Laboratory safety intelligent monitoring and early warning method and system

Abstract

The invention relates to the technical field of safety management and risk assessment, and discloses a laboratory safety intelligent monitoring and early warning method and system, and the method comprises the following steps: S1, building a three-dimensional digital model of a laboratory; s2, deploying a sensor network covering a laboratory; s3, monitoring an operation behavior in real time through a sensor network, and comparing the operation behavior with the operation logic chain; s4, when the sensor network detects that the physical environment is abnormal; s5, collaborating the logic abnormal state, the position and strength of the risk source and the risk type; and S6, performing evolution prediction based on the risk type and the risk source. According to the method, Bayesian fusion reasoning is performed on context information such as multi-dimensional sensor evidence and real-time operation regulations, so that high-precision and high-confidence identification of risk types is realized, and the problem that the risk types cannot be identified due to lack of cognition on field operation activities is solved. And the risk qualitative is fuzzy, the false alarm rate is high, and the real dangerous case and compliance operation interference cannot be distinguished.

Description

Technical Field

[0001] This invention relates to the field of safety management and risk assessment technology, and in particular to a method and system for intelligent monitoring and early warning of laboratory safety. Background Technology

[0002] In current industrial production and laboratory operating environments, technologies for safety management and risk assessment are gradually developing. However, existing technological systems still have many shortcomings, affecting overall safety and compliance.

[0003] Most existing safety management systems still rely on single sensors for data monitoring. A major drawback of this approach is its inability to fully utilize multi-dimensional evidence for comprehensive judgment. For example, alarm thresholds for single sensors often lead to frequent false alarms. Due to a lack of comprehensive understanding of on-site operations, traditional methods cannot clearly distinguish between safe operations and potential risks. Ultimately, this ambiguity increases the potential threats to the work and introduces risks into safety management.

[0004] Many security audit mechanisms still rely on manual supervision or post-incident review, resulting in insufficient response efficiency. Operator compliance reviews are often not conducted in real time. For example, violations may not be discovered until some time after they occur, meaning that the best opportunity to prevent potential incidents may be missed at critical moments. This lack of real-time monitoring in management directly leads to an increasing frequency of security incidents caused by procedural errors.

[0005] Current safety systems are generally reactive. Many technologies are unable to effectively assess latent risks or identify operations that, while below alarm thresholds, still pose a risk. Meanwhile, complex interactions may arise between different operations due to potential hazards, the safety of which is often overlooked. This lack of awareness of potential dangers makes the development of preventative measures virtually impossible, posing significant risks. Summary of the Invention

[0006] The purpose of this invention is to provide a laboratory safety intelligent monitoring and early warning method and system, which solves the problems of insufficient risk identification, compliance auditing and latent hazard monitoring in complex working environments in the prior art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a laboratory safety intelligent monitoring and early warning method, comprising the following steps: S1. Establish a three-dimensional digital model of the laboratory and construct a risk event feature library that predefines various risk events and their triggering conditions; S2. Deploy a sensor network covering the laboratory and logicalize the laboratory's standard operating procedures to form an operational logic chain that can be matched in real time. S3. Monitor operational behavior in real time through sensor network and compare it with the operation logic chain to determine the current compliance operation status or logic abnormality status of the laboratory. S4. When the sensor network detects an anomaly in the physical environment, perform risk field inversion to determine the location and intensity of the risk source, and perform risk type identification to determine the risk type of the physical environment anomaly. S5. Identify latent risk fields by identifying abnormal states of collaborative logic, the location and intensity of risk sources, and the type of risk, and proactively constrain operational procedures that may interact dangerously with the latent risk fields in the future. S6. Based on the evolution prediction of the risk type and the risk source, generate structured early warning information containing precise handling suggestions.

[0008] Preferably, in step S1, constructing the risk event feature library involves associating a risk event with a trigger condition vector consisting of trigger conditions. The trigger condition vector is defined by an event feature signature consisting of readings from a single sensor or a combination of readings from multiple different types of sensors.

[0009] Furthermore, a concrete and computable digital profile is established for each abstract risk event. Traditional threshold alarms can only respond to changes in a single-dimensional physical quantity, while this invention, by constructing an "event feature signature," can capture the multi-dimensional, synchronously changing composite features when a risk occurs. This methodology enables the system to distinguish real risk signals from complex background noise, greatly enhancing the robustness and accuracy of identification. The triggering logic of this event feature signature can be formally described as a weighted confidence function: In the formula, C(R) k ) indicates the specific risk event R k The confidence level assessment value of the occurrence, R k This represents the k-th predefined risk event, where k represents the unique identifier index of the risk event, i represents the sensor index in the event feature signature, ranging from 1 to n, where n represents the total number of sensors included in the specific event feature signature, and w i This indicates that the i-th sensor is assessing risk event R. k Weight of time, Let f(·,·) represent an indicator function, and d represent a feature judgment function. i Δd represents the real-time measurement reading of the i-th sensor. i This indicates the trend of the reading change of the i-th sensor.

[0010] Preferably, the establishment of the event feature signature specifically includes the following steps; For a specific risk event, identify a group of multiple types of sensors that are associated with the occurrence of the risk event from the perspective of human factors engineering or experimental mechanism; For each type of sensor in this group, define the characteristic readings that it presents when the risk event occurs. The characteristic readings include a range of values ​​or a trend of value change. Establish logical combination relationships between the characteristic readings of multiple sensors in the group to form an event feature signature that can uniquely identify a specific risk event.

[0011] Furthermore, by introducing ergonomics or experimental mechanisms as prior knowledge, it is ensured that the selected sensor combination has a strong causal relationship with the target risk event at the physical or chemical level, fundamentally guaranteeing the validity of the signature. The process of establishing this logical combination relationship is essentially about defining a specific risk event R within a multi-dimensional feature space. k Find a decision boundary. This decision boundary can be defined by a discriminant function g(x); g(x) = w T x+b: In the formula, g(x) represents the judgment function, x represents the feature vector of the sensor combination, and w represents the weight vector. T represents the transpose of the weight vector w, and b represents the bias term.

[0012] Preferably, in step S2, the standard operating procedure of the laboratory is logically decomposed into an ordered sequence of multiple event nodes. Each event node defines the preconditions that must be met to perform the operation of that node, as well as the operation action and operation object of that node.

[0013] Furthermore, static, unstructured text-based Standard Operating Procedures (SOPs) are transformed into dynamic, machine-readable operational logic chains. This transformation is not simply digitization, but a deep logical deconstruction and reorganization, translating human operational guidelines into a state machine model that computers can understand and execute. Each event node is a state of the state machine, containing all the elements of state transition (preconditions, actions, and objects), providing a structured foundation for subsequent real-time, automated compliance auditing of operational behaviors.

[0014] Preferably, the determination of the current compliant operation status or logical abnormal status of the laboratory in step S3 specifically includes the following steps: Receive an event characterizing real-time operational behavior from the sensor network; The event is matched and verified with the next expected event node in all currently active operation logic chains; when the event matches the next expected event node in any operation logic chain and meets the preconditions bound to that node, the state of that operation logic chain is advanced and determined to be a compliant operation state. An event is considered to be in a logically abnormal state if it does not match the next expected event node in any operation logic chain, or if the occurrence of the event violates the preset preconditions of its matching node.

[0015] Furthermore, a closed-loop real-time monitoring cycle of expectation-observation-comparison is implemented. Instead of passively receiving data, it proactively anticipates the next step based on the activated operational logic chain. When the sensor network observes an actual operational behavior, it immediately performs a comparison. This mechanism enables the system to instantly capture any deviation from the preset script, whether it's an omitted step, an incorrect sequence, or an operation performed at the wrong time or place. This deviation is defined as a logical anomaly state, a key signal triggering subsequent in-depth risk analysis. Its determination process can be described using a state transition function: In the formula, S t+1 Let F(·) represent the state of the system at time t+1, and let S represent the state transition function. t E represents the state of the system at the current time t. t This represents the actual event received by the sensing network at time t, and Validate(·) represents a validation function, E i,j+1 This represents the next predicted event node for the i-th operation node, (SOP) i,j+1 ) indicates that the system has entered the next standard state, Anomaly indicates that the system has entered an abnormal state, true indicates that the verification has passed, and false indicates that the verification has failed.

[0016] Preferably, step S4 involves risk type identification to determine the risk type of the physical environment anomaly, specifically including the following steps: The readings of sensors that trigger physical environmental anomalies and have a clear chemical orientation are matched with single sensor reading conditions in the risk event feature library to directly identify the risk type. If direct identification fails, the readings of multiple sensors that detect physical environment anomalies will be combined and matched with the preset event feature signatures in the risk event feature database to identify the risk type. If the signature match fails, context inference is performed to ultimately determine the risk type by querying the operational logic chain or digital bill of materials associated with the risk source location.

[0017] Furthermore, the system prioritizes the use of the most certain information (direct identification), gradually escalating to more complex pattern matching (feature signature matching) when information is insufficient, and finally introducing environmental information for logical reasoning (contextual inference) when information is ambiguous. Particularly in the contextual inference stage, the system correlates physical space risks with logical space operational behaviors, which is a concentrated embodiment of the "physical-logical coupling" concept of this invention. This inference process can be modeled as a Bayesian inference problem to find the most likely risk type in the presence of uncertainty. P(R k |E,C)∝P(E|P(R k |E,C))∝P(E|R k )·P(R k |C): In the formula, P(R) k |E,C) represents the posterior probability, that is, the probability that the risky event R will occur given the observation of sensor evidence E and contextual information C. k The probability, ∝, indicates that it is proportional to, P(E|R k ) represents the likelihood probability, i.e., the probability of occurrence of risk event R. k Under the conditions that occur, the probability of observing sensor evidence E, P(R k |C) represents the prior probability, that is, given the contextual information C, the probability of risk event R. k The probability of it occurring, R k Let E represent the k-th possible risk event, E represent observational evidence from sensors that may be vague or ambiguous, and C represent contextual information associated with the location of the risk source.

[0018] Preferably, the identification of latent risk fields in step S5 is triggered when it is determined that there is a risk source with a source intensity lower than a preset alarm threshold and the risk source cannot be explained by any compliant operation status, and the latent risk field is marked with its spatial location and the identified risk type.

[0019] Furthermore, the determination of a latent physical anomaly is based on the logical constraint that it cannot be explained by any compliant operational status, thus confirming its unexpectedness. This means that a weak signal, when it should not appear in the current operational process, will be identified by the system as a latent risk requiring attention, thereby greatly improving the system's security sensitivity.

[0020] Preferably, the active operation constraint in step S5 specifically includes the following steps: Upon receiving a request to initiate a new operating procedure, extract the planned operation action and spatial location of the operating procedure; determine whether the planned execution spatial location overlaps with the spatial location of a marked latent risk field; If an overlap occurs, it is further determined whether the operation actions of the operating procedure will have a dangerous interaction with the risk type of the latent risk field; If a dangerous interaction is detected, the execution of that operation procedure will be prevented.

[0021] Furthermore, the future behavior (operational actions, spatial location) of the process is pre-enacted and collision detected against the currently known physical environment (spatial location of potential risk fields, risk types). This "pre-execution" risk assessment mechanism enables the system to prevent operations that are fully compliant under normal circumstances but would cause disasters under specific potential risk contexts. The judgment logic of this dangerous interaction can be abstracted into a dangerous interaction judgment function; In the formula, Constraint represents the final constrained decision result. The function indicates the overlap; Overlap(·,·) indicates the spatial overlap judgment function; L O L indicates the spatial location where the new operating procedure is scheduled to be executed. P H(·,·) represents the spatial location of the marked latent risk field, and H(·,·) represents the hazard interaction judgment function. O Indicates the operational actions included in the new operating procedure, Type P This indicates the risk type of the identified latent risk field.

[0022] Preferably, the generation of structured early warning information containing precise handling suggestions in step S6 specifically includes the following steps: Based on the identified risk types, retrieve and extract precise handling suggestions corresponding to the risk type from the risk event feature database; The evolution prediction of risk sources is visualized to generate a risk field evolution prediction map; The precise handling suggestions, risk field evolution prediction map, location and intensity of risk sources, and cause analysis based on logical anomalies are combined into a single data structure for structured early warning information.

[0023] Furthermore, all outputs (qualitative risk assessment, location, quantitative risk analysis, causal analysis, and trend prediction) were organically integrated with pre-stored expert knowledge (precise response recommendations). This structured information minimizes the information acquisition and decision-making burden on emergency personnel under high-stress conditions, and is a key guarantee for ensuring that intelligent analysis results can be effectively translated into correct emergency actions.

[0024] A laboratory safety intelligent monitoring and early warning system includes; A 3D modeling module is used to create a 3D digital model of the laboratory and build a risk event feature library. The sensing and logic module is used to manage the sensor network and logicize standard operating procedures to form an operational logic chain; the real-time analysis module, which is connected to the 3D modeling module and the sensing and logic module, is used to perform compliance judgment of operational behavior, risk field inversion and risk type identification. The decision and constraint module, which is connected to the real-time analysis module, is used to identify latent risk fields and execute proactive operational constraints. The early warning generation module, which is connected to the decision and constraint module, is used to generate structured early warning information.

[0025] In summary, the present invention has at least one of the following beneficial technical effects: 1. This invention achieves high-precision, high-confidence identification of risk types by performing Bayesian fusion reasoning with multi-dimensional sensor evidence and contextual information such as real-time operating procedures. Compared with existing technologies that rely solely on a single sensor threshold for alarms, this invention solves the problems of vague risk characterization, high false alarm rates, and inability to distinguish between genuine hazards and compliant operational interference caused by a lack of understanding of on-site operational activities.

[0026] 2. This invention constructs an operational logic chain of standard operating procedures and compares it with a sensor network in real time, enabling immediate and automated compliance auditing of experimental personnel's operational behavior. Compared to existing technologies that rely on manual supervision or post-event traceability, this invention solves the shortcomings of these technologies, such as the inability to detect and stop violations in a timely manner and the difficulty in preventing safety accidents caused by program errors.

[0027] 3. This invention, by introducing latent risk field identification and proactive operational constraint mechanisms, can proactively prevent future compliant operations from interacting fatally with current potential hazards. Compared to existing security systems that are entirely reactive and unable to assess the safety of an impending operation in the current specific environment, this invention has a significant weakness in preventing safety incidents caused by the superposition of latent hazards and compliant operations.

[0028] 4. By integrating risk type, precise location, evolution prediction, and emergency response procedures during the early warning phase, a structured, multi-dimensional early warning information package is generated and pushed out, greatly improving the usability and decision support value of the early warning information. Compared with existing early warning methods that only provide audio-visual signals or single alarm text, this overcomes the shortcomings of their single information dimension and insufficient content, and significantly reduces emergency decision-making time. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the physical-logical coupling model of the present invention; Figure 3 This is a schematic diagram of the risk identification sequential reasoning strategy of the present invention; Figure 4 This is a schematic diagram of the latent risk field and active constraint mechanism of the present invention; Figure 5 This is a schematic diagram illustrating the structured early warning information generation and push of the present invention; Figure 6 This is a schematic diagram of the system framework of the present invention. Detailed Implementation

[0030] The following is in conjunction with the appendix Figure 1 - Appendix Figure 5 The present invention will be further described in detail below.

[0031] This invention provides a laboratory safety intelligent monitoring and early warning method, comprising the following steps: S1. Establish a three-dimensional digital model of the laboratory and construct a risk event feature library that predefines various risk events and their triggering conditions; Specifically, digital modeling of the target laboratory environment is performed to create a high-fidelity three-dimensional digital model.

[0032] The creation of the 3D digital model can be accomplished through methods including, but not limited to, laser scanning, photogrammetry, or direct import of existing Building Information Modeling (BIM) data. This model accurately reproduces the internal spatial layout of the laboratory in geometric dimensions. For example, the model includes static building structures such as walls, floors, doors, and windows, and its spatial position and dimensions in a unified coordinate system are calibrated with centimeter-level precision.

[0033] Furthermore, the 3D digital model also includes digitized objects of key equipment and facilities within the laboratory. These objects include lab benches, fume hoods, medicine cabinets, gas cylinder storage areas, eyewash stations, emergency sprinkler systems, and fire-fighting equipment. Each object is assigned precise 3D spatial coordinates and geometric attributes, providing the necessary data foundation for subsequent spatial location of risk sources and visualization of the risk field evolution process.

[0034] While establishing a three-dimensional digital model, this method also constructs a risk event feature library. This library, as a structured knowledge system, is used to predefine various risk events that may occur in the laboratory and their corresponding triggering conditions, providing core judgment criteria for the system's risk identification function.

[0035] According to one embodiment of the present invention, the core of constructing a risk event feature database lies in associating each preset risk event with a trigger condition vector consisting of triggering conditions. This vector is the direct basis for the system to identify and confirm the occurrence of a specific risk event.

[0036] The trigger condition vector can be composed of readings from a single sensor or an event feature signature consisting of a combination of readings from multiple different types of sensors. This design can address the identification needs of various risk events, from simple to complex.

[0037] Risk events that can be clearly characterized by a single physical quantity can have their triggering conditions directly defined as the reading conditions of a single sensor. For example, for a "carbon monoxide leak" event, its triggering condition vector can be defined as "the average reading of the carbon monoxide sensor associated with a specific area exceeds 50 ppm within a preset time window".

[0038] For more complex risk events that occur concurrently with multiple physical phenomena, this invention preferably uses event signatures for definition. The process of establishing the event signature specifically includes the following steps: For specific risk events, a group of multi-type sensors with a strong causal relationship to the occurrence of the risk event is identified based on human factors engineering or experimental mechanisms. This step ensures the scientific validity and reliability of the selected sensor combination. Taking an initial leak risk event of a flammable solvent (such as acetone) as an example, based on experimental mechanisms (liquid evaporation absorbing heat leading to localized cooling) and human factors engineering (human avoidance behavior upon noticing anomalies), a group of associated sensors can be identified. This group may include: volatile organic compound (VOC) sensors, microenvironmental temperature sensors, and machine vision sensors deployed above the operating area.

[0039] For each sensor in this group, define the characteristic reading it should exhibit when the aforementioned risk event occurs. This characteristic reading is not limited to a static numerical range but can also be a dynamic trend of numerical change. In the acetone leakage example above: For VOC sensors, a characteristic reading can be defined as a value that jumps from a background value (below 5 ppm) to above 50 ppm within 5 seconds.

[0040] For a temperature sensor, its characteristic reading can be defined as a rapid decreasing trend of more than 0.5°C within the synchronous time window of the VOC reading jump.

[0041] For machine vision sensors, feature readings can be defined as the action patterns detected by built-in behavior recognition algorithms, such as people rapidly retreating or waving to disperse non-typical experimental operations.

[0042] A logical combination relationship is established between the characteristic readings of multiple sensors in the group to form an event feature signature that uniquely identifies the specific risk event. This logical combination relationship is typically expressed as an AND relationship within a preset time window, meaning that the characteristic reading conditions of all sensors must be met.

[0043] To provide a quantitative and computable description of this logical combination relationship, one specific implementation of the present invention introduces a weighted confidence function to evaluate the matching degree of the event feature signature. This function can be expressed as: In the formula, C(R) k ) indicates the specific risk event R k The confidence level assessment value of the occurrence, R k This represents the k-th predefined risk event, where k represents the unique identifier index of the risk event, i represents the sensor index in the event feature signature, ranging from 1 to n, where n represents the total number of sensors included in the specific event feature signature, and w i This indicates that the i-th sensor is assessing risk event R. k Weight of time, Let f(·,·) represent an indicator function, and d represent a feature judgment function. i Δd represents the real-time measurement reading of the i-th sensor. i This indicates the trend of the reading change of the i-th sensor.

[0044] If and only if the calculated overall confidence level C(R) is... k Only when the threshold value exceeds a preset threshold will the system finally confirm that the event signature has been successfully matched, thus confirming that the corresponding risk event has occurred.

[0045] Furthermore, in a machine learning-based implementation, the event feature signature matching process can be modeled as a classification problem. This process is achieved by finding a decision boundary in a multidimensional feature space consisting of multiple sensor feature readings. This decision boundary can be defined by a discriminant function of the following form: g(x) = w T x+b; In the formula, g(x) represents the judgment function, x represents the feature vector of the sensor combination, and w represents the weight vector. T represents the transpose of the weight vector w, and b represents the bias term.

[0046] The weight vector w and bias term b can be obtained through supervised learning by analyzing historical security event data, or set based on the knowledge of domain experts.

[0047] S2. Deploy a sensor network covering the laboratory and logicalize the laboratory's standard operating procedures to form an operational logic chain that can be matched in real time. Specifically, the first step is to deploy a sensor network covering the entire laboratory area, possessing multi-layered sensing capabilities. This sensor network, as a physical sensing system, continuously collects key data characterizing the laboratory's internal physical environment, equipment status, and the dynamics of personnel and objects.

[0048] In one specific implementation, the sensor network exemplarily includes: Environmental sensors used to monitor environmental parameters include, for example, volatile organic compound (VOC) sensors and electrochemical gas sensors for detecting specific chemical vapor concentrations, as well as temperature and humidity sensors and pressure sensors for monitoring changes in the microenvironment.

[0049] Equipment status sensors used to obtain the operating status of equipment, such as wind speed sensors installed in the exhaust duct of a fume hood to monitor its operating status, or smart meters or current transformers connected to the power line of heating equipment to monitor its start-up and shutdown status.

[0050] Personnel and object tracking sensors used to track the movement of personnel and critical materials, such as ultra-wideband (UWB) positioning tags and their associated base stations worn by laboratory personnel, or radio frequency identification (RFID) tags and their corresponding readers affixed to critical materials such as reagent bottles, as well as machine vision sensors used for behavior recognition and target tracking.

[0051] Following the deployment of the sensor network, another core task in this step is to logicalize the laboratory's Standard Operating Procedures (SOPs) to form an operational logic chain that the system can match in real time. This process transforms the originally unstructured text procedures into a machine-readable and executable structured sequence of states.

[0052] The logical process involves breaking down each standard operating procedure into an ordered sequence of multiple event nodes. This sequence precisely defines the steps required to perform a complete experimental task.

[0053] Each event node in the sequence is structurally defined with three core elements necessary to execute the operation of that node: the preconditions that the node must meet, the operation action contained in the node, and the operation object of the node.

[0054] Preconditions define the conditions that must be met before starting the operation of the current node. For example, the operator must wear protective gloves or the air velocity inside the fume hood must reach 0.5 m / s or higher.

[0055] The operation action defines the specific behavior that the current node needs to perform. For example, turning on the centrifuge or taking out a reagent from the medicine cabinet.

[0056] The object of operation defines the specific entity to which the operation is performed. For example, a centrifuge or a hydrochloric acid reagent bottle with a specific number.

[0057] To illustrate this process more clearly, let's take a standard operating procedure for "removing acetone solvent from a fume hood" as an example. The logical implementation process is as follows: This SOP can be decomposed into an operational logic chain, which includes, but is not limited to, event nodes arranged in the following order: Event Node 1: Operation Action: Start Ventilation.

[0058] Object to be operated: Fume hood.

[0059] Prerequisite: The real-time location of the operator initiating this operating procedure must be within the operating range of the fume hood.

[0060] Event Node 2: Procedure: Wear personal protective equipment.

[0061] Items to be handled: Chemical-resistant gloves and goggles.

[0062] Prerequisite: Event node 1 has been successfully completed.

[0063] Event Node 3: Operation: Take the reagent from the medicine cabinet.

[0064] Object to be handled: Acetone reagent bottle.

[0065] Prerequisites: Event node 2 has been successfully completed, and the cabinet door for storing acetone is unlocked.

[0066] In this way, the static text procedure is transformed into a dynamic, state-transition-based logical model.

[0067] This involves establishing data-level associations and bindings between physical sensors in a sensor network and logical elements in the operational logic chain. This binding relationship establishes a direct mapping between real-time perception of the physical world and behavioral rules in the logical world.

[0068] Specifically, association binding involves associating an action, object, or precondition defined in an event node with the readings or changes of a specific sensor or set of sensors. In the example above: The activation of ventilation in event node 1 is bound to the wind speed sensor in the fume hood duct, and its completion is marked by the sensor reading changing from below the safety threshold to above the safety threshold.

[0069] The operator's location prerequisite for event node 1 is bound to the UWB positioning tag worn by the operator, and is verified by determining whether the real-time coordinates of the tag are within the preset geofence.

[0070] The acetone reagent bottle operation object of event node 3 is bound to the unique identification code of the RFID tag affixed to the reagent bottle.

[0071] By implementing step S2, the present invention can seamlessly connect the physical reality of the laboratory with the logical requirements of the operating procedures, forming a data-driven, automatically verifiable closed-loop foundation.

[0072] S3. Monitor operational behavior in real time through sensor network and compare it with the operation logic chain to determine the current compliance operation status or logic abnormality status of the laboratory. Specifically, in step S3 of this embodiment, the sensor network deployed in step S2 continuously receives one or a series of events characterizing real-time operational behavior. These events are structured representations of the raw sensor data. For example, when the data stream from the fume hood's wind speed sensor shows a wind speed value changing in a predetermined pattern, it is parsed into a structured event indicating that ventilation has been turned on, carrying a timestamp and device ID.

[0073] The received real-time events are matched and verified against the next expected event node in all currently active operation logic chains. Here, the next expected event node refers to the node immediately following the current state of the operation logic chain.

[0074] The matching verification is a dual verification process. First, it verifies whether the core attributes of the received event, such as its type and the target of the operation, match the definitions of the expected event node. Second, it verifies whether all the preconditions bound to the expected event node are met when the event occurs. These preconditions might require that there are no other people in a specific area, or that the ambient temperature is below a certain threshold. Based on the matching and verification results, a determination is made regarding the current state. When the received event completely matches the next expected event node in any active operation logic chain, and all preconditions bound to that node are met, the current state is determined to be compliant. At this point, the state of the operation logic chain is advanced one node, and the system begins waiting for the next new event in the sequence.

[0075] When a received event does not match the next expected event node in any active operation logic chain, such as when the operation sequence is incorrect; or, even if the event type matches, but it occurs in violation of the node's preset preconditions, such as when the operation was performed at the wrong time or place, the system determines that the current state is a logical exception.

[0076] To formally describe this dynamic determination and state update process, a specific implementation of this invention introduces a state transition function for modeling. This function precisely defines how the system state evolves based on the current state and new input events, and its expression is as follows: In the formula, S t+1 Let F(·) represent the state of the system at time t+1, and let S represent the state transition function. t E represents the state of the system at the current time t. t This represents the actual event received by the sensing network at time t, and Validate(·) represents a validation function, E i,j+1 This represents the next predicted event node for the i-th operation node, (SOP) i,j+1 ) indicates the next compliant state the system enters, Anomaly indicates the system enters an abnormal state, true represents a logical value indicating successful verification, and false represents a logical value indicating failed verification.

[0077] Taking the operation logic chain of removing acetone solvent from a fume hood as an example, when system state S t For (SOP) 丙酮 ,0), which is the initial state, and the expected next node E. 丙酮，1 To initiate ventilation. If event e is received at this time. t If ventilation is initiated, and the prerequisite of personnel being present in front of the fume hood is met, the Validate function returns true, and the system state is updated to S. t+1 =(SOP) 丙酮 ,1). Conversely, if the received event e t The acetone reagent bottle was used, in accordance with the expected E 丙酮，1 If the condition is not met, the Validate function returns false, and the system status is immediately updated to Anomaly.

[0078] By implementing step S3, the present invention can transform operating procedures into logical constraints that can be audited in real time and automatically, thereby achieving refined and procedural control over the operating process.

[0079] S4. When the sensor network detects an anomaly in the physical environment, perform risk field inversion to determine the location and intensity of the risk source, and perform risk type identification to determine the risk type of the physical environment anomaly. Specifically, in this embodiment, when step S3 determines that a logical anomaly has occurred, or when the sensor network directly detects an anomaly in the physical environment (such as any sensor reading exceeding its preset safety threshold), this method executes step S4. This step aims to perform in-depth analysis of the occurring physical environment anomaly to determine the specific location, intensity, and exact type of risk source.

[0080] When anomalies in the physical environment are detected, risk field inversion can be performed to determine the location and intensity of the risk source. This process utilizes readings from multiple sensors of the same or different types deployed in a three-dimensional laboratory space, combined with a pre-defined material diffusion model, such as a Gaussian plume model or a computational fluid dynamics (CFD) model, to inversely calculate and determine the three-dimensional spatial coordinates and source intensity (such as leakage rate) of the pollution or hazard source most likely to cause the current sensor reading distribution. This result provides a precise spatial indication for subsequent risk management and contextual inference.

[0081] At the same time, risk type identification is performed to determine the exact risk type of the physical environment anomaly.

[0082] The identification process first attempts direct identification. This process triggers readings from sensors exhibiting physical environmental anomalies and clear chemical orientations, directly matching them with single sensor readings in the risk event feature library constructed in step S1. If the alarm sensor is a dedicated carbon monoxide sensor, it can be directly matched from the feature library and identified as a carbon monoxide leak risk; this is the highest priority identification path.

[0083] When direct identification fails, if the anomaly is triggered by a general-purpose sensor (such as a VOC sensor), the process proceeds to the second level of identification. This involves combining readings from multiple sensors that triggered the physical environment anomaly and matching them with pre-defined event signatures in a risk event signature database. The readings and trends of multiple sensors (such as VOC, temperature, and air pressure) around the anomaly point are extracted within a time window to form a real-time feature vector. This vector is then compared with the event signatures stored in the database to identify the risk type. If this combination pattern matches the signature of an initial acetone evaporation leak, the risk type is identified.

[0084] When signature matching fails or the matching result is ambiguous, the highest-level context inference is performed. This step ultimately determines the risk type by querying the operational logic chain or digital bill of materials associated with the risk source location (determined by risk field inversion). This process is a concentrated embodiment of the physical-logic coupling concept of this invention, associating anomalies in the physical space with behavioral or entity information in the logical space to eliminate uncertainty.

[0085] To formally model the contextual inference process with inherent uncertainties, a specific implementation of this invention introduces a Bayesian inference framework. This framework is used to calculate and identify the most likely risk event given incomplete sensor evidence and additional contextual information. Its core logic can be represented by the following proportional relationship; P(R k|E,C)∝P(E|P(R k |E,C))∝P(E|R k )·P(R k |C); In the formula, P(R) k |E,C) represents the posterior probability, that is, the probability that the risky event R will occur given the observation of sensor evidence E and contextual information C. k The probability, ∝, indicates that it is proportional to, P(E|R k ) represents the likelihood probability, i.e., the probability of occurrence of risk event R. k Under the conditions that occur, the probability of observing sensor evidence E, P(R k |C) represents the prior probability, that is, given the contextual information C, the probability of risk event R. k The probability of it occurring, R k Let E represent the k-th possible risk event, E represent observational evidence from sensors that may be vague or ambiguous, and C represent contextual information associated with the location of the risk source.

[0086] Sensor evidence E indicates an excessive VOC reading, and risk field inversion locates the vicinity of the risk source. Contextual information C shows an acetone removal operation is underway. At this point, regarding the risk R of acetone leakage... k In terms of its prior probability P(R) k |C) will be assigned an extremely high value by the system, while the prior probability of other irrelevant risks (such as ethanol leakage) is very low. Therefore, even if the likelihood probability P(E|R) is high, the system will assign a very high value to the risk. k The posterior probability P(R) is similar to that of the given probability, and the final calculated posterior probability is... k |E,C) also significantly indicate acetone leakage.

[0087] By implementing step S4, an isolated, raw physical environmental anomaly signal is transformed into a structured risk perception that includes precise location, quantified intensity, and clear type.

[0088] S5. Identify latent risk fields by identifying abnormal states of collaborative logic, the location and intensity of risk sources, and the type of risk, and proactively constrain operational procedures that may interact dangerously with latent risk fields in the future. Specifically, following step S4, which completes the in-depth analysis of physical environment anomalies, this embodiment executes step S5. This step, in conjunction with the logical anomaly state determined in step S3 and the risk source location, intensity, and type information output in step S4, aims to identify latent risks and, based on the identification results, execute forward-looking and proactive operational constraints.

[0089] This step involves identifying latent risk fields. This identification process does not target all physical environmental anomalies, but rather focuses on low-risk, highly concealed threats that are easily overlooked by conventional monitoring methods.

[0090] The identification of latent risk fields is strictly limited to the following triggering conditions: the identification process is triggered only when a risk source with a source strength lower than a preset alarm threshold is identified, and the existence of this risk source cannot be explained by any operational logic chain under compliant operation. This design aims to accurately capture unexpected, unexplained, and subtle leaks or anomalies.

[0091] When the above triggering conditions are met, the risk field inversion and risk type identification method described in step S4 is invoked to determine the spatial location, impact range, and risk type of the weak risk source. A latent risk field data object is generated and marked in the three-dimensional digital model. This object encapsulates information such as the spatial boundary, risk type, and source intensity of the risk field.

[0092] After identifying and flagging potential risk fields, proactive operational constraints are further implemented to prevent dangerous interactions between future compliant operations and existing potential risk fields.

[0093] The process begins when the system receives a start request initiated by an operator to execute a new Standard Operating Procedure (SOP). The system first parses the request, extracts the key operational actions contained in the SOP, and the spatial location and scope where these actions are scheduled to occur.

[0094] Perform spatial overlap determination. This determination involves geometrically comparing the predetermined spatial location of the operation to be performed with the spatial locations of all marked latent risk fields in the system to determine whether there is any spatial overlap or proximity relationship.

[0095] If spatial overlap is detected, a hazard interaction judgment is immediately executed. This judgment queries a pre-defined hazard interaction knowledge base to determine whether there is a known hazard combination between the operation to be performed and the risk type of the potential risk field. This knowledge base, for example, stores hazard interaction rules such as high-temperature heat sources and flammable gases, vibration and sensitive explosives.

[0096] When the result of the dangerous interaction judgment is that a dangerous interaction exists, the final constraint action is executed, that is, the start request of the standard operating procedure is rejected, and a structured warning message is pushed to the operator's interface, clearly indicating the reason for rejection, the type of potential risk, and the location of the latent risk field of the conflict, thereby achieving early intervention for potential accidents.

[0097] To ensure accurate and repeatable calculation of this constraint decision logic, one specific implementation of the present invention introduces the following constraint decision function: In the formula, Constraint represents the final constrained decision result. The function indicates the overlap; Overlap(·,·) indicates the spatial overlap judgment function; L O L indicates the spatial location where the new operating procedure is scheduled to be executed. P H(·,·) represents the spatial location of the marked latent risk field, and H(·,·) represents the hazard interaction judgment function. O Indicates the operational actions included in the new operating procedure, Type P This indicates the risk type of the identified latent risk field.

[0098] The system executes operational constraints only if the result of Constraint is 1, i.e., the spaces overlap and the interaction is dangerous.

[0099] S6. Based on the evolution prediction of risk type and risk source, generate structured early warning information containing precise handling suggestions.

[0100] Specifically, after the risk identification, analysis and assessment are completed through the aforementioned steps, this embodiment continues to execute step S6, the purpose of which is to transform the complex analysis results within the system into highly usable structured early warning information that can be directly understood and executed by human emergency response personnel.

[0101] It is triggered either by identifying the exact risk type in step S4 or by executing proactive operational constraints in step S5. Its core lies in generating structured early warning information containing precise handling recommendations based on the evolutionary prediction of risk type and risk source.

[0102] First, based on the risk type finally determined in step S4, precise handling recommendations are retrieved. This process involves using the risk type as a query index to search the risk event feature database established in step S1 and extracting the pre-compiled emergency response procedures that are uniquely associated with that risk type.

[0103] For example, when the risk type identified in step S4 is a high concentration of acetone vapor leak, the system automatically retrieves the corresponding handling recommendations from the database. The recommendations may be: emergency personnel must wear Class A chemical protective clothing and positive pressure air respirators, and use alcohol-resistant foam or dry powder extinguishing agents for covering and extinguishing, and water is strictly prohibited as a handling agent.

[0104] Simultaneously, based on the risk source locations and intensities determined through risk field inversion in step S4, the system performs a forward evolution prediction of the risk field. This prediction aims to provide emergency responders with an intuitive understanding of the future development trend of risks.

[0105] Evolutionary prediction is achieved by using a positive mass diffusion model combined with laboratory environmental parameters (such as ventilation system status and airflow distribution). This process can be formally described as an evolutionary function. In the formula, This indicates the spatial location at a future time t. The predicted concentration of hazardous substances is given by G(·), which represents a forward propagation function model, and Q represents the intensity of the risk source determined in step S4. This represents the three-dimensional spatial location vector of the risk source determined in step S4. Indicates the location inside the laboratory The environmental wind field vector, where t represents the future time calculated from the current moment.

[0106] By solving this function, a series of snapshots of the spatial distribution of the risk field at different future time points are obtained. These snapshots are then loaded into the three-dimensional digital model established in step S1, generating a dynamic and intuitive risk field evolution prediction map in the form of a three-dimensional visualized cloud map.

[0107] All the information obtained in the above process is integrated into a standardized early warning information package with a single data structure. This information package, as a whole, encapsulates all the core elements of this early warning event.

[0108] Specifically, the structured early warning information package includes the following data fields: Qualitative risk field: Contains the specific risk type identified in step S4.

[0109] Risk location and quantification fields: These include the coordinates of the risk source locations and their real-time source intensities, which are highlighted on the 3D model as determined in step S4.

[0110] Risk Trend Field: Contains the aforementioned dynamic risk field evolution prediction graph.

[0111] Cause Analysis Field: When the risk is associated with the logical anomaly determined in step S3, this field contains a textual description of the possible causes.

[0112] The handling suggestion field contains the precise emergency handling steps retrieved from the risk event feature database.

[0113] This highly integrated, structured early warning information package will be simultaneously pushed out through multiple pre-set information dissemination channels. These channels may include on-site audible and visual alarm devices in the laboratory, handheld application (APP) for safety management personnel, and the emergency command screen in the central control center.

[0114] The intelligent laboratory safety monitoring and early warning system described below can be referred to in correspondence with the intelligent laboratory safety monitoring and early warning method described above.

[0115] Please see the appendix Figure 6 The present invention also provides a laboratory safety intelligent monitoring and early warning system, comprising: A 3D modeling module is used to create a 3D digital model of the laboratory and build a risk event feature library. The sensing and logic module is used to manage the sensor network and logicize standard operating procedures to form an operational logic chain. The real-time analysis module, which is connected to the 3D modeling module and the sensing and logic module, is used to perform compliance determination of operational behavior, risk field inversion, and risk type identification. The decision and constraint module, which is connected to the real-time analysis module, is used to identify latent risk fields and execute proactive operational constraints. The early warning generation module, which is connected to the decision and constraint module, is used to generate structured early warning information.

[0116] The system in this embodiment can be used to execute the above method embodiments, and its principle and technical effect are similar, so they will not be described again here.

Claims

1. A method for intelligent monitoring and early warning of laboratory safety, characterized in that, Includes the following steps: S1. Establish a three-dimensional digital model of the laboratory and construct a risk event feature library that predefines various risk events and their triggering conditions; S2. Deploy a sensor network covering the laboratory and logicalize the laboratory's standard operating procedures to form an operational logic chain that can be matched in real time. S3. Monitor operational behavior in real time through sensor network and compare it with the operation logic chain to determine the current compliance operation status or logic abnormality status of the laboratory. S4. When the sensor network detects an anomaly in the physical environment, perform risk field inversion to determine the location and intensity of the risk source, and perform risk type identification to determine the risk type of the physical environment anomaly. S5. Identify latent risk fields by identifying abnormal states of collaborative logic, the location and intensity of risk sources, and the type of risk, and proactively constrain operational procedures that may interact dangerously with the latent risk fields in the future. S6. Based on the evolution prediction of the risk type and the risk source, generate structured early warning information containing precise handling suggestions.

2. The intelligent monitoring and early warning method for laboratory safety according to claim 1, characterized in that, In step S1, the risk event feature library is constructed by associating a risk event with a trigger condition vector consisting of trigger conditions. The trigger condition vector is defined by the event feature signature consisting of the reading conditions of a single sensor or the combination of readings from multiple different types of sensors.

3. The intelligent monitoring and early warning method for laboratory safety according to claim 2, characterized in that, The establishment of the event signature specifically includes the following steps; For a specific risk event, identify a group of multiple types of sensors that are associated with the occurrence of the risk event from the perspective of human factors engineering or experimental mechanism; For each type of sensor in this group, define the characteristic readings that it presents when the risk event occurs. The characteristic readings include a range of values ​​or a trend of value change. Establish logical combination relationships between the characteristic readings of multiple sensors in the group to form an event feature signature that can uniquely identify a specific risk event.

4. The intelligent monitoring and early warning method for laboratory safety according to claim 1, characterized in that, In step S2, the standard operating procedure of the laboratory is logically decomposed into an ordered sequence of multiple event nodes. Each event node defines the preconditions that must be met to perform the operation of that node, as well as the operation action and operation object of that node.

5. The intelligent monitoring and early warning method for laboratory safety according to claim 1, characterized in that, The S3 step, which determines the current compliant operation status or logical anomaly status of the laboratory, specifically includes the following steps: Receive an event characterizing real-time operational behavior from the sensor network; The event is matched and verified with the next expected event node in all currently active operation logic chains; When an event matches the next expected event node in any operation logic chain and satisfies the preconditions bound to that node, the state of the operation logic chain is advanced and determined to be a compliant operation state. An event is considered to be in a logically abnormal state if it does not match the next expected event node in any operation logic chain, or if the occurrence of the event violates the preset preconditions of its matching node.

6. The intelligent monitoring and early warning method for laboratory safety according to claim 1, characterized in that, Step S4 involves risk type identification to determine the risk type of the physical environment anomaly, specifically including the following steps: The readings of sensors that trigger physical environmental anomalies and have a clear chemical orientation are matched with single sensor reading conditions in the risk event feature library to directly identify the risk type. If direct identification fails, the readings of multiple sensors that detect physical environment anomalies will be combined and matched with the preset event feature signatures in the risk event feature database to identify the risk type. If the signature match fails, context inference is performed to ultimately determine the risk type by querying the operational logic chain or digital bill of materials associated with the risk source location.

7. The intelligent monitoring and early warning method for laboratory safety according to claim 1, characterized in that, The identification of latent risk fields in step S5 is triggered when it is determined that there is a risk source with a source intensity lower than a preset alarm threshold and the risk source cannot be explained by any compliant operation status. The latent risk field is then marked with its spatial location and the identified risk type.

8. The intelligent monitoring and early warning method for laboratory safety according to claim 1, characterized in that, The active operation constraint in step S5 specifically includes the following steps: Upon receiving a request to initiate a new operating procedure, extract the scheduled actions and spatial locations of that operating procedure. Determine whether the spatial location of the predetermined execution overlaps with the spatial location of the marked latent risk field; If an overlap occurs, it is further determined whether the operation actions of the operating procedure will have a dangerous interaction with the risk type of the latent risk field; If a dangerous interaction is detected, the execution of that operation procedure will be prevented.

9. The intelligent monitoring and early warning method for laboratory safety according to claim 1, characterized in that, Step S6 generates structured early warning information containing precise handling suggestions, specifically including the following steps: Based on the identified risk types, retrieve and extract precise handling suggestions corresponding to the risk type from the risk event feature database; The evolution prediction of risk sources is visualized to generate a risk field evolution prediction map; The precise handling suggestions, risk field evolution prediction map, location and intensity of risk sources, and cause analysis based on logical anomalies are combined into a single data structure for structured early warning information.

10. A laboratory safety intelligent monitoring and early warning system, applied to the laboratory safety intelligent monitoring and early warning method as described in any one of claims 1-9, characterized in that, include; A 3D modeling module is used to create a 3D digital model of the laboratory and build a risk event feature library. The sensing and logic module is used to manage the sensor network and logicize standard operating procedures to form an operational logic chain. The real-time analysis module, which is connected to the 3D modeling module and the sensing and logic module, is used to perform compliance determination of operational behavior, risk field inversion, and risk type identification. The decision and constraint module, which is connected to the real-time analysis module, is used to identify latent risk fields and execute proactive operational constraints. The early warning generation module, which is connected to the decision and constraint module, is used to generate structured early warning information.

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