Multi-parameter real-time monitoring and early warning system for dangerous chemical storage environment

Abstract

This invention relates to the field of monitoring and early warning technology, specifically disclosing a real-time monitoring and early warning system for multiple parameters of hazardous chemical storage environments. The system includes modules for data acquisition, data processing, trend prediction, and early warning decision-making. The data acquisition module employs a dynamically adjusted frequency, adaptively switching the sampling density based on the parameter change rate. The data processing module uses a deep residual recurrent neural network, employing static nonlinear compensation and dynamic non-steady-state compensation based on the change rate. The trend prediction module constructs a spatiotemporal sequence matrix and uses a multi-head attention spatiotemporal fusion network to collaboratively model time, space, and feature dimensions, outputting a predicted environmental risk value for the next time period. The early warning decision-making module nonlinearly fuses the predicted value with a real-time environmental fluctuation index calculated based on real-time concentration peaks and change rates. This cleverly solves the problems of delayed early warnings and high false alarm rates in traditional monitoring systems, improving the accuracy and reliability of the data.

Description

Technical Field

[0001] This invention relates to the field of monitoring and early warning technology, and in particular to a real-time monitoring and early warning system for multiple parameters of hazardous chemical storage environment. Background Technology

[0002] With the expansion of industrial scale, the safety monitoring of storage environments for hazardous chemicals has become a critical aspect of public safety. These environments not only experience drastic fluctuations in thermodynamic parameters such as temperature, humidity, and pressure, but also face the potential risk of leaks of toxic, harmful, or flammable gases.

[0003] Existing hazardous chemical storage monitoring systems typically use distributed sensor networks to collect environmental parameters and determine alarms based on preset static thresholds. However, this passive monitoring mode relies mainly on the triggering of single-point parameters exceeding limits, lacking in-depth analysis of the spatiotemporal evolution of multi-dimensional monitoring data. This makes it difficult for the system to effectively predict trends in the early stages of a hazard, often resulting in delayed warnings and insufficient emergency response time.

[0004] More importantly, hazardous chemical storage environments are typically complex, exhibiting non-steady-state characteristics such as drastic fluctuations in temperature and humidity. Existing gas sensors (especially electrochemical or semiconductor types) are highly susceptible to cross-coupling interference from environmental thermodynamic parameters, resulting in nonlinear zero-point drift or sensitivity drift. Traditional monitoring systems primarily employ fixed-frequency sampling and linear compensation methods, which struggle to effectively decouple temperature and humidity coupling interference. This leads to environmental noise contaminating the monitoring data, significantly increasing the system's false alarm rate and data uncertainty. Summary of the Invention

[0005] The main objective of this invention is to provide a multi-parameter real-time monitoring and early warning system for the storage environment of hazardous chemicals, aiming to solve the problems of insufficient robustness and accuracy of sensor data and lack of efficient spatiotemporal correlation prediction models in the existing technology, which lead to delayed early warning and inaccurate risk assessment.

[0006] To achieve the above objectives, the present invention provides a real-time monitoring and early warning system for multiple parameters of hazardous chemical storage environment, the system comprising:

[0007] The data acquisition module includes multiple sensor nodes distributed in the hazardous chemical storage area. Each sensor node is used to collect multi-dimensional environmental monitoring data in the hazardous chemical storage area and dynamically adjusts the frequency when collecting multi-dimensional environmental monitoring data.

[0008] Among them, multidimensional environmental monitoring data includes environmental thermodynamic parameters and gas concentration parameters with spatial coordinate information;

[0009] The data processing module, connected to the data acquisition module, is used to receive multidimensional environmental monitoring data and perform multi-state compensation processing on the gas concentration parameters through its internal preset nonlinear optimization model to output calibrated gas concentration characteristic data.

[0010] The trend prediction module, connected to the data processing module, is used to receive calibrated gas concentration characteristic data and process it to output the environmental risk prediction value for the next time period.

[0011] The early warning decision module is connected to the data processing module and the trend prediction module respectively. It is used to receive environmental risk prediction values ​​and calibrated gas concentration characteristic data, and to perform risk assessment to generate corresponding early warning signals.

[0012] Furthermore, the specific process of dynamically adjusting the frequency when collecting multidimensional environmental monitoring data includes:

[0013] Set a sliding time window and calculate the first derivative of each parameter in real time within the window to obtain the rate of change;

[0014] Determine whether the rate of change exceeds a preset fluctuation threshold;

[0015] If the fluctuation threshold is exceeded, a high-frequency sampling mode will be executed to collect multi-dimensional environmental monitoring data.

[0016] If the fluctuation threshold is not exceeded, the low-frequency sampling mode is executed to collect multi-dimensional environmental monitoring data.

[0017] Furthermore, the nonlinear optimization model in the data processing module is a deep residual recurrent neural network, and its polymorphic compensation process includes:

[0018] Static nonlinear compensation is performed to separate the coupling interference of environmental thermodynamic parameters on gas concentration parameters, and to provide the reference calibration value of gas concentration parameters collected by the data acquisition module during the environmental stabilization phase.

[0019] Dynamic unsteady-state compensation is performed. Based on the benchmark calibration value, the rate of change is used as a dynamic adjustment parameter to adaptively adjust the key weights of the deep residual recurrent neural network in real time to eliminate the unsteady-state drift generated by the data acquisition module and output the compensated intermediate gas characteristic data.

[0020] Furthermore, the dynamic compensation process also includes:

[0021] After performing dynamic non-steady-state compensation, a confidence assessment of abrupt change feature frames is introduced to evaluate the compensated intermediate gas feature data, thereby outputting the final calibrated gas concentration feature data; among which,

[0022] The specific process of the evaluation treatment includes:

[0023] The rate of change of each parameter in multidimensional environmental monitoring data is identified as a data fluctuation indicator factor.

[0024] The process of comparing the data fluctuation indicator with a preset confidence threshold and then applying confidence weights is as follows:

[0025] If the data fluctuation indicator factor exceeds the confidence threshold, then the corresponding data frame in the compensated intermediate gas characteristic data is marked with a low confidence weight.

[0026] If the data fluctuation indicator does not exceed the confidence threshold, the corresponding data frame in the intermediate gas feature data will be marked with a high confidence weight.

[0027] The corresponding data frames in the intermediate gas feature data after being labeled with confidence weights are integrated to form the calibrated gas concentration feature data output.

[0028] Furthermore, in the trend prediction module, the specific process for receiving and processing calibrated gas concentration characteristic data to output the environmental risk prediction value for the next time period includes: constructing a spatiotemporal sequence matrix and deep learning calculation.

[0029] Furthermore, the process of constructing the spatiotemporal sequence matrix includes:

[0030] It receives calibrated gas concentration characteristic data, calls up the spatial coordinate information of each sensor node in the data acquisition module, and obtains the rate of change of each parameter.

