperature Wi, where i is the number of collections, i ≥ 10, and the preset time interval is set for each collection. When any one of Wi is greater than the warning value, directly export the temperature greater than the warning value as the operating temperature parameter of the ventilation equipment, and determine that the operating temperature parameter of the ventilation equipment is abnormal, generating a laboratory environment warning; When all the operating temperatures Wi are less than the warning value, after removing the maximum and minimum values from the operating temperature Wi, calculate the mean value of the remaining Wi to obtain the operating temperature parameter of the ventilation equipment. When the operating temperature parameter of the ventilation equipment is greater than the preset value, it is determined that the operating temperature parameter of the ventilation equipment is abnormal, generating a laboratory environment warning; The process of obtaining the environmental suspended particulate matter concentration parameter is as follows: Continuously collect the environmental suspended particulate matter concentration parameter Mi, where i is the number of collections, i ≥ 10, and the preset time interval is set for each collection. Calculate the mean value of the environmental suspended particulate matter concentration Mi to obtain the environmental suspended particulate matter concentration parameter. When the environmental suspended particulate matter concentration parameter is greater than f1, it indicates that there is an abnormality, and a laboratory environment warning is generated; The process of obtaining the environmental humidity parameter is as follows: Continuously collect the environmental humidity Si, where i is the number of collections and i ≥ 10. Each collection is spaced at a preset time interval. Calculate the mean value of the environmental humidity Si, that is, obtain the environmental humidity parameter. When the environmental humidity is greater than the preset humidity u1, it indicates an abnormality, and a laboratory environment warning is generated.

[0031] When the number of fan blade rotation speeds Vi of the ventilation equipment that are less than the currently set rotation speed exceeds i / 3 or the number of operating temperatures Wi that are greater than the standard temperature exceeds i / 3, extract the environmental suspended particulate matter concentration Mi and the environmental humidity Si; The duration Mt during which the environmental suspended particulate matter concentration Mi is greater than the preset value f2 and the duration St during which the environmental humidity Si is greater than the preset value u2; When both Mt and St are greater than the preset value, a laboratory environment warning is generated; f2 < f1, u2 < u1; When the fan rotation speed is abnormal (such as the number of speeds lower than the set speed exceeding 1 / 3) or the motor temperature is abnormal (the number of temperatures higher than the standard temperature exceeding 1 / 3), trigger a in-depth analysis of the environmental particulate matter (Mi) and humidity (Si), reflecting the causal relationship between equipment failures and environmental risks. For example: A decrease in the rotation speed of the ventilation equipment may lead to insufficient exhaust air, causing particulate matter accumulation or humidity increase. The system can identify potential risks in advance through correlation analysis, rather than monitoring a single parameter in isolation.

[0032] Set f2 < f1, u2 < u1 (that is, the trigger threshold for the linkage scenario is lower than the independent warning threshold), so as to lower the environmental parameter warning threshold when the equipment is abnormal and strengthen the environmental risk investigation for equipment failure scenarios; Not only focus on the absolute value of the parameter, but also introduce the proportion of abnormal data (such as the number of abnormal rotation speeds / temperatures exceeding i / 3) and the over-standard duration (Mt / St greater than the preset value), avoiding frequent false alarms caused by occasional fluctuations, and at the same time capturing persistent hidden dangers (such as long-term inefficient operation of the ventilation equipment leading to pollutant accumulation).

[0033] For example: If the humidity occasionally exceeds u1 but quickly returns to normal, the system does not alarm; however, if the humidity continuously exceeds u2 (a lower threshold) and the duration exceeds the limit when the equipment rotation speed is abnormal, it is determined as an environmental risk caused by ventilation failure, accurately locating the root cause of the problem.

[0034] In view of the characteristics of diverse and frequently used equipment in university laboratories, through monitoring the rotation speed and motor temperature, timely detect mechanical failures (such as fan blade jamming, bearing wear) or electrical hidden dangers (such as coil overheating) of ventilation equipment (such as fume hoods, exhaust fans), and avoid the accumulation of toxic gases / dust caused by equipment failure, ensuring the safety of teachers and students.

[0035] The calculation of the mean values of particulate matter concentration and humidity and the determination of duration are applicable to risk scenarios such as common chemical experiment pollution and reagent deliquescence in university laboratories. For example, continuous high humidity may cause corrosion of precision instruments or deterioration of reagents. The system monitors through the Si mean value and St duration to give early warnings of unsuitable environmental conditions; When the particulate matter concentration Mt exceeds the limit, it indicates insufficient exhaust ventilation in dust experiments (such as metal processing and powder preparation), avoiding teachers and students from inhaling harmful substances.

