Hydrogen explosion-proof monitoring and early warning management and control system for multiple risk sources

Abstract

The invention relates to the technical field of flammable gas monitoring and new-generation information, in particular to a multi-risk-source-oriented hydrogen explosion-proof monitoring and early-warning management and control system which comprises the steps that a monitoring device synchronously collects hydrogen leakage concentration, equipment pressure, fire source signals, seismic intensity, waterlogging water level, environment temperature and wind speed data to form a multi-risk-source data set; the communication device transmits the data set to a management and control center; the data processing device performs normalization processing on the data, adopts an entropy weight method to dynamically correct a weight coefficient, outputs a comprehensive risk probability value through a risk probability prediction model and generates an early warning instruction; the early warning display device carries out three-dimensional visual warning and sends an early warning message to a specified terminal; and the field management and control device automatically executes disaster reduction operations such as starting exhaust equipment, triggering a fire extinguishing device, opening a safety valve or cutting off a power supply according to the early warning instruction. The technical problem that single-risk-source monitoring cannot deal with composite disasters is solved, and cooperative monitoring and intelligent management and control of multiple risk sources are achieved.

Description

Technical Field

[0001] This invention relates to the field of flammable gas monitoring and next-generation information technology, and in particular to a hydrogen explosion-proof monitoring, early warning and control system for multiple risk sources. Background Technology

[0002] Hydrogen energy facilities, such as hydrogen production plants, hydrogen storage stations, and hydrogen refueling stations, utilize integrated safety monitoring systems that deploy sensor networks to collect parameters such as hydrogen concentration, pressure, and temperature in real time. Intelligent early warning technology uses data fusion and machine learning algorithms to analyze information flow, identify abnormal patterns that deviate from normal conditions, and trigger early warning signals. This achieves closed-loop management from real-time monitoring to early risk warning during hydrogen production, storage, and refueling, thereby improving the safety and reliability of facility operation.

[0003] Existing hydrogen monitoring technologies suffer from the following technical challenges: Due to a relatively late start in technology development, current system designs primarily monitor single risk sources such as hydrogen leaks, failing to integrate risk factors arising from multiple natural disasters (earthquakes, storms, etc.) and human activities. This results in a lack of comprehensive early warning capabilities in hydrogen production, storage, and refueling scenarios. For example, within hydrogen production facilities, relying solely on leak sensors means that when an earthquake causes a pipeline rupture or a storm ignites an external fire source, the single monitoring mechanism cannot identify the synergistic effect between the leak and the earthquake vibration or meteorological disaster. This leads to a delayed response from the early warning system, and the risk of an explosion significantly increases when hydrogen accumulation combines with an ignition source. This challenge stems from insufficient multi-source risk data fusion capabilities and a lack of dynamic assessment of complex disaster chains in the early warning logic, thereby weakening the overall safety and control effectiveness. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources. This system solves the technical problem that existing hydrogen monitoring technologies can only monitor single-type risk sources (such as leaks), resulting in the inability to provide comprehensive early warning of complex disasters involving multiple risk sources such as earthquakes and storms during hydrogen production, storage, and refueling, thus significantly increasing the risk of hydrogen explosion accidents.

[0005] To solve the above-mentioned technical problems, the specific contents of the present invention are as follows: The hydrogen explosion prevention monitoring, early warning and control system for multiple risk sources provided by this invention includes: The monitoring device is used to simultaneously collect data on hydrogen leakage concentration, equipment pressure, ignition source signals, seismic intensity, water level, ambient temperature and wind speed in the environment of hydrogen production, storage or refueling facilities, forming a multi-risk source dataset. A communication device, connected to the monitoring device, is used to transmit the multi-risk source dataset to the control center; A data processing device, located in the control center, is used to receive and integrate the multi-risk source dataset, make intelligent judgments by calculating the changing trends of risk indicators, and generate an early warning instruction when any type of risk data exceeds a preset threshold. The early warning display device is connected to the data processing device and is used to receive the early warning command and execute visual and audible warnings, while sending an early warning message including GPS positioning information to a designated terminal. The on-site control device is connected to the data processing device and is used to receive the early warning command and automatically execute disaster mitigation operations; the disaster mitigation operations include: starting the exhaust equipment to reduce the hydrogen concentration, triggering the fire extinguishing device to extinguish the fire, opening the safety valve to depressurize the equipment, or cutting off the power supply to the equipment to stop operation.