[0031] The calibrated gas concentration feature data, the rate of change of each parameter, and the spatial coordinate information of the sensor nodes are fused in three dimensions to reconstruct a multidimensional feature tensor containing time, space, and feature dimensions. The time dimension contains the calibrated gas concentration feature data and is arranged in the order of data acquisition. The spatial dimension contains the spatial location association data of the sensor nodes and the coordinate distribution of the sensor nodes. The feature dimension contains the calibrated gas concentration features and the rate of change of each parameter.

[0032] The feature data of each sensor node in the multidimensional feature tensor are arranged in a structured manner according to the time series to construct a spatiotemporal sequence matrix.

[0033] Furthermore, the deep learning computation process includes:

[0034] A multi-head attention spatiotemporal fusion network is used as a deep learning network, and a spatiotemporal sequence matrix is ​​used as input to perform collaborative modeling of the time dimension, spatial dimension and feature dimension.

[0035] Through a multi-head attention mechanism, dynamic spatial weights and feature weights are simultaneously assigned to different feature channels and different sensor nodes in the spatiotemporal sequence matrix; where different feature channels correspond to the calibrated gas concentration characteristics and the rate of change of each parameter, and different sensor nodes correspond to the coordinate correlation data of the spatial dimension.

[0036] The features after collaborative modeling are mapped and transformed through the fully connected layer of the multi-head attention spatiotemporal fusion network to output the environmental risk prediction value for the next time period.

[0037] Furthermore, the multi-head attention spatiotemporal fusion network comprises the following sequentially connected components:

[0038] An input layer is used to receive a spatiotemporal sequence matrix and convert it into a high-dimensional feature vector.

[0039] A multi-head attention layer, comprising multiple attention heads configured in parallel, wherein each attention head extracts the correlation information of the time dimension, spatial dimension and feature dimension in the high-dimensional feature vector and outputs a local feature vector.

[0040] The feature fusion layer uses a weighted summation algorithm to integrate local feature vectors to generate a global fused feature vector;

[0041] The fully connected layer performs non-linear mapping and dimensionality compression on the global fused feature vector, and outputs low-dimensional abstract features.

[0042] The output layer performs regression calculations on low-dimensional abstract features to output the predicted environmental risk value for the next time period.

[0043] Furthermore, the specific process of risk assessment performed by the early warning decision module is as follows:

[0044] Receive the environmental risk prediction value output by the trend prediction module;

[0045] Receive the calibrated gas concentration characteristic data and the rate of change of each parameter output by the data processing module;

[0046] Based on the currently received calibrated gas concentration characteristic data, the concentration peak is extracted in real time, and the real-time environmental fluctuation index is calculated according to the concentration peak and the rate of change of each parameter.

[0047] The predicted environmental risk values ​​are non-linearly weighted and fused with the real-time environmental fluctuation index to generate the final weighted comprehensive risk index.

[0048] The final weighted comprehensive risk index is compared with a preset risk threshold to determine the risk level of the environment and output a corresponding early warning signal.

[0049] Compared with the prior art, the beneficial effects that the present invention can achieve are as follows:

[0050] This invention addresses the problems of inefficient and real-time acquisition of environmental change data and insufficient data accuracy caused by environmental coupling interference in gas sensors under traditional fixed-frequency sampling strategies by employing a dynamically adjusted acquisition frequency in the data acquisition module and a nonlinear optimization model (deep residual recurrent neural network) built into the data processing module to perform multi-state compensation processing on gas concentration parameters. Simultaneously, the system constructs a spatiotemporal sequence matrix containing time, space, and feature dimensions through a trend prediction module and uses a multi-head attention spatiotemporal fusion network for collaborative modeling. This eliminates the deficiency of existing technologies in lacking in-depth analysis of the spatiotemporal evolution of multidimensional data, enabling the identification of subtle evolutionary trends in the early stages of a hazard. Finally, the early warning decision module integrates predicted values ​​with real-time fluctuation indices through nonlinear weighted fusion, ensuring the timeliness and reliability of early warning signals and avoiding the problems of delayed early warnings and high false alarm rates in traditional monitoring systems.

[0051] Furthermore, this invention also performs static nonlinear compensation and dynamic unsteady-state compensation based on the rate of change through the data processing module, which specifically separates the complex influence of environmental thermodynamic parameters on gas concentration measurement, significantly improving the accuracy and reliability of environmental monitoring data. At the same time, by integrating environmental risk prediction values ​​and real-time environmental fluctuation index through the early warning decision module, the forward-looking and robust risk assessment is ensured, avoiding the lag and misjudgment caused by traditional static threshold-triggered early warning. Ultimately, it achieves early and accurate early warning of hazardous chemical leakage risks, greatly improving the safety management level of hazardous chemical storage environment. Attached Figure Description

[0052] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0053] Figure 1 This is a system block diagram of the present invention.

[0054] 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

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0056] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0057] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0058] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0059] Example:

[0060] Please refer to Figure 1 This embodiment provides a real-time monitoring and early warning system for multiple parameters of the hazardous chemical storage environment. Specifically, the system includes:

[0061] The data acquisition module includes multiple sensor nodes distributed in the hazardous chemical storage area. Each sensor node is used to collect multi-dimensional environmental monitoring data in the hazardous chemical storage area and dynamically adjusts the frequency when collecting multi-dimensional environmental monitoring data.

[0062] Among them, multidimensional environmental monitoring data includes environmental thermodynamic parameters and gas concentration parameters with spatial coordinate information;

[0063] The data processing module, connected to the data acquisition module, is used to receive multidimensional environmental monitoring data and perform multi-state compensation processing on the gas concentration parameters through its internal preset nonlinear optimization model to output calibrated gas concentration characteristic data.

[0064] The trend prediction module, connected to the data processing module, is used to receive calibrated gas concentration characteristic data and process it to output the environmental risk prediction value for the next time period.

[0065] The early warning decision module is connected to the data processing module and the trend prediction module respectively. It is used to receive environmental risk prediction values ​​and calibrated gas concentration characteristic data, and to perform risk assessment to generate corresponding early warning signals.

[0066] It should be noted that, according to analysis, existing hazardous chemical storage monitoring systems have fundamental defects in their functionality, mainly in two core technical challenges: first, how to eliminate the nonlinear interference and drift of sensors caused by complex environments to ensure the robustness and accuracy of data; second, how to efficiently and in real time collect sudden change data and, based on multidimensional feature data, establish a complex spatiotemporal correlation prediction model that can effectively capture the diffusion of gas leaks, thereby achieving accurate early warning of hazardous chemical leak risks.

[0067] Further analysis reveals that traditional monitoring systems, employing fixed-frequency sampling and linear compensation methods, struggle to cope with cross-coupling interference and non-steady-state drift in complex environments, resulting in poor data quality. Furthermore, they lack the capability for in-depth spatiotemporal analysis of multidimensional data, causing early warning functions to often remain reactive and delayed. These shortcomings are the key obstacles that this invention aims to overcome through system architecture optimization.

[0068] Therefore, the multi-parameter real-time monitoring and early warning system for hazardous chemical storage environments provided in this application systematically solves the above-mentioned problems by setting up a data acquisition module with dynamically adjusted frequency, a data processing module with a nonlinear optimization model, and a trend prediction module. Specifically:

[0069] First, in this scheme, the data acquisition module adopts a dynamically adjusted frequency to collect multi-dimensional environmental monitoring data. Compared with the traditional fixed-frequency acquisition method, it can adjust the sampling density according to the actual changes in environmental parameters, thus ensuring the timeliness of data acquisition at critical moments while taking into account system resources. Second, the core of the data processing module of this scheme lies in its built-in nonlinear optimization model. This model is used to perform multi-state compensation processing on gas concentration parameters. It is specifically designed to solve the cross-coupling interference of environmental thermodynamic parameters on gas concentration and the non-steady-state drift of the sensor itself, which cannot be effectively separated and eliminated by traditional linear methods. Through this nonlinear optimization processing, the accuracy and robustness of the calibrated gas concentration characteristic data output by the system are greatly improved, laying a reliable data foundation for subsequent accurate prediction. Furthermore, based on this high-quality data, the trend prediction module receives the calibrated characteristic data and performs in-depth processing to output the environmental risk prediction value for the next time period, thus providing the system with a forward-looking early warning capability and overcoming the lag of existing systems that only rely on real-time over-limit alarms. Finally, the early warning decision module receives the risk prediction value from the prediction module and the real-time calibration data from the data processing module, and generates corresponding early warning signals by performing risk assessment.

[0070] Understandably, in this solution, the modules are closely integrated, forming a complete closed-loop optimization mechanism from data acquisition to risk warning. This effectively solves the problem of sensor cross-coupling interference and drift caused by drastic fluctuations in environmental thermodynamic parameters in the hazardous chemical storage environment. At the same time, it realizes real-time trend prediction of subtle changes in the early stage of danger, significantly reducing the risk of warning lag and false alarms.

[0071] In some embodiments, the dynamic frequency adjustment in the data acquisition module can be understood as a technique for adaptively adjusting the sampling period according to environmental changes. Specifically, this can be achieved by setting upper and lower thresholds for the number of samples within a fixed time interval. For example, a lower sampling frequency is used when environmental parameters fluctuate little, while automatically switching to a higher sampling frequency when abnormal fluctuations are detected. This mechanism ensures that appropriate data density is obtained under different environmental conditions.

[0072] In some embodiments, the environmental thermodynamic parameters in the multidimensional environmental monitoring data may include physical quantities such as temperature, humidity, and pressure. These parameters are collected by sensor nodes with positioning functions to form a monitoring data set with spatial distribution characteristics. The gas concentration parameters mainly refer to the concentration values ​​of various hazardous gases. The acquisition process needs to consider the sensor's detection sensitivity and response time for different gases.

[0073] In some embodiments, the nonlinear optimization model in the data processing module can be implemented using various machine learning algorithms, such as support vector machines, random forests, or neural networks. These models establish a complex mapping relationship between environmental thermodynamic parameters and gas concentration parameters by learning from historical data, thereby achieving error compensation for gas concentration measurements.

[0074] In some embodiments, the trend prediction module can be implemented using time series analysis methods, such as the Autoregressive Integrated Moving Average (ARIMA) model or exponential smoothing. These methods predict the trend of environmental risk changes over a future period by identifying patterns in historical data.

[0075] In some embodiments, the risk assessment process of the early warning decision module can be implemented based on rule-based reasoning or fuzzy logic. For example, by setting different weight coefficients to perform weighted calculations on various input parameters, a comprehensive risk assessment result can be obtained.

[0076] As a further preferred embodiment of this application, the process of dynamically adjusting the frequency when the data acquisition module collects multi-dimensional environmental monitoring data is described in detail below. Specifically, the process includes the following steps:

[0077] Set a sliding time window and calculate the first derivative of each parameter in real time within the window to obtain the rate of change;

[0078] Determine whether the rate of change exceeds a preset fluctuation threshold;

[0079] If the fluctuation threshold is exceeded, a high-frequency sampling mode will be executed to collect multi-dimensional environmental monitoring data.

[0080] If the fluctuation threshold is not exceeded, the low-frequency sampling mode is executed to collect multi-dimensional environmental monitoring data.

[0081] Understandably, in some specific embodiments, a sliding time window refers to a dynamic time interval with a fixed length that is continuously updated over time. It can be implemented using a data structure based on a circular buffer or a queue, with the aim of continuously tracking the instantaneous change trend of environmental parameters. The first derivative refers to the rate of change of environmental parameters per unit time, which can be calculated using difference algorithms or differential approximation methods, with the aim of quantifying the severity of changes in environmental parameters. The fluctuation threshold is a critical value used to distinguish whether environmental parameters are in a state of severe fluctuation. It can be determined based on historical data statistical analysis or empirical settings, with the aim of providing a decision basis for switching sampling frequencies.

[0082] As an example, multidimensional environmental monitoring data In time Nearby rate of change The expression satisfies:

[0083] ;

[0084] in, Indicates the current moment The calculated rate of change of the parameters (i.e., the first derivative). Indicates the current moment Collected environmental monitoring data; Indicates the previous adoption time Collected environmental monitoring data; Indicates the current sampling time interval.

[0085] The rate of change is obtained through the above expression. Then, the sampling frequency is dynamically adjusted according to the following decision rules. Its decision-making rules satisfy:

[0086] ;

[0087] in, This is the preset high-frequency sampling mode frequency (e.g., 2Hz, which means sampling twice per second). This is the preset low-frequency sampling mode frequency (e.g., 0.1Hz, which means sampling once every ten seconds). The sampling frequency is adaptively switched by using a preset fluctuation threshold (which can be determined based on the maximum allowable rate of change of the monitored gas concentration within a ten-second sliding window, for example, 0.5 ppm / s. When the rate of change of gas concentration exceeds this threshold, it is immediately judged as a potential sudden change and switched to high-frequency sampling).

[0088] In detail, this solution effectively solves the core problem of mismatch between sampling frequency and parameter changes in environmental monitoring by constructing a specific mechanism for dynamically adjusting the frequency. Overall, it uses the real-time rate of change of environmental parameters as the decision-making basis to achieve adaptive switching of the sampling frequency. This avoids the limitations of fixed-frequency sampling in complex environments while ensuring data capture capabilities during critical change phases. Specifically, the dynamic characteristics of the sliding time window continuously track the instantaneous change trend of environmental parameters, avoiding the response lag caused by fixed time interval calculations, and providing a highly timely quantitative basis for frequency adjustment. Based on the precise comparison of the rate of change and threshold, intelligent identification of environmental states is achieved, ensuring that the system only triggers high-frequency sampling when parameter fluctuations exceed safe ranges, thus avoiding resource redundancy caused by indiscriminate high-frequency sampling. Actively increasing the sampling frequency during periods of drastic parameter fluctuations can fully capture the detailed features of sudden environmental changes, significantly enhancing the system's early perception capability of potential risks. Reducing the sampling frequency under stable environmental conditions effectively reduces data processing burden and sensor energy consumption, extends equipment lifespan, and ensures the long-term stability and economy of the system.

[0089] Therefore, this solution, by combining the aforementioned dynamic frequency adjustment mechanism with the distributed sensor nodes deployed in the hazardous chemical storage area and the multi-dimensional environmental monitoring data acquisition process, further improves the overall performance of the system. By dynamically adjusting the sampling frequency, it can not only more accurately capture the key changing trends of environmental parameters, but also optimize resource utilization efficiency when the environment is stable, thus providing a high-quality data foundation for subsequent nonlinear optimization model processing and environmental risk prediction.

[0090] As a further preferred embodiment, this application will provide a detailed description, wherein the nonlinear optimization model in the data processing module is a deep residual recurrent neural network, and its polymorphic compensation process includes:

[0091] Static nonlinear compensation is performed to separate the coupling interference of environmental thermodynamic parameters on gas concentration parameters, and to provide the reference calibration value of gas concentration parameters collected by the data acquisition module during the environmental stabilization phase.

[0092] Dynamic unsteady-state compensation is performed. Based on the benchmark calibration value, the rate of change is used as a dynamic adjustment parameter to adaptively adjust the key weights of the deep residual recurrent neural network in real time to eliminate the unsteady-state drift generated by the data acquisition module and output the compensated intermediate gas characteristic data.

[0093] Understandably, a deep residual recurrent neural network (RNN) is a deep learning model that combines residual structures and recurrent mechanisms. It can be implemented using multiple recurrent units and skip connections, aiming to enhance the network's ability to memorize long-term data series and alleviate the gradient vanishing problem. Static nonlinear compensation refers to separating the interference of environmental thermodynamic parameters on gas concentration parameters through mathematical modeling under relatively stable environmental parameters. It can be implemented through algorithms such as polynomial fitting or support vector machines, aiming to establish reliable benchmark calibration values. Dynamic non-steady-state compensation refers to dynamically adjusting network weights according to the real-time changes in environmental parameters to adapt to drift characteristics under non-steady-state conditions. It can be implemented through adaptive filtering algorithms or online learning mechanisms, aiming to improve the system's adaptability to complex environments.

[0094] For example, static nonlinear compensation aims to establish a nonlinear coupling interference model of environmental thermodynamic parameters on the sensor's original output, thereby obtaining a reference calibration, whose expression satisfies:

[0095] ;

[0096] in, The reference calibration value represents the stable output of the gas concentration parameter after static disturbance decoupling, and is also the input data for dynamic unsteady-state compensation. This represents the raw gas concentration parameters collected by the sensor (input to the data processing module). A characteristic vector representing environmental thermodynamic parameters (such as temperature, humidity, etc.); This indicates that the static nonlinear compensation model, trained during the stable phase of the environment, is used to separate... right Coupling interference.

[0097] Furthermore, for dynamic unsteady-state compensation, based on the benchmark calibration value... First, the real-time rate of change is introduced. As tuning parameters, key weights of deep residual recurrent neural networks (DRNNs) are implemented. Real-time adaptive adjustment, specifically, its time step Key weights The adaptive adjustment mechanism satisfies:

[0098] ;

[0099] Subsequently, the DRNN further processes the baseline calibration values ​​using the adjusted key weights to output compensated intermediate gas feature data, the process of which satisfies:

[0100] ;

[0101] in, This represents the intermediate gas characteristic data after compensation (i.e., the output of dynamic compensation). Indicates the current time Key weights of deep residual recurrent neural networks; The baseline weights are obtained from training during the static nonlinear compensation phase. Indicates based on real-time rate of change The calculated dynamic adjustment amount; This represents the nonlinear processing function of a deep residual recurrent neural network.

[0102] As shown above, this scheme achieves effective calibration of gas concentration data through a phased compensation strategy. First, in the static nonlinear compensation phase, an interference decoupling model is constructed using data collected during the environmental stability phase. This process effectively separates the coupling interference of environmental thermodynamic parameters on gas concentration parameters, thus providing accurate benchmark calibration values ​​for subsequent dynamic compensation. On this basis, the rate of change is introduced as a key adjustment parameter in the dynamic unsteady-state compensation phase. By adaptively adjusting the key weights of the deep residual recurrent neural network in real time, the network can accurately capture the drift characteristics under unsteady-state conditions.

[0103] Therefore, this embodiment, based on a dynamic adjustment mechanism of the rate of change, can not only reflect the real-time fluctuation of environmental parameters but also avoid the failure problem of traditional fixed compensation methods in environments with severe fluctuations. Furthermore, its combination with the dynamic adjustment frequency of the data acquisition module further improves the monitoring accuracy and robustness of the system in complex environments, significantly reducing false alarm rates and data uncertainty. This enables precise calibration of gas concentration data in hazardous chemical storage environments, especially under non-steady-state conditions such as severe temperature and humidity fluctuations. It effectively eliminates zero-point drift and sensitivity drift of the sensor, improving the reliability and accuracy of monitoring data and greatly enhancing the system's precision.

[0104] In a further preferred embodiment, this application further proposes that the dynamic compensation process also includes:

[0105] After performing dynamic non-steady-state compensation, a confidence assessment of abrupt change feature frames is introduced to evaluate the compensated intermediate gas feature data, so as to output the final calibrated gas concentration feature data; wherein, the specific process of the evaluation process includes: identifying the rate of change of each parameter in the multidimensional environmental monitoring data as a data fluctuation indicator factor;

[0106] The data fluctuation indicator is compared with a preset confidence threshold and a confidence weight is applied. The process is as follows: if the data fluctuation indicator exceeds the confidence threshold, a low confidence weight is applied to the corresponding data frame in the compensated intermediate gas feature data; if the data fluctuation indicator does not exceed the confidence threshold, a high confidence weight is applied to the corresponding data frame in the intermediate gas feature data.

[0107] The corresponding data frames in the intermediate gas feature data after being labeled with confidence weights are integrated to form the calibrated gas concentration feature data output.

[0108] Understandably, the data fluctuation indicator is a key indicator that quantifies the degree of environmental abrupt change by calculating the rate of change of various parameters. It can be implemented by using a moving average algorithm or a difference algorithm to extract the rate of change, with the aim of sensitively capturing the impact of drastic fluctuations in environmental thermodynamic parameters on the quality of sensor data. On the other hand, confidence weight labeling is the process of assigning different confidence levels to data frames based on the comparison results of the rate of change and a preset threshold. It can be implemented by setting high and low confidence weight coefficients (for example, the low confidence weight coefficient is set to 0.3, while the high confidence weight coefficient is set to 0.9) to achieve graded labeling, with the aim of distinguishing the reliability of data in stable environments and abrupt change environments.

[0109] Specifically, this solution effectively addresses the issue of insufficient reliability of compensation data under environmental abrupt changes by constructing a dynamic confidence assessment mechanism. First, by introducing confidence assessment of abrupt change characteristic frames on top of dynamic non-steady-state compensation, the impact of environmental abrupt changes on data quality can be identified in a timely manner, avoiding the direct use of potentially distorted intermediate data in the final output. Second, by using the rate of change as an indicator of data fluctuation, combined with a preset confidence threshold for dynamic grading, accurate judgment of data reliability is ensured. When the rate of change exceeds the confidence threshold, the influence of anomalous data is reduced by marking it with a low confidence weight, preventing it from distorting the overall calibration results. Conversely, when the rate of change does not exceed the confidence threshold, high-quality data under stable conditions is retained by marking it with a high confidence weight. Finally, by integrating data frames with different confidence weights, effective suppression of environmental noise is achieved, resulting in more robust calibration data output.

[0110] Therefore, this solution, combined with the dynamic non-steady-state compensation process in the aforementioned system, forms a complete processing chain from data compensation to confidence assessment. By introducing a confidence assessment mechanism, not only is the reliability of calibration data improved, but a highly reliable input basis is also provided for subsequent risk prediction, significantly reducing the false alarm rate and enhancing the system's early warning capability.

[0111] In a further embodiment, the specific process of receiving calibrated gas concentration feature data and processing it to output the environmental risk prediction value for the next time period in the trend prediction module includes: constructing a spatiotemporal sequence matrix and deep learning calculation.

[0112] It should be understood that constructing a spatiotemporal sequence matrix refers to the process of structurally integrating calibrated gas concentration characteristic data with time, space, and feature dimensions. This can be achieved using three-dimensional data fusion technology, aiming to capture the spatiotemporal characteristics of risk evolution in hazardous chemical storage environments through joint modeling of multi-dimensional data. Deep learning computing can be understood as a technique for automatically extracting nonlinear correlations and dynamic patterns from the spatiotemporal sequence matrix based on complex neural network models. This can be achieved through multi-head attention mechanisms or convolutional neural networks, aiming to mine deep spatiotemporal features from historical data, thereby improving prediction accuracy.

[0113] In detail, this solution achieves comprehensive integration of multidimensional monitoring data by constructing a spatiotemporal sequence matrix. Specifically, it first receives calibrated gas concentration characteristic data and combines it with the spatial coordinate information of sensor nodes and the rate of change of each parameter to form a multidimensional feature tensor containing time, space, and feature dimensions. Based on this, the data in the multidimensional feature tensor are arranged in time sequence to finally generate the spatiotemporal sequence matrix. Thus, this structured data organization not only preserves the dynamic changes in the time series but also incorporates spatial distribution correlation information, enabling the system to identify the spatial propagation path and temporal gradual trend of risk. Then, deep learning computation collaboratively models the spatiotemporal sequence matrix, solving the problem of traditional methods struggling to cope with complex environmental interference. In this embodiment, a multi-head attention mechanism is used to assign dynamic weights to different feature channels and sensor nodes, thereby highlighting the importance of key features. By mapping and transforming the collaboratively modeled features, the environmental risk prediction value for the next time period is finally output, effectively capturing early weak signals and significantly improving the predictive foresight and reliability.

[0114] Thus, through the above technical solution, the system not only overcomes the limitations of traditional methods that focus only on a single point in time or isolated spatial location, but also extracts deep spatiotemporal features from complex multidimensional monitoring data, thereby achieving more accurate risk prediction. This provides strong technical support for the safety monitoring of hazardous chemical storage environments, significantly improving the timeliness and accuracy of early warnings.

[0115] As a preferred embodiment, the process of constructing the spatiotemporal sequence matrix is ​​described in detail below, including:

[0116] It receives calibrated gas concentration characteristic data, calls up the spatial coordinate information of each sensor node in the data acquisition module, and obtains the rate of change of each parameter.

[0117] The calibrated gas concentration feature data, the rate of change of each parameter, and the spatial coordinate information of the sensor nodes are fused in three dimensions to reconstruct a multidimensional feature tensor containing time, space, and feature dimensions. The time dimension contains the calibrated gas concentration feature data and is arranged in the order of data acquisition. The spatial dimension contains the spatial location association data of the sensor nodes and the coordinate distribution of the sensor nodes. The feature dimension contains the calibrated gas concentration features and the rate of change of each parameter.

[0118] The feature data of each sensor node in the multidimensional feature tensor are arranged in a structured manner according to the time series to construct a spatiotemporal sequence matrix.

[0119] Understandably, a multidimensional feature tensor is a data structure capable of simultaneously encoding temporal evolution, spatial correlation, and feature dynamics, which can be implemented through tensor operations in deep learning frameworks. In this embodiment, the temporal dimension of the multidimensional feature tensor can employ a sliding window mechanism to preserve the continuity of historical data, aiming to capture long-term dependencies of environmental parameters; the spatial dimension can generate an adjacency matrix based on the physical layout of sensor nodes to reflect spatial correlation, aiming to identify the diffusion path of gas leaks; and the feature dimension can integrate multiple monitoring parameters to comprehensively characterize the environmental state, aiming to overcome the limitations of traditional methods that rely on only a single parameter.

[0120] Specifically, this scheme addresses the problem of insufficient capture of spatiotemporal evolution patterns in trend prediction by introducing the spatial coordinate information and parameter change rates of sensor nodes. First, receiving calibrated gas concentration characteristic data ensures the reliability of the input data and avoids noise interference caused by environmental thermodynamic parameters. Simultaneously, utilizing the spatial coordinate information of sensor nodes enables the system to identify spatial distribution differences in gas concentrations across different regions. The change rates of each parameter are acquired as dynamic indicators, providing real-time fluctuation characteristics for subsequent analysis. Based on this, a multidimensional feature tensor is formed through three-dimensional data fusion and reconstruction. The time dimension, arranged in the order of acquisition, preserves the time-series characteristics of historical data; the spatial dimension reflects the spatial layout and correlation of sensors; and the feature dimension integrates concentration characteristics and change rates to comprehensively characterize the environmental state. This multidimensional fusion approach provides a structured information foundation for subsequent deep learning. Furthermore, the feature data of each sensor node in the multidimensional feature tensor are structured and arranged to construct a spatiotemporal sequence matrix, ensuring that the matrix accurately maps the spatiotemporal propagation patterns of environmental risks, thereby improving the foresight and accuracy of early warnings.

[0121] Clearly, this scheme fully leverages the reliability of calibrated gas concentration characteristic data when integrating the spatial location information and parameter change rates of sensor nodes. By combining the synergistic effect of spatial and characteristic dimensions, it significantly improves the accuracy and timeliness of trend prediction. In this way, it can not only effectively capture the spatiotemporal propagation dynamics of gas concentration in hazardous chemical storage environments but also provide high-quality input data for subsequent deep learning calculations, thereby further optimizing the effectiveness of environmental risk prediction.

[0122] As a further preferred embodiment, the deep learning computation process is described in detail below, including:

[0123] A multi-head attention spatiotemporal fusion network is used as a deep learning network, and a spatiotemporal sequence matrix is ​​used as input to perform collaborative modeling of the time dimension, spatial dimension and feature dimension.

[0124] Through a multi-head attention mechanism, dynamic spatial weights and feature weights are simultaneously assigned to different feature channels and different sensor nodes in the spatiotemporal sequence matrix; where different feature channels correspond to the calibrated gas concentration characteristics and the rate of change of each parameter, and different sensor nodes correspond to the coordinate correlation data of the spatial dimension.

[0125] The features after collaborative modeling are mapped and transformed through the fully connected layer of the multi-head attention spatiotemporal fusion network to output the environmental risk prediction value for the next time period.

[0126] Specifically, the multi-head attention spatiotemporal fusion network refers to a deep learning architecture based on an attention mechanism, which can be implemented using multi-head self-attention modules, residual connection modules, and feedforward neural network modules. The spatiotemporal sequence matrix can be understood as a multi-dimensional data structure containing time, space, and feature dimensions, aiming to provide a unified representation of multi-parameter monitoring data in hazardous chemical storage environments. In practical applications, the multi-head attention mechanism is a technique capable of dynamically allocating weights. It can dynamically adjust weights by calculating the similarity between query vectors, key vectors, and value vectors, thereby enhancing the model's ability to focus on high-risk information.

[0127] In detail, this solution achieves deep spatiotemporal correlation analysis of multi-parameter monitoring data of hazardous chemical storage environments through the core architecture of a multi-head attention spatiotemporal fusion network, thereby accurately identifying risk evolution patterns and improving prediction reliability. In the specific implementation process, firstly, based on the multidimensional structural characteristics of the spatiotemporal sequence matrix, a multi-head attention spatiotemporal fusion network is used to collaboratively model the time, spatial, and feature dimensions. This allows the model to simultaneously analyze the dynamic evolution of environmental parameters over time, the spatial distribution characteristics of sensor nodes, and the intrinsic interaction between gas concentration characteristics and change rates. Furthermore, based on this, a multi-head attention mechanism simultaneously assigns dynamically adjusted spatial and feature weights to different feature channels and sensor nodes in the spatiotemporal sequence matrix. This automatically optimizes weight allocation based on real-time data fluctuation characteristics; for example, it strengthens the weights of corresponding nodes in areas of abrupt gas concentration changes or when key parameters change drastically, enabling the model to adaptively focus on high-risk information. Finally, a fully connected layer maps and transforms the collaboratively modeled features, extracting the high-dimensional fusion features into environmental risk prediction values ​​for the next time period. This transformation process is based on nonlinear mapping to compress redundant information, ensuring that the prediction results retain key risk characteristics while maintaining operability.

[0128] Thus, through the above technical solution, this application effectively solves the problem of the lack of in-depth analysis of the spatiotemporal evolution law of multidimensional monitoring data in existing systems, significantly suppresses the unsteady drift caused by environmental thermodynamic parameter interference, and fundamentally overcomes the risk of early warning lag caused by prediction deviation in existing systems.

[0129] In a further embodiment, a multi-head attention spatiotemporal fusion network is specifically described herein, which includes sequentially connected:

[0130] An input layer is used to receive a spatiotemporal sequence matrix and convert it into a high-dimensional feature vector.

[0131] A multi-head attention layer, comprising multiple attention heads configured in parallel, wherein each attention head extracts the correlation information of the time dimension, spatial dimension and feature dimension in the high-dimensional feature vector and outputs a local feature vector.

[0132] The feature fusion layer uses a weighted summation algorithm to integrate local feature vectors to generate a global fused feature vector;

[0133] The fully connected layer performs non-linear mapping and dimensionality compression on the global fused feature vector, and outputs low-dimensional abstract features.

[0134] The output layer performs regression calculations on low-dimensional abstract features to output the predicted environmental risk value for the next time period.

[0135] Understandably, in this embodiment, the input layer refers to the processing unit that transforms the spatiotemporal sequence matrix into a high-dimensional feature vector. It can be implemented using an embedded encoder or matrix transformer, aiming to preserve the complete details of multidimensional parameters in the hazardous chemical storage environment and provide a high-information-density input foundation for subsequent processing. The multi-head attention layer refers to a structure that extracts information related to different dimensions in the high-dimensional feature vector through multiple parallel attention heads. It can be implemented using a self-attention mechanism or a cross-dimensional interaction module, aiming to simultaneously focus on the interaction relationships of time, space, and feature dimensions, enhancing the model's adaptability to complex environmental fluctuations. The feature fusion layer refers to the processing unit that integrates local feature vectors using a weighted summation algorithm. It can be implemented using a dynamic weight allocator or a confidence assessment module, aiming to effectively fuse features based on their importance and suppress environmental noise interference. The fully connected layer refers to the processing unit that performs nonlinear mapping and dimensionality compression on the globally fused feature vector. It can be implemented using an activation function combined with a dimensionality reduction matrix, aiming to capture the nonlinear relationships between features and focus on core risk indicators. The output layer refers to the processing unit that performs regression calculations based on low-dimensional abstract features. It can be implemented using a linear regressor or a nonlinear predictor, with the aim of transforming abstract features into quantifiable risk indicators.

[0136] Specifically, the aforementioned multi-head attention spatiotemporal fusion network achieves efficient prediction of environmental risks in hazardous chemical storage through the organic cooperation between its layers. Furthermore, the input layer transforms the spatiotemporal sequence matrix containing temporal, spatial, and feature-dimensional information into a high-dimensional feature vector, ensuring the integrity of the original data. The multi-head attention layer extracts the rate of change in the temporal dimension, sensor coordinate correlation in the spatial dimension, and the coupling information of gas concentration and thermodynamic parameters in the feature dimension through multiple parallel attention heads, enabling it to simultaneously focus on feature interactions across different dimensions, overcoming the limitations of a single attention head in processing multidimensional spatiotemporal data. Additionally, the feature fusion layer dynamically adjusts the weights based on the importance of local features output by each attention head, integrating them to generate a global fusion feature vector, effectively suppressing environmental noise and abrupt changes. For the fully connected layer, it simplifies data complexity and focuses on core risk indicators by performing nonlinear mapping and dimensional compression on the global fusion feature vector. Finally, the output layer performs regression calculations based on low-dimensional abstract features, allowing the prediction results to directly reflect the evolution trend of environmental risks.

[0137] This solution provides a high-quality data foundation for input by using the multidimensional feature tensor formed by the fusion and reconstruction of three-dimensional data, thereby further improving the accuracy and real-time performance of environmental risk prediction and effectively addressing the complex interference caused by drastic fluctuations in temperature and humidity and unsteady drift of gas concentration in the hazardous chemical storage environment.

[0138] In a further preferred embodiment, the specific process of the risk assessment performed by the early warning decision module is as follows:

[0139] Receive the environmental risk prediction value output by the trend prediction module;

[0140] Receive the calibrated gas concentration characteristic data and the rate of change of each parameter output by the data processing module;

[0141] Based on the currently received calibrated gas concentration characteristic data, the concentration peak is extracted in real time, and the real-time environmental fluctuation index is calculated according to the concentration peak and the rate of change of each parameter.

[0142] The predicted environmental risk values ​​are non-linearly weighted and fused with the real-time environmental fluctuation index to generate the final weighted comprehensive risk index.

[0143] The final weighted comprehensive risk index is compared with a preset risk threshold to determine the risk level of the environment and output a corresponding early warning signal.

[0144] Clearly, the real-time environmental fluctuation index is a comprehensive indicator constructed by quantifying the intensity of dynamic changes in environmental parameters. It can be implemented using mathematical models or algorithms, such as weighted summation or nonlinear mapping, aiming to accurately reflect the true risk level during periods of drastic fluctuations in environmental thermodynamic parameters. Nonlinear weighted fusion is a fusion mechanism that dynamically adjusts weights. It can be implemented by introducing adaptive weight allocation algorithms or machine learning models, aiming to balance the contributions of predicted environmental risks and the real-time environmental fluctuation index, thereby improving the robustness and accuracy of risk assessment. The final weighted comprehensive risk index is a global risk assessment result generated after integrating multi-dimensional information. It can be implemented through regression analysis, fuzzy logic, or other numerical calculation methods, aiming to provide a basis for tiered early warning.

[0145] As an example, the calculation expression for the final weighted composite risk index satisfies:

[0146] ;

[0147] in, This represents the final weighted composite risk index, which serves as the basis for tiered early warning systems. This represents the predicted environmental risk value, derived from the trend prediction module. This represents a real-time environmental fluctuation index, calculated based on peak concentration and rate of change. and These are the dynamic weighting coefficients for predicted risk and real-time volatility index, used to balance the contributions of the two. This represents a nonlinear mapping function used to capture complex nonlinear risk relationships.

[0148] Preferably, the preset risk threshold can be set as a tiered threshold, for example:

[0149] Level 1 risk threshold, corresponding to low risk, can be set as follows: ;

[0150] The level 2 risk threshold, corresponding to medium risk, can be set as follows: ;

[0151] The third-level risk threshold, corresponding to high risk, can be set as follows: Obviously, this preset risk threshold is usually based on the safety management standards for hazardous chemicals or historical data statistics.

[0152] In detail, the above-mentioned scheme achieves efficient risk assessment in hazardous chemical storage environments through the organic combination of several key steps. First, the environmental risk prediction value output by the trend prediction module provides a forward-looking judgment based on historical data evolution. This information, as an extension of the time dimension, can capture the development trend of potential risks. Second, the calibrated gas concentration characteristic data and the rate of change of each parameter output by the data processing module lay a high-precision and dynamic input foundation for risk assessment. The calibrated data eliminates the cross-coupling interference of environmental thermodynamic parameters, while the rate of change quantifies the intensity of parameter fluctuations. On this basis, by extracting the concentration peak in real time and combining it with the rate of change to calculate the real-time environmental fluctuation index, it is possible to effectively distinguish between environmental noise and real leakage signals, avoiding the limitations of triggering single parameter thresholds. Subsequently, the environmental risk prediction value and the real-time environmental fluctuation index are integrated through a nonlinear weighted fusion mechanism, dynamically adjusting the contribution weights of the two, which retains the long-term perspective of trend prediction while strengthening the sensitive response to real-time fluctuations. Finally, by comparing the final weighted comprehensive risk index with the preset risk threshold, accurate output of graded early warning is achieved, significantly improving the system's ability to suppress false alarms and the reliability of early warnings in complex environments.

[0153] Therefore, it can be clearly stated that this scheme further enhances the real-time performance and accuracy of risk assessment by introducing the rate of change in multidimensional environmental monitoring data as a dynamic indicator. At the same time, the nonlinear weighted fusion mechanism and the polymorphic compensation processing of the deep residual recurrent neural network complement each other and jointly solve the problem of high false alarm rate under drastic fluctuations in environmental thermodynamic parameters, thereby improving the overall early warning performance and reliability of the system.

[0154] In addition, in the present solution, it is further preferred that after the early warning decision module generates a corresponding early warning signal, the module can send the signal to an external alarm device, a fire protection system or an emergency command platform through a communication interface to achieve linkage. For example, when the final weighted comprehensive risk index exceeds the three-level risk threshold, the system automatically triggers the on-site audible and visual alarm to keep alarming, links the ventilation equipment to start automatically, and pushes early warning short messages and real-time risk data to the mobile terminals of on-site management personnel at the same time.

[0155] In addition, it can be understood that the data transmission and connection between the data acquisition module, data processing module, trend prediction module and early warning decision module involved in the present solution is preferably implemented through wired or wireless communication means (such as Ethernet, Wi-Fi, LoRa or CAN bus, etc.) to realize real-time exchange of data and control signals, ensuring smooth and closed-loop data flow between all modules.

[0156] Finally, it should be noted that in this document, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system comprising a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. Without more restrictions, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or system that comprises said element.

[0157] The serial numbers of the embodiments of the present invention described above are for description only, and do not represent the advantages or disadvantages of the embodiments.

[0158] Through the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiments can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation mode. Based on this understanding, the technical solution of the present invention, essentially or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disc), and includes a number of instructions to enable a multimedia terminal device (which can be a mobile phone, a computer, a television receiver, or a network device, etc.) to execute the method described in various embodiments of the present invention.

[0159] The above are only preferred embodiments of the present invention, and are not used to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the description and drawings of the present invention, whether directly or indirectly applied to other related technical fields, is similarly included in the scope of patent protection of the present invention.

Claims

1. A real-time monitoring and early warning system for multiple parameters of hazardous chemical storage environment, characterized in that, The system includes: The data acquisition module includes multiple sensor nodes distributed in the hazardous chemical storage area. Each sensor node is used to collect multi-dimensional environmental monitoring data in the hazardous chemical storage area and dynamically adjusts the frequency when collecting multi-dimensional environmental monitoring data. Among them, multidimensional environmental monitoring data includes environmental thermodynamic parameters and gas concentration parameters with spatial coordinate information; The data processing module, connected to the data acquisition module, is used to receive multidimensional environmental monitoring data and perform multi-state compensation processing on the gas concentration parameters through its internal preset nonlinear optimization model to output calibrated gas concentration characteristic data. The trend prediction module, connected to the data processing module, is used to receive calibrated gas concentration characteristic data and process it to output the environmental risk prediction value for the next time period. The early warning decision module is connected to the data processing module and the trend prediction module respectively. It is used to receive environmental risk prediction values ​​and calibrated gas concentration characteristic data, and to perform risk assessment to generate corresponding early warning signals.

2. The multi-parameter real-time monitoring and early warning system for hazardous chemical storage environment according to claim 1, characterized in that, The specific process of dynamically adjusting the frequency when collecting multidimensional environmental monitoring data includes: Set a sliding time window and calculate the first derivative of each parameter in real time within the window to obtain the rate of change; Determine whether the rate of change exceeds a preset fluctuation threshold; If the fluctuation threshold is exceeded, a high-frequency sampling mode will be executed to collect multi-dimensional environmental monitoring data. If the fluctuation threshold is not exceeded, the low-frequency sampling mode is executed to collect multi-dimensional environmental monitoring data.

3. The real-time monitoring and early warning system for multiple parameters of hazardous chemical storage environment according to claim 1, characterized in that, The nonlinear optimization model in the data processing module is a deep residual recurrent neural network, and its polymorphic compensation process includes: Static nonlinear compensation is performed to separate the coupling interference of environmental thermodynamic parameters on gas concentration parameters, and to provide the reference calibration value of gas concentration parameters collected by the data acquisition module during the environmental stabilization phase. Dynamic unsteady-state compensation is performed. Based on the benchmark calibration value, the rate of change is used as a dynamic adjustment parameter to adaptively adjust the key weights of the deep residual recurrent neural network in real time to eliminate the unsteady-state drift generated by the data acquisition module and output the compensated intermediate gas characteristic data.

4. The real-time monitoring and early warning system for multiple parameters of hazardous chemical storage environment according to claim 3, characterized in that, The dynamic compensation process also includes: After performing dynamic non-steady-state compensation, a confidence assessment of abrupt change feature frames is introduced to evaluate the compensated intermediate gas feature data, thereby outputting the final calibrated gas concentration feature data; among which, The specific process of the evaluation treatment includes: The rate of change of each parameter in multidimensional environmental monitoring data is identified as a data fluctuation indicator factor. The process of comparing the data fluctuation indicator with a preset confidence threshold and then applying confidence weights is as follows: If the data fluctuation indicator factor exceeds the confidence threshold, then the corresponding data frame in the compensated intermediate gas characteristic data is marked with a low confidence weight. If the data fluctuation indicator does not exceed the confidence threshold, the corresponding data frame in the intermediate gas feature data will be marked with a high confidence weight. The corresponding data frames in the intermediate gas feature data after being labeled with confidence weights are integrated to form the calibrated gas concentration feature data output.

5. The real-time monitoring and early warning system for multiple parameters of hazardous chemical storage environment according to claim 2, characterized in that, The specific process of receiving and processing calibrated gas concentration characteristic data in the trend prediction module to output the environmental risk prediction value for the next time period includes: constructing a spatiotemporal sequence matrix and deep learning calculation.

6. The multi-parameter real-time monitoring and early warning system for hazardous chemical storage environment according to claim 5, characterized in that, The process of constructing the spatiotemporal sequence matrix includes: It receives calibrated gas concentration characteristic data, calls up the spatial coordinate information of each sensor node in the data acquisition module, and obtains the rate of change of each parameter. The calibrated gas concentration feature data, the rate of change of each parameter, and the spatial coordinate information of the sensor nodes are fused in three dimensions to reconstruct a multidimensional feature tensor containing time, space, and feature dimensions. The time dimension contains the calibrated gas concentration feature data and is arranged in the order of data acquisition. The spatial dimension contains the spatial location association data of the sensor nodes and the coordinate distribution of the sensor nodes. The feature dimension contains the calibrated gas concentration features and the rate of change of each parameter. The feature data of each sensor node in the multidimensional feature tensor are arranged in a structured manner according to the time series to construct a spatiotemporal sequence matrix.

7. The real-time monitoring and early warning system for multiple parameters of hazardous chemical storage environment according to claim 6, characterized in that, The deep learning computation process includes: A multi-head attention spatiotemporal fusion network is used as a deep learning network, and a spatiotemporal sequence matrix is ​​used as input to perform collaborative modeling of the time dimension, spatial dimension and feature dimension. Through a multi-head attention mechanism, dynamic spatial weights and feature weights are simultaneously assigned to different feature channels and different sensor nodes in the spatiotemporal sequence matrix; where different feature channels correspond to the calibrated gas concentration characteristics and the rate of change of each parameter, and different sensor nodes correspond to the coordinate correlation data of the spatial dimension. The features after collaborative modeling are mapped and transformed through the fully connected layer of the multi-head attention spatiotemporal fusion network to output the environmental risk prediction value for the next time period.

8. The real-time monitoring and early warning system for multiple parameters of hazardous chemical storage environment according to claim 7, characterized in that, The multi-head attention spatiotemporal fusion network comprises, in sequence: An input layer is used to receive a spatiotemporal sequence matrix and convert it into a high-dimensional feature vector. A multi-head attention layer, comprising multiple attention heads configured in parallel, wherein each attention head extracts the correlation information of the time dimension, spatial dimension and feature dimension in the high-dimensional feature vector and outputs a local feature vector. The feature fusion layer uses a weighted summation algorithm to integrate local feature vectors to generate a global fused feature vector; The fully connected layer performs non-linear mapping and dimensionality compression on the global fused feature vector, and outputs low-dimensional abstract features. The output layer performs regression calculations on low-dimensional abstract features to output the predicted environmental risk value for the next time period.

9. The real-time monitoring and early warning system for multiple parameters of hazardous chemical storage environment according to claim 1, characterized in that, The specific process of risk assessment by the early warning decision module is as follows: Receive the environmental risk prediction value output by the trend prediction module; Receive the calibrated gas concentration characteristic data and the rate of change of each parameter output by the data processing module; Based on the currently received calibrated gas concentration characteristic data, the concentration peak is extracted in real time, and the real-time environmental fluctuation index is calculated according to the concentration peak and the rate of change of each parameter. The predicted environmental risk values ​​are non-linearly weighted and fused with the real-time environmental fluctuation index to generate the final weighted comprehensive risk index. The final weighted comprehensive risk index is compared with a preset risk threshold to determine the risk level of the environment and output a corresponding early warning signal.

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