[0036] By means of data denoising, correlation analysis and hierarchical threshold setting, invalid alarms (such as sudden changes in rotation speed caused by instantaneous voltage fluctuations) are reduced, enabling management personnel to focus on real risks and improving response efficiency.

[0037] Automated multi-parameter comprehensive determination replaces the subjectivity and lag of manual inspections, especially suitable for distributed control scenarios of multiple laboratories.

[0038] The specific process of the identity verification subsystem for collecting and verifying identity information is as follows: Collect the real-time face images of experimental personnel, and retrieve the face information of the corresponding experimental personnel on the identity card from the database, that is, the standard face information; Compare the real-time face image collection with the retrieved standard face information. After the comparison passes, the person is allowed to enter the preset detection area for human liveness verification and status verification; When both the human liveness verification and status verification pass, it means the verification is passed and the experimental personnel are allowed to enter the laboratory.

[0039] The process of human liveness verification is as follows: After the entering person enters the preset detection area, a notification message is sent to notify the verified experimental personnel to shake their faces in a clockwise direction. At this time, the face features are collected again and compared with the standard face information. If the comparison fails, it means the human liveness verification fails; If the comparison passes, it means the preliminary human liveness verification passes. At this time, simple dust removal of the human body is carried out, and a notification message is sent in real time to notify the verified experimental personnel to move according to the content of the notification message. At this time, the image acquisition device set in the preset detection area collects the human body image of the experimental personnel, processes the human body image of the experimental personnel to extract secondary verification feature points, processes the secondary verification feature points to obtain the step distance evaluation parameter and the step number evaluation parameter, and compares the step distance evaluation parameter and the step number evaluation parameter with the standard step distance and standard step number of the corresponding experimental personnel in the database to obtain the step distance and step number comparison result; A detector is also set to detect the experimental personnel. During the collection process of the step distance evaluation parameter and the step number evaluation parameter, the detector collects the body temperature information of the experimental personnel through an infrared temperature measurement device and records the number of sneezes and coughs of the experimental personnel; When the step distance and number comparison result is passed, the experimenter's body temperature information meets the standard, and the number of sneezes and coughs of the experimenter within the preset time is less than the preset value, it means that the identity authentication is passed. After completing simple dust removal, they are allowed to enter the laboratory.

[0040] The specific process of step length evaluation parameters and step number evaluation parameters is as follows: Extract the foot image from the human body image of the experimenter, mark the rearmost end of one foot as point a1, and mark the frontmost end of the other foot as point a2; Continuously collect the distance between point a1 and point a2 m times, calculate the average value of the distance between point a1 and point a2 m times, that is, obtain the step evaluation parameter, where m is a positive integer; At the same time, the number of times points a1 and a2 overlap within the preset distance is collected, that is, the step evaluation parameter is obtained; Calculate the difference between the step evaluation parameter and the standard step, and obtain the step difference; Calculate the difference between the step evaluation parameter and the standard step number to obtain the step difference; When the step difference and the step number difference are both within the preset range, the step difference and step number comparison result is a pass; if either the step difference or the step number difference exceeds the preset range, the step difference and step number comparison result is a fail. Dust removal and gait collection are completed simultaneously, avoiding time waste caused by step-by-step operations. This is especially suitable for scenarios where students enter the laboratory in batches (such as unified sign-in for laboratory classes). After dust removal, there is no obvious obstruction on the surface of the clothing, which makes it easier for the imaging device to clearly capture the feature points of the foot (such as heels and toes), improve the accuracy of stride / number of steps calculation, and avoid clothing wrinkles or external debris interfering with feature extraction.

[0041] The triple liveness verification mechanism prevents identity fraud and abnormal status: The first level of verification is face comparison: quickly exclude unauthorized persons and solve the problem of whether it is this person; The second level of verification is to shake the face: to verify the authenticity of the live body through dynamic expressions, to prevent photo / video forgery, and to solve the problem of whether it is a real person; The third level of verification, gait analysis: using the individual uniqueness of stride length / number of steps (different heights, leg lengths, and habits lead to gait differences) to solve the problem of whether the person is acting autonomously.

[0042] The three-layer verification is progressive and is particularly suitable for scenarios where university laboratories have a large student base and identity cards are easily lost / borrowed, preventing irrelevant personnel or identity impersonators from entering from the source.

[0043] Temperature detection: quickly identify abnormal conditions such as fever to prevent the spread of infectious diseases; Cough and Sneeze Count: Directly targeting the stability of the experimental environment, university laboratories (such as chemical analysis rooms and biological culture rooms) are sensitive to environmental disturbances. Frequent coughing / sneezing may cause: Biological laboratory: Droplet contamination of culture dishes and PCR samples; Chemical laboratory: Airflow fluctuations affect the weighing accuracy of balances and the accuracy of reagent dripping; Precision instrument room: Vibration interferes with the stable imaging of equipment such as microscopes and spectrometers.

[0044] By setting the threshold for the number of coughs / sneezes within a preset time (e.g., ≥3 times within 5 minutes), personnel who may interfere with the experimental environment can be intercepted in advance to ensure the reliability of experimental data.

[0045] Simple dust removal procedures can reduce the introduction of external pollution sources and protect experimental accuracy; Common pollution scenarios in university laboratories: Materials laboratory: Dust particles carried by clothing may contaminate the process of preparing nanomaterials; Biological laboratory: DNA / RNA in hair and dandruff may cause cell contamination; Physics laboratory: Fiber impurities may scratch the wafers of lithography machines.

[0046] Simple dust removal (such as blowing with air) can remove particulate pollutants on the surface of clothing. Combined with the movement during subsequent gait collection, the dust will fall off in advance before entering the laboratory, avoiding the spread of pollutants in the room due to personnel movement.

[0047] Linked with gait analysis to improve verification efficiency. The airflow direction design of the dust removal equipment can assist experimental personnel in maintaining a stable walking posture, facilitating the standardized collection of step distances and steps by imaging equipment (such as requiring a straight walk of 1.5 meters and collecting step distance data 3 times); During the dust removal process, experimental personnel need to remove blocking objects such as hats and scarves to ensure that there is no obstruction to subsequent facial and gait features, and solve the problem of verification errors caused by thick winter clothing.

[0048] Analysis of the trend of health status: By statistically analyzing abnormal cough and sneeze data, potential risks in the laboratory environment can be identified. For example, if multiple students cough during a certain period, it may indicate that the ventilation system fails, resulting in the accumulation of irritating gases, and inversely optimize the warning threshold of the environmental monitoring subsystem; For lower-grade students who are prone to neglecting pre-experiment preparations (such as not cleaning their clothes and not wearing experimental clothes properly), the system, through multiple mandatory verifications of dust removal, gait, and health, transforms the environmental access requirements into a standardized process to cultivate students' awareness of norms before experiments; For postgraduate students / researchers, gait verification can be compatible with scenarios such as wearing protective clothing and masks (identifying through gait rather than facial features), without affecting the access of personnel to high-protection-level experiments. The system supports differential control of multiple types of laboratories: High-cleanliness laboratories (such as cell rooms): Strengthen the dust removal efficiency (configure high-efficiency filters), set the cough / sneeze threshold to 0 times, and eliminate any potential pollution risks; Ordinary teaching laboratories: Relax the step distance / number ratio threshold (allow certain individual differences), focus on monitoring body temperature and the number of coughs, and balance safety and passage efficiency.

[0049] The generation process of the internal anomaly warning is as follows: Extract the images inside the laboratory and perform personnel image recognition on the images inside the laboratory; After identifying the personnel images, record the number of personnel images, obtain the number of personnel inside the laboratory, and then collect the area inside the laboratory; Calculate the ratio of the area inside the laboratory to the number of personnel inside the laboratory to obtain the unit personnel parameter. When the unit personnel parameter is greater than the preset value, an internal anomaly warning is generated; At the same time, collect the distances between individuals. When there are more than the preset number of distances between individuals that are less than the preset value and greater than the preset duration, an internal anomaly warning is generated; After identifying the human body image, monitor the moving speed of the human body in real time. When there is any human body moving speed greater than the preset value, an internal anomaly warning is generated; At the same time, process the human body image and collect the swinging amplitude of the arm and the swinging amplitude of the leg; When any one of the swinging amplitudes of the arm or the swinging amplitude of the leg is abnormal, an internal anomaly warning is generated.

[0050] Furthermore, the acquisition process and anomaly determination process of the swinging amplitude of the arm are as follows: Perform personnel image recognition on the images inside the laboratory to obtain the human body image including the left human body image and the right human body image. Process the left human body image and extract the key points of the left upper limb of the human body. The key points of the left upper limb include the shoulder joint point, the elbow joint point, and the wrist joint point; Taking the shoulder joint point as the reference point, establish an upper body coordinate system, mark the positions of the elbow joint point and the wrist joint point on the upper half coordinate system, and connect them to obtain the left arm line. Calculate the swinging angle of the arm line in the horizontal direction, and calculate the included angle between the maximum swinging angle of the arm in the horizontal direction and the x-axis of the upper body coordinate system, that is, obtain the swinging amplitude of the left arm; Record the number of times L1 that the swinging amplitude of the left arm is greater than the preset value within the preset duration; Perform the same processing on the right human body image as on the left human body image to obtain the swinging amplitude of the right arm; Record the number of times R1 that the swing amplitude of the right arm is greater than the preset value within the preset time length; The process of obtaining the leg swing amplitude and determining abnormalities is as follows: Process the left human body image and extract the key points of the left lower limb of the human body, which include the hip joint point, knee joint point and ankle joint point; Take the position of the hip joint as the reference point, establish the lower limb coordinate system, mark the positions of the knee joint and the ankle joint on the lower limb coordinate system, obtain the leg line, calculate the angle between the leg line and the x-axis in the horizontal direction, obtain the swing angle of the lower limb, and record the maximum swing angle of the leg line in the horizontal direction, that is, the swing amplitude of the left leg; Record the number of times L2 that the left leg swing amplitude is greater than the preset value within the preset time length; The right human body image is processed in the same way as the left human body image to obtain the swing amplitude of the right leg; Record the number of times R2 that the swing amplitude of the right leg is greater than the preset value within the preset time length; Extract R1 and L1. When either R1 or L1 is greater than the preset value, it means that the arm swing amplitude is abnormal. Extract R2 and L2. When either R2 or L2 is greater than a preset value, it indicates that the leg swing amplitude is abnormal. Through unit personnel parameter monitoring, real-time identification of personnel overload (for example, the average safety area per person in a standard chemical laboratory must be ≥ 2.5 m2): For basic teaching laboratories (such as inorganic chemistry laboratories), it is easy to have a large number of people when students concentrate on operating. Exceeding the standard of unit personnel parameters can warn of insufficient ventilation and reagent collision risks; For precision instrument rooms (such as transmission electron microscope laboratories), strictly controlling the number of people can avoid equipment temperature fluctuations caused by human body heat dissipation and ensure instrument stability.

[0051] Dynamic monitoring of personnel distance to prevent cross contamination and safety hazards. When a preset number of people are in close contact (e.g. <1.2 meters) and the duration is >5 minutes, an alarm is triggered to accurately respond to typical scenarios in university laboratories: In group experiments, students gather together to observe phenomena and talk in close proximity, which may cause droplets to contaminate biological samples; With many people working closely together at a chemical lab bench, reagent bottles (such as concentrated sulfuric acid and strong oxidants) may be easily knocked over due to elbow collisions, causing safety accidents.

[0052] Monitor human movement speed to curb dangerous behaviors and environmental disturbances; When the speed of a person moves > 1.5m / s (critical value for fast walking), an immediate warning is issued, with the focus on preventing: students running in panic due to operational errors (such as reagent spillage), leading to chain collision accidents; Moving quickly when carrying flammable and explosive reagents (such as ether and hydrogen cylinders) increases the risk of dumping and leakage; Linked with motion amplitude detection: High-speed movement is often accompanied by large body movements. The system makes dual judgments on speed abnormalities and amplitude abnormalities to improve the accuracy of dangerous behavior identification and avoid false alarms of a single parameter.

[0053] Conduct motion amplitude detection to control dust and experimental interference from the source: Through the modeling of the shoulder, elbow, and wrist joint coordinates, the horizontal swing angle is calculated (if the preset threshold is greater than 60°, it is considered abnormal), effectively capturing common dust risk behaviors in university laboratories, such as students violently swinging test tubes and beakers (if the oscillator is not used in a standardized manner), causing the solution to splash or powder to be raised; When moving large instruments (such as centrifuges and ovens), the arms move too far and collide with surrounding equipment, generating metal debris.

[0054] Dust linkage mechanism: When the number of abnormal arm swings (L1 / R1) is greater than 5 times / minute, the suspended particle concentration data of the environmental monitoring subsystem is automatically retrieved. If Mi>f2 (linkage threshold) at this time, it is determined that improper operation has caused dust, and the ventilation equipment is simultaneously triggered to increase operation, forming a closed loop of behavioral warning, pollution monitoring and equipment linkage.

[0055] Leg swing amplitude monitoring to suppress ground dust and vibration interference; A lower limb coordinate system is established based on the hip, knee, and ankle joints to monitor the stride angle (if the horizontal swing angle of a single step is greater than 30°, it is considered abnormal), and targeted solutions are provided to solve the problem of dust being raised on the ground due to fast walking or stomping (especially in laboratories that are not cleaned in time, dust accumulated in the gaps between floor tiles can be easily stirred up).

[0056] The fusion analysis of multiple parameters can improve the intelligence level of abnormality judgment: combining the number of abnormalities within a preset time period, such as ≥10 abnormal arm swings within 10 minutes, rather than a single abnormality, filtering out accidental action interference, and focusing on persistent violations (such as students repeatedly stirring vigorously, causing reagents to splash).

[0057] Set different thresholds for different laboratory types: Biological clean room: The arm swing amplitude threshold is set to 45° (to avoid contamination caused by excessive amplitude when touching the sterile operating table); Mechanical Laboratory: The leg swing threshold is relaxed to 40° (to accommodate the natural gait when carrying heavy equipment), but the movement speed threshold is tightened to 1.2 m / s.

[0058] In addition, the terms "first" and "second" are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0059] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in a suitable manner in any one or more embodiments or examples. In addition, without contradiction, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0060] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. An intelligent laboratory monitoring system, characterized in that: include: Environmental monitoring subsystem, used to monitor environmental information in the laboratory; Internal image acquisition subsystem, used for image acquisition within the laboratory; Identity verification subsystem, used to collect and verify identity information; The intelligent supervision system processes environmental information to generate laboratory environmental warnings; The intelligent monitoring system processes images in the laboratory and generates internal abnormality alerts; The environmental monitoring subsystem includes: Ventilation equipment monitoring module, used to continuously collect ventilation equipment information in the laboratory; The particle detection module is used to collect the concentration value of suspended particles in the air in the laboratory; Humidity sensor module, real-time monitoring of the ambient humidity in the laboratory; The specific process of processing environmental information to generate laboratory environmental warnings is as follows: Extracting ventilation equipment information, the ventilation equipment information includes the operating status of the ventilation equipment and the environmental information of the ventilation equipment; The operating status of the ventilation equipment includes the fan blade speed of the ventilation equipment and the operating temperature information of the fan motor; Environmental information of ventilation equipment, including the concentration of suspended particulate matter and humidity of the environment in which the ventilation equipment is located; Calculate and process the fan blade speed to obtain the speed evaluation parameter, process the operating temperature information of the fan motor to obtain the ventilation equipment operating temperature parameter; Process the suspended particulate matter concentration value of the environment in which the ventilation equipment is located to obtain the environmental suspended particulate matter concentration parameter; Process the ambient humidity in the laboratory to obtain ambient humidity parameters; When any of the speed evaluation parameters, operating temperature parameters, humidity parameters and ambient suspended particulate matter concentration parameters is abnormal, a laboratory environment warning is generated.

2. The intelligent laboratory monitoring system according to claim 1, characterized in that: The specific process of identity information collection and verification by the identity authentication subsystem is as follows: Collect the real-time facial images of the experimenters, and retrieve the facial information of the experimenters corresponding to the ID cards from the database, i.e., the standard facial information; The real-time facial image is collected and compared with the retrieved standard facial information. After the comparison is passed, the person is allowed to enter the preset detection area for person liveness verification and status verification; When both the liveness verification and status verification of the person are passed, it means that the verification is passed and the experimenter is allowed to enter the laboratory.

3. The intelligent laboratory monitoring system according to claim 2, characterized in that: The process of liveness verification is as follows: after a person enters the preset detection area, a notification message is sent to notify the verification experimenter to shake his face in a clockwise direction. At this time, the facial features are collected and compared with the standard facial information. If the comparison fails, it means that the person's liveness verification has failed; If the comparison is passed, it means that the preliminary liveness verification of the person has passed. At this time, simple dust removal of the human body is performed, and notification information is sent in real time to notify the verified experimenter to move according to the content of the notification information. At this time, the image acquisition device set in the preset detection area collects the human body image of the experimenter, processes the human body image of the experimenter, extracts the secondary verification feature points, processes the secondary verification feature points, obtains the step distance evaluation parameters and the step number evaluation parameters, compares the step distance evaluation parameters and the step number evaluation parameters with the standard step distance and the standard step number of the corresponding experimenter in the database, and obtains the step distance and step number comparison results; A tester was also set up to test the experimenters. During the collection of step distance evaluation parameters and step number evaluation parameters, the tester collected the experimenters' body temperature information through infrared temperature measurement equipment and recorded the number of sneeze and cough of the experimenters. When the step distance and number comparison result is passed, the experimenter's body temperature information meets the standard, and the number of sneezes and coughs of the experimenter within the preset time is less than the preset value, it means that the identity authentication is passed. After completing simple dust removal, they are allowed to enter the laboratory.

4. The intelligent laboratory monitoring system according to claim 3, characterized in that: The specific process of step length evaluation parameters and step number evaluation parameters is as follows: Extract the foot image from the human body image of the experimenter, mark the rearmost end of one foot as point a1, and mark the frontmost end of the other foot as point a2; Continuously collect the distance between point a1 and point a2 m times, calculate the average value of the distance between point a1 and point a2 m times, and obtain the step evaluation parameter; At the same time, the number of times points a1 and a2 overlap within the preset distance is collected, that is, the step evaluation parameter is obtained; Calculate the difference between the step evaluation parameter and the standard step, and obtain the step difference; Calculate the difference between the step evaluation parameter and the standard step number to obtain the step difference; When the step difference and the step number difference are both within the preset range, the step-to-step comparison result is a passed comparison; if either the step difference or the step number difference exceeds the preset range, the step-to-step comparison result is a failed comparison.

5. The intelligent laboratory monitoring system according to claim 1, characterized in that: The generation process of the internal abnormal warning is as follows: Extract the images in the laboratory and perform personnel image recognition on the images in the laboratory; After identifying the person images, the number of person images is recorded to obtain the number of people in the laboratory, and then the area inside the laboratory is collected; Calculate the ratio of the area inside the laboratory to the number of people in the laboratory, and obtain the unit personnel parameter. When the unit personnel parameter is greater than the preset value, an internal abnormality warning is generated; At the same time, the distance between each person is collected. When the distance between more than a preset number of people is less than a preset value and longer than a preset time, an internal abnormality warning is generated; After identifying the human image, the human body movement speed is monitored in real time. When any human body movement speed is greater than the preset value, an internal abnormality warning is generated; At the same time, the human body image is processed to collect the arm swing amplitude and leg swing amplitude; When the swing range of any arm or leg is abnormal, an internal abnormality alarm is generated.

6. The intelligent monitoring system for a laboratory according to claim 5, characterized in that: The process of obtaining the arm swing amplitude and determining abnormalities is as follows: Performing personnel image recognition on the images in the laboratory, obtaining human body images including left human body images and right human body images, processing the left human body images, extracting the key points of the left upper limb of the human body, including the shoulder joint points, elbow joint points and wrist joint points; Taking the shoulder joint as the reference point, establish the upper body coordinate system, mark the positions of the elbow joint and the wrist joint on the upper body coordinate system, and connect them to obtain the left arm line, calculate the swing angle of the arm line in the horizontal direction, calculate the angle between the maximum swing angle of the arm in the horizontal direction and the x-axis of the upper body coordinate system, and obtain the swing amplitude of the left arm; Record the number of times L1 that the left arm swing amplitude is greater than the preset value within the preset time length; The right human body image is processed in the same way as the left human body image to obtain the swing amplitude of the right arm; Record the number of times R1 that the swing amplitude of the right arm is greater than the preset value within the preset time length; The process of obtaining the leg swing amplitude and determining abnormalities is as follows: Process the left human body image and extract the key points of the left lower limb of the human body, which include the hip joint point, knee joint point and ankle joint point; Take the position of the hip joint as the reference point, establish the lower limb coordinate system, mark the positions of the knee joint and the ankle joint on the lower limb coordinate system, obtain the leg line, calculate the angle between the leg line and the x-axis in the horizontal direction, obtain the swing angle of the lower limb, and record the maximum swing angle of the leg line in the horizontal direction, that is, the swing amplitude of the left leg; Record the number of times L2 that the left leg swing amplitude is greater than the preset value within the preset time length; The right human body image is processed in the same way as the left human body image to obtain the swing amplitude of the right leg; Record the number of times R2 that the swing amplitude of the right leg is greater than the preset value within the preset time length; Extract R1 and L1. When either R1 or L1 is greater than the preset value, it means that the arm swing amplitude is abnormal. R2 and L2 are extracted. When either R2 or L2 is greater than a preset value, it indicates that the leg swing amplitude is abnormal.

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