Laboratory safety monitoring alarm method, apparatus and device

Abstract

The application relates to a laboratory safety monitoring alarm method, device and equipment. The method comprises the following steps: acquiring environment monitoring data corresponding to a laboratory, equipment monitoring data corresponding to each device in the laboratory and personnel density data corresponding to each grid in the laboratory; performing secondary disaster probability calculation processing according to the environment monitoring data and the equipment monitoring data, to obtain a secondary disaster occurrence probability corresponding to the laboratory; for each grid, performing fusion analysis processing according to the environment monitoring data, the equipment monitoring data and the personnel density data corresponding to the grid, to obtain grid risk level data corresponding to the grid; and generating an alarm instruction in the case that the grid risk level data and the secondary disaster occurrence probability meet an alarm condition. The method can improve the accuracy of laboratory safety monitoring alarm.

Description

Technical Field

[0001] This application relates to the field of laboratory safety monitoring technology, and in particular to a laboratory safety monitoring alarm method, device, and equipment. Background Technology

[0002] With the increasing complexity of scientific research activities, laboratory safety management has become a core aspect of ensuring the safety of researchers' lives and the security of equipment and property. Typically, laboratories are equipped with alarm devices for smoke detection, gas leaks, and power failures. However, many related technologies employ a single-point parallel triggering mode of "smoke detector-alarm" or "gas detector-alarm," resulting in low precision in laboratory safety monitoring and alarm systems. Summary of the Invention

[0003] Therefore, it is necessary to provide a laboratory safety monitoring and alarm method, device, and equipment that can improve the accuracy of laboratory safety monitoring and alarms, addressing the aforementioned technical problems.

[0004] Firstly, this application provides a laboratory safety monitoring and alarm method, including:

[0005] Obtain environmental monitoring data for the laboratory, equipment monitoring data for each piece of equipment in the laboratory, and personnel density data for each grid in the laboratory;

[0006] The probability of secondary disasters is calculated based on environmental monitoring data and monitoring data from various devices to obtain the probability of secondary disasters occurring in the laboratory.

[0007] For each grid, environmental monitoring data, monitoring data from various devices, and personnel density data corresponding to the grid are integrated and analyzed to obtain the grid risk level data.

[0008] An alarm command is generated when the grid risk level data and the probability of secondary disasters meet the alarm conditions.

[0009] In one embodiment, environmental monitoring data, monitoring data from various devices, and personnel density data corresponding to the grid are fused and analyzed to obtain grid risk level data corresponding to the grid, including:

[0010] Based on environmental monitoring data and the personnel density data corresponding to the grid, the leakage level data corresponding to the grid is determined;

[0011] The fire level data corresponding to the grid is determined based on the environmental monitoring data corresponding to the neighboring grids;

[0012] Based on the equipment monitoring data of the associated devices corresponding to the grid, determine the power fault level data corresponding to the grid;

[0013] By using leakage risk weights, fire risk weights, and power failure risk weights, the leakage level data, fire level data, and power failure level data are weighted and summed to obtain the grid risk level data corresponding to the grid.

[0014] In one embodiment, the process of calculating the probability of secondary disasters based on environmental monitoring data and monitoring data from various devices to obtain the probability of secondary disasters occurring in the laboratory further includes:

[0015] Identify the target secondary disaster types, which include leakage disasters, fire disasters, and power outage disasters;

[0016] The method also includes:

[0017] When the probability of a secondary disaster is greater than a preset probability threshold, the risk weights corresponding to the target secondary disaster type are updated based on the probability of the secondary disaster, resulting in updated risk weights.

[0018] In one embodiment, the probability of secondary disasters is calculated based on environmental monitoring data and monitoring data from various devices to obtain the probability of secondary disasters occurring in the laboratory, including:

[0019] Given that a source disaster event occurs in either environmental monitoring data or equipment monitoring data, obtain the basic probability of secondary disasters corresponding to the source disaster event and the source grid corresponding to the source disaster event;

[0020] Identify the secondary grid where the secondary disaster-related equipment corresponding to the primary disaster event is located;

[0021] Based on the secondary mesh and the source mesh, the probability correction value and the probability correction direction are obtained;

[0022] Based on the basic probability, probability correction value, and probability correction direction of secondary disasters, the probability of secondary disasters occurring in the corresponding laboratory is determined.

[0023] In one embodiment, when the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions, an alarm command is generated, including:

[0024] If at least one grid has a grid risk level data greater than the first grid risk threshold, or if the probability of a secondary disaster occurs is greater than the first probability threshold, a first alarm command is generated. The first alarm command is used to execute a global emergency operation based on the first alarm command.

[0025] In one embodiment, when the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions, an alarm command is generated, including:

[0026] If at least one grid has a grid risk level data that is less than or equal to the first grid risk threshold and greater than the second grid risk threshold, or if the probability of a secondary disaster is less than or equal to the first probability threshold and greater than the second probability threshold, a second alarm command is generated. The second alarm command is used to perform emergency operations on the target area based on the second alarm command.

[0027] Where at least one grid has grid risk level data that is less than or equal to the first grid risk threshold and greater than the second grid risk threshold, the target area includes the target grid and the neighboring grids of the target grid. The target grid is the grid whose grid risk level data is less than or equal to the first grid risk threshold and greater than the second grid risk threshold.

[0028] When the probability of a secondary disaster occurring is less than or equal to the first probability threshold and greater than the second probability threshold, the target area is determined based on the source grid corresponding to the source disaster event and the secondary grid where the secondary disaster-related equipment is located.

[0029] In one embodiment, when the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions, an alarm command is generated, including:

[0030] If at least one grid has a grid risk level data that is less than or equal to the second grid risk threshold and greater than the third grid risk threshold, a third alarm command is generated. The third alarm command is used to generate alarm prompt information for the target grid based on the third alarm command.