[0006] Furthermore, in the hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources described in this invention, the monitoring device collects and constructs a multi-risk source dataset including: By deploying multiple sets of sensors in different spatial locations within the facility, hydrogen concentration, pressure, and temperature data are periodically captured; It receives earthquake intensity and wind speed data in real time from the earthquake monitoring network and meteorological early warning platform. The periodically captured local data and the real-time received external early warning data are aligned and integrated according to a unified timestamp to generate a structured dataset with time series markers and data source labels, which serves as the multi-risk source dataset.

[0007] Furthermore, in the hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources described in this invention, the data processing device integrates the multi-risk source dataset and performs intelligent judgment, including: Normalize the different types of data in the structured dataset to transform the original data of different units and magnitudes to the [0,1] interval; Based on the historical accident frequency and severity scores of risks such as hydrogen leakage, fire source, pressure, earthquake, waterlogging, and wind speed, an initial weight coefficient is assigned to each type of data, and the weight coefficients constitute an initial weight vector. The entropy weight method is used to perform a secondary analysis on the volatility of various data in the current monitoring period, and a dynamic adjustment factor is calculated to correct the initial weight vector to obtain the fused weight vector. The normalized data vector is multiplied by the fused weight vector, and the result is input into the risk probability prediction model trained with historical data to output a comprehensive risk probability value. The comprehensive risk probability value is compared with a multi-level early warning threshold. When the comprehensive risk probability value falls into different threshold ranges, an early warning instruction of the corresponding level is generated.

[0008] Furthermore, in the hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources described in this invention, the data processing device for generating early warning commands further includes: In the risk probability prediction model, a contribution analysis branch is set up; Through the contribution analysis branch, the contribution of each dimension in the current data vector to the comprehensive risk probability value is calculated retrospectively. Identify at least one risk data type with the highest contribution and mark the risk data type as the dominant risk source identifier of the early warning instruction.

[0009] Furthermore, in the hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources described in this invention, the on-site control device automatically performs disaster mitigation operations, including: Read the warning level and the primary risk source identifier from the warning instruction; The system invokes a predefined risk and response mapping rule base, which stores the device linkage sequences corresponding to combinations of different warning levels and dominant risk source identifiers; Based on the equipment linkage sequence, control signals are sent sequentially or synchronously to the exhaust equipment, fire extinguishing device, safety valve, or power controller to execute disaster mitigation operations that are precisely matched with the combination.

[0010] Furthermore, the hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources described in this invention employs the entropy weight method to calculate the dynamic adjustment factor, including: Calculate the information entropy of each type of data within the current monitoring period; Determine the entropy weight for each type of data based on the information entropy; The dynamic adjustment factor is calculated by weighting the entropy weight and the initial weight coefficient into a harmonic average. In the weighted harmonic average, the weight of the entropy weight is greater than the weight of the initial weight coefficient.

[0011] Furthermore, in the hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources described in this invention, the early warning display device performs visual and audible warnings, including: In the 3D virtual scene interface of the control center, locate and zoom in to display the real-time 3D model of the equipment or area associated with the warning command; The surface color of the real-time 3D model is switched to a preset warning color according to the level of the warning instruction; On the information panel of the three-dimensional factory virtual scene interface, the fusion weight vector, the log of the comprehensive risk probability value calculation process, and the contribution analysis results from the data processing device are displayed in a scrolling manner.

[0012] Furthermore, in the hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources described in this invention, the communication device transmits the multi-risk source dataset to the control center, including: The sensors in the monitoring device that collect hydrogen concentration and pressure are configured with the first priority and transmitted via a wired industrial ring network. The sensors that collect fire source signals and wind speed data are configured with a second priority and transmitted via an industrial wireless mesh network. A dual-channel receiving unit with different queue caching strategies is set at the network interface board of the control center; The dual-channel receiving unit enables a zero-buffered pass-through queue for data transmitted via the wired industrial ring network and enables a buffered queue with a packet loss retransmission request mechanism for data transmitted via the industrial wireless mesh network. The data stream processed by the dual-channel receiving unit is then forwarded to the data processing device.

