Laboratory safety management and control method and device, computer equipment and readable storage medium

Abstract

The application relates to a laboratory safety control method and device, computer equipment and a readable storage medium. The method comprises the following steps: determining a comprehensive risk index of a laboratory according to sensor data of the laboratory at a current time; determining an abnormality degree value of the laboratory between a historical time and the current time based on the comprehensive risk index and configuration parameters of each sensor, wherein the historical time is earlier than a previous time of the current time; determining a safety warning threshold of the laboratory at the current time based on the abnormality degree value and a data change amount of each sensor, wherein the data change amount is a change amount between the sensor data at the current time and the sensor data at the previous time; and controlling the safety of the laboratory based on the comprehensive risk index and the safety warning threshold. The method can improve the response sensitivity to the safety hidden danger of the laboratory.

Description

Technical Field

[0001] This application relates to the field of laboratory safety management technology, and in particular to a laboratory safety control method, apparatus, computer equipment, and readable storage medium. Background Technology

[0002] With the rapid development of science and technology and the increasing complexity of laboratory work, laboratory safety management faces more and more challenges. In many high-precision, high-risk experiments, factors such as temperature, humidity, gas concentration, and equipment status in the laboratory environment have a direct impact on experimental results and personnel safety. Therefore, laboratory safety management is extremely important.

[0003] In related technologies, laboratory safety management is mainly achieved through manual inspections and basic sensor monitoring. Both manual inspections and sensor monitoring use fixed warning thresholds to compare laboratory data and manage safety based on the comparison results. However, this method lacks sufficient sensitivity to safety hazards, resulting in delayed or excessively slow responses, making it difficult to address emergencies in the laboratory in a timely manner. Summary of the Invention

[0004] Therefore, it is necessary to provide a laboratory safety management method, device, computer equipment, and readable storage medium that can improve the response sensitivity to laboratory safety hazards in response to the above-mentioned technical problems.

[0005] Firstly, this application provides a laboratory safety management method, including:

[0006] The comprehensive risk index of the laboratory is determined based on the sensor data of the laboratory at the current moment;

[0007] Based on the comprehensive risk index and the configuration parameters of each sensor, the degree of anomaly of the laboratory between the historical time and the current time is determined; wherein the historical time is earlier than the time before the current time.

[0008] The safety warning threshold for the laboratory at the current moment is determined based on the anomaly level value and the data change of each of the sensors; wherein, the data change is the change between the sensor data at the current moment and the sensor data at the previous moment;

[0009] The laboratory is subject to safety management based on the comprehensive risk indicators and the safety warning thresholds.

[0010] In one embodiment, the configuration parameters include at least one of the following parameters of the sensor: the expected value at the current moment, a weighting coefficient, a sensitivity factor, a spatiotemporal anomaly adjustment factor, and a time standard deviation; wherein the sensitivity factor is used to characterize the degree of abnormal fluctuation of the sensor data, the spatiotemporal anomaly adjustment factor is used to characterize the rate at which the impact of the sensor's historical abnormal data on the current risk assessment decays over time, and the time standard deviation is used to characterize the degree of fluctuation of the sensor data in the time dimension.

[0011] In one embodiment, determining the degree of anomaly of the laboratory between historical and current times based on the comprehensive risk index and the configuration parameters of each sensor includes: calculating the absolute value of the difference between the comprehensive risk index and each of the expected values; determining an attenuation factor based on the spatiotemporal anomaly adjustment factor, the time standard deviation, and the time difference between the current time and the historical time; wherein the attenuation factor is used to characterize the degree of influence of the abnormal data of the historical time on the risk assessment of the current time; and determining the degree of anomaly of the laboratory between historical and current times based on the attenuation factor, the absolute value of the difference, the sensitivity factor, and the weighting coefficient.

[0012] In one embodiment, determining the laboratory's safety warning threshold at the current moment based on the anomaly level value and the data change amount of each of the sensors includes: calculating the average value of the data change amount of each of the sensors; determining a sensitivity amplification factor based on the anomaly level value; wherein the sensitivity amplification factor is negatively correlated with the anomaly level value; and determining the laboratory's safety warning threshold at the current moment based on the average value and the sensitivity amplification factor.

[0013] In one embodiment, the safety management of the laboratory based on the comprehensive risk index and the safety warning threshold includes: when the comprehensive risk index is greater than the safety warning threshold, determining at least one target safety measure from a preset safety measure database based on the comprehensive risk index, the safety warning threshold, and the sensor data; determining the target response intensity of the target safety measure based on the comprehensive risk index, the safety warning threshold, and the characteristic parameters of the target safety measure; and executing the target safety measure according to the target response intensity.

[0014] In one embodiment, determining the target response intensity of the target security measure based on the comprehensive risk index, the security warning threshold, and the characteristic parameters of the target security measure includes: determining the initial response intensity of the target security measure based on the comprehensive risk index and the characteristic parameters of the target security measure; and adjusting the initial response intensity based on the security warning threshold to obtain the target response intensity.

[0015] In one embodiment, the sensor data includes environmental perception data, equipment status data, and personnel behavior data; determining the comprehensive risk index of the laboratory based on the sensor data of the laboratory at the current moment includes: determining an environmental risk index based on the environmental perception data; determining an equipment risk index based on the equipment status data; determining a behavioral risk index based on the personnel behavior data; and determining the comprehensive risk index based on the environmental risk index, the equipment risk index, and the behavioral risk index.

[0016] Secondly, this application also provides a laboratory safety management device, using the laboratory safety management method provided in the first aspect of this application, including the following modules:

[0017] The first determining module is used to determine the comprehensive risk index of the laboratory based on the sensor data of the laboratory at the current moment;

[0018] The second determining module is used to determine the degree of anomaly of the laboratory between a historical time and the current time based on the comprehensive risk index and the configuration parameters of each sensor; wherein the historical time is earlier than the time before the current time;

[0019] The third determining module is used to determine the safety warning threshold of the laboratory at the current moment based on the anomaly degree value and the data change amount of each of the sensors; wherein, the data change amount is the change amount between the sensor data at the current moment and the sensor data at the previous moment;

[0020] The control module is used to manage the safety of the laboratory based on the comprehensive risk indicators and the safety warning threshold.

[0021] 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 laboratory safety management method provided in the first aspect of this application.