[0031] In one embodiment, the environmental monitoring data includes multiple types of environmental monitoring sub-data, and the equipment monitoring data includes multiple types of equipment monitoring sub-data;

[0032] The method also includes:

[0033] If there is an anomaly in the target monitoring sub-data and the duration of the anomaly exceeds a preset time threshold, a fourth alarm instruction is generated. The fourth alarm instruction is used to generate alarm prompt information for the device corresponding to the target monitoring sub-data based on the fourth alarm instruction.

[0034] Among them, target monitoring sub-data is one of multiple environmental monitoring sub-data and multiple equipment monitoring sub-data.

[0035] Secondly, this application also provides a laboratory safety monitoring and alarm device, comprising:

[0036] The data acquisition module is used to acquire environmental monitoring data corresponding to the laboratory, equipment monitoring data corresponding to each piece of equipment in the laboratory, and personnel density data corresponding to each grid in the laboratory.

[0037] The data analysis module is used to perform secondary disaster probability calculation processing based on the environmental monitoring data and the monitoring data of each of the devices to obtain the probability of secondary disaster occurrence corresponding to the laboratory; and, for each of the grids, to perform fusion analysis processing based on the environmental monitoring data, the monitoring data of each of the devices and the personnel density data corresponding to the grid to obtain the grid risk level data corresponding to the grid.

[0038] The decision module is used to generate an alarm command when the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions.

[0039] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method described in the first aspect.

[0040] The aforementioned laboratory safety monitoring and alarm method, device, and equipment acquire environmental monitoring data corresponding to the laboratory, equipment monitoring data corresponding to each piece of equipment in the laboratory, and personnel density data corresponding to each grid in the laboratory. Based on the environmental monitoring data and the equipment monitoring data, the probability of secondary disasters is calculated to obtain the probability of secondary disasters occurring in the laboratory. For each grid, the environmental monitoring data, equipment monitoring data, and personnel density data corresponding to the grid are fused and analyzed to obtain the grid risk level data. When the grid risk level data and the probability of secondary disasters occur meet the alarm conditions, an alarm command is generated. In this way, the evaluation of alarm command generation based on the grid risk level data and the probability of secondary disasters considers the spatial dimension of the laboratory, i.e., using the grid as the analysis unit, giving the alarm command spatial guidance. Simultaneously, considering the probability of secondary disasters, it achieves the function of early warning of secondary disaster accidents, thereby improving the precision of laboratory safety monitoring and alarms. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is an application environment diagram of a laboratory safety monitoring and alarm method in one embodiment;

[0043] Figure 2 This is a flowchart illustrating a laboratory safety monitoring and alarm method in one embodiment;

[0044] Figure 3 This is a flowchart illustrating the steps for obtaining the grid risk level data corresponding to a grid in one embodiment;

[0045] Figure 4 This is a flowchart illustrating the steps for determining the probability of secondary disasters occurring in a laboratory, as shown in one embodiment.

[0046] Figure 5 This is a structural block diagram of a laboratory safety monitoring and alarm device in one embodiment;

[0047] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0049] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0050] The laboratory safety monitoring and alarm method provided in this application embodiment can be applied to, for example... Figure 1 The application environment shown is as follows. A multimodal sensor network 110 is deployed throughout the laboratory. This multimodal sensor network 110 includes an environmental monitoring unit 112, an equipment status monitoring unit 114, and a spatial positioning unit 116. The multimodal sensor network transmits the collected monitoring data to the control device 130 for analysis and decision-making processing via a gateway device.

[0051] For example, the environmental monitoring unit 112 is used to acquire the concentration of volatile organic compounds (VOCs), smoke density, and temperature rise rate in key locations such as fume hood areas and hazardous chemical storage areas; the equipment status monitoring unit 114 is used to monitor the insulation resistance of high-voltage equipment, power harmonic distortion rate, and container pressure fluctuation data; and the spatial positioning unit 116 is used to dynamically track personnel distribution through UWB (Ultra-Wideband) positioning tags and bind and map personnel positions to a digital grid of a preset size.

[0052] For example, gateway device 120 is an industrial gateway device that supports multiple communication protocols. Exemplary gateway device 120 supports Modbus RTU (Modbus Remote Terminal Unit Protocol), ZigBee, and CAN (Controller Area Network Protocol) protocols.

[0053] For example, the control device 130 can be a terminal device, such as various personal computers, laptops, smartphones, tablets, IoT devices and portable wearable devices; the control device 130 can also be a server, such as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0054] In one exemplary embodiment, such as Figure 2 As shown, a laboratory safety monitoring and alarm method is provided, which can be applied to... Figure 1 The following steps are described using the control device 130 as an example, including steps 202 to 206.

[0055] Step 202: Obtain the environmental monitoring data corresponding to the laboratory, the equipment monitoring data corresponding to each piece of equipment in the laboratory, and the personnel density data corresponding to each grid in the laboratory.

[0056] Environmental monitoring data is acquired through an environmental monitoring unit. In one possible implementation, the environmental monitoring data includes various types of environmental monitoring sub-data. For example, the environmental monitoring sub-data may be VOCs concentration, smoke density, or temperature rise rate. The sampling range for VOCs concentration is 0-1000 ppm (Parts Per Million), the sampling accuracy for smoke density is ±0.1%obs / m (Obscuration per Meter), and the resolution for temperature rise rate is 0.5℃ / s (degrees Celsius per second).

[0057] The laboratory is pre-divided into multiple grids of preset sizes, and monitoring data and safety analysis are acquired on a grid-by-grid basis. For example, the laboratory is pre-divided into multiple grids of 1m x 1m size. Personnel density data within each grid can be obtained using spatial positioning units.

[0058] In one possible implementation, environmental monitoring units are installed in locations such as fume hood areas and hazardous chemical storage areas. Environmental monitoring data corresponding to the grid is obtained through the environmental monitoring units, and the environmental monitoring data corresponding to each grid is determined based on the distance relationship between each grid and the location of the environmental monitoring unit.

[0059] In one possible implementation, in addition to setting up environmental monitoring units in locations such as fume hood areas and hazardous chemical storage areas, environmental monitoring units are also distributed in other areas of the laboratory to form an environmental monitoring sensor network.

[0060] Equipment monitoring data is acquired through an equipment status monitoring unit. In one possible implementation, the equipment monitoring data includes various types of equipment monitoring sub-data. For example, the equipment monitoring sub-data includes high-voltage equipment insulation resistance, power harmonic distortion rate, and container pressure fluctuation.

[0061] In some implementations, if the power harmonic distortion rate is less than or equal to 5%, it is not collected; if the power harmonic distortion rate is greater than 5%, it is collected and reported to the control equipment. If the container pressure fluctuation is less than or equal to 10%, it is not collected; if the container pressure fluctuation is greater than 10%, it is collected and reported to the control equipment.

[0062] Step 204: Calculate the probability of secondary disasters based on environmental monitoring data and monitoring data from various devices to obtain the probability of secondary disasters occurring in the laboratory.

[0063] Specifically, when an abnormal trend is detected in a certain data point based on environmental monitoring data and equipment monitoring data, a source disaster event corresponding to the abnormal monitoring data is identified, and the probability of this source disaster event triggering a secondary disaster is determined. In some embodiments, the source disaster event may also be referred to as the initial event.

[0064] For example, if the source disaster event is determined to be "hydrochloric acid leak" based on environmental monitoring data, the secondary disaster chain that it may trigger includes: the leaked hydrochloric acid mist corroding the insulation layer of the wiring in the nearby electrical cabinet, eventually causing "electrical short circuit fire"; or, the leaked hydrochloric acid reacting chemically with certain metal containers to release "hydrogen gas", and when the hydrogen gas accumulates to the explosion limit in a confined space, it may "explode" when it comes into contact with an open flame.

[0065] In one possible implementation, environmental monitoring data and equipment monitoring data are input into a pre-trained Bayesian network to obtain the probability of secondary disasters.

[0066] In one possible implementation, the basic probabilities of secondary disasters corresponding to various source disaster events are preset, and then the basic probabilities of secondary disasters are corrected based on the monitored real-time data to obtain the probability of secondary disaster occurrence.

[0067] Step 206: For each grid, perform integrated analysis and processing based on environmental monitoring data, monitoring data from each device, and personnel density data corresponding to the grid to obtain the grid risk level data corresponding to the grid.

[0068] This method uses grids as the unit of analysis to obtain the risk level data for each grid. In this way, in the event of a safety incident, the precise location of the incident can be determined, enabling more precise alarm and response.

[0069] Step 208: If the grid risk level data and the probability of secondary disasters occur meet the alarm conditions, generate an alarm command.

[0070] The laboratory safety monitoring and alarm method provided in the above embodiments acquires environmental monitoring data corresponding to the laboratory, equipment monitoring data corresponding to each piece of equipment in the laboratory, and personnel density data corresponding to each grid in the laboratory. It then calculates the probability of secondary disasters based on the environmental monitoring data and the equipment monitoring data to obtain the probability of secondary disasters occurring in the laboratory. For each grid, it performs fusion analysis based on the environmental monitoring data, the equipment monitoring data, and the personnel density data corresponding to the grid to obtain the grid risk level data. When the grid risk level data and the probability of secondary disasters occur meet the alarm conditions, an alarm command is generated. In this way, the evaluation of alarm command generation based on the grid risk level data and the probability of secondary disasters considers the spatial dimension of the laboratory, i.e., using the grid as the analysis unit, giving the alarm command spatial guidance. Simultaneously, it considers the probability of secondary disasters, achieving the function of early warning of secondary disaster accidents, thereby improving the precision of laboratory safety monitoring and alarms.

[0071] In one exemplary embodiment, please refer to Figure 3 ,based on Figure 2 The embodiment shown illustrates a laboratory safety monitoring and alarm method that involves calculating the probability of secondary disasters based on environmental monitoring data and monitoring data from various devices to obtain the probability of secondary disasters occurring in the laboratory. For example... Figure 3 The process shown includes steps 302 to 308.

[0072] Step 302: Determine the leakage level data corresponding to each grid based on environmental monitoring data and personnel density data of each grid.

[0073] This involves mapping environmental monitoring data to various grids and using the corresponding personnel density data for each grid to calculate the leakage level data for each grid.

[0074] For example, based on the distance relationship between the location of the environmental monitoring unit and each grid location, the environmental monitoring data is mapped to each grid according to a preset environmental data diffusion relationship, thus obtaining the environmental monitoring data corresponding to each grid. For instance, if there are two environmental monitoring units in the laboratory, where grid A is the grid containing environmental monitoring unit 1, grid B is the grid containing environmental monitoring unit 2, and no environmental monitoring unit is set up in grid C, then the environmental monitoring data corresponding to grid A is the environmental monitoring data collected by environmental monitoring unit 1, the environmental monitoring data corresponding to grid B is the environmental monitoring data collected by environmental monitoring unit 2, and the environmental monitoring data corresponding to grid C is determined based on the data collected by environmental monitoring units 1 and 2, the relative positional relationship between grid C and grid A, and the relative positional relationship between grid C and grid B.

[0075] For example, L_local = 0.4 × [VOCs] + 0.3 × [diffusion rate] + 0.3 × [toxicity coefficient] × [grid personnel density]; where L_local represents the leakage level data corresponding to the grid, [VOCs] represents the concentration of volatile organic compounds, [diffusion rate] refers to the diffusion rate of volatile organic compounds, [toxicity coefficient] represents the toxicity coefficient of the volatile organic compound, and [grid personnel density] refers to the personnel density of the current grid.

[0076] For example, the [toxicity coefficient] can be determined through the proprietary database of the associated laboratory; for instance, the toxicity coefficient of hydrochloric acid is 0.9, that of ethanol is 0.6, and that of sodium metal is 1.

[0077] The diffusion rate can be approximated by calculating the VOC concentration gradient between the current grid and its neighboring grids at the same moment. For example, the diffusion rate can be calculated as follows: the diffusion rate equals the average VOC concentration of neighboring grids minus the VOC concentration of the current grid, divided by the characteristic distance between the grids, where the characteristic distance between the grids can be approximated by the spacing between VOC sensors. For instance, if the VOC concentration of the current grid is 200 ppm, the average concentration of its surrounding neighboring grids is 150 ppm, and the VOC sensor spacing is 1 meter, then the diffusion rate at this moment is -50 ppm per meter.

[0078] The sign of the diffusion concentration indicates whether the leaked material diffuses towards or converges from the surrounding area, while its absolute value characterizes the intensity of the diffusion. Leakage level data is calculated based on the absolute value of the diffusion rate.

[0079] In one possible implementation, by calculating the diffusion rate in real time, the development trend of a leakage event can be dynamically perceived and quantified as an input variable for accurate assessment of local leakage risk.

[0080] Step 304: Determine the fire level data corresponding to the grid based on the environmental monitoring data corresponding to the neighboring grids.

[0081] For example, the fire severity level data F_adjacent of a grid is obtained by weighting the fire severity data of the 8 neighboring grids around the grid; the fire severity data of a grid is F=0.6×[dT / dt]+0.4×[smoke concentration], which reflects the fire spread trend, where dT / dt represents the rate of temperature rise.

[0082] Step 306: Determine the power fault level data corresponding to the grid based on the equipment monitoring data of the associated devices.

[0083] In this context, the associated devices corresponding to a grid are the devices within that grid and the devices that have an electrical connection with it. For example, the power fault level data corresponding to a grid is determined by comprehensively considering the power fault risks of the associated devices within that grid. All devices directly connected to the devices in that grid are identified through the electrical topology diagram. The power fault data for each directly connected device is calculated as E = 0.8 × [insulation resistance] + 0.2 × [harmonic distortion rate]. Then, the power fault level data corresponding to that grid is obtained by summing the power fault data of each directly connected device and the power fault data of the devices in that grid.

[0084] Step 308: Using the leakage risk weight, fire risk weight, and power failure risk weight, perform weighted summation on the leakage level data, fire level data, and power failure level data to obtain the grid risk level data corresponding to the grid.

[0085] For example, R_grid=W L ×L_local +W F ×F_adjacent+W E ×E_related, where W L W represents the risk weight of leakage. F W represents the fire risk weight. E E_related represents the power failure risk weight, and R_grid represents the grid risk level data.

[0086] In some examples, the leakage level data, fire level data, and power failure level data are first normalized, and then weighted summation is performed.

[0087] In one exemplary embodiment, based on Figure 3 The illustrated embodiment, in the process of calculating the probability of secondary disasters based on the environmental monitoring data and equipment monitoring data corresponding to each grid, further includes determining the target secondary disaster type. The secondary disaster types include leakage disasters, fire disasters, and power outage disasters.

[0088] Correspondingly, the laboratory safety monitoring and alarm method provided in this embodiment further includes: when the probability of a secondary disaster is greater than a preset probability threshold, updating the risk weight corresponding to the target secondary disaster type based on the probability of the secondary disaster to obtain the updated risk weight.

[0089] Specifically, if the target secondary disaster type is a leakage disaster, and if the probability of the secondary disaster occurring is greater than a preset probability threshold, then the leakage risk weight is updated based on the probability of the secondary disaster occurring, and the updated leakage risk weight is obtained. The process of obtaining grid risk level data is then performed based on the updated leakage risk weight.

[0090] Specifically, if the target secondary disaster type is a fire disaster, and if the probability of the secondary disaster occurring is greater than a preset probability threshold, then the fire risk weight is updated based on the probability of the secondary disaster occurring, and the updated fire risk weight is obtained. The process of obtaining grid risk level data is then performed based on the updated fire risk weight.

[0091] Specifically, if the target secondary disaster type is a power failure disaster, and if the probability of the secondary disaster occurring is greater than a preset probability threshold, then the power failure risk weight is updated based on the probability of the secondary disaster occurring, and the updated power failure risk weight is obtained. The process of obtaining grid risk level data is then performed based on the updated power failure risk weight.

[0092] In this embodiment, the leakage level data, fire level data, and power failure level data are weighted and summed to obtain the grid risk level data corresponding to the grid. Each risk weight in this process has a preset fixed initial value under the initial state of the control equipment. During the execution of the laboratory safety monitoring and alarm method, if a secondary disaster is likely to occur, and the probability of the secondary disaster is high, the risk weight corresponding to that secondary disaster type is dynamically updated. This ensures that the fusion calculation of the grid risk level data evolves in real time with the threat of the accident, making it easier for the grid risk level to meet the alarm conditions and improving the efficiency of safety monitoring and alarm.

[0093] For example, the initial values ​​for the leakage risk weight, fire risk weight, and power failure risk weight are 0.5, 0.3, and 0.2, respectively. The formula for calculating the grid risk level data is R_grid=0.5×L_local+0.3×F_adjacent+0.2×E_related. Taking "hydrochloric acid leakage may cause circuit corrosion" as an example: In the initial state of the system, the power failure risk weight is fixed at 0.2. When the calculated probability of this secondary disaster (P_secondary) exceeds the preset probability threshold (e.g., 0.3), the weight adjustment formula will be triggered, for example: the new power failure risk weight W... E =0.2×(1+0.5×P_secondary / 0.3). If P_secondary is 0.8, the calculated new weight is approximately 0.47. The system will then immediately update the grid risk level data calculation formula to R_grid=0.5×L_local+0.3×F_adjacent+0.47×E_related. In this way, the power failure risk weight is dynamically increased from 0.2 to 0.47. Even if the original equipment monitoring data remains unchanged, its contribution to the overall risk assessment is significantly amplified, making it easier for the comprehensive risk value to reach the response threshold and achieving early warning of secondary disasters. This mechanism also applies to adjusting the weight of leakage or fire items, thereby enabling the entire risk assessment model to evolve in real time according to the threat of the accident chain.

[0094] In one exemplary embodiment, based on Figure 2 The embodiment shown illustrates a laboratory safety monitoring and alarm method that involves calculating the probability of secondary disasters based on environmental monitoring data and monitoring data from various devices, thereby obtaining the probability of secondary disasters occurring in the laboratory. For example... Figure 4 As shown, the process includes steps 402 to 408.

[0095] Step 402: If a source disaster event occurs in one of the environmental monitoring data and equipment monitoring data, obtain the basic probability of secondary disasters corresponding to the source disaster event and the source grid corresponding to the source disaster event.

[0096] In this embodiment, when environmental monitoring data or equipment monitoring data detects the occurrence or potential occurrence of a source disaster event, the basic probability of the occurrence of a secondary disaster event corresponding to that source disaster event is obtained. This probability is denoted as the basic probability of secondary disaster and is a preset fixed value obtained through experience or big data statistics. For example, a correspondence between a source disaster event, a secondary disaster event, and the basic probability of secondary disaster is pre-set. When monitoring data indicates the occurrence or potential occurrence of a source disaster event, the corresponding secondary disaster event and the basic probability of secondary disaster are obtained. In some examples, a correspondence between environmental monitoring data and equipment monitoring data, a secondary disaster event, and the basic probability of secondary disaster can also be set. For example, if the VOCs concentration reaches a certain level, a source disaster event is triggered. The secondary disaster event is determined based on the VOCs concentration data type, and the basic probability of secondary disaster is determined based on the specific value of the VOCs concentration.

[0097] For example, if environmental monitoring data indicates the occurrence of a hydrochloric acid leak, then the basic probability of secondary disasters corresponding to the hydrochloric acid leak can be obtained, such as "under the condition of a hydrochloric acid leak, the probability of circuit corrosion is 0.8".

[0098] Among them, obtaining the source grid corresponding to the source disaster event refers to the location of the equipment corresponding to the source disaster event. For example, if the source disaster event is a hydrochloric acid leak event, the source grid is the grid where the hydrochloric acid storage tank is located.

[0099] Step 404: Determine the secondary grid where the secondary disaster-related equipment corresponding to the source disaster event is located.

[0100] The secondary grid refers to the grid where the equipment related to the secondary disaster is located. For example, if the source disaster event is a hydrochloric acid leak, the corresponding secondary disaster events are circuit corrosion and a chemical reaction in a metal container that releases hydrogen gas and causes an explosion. In this example, the grid corresponding to the location of the electrical cabinet and the grid corresponding to the location of the metal container are determined. In this example, the number of secondary disaster events is 2, and the corresponding basic probability of the secondary disaster is also the basic probability corresponding to these 2 secondary disaster events.

[0101] Step 406: Based on the secondary mesh and the source mesh, obtain the probability correction value and the probability correction direction.

[0102] Specifically, the relative distance between secondary disasters is determined based on the secondary grid and the source grid, and the probability correction value and direction are determined based on the relative distance between the secondary disasters. For example, if the relative distance between the secondary disasters is greater than a preset distance threshold, the probability correction direction is to decrease, and the probability correction value is determined based on the magnitude of the relative distance between the secondary disasters. For example, if the relative distance between the secondary disasters is less than a preset distance threshold, the probability correction direction is to increase, and the probability correction value is determined based on the magnitude of the relative distance between the secondary disasters.

[0103] Step 408: Based on the basic probability of secondary disasters, the probability correction value, and the probability correction direction, determine the probability of secondary disasters occurring in the laboratory.

[0104] Specifically, the basic probability of secondary disasters is corrected according to the probability correction direction and probability correction value to determine the probability of secondary disasters occurring in the laboratory.

[0105] In the above embodiments, the basic probability of secondary disasters is corrected by the relative positional relationship between the equipment related to the primary disaster event and the secondary disaster event, so as to obtain a secondary disaster occurrence probability that is closer to the actual situation, thereby improving the accuracy of laboratory safety monitoring alarms and reducing the false alarm rate.

[0106] In one exemplary embodiment, based on Figure 2 The embodiment shown illustrates a laboratory safety monitoring and alarm method. The process of generating an alarm command when the grid risk level data and the probability of secondary disaster occurrence meet alarm conditions includes: generating a first alarm command when at least one grid's corresponding grid risk level data is greater than a first grid risk threshold, or when the probability of secondary disaster occurrence is greater than a first probability threshold. The first alarm command is used to execute a comprehensive emergency response based on the first alarm command.

[0107] In this embodiment, if the grid risk level data corresponding to at least one grid is greater than the first grid risk threshold, it indicates that the laboratory currently faces an extremely high safety risk. If the probability of secondary disasters is greater than the first probability threshold, it indicates that the possibility of future extended disasters is extremely high. Therefore, if at least one of these two situations occurs, the most comprehensive emergency response is initiated, and a full-area emergency operation is performed for the entire laboratory. This operation is global and mandatory. For example, based on the first alarm command, the laboratory's main power supply and specific gas valves are shut off in conjunction with the release of a full-area fire extinguishing agent, and the most urgent evacuation command is issued through all channels (such as sound, light, and projection).

[0108] In an exemplary embodiment, when the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions, the process of generating an alarm command includes: generating a second alarm command when at least one grid's corresponding grid risk level data is less than or equal to a first grid risk threshold and greater than a second grid risk threshold, or when the probability of secondary disaster occurrence is less than or equal to a first probability threshold and greater than a second probability threshold. The second alarm command is used to enable emergency operations targeting the target area to be performed based on the second alarm command.

[0109] Where at least one grid has a grid risk level data that is less than or equal to a first grid risk threshold and greater than a second grid risk threshold, the target area includes the target grid and its neighboring grids. The target grid is defined as the grid whose grid risk level data is less than or equal to the first grid risk threshold and greater than the second grid risk threshold.

[0110] Specifically, when the probability of a secondary disaster is less than or equal to the first probability threshold and greater than the second probability threshold, the target area is determined based on the source grid corresponding to the source disaster event and the secondary grid where the secondary disaster-related equipment is located.