[0013] Furthermore, in the hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources described in this invention, the early warning display device sends early warning messages to designated terminals in the following ways: Extract the warning level and the primary risk source identifier from the warning instruction; Based on the mapping relationship between the warning level and the dominant risk source identifier, a dynamic message including an animation of the risk evolution situation is generated; The dynamic message is pushed to the terminal group determined by the warning level; the terminal group includes at least on-site mobile terminals, workshop fixed terminals and remote emergency platforms.

[0014] Furthermore, the hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources described in this invention also includes: An adaptive optimization device is used to continuously record the metadata of early warning events; the metadata includes at least the data vector at the time of triggering, the fusion weight vector, the generated early warning command, the executed device linkage sequence, and the subsequent risk data change trajectory. Establish an evaluation function with disaster reduction efficiency and risk mitigation speed as joint optimization objectives; Using reinforcement learning algorithms, with the metadata as training data, the parameters of the device linkage sequence in the risk and response mapping rule base and the risk probability prediction model are iteratively optimized until the evaluation function converges to the optimal state.

[0015] Beneficial effects of this invention; This invention utilizes a monitoring device to simultaneously collect and fuse data on hydrogen leakage concentration, equipment pressure, ignition signals, seismic intensity, floodwater levels, ambient temperature, and wind speed, constructing a multi-risk source dataset. This addresses the blind spots in early warning of complex disasters caused by existing technologies that only monitor single-type risk sources. The data processing device employs an entropy weighting method to dynamically adjust weight coefficients, combining a risk probability prediction model with a contribution analysis branch to achieve collaborative evaluation of multi-source risk factors and identification of dominant risk sources, significantly improving early warning accuracy. The early warning display device dynamically visualizes the risk situation through a 3D virtual scene of the plant, enhancing information transmission efficiency. The on-site control device automatically executes equipment linkage sequences based on early warning commands, forming a closed-loop control from risk perception to disaster mitigation operations, effectively reducing the risk of hydrogen explosion accidents and improving the safety and control capabilities of hydrogen energy facilities. Attached Figure Description

[0016] To more clearly illustrate the technical solution of the present invention, the drawings used in the embodiments will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on the drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the system architecture of the hydrogen explosion prevention monitoring, early warning and control system for multiple risk sources according to the present invention. Detailed Implementation

[0018] To make the technical solution of the present invention clearer, the present invention will be clearly and completely described below with reference to specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. The present invention provided by various embodiments will be described in detail below with reference to the accompanying drawings. To better understand the purpose of the present invention, the present invention will be described in further detail below.

[0019] Please see Figure 1 The hydrogen explosion prevention monitoring, early warning and control system for multiple risk sources provided by this invention includes: The monitoring device is used to simultaneously collect data on hydrogen leakage concentration, equipment pressure, ignition source signals, seismic intensity, water level, ambient temperature and wind speed in the environment of hydrogen production, storage or refueling facilities, forming a multi-risk source dataset. A communication device, connected to the monitoring device, is used to transmit the multi-risk source dataset to the control center; A data processing device, located in the control center, is used to receive and integrate the multi-risk source dataset, make intelligent judgments by calculating the changing trends of risk indicators, and generate an early warning instruction when any type of risk data exceeds a preset threshold. The early warning display device is connected to the data processing device and is used to receive the early warning command and execute visual and audible warnings, while sending an early warning message including GPS positioning information to a designated terminal. The on-site control device is connected to the data processing device and is used to receive the early warning command and automatically execute disaster mitigation operations; the disaster mitigation operations include: starting the exhaust equipment to reduce the hydrogen concentration, triggering the fire extinguishing device to extinguish the fire, opening the safety valve to depressurize the equipment, or cutting off the power supply to the equipment to stop operation.