[0022] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the laboratory safety management method provided in the first aspect of this application.

[0023] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the laboratory safety management method provided in the first aspect of this application.

[0024] The aforementioned laboratory safety management method, apparatus, computer equipment, computer-readable storage medium, and computer program product determine the laboratory's comprehensive risk index based on sensor data at the current moment; determine the degree of anomaly between the laboratory and the current moment based on the comprehensive risk index and the configuration parameters of each sensor, wherein the historical moment is earlier than the moment before the current moment; determine the laboratory's safety warning threshold at the current moment based on the degree of anomaly and the data change of each sensor, wherein the data change is the change between the sensor data at the current moment and the sensor data at the previous moment; and conduct safety management of the laboratory based on the comprehensive risk index and the safety warning threshold. Therefore, in this embodiment, the laboratory's safety warning threshold is dynamically adjusted based on the comprehensive risk index, the configuration parameters of each sensor, and the data change of the sensors, achieving adaptive dynamic adjustment of the safety warning threshold. Compared to using a fixed warning threshold for laboratory safety management, this embodiment can improve the response sensitivity to laboratory safety hazards, reduce or avoid response delays or excessive sluggishness, and thus enable timely response to laboratory emergencies. Attached Figure Description

[0025] 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.

[0026] Figure 1 This is a diagram illustrating the application environment of a laboratory safety management method in one embodiment.

[0027] Figure 2 This is a flowchart illustrating a laboratory safety management method in one embodiment;

[0028] Figure 3 This is a flowchart illustrating step 202 in one embodiment;

[0029] Figure 4 This is a flowchart illustrating step 203 in one embodiment;

[0030] Figure 5 This is a flowchart illustrating step 204 in one embodiment;

[0031] Figure 6 This is a flowchart illustrating a laboratory safety management method in another embodiment;

[0032] Figure 7 This is a structural block diagram of a laboratory safety control device in one embodiment;

[0033] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0034] 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.

[0035] 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.

[0036] In related technologies, laboratory safety management mainly relies on manual inspections and basic sensor monitoring. However, this method suffers from slow response times, narrow coverage, and poor real-time performance, often failing to detect potential safety hazards in a timely manner and easily leading to accidents. With the increasing variety of laboratory equipment and the growing complexity of experimental operations, how to achieve comprehensive and accurate monitoring of the laboratory environment and equipment status, and improve the intelligence and automation level of safety management, has become an urgent problem to be solved.

[0037] The aforementioned technologies have at least the following technical problems: they use fixed warning thresholds and fail to automatically adjust safety response thresholds according to dynamic changes in the laboratory environment, resulting in delayed or excessively slow responses that cannot effectively address sudden safety incidents; the emergency response mechanism lacks flexibility and cannot flexibly select appropriate safety measures based on the actual situation, often resulting in inaccurate execution of response measures or errors in priority judgment.

[0038] To address these issues, this application proposes a laboratory safety management method to solve the problems in related technologies that use fixed early warning thresholds, which fail to automatically adjust safety response thresholds according to dynamic changes in the laboratory environment, resulting in delayed or excessively slow responses and an inability to effectively respond to sudden safety incidents; and the lack of flexibility in emergency response mechanisms, which cannot flexibly select appropriate safety measures according to actual conditions, often leading to inaccurate execution of response measures or errors in priority judgment.

[0039] The laboratory safety management method provided in this application embodiment can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or located on the cloud or other network servers. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, projection devices, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted devices, etc. Head-mounted devices can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. Server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0040] In one exemplary embodiment, such as Figure 2 As shown, a laboratory safety management method is provided, which is applied to... Figure 1 Taking the server in the example, the explanation includes the following steps 201 to 204. Wherein:

[0041] Step 201: Determine the laboratory's comprehensive risk indicators based on the sensor data at the current moment.

[0042] The comprehensive risk index characterizes the laboratory's safety risk level at a given moment, with a value between 0 and 1. A higher comprehensive risk index indicates a higher safety risk level, while a lower index indicates a lower safety risk level. The laboratory can be a smart laboratory. A smart laboratory is a laboratory equipped with multi-source sensors, data processing units, control units, and actuators, enabling real-time perception of all elements within the laboratory, including the environment, equipment, and personnel, and allowing for safety management based on the sensor data.

[0043] Optionally, sensor data includes multiple types of data, such as environmental perception data, equipment status data, and personnel behavior data. Environmental perception data characterizes the physical and chemical information of the laboratory environment, including temperature, humidity, and environmental noise data, and can be collected by environmental sensors deployed within the laboratory. Equipment status data characterizes the operating and status information of all or some key equipment within the laboratory, such as centrifuge speed and temperature data, and is collected by sensors built into the equipment or sensors set on the corresponding equipment. Personnel behavior data characterizes personnel activities within the laboratory and the interaction information between personnel and equipment, the environment, and hazards, such as personnel location and personnel operation logs, and is collected by behavioral sensors and wearable devices within the laboratory.

[0044] Since the current safety risk level of a laboratory is related to its current sensor data—for example, high temperatures, high concentrations of specific gases, or high smoke concentrations indicate a potential fire or explosion risk; high concentrations of toxic gases suggest a possible leak of toxic or hazardous substances—meaning the sensor data originates within the laboratory and may be physically or chemically related to a potential laboratory safety incident. Therefore, a comprehensive risk index can be determined based on the laboratory's current sensor data to obtain its current safety risk level.

[0045] For example, firstly, multiple sensors are deployed in the laboratory to monitor the experimental environment, equipment status, and personnel behavior in real time. These sensors may include temperature and humidity sensors, gas leak detectors, equipment status monitoring sensors, and personnel behavior monitoring equipment, ensuring comprehensive perception of various safety hazards in the laboratory. Sensor data is acquired from these sensors to obtain current data on temperature and humidity, gas leak levels, equipment status, and personnel behavior. Then, the data from these sensors is aggregated or fused to obtain a comprehensive risk index. Because there are many types of sensors, and their output data have different physical units and precisions, data aggregation requires standardized processing to obtain standardized sensor data. This allows sensor data from different sources to be effectively fused and compared on the same data platform. Finally, based on the importance of the risk dimensions corresponding to the standardized sensor data to the overall safety of the laboratory, weights are assigned to each standardized sensor data point, and the standardized sensor data are weighted and fused to obtain a comprehensive risk index characterizing the overall perception status of the laboratory at the current moment. This comprehensive risk index reflects the current safety status of the laboratory.

[0046] Step 202: Based on the comprehensive risk indicators and the configuration parameters of each sensor, determine the degree of anomaly of the laboratory between the historical time and the current time; wherein the historical time is earlier than the previous time of the current time.

[0047] The sensor configuration parameters refer to predefined parameters used to quantify the contribution weight of historical sensor data to the laboratory's current risk assessment. These parameters can be static or dynamically changing. Configuration parameters can be understood as benchmark parameters for anomaly judgment based on the corresponding sensor. Configuration parameters include at least one of the following sensor parameters: expected value at the current moment, weighting coefficient, sensitivity factor, spatiotemporal anomaly adjustment factor, and time standard deviation. The sensitivity factor characterizes the degree of anomalous fluctuation in sensor data, the spatiotemporal anomaly adjustment factor characterizes the rate at which the impact of historical anomalous data on the current risk assessment decays over time, and the time standard deviation characterizes the degree of fluctuation in sensor data over time.

[0048] The anomaly degree value represents the degree of anomaly within the laboratory between the current time t and the historical time s. The larger the anomaly degree value, the more abnormal the laboratory is, the greater the deviation between the current time and the historical normal state, and the higher the possibility of safety hazards in the laboratory. The smaller the anomaly degree value, the more normal the laboratory is, the smaller the deviation between the current time and the historical normal state, and the lower the possibility of safety hazards in the laboratory.

[0049] It should be noted that when there are no faults or anomalies, the internal state of the laboratory will not change abruptly, but will remain smooth. However, when there are faults or anomalies in the internal state of the laboratory, its state may change abruptly. The comparison benchmarks for smoothness and abrupt change mentioned above are both based on the normal state of the laboratory, comparing the deviation between the comprehensive risk index and the normal state. The normal state is not fixed, but changes dynamically with the experimental stage, time, environment, and sensor properties. Therefore, it is necessary not only to base it on the comprehensive risk index, but also to combine the sensor configuration parameters to determine the degree of anomaly between the laboratory at a historical time and the current time. In order to detect long-term, slow, or periodic anomalies, the historical time must be earlier than the previous time. The time between the historical time and the current time may be minutes, hours, or days.

[0050] For example, after fusing data from multiple sensors, it is necessary to monitor potential safety hazards in the laboratory. To more accurately identify potential safety risks, such as equipment failure, abnormal temperature, or gas leaks, a spatiotemporal anomaly measurement model is adopted. This model analyzes the fluctuations of the laboratory's comprehensive risk index over time and changes in equipment status, and combines this with the configuration parameters of each sensor to determine the degree of anomaly between historical and current times. This is used to identify potential risks arising from changes in the laboratory. The comprehensive risk index is a static risk score, the configuration parameters are a set of baseline parameters corresponding to each sensor, and the degree of anomaly represents the changes in the laboratory from historical to current times. For instance, if the comprehensive risk index indicates a low risk level, but based on this comprehensive risk index and the configuration parameters of each sensor, the degree of anomaly is determined to be an abnormal change in the laboratory's safety status over the three minutes from the 4th minute (historical time) to the 7th minute (current time), indicating a potential serious accident in the laboratory. Therefore, without the baseline provided by the configuration parameters, a low risk level comprehensive risk index might not trigger an alert. However, a high degree of anomaly calculated using the configuration parameters may trigger an alert, making the alert more timely and accurate. The comprehensive risk index obtained in step 201 reflects the level of safety risk in the laboratory at the current moment. Step 202 combines this level of safety risk with the configuration parameters of the sensors to detect how abnormal the rate of change in the laboratory is over a period of time, thereby enabling early and accurate warnings.

[0051] Step 203: Determine the laboratory's safety warning threshold at the current moment based on the anomaly level value and the data changes of each sensor.

[0052] The data change refers to the change between the sensor data at the current moment and the sensor data at the previous moment.

[0053] Based on natural laws, a higher anomaly level corresponds to a lower safety warning threshold, thus preventing missed alarms and improving response sensitivity; similarly, a greater change in sensor data corresponds to a higher safety warning threshold, thus preventing false alarms. However, in actual laboratory operation scenarios, anomaly levels and data changes may coexist under natural conditions. If the safety warning threshold is determined solely based on either anomaly level or data change, a balance cannot be achieved between preventing false alarms and missed alarms. Therefore, it is necessary to combine anomaly levels and data changes for collaborative calculation to determine the safety warning threshold that best matches the current risk situation.

[0054] It should be noted that, in order to better achieve laboratory control, a dynamic balance needs to be achieved between preventing false alarms (stability) and preventing missed alarms (sensitivity). This application embodiment adjusts this dynamic balance through anomaly severity values. The adjustment follows these natural laws: During laboratory operation, if the anomaly severity value is small, it indicates that the laboratory's safety status is stable and has not deteriorated rapidly, indicating that the overall risk is controllable. In this case, priority is given to preventing false alarms. Therefore, the safety warning threshold is mainly determined based on the amount of data change, and the safety warning threshold can be increased to maintain a high threshold. Conversely, if the anomaly severity value is large, it indicates that the laboratory's safety status is deteriorating rapidly and drastically, with an urgent risk situation. In this case, priority is given to preventing missed alarms. Therefore, the safety warning threshold is mainly determined based on the anomaly severity value. Even if the amount of data change indicates a change in laboratory data, this application embodiment will lower the safety warning threshold to maintain a low threshold, thereby ensuring timely warnings of potential hazards.

[0055] For example, after obtaining the anomaly level value, the safety warning threshold needs to be dynamically adjusted based on real-time changes in the laboratory to ensure sufficient sensitivity and timeliness in responding to potential safety risks. The safety warning threshold is dynamically adjusted according to changes in the laboratory, especially when abnormal data fluctuations occur. To this end, this embodiment introduces an adaptive threshold adjustment mechanism. By calculating the changes in data from each sensor in the laboratory, the dynamic changes of various physical quantities in the laboratory are reflected. Then, the safety warning threshold is adjusted in conjunction with the anomaly level value to ensure that the warning system can respond quickly. In addition, the safety warning threshold will automatically decrease as the magnitude of data changes and the anomaly level value increase, increasing the sensitivity to abnormal situations and avoiding missing potential safety hazards. Optionally, firstly, the sensor data of the previous moment is obtained from the historical data of each sensor, and the absolute value of the difference between the current sensor data and the previous sensor data is calculated to obtain the data change of the corresponding sensor. Then, the average value of the data change of each sensor is calculated to obtain a value characterizing the current overall environmental fluctuation level of the laboratory. Finally, the average value of the change and the anomaly degree value obtained in step 202 are processed or calculated according to preset rules to obtain the safety warning threshold of the laboratory at the current moment. This realizes the dynamic adjustment of the safety warning threshold based on the anomaly degree value and the rapid response to potential safety risks.

[0056] Step 204: Conduct safety management and control of the laboratory based on comprehensive risk indicators and safety early warning thresholds.

[0057] For example, the overall risk index and the safety warning threshold are compared. If the overall risk index does not exceed the safety warning threshold, there is no need to trigger the emergency response mechanism. If the overall risk index exceeds the safety warning threshold, the emergency response mechanism is triggered. The overall risk index and the safety warning threshold are combined to automatically issue a warning and activate appropriate target safety measures to reduce laboratory safety risks and ensure the safety and stability of the laboratory.

[0058] For example, consider a fire in a laboratory. Temperature sensor data shows the current temperature is 50 degrees Celsius, and flame sensor detects an open flame. Step 201 aggregates the sensor data, resulting in a high-risk comprehensive risk index (e.g., 0.7), indicating a high level of risk in the laboratory at that moment. Then, step 202, based on the comprehensive risk index and sensor configuration parameters, determines that the laboratory's anomaly level within three minutes (between historical and current times) is high (e.g., 0.9), indicating a significant deviation from the laboratory's historical normal state and a very high probability of safety hazards. Next, step 203 dynamically determines a safety warning threshold. If the temperature sensor data shows significant changes and the anomaly level is high (e.g., 0.9), the safety warning threshold can be dynamically adjusted to a lower value (e.g., 0.3). Finally, because the comprehensive risk index (e.g., 0.7) is higher than the safety warning threshold (e.g., 0.3), an alert is issued, and the emergency response mechanism is activated, implementing targeted safety measures such as cutting off the laboratory power and activating the ventilation system.

[0059] In the aforementioned laboratory safety management method, a comprehensive risk index for the laboratory is determined based on the sensor data at the current moment. Based on the comprehensive risk index and the configuration parameters of each sensor, the degree of anomaly between the laboratory at a historical moment and the current moment is determined; where the historical moment is earlier than the moment before the current moment. A safety warning threshold for the laboratory at the current moment is determined based on the degree of anomaly and the data change of each sensor; where the data change is the change between the sensor data at the current moment and the sensor data at the previous moment. Based on the comprehensive risk index and the safety warning threshold, safety management of the laboratory is implemented. Therefore, this embodiment dynamically adjusts the laboratory's safety warning threshold based on the comprehensive risk index, the configuration parameters of each sensor, and the data change of the sensors, achieving adaptive dynamic adjustment of the safety warning threshold. Compared to using a fixed warning threshold for laboratory safety management, this embodiment improves the response sensitivity to laboratory safety hazards, reduces or avoids response delays or excessive sluggishness, and thus enables timely response to laboratory emergencies.

[0060] In one exemplary embodiment, such as Figure 3As shown, step 202 includes steps 301 to 303. Wherein:

[0061] Step 301: Calculate the absolute values ​​between the comprehensive risk index and each expected value.

[0062] The expected value is a baseline value of the sensor obtained in advance based on the sensor's historical operating data, experimental verification, or model prediction.

[0063] Step 302: Determine the attenuation factor based on the spatiotemporal anomaly adjustment factor, the time standard deviation, and the time difference between the current moment and historical moments.

[0064] The attenuation factor characterizes the impact of historical anomalies on the risk assessment at the current time. Its value ranges from 0 to 1 and decreases as the time difference increases. The attenuation factor is a measure of the reduction in the impact of historical anomalies on the present; it quantifies the anomaly at historical time s and serves as a reference for calculating the degree of anomaly at the current time t.

[0065] For example, for each sensor, the attenuation factor is calculated based on the time difference, the spatiotemporal anomaly adjustment factor, and the time standard deviation, using a Gaussian attenuation function.

[0066] Step 303: Determine the degree of anomaly of the laboratory between historical time and current time based on the attenuation factor, absolute value of difference, sensitivity factor and weighting coefficient.

[0067] For example, after obtaining the absolute value of the difference between the comprehensive risk index and the expected value, and the attenuation factor, for each sensor, the p-th power of the absolute value of the difference is calculated and multiplied by the corresponding sensor's weight coefficient to obtain the first product value. The product of the first product value and the attenuation factor is then calculated to obtain the second product value. Therefore, the second product value corresponding to each sensor is calculated. Then, the sum of the second product values ​​corresponding to all sensors is calculated to obtain the anomaly degree value. Optionally, the formula for calculating the anomaly degree value is:

[0068]

[0069] in, The anomaly score represents the degree of anomaly in the laboratory environment between the current time t and the historical time s. The higher the value, the greater the deviation between the current moment and the historical normal state, indicating a higher possibility of potential safety hazards; n refers to the number of environmental sensors, and m refers to the number of non-environmental sensors. This represents the laboratory's comprehensive risk index at the current time t. Let be the expected value of the k-th sensor at the current time t, representing the normal state of the sensor data; It is the weight coefficient of the k-th sensor, which represents the relative importance of the k-th sensor in spatiotemporal anomaly detection. It is set based on historical experience and actual needs, and is usually greater than 0. It is the sensitivity factor for anomaly measurement, used to adjust the sensitivity to abnormal fluctuations in sensor data, and commonly has a value of 1 or 2. It is the attenuation factor of the impact of historical anomalies on the present. It is a Gaussian decay function used to measure how much reference value the anomaly at historical time s still has for the risk assessment at the current time t. This is the spatiotemporal anomaly adjustment factor for the k-th sensor, used to adjust the decay rate of the Gaussian decay function. It is set based on historical experience and actual needs, and is usually greater than 0. It is the time standard deviation of the k-th sensor itself in the time dimension, representing the time variability of the current sensor data, and its value is greater than 0.

[0070] Therefore, this embodiment incorporates the laboratory's comprehensive risk indicators into anomaly detection to capture potential dangerous changes in the laboratory (such as equipment failure, gas leaks, etc.), and uses a Gaussian decay function to decay the impact of historical anomalies over time, and uses weighting coefficients to reflect the spatial importance and reliability differences of each sensor. The combination of these three factors enables the anomaly degree value to reflect the severity of abnormal fluctuations in the laboratory's safety status in both time and space, ensuring that the safety warning threshold can be reliably calculated subsequently and used to trigger the corresponding safety response.

[0071] After obtaining the anomaly level value, step 203 is executed, which is to determine the laboratory's safety warning threshold at the current moment based on the anomaly level value and the data change of each sensor.

[0072] In one exemplary embodiment, such as Figure 4 As shown, step 203 includes steps 401 to 403. Wherein:

[0073] Step 401: Calculate the average value of the data changes of each sensor.

[0074] The average of all data changes represents the average drastic degree of data change across all sensors at the current moment. In actual laboratory operation, the average of the data changes is positively correlated with the safety warning threshold.

[0075] For example, for each sensor, the absolute value of the difference between the sensor data at the current moment and the sensor data at the previous moment is calculated, which is the amount of data change. The average value of all data changes is calculated to characterize the average drastic degree of change of each sensor.

[0076] Step 402: Determine the sensitivity amplification factor based on the anomaly degree value; wherein the sensitivity amplification factor is negatively correlated with the anomaly degree value.

[0077] For example, the sensitivity amplification factor is calculated using an attenuation function based on the sensitivity adjustment factor, dynamic adjustment coefficient, and anomaly level value: ,in, This is a sensitivity adjustment factor for adjusting the safety warning threshold. It is used to adjust the degree of influence of the anomaly level value on the safety warning threshold, and its value is a positive real number. The larger the value, the more significant the impact of the anomaly on threshold downsetting; It is a dynamic adjustment coefficient that controls the degree of influence of the anomaly level on the adjustment of the early warning threshold, and its value is a positive real number. It is a decay function; the more severe the detected historical anomalies ( The larger the value of the regulator, the closer the output value of the entire regulator is to 1 (i.e., the regulatory effect weakens), thus reducing the final safety warning threshold. The lower the level, the more sensitive and alert the system becomes.

[0078] Step 403: Based on the average value and sensitivity amplification factor, determine the laboratory's safety warning threshold at the current moment.

[0079] For example, the product between the average value and the sensitivity amplification factor is calculated, and the product is adjusted using an initial warning threshold adjustment factor to obtain the safety warning threshold at the current moment. The initial warning threshold adjustment factor is used to adjust the initial setting of the safety warning threshold and takes the value of a positive real number.

[0080] Optionally, the formula for calculating the safety warning threshold is:

[0081]

[0082] in, The current time t is the safety warning threshold, which is used to determine whether emergency response measures need to be triggered. If the laboratory's comprehensive risk index exceeds the safety warning threshold, the system will issue a warning. This is the initial warning threshold adjustment factor, which controls the initial setting of the warning threshold and takes the value of a positive real number. It is the average value of the data changes from all sensors, representing the average drastic degree of data change from all sensors at the current moment; The change in data of the k-th sensor between the current time and the previous time represents the degree of change in the laboratory environment. It is a sensitivity amplification factor obtained based on the historical degree of anomaly (i.e., the anomaly degree value). It is a decay function; the more severe the historical anomalies detected by the system (i.e., The larger the value of the damping function regulator, the closer the output value of the entire damping function regulator is to 1 (i.e., the regulation effect is weakened), thus reducing the final safety warning threshold. The lower the level, the more sensitive and alert the system becomes; The sensitivity adjustment factor for adjusting the safety warning threshold is used to adjust the degree of influence of the anomaly level value on the safety warning threshold. It is a dynamic adjustment coefficient that controls the degree of influence of the anomaly level value on the adjustment of the safety warning threshold.

[0083] Therefore, this embodiment automatically adjusts the safety warning threshold based on the sensitivity to changes in the current laboratory environment. By weighting the changes in sensor data and comprehensively considering the degree of anomalies in the spatiotemporal dimensions, it can automatically issue warnings based on drastic changes in the laboratory environment, thus identifying potential safety hazards in advance.

[0084] After obtaining the laboratory's real-time safety warning threshold, step 204 is executed, which involves conducting safety management of the laboratory based on comprehensive risk indicators and safety warning thresholds.

[0085] In one exemplary embodiment, such as Figure 5 As shown, step 204 includes steps 501 to 503. Wherein:

[0086] Step 501: If the comprehensive risk index is greater than the safety warning threshold, determine at least one target safety measure from the preset safety measure database based on the comprehensive risk index, the safety warning threshold, and sensor data.

[0087] Among them, target safety measures refer to the safety measures that are most suitable for the current safety status of the laboratory.

[0088] For example, if the comprehensive risk index exceeds the safety warning threshold, it indicates a safety hazard in the experiment, triggering the safety warning threshold and issuing a warning message to prompt personnel inside the laboratory to evacuate. Simultaneously, based on the current comprehensive risk index and the severity of the warning, a multi-stage emergency response mechanism is automatically activated. The first stage involves safety measure decision-making. Based on the real-time comprehensive risk index, safety warning threshold, and risk types reflected by sensor data (such as abnormal temperature, gas leaks, etc.), a predefined safety measure database is used for matching and reasoning. This database is an irregular knowledge base that stores the mapping relationship between different risk scenarios and safety measures, such as the mapping relationship between abnormal temperature and power cut-off. A decision tree matching model is used to output a list of safety measures to be activated. This list includes at least one target safety measure that should be activated (to be activated), such as power cut-off or activation of the fire suppression system, thus obtaining the safety measures most suitable for the current safety status of the laboratory.

[0089] Optionally, the list of security measures may include not only the target security measures, but also the activation priority of each target security measure, so that the target security measures can be executed according to the priority when they are executed later.

[0090] Step 502: Based on comprehensive risk indicators, safety warning thresholds, and characteristic parameters of target safety measures, determine the target response intensity of the target safety measures.

[0091] The characteristic parameters of the target safety measures are used to characterize the features of the target safety measures, such as performance, usage conditions, and control logic. These characteristic parameters can be stored in the safety measures database. For example, taking the exhaust system as an example, its performance parameters may include maximum air volume, and its usage conditions may include gas leakage. The safety measures database also includes the mapping relationship between the comprehensive risk index and the response intensity of the safety measures. For example, when the comprehensive risk index is low, the response intensity of the exhaust system is medium; when the comprehensive risk index is high, the response intensity of the exhaust system is high.

[0092] For example, after obtaining the target security measures through the first stage of the multi-stage emergency response mechanism, the process enters the second stage, which optimizes the implementation intensity through response strength synthesis. In the second stage, based on comprehensive risk indicators, security warning thresholds, and characteristic parameters of the target security measures, the initial response strength of the target security measures is first determined, and then the initial response strength is optimized to obtain the target response strength.

[0093] Step 503: Implement target safety measures based on the target response intensity.

[0094] In one example, the laboratory control system sends execution instructions to the corresponding actuators, such as those in the exhaust system, based on the target response strength for each target safety measure. These instructions include not only on / off commands but also response strength parameters; for instance, sending a command to the exhaust system's frequency converter to "start, target frequency is maximum frequency." Upon receiving the execution instructions, the actuators execute the target safety measure according to the target response strength, thus achieving the desired response to the target safety measure.

[0095] Example 2: For each target safety measure, the laboratory control system executes the target safety measure according to its target response intensity and priority, and sends execution instructions to the corresponding execution equipment, such as the relevant equipment of the exhaust system, to execute the target safety measure.

[0096] Therefore, in this embodiment, a two-stage process of decision-making followed by intensive adjustment is used to assess the laboratory safety status and select the most appropriate target safety measures (such as shutting off the power and activating fire extinguishers), while dynamically adjusting the intensity of their implementation to ensure that the response matches the urgency of the risk.

[0097] In an exemplary embodiment, step 502 includes: determining the initial response strength of the target security measures based on the comprehensive risk index and the characteristic parameters of the target security measures; and adjusting the initial response strength based on the security warning threshold to obtain the target response strength.

[0098] For example, after identifying at least one target security measure, the process first involves using a predefined mapping relationship, such as a control function, based on a comprehensive risk index and the characteristic parameters of the target security measure. The initial response strength of each target safety measure is determined. Then, a safety warning threshold is introduced to adjust each initial response strength to obtain the final target response strength. Optionally, the adjustment logic is as follows: an adjustment factor is calculated based on the safety warning threshold, and the product between the initial response strength and the adjustment factor is taken as the target response strength of the corresponding target safety measure. When the safety warning threshold is low, it indicates that the risk to the laboratory is imminent, and the adjustment factor is close to 1 to ensure accurate implementation of the target safety measures; when the safety warning threshold is high, it indicates that the risk to the laboratory is far away, and the adjustment factor is greater than 1, amplifying the initial response strength to allow for early response.

[0099] Optionally, the target response strength of each target security measure can be calculated using the following formula:

[0100]

[0101] in, It is the target response intensity of the r-th target security measure; It is a control function, whose input is the laboratory comprehensive risk index. Characteristic parameters of the r-th target security measure The output is the initial response strength of the r-th target security measure; It is a regulating factor, which can be understood as an urgency amplifier, based on how close the current danger is (i.e., the safety warning threshold). To fine-tune the response intensity, and It's an adjustment knob that controls how strong the urgency amplification effect is. It is the sensitivity adjustment coefficient, used to control the activation speed of the emergency response, and determines the maximum possible intensity of the urgency amplification effect. It is generally a positive real number. It is the sensitivity coefficient of the emergency response to the safety warning threshold, which determines the amplification effect as the warning threshold increases. The rate at which an increase occurs and then decreases is generally a positive real number.

[0102] Thus, in this embodiment, the initial response intensity of the target safety measure is first determined according to the comprehensive risk indicator and the characteristic parameters of the target safety measure, and then the initial response intensity is adjusted based on the safety warning threshold to obtain the target response intensity, which can ensure the accuracy of the target response intensity, make the execution of the target safety measure more reliable, and ensure the precise response to the safety hazards in the laboratory. In addition, through the above two-stage linkage mechanism, the system can not only intelligently select the most suitable target safety measure, but also dynamically adjust the response intensity according to the real-time evolution of the risk, thus achieving the precise matching of the target safety measure and the execution intensity, ensuring rapid, accurate and moderately graded emergency response, and maximizing the safety of the laboratory.

[0103] In an exemplary embodiment, the sensor data includes environmental perception data, equipment status data, and personnel behavior data. The environmental perception data can be used to determine whether the environment inside the laboratory is safe at the current moment, the equipment status data can be used to determine whether each device inside the laboratory is in a normal state, and the personnel behavior data can be used to determine the behavior of the personnel inside the laboratory, such as whether the experimental operations are standardized.

[0104] In this embodiment, step 201 includes: determining an environmental risk indicator according to the environmental perception data; determining an equipment risk indicator according to the equipment status data; determining a behavior risk indicator according to the personnel behavior data; and determining a comprehensive risk indicator according to the environmental risk indicator, the equipment risk indicator, and the behavior risk indicator.

[0105] Exemplarily, after standardizing and assigning weights to the environmental perception data, the equipment status data, and the personnel behavior data, calculate the weighted fusion value of each standardized environmental perception data (such as standardized temperature, humidity, gas concentration data, etc.) to obtain the environmental risk indicator, calculate the weighted fusion value of each standardized equipment status data to obtain the equipment risk indicator, and calculate the weighted fusion value of each standardized personnel behavior data to obtain the behavior risk indicator. Then, calculate the weighted fusion value of the environmental risk indicator, the equipment risk indicator, and the behavior risk indicator to obtain the comprehensive risk indicator. The calculation formula is:

[0106]

[0107] where is the comprehensive risk indicator, and the higher the value, the higher the overall safety risk of the laboratory; is the environmental risk indicator, which synthesizes all environmental factors of the laboratory; is the equipment risk indicator, which synthesizes the status of key equipment in the laboratory; is the behavior risk indicator, which synthesizes the monitoring data of personnel behavior; are the weight coefficients of the environmental risk indicator, the equipment risk indicator, and the behavior risk indicator respectively, satisfying Its size reflects the relative importance of the three major categories of risks—environment, equipment, and personnel—to the overall safety of the laboratory.

[0108] Therefore, this embodiment uses three major categories of risk indicators—environment, equipment, and personnel—to achieve full-element perception of laboratory risks, which can improve the accuracy and reliability of comprehensive risk indicators, thereby ensuring better management of the laboratory's safety status.

[0109] The laboratory control method of this application embodiment is described below through a specific example. For example... Figure 6 As shown, the following steps are used to achieve security management in a smart laboratory:

[0110] Step 601: Standardize the environmental perception data, equipment status data, and personnel behavior data of the laboratory at the current moment;

[0111] Step 602: Determine environmental risk indicators based on environmental perception data, determine equipment risk indicators based on equipment status data, and determine behavioral risk indicators based on personnel behavior data.

[0112] Step 603: Determine the comprehensive risk index based on environmental risk indicators, equipment risk indicators, and behavioral risk indicators;

[0113] Step 604: Calculate the absolute value of the difference between the comprehensive risk index and the expected value of each sensor;

[0114] Step 605: Determine the attenuation factor based on the spatiotemporal anomaly adjustment factor, the time standard deviation, and the time difference between the current moment and historical moments;

[0115] Step 606: Determine the degree of anomaly of the laboratory between historical time and current time based on the attenuation factor, absolute value of difference, sensitivity factor and weighting coefficient;

[0116] Step 607: Calculate the average value of the data changes from each sensor;

[0117] Step 608: Determine the sensitivity amplification factor based on the anomaly degree value; wherein, the sensitivity amplification factor is negatively correlated with the anomaly degree value;

[0118] Step 609: Based on the average value and sensitivity amplification factor, determine the laboratory's safety warning threshold at the current moment;

[0119] Step 610: If the comprehensive risk index is greater than the safety warning threshold, determine at least one target safety measure from the preset safety measure database based on the comprehensive risk index, the safety warning threshold and sensor data.

[0120] Step 611: Determine the initial response strength of the target security measures based on the comprehensive risk indicators and the characteristic parameters of the target security measures;

[0121] Step 612: Adjust the initial response intensity based on the safety warning threshold to obtain the target response intensity;

[0122] Step 613: Implement target safety measures based on the target response intensity.

[0123] In summary, the embodiments of this application have the following beneficial effects:

[0124] (1) By weighted fusion processing, sensor data from different types of sensors are standardized and weighted, eliminating the differences in physical units and precision of sensor data, enabling data from different sources to be effectively fused and compared on the same data platform, thereby providing a unified and accurate comprehensive risk indicator for the laboratory.

[0125] (2) Based on comprehensive risk indicators and sensor configuration parameters, the laboratory environment is dynamically detected to change in time and space, effectively capturing potential abnormal situations such as environmental fluctuations, equipment failures, and gas leaks; by measuring and comparing the differences between the current moment and historical data in real time, the risk changes in the laboratory are identified and timely warnings are issued;

[0126] (3) An adaptive threshold adjustment mechanism was introduced to dynamically adjust the safety warning threshold according to the changes in the laboratory environment (i.e., the degree of abnormality), thereby enhancing the system's response sensitivity to safety hazards; especially when the laboratory environment undergoes drastic changes, the warning threshold can be adjusted in real time to improve the accuracy and speed of the warning.

[0127] (4) Through the two-stage emergency response mechanism, the system can not only intelligently select the most suitable target safety measures, but also dynamically adjust the response intensity of the target safety measures according to the real-time evolution of the risk, thereby achieving a precise match between response priority and execution intensity, ensuring that the emergency response is rapid, accurate and appropriately graded, and maximizing laboratory safety.

[0128] 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.

[0129] Based on the same inventive concept, this application also provides a laboratory safety management device for implementing the laboratory safety management 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 laboratory safety management device embodiments provided below can be found in the limitations of the laboratory safety management method described above, and will not be repeated here.

[0130] In one exemplary embodiment, such as Figure 7 As shown, a laboratory safety control device is provided, comprising: a first determining module 701, a second determining module 702, a third determining module 703, and a control module 704, wherein:

[0131] The first determining module 701 is used to determine the comprehensive risk index of the laboratory based on the sensor data of the laboratory at the current moment;

[0132] The second determining module 702 is used to determine the degree of anomaly of the laboratory between a historical time and the current time based on the comprehensive risk index and the configuration parameters of each sensor; wherein the historical time is earlier than the time before the current time;

[0133] The third determining module 703 is used to determine the safety warning threshold of the laboratory at the current moment based on the anomaly degree value and the data change amount of each of the sensors; wherein, the data change amount is the change amount between the sensor data at the current moment and the sensor data at the previous moment;

[0134] The control module 704 is used to conduct safety control of the laboratory based on the comprehensive risk indicators and the safety warning threshold.

[0135] In one embodiment, the configuration parameters include at least one of the following parameters of the sensor: the expected value at the current moment, the weighting coefficient, the sensitivity factor, the spatiotemporal anomaly adjustment factor, and the time standard deviation; wherein the sensitivity factor is used to characterize the degree of abnormal fluctuation of the sensor data, the spatiotemporal anomaly adjustment factor is used to characterize the rate at which the impact of the sensor's historical abnormal data on the current risk assessment decays over time, and the time standard deviation is used to characterize the degree of fluctuation of the sensor data in the time dimension.

[0136] In one embodiment, the second determining module 702 is specifically used to: calculate the absolute value of the difference between the comprehensive risk index and each of the expected values; determine an attenuation factor based on the spatiotemporal anomaly adjustment factor, the time standard deviation, and the time difference between the current time and the historical time; wherein the attenuation factor is used to characterize the degree of influence of the abnormal data of the historical time on the risk assessment of the current time; and determine the degree of anomaly of the laboratory between the historical time and the current time based on the attenuation factor, the absolute value of the difference, the sensitivity factor, and the weighting coefficient.

[0137] In one embodiment, the third determining module 703 is specifically used to: calculate the average value of the data change of each of the sensors; determine the sensitivity amplification coefficient based on the anomaly degree value; wherein the sensitivity amplification coefficient is negatively correlated with the anomaly degree value; and determine the safety warning threshold of the laboratory at the current moment based on the average value and the sensitivity amplification coefficient.

[0138] In one embodiment, the control module 704 is specifically configured to: when the comprehensive risk index is greater than the safety warning threshold, determine at least one target safety measure from a preset safety measure database based on the comprehensive risk index, the safety warning threshold, and the sensor data; determine the target response intensity of the target safety measure based on the comprehensive risk index, the safety warning threshold, and the characteristic parameters of the target safety measure; and execute the target safety measure according to the target response intensity.

[0139] In one embodiment, when the control module 704 determines the target response strength of the target security measure based on the comprehensive risk index, the security warning threshold, and the characteristic parameters of the target security measure, it is specifically used to: determine the initial response strength of the target security measure based on the comprehensive risk index and the characteristic parameters of the target security measure; and adjust the initial response strength based on the security warning threshold to obtain the target response strength.

[0140] In one embodiment, the sensor data includes environmental perception data, equipment status data, and personnel behavior data; the first determining module 701 is specifically used to: determine environmental risk indicators based on the environmental perception data; determine equipment risk indicators based on the equipment status data; determine behavioral risk indicators based on the personnel behavior data; and determine the comprehensive risk indicator based on the environmental risk indicators, the equipment risk indicators, and the behavioral risk indicators.

[0141] Each module in the aforementioned laboratory safety control 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.

[0142] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As 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 operation of the operating system and computer programs stored in the non-volatile storage media. The database stores laboratory sensor data. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a laboratory safety management method.

[0143] Those skilled in the art will understand that Figure 8 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.

[0144] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a laboratory safety management method.

[0145] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a laboratory safety management method.

[0146] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements a laboratory safety management method.

[0147] It should be noted that the personnel 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.

[0148] 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.

[0149] 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.

[0150] 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 management method, characterized in that, The method includes: The comprehensive risk index of the laboratory is determined based on the sensor data of the laboratory at the current moment; Based on the comprehensive risk index and the configuration parameters of each sensor, the degree of anomaly of the laboratory between the historical time and the current time is determined; wherein the historical time is earlier than the time before the current time. The safety warning threshold for the laboratory at the current moment is determined based on the anomaly level value and the data change of each of the sensors; wherein, the data change is the change between the sensor data at the current moment and the sensor data at the previous moment; The laboratory is subject to safety management based on the comprehensive risk indicators and the safety warning thresholds.

2. The method according to claim 1, characterized in that, The configuration parameters include at least one of the following parameters of the sensor: the expected value at the current moment, the weighting coefficient, the sensitivity factor, the spatiotemporal anomaly adjustment factor, and the time standard deviation; wherein, the sensitivity factor is used to characterize the degree of abnormal fluctuation of the sensor data, the spatiotemporal anomaly adjustment factor is used to characterize the rate at which the impact of the sensor's historical abnormal data on the current risk assessment decays over time, and the time standard deviation is used to characterize the degree of fluctuation of the sensor data in the time dimension.

3. The method according to claim 2, characterized in that, The determination of the degree of anomaly of the laboratory between historical and current times, based on the comprehensive risk index and the configuration parameters of each sensor, includes: Calculate the absolute value of the difference between the comprehensive risk index and each of the expected values; A decay factor is determined based on the spatiotemporal anomaly adjustment factor, the time standard deviation, and the time difference between the current moment and the historical moment; wherein, the decay factor is used to characterize the degree of influence of the abnormal data of the historical moment on the risk assessment of the current moment; The degree of anomaly of the laboratory between historical and current times is determined based on the attenuation factor, the absolute value of the difference, the sensitivity factor, and the weighting coefficient.

4. The method according to claim 1, characterized in that, Determining the laboratory's safety warning threshold at the current moment based on the anomaly level value and the data changes of each sensor includes: Calculate the average value of the data changes from each of the aforementioned sensors; The sensitivity amplification factor is determined based on the anomaly level value; wherein the sensitivity amplification factor is negatively correlated with the anomaly level value; Based on the average value and the sensitivity amplification factor, the safety warning threshold of the laboratory at the current moment is determined.

5. The method according to any one of claims 1-4, characterized in that, The safety management of the laboratory based on the comprehensive risk indicators and the safety warning threshold includes: If the comprehensive risk index is greater than the safety warning threshold, at least one target safety measure is determined from a preset safety measure database based on the comprehensive risk index, the safety warning threshold, and the sensor data. Based on the comprehensive risk index, the security early warning threshold, and the characteristic parameters of the target security measures, the target response intensity of the target security measures is determined; The target security measures are executed based on the target response strength.

6. The method according to claim 5, characterized in that, The determination of the target response intensity of the target security measure based on the comprehensive risk index, the security early warning threshold, and the characteristic parameters of the target security measure includes: Based on the comprehensive risk index and the characteristic parameters of the target security measures, the initial response intensity of the target security measures is determined; The initial response intensity is adjusted based on the security warning threshold to obtain the target response intensity.

7. The method according to any one of claims 1-4, characterized in that, The sensor data includes environmental perception data, equipment status data, and personnel behavior data; The determination of the laboratory's comprehensive risk index based on the laboratory's current sensor data includes: Environmental risk indicators are determined based on the environmental perception data. Determine equipment risk indicators based on the equipment status data; Determine behavioral risk indicators based on the aforementioned personnel behavior data; The comprehensive risk index is determined based on the environmental risk index, the equipment risk index, and the behavioral risk index.

8. A laboratory safety control device, using the laboratory safety control method as described in any one of claims 1-7, characterized in that, Includes the following modules: The first determining module is used to determine the comprehensive risk index of the laboratory based on the sensor data of the laboratory at the current moment; The second determining module is used to determine the degree of anomaly of the laboratory between a historical time and the current time based on the comprehensive risk index and the configuration parameters of each sensor; wherein the historical time is earlier than the time before the current time; The third determining module is used to determine the safety warning threshold of the laboratory at the current moment based on the anomaly degree value and the data change amount of each of the sensors; wherein, the data change amount is the change amount between the sensor data at the current moment and the sensor data at the previous moment; The control module is used to manage the safety of the laboratory based on the comprehensive risk indicators and the safety warning threshold.

9. 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 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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