[0111] In this embodiment, if at least one grid has a grid risk level data that is less than or equal to a first grid risk threshold and greater than a second grid risk threshold, or if the probability of a secondary disaster is less than or equal to a first probability threshold and greater than a second probability threshold, it indicates that there is currently a medium-risk area in the laboratory that requires proactive intervention. If only at least one grid has a grid risk level data that is less than or equal to a first grid risk threshold and greater than a second grid risk threshold, then based on which specific grids have grid risk level data between the second and first grid risk thresholds, these grids and their neighboring grids are identified as target areas. If only the probability of a secondary disaster is less than or equal to a first probability threshold and greater than a second probability threshold, then a secondary risk warning area is delineated based on the source grid corresponding to the primary disaster event and the secondary grid where the secondary disaster-related equipment is located, serving as the target area. If both of these situations exist, the target area for emergency operations includes the target areas determined in each of these two situations.

[0112] For example, the emergency operation performed on the target area based on the second alarm command includes: turning off the power to the target area, starting the ventilation system corresponding to the target area, issuing warnings and clearing personnel from the target area through directional sound beams and laser projection, and controlling the access control system to implement "exit only, no entry" management for the area.

[0113] In one exemplary embodiment, when the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions, the process of generating an alarm command includes: generating a second alarm command when at least one grid's corresponding grid risk level data is less than or equal to a first grid risk threshold and greater than a second grid risk threshold, or when the probability of secondary disaster occurrence is less than or equal to a first probability threshold and greater than a second probability threshold. The second alarm command is used to enable emergency operations targeting the target area to be performed based on the second alarm command.

[0114] In this embodiment, if there is at least one grid whose grid risk level data is less than or equal to the second grid risk threshold and greater than the third grid risk threshold, a third alarm instruction is generated. The third alarm instruction is used to generate alarm indication information for the target grid based on the third alarm instruction.

[0115] The target grid refers to the grid whose grid risk level data falls between the second grid risk threshold and the third grid risk threshold.

[0116] In this embodiment, if the risk level data corresponding to at least one grid is less than or equal to the second grid risk threshold and greater than the third grid risk threshold, the laboratory is considered to be in a low-risk situation or exhibiting an abnormal trend. An alarm message for the target grid is then generated via a third alarm command. For example, a gentle blue warning light spot is projected above the target grid, and a detailed text alarm message is sent to the mobile terminal of the laboratory manager or safety officer, specifying the abnormal parameters and location. The staff then decides whether to conduct an on-site inspection, avoiding excessive interference with normal experimental activities.

[0117] In one exemplary embodiment, based on Figure 2 The illustrated embodiment includes environmental monitoring data comprising various types of environmental monitoring sub-data, and equipment monitoring data comprising various types of equipment monitoring sub-data. The provided laboratory safety monitoring alarm method further includes generating a fourth alarm command when an anomaly is detected in the target monitoring sub-data and the duration of the anomaly exceeds a preset duration threshold.

[0118] The fourth alarm command is used to generate alarm prompts for the devices corresponding to the target monitoring sub-data based on the fourth alarm command; the target monitoring sub-data is one of various environmental monitoring sub-data and various device monitoring sub-data.

[0119] For example, when the VOCs concentration or equipment temperature exceeds the standard for a certain period of time, a fourth alarm command is triggered to alert the staff.

[0120] In one exemplary embodiment, a laboratory safety monitoring alarm method is provided, which is applied to... Figure 1 Taking the control device 130 as an example, the explanation includes the following steps S2 to S26.

[0121] Step S2: Obtain environmental monitoring data corresponding to the laboratory, equipment monitoring data corresponding to each piece of equipment in the laboratory, and personnel density data corresponding to each grid in the laboratory.

[0122] Step S4: If one of the environmental monitoring data and the equipment monitoring data indicates the occurrence of a source disaster event, obtain the basic probability of secondary disasters corresponding to the source disaster event and the source grid corresponding to the source disaster event.

[0123] Step S6: Determine the secondary grid where the secondary disaster-related equipment corresponding to the source disaster event is located.

[0124] Step S8: Based on the secondary mesh and the source mesh, obtain the probability correction value and the probability correction direction.

[0125] Step S10: Based on the basic probability of secondary disasters, the probability correction value, and the probability correction direction, determine the probability of secondary disasters occurring in the laboratory.

[0126] Step S12: Determine the leakage level data corresponding to the grid based on environmental monitoring data and personnel density data corresponding to the grid.

[0127] Step S14: Determine the fire level data corresponding to the grid based on the environmental monitoring data corresponding to the neighboring grids.

[0128] Step S16: Determine the power fault level data corresponding to the grid based on the equipment monitoring data of the associated devices corresponding to the grid.

[0129] Step S18: Using the leakage risk weight, fire risk weight, and power failure risk weight, perform weighted summation on the leakage level data, fire level data, and power failure level data to obtain the grid risk level data corresponding to the grid.

[0130] Optionally, the provided method further includes: determining the target secondary disaster type, and when the probability of secondary disaster occurrence is greater than a preset probability threshold, updating the risk weight corresponding to the target secondary disaster type based on the probability of secondary disaster occurrence to obtain the updated risk weight.

[0131] Step S20: If the grid risk level data corresponding to at least one grid is greater than the first grid risk threshold or the probability of secondary disasters is greater than the first probability threshold, a first alarm command is generated. The first alarm command is used to perform a global emergency operation based on the first alarm command.

[0132] Step S22: If there is at least one grid with grid risk level data that is less than or equal to the first grid risk threshold and greater than the second grid risk threshold, or if the probability of secondary disaster occurrence is less than or equal to the first probability threshold and greater than the second probability threshold, a second alarm command is generated. The second alarm command is used to perform emergency operations on the target area based on the second alarm command.

[0133] Specifically, if at least one grid has grid risk level data that is less than or equal to the first grid risk threshold and greater than the second grid risk threshold, the target area includes the target grid and its neighboring grids. The target grid is the grid whose grid risk level data is less than or equal to the first grid risk threshold and greater than the second grid risk threshold. If the probability of a secondary disaster is less than or equal to the first probability threshold and greater than the second probability threshold, the target area is determined based on the source grid corresponding to the source disaster event and the secondary grid where the secondary disaster-related equipment is located.

[0134] Step S24: If there is at least one grid whose grid risk level data is less than or equal to the second grid risk threshold and greater than the third grid risk threshold, a third alarm instruction is generated. The third alarm instruction is used to generate alarm prompt information for the target grid based on the third alarm instruction.

[0135] Step S26: If there is an anomaly in the target monitoring sub-data and the duration of the anomaly in the target monitoring sub-data is greater than a preset duration threshold, a fourth alarm instruction is generated. The fourth alarm instruction is used to generate alarm prompt information for the device corresponding to the target monitoring sub-data based on the fourth alarm instruction.

[0136] Among them, environmental monitoring data includes various types of environmental monitoring sub-data, equipment monitoring data includes various types of equipment monitoring sub-data, and target monitoring sub-data is one of the various environmental monitoring sub-data and various equipment monitoring sub-data.

[0137] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0138] It is understood that the term "based on" as used in this application is used to describe one or more factors that influence the determination, but does not exclude other factors that may influence the determination. For example, the phrase "determine A based on B" means that the determination of A can be based entirely or at least partially on factor B. That is, B is a factor that influences the determination of A, but does not exclude the fact that the determination of A is also based on C.

[0139] Based on the same inventive concept, this application also provides a laboratory safety monitoring and alarm device for implementing the laboratory safety monitoring and alarm method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the laboratory safety monitoring and alarm device provided below can be found in the limitations of the laboratory safety monitoring and alarm method described above, and will not be repeated here.

[0140] In one exemplary embodiment, such as Figure 5 As shown, a laboratory safety monitoring and alarm device is provided, comprising: a data acquisition module 502, a data analysis module 504, and a decision module 506, wherein:

[0141] The data acquisition module 502 is used to acquire environmental monitoring data corresponding to the laboratory, equipment monitoring data corresponding to each piece of equipment in the laboratory, and personnel density data corresponding to each grid in the laboratory.

[0142] The data analysis module 504 is used to calculate the probability of secondary disasters based on environmental monitoring data and monitoring data of various equipment to obtain the probability of secondary disasters occurring in the laboratory; and, for each grid, to perform fusion analysis based on environmental monitoring data, monitoring data of various equipment and personnel density data corresponding to the grid to obtain the grid risk level data corresponding to the grid.

[0143] The decision module 506 is used to generate alarm commands when the grid risk level data and the probability of secondary disasters meet the alarm conditions.

[0144] In an exemplary embodiment, the data analysis module 504 is used to determine the leakage level data corresponding to the grid based on environmental monitoring data and personnel density data corresponding to the grid; determine the fire level data corresponding to the grid based on environmental monitoring data corresponding to the neighboring grids; determine the power failure level data corresponding to the grid based on equipment monitoring data of the associated equipment corresponding to the grid; and perform weighted summation processing on the leakage level data, fire level data, and power failure level data using leakage risk weight, fire risk weight, and power failure risk weight to obtain the grid risk level data corresponding to the grid.

[0145] In an exemplary embodiment, the data analysis module 504 is used to determine the target secondary disaster type, wherein the secondary disaster type includes leakage disaster, fire disaster and power failure disaster, and, when the probability of occurrence of the secondary disaster is greater than a preset probability threshold, to update the risk weight corresponding to the target secondary disaster type based on the probability of occurrence of the secondary disaster, to obtain the updated risk weight.

[0146] In an exemplary embodiment, the data analysis module 504 is used to obtain the basic probability of secondary disasters corresponding to the source disaster event and the source grid corresponding to the source disaster event when one of the environmental monitoring data and equipment monitoring data represents the occurrence of a source disaster event; determine the secondary grid where the secondary disaster-related equipment corresponding to the source disaster event is located; obtain the probability correction value and probability correction direction based on the secondary grid and the source grid; and determine the probability of secondary disaster occurrence corresponding to the laboratory based on the basic probability of secondary disasters, the probability correction value and the probability correction direction.

[0147] In an exemplary embodiment, the decision module 506 is used to generate a first alarm command when there is at least one grid risk level data corresponding to a grid that is greater than a first grid risk threshold or when the probability of a secondary disaster is greater than a first probability threshold. The first alarm command is used to enable the execution of a global emergency operation based on the first alarm command.

[0148] In an exemplary embodiment, the decision module 506 is configured to generate a second alarm command when at least one grid corresponds to a grid risk level data that is less than or equal to a first grid risk threshold and greater than a second grid risk threshold, or when the probability of a secondary disaster occurring is less than or equal to a first probability threshold and greater than a second probability threshold. The second alarm command is used to execute emergency operations for a target area based on the second alarm command. Wherein, when at least one grid corresponds to a grid risk level data that is less than or equal to the first grid risk threshold and greater than the second grid risk threshold, the target area includes the target grid and its neighboring grids. The target grid is the grid whose grid risk level data is less than or equal to the first grid risk threshold and greater than the second risk threshold. When the probability of a secondary disaster occurring is less than or equal to the first probability threshold and greater than the second probability threshold, the target area is determined based on the source grid corresponding to the source disaster event and the secondary grid where the secondary disaster-related equipment is located.

[0149] In an exemplary embodiment, the decision module 506 is used to generate a third alarm instruction when there is at least one grid risk level data corresponding to a grid that is less than or equal to a second grid risk threshold and greater than a third grid risk threshold. The third alarm instruction is used to generate alarm prompt information for the target grid based on the third alarm instruction.

[0150] In an exemplary embodiment, the environmental monitoring data includes multiple types of environmental monitoring sub-data, and the equipment monitoring data includes multiple types of equipment monitoring sub-data. The decision module 506 is used to generate a fourth alarm instruction when the target monitoring sub-data is abnormal and the duration of the abnormality is greater than a preset duration threshold. The fourth alarm instruction is used to generate alarm prompt information for the equipment corresponding to the target monitoring sub-data based on the fourth alarm instruction. The target monitoring sub-data is one of multiple environmental monitoring sub-data and multiple equipment monitoring sub-data.

[0151] Each module in the aforementioned laboratory safety monitoring and alarm device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0152] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data required for implementing the laboratory safety monitoring and alarm method. The I / O interfaces allow the processor to exchange information with external devices. The communication interface allows communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a laboratory safety monitoring and alarm method.

[0153] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0154] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0155] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0156] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0157] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0158] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0159] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0160] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A laboratory safety monitoring and alarm method, characterized in that, The method includes: Acquire environmental monitoring data corresponding to the laboratory, equipment monitoring data corresponding to each piece of equipment in the laboratory, and personnel density data corresponding to each grid in the laboratory; The probability of secondary disasters is calculated based on the environmental monitoring data and the monitoring data of each of the aforementioned devices to obtain the probability of secondary disasters occurring in the laboratory. For each grid, the environmental monitoring data, the monitoring data of each device, and the personnel density data corresponding to the grid are fused and analyzed to obtain the grid risk level data corresponding to the grid. An alarm command is generated when the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions.

2. The method according to claim 1, characterized in that, The process of fusing and analyzing the environmental monitoring data, the monitoring data of each device, and the personnel density data corresponding to the grid to obtain the grid risk level data corresponding to the grid includes: Based on the environmental monitoring data and the personnel density data corresponding to the grid, the leakage level data corresponding to the grid is determined; The fire level data corresponding to the grid is determined based on the environmental monitoring data corresponding to the neighboring grids of the grid. Based on the equipment monitoring data of the associated devices corresponding to the grid, the power fault level data corresponding to the grid is determined; By using leakage risk weights, fire risk weights, and power failure risk weights, the leakage level data, fire level data, and power failure level data are weighted and summed to obtain the grid risk level data corresponding to the grid.

3. The method according to claim 2, characterized in that, The process of calculating the probability of secondary disasters based on the environmental monitoring data and the monitoring data of each of the devices to obtain the probability of secondary disasters occurring corresponding to the laboratory also includes: Identify the target secondary disaster types, which include leakage disasters, fire disasters, and power outage disasters; The method further includes: If the probability of the secondary disaster occurring is greater than a preset probability threshold, the risk weight corresponding to the target secondary disaster type is updated based on the probability of the secondary disaster occurring, resulting in an updated risk weight.

4. The method according to claim 1, characterized in that, The step of calculating the probability of secondary disasters based on the environmental monitoring data and the monitoring data of each of the devices to obtain the probability of secondary disasters occurring for the laboratory includes: If either the environmental monitoring data or the equipment monitoring data indicates the occurrence of a source disaster event, obtain the basic probability of secondary disasters corresponding to the source disaster event and the source grid corresponding to the source disaster event; Determine the secondary grid where the secondary disaster-related equipment corresponding to the primary disaster event is located; Based on the secondary mesh and the source mesh, the probability correction value and the probability correction direction are obtained; Based on the basic probability of secondary disasters, the probability correction value, and the probability correction direction, the probability of secondary disasters occurring corresponding to the laboratory is determined.

5. The method according to claim 1, characterized in that, When the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions, an alarm command is generated, including: If at least one grid has a grid risk level data greater than the first grid risk threshold, or if the probability of the secondary disaster is greater than the first probability threshold, a first alarm command is generated. The first alarm command is used to enable the execution of a global emergency operation based on the first alarm command.

6. The method according to claim 5, characterized in that, When the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions, an alarm command is generated, including: If at least one grid has a grid risk level data that is less than or equal to the first grid risk threshold and greater than the second grid risk threshold, or if the probability of the secondary disaster is less than or equal to the first probability threshold and greater than the second probability threshold, a second alarm command is generated. The second alarm command is used to perform emergency operations on the target area based on the second alarm command. Wherein, if there is at least one grid whose grid risk level data is less than or equal to the first grid risk threshold and greater than the second grid risk threshold, the target area includes the target grid and the neighboring grids of the target grid, and the target grid is the grid whose grid risk level data is less than or equal to the first grid risk threshold and greater than the second grid risk threshold; If the probability of secondary disaster occurrence is less than or equal to the first probability threshold and greater than the second probability threshold, the target area is determined based on the source grid corresponding to the source disaster event and the secondary grid where the secondary disaster-related equipment is located.

7. The method according to claim 6, characterized in that, When the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions, an alarm command is generated, including: If at least one grid has a grid risk level data that is less than or equal to the second grid risk threshold and greater than the third grid risk threshold, a third alarm instruction is generated. The third alarm instruction is used to generate alarm prompt information for the target grid based on the third alarm instruction.

8. The method according to claim 1, characterized in that, The environmental monitoring data includes various types of environmental monitoring sub-data, and the equipment monitoring data includes various types of equipment monitoring sub-data. The method further includes: If there is an anomaly in the target monitoring sub-data and the duration of the anomaly in the target monitoring sub-data is greater than a preset duration threshold, a fourth alarm instruction is generated. The fourth alarm instruction is used to generate alarm prompt information for the device corresponding to the target monitoring sub-data based on the fourth alarm instruction. The target monitoring sub-data is one of the various environmental monitoring sub-data and the various equipment monitoring sub-data.

9. A laboratory safety monitoring and alarm device, characterized in that, The device includes: The data acquisition module is used to acquire environmental monitoring data corresponding to the laboratory, equipment monitoring data corresponding to each piece of equipment in the laboratory, and personnel density data corresponding to each grid in the laboratory. The data analysis module is used to perform secondary disaster probability calculation processing based on the environmental monitoring data and the monitoring data of each of the devices to obtain the probability of secondary disaster occurrence corresponding to the laboratory; and, for each of the grids, to perform fusion analysis processing based on the environmental monitoring data, the monitoring data of each of the devices and the personnel density data corresponding to the grid to obtain the grid risk level data corresponding to the grid. The decision module is used to generate an alarm command when the grid risk level data and the probability of secondary disaster occurrence meet the alarm conditions.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

Images