[0020] The hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources integrates multiple devices to achieve simultaneous monitoring and intelligent early warning of various risk sources in hydrogen production, storage, or refueling facilities. The system encompasses monitoring devices, communication devices, data processing devices, early warning display devices, and on-site control devices, forming a closed-loop management process from data acquisition to automated disaster mitigation. Monitoring devices collect data on hydrogen leakage concentration, equipment pressure, ignition signals, seismic intensity, floodwater levels, ambient temperature, and wind speed, forming a multi-risk source dataset. The communication device connects to the monitoring devices, transmitting the multi-risk source dataset to the control center. The data processing device, located at the control center, receives and integrates the multi-risk source dataset, intelligently assesses risk indicators by calculating trends, and generates an early warning command when any type of risk data exceeds a preset threshold. The early warning display device connects to the data processing device, receives the early warning command, executes visual and audible alerts, and simultaneously sends an early warning message, including GPS location information, to designated terminals. The on-site control device is connected to the data processing device, receives early warning instructions and automatically executes disaster mitigation operations, including starting the exhaust equipment to reduce the hydrogen concentration, triggering the fire extinguishing device to extinguish the fire, opening the safety valve to depressurize the equipment or cutting off the power to the equipment to stop the operation.

[0021] The underlying technical solution for the monitoring device involves deploying multiple sets of sensors at different spatial locations within the facility to periodically capture hydrogen concentration, pressure, and temperature data. The monitoring device receives real-time earthquake intensity and wind speed data broadcast from earthquake monitoring networks and meteorological early warning platforms. It aligns and integrates the periodically captured local data with the real-time received external early warning data using a unified timestamp, generating a structured dataset with time-series markers and data source labels. For example, hydrogen concentration monitoring instruments can be installed under the roof of a hydrogen production plant workshop, while dedicated probes can be deployed above hydrogen production equipment and pipelines to ensure comprehensive coverage of risk sources. This deployment method can capture the synergistic effects of leaks and external disasters, improving data integrity.

[0022] The lower-level technical solution for the communication device includes configuring the sensors collecting hydrogen concentration and pressure in the monitoring device with first priority, transmitting data via a wired industrial ring network. Sensors collecting fire source signals and wind speed data are configured with second priority, transmitting data via an industrial wireless mesh network. A dual-channel receiving unit with different queue buffering strategies is installed on the network interface board of the control center. The dual-channel receiving unit uses a zero-buffered direct-pass queue for data transmitted via the wired industrial ring network, and a buffer queue with a packet loss retransmission request mechanism for data transmitted via the industrial wireless mesh network. The data stream processed by the dual-channel receiving unit is then forwarded to the data processing device to ensure the reliability and real-time performance of data transmission, preventing data loss or delay from affecting the early warning response.

[0023] The lower-level technical solution of the data processing device first normalizes different types of data in the structured dataset, transforming the raw data of different units and magnitudes into a zero-to-one range. Based on the historical accident frequency and severity scores of hydrogen leaks, fire sources, pressure, earthquakes, flooding, and wind speed risks, initial weight coefficients are assigned to each data type, forming an initial weight vector. The entropy weight method is used to perform a secondary analysis of the volatility of various data types within the current monitoring period, calculating a dynamic adjustment factor to correct the initial weight vector, resulting in a fused weight vector. The normalized data vector and the fused weight vector are then multiplied by a dot product, and the result is input into a risk probability prediction model trained on historical data, outputting a comprehensive risk probability value. The comprehensive risk probability value is compared with multi-level warning thresholds; when the comprehensive risk probability value falls within different threshold ranges, a corresponding level of warning instruction is generated. A contribution analysis branch is set up in the risk probability prediction model. This branch back-calculates the contribution of each dimension in the current data vector to the comprehensive risk probability value, identifies the risk data type with the highest contribution, and marks this risk data type as the dominant risk source identifier for the warning instruction. This fusion approach enhances the ability to dynamically assess complex disaster chains.

[0024] The lower-level technical solution of the early warning display device includes locating and enlarging a real-time 3D model of the equipment or area associated with the early warning command within the 3D virtual scene interface of the control center. The surface color of the real-time 3D model is switched to a preset warning color according to the level of the early warning command, such as green for low risk, yellow for medium risk, and red for high risk. On the information panel of the 3D virtual scene interface, the fusion weight vector, the log of the comprehensive risk probability value calculation process, and the contribution analysis results from the data processing device are displayed on a scrolling basis. The early warning display device extracts the early warning level and the dominant risk source identifier from the early warning command, and generates a dynamic message including a risk evolution trend animation based on the mapping relationship. This dynamic message is pushed to a terminal group determined by the early warning level, including on-site mobile terminals, workshop fixed terminals, and a remote emergency platform. This visualization method helps operators quickly understand the risk situation.

