LIMS-based methods for monitoring the entire lifecycle of hazardous chemicals and predicting safety hazards

Abstract

This invention relates to the field of hazardous chemical monitoring technology, and more particularly to a method for monitoring the entire lifecycle of hazardous chemicals and predicting safety hazards based on LIMS. The method includes the following steps: Hazardous chemical information is entered into the LIMS by scanning electronic tags, and the operation steps and status are updated in real time when the chemical moves, forming updated data. The system compares the updated data with early warning rules, generates early warning information when an anomaly is triggered, and pushes it to the terminal server. The terminal server performs risk prediction based on the updated data and automatically generates disposal suggestions and graded contingency plans, ultimately forming a record of hazardous chemical hazard handling information, achieving intelligent monitoring and early warning throughout the entire process. This invention, by combining dynamic monitoring throughout the entire lifecycle, intelligent status recognition, and contextualized risk prediction, achieves accurate identification, early prediction, and graded disposal of hazardous chemical hazards, significantly improving the safety and emergency response capabilities of hazardous chemical management.

Description

Technical Field

[0001] This invention relates to the field of hazardous chemical monitoring technology, and in particular to a method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS. Background Technology

[0002] In recent years, with the rapid development of the chemical industry, the safety management of hazardous chemicals (hereinafter referred to as "hazardous chemicals") throughout their entire life cycle, including production, storage, transportation, use, and disposal, has become increasingly prominent. Traditional manual record-keeping and decentralized management systems are insufficient for real-time monitoring and risk warning of the entire hazardous chemicals process, leading to frequent safety accidents and causing significant casualties and environmental pollution.

[0003] Currently, although some companies have introduced Laboratory Information Management Systems (LIMS) for sample management and data recording, their functions are mainly concentrated in the laboratory and fail to cover the entire process of hazardous chemicals from procurement, warehousing, requisition, use, and disposal. Furthermore, they lack data-based intelligent early warning and safety hazard prediction capabilities.

[0004] In addition, existing hazardous chemical management systems are mostly based on independent deployment and operate in information silos, making data integration difficult and unable to achieve cross-departmental and cross-level safety collaborative management and emergency response, thus failing to meet the safety management needs of modern smart chemical industrial parks and enterprises. Summary of the Invention

[0005] Therefore, it is necessary to provide a LIMS-based method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards in order to solve at least one of the above-mentioned technical problems.

[0006] To achieve the above objectives, a method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS is proposed, the method comprising the following steps: Step S1: Enter hazardous chemical information into the LIMS system by scanning the electronic tags attached to the hazardous chemicals; Step S2: Respond to the operation of updating hazardous chemicals in the operation of moving hazardous chemicals, and update the status of the hazardous chemicals entered information according to the operation to obtain the updated hazardous chemicals data; Step S3: Compare the updated hazardous chemical data with the preset early warning rules. When the updated hazardous chemical data meets the preset early warning rules, generate the corresponding early warning information. Step S4: Push the early warning trigger information to the terminal server to predict the risk of hazardous chemicals, and generate disposal suggestions and contingency plans based on the prediction results, and obtain information records of hazardous chemical hazard handling.

[0007] The present invention has the following beneficial effects: I. Through automated electronic tag entry and dynamic movement monitoring based on GIS data, continuous tracking of hazardous chemicals throughout their entire lifecycle—from warehousing and storage to requisition and transportation—is achieved. Utilizing multi-dimensional indicators such as time-series movement distance, state switching frequency, and segment proportion characteristics, the system automatically identifies typical operational phases such as continuous movement, short-distance switching, and prolonged static placement, effectively avoiding the shortcomings of manual recording delays and coarse state judgments. Through rhythmic change analysis and fluctuation difference identification, this technology can perform secondary confirmation based on operational environment change patterns, significantly improving the precision and robustness of hazardous chemical operation behavior identification, enabling the system to stably and accurately determine the status of hazardous chemicals even in complex scenarios.

[0008] Second, based on updated hazardous chemicals data and early warning rules, a hazardous chemicals risk prediction model is constructed by calling historical accident information through a terminal server, forming an intelligent risk prediction mechanism that combines rule-driven and data-driven approaches. By conducting quantitative risk prediction on early warning trigger information, the system can identify possible abnormal states and risk trends in advance, achieving predictive judgment of potential accidents. This mechanism overcomes the shortcomings of traditional methods that rely on single rule triggers, resulting in high false alarm rates and an inability to predict complex risk evolution, thus giving the early warning system higher accuracy, adaptability, and real-time performance.

[0009] Third, based on the prediction results, potential risk evolution scenarios are constructed. Multiple risk evolution paths are generated through steps such as key time node extraction, causal analysis, and scenario linking, thereby selecting potential risk evolution scenarios that better fit reality. Combined with a hazardous chemicals disposal knowledge base, the system can automatically generate disposal suggestions adapted to different scenarios and conduct graded contingency plan assessments based on scenario severity, response time, and controllability. The final generated hazardous chemicals hazard handling information record is structured, scenario-based, and executable, effectively overcoming the problems of traditional emergency plans such as strong generalization, lack of dynamic matching, and insufficient specificity, significantly improving the scientific decision-making ability and response efficiency of hazardous chemicals emergency response. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the steps of a LIMS-based method for monitoring the entire lifecycle of hazardous chemicals and predicting safety hazards. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2. Figure 3 This is a flowchart illustrating the implementation process of a LIMS-based method for monitoring the entire lifecycle of hazardous chemicals and predicting safety hazards, as proposed in this application. Figure 4 This is a flowchart illustrating the workflow of a LIMS-based method for monitoring the entire lifecycle of hazardous chemicals and predicting safety hazards, as proposed in this application. The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0011] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0012] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0013] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0014] To achieve the above objectives, please refer to Figures 1 to 4 A method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS, the method comprising the following steps: Step S1: Enter hazardous chemical information into the LIMS system by scanning the electronic tags attached to the hazardous chemicals; In one embodiment, the hazardous chemicals management system first deploys electronic tag scanning equipment at the hazardous chemicals warehousing stage. The scanning equipment includes an UHF RFID reader / writer, a QR code recognition module, and a communication interface connected to a LIMS (Laboratory Information Management System). Each hazardous chemical is bound with a uniquely coded electronic tag during the production or procurement stage, and this electronic tag records the basic attribute information of the hazardous chemical.

[0015] Once the hazardous chemicals arrive at the storage receiving area, operators place them on a scanning table, where the scanning equipment automatically identifies the electronic tags. If the electronic tag is an RFID tag, the system reads its stored UID code, hazardous chemical name, specifications, production batch number, supplier information, and tag writing time, among other basic data. If the electronic tag is a QR code tag, the QR code recognition module retrieves the associated text-coded data.

[0016] After the scanning device completes the tag reading, it transmits the acquired data to the LIMS system in real time via wired or wireless means. The LIMS system matches the received tag data with its internal database, automatically creates new records for unregistered hazardous chemicals, and generates a hazardous chemical entry number. Simultaneously, it loads the corresponding safety attribute templates based on the hazardous chemical category, including hazard classification, storage conditions, expiration date, required control level, temperature and humidity requirements, and other information.

[0017] During the data entry process, the LIMS system automatically records the scanning time, scanning location, scanning device number, and operator identification information to ensure traceability. It also verifies the consistency between the information on the electronic tags and the information manually entered by the operator. If any abnormalities, missing items, or discrepancies with the preset format are found, a warning pops up on the operating terminal, prompting manual review.

[0018] After the hazardous chemicals are successfully entered, the LIMS system will generate a corresponding hazardous chemical entry form and push the entered information to the warehouse management module, environmental monitoring module, and safety early warning module as the basic data for subsequent full life cycle monitoring, circulation tracking, and safety early warning.

[0019] It should be noted that the LIMS system can be referenced. Figure 3It adopts a four-layer architecture to achieve management and risk monitoring of hazardous chemicals throughout their entire life cycle. The perception layer is responsible for real-time data collection of hazardous chemicals and their environment, including RFID readers, QR code scanners, temperature and humidity sensors, pressure sensors, gas humidity sensors, and other IoT devices. The collected data directly reflects the physical state of the hazardous chemicals. The network layer provides communication between the perception layer devices and the platform layer, supporting technologies such as 5G, NB-IoT, and LoRa to achieve high-speed, low-power, and wide-coverage data transmission. The platform layer is a cloud-native LIMS extension platform, whose core functions consist of a business middle platform, a data middle platform, and an algorithm middle platform. The business middle platform includes a process engine and rule base for hazardous chemical operation process management and rule execution. The data middle platform cleans, analyzes, and stores the collected data to form a structured data warehouse. The algorithm middle platform applies AI / ML models and early warning algorithms to perform intelligent analysis and risk prediction of the data. At the same time, the platform layer provides data and algorithm support to the application layer and opens service interfaces to users and managers. The application layer provides users with business functions such as hazardous chemical traceability, risk warning, emergency command, and compliance reports, enabling users and managers to monitor the status of hazardous chemicals in real time and make management decisions.

[0020] Step S2: Respond to the operation of updating hazardous chemicals in the operation of moving hazardous chemicals, and update the status of the hazardous chemicals entered information according to the operation to obtain the updated hazardous chemicals data; In one embodiment, after the initial entry of hazardous chemicals is completed, when it is detected that the warehouse management personnel execute a hazardous chemical movement operation command on the operating terminal, the movement trajectory data of the hazardous chemicals is first obtained from the real-time positioning unit, electronic tag reader / writer, or logistics trajectory recording device. The movement trajectory data includes information such as the current storage location of the hazardous chemicals, the target movement location, the movement timestamp, and the operator identification during the movement process.

[0021] Based on the acquired movement trajectory data, the operation steps for hazardous chemicals are automatically matched from preset operation steps such as "awaiting inspection upon arrival," "in storage," "preparing for operation," and "in transit" to the corresponding step for the current movement. For example, when it is detected that hazardous chemicals have been moved from the fixed storage area to the operation buffer zone, the operation step is updated to "preparing for operation"; when hazardous chemicals are loaded onto transport pallets or internal logistics vehicles, the operation step is updated to "in transit."

[0022] After confirming the operational steps, the key status fields in the hazardous chemical entry information are synchronously updated based on the operational steps. The status update fields include, but are not limited to: location status field (recording the current location and coordinates of the hazardous chemical), safety status field (recording the safety risk level corresponding to the current step), usage status field (recording whether the hazardous chemical is in an operable or controlled state), and time status field (recording the start time and expected end time of the operational step).

[0023] During the field update process, preset safety management rules are automatically invoked based on the operational steps. For example, if hazardous chemicals are moved to a high-risk work area, the safety status will be updated from "normal" to "high-risk pending control"; when hazardous chemicals are transferred to a low-temperature storage area, low-temperature environment monitoring requirements will be automatically added and corresponding risk labels will be appended.

[0024] The updated data is structured into a hazardous chemical update data package, including operation step identifiers, changed status fields, operator information, movement trajectory data, and timestamps. This updated data is written to the LIMS (Laboratory Information Management System) database, simultaneously triggering the risk monitoring module to compare the new operation steps against early warning rules. This enables subsequent steps to perform full lifecycle monitoring and hazard prediction of hazardous chemicals based on the latest status.

[0025] Step S3: Compare the updated hazardous chemical data with the preset early warning rules. When the updated hazardous chemical data meets the preset early warning rules, generate the corresponding early warning information. In one embodiment, reference may be made to Figure 4 After receiving the hazardous chemical update data generated in step S2, the LIMS system server automatically imports the update data into the preset early warning rule matching module. The early warning rule matching module pre-stores a set of multiple early warning rules related to the full life cycle management of hazardous chemicals, including storage time limit rules, container status abnormality rules, operator permission matching rules, transportation trajectory deviation rules, environmental factor triggering rules, and risk level escalation rules.

[0026] When making an early warning judgment, the system first retrieves the applicable scope of the corresponding rules based on the unique identifier of the hazardous chemical, and then filters out a subset of early warning rules related to the type, hazard level, and current operation of the hazardous chemical. For example, when the hazardous chemical is in the process of being moved, retrieved, stored, or transferred, different operation status rule templates are automatically matched.

[0027] Subsequently, key fields in the updated hazardous chemical data (including operation time, operator number, operation location, container status parameters, environmental sensor parameters, movement trajectory information, etc.) are compared with the selected warning rules one by one. The comparison process uses a multi-condition logical judgment method. For example: if hazardous chemicals move to an unauthorized area, an "area crossing" warning is triggered; if the container temperature, pressure, or sealing status exceeds the safe range, an "abnormal container status" warning is triggered; if the operator's permission level does not match the current operation, an "permission violation" warning is triggered; if hazardous chemicals have not undergone necessary inventory checks for a long time, an "inventory check timeout" warning is triggered; if environmental monitoring data shows an increase in the concentration of volatile substances, an "environmental risk" warning is triggered.

[0028] When any warning rule is triggered, the system immediately records the name of the triggering rule, the trigger time, the corresponding hazardous chemical number, and the trigger reason, and automatically generates a structured warning information data packet. This data packet includes: warning level, warning type, risk source field, hazardous chemical location information, and suggested response measures.

[0029] In a preferred embodiment, to avoid false triggering, a secondary verification is performed on the triggering result. For example, for a container temperature anomaly warning, it is verified whether the difference between two consecutive temperature sensor samples is within a reasonable range; for a trajectory deviation warning, it is necessary to confirm whether the duration of the deviation exceeds a prescribed threshold. This secondary verification effectively reduces false alarms caused by sensor data fluctuations or brief anomalies.

[0030] Step S4: Push the early warning trigger information to the terminal server to predict the risk of hazardous chemicals, and generate disposal suggestions and contingency plans based on the prediction results, and obtain information records of hazardous chemical hazard handling.

[0031] In one embodiment, after the monitoring system completes the identification of the hazardous chemical status and triggers the early warning, it first transmits the early warning trigger information to the terminal server via an industrial communication bus or a secure encrypted network. The early warning trigger information includes the identification code of the hazardous chemical, the current operating procedure, the triggered early warning level, the corresponding environmental parameter changes, and historical status comparison results. Upon receiving the early warning trigger information, the terminal server immediately enters the risk prediction process.

[0032] The terminal server parses key fields in the early warning trigger information based on its built-in hazardous chemical risk prediction model. The server first calls the hazardous chemical risk database to extract basic data such as the hazardous characteristics, storage requirements, possible failure modes, common leakage paths, reactivity risk level, and diffusion characteristics of the corresponding hazardous chemical. Combined with the currently monitored environmental changes (including temperature, pressure, concentration, and changes in operating procedures), it generates a set of input parameters for risk prediction.

[0033] The server then performs a multi-dimensional risk assessment on the input parameter set. The assessment includes judging the evolution trend of potential accidents, estimating the speed of risk progression, analyzing the probability of triggering higher-level risks again, and identifying nodes in the accident chain. For example, when a hazardous chemical is detected to have experienced abnormally high temperatures and unstable container pressure fluctuations within a short period of time, the server will predict the possible risks of thermal runaway or container rupture and provide a risk probability level, thus forming a prediction result.

[0034] Based on the risk prediction results, the terminal server automatically generates disposal recommendations and emergency plans. Disposal recommendations include immediate operational instructions for on-site personnel, such as reducing operational intensity, switching to backup storage containers, and activating local exhaust ventilation or sprinkler systems. Emergency plans include hazardous chemical isolation strategies, emergency shutdown logic for surrounding equipment, personnel evacuation route suggestions, and a list of emergency resources that may need to be activated (such as fire extinguishers, neutralizing agents, and spill containment materials).

[0035] In a preferred embodiment, the system will also categorize and push relevant information to responsible parties based on disposal recommendations and contingency plans, and simultaneously send risk warnings to on-site terminal equipment, the safety management center, and the cloud monitoring platform to ensure timely awareness by all parties. The server will also record the trigger time of this risk prediction, prediction process parameters, generated contingency plan content, and the execution status of each step, forming a hazardous chemical hazard handling information record.

[0036] As an example of the present invention, reference is made to Figure 2 As shown, in this example, step S2 includes: Step S21: Obtain the GIS location information of hazardous chemicals; Step S22: Update the GIS location information in response to the hazardous chemical movement operation at a preset time interval, and confirm the time-series movement distance of the hazardous chemical based on the updated GIS location information; Step S23: Confirm the operational procedures for hazardous chemicals based on the time-series movement distance; Step S24: Update the status of hazardous chemicals based on the information entered in the operation procedure to obtain the updated data of hazardous chemicals.

[0037] In one embodiment, the spatial location of each hazardous chemical is first monitored based on the built-in on-site positioning module. Specifically, in step S21, the geographic information system (GIS) positioning data of the hazardous chemical is acquired through a positioning unit attached to the outside of the hazardous chemical packaging. This positioning data includes the longitude, latitude, and altitude information of the hazardous chemical within the storage area or factory area, as well as a positioning timestamp for subsequent time-series change analysis. The initially acquired positioning information is used as the initial spatial reference data for the hazardous chemical and stored in the hazardous chemical management database for comparison with subsequent positioning records.

[0038] The movement of hazardous chemicals is continuously monitored according to a preset sampling period. Taking a typical ten-second sampling period as an example, new GIS positioning information is read at each sampling moment, and spatial difference calculation is performed with the positioning record of the previous period to obtain the movement distance of the hazardous chemicals within that time period. To improve measurement stability, the difference results of multiple consecutive periods are smoothed to avoid sudden changes in movement distance caused by short-term positioning errors. When the detected movement distance exceeds the minimum movement discrimination threshold, it is determined that a valid movement operation of the hazardous chemicals has occurred, and the corresponding time-series movement distance is recorded.

[0039] The system intelligently identifies the operational stage of hazardous chemicals based on the aforementioned time-series movement distance. For example, when the movement distance of hazardous chemicals is within the typical "warehouse to temporary storage area" range, it can be determined that they are in the outbound preparation stage; when the movement distance gradually changes from short to long and the movement path tends towards the external transportation channel of the plant area, it can be identified that the hazardous chemicals are in the loading or external transportation stage; if the monitored movement distance of hazardous chemicals is continuously maintained in a low range and the location information remains near the detection point, it can be determined that the hazardous chemicals are performing fixed operation stages such as inspection, weighing, or barcode registration. According to the preset operation stage mapping table, the time-series movement distance is matched with the specific operation stage, and the current operation stage of the hazardous chemicals is finally confirmed.

[0040] The lifecycle records of hazardous chemicals are updated based on the identified operational stages. Specifically, if a hazardous chemical is determined to be in the outbound preparation stage, its inventory status in the entered information is updated to "Pending Outbound"; if it is determined to be in the transportation stage, the status is updated to "Transporting"; if it is determined to be in the operation point area within the plant, the status is updated to "Processing" or "Pending Inspection". The updated hazardous chemical status, along with the corresponding timestamp, location record, and operation stage tag, constitutes the hazardous chemical update data, which is written to the LIMS system in real time for subsequent anomaly detection, full-process monitoring, and risk warning analysis.

[0041] Preferably, the operational steps for identifying hazardous chemicals based on time-series travel distance include: The movement status of hazardous chemicals is preliminarily determined based on the time-series movement distance, and a preliminary determination result is obtained. The preliminary determination is used to determine whether the hazardous chemicals are in a moving, issued or stationary state. Based on the frequency and timing of state switching in the preliminary assessment, the operating environment was reconfirmed to identify the operational steps for hazardous chemicals. These operational steps include continuous movement in transportation, short-range location switching in requisition, and long-term static storage in storage.

[0042] In one embodiment, based on the periodic location feedback information from the electronic tags of hazardous chemicals, a spatial coordinate sequence of the hazardous chemicals at multiple adjacent sampling times is obtained. The main control module calculates the coordinate difference between each adjacent sampling time to obtain the temporal movement distance of the hazardous chemicals within each time period. This temporal movement distance is sampled at a period of seconds or minutes, which can truly reflect the subtle positional changes of the hazardous chemicals during storage, transportation, and use.

[0043] Based on the acquired time-series movement distance, a preliminary determination of the movement status of hazardous chemicals is made. Specifically, if the movement distance in multiple consecutive sampling periods is greater than a set movement threshold (e.g., 0.5 meters), the hazardous chemical is determined to be in a moving state; if the movement distance changes occasionally with small fluctuations but the changes are discontinuous, and there are short periods of stillness between multiple sampling intervals, the hazardous chemical is determined to be in a stationary state; if the movement distance is lower than a micro-motion detection threshold (e.g., 0.05 meters) for a relatively long period of time (e.g., more than 30 minutes), the hazardous chemical is determined to be in a stationary state. The above preliminary determination results serve as the basis for confirmation in subsequent operational procedures.

[0044] Subsequently, the frequency of state transitions in the preliminary assessment results was statistically analyzed. For example, if within a monitoring period, hazardous chemicals exhibit multiple short-term transitions between "movement-stopping-movement," with each movement covering a short distance (e.g., 1-5 meters), it is suspected that short-distance manual handling may have occurred. If the state remains static for extended periods with almost no state transitions, it indicates that the hazardous chemicals are in a typical storage stage. If multiple consecutive sampling periods are determined to be in a moving state, and the direction of movement is continuous with a significant cumulative distance (e.g., exceeding tens of meters or cross-regional movement), it can be confirmed that the hazardous chemicals are in a transportation stage.

[0045] Furthermore, the operating environment is further confirmed by incorporating temporal characteristics. The control module generates a state change sequence based on the preliminary judgment results and analyzes the duration, switching rhythm, and cumulative movement distance within the sequence. For example, for continuous movement in transportation, the module continuously tracks the positional changes of hazardous chemicals in storage areas, loading and unloading areas, and transportation channels to verify whether they conform to the characteristics of the operational path; for short-distance position switching in requisition, the module can identify whether hazardous chemicals are moving back and forth between fixed workstations to confirm whether they are in actual use; for long-term static storage in storage, the module simultaneously compares environmental data from other sensors in the warehouse (such as temperature, humidity, and storage location signals) to complete the final confirmation of the static state.

[0046] Finally, based on the above secondary confirmation results, the operational information for hazardous chemicals is output. Records of long-distance continuous movement are marked as transportation-related processes; short-distance, frequent location changes are marked as requisition-related processes; and long periods without movement records are marked as storage-related processes. This method achieves automatic identification of operational processes based on time-series movement distance, effectively improving the safety and traceability of hazardous chemicals throughout their entire lifecycle management.

[0047] Preferably, the secondary verification of the operating environment based on the frequency and timing of state transitions determined in the preliminary assessment includes: By performing time-segmented statistical analysis on the frequency of state transitions in the preliminary judgment results, the temporal distribution data of state transitions is obtained. Based on the temporal distribution data of state transitions, the duration of transition and stable segments is analyzed to obtain the persistence characteristic data of state segments. By using the continuous characteristic data of state segments, the proportion of continuous movement segments, short-range switching segments, and stationary segments is identified, and segment proportion characteristic data is obtained. The change patterns of the operating environment are further confirmed by paragraph proportion feature data, and corresponding operation behavior type data is generated. Based on the operational behavior type data, the continuous movement behavior of the transportation link, the short-distance location switching behavior of the requisition link, and the long-term static behavior of the storage link are identified, thereby obtaining the operational links of hazardous chemicals.

[0048] In one embodiment, a preliminary judgment result is first obtained, which includes the status information of each time point in the hazardous chemical handling process. The main control unit performs time-segmented statistics on the status switching events in the preliminary judgment result, dividing the entire operation process into several fixed time windows, such as 5 seconds per time period. By counting the number of status switching events within each time period, temporal distribution data of status switching is generated to reflect the changes in the frequency and rhythm of actions in the operating environment.

[0049] Based on the generated state transition timing distribution data, the duration of each state segment is analyzed. Specifically, time periods in which the same state appears consecutively are identified as stable segments, while frequently switching state segments are identified as transition segments. By statistically analyzing the duration of each stable and transition segment, the persistence characteristic data of state segments is obtained, which is used to describe the continuity and discontinuity of operational behavior.

[0050] The main control unit further utilizes the persistence characteristic data of state segments to divide the entire operation process into three typical segments: continuous movement segments, short-range switching segments, and stationary segments. Continuous movement segments correspond to continuous, continuous state switching behavior; short-range switching segments correspond to states that switch repeatedly within a short period of time; and stationary segments correspond to stages where the state remains unchanged for a long time. The proportion of each type of segment in the total operation process is calculated to generate segment proportion characteristic data, thereby quantifying the temporal distribution characteristics of the operation behavior.

[0051] Based on segment proportion feature data, a secondary confirmation is performed on the operational environment change pattern. Specifically, when the proportion of continuous movement segments is significant, the operation can be identified as a transportation operation; when the proportion of short-range switching segments is high and the switching amplitude is small, it is identified as a requisition operation; when the proportion of stationary segments is the largest and the duration is long, it is identified as a storage operation. This secondary confirmation process can eliminate misjudgments caused by single-frame judgment and improve the reliability of operation segment judgment.

[0052] Finally, the main control unit, combining the operational behavior type data, categorized the various stages of hazardous chemical handling into transportation, requisition, and storage stages. Transportation stages correspond to continuous movement and can be used to monitor the handling path and duration of hazardous chemicals; requisition stages correspond to short-distance location switching and can be used to confirm material receipt and usage; and storage stages correspond to long-term static storage and can be used to monitor the safety status of the storage facility.

[0053] Preferably, the methods for obtaining the changing patterns of the operating environment include: By summarizing the distribution of state switching frequency within continuous time slices, time slice characteristic data representing the stability of the operating environment are obtained. By comparing the fluctuation differences between adjacent time slices segment by segment based on the time slice characteristic data, fluctuation difference data reflecting the changing trend of the operating environment is obtained. By using fluctuation difference data to identify persistent and intermittent fluctuation segments in the changing trend, the rhythmic change characteristics of the operating environment can be obtained. Based on the rhythmic change characteristic data analysis, the switching rhythm and dwell rhythm of operational behavior are analyzed to obtain the change pattern of the operational environment.

[0054] In one embodiment, an environmental monitoring unit continuously collects status information from the operating environment, including equipment status, execution status of operating commands, and environmental response signals. The data is divided into preset time slices, each lasting from 1 to 5 seconds. The frequency of state switching within each time slice is counted, and the frequency distribution is summarized as time slice characteristic data to characterize the stability of the operating environment within that time slice.

[0055] Next, based on the summarized time-slice characteristic data, the stability indices of adjacent time slices are compared segment by segment. Specifically, this involves analyzing the differences in the number of state transitions and fluctuation ranges for each pair of adjacent time slices, thereby generating fluctuation difference data reflecting the changing trends of the operating environment. This fluctuation difference data can characterize the magnitude and rate of change of the operating environment state within different time slices.

[0056] Subsequently, a continuity analysis was performed on the fluctuation difference data to distinguish between persistent and intermittent fluctuation segments in the trend of changes in the operating environment. Persistent fluctuation segments refer to regions where state switching maintains a high frequency or amplitude across several consecutive time slices, while intermittent fluctuation segments refer to a set of time slices where the frequency or amplitude of state switching is discontinuously distributed. By labeling and statistically analyzing different types of fluctuation segments, the rhythmic change characteristics data of the operating environment were obtained.

[0057] Based on the rhythmic variation characteristic data, the switching rhythm and dwell rhythm of operational behavior are further analyzed. The switching rhythm represents the pattern of rapid changes in environmental conditions or continuous operational behavior, while the dwell rhythm represents the pattern of stable environmental conditions or operational pauses. Through quantitative analysis of the switching rhythm and dwell rhythm, the change pattern of the operational environment within each time period is established, including high-frequency switching zones, low-frequency switching zones, and stable dwell zones.

[0058] In a preferred embodiment, the analyzed operating environment change patterns can be compared with historical operating data to identify abnormal operating behaviors or potential risk areas. The change pattern data can be used for subsequent operation optimization, predictive maintenance, or safety early warning systems, enabling intelligent monitoring and management of the operating environment.

[0059] Preferably, identifying persistent and intermittent fluctuation segments in a trend using fluctuation difference data includes: When the amplitude of a single fluctuation remains between 0.5 and 2.0 units and the duration is between 3 and 10 seconds, it is determined to be a continuous fluctuation segment. When the fluctuation amplitude is less than 0.5 units or occurs only within 1s to 3s, it is determined to be an intermittent fluctuation segment.

[0060] In one embodiment, the fluctuation difference data generated in the aforementioned steps is first acquired. This data includes information on the fluctuation amplitude and duration of the operating environment state within each consecutive time slice. A threshold judgment is performed on the fluctuation amplitude of each time slice. Specifically, when the amplitude of a single fluctuation is continuously within the range of 0.5 to 2.0 units and the continuous duration reaches 3 to 10 seconds, the time slice is marked as a continuous fluctuation segment.

[0061] For time slices with fluctuation amplitudes below 0.5 units, or fluctuations lasting only 1 to 3 seconds, they are classified as intermittent fluctuation segments. By scanning and marking continuous fluctuation segments throughout the entire time series, it is possible to distinguish between persistent and intermittent fluctuation areas in the changing trend of the operating environment.

[0062] During the labeling process, the start and end times, duration, and amplitude range of each fluctuation segment are further recorded to facilitate subsequent rhythmic analysis and pattern extraction. The obtained data on continuous and intermittent fluctuation segments can serve as the basis for the rhythmic change characteristics of the operating environment, providing a reliable basis for operational behavior analysis and anomaly detection.

[0063] Preferably, the switching rhythm and dwell rhythm of operational behavior based on rhythmic change characteristic data analysis include: Identify state transition points based on rhythmic change characteristic data; The switching rhythm parameters are obtained based on the distribution density of state switching points and the dwell time of adjacent states; The rhythm difference in switching rhythm parameters distinguishes between the continuous change phase and the stable dwell phase. By analyzing the fluctuations in the continuous change phase and the stable residence phase, the change path of operational behavior can be determined in order to obtain the change pattern of the operational environment.

[0064] In one embodiment, the rhythmic change characteristic data of the operating environment obtained in the aforementioned steps is first acquired. This data includes the fluctuation segment type (continuous or intermittent), duration, and fluctuation amplitude for each time slice. State switching points are identified based on the rhythmic change characteristic data, i.e., the time points at which the operating state switches from one fluctuation mode to another.

[0065] For the identified state transition points, their distribution density is statistically analyzed, and the transition rhythm parameter is calculated by combining this with the dwell time of adjacent states. Specifically, if the interval between state transition points is short and the dwell time fluctuates little, it is determined to be a high-frequency transition rhythm; if the interval between state transition points is long and the dwell time is stable, it is determined to be a low-frequency transition rhythm or a stable dwell rhythm.

[0066] Based on the analysis of switching rhythm parameters, the entire operating environment sequence is divided into a continuous change phase and a stable dwell phase. In the continuous change phase, the operating environment switches rapidly, reflecting frequent adjustments in operational behavior; in the stable dwell phase, the operating environment is relatively stable, reflecting the pause or suspension of operational behavior.

[0067] Further analysis of the fluctuations during the continuous change phase and the stable residence phase reveals the change path of operational behavior, including the state switching sequence, duration, and fluctuation amplitude, forming a complete time series of operational behavior. This allows for the acquisition of change patterns in the operational environment, providing a basis for subsequent abnormal behavior identification, optimized control, or rhythmic pattern modeling.

[0068] Preferably, step S4 includes the following steps: Step S41: Push the warning trigger information to the terminal server for local storage, and retrieve the historical accident information in the terminal server; Step S42: Construct a hazardous chemical risk prediction model based on updated hazardous chemical data and historical accident information; Step S43: Use the hazardous chemical risk prediction model to predict the hazardous chemical risk based on the early warning trigger information, and obtain the prediction results of the hazardous chemical risk; Step S44: Generate disposal suggestions and contingency plans based on the prediction results, and obtain information records on the handling of hazardous chemical hazards.

[0069] In one embodiment, when an abnormal state of hazardous chemicals is detected and an early warning is triggered, the warning information is first pushed to a terminal server for storage via a network interface or local communication bus. After receiving the warning information, the terminal server automatically generates a corresponding local record file and stores historical accident information in its local database, including detailed data on past hazardous chemical leaks, fires, explosions, or other abnormal events, such as the time of occurrence, location, types of substances involved, handling methods, and results.

[0070] Subsequently, based on the latest updated data on hazardous chemicals, including substance type, storage quantity, environmental parameters (such as temperature and humidity), transportation or operational status, and historical accident information, a hazardous chemical risk prediction model is automatically constructed. This model can employ rule-based reasoning, statistical analysis, or machine learning methods. Rule-based reasoning can assign weights based on the hazardous chemical's hazard level and historical accident frequency; statistical analysis can utilize the conditional probability of accidents; and machine learning methods can combine historical data to train prediction algorithms, thereby achieving risk level assessments for different types of hazardous chemicals.

[0071] Once the hazardous chemicals risk prediction model is established, the early warning trigger information is used as input data to perform hazardous chemicals risk prediction. The model output includes the risk level (e.g., low, medium, high), the location of risk distribution, the potential impact range, and possible accident types. Based on the prediction results, a hazardous chemicals risk prediction report is generated and updated in real time to the terminal server and operator terminals to ensure the traceability and timeliness of the information.

[0072] Based on this, response recommendations and emergency plans are automatically generated according to the prediction results. Response recommendations include specific operational steps such as safety measures to be taken, personnel evacuation plans, physical isolation or ventilation measures, alarm prompts, and enhanced environmental monitoring; emergency plans include detailed resource allocation plans, emergency team mobilization plans, on-site operating procedures, and accident information feedback mechanisms. All response recommendations and plans are recorded in the hazardous chemical hazard handling information record, including trigger time, risk level, associated hazardous chemicals, prediction results, recommended measures, and plan content, for subsequent auditing, optimization, and historical reference.

[0073] To ensure the reliability of implementation, the hazardous chemical risk prediction model can be periodically reviewed and corrected in this embodiment. By comparing the prediction results with actual accidents or experimental verification results, the model parameters and weights can be adjusted to improve the accuracy of hazardous chemical risk prediction and the effectiveness of the disposal plan.

[0074] Preferably, step S44 includes: Based on the prediction results, scenario-based simulations are conducted to identify several potential risk evolution scenarios; Based on the severity, controllability, and corresponding response time requirements of potential risk evolution scenarios, disposal measures that meet the current scenario conditions are selected from the pre-set hazardous chemical disposal knowledge base to obtain disposal recommendations; Based on the disposal recommendations, the feasibility of different potential risk evolution scenarios is assessed to obtain tiered disposal plans; Integrated disposal recommendations and tiered disposal plans serve as records of information on hazardous chemical hazard handling.

[0075] In one embodiment, the risk prediction results output by the hazardous chemicals lifecycle monitoring module or prediction module are first input into the scenario simulation unit. Based on the changing trends, occurrence probabilities, and historical accident cases of various risk indicators in the prediction results, the scenario simulation unit generates several potential risk evolution scenarios using simulation or rule-based reasoning methods. Each potential risk scenario includes information such as the risk occurrence time window, the possible scope of impact, the types of hazardous chemicals involved, and the potential consequence level.

[0076] Once potential risk scenarios are identified, appropriate disposal measures are selected from a pre-defined hazardous chemicals disposal knowledge base based on the severity level, controllability assessment, and response time requirements of each scenario. The knowledge base includes emergency response manuals, isolation measures, ventilation and emission control plans, fire extinguishing methods, and personnel evacuation plans, with each type of measure labeled with applicable conditions, operational steps, and resource requirements. By matching scenario parameters with the applicable conditions of the disposal measures, disposal recommendations that meet the current risk scenario are obtained.

[0077] Subsequently, the selected disposal recommendations are input into the feasibility assessment module. This module performs a feasibility analysis on each disposal recommendation based on scenario conditions (such as available equipment, personnel, and environmental constraints) and the complexity of implementation, including dimensions such as implementation time, resource consumption, and operational safety. Based on the analysis results, a tiered disposal plan is generated, with each level corresponding to a different risk level and response priority to ensure rapid and effective execution in actual operation.

[0078] Finally, the selected disposal recommendations are integrated with the tiered disposal plans to form a complete record of hazardous chemical hazard handling information. This record includes a description of potential risk evolution scenarios, corresponding disposal measures, tiered disposal plans, and execution sequences, for operators' reference or to be triggered by automated control systems. The record may also include execution status indicators, disposal history records, and feedback data to support subsequent risk analysis and continuous optimization.

[0079] In a preferred embodiment, potential risk scenarios and disposal recommendations can be dynamically updated based on real-time environmental changes and risk evolution trends, enabling real-time response and closed-loop control of hazardous chemical safety management. Through the above steps, this embodiment can effectively transform risk prediction results into executable tiered disposal plans, improving the safety and reliability of hazardous chemical lifecycle management.

[0080] Preferably, based on the prediction results, scenario-based extrapolation is conducted to identify several potential risk evolution scenarios, including: Based on the risk change trends in the prediction results, key time points where anomalies may occur are extracted; Based on the analysis of key time points, the main causes of hazardous chemicals are identified, resulting in several scenarios for changes in hazardous chemicals. By connecting the changing scenarios of hazardous chemicals in sequence according to key time nodes, different risk evolution paths can be obtained; Potential risk evolution scenarios are identified from the risk evolution paths.

[0081] In one embodiment, the risk prediction results output by the hazardous chemicals lifecycle prediction module are first input into the scenario simulation unit. Based on the changing trends of various risk indicators in the prediction results, the scenario simulation unit identifies key time points where abnormal changes may occur, such as sudden temperature increases, abnormal pressure, or sudden increases in concentration fluctuations. Each key time point corresponds to a potential risk change trigger condition, providing a time reference for subsequent scenario construction.

[0082] For each key time point, the main contributing factors to hazardous chemicals are analyzed, including external environmental factors (such as temperature, humidity, and air pressure), operational factors (such as handling, filling, or mixing), and equipment condition factors (such as valve malfunctions and pipeline leaks). By comprehensively analyzing the scope and potential changes of the various contributing factors, several hazardous chemical change scenarios are generated. Each scenario describes the possible changes in the state of hazardous chemicals at a specific time point.

[0083] The generated hazardous chemical change scenarios are logically linked together according to key time nodes to form different risk evolution paths. Each evolution path includes a complete evolution process from the initial state to the key node and then to the possible consequences, and marks the time, hazardous chemical status and potential impact of each node. By combining multiple evolution paths, different development directions of potential risks can be comprehensively covered.

[0084] Finally, paths with a high probability of occurrence, significant potential harm, or low controllability are selected from the generated risk evolution paths and defined as potential risk evolution scenarios. Each potential risk evolution scenario includes key time points, changes in the state of hazardous chemicals, triggering information, and potential impacts, providing basic data for subsequent graded response and emergency decision-making.

[0085] In a preferred embodiment, the scenario-based simulation process can be updated in real time, dynamically adjusting key time points and potential risk evolution scenarios based on the latest risk prediction data, thereby achieving dynamic prediction and proactive management of hazardous chemical risks.

[0086] Of particular importance is the analysis of the main triggers for hazardous chemicals based on key time points, including: Based on key time nodes, the continuous behavioral trajectory of the operation process is coupled with the slight fluctuations in the environmental state to obtain the causal weight distribution. By using causal weight distribution to combine and map potential triggering factors, a multi-path evolution trajectory of hazardous chemical states is generated; Based on the synchronicity and delay characteristics of the evolution trajectory, several different hazardous chemical change scenarios are divided; By logically reconstructing the hazardous chemical change scenarios through time correlation and stage characteristics, hazardous chemical change scenarios that can be used for hazard prediction are obtained.

[0087] In one embodiment, the key time nodes identified in the previous step are first used as the starting point for analysis. Data integration is then performed on the continuous behavioral trajectories and environmental state parameters during the hazardous chemical handling process. The operational trajectory includes sequences of actions such as handling, filling, and mixing, while the environmental state parameters include minute fluctuations in temperature, humidity, pressure, and vibration. The system uses a coupled calculation method to correlate operational behaviors with these minute fluctuations in environmental state, obtaining the causal weight distribution of each potential triggering factor on changes in the state of the hazardous chemicals.

[0088] Based on the obtained causal weight distribution, potential triggering factors are combined and mapped. Multi-path simulations are then performed on the state change paths of hazardous chemicals corresponding to different combinations of factors, generating multiple evolution trajectories for the hazardous chemical state. Each evolution trajectory records the state change process of the hazardous chemical under the influence of a specific combination of triggering factors, and marks relevant time points and the magnitude of changes in state indicators.

[0089] Based on the synchronicity and lag characteristics of the evolution trajectories, the trajectories are classified into several different hazardous chemical change scenarios. Trajectories with high synchronicity are grouped into the same scenario, representing the same type of risk evolution that may occur under similar operating and environmental conditions; trajectories with obvious lag characteristics form separate change scenarios, representing potential lagged risks.

[0090] The segmented hazardous chemical change scenarios were logically reconstructed. Combining temporal correlation and stage-specific characteristics, each change scenario was organized into a continuous and traceable sequence of state changes, forming a hazardous chemical change scenario dataset that can be used for hazard prediction and risk assessment. This dataset includes the triggering factor combinations, key time nodes, evolution trajectories, and potential risk impacts for each change scenario, providing a foundation for further scenario-based simulations and tiered response.

[0091] Of particular importance is the logical reconstruction of hazardous chemical change scenarios through time correlation and phased characteristics, including: Based on the changing scenarios of hazardous chemicals, extract the order of occurrence and duration of key time nodes in the scenario; Analyze the behavioral changes in hazardous chemical change scenarios based on the order of occurrence and duration to obtain correlation indicators for hazardous chemical change stages; By logically integrating and correcting conflicts in hazardous chemical change scenarios through correlation indicators of hazardous chemical change stages, hazardous chemical change scenarios that can be used for hazard prediction are obtained.

[0092] In one embodiment, hazardous chemical change data acquired by the hazardous chemical lifecycle monitoring module or sensor acquisition module is first input into the scenario reconstruction unit. The scenario reconstruction unit extracts key time nodes based on various change events in the data, including the time of occurrence, duration, and sequence of events. Key time nodes may include hazardous chemical loading and unloading time, storage condition change time, abnormal temperature and humidity time, equipment operation time, etc.

[0093] Furthermore, based on the occurrence sequence and duration of key time nodes, the behavioral change relationships between each time node are analyzed. For example, the time intervals between consecutive events, the superposition effect of events, and the cumulative impact of events on the state of hazardous chemicals can be statistically analyzed to generate a hazardous chemical change stage correlation index. This index is used to describe the time dependence and stage correlation of hazardous chemical behavior at different stages, including high-risk period identification, event triggering sequence priority, and inter-stage coupling relationship.

[0094] Subsequently, the original change scenarios were logically integrated using indicators related to the stages of hazardous chemical change. During the integration process, conflicting event sequences or inconsistent stage characteristics were corrected. For example, when monitoring data from different sources overlapped in time or conflicted in event priorities, rule-based reasoning or priority adjustment algorithms were used to reorder and correct the conflicting events, ensuring logical consistency in the hazardous chemical change scenarios.

[0095] Finally, after logical integration and conflict correction, a hazardous chemical change scenario was generated that can be used for hazard prediction. This scenario not only retains the time relevance and phased characteristics, but also clarifies the sequence of key events and the relationships between phases. Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0096] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS, characterized in that, Includes the following steps: Step S1: Enter hazardous chemical information into the LIMS system by scanning the electronic tags attached to the hazardous chemicals; Step S2: In response to the operation of updating hazardous chemicals during the movement of hazardous chemicals, update the status of the hazardous chemicals entered information according to the operation steps to obtain the updated hazardous chemicals data; Step S3: Compare the updated hazardous chemical data with the preset early warning rules. When the updated hazardous chemical data meets the preset early warning rules, generate the corresponding early warning information. Step S4: Push the early warning trigger information to the terminal server to predict the risk of hazardous chemicals, and generate disposal suggestions and contingency plans based on the prediction results, and obtain information records of hazardous chemical hazard handling.

2. The method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Obtain the GIS location information of hazardous chemicals; Step S22: Update the GIS location information in response to the hazardous chemical movement operation at a preset time interval, and confirm the time-series movement distance of the hazardous chemical based on the updated GIS location information; Step S23: Confirm the operational procedures for hazardous chemicals based on the time-series movement distance; Step S24: Update the status of hazardous chemicals based on the information entered in the operation procedure to obtain the updated data of hazardous chemicals.

3. The method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS according to claim 2, characterized in that, The operational steps for confirming hazardous chemicals based on time-series travel distance include: The movement status of hazardous chemicals is preliminarily determined based on the time-series movement distance, and a preliminary determination result is obtained. The preliminary determination is used to determine whether the hazardous chemicals are in a moving, issued or stationary state. Based on the frequency and timing of state switching in the preliminary assessment, the operating environment was reconfirmed to identify the operational steps for hazardous chemicals. These operational steps include continuous movement in transportation, short-range location switching in requisition, and long-term static storage in storage.

4. The method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS according to claim 3, characterized in that, Based on the frequency and timing of state transitions determined in the initial assessment, a secondary verification of the operating environment was conducted, including: By performing time-segmented statistical analysis on the frequency of state transitions in the preliminary judgment results, the temporal distribution data of state transitions is obtained. Based on the temporal distribution data of state transitions, the duration of transition and stable segments is analyzed to obtain the persistence characteristic data of state segments. By using the continuous characteristic data of state segments, the proportion of continuous movement segments, short-range switching segments, and stationary segments is identified, and segment proportion characteristic data is obtained. The change patterns of the operating environment are further confirmed by paragraph proportion feature data, and corresponding operation behavior type data is generated. Based on the operational behavior type data, the continuous movement behavior of the transportation link, the short-distance location switching behavior of the requisition link, and the long-term static behavior of the storage link are identified, thereby obtaining the operational links of hazardous chemicals.

5. The method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS according to claim 4, characterized in that, Methods for obtaining the changing patterns of the operating environment include: By summarizing the distribution of state switching frequency within continuous time slices, time slice characteristic data representing the stability of the operating environment are obtained. By comparing the fluctuation differences between adjacent time slices segment by segment based on the time slice characteristic data, fluctuation difference data reflecting the changing trend of the operating environment is obtained. By using fluctuation difference data to identify persistent and intermittent fluctuation segments in the changing trend, the rhythmic change characteristics of the operating environment can be obtained. Based on the rhythmic change characteristic data analysis, the switching rhythm and dwell rhythm of operational behavior are analyzed to obtain the change pattern of the operational environment.

6. The method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS according to claim 5, characterized in that, Identifying persistent and intermittent fluctuation segments in a trend using fluctuation difference data includes: When the amplitude of a single fluctuation remains between 0.5 and 2.0 units and the duration is between 3 and 10 seconds, it is determined to be a continuous fluctuation segment. When the fluctuation amplitude is less than 0.5 units or occurs only within 1s to 3s, it is determined to be an intermittent fluctuation segment.

7. The method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS according to claim 5, characterized in that, Based on data analysis of rhythmic change characteristics, the switching rhythm and dwell rhythm of operational behavior include: Identify state transition points based on rhythmic change characteristic data; The switching rhythm parameters are obtained based on the distribution density of state switching points and the dwell time of adjacent states; The rhythm difference in switching rhythm parameters distinguishes between the continuous change phase and the stable dwell phase. By analyzing the fluctuations in the continuous change phase and the stable residence phase, the change path of operational behavior can be determined in order to obtain the change pattern of the operational environment.

8. The method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Push the warning trigger information to the terminal server for local storage, and retrieve the historical accident information in the terminal server; Step S42: Construct a hazardous chemical risk prediction model based on updated hazardous chemical data and historical accident information; Step S43: Use the hazardous chemical risk prediction model to predict the hazardous chemical risk based on the early warning trigger information, and obtain the prediction results of the hazardous chemical risk; Step S44: Generate disposal suggestions and contingency plans based on the prediction results, and obtain information records on the handling of hazardous chemical hazards.

9. The method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS according to claim 1, characterized in that, Step S44 includes: Based on the prediction results, scenario-based simulations are conducted to identify several potential risk evolution scenarios; Based on the severity, controllability, and corresponding response time requirements of potential risk evolution scenarios, disposal measures that meet the current scenario conditions are selected from the pre-set hazardous chemical disposal knowledge base to obtain disposal recommendations; Based on the disposal recommendations, the feasibility of different potential risk evolution scenarios is assessed to obtain tiered disposal plans; Integrated disposal recommendations and tiered disposal plans serve as records of information on hazardous chemical hazard handling.

10. The method for monitoring the entire life cycle of hazardous chemicals and predicting safety hazards based on LIMS according to claim 9, characterized in that, Based on the prediction results, scenario-based simulations were conducted to identify several potential risk evolution scenarios, including: Based on the risk change trends in the prediction results, key time points where anomalies may occur are extracted; Based on the analysis of key time points, the main causes of hazardous chemicals are identified, resulting in several scenarios for changes in hazardous chemicals. By connecting the changing scenarios of hazardous chemicals in sequence according to key time nodes, different risk evolution paths can be obtained; Potential risk evolution scenarios are identified from the risk evolution paths.

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