Intelligent laboratory environment safety monitoring system and monitoring method

Abstract

The invention provides an intelligent laboratory environment safety monitoring system, which comprises a region division unit, an environment monitoring point, a partition environment data management unit, a data recording alarm unit and a visual display background, and is characterized in that the region division unit firstly performs gridding processing on a laboratory space to form a plurality of laboratory partitions; the environment monitoring points are arranged in the laboratory subareas, monitoring layers are divided in each laboratory subarea, at least two sets of environment monitoring points are arranged in the corresponding monitoring layers in each laboratory subarea, environment data collected by the environment monitoring points are sent to the subarea environment data management unit, and the subarea environment data management unit summarizes the data in all the environment monitoring points and sends the summarized data to the environment monitoring points. And a time data relation curve is drawn according to the environment data of the corresponding monitoring layer in each laboratory partition, and the data recording and alarming unit records the time data relation curve and sets limiting intervals in different time periods.

Description

Technical Field

[0001] The present invention belongs to the field of laboratory monitoring, and in particular relates to a smart laboratory environment safety monitoring system and a monitoring method. Background Art

[0002] With the rapid development of science and technology, laboratory environmental safety monitoring has received more and more attention, but the existing monitoring systems and methods still have many shortcomings. Traditional laboratory environmental safety monitoring systems usually use single-point or a small number of fixed points for monitoring, which cannot fully cover the laboratory space, resulting in incomplete and inaccurate monitoring data. At the same time, existing systems often only focus on a single or a few environmental parameters, such as temperature and humidity, and ignore other potential safety hazards. In addition, most monitoring systems lack real-time data analysis and intelligent early warning functions, and cannot detect and predict potential risks in a timely manner. Existing monitoring methods usually use manual regular inspections or simple automated monitoring, which is inefficient and prone to omissions. Data collection, storage and analysis are often carried out in a decentralized manner, lacking a unified management platform, resulting in the formation of information islands and affecting the effective use of data.

[0003] In terms of alarm mechanism, most existing systems use fixed thresholds to trigger alarms, which cannot be adjusted dynamically according to different time periods and experimental conditions, and are prone to false alarms or missed alarms. In terms of visualization, many systems only provide simple data display and lack an intuitive and easy-to-understand graphical interface, which is not conducive to managers quickly grasping the laboratory environment status. In addition, the existing systems have poor linkage with other laboratory safety equipment and cannot automatically trigger corresponding emergency plans in the event of an emergency. In terms of data storage and analysis, many systems only focus on short-term data and lack the accumulation and analysis of long-term data, which cannot provide strong support for laboratory environment optimization and safety management decisions. Another common problem is that most existing systems are independently operated and lack integration with other systems such as laboratory management systems and experimental process monitoring systems, making it impossible to achieve high-level information sharing and collaborative management. In terms of the layout of monitoring points, existing systems often adopt a uniform distribution method, without considering the special needs and potential risks of different areas in the laboratory, resulting in unreasonable allocation of monitoring resources. In addition, most systems lack self-learning and adaptive capabilities and cannot automatically optimize monitoring strategies and alarm thresholds based on historical data and environmental changes.

[0004] In terms of user interaction, many systems are complex to operate and require professional maintenance, which is not conducive to the use of daily laboratory managers. Finally, the existing systems are also insufficient in data security and privacy protection, which can easily lead to the leakage of sensitive information. These problems seriously restrict the effectiveness of laboratory environmental safety monitoring and cannot meet the needs of modern laboratories for safe, efficient and intelligent management. A new type of intelligent laboratory environmental safety monitoring system and monitoring method are urgently needed to solve these problems.

[0005] Therefore, there is an urgent need for a smart laboratory environment safety monitoring system and monitoring method. Summary of the invention

[0006] The present invention proposes a smart laboratory environmental safety monitoring system and a monitoring method, which solve the following technical problems: comprehensive grid monitoring of laboratory space is realized, and the accuracy and coverage of data collection are improved; the detection capability of environmental anomalies is enhanced through multi-level monitoring and real-time data analysis; the accuracy and timeliness of alarms are improved by adopting dynamic limit intervals and time data relationship curves; and data management and emergency response capabilities are improved through visual display and data backup.

[0007] The technical solution of the present invention is implemented as follows: a smart laboratory environment safety monitoring system, including an area division unit, an environmental monitoring point, a partitioned environmental data management unit, a data recording and alarming unit and a visual display background, wherein the area division unit first performs grid processing on the laboratory space to form a number of laboratory partitions, and divides the monitoring layer in each laboratory partition, and the environmental monitoring point is provided with at least two groups of corresponding monitoring layers in each experimental partition, and the environmental data collected by the environmental monitoring point is sent to the partitioned environmental data management unit, the partitioned environmental data management unit summarizes the data in all environmental monitoring points, and draws a time data relationship curve for the environmental data of the corresponding monitoring layer in each laboratory partition according to time, the data recording and alarming unit records the time data relationship curve, and sets a limit interval in different time periods, and after the time data relationship curve exceeds or falls below the limit interval, an alarm is issued to the visual display background, the visual display background retrieves the data of each environmental monitoring point and the time data relationship curve for data display, and when the data recording and alarming unit issues an alarm, the displayed data is backed up.

[0008] Compared with the existing technology, the intelligent laboratory environmental safety monitoring system has significant innovation and advantages. The system uses a grid processing method to divide the laboratory space. Compared with the traditional single-point or a small number of fixed-point monitoring, this method can cover the entire laboratory space more comprehensively and finely, greatly improving the accuracy and comprehensiveness of monitoring. The system divides multiple monitoring layers in each laboratory partition and sets at least two groups of environmental monitoring points on each layer. This multi-level and multi-point monitoring layout can capture the changes in environmental parameters at different heights and locations, effectively avoiding the data deviation that may be caused by traditional single-height monitoring. The system summarizes and analyzes the data of all monitoring points through the partitioned environmental data management unit and draws a time data relationship curve. This method can not only reflect the changing trend of environmental parameters in real time, but also predict potential environmental risks through the changes in the curve, greatly improving the early warning ability of the system. The data recording alarm unit of the system adopts a dynamic limit interval setting method, which can flexibly adjust the alarm threshold according to the experimental requirements and safety standards of different time periods. This is more intelligent and accurate than the traditional fixed threshold alarm method, effectively reducing false alarms and missed alarms. The system's visual display background can not only display the data of each monitoring point and the time data relationship curve in real time, but also automatically save the display data when an alarm is triggered. This design greatly improves the traceability of data and the convenience of post-analysis. Compared with existing technologies, the system also realizes intelligent management of the entire process from data collection, processing, analysis to display and storage, avoiding the problem of scattered and isolated links in traditional systems.

[0009] This smart laboratory environmental safety monitoring system greatly improves the accuracy, comprehensiveness, real-time and intelligence of laboratory environmental monitoring through innovative designs such as grid space division, multi-level monitoring layout, real-time data analysis, dynamic alarm threshold setting, and intelligent data management and display, providing more reliable and efficient technical support for laboratory safety management.

[0010] As a preferred embodiment, when the area division unit performs grid processing on the laboratory space, it divides the space into several laboratory partitions according to the characteristics of the laboratory structure, and divides the space into at least three layers from top to bottom in each laboratory partition to comprehensively monitor the environment inside the laboratory.

[0011] As a preferred embodiment, the environmental monitoring points are arranged in different monitoring layers in each experimental partition. Each group of environmental monitoring points includes temperature, humidity, and gas concentration environmental parameter sensors and a data transmission unit. The environmental conditions in the area are monitored in real time through the environmental parameter sensors, and the collected data are fed back to the partition environmental data management unit through the data transmission unit.

[0012] As a preferred embodiment, the partitioned environmental data management unit summarizes and integrates the data collected from all environmental monitoring points, generates an environmental data report for the corresponding monitoring layer in each laboratory partition, updates the data regularly, and feeds back the updated data to the visualization background.

[0013] As a preferred embodiment, the data recording and alarm unit records and analyzes the time data relationship curve, compares the recorded data with the standard parameters, makes trend predictions on the changing rules of the environmental data, sets and adjusts the limit interval, and responds accordingly to the status exceeding or falling below the limit interval.

[0014] A method for monitoring the safety of a smart laboratory environment, the method comprising the following steps:

[0015] The entire laboratory space is processed into a three-dimensional grid and divided into several laboratory partitions; in each laboratory partition, at least three monitoring layers are set up from top to bottom, and at least two groups of environmental monitoring points are arranged on each layer;

[0016] Each group of environmental monitoring points collects environmental parameter data in real time and transmits the data to the partition environmental data management unit. The partition environmental data management unit summarizes the data of all monitoring points and draws the environmental parameter time relationship curve for each monitoring layer in each partition in chronological order;

[0017] The data recording alarm unit sets a dynamic limit interval for the environmental parameters according to the security requirements of the time period. The data recording alarm unit monitors the time relationship curve of the environmental parameters in real time. When the curve exceeds or falls below the limit interval, an alarm signal is sent to the visualization display background;

[0018] The visualization background retrieves and displays the monitoring point data and time relationship curve in real time. When an alarm signal is received, the visualization background backs up and stores all the currently displayed data;

[0019] Based on historical environmental data and the changing trend of the current time relationship curve, machine learning algorithms are used to predict potential risks and issue warnings before abnormalities occur.

[0020] This smart laboratory environmental safety monitoring method has several significant innovations and advantages compared with the existing technology. This method uses three-dimensional grid processing to divide the laboratory space. This method is more refined and comprehensive than the traditional two-dimensional plane division, and can more accurately reflect the complex structure and environmental characteristics of the laboratory space. At least three monitoring layers are set in each partition, and at least two groups of environmental monitoring points are set in each layer. This multi-level, high-density monitoring layout greatly improves the accuracy and spatial coverage of data collection, and can capture subtle environmental changes at different heights and locations. This method summarizes and processes monitoring data in real time through the partition environmental data management unit, and draws the time relationship curve of environmental parameters. This dynamic and visual data processing method helps to quickly identify the changing trends and abnormal fluctuations of environmental parameters. This method introduces the concept of dynamic limit intervals, and flexibly adjusts the alarm threshold according to the safety requirements of different time periods. This is more intelligent and accurate than the traditional fixed threshold alarm, and can effectively reduce false alarms and missed alarms. In terms of data display and storage, this method realizes real-time data retrieval and display, and automatically backs up data when an alarm is issued. This design greatly improves the traceability of data and the convenience of post-analysis.

[0021] The most innovative thing is that this method introduces machine learning algorithms to predict and warn potential risks based on historical data and current trends. This forward-looking risk management method is rare in traditional monitoring systems. Compared with existing technologies, this method realizes the intelligence of the entire process from data collection, processing, analysis to warning, avoiding the problem of scattered and isolated links in traditional methods.

[0022] After adopting the above technical scheme, the beneficial effects of the present invention are as follows: through three-dimensional grid processing and multi-level high-density monitoring point arrangement, all-round and high-precision monitoring of laboratory space is achieved, greatly improving the accuracy and comprehensiveness of environmental parameter collection. The partitioned environmental data management unit is used to summarize and process data in real time, and the time relationship curve of environmental parameters is drawn, so that managers can intuitively grasp the trend of environmental changes and quickly identify abnormal situations. The dynamic limit interval setting is adopted to automatically adjust the alarm threshold according to the security requirements of different time periods, which significantly improves the accuracy of the alarm and effectively reduces false alarms and missed alarms. The data is displayed in real time through the visual display background, and it is automatically backed up when the alarm is issued, which improves the traceability of the data and the convenience of post-analysis. The machine learning algorithm is introduced to predict and warn potential risks based on historical data and current trends, realizing the transition from passive response to active prevention. Through precise environmental monitoring and data analysis, it helps laboratory managers better understand the environmental conditions of each area, thereby optimizing resource allocation and experimental layout. The comprehensive monitoring and intelligent early warning functions of the system greatly improve the safety management level of the laboratory and reduce the possibility of safety accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0024] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] Example:

[0027] like Figure 1 As shown, a smart laboratory environmental safety monitoring system includes an area division unit, an environmental monitoring point, a partitioned environmental data management unit, a data recording and alarming unit, and a visual display background, wherein the area division unit first performs grid processing on the laboratory space to form a plurality of laboratory partitions, and divides the monitoring layer in each laboratory partition, and the environmental monitoring point is provided with at least two groups of corresponding monitoring layers in each experimental partition, and the environmental data collected by the environmental monitoring point is sent to the partitioned environmental data management unit, and the partitioned environmental data management unit summarizes the data in all environmental monitoring points, and draws a time data relationship curve according to time for the environmental data of the corresponding monitoring layer in each laboratory partition, and the data recording and alarming unit records the time data relationship curve, and sets a limit interval in different time periods, and after the time data relationship curve exceeds or falls below the limit interval, an alarm is issued to the visual display background, and the visual display background retrieves the data of each environmental monitoring point and the time data relationship curve for data display, and when the data recording and alarming unit issues an alarm, the displayed data is stored for backup.

[0028] The smart laboratory environment safety monitoring system is a new type of efficient laboratory safety monitoring solution. It realizes the working principle and workflow of the smart laboratory environment safety monitoring system by finely dividing the laboratory space into different areas:

[0029] The system first grids the laboratory space through the area division unit to form multiple laboratory partitions, and divides the monitoring layer in each laboratory partition. At least two sets of environmental monitoring points are set in the corresponding monitoring layer in each experimental partition to collect environmental data. These environmental monitoring points send the collected environmental data to the partition environmental data management unit. The partition environmental data management unit is responsible for summarizing the data from all environmental monitoring points and drawing a time data relationship curve for the environmental data of the corresponding monitoring layer in each laboratory partition according to time.

[0030] The data recording and alarm unit records the time data relationship curve and sets the limit interval in different time periods. Once the time data relationship curve exceeds or falls below the limit interval, the data recording and alarm unit sends an alarm signal to the visualization background. The visualization background can retrieve the data of each environmental monitoring point and the time data relationship curve for data display. At the same time, when the data recording and alarm unit issues an alarm, the visualization background will store the display data for backup.

[0031] Compared with the existing technology, the difference of this intelligent laboratory environment safety monitoring system is that it grids the laboratory space through area division units, realizes the refined monitoring and management of the laboratory environment, and at the same time, through the collaborative work of the data recording alarm unit and the visual display background, it realizes real-time monitoring, analysis and display of laboratory environmental data, as well as timely response and backup to abnormal situations, thereby improving the efficiency and accuracy of laboratory environmental safety monitoring.

[0032] When the area division unit performs grid processing on the laboratory space, it divides the space according to the characteristics of the laboratory structure to form several laboratory partitions, and divides the space into at least three layers from top to bottom in each laboratory partition to comprehensively monitor the environment in the laboratory. The area division unit in the smart laboratory environmental safety monitoring system adopts an innovative grid processing method, which has significant differences and advantages compared with the existing technology. Existing laboratory environmental monitoring systems usually use single-point monitoring at a fixed position or simple area division, which cannot comprehensively and accurately reflect the environmental conditions of the entire laboratory space.

[0033] The regional division unit of this system is divided according to the specific structural characteristics of the laboratory to form several laboratory partitions, and the space is divided into at least three layers from top to bottom in each laboratory partition. This design can monitor the environmental changes in the laboratory more comprehensively and finely. This multi-level, grid-based monitoring method can capture the differences in environmental parameters at different heights and in different areas in the laboratory, thereby providing more accurate and comprehensive environmental data. The advantage of this setting is that it can achieve three-dimensional and refined monitoring of the laboratory environment, effectively avoiding the blind spots or data deviations that may exist in traditional monitoring methods. By finely partitioning and multi-level monitoring of the laboratory space, the system can better identify and locate potential environmental safety hazards, such as harmful gas leakage, temperature anomalies, etc., thereby improving the efficiency and accuracy of laboratory safety management. The general state of the laboratory environment monitoring industry is to use relatively simple monitoring methods, such as single-point monitoring or rough regional division, which is difficult to meet the needs of modern laboratories for refined and intelligent environmental monitoring. The innovative design of this system can not only provide more comprehensive and accurate environmental data, but also provide laboratory managers with more intuitive and detailed environmental information, which is helpful to formulate more scientific and effective safety management strategies, thereby promoting the entire industry to develop in a more intelligent and safe direction.

[0034] The environmental monitoring points are set at different monitoring layers in each experimental partition. Each group of environmental monitoring points includes temperature, humidity, gas concentration environmental parameter sensors and data transmission units. The environmental conditions in the area are monitored in real time through the environmental parameter sensors, and the collected data are fed back to the partition environmental data management unit through the data transmission unit. The setting method of environmental monitoring points in the smart laboratory environmental safety monitoring system has significant innovation and advantages compared with the existing technology.

[0035] Traditional laboratory environment monitoring systems usually use a single or limited monitoring point, and the monitoring parameters are relatively simple, which makes it difficult to fully reflect the environmental conditions in the laboratory. However, this system sets up environmental monitoring points at different monitoring layers in each experimental partition. Each group of monitoring points contains multiple environmental parameter sensors such as temperature, humidity, gas concentration, and data transmission units. This design can achieve multi-dimensional and all-round monitoring of the laboratory environment. Compared with the existing technology, the main differences are: the distribution of monitoring points is more comprehensive and dense, covering all areas and height levels of the laboratory space; each monitoring point integrates a variety of environmental parameter sensors, which can monitor multiple environmental indicators at the same time; equipped with a special data transmission unit to ensure that the data can be transmitted to the partition environment data management unit in real time and accurately. The reason for this setting is to obtain more comprehensive and accurate laboratory environmental data in order to better evaluate and manage the safety status of the laboratory.

[0036] The benefits of this setting are mainly reflected in the following aspects: First, it can realize all-round and multi-parameter monitoring of the laboratory environment, avoiding the monitoring blind spots or parameter missing that may exist in traditional monitoring methods; second, through real-time data transmission, it can timely detect and respond to environmental anomalies and improve the efficiency of safety management; third, multi-point and multi-parameter monitoring data provide rich data support for the analysis and optimization of the laboratory environment, which is helpful to formulate more scientific and effective laboratory management strategies. The general state of the laboratory environment monitoring industry is that the distribution of monitoring points is relatively sparse, the monitoring parameters are relatively single, and there is a certain delay in data transmission and processing. This situation makes it difficult to meet the high standards of modern laboratories for environmental safety. In contrast, the innovative design of this system can not only provide more comprehensive and accurate environmental data, but also realize real-time monitoring and rapid response, which greatly improves the level of laboratory environmental safety management and promotes the entire industry to develop in a more intelligent and refined direction.

[0037] The partitioned environmental data management unit summarizes and integrates the data collected from all environmental monitoring points, generates an environmental data report for the corresponding monitoring layer in each laboratory partition, and regularly updates the data, and feeds back the updated data to the visual display background. The partitioned environmental data management unit in the smart laboratory environmental safety monitoring system shows significant innovation in data processing and management, and has obvious advantages over the prior art. Traditional laboratory environmental monitoring systems usually adopt simple data collection and storage methods, lacking in-depth integration and analysis of data. The partitioned environmental data management unit of this system can not only summarize and integrate the data of all environmental monitoring points, but also generate environmental data reports for the corresponding monitoring layer in each laboratory partition, and regularly update the data, and feed back to the visual display background in real time. The main difference between this design and the prior art is that it realizes the centralized management and integration of the data of the dispersed monitoring points, and provides a more comprehensive overview of the environmental status; by generating environmental data reports for the partitions and monitoring layers, the data is structured and hierarchical; the regular update and real-time feedback mechanism ensure the timeliness and availability of the data. The reason for this setting is to more effectively utilize and manage a large amount of environmental monitoring data and improve the value and availability of the data.

[0038] The benefits of this setting are mainly reflected in the following aspects: First, through the aggregation and integration of data, managers can quickly obtain a global view of the entire laboratory environment, which is convenient for making overall decisions; second, the partitioned and layered data reports enable managers to accurately locate and analyze environmental problems in specific areas; third, the regular update and real-time feedback mechanism ensure that managers can obtain the latest environmental data in a timely manner, improving the response speed and efficiency of safety management; fourth, it provides a rich and structured data foundation for subsequent data analysis and trend prediction. As a preferred embodiment, the data recording and alarm unit records and analyzes the time data relationship curve, compares the recorded data with the standard parameters, predicts the trend of the change law of the environmental data, sets and adjusts the limit interval, and responds to the state exceeding or falling below the limit interval.

[0039] A method for monitoring the safety of a smart laboratory environment, the method comprising the following steps:

[0040] The entire laboratory space is processed into a three-dimensional grid and divided into several laboratory partitions; in each laboratory partition, at least three monitoring layers are set up from top to bottom, and at least two groups of environmental monitoring points are arranged on each layer;

[0041] Each group of environmental monitoring points collects environmental parameter data in real time and transmits the data to the partition environmental data management unit. The partition environmental data management unit summarizes the data of all monitoring points and draws the environmental parameter time relationship curve for each monitoring layer in each partition in chronological order;

[0042] The data recording alarm unit sets a dynamic limit interval for the environmental parameters according to the security requirements of the time period. The data recording alarm unit monitors the time relationship curve of the environmental parameters in real time. When the curve exceeds or falls below the limit interval, an alarm signal is sent to the visualization display background;

[0043] The visualization background retrieves and displays the monitoring point data and time relationship curve in real time. When an alarm signal is received, the visualization background backs up and stores all the currently displayed data;

[0044] Based on historical environmental data and the changing trend of the current time relationship curve, machine learning algorithms are used to predict potential risks and issue warnings before abnormalities occur.

[0045] Traditional laboratory environment monitoring methods usually use simple two-dimensional plane monitoring or fixed point monitoring, which lacks spatial stereoscopicity and comprehensiveness. However, this method uses three-dimensional grid processing to divide the entire laboratory space into multiple partitions, and sets at least three monitoring layers in each partition, with at least two groups of environmental monitoring points arranged on each layer. This design realizes all-round and three-dimensional monitoring of the laboratory environment. Compared with the existing technology, the main differences are: three-dimensional grid processing and multi-layer monitoring point arrangement provide more comprehensive and detailed environmental data; the partition environmental data management unit can summarize data in real time and draw time relationship curves, providing a dynamic view of environmental changes; the data recording alarm unit adopts dynamic limit intervals, which can flexibly adjust the warning threshold according to the safety requirements of different time periods; the visual display background not only displays data in real time, but also backs up data when an alarm is issued, ensuring the integrity and traceability of the data; the introduction of machine learning algorithms for risk prediction and early warning realizes the transformation from passive response to active prevention. The reason for this setting is to build a more comprehensive, accurate and intelligent laboratory environment safety monitoring system to meet the high standards of modern laboratories for safety management.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A smart laboratory environment safety monitoring system, characterized in that: It includes an area division unit, an environmental monitoring point, a partitioned environmental data management unit, a data recording and alarming unit and a visualization display background, wherein the area division unit first performs grid processing on the laboratory space to form a number of laboratory partitions, and divides the monitored layers in each laboratory partition, and the corresponding monitoring layers of the environmental monitoring points in each experimental partition are at least set with two groups, and the environmental data collected by the environmental monitoring points are sent to the partitioned environmental data management unit, the partitioned environmental data management unit summarizes the data in all environmental monitoring points, and draws a time data relationship curve according to time for the environmental data of the corresponding monitoring layer in each laboratory partition, the data recording and alarming unit records the time data relationship curve, and sets a limit interval in different time periods, and after the time data relationship curve exceeds or falls below the limit interval, an alarm is issued to the visualization display background, the visualization display background retrieves the data of each environmental monitoring point and the time data relationship curve for data display, and when the data recording and alarming unit issues an alarm, the displayed data is backed up.

2. The smart laboratory environment safety monitoring system according to claim 1, characterized in that: When the area division unit performs grid processing on the laboratory space, it divides the space according to the laboratory structure characteristics to form a number of laboratory partitions, and divides the space into at least three layers from top to bottom in each laboratory partition to comprehensively monitor the environment inside the laboratory.

3. The intelligent laboratory environment safety monitoring system according to claim 1, characterized in that: The environmental monitoring points are set in different monitoring layers in each experimental partition. Each group of environmental monitoring points includes temperature, humidity, gas concentration environmental parameter sensors and data transmission units. The environmental conditions in the area are monitored in real time through the environmental parameter sensors, and the collected data are fed back to the partition environmental data management unit through the data transmission unit.

4. The smart laboratory environment safety monitoring system according to claim 1, characterized in that: The partitioned environmental data management unit summarizes and integrates the data collected from all environmental monitoring points, generates an environmental data report for the corresponding monitoring layer in each laboratory partition, updates the data regularly, and feeds back the updated data to the visualization display background.

5. The smart laboratory environment safety monitoring system according to claim 1, characterized in that: The data recording and alarm unit records and analyzes the time data relationship curve, compares the recorded data with the standard parameters, predicts the trend of the change law of the environmental data, sets and adjusts the limit interval, and responds accordingly to the state exceeding or falling below the limit interval.

6. A method for monitoring the environmental safety of a smart laboratory, characterized in that: The method comprises the following steps: The entire laboratory space is processed into a three-dimensional grid and divided into several laboratory partitions; in each laboratory partition, at least three monitoring layers are set up from top to bottom, and at least two groups of environmental monitoring points are arranged on each layer; Each group of environmental monitoring points collects environmental parameter data in real time and transmits the data to the partition environmental data management unit. The partition environmental data management unit summarizes the data of all monitoring points and draws the environmental parameter time relationship curve for each monitoring layer in each partition in chronological order; The data recording alarm unit sets a dynamic limit interval for the environmental parameters according to the security requirements of the time period. The data recording alarm unit monitors the time relationship curve of the environmental parameters in real time. When the curve exceeds or falls below the limit interval, an alarm signal is sent to the visualization display background; The visualization background retrieves and displays the monitoring point data and time relationship curve in real time. When an alarm signal is received, the visualization background will back up and store all the currently displayed data; Based on historical environmental data and the changing trend of the current time relationship curve, machine learning algorithms are used to predict potential risks and issue warnings before abnormalities occur.

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