[0025] The lower-level technical solution of the on-site control device includes reading the warning level and dominant risk source identifier from the warning command, and calling a predefined risk and response mapping rule library. This rule library stores the equipment linkage sequences corresponding to different combinations of warning levels and dominant risk source identifiers. Based on the equipment linkage sequence, control signals are sent sequentially or synchronously to exhaust equipment, fire extinguishing devices, safety valves, or power controllers to execute disaster reduction operations precisely matched to the combination. For example, when the dominant risk source identifier is hydrogen leakage, the system automatically opens workshop windows or starts the exhaust equipment; when the dominant risk source identifier is fire, it directly triggers the suspended dry powder fire extinguishing device. The on-site control device also integrates an adaptive optimization device to continuously record the metadata of warning events. This metadata includes the data vector at the time of triggering, the fusion weight vector, the generated warning command, the executed equipment linkage sequence, and the subsequent risk data change trajectory. An evaluation function with disaster reduction efficiency and risk suppression speed as joint optimization objectives is established. Reinforcement learning algorithms are used to iteratively optimize the parameters of the equipment linkage sequence and risk probability prediction model in the risk and response mapping rule library until the evaluation function converges to the optimal state, achieving system self-improvement.

[0026] When multiple sensors are deployed in different spatial locations within the facility, a three-dimensional deployment strategy is adopted. Hydrogen concentration monitors are installed 5 and 10 centimeters below the roof of the hydrogen production workshop, while dedicated monitoring probes are positioned 20 and 30 centimeters above the hydrogen production equipment, pipelines, and storage tanks. The fire and smoke detectors are installed at dual points, covering both the roof area and providing enhanced monitoring in densely populated equipment areas. Pressure change sensors mounted on the surface of the high-pressure hydrogen equipment are threaded and directly contact the equipment surface to sense pressure fluctuations. This arrangement captures three-dimensional risk data from the roof space to the equipment itself, forming a complete monitoring network.

[0027] When the monitoring device periodically captures data, different sampling frequencies are set. Hydrogen concentration data is sampled every second, pressure data every 5 seconds, and temperature data every 10 seconds. When receiving external early warning data in real time, earthquake intensity data is obtained through a dedicated data receiving terminal connected to the earthquake monitoring network, and wind speed data is obtained through a meteorological data interface. During data integration, GPS clock synchronization technology is used to assign a unified timestamp to all data, achieving millisecond-level alignment between locally captured data and external early warning data in the time dimension. The generated structured dataset includes data source labels, such as "local sensor rooftop hydrogen monitor" or "external data earthquake intensity," facilitating data source tracing during subsequent processing.

[0028] When normalizing the structured dataset, the data processing unit employs minimum and maximum normalization algorithms. The original value of each risk data type is mapped to a zero-to-one interval, calculated as (current value + historical minimum) / (historical maximum + historical minimum). Historical incident frequency weighting is based on incident records in the enterprise's safety database, and the severity of consequences is determined with reference to industry safety standards. The initial weight vector comprises seven dimensions, each corresponding to one of the seven risk data types.

[0029] When calculating the dynamic adjustment factor using the entropy weight method, the standard deviation of various data types within each monitoring period is first calculated, and then the information entropy is calculated based on the standard deviation. The information entropy value reflects the uncertainty of data fluctuations, and the entropy weight is inversely proportional to the information entropy value. In the weighted harmonic average calculation, the entropy weight accounts for 70% of the weight, and the initial weight coefficient accounts for 30%. The fused weight vector is obtained through matrix operations, ensuring that the dynamically adjusted weights reflect both historical patterns and real-time changes.

[0030] The risk probability prediction model employs a three-layer neural network structure. The number of nodes in the input layer matches the number of risk data types, the hidden layer contains 20 neurons, and the output layer generates a comprehensive risk probability value. The model is trained using historical monitoring data from the past five years, and parameters are optimized using a backpropagation algorithm. The multi-level early warning thresholds are set in three intervals: 0 to 0.3 is the safe interval, 0.3 to 0.7 is the early warning interval, and 0.7 to 1.0 is the alarm interval.

[0031] The contribution analysis branch is integrated after the fully connected layer of the risk probability prediction model. The gradient backpropagation algorithm is used to calculate the contribution of each input dimension to the output result, achieved by calculating the partial derivatives of the output layer nodes with respect to the input layer nodes. The contribution values ​​are normalized so that the sum of the contributions of all dimensions is 1.

[0032] When identifying the dominant risk source, a contribution threshold is set. When the contribution of a certain risk data type exceeds 0.4, it is marked as the dominant risk source. The contribution analysis results are stored in the early warning instruction data structure in vector form, including the contribution value of each risk data type and the dominant risk source marker. This design ensures that the early warning instruction not only includes risk level information but also clearly indicates the main risk source, providing a basis for subsequent precise handling.

[0033] The risk and response mapping rule base uses a tree structure to store device linkage sequences. First-level nodes correspond to warning levels, second-level nodes correspond to the dominant risk source identifier, and leaf nodes store specific device control command sequences. When reading a warning command, the system indexes the corresponding device linkage sequence based on the combination of the warning level and the dominant risk source identifier.

[0034] The equipment linkage sequence includes three elements: equipment address, control command, and execution timing. For example, the linkage sequence for the combination of "hydrogen leak + red alert" includes: immediately activating the designated exhaust equipment, activating the backup ventilation system after a 3-second delay, and simultaneously sending a command to the power controller to cut off power to non-essential equipment. Control signals are transmitted using the industry-standard Modbus TCP protocol, and each control command includes the equipment address code, function code, and data field.

[0035] The 3D virtual factory scene is constructed using Building Information Modeling (BIM) technology, with each device corresponding to a unique model number. Upon receiving an early warning command, the system locates the target device in the scene based on its number and uses a zoom-in effect to highlight the risk area. The model surface shading uses the HSL color space conversion algorithm, dynamically adjusting the hue value according to the warning level: green corresponds to a safe state (hue 120°), yellow corresponds to a warning state (hue 60°), and red corresponds to an alarm state (hue 0°).

[0036] The information panel employs a multi-window layout. The left window displays the real-time change curve of the fused weight vector, the middle window shows the calculation process log of the comprehensive risk probability value, and the right window scrolls to display the contribution analysis results. Log information is refreshed every 5 seconds, arranged in reverse chronological order, with the latest data always displayed at the top. This design allows operators to intuitively grasp the development of the risk situation.

[0037] The wired industrial ring network uses fiber optic transmission and has a ring topology. Each monitoring node is equipped with dual network cards for redundant connections, automatically switching transmission paths in case of a single point of failure. The industrial wireless mesh network uses dual-band transmission of 2.4GHz and 5GHz, automatically forming a network between nodes and supporting multi-hop routing.

[0038] The dual-channel receiving unit employs hardware acceleration technology to process the data stream. Data packets from the wired channel enter a zero-buffer queue and are directly parsed by a dedicated processor, reducing software-layer processing latency. Data packets from the wireless channel undergo checksum verification and reassembly in a buffer queue, and a selective retransmission mechanism is used to handle packet loss. After protocol conversion, data from both channels is uniformly encapsulated into a standard data format and forwarded to the data processing device.

[0039] The risk evolution simulation animation uses a particle system to model the risk diffusion process. Different particle effects are selected based on the type of risk: gray particles simulate diffusion for hydrogen leaks, red particles simulate the spread of fires, and yellow pulse waves indicate abnormal pressure. The animation duration is dynamically adjusted according to the warning level, with a 30-second simulation for a yellow warning and a 60-second simulation for a red warning.

[0040] Terminal group management employs a tiered push strategy. On-site mobile terminals receive complete dynamic messages, including detailed risk analysis data; fixed terminals in the workshop display concise handling instructions; and the remote emergency platform obtains comprehensive reports, including geographic information. Message pushes utilize priority queue management, sending high-alert-level messages first to ensure timely delivery of emergency information.

[0041] The hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources addresses the shortcomings of existing technologies that only monitor single risk sources. By integrating multi-source data and intelligent analysis, it achieves simultaneous monitoring and coordinated response to multiple risk factors in the hydrogen facility environment. The system is deployed in scenarios such as hydrogen production plants, hydrogen storage stations, and hydrogen refueling stations, focusing on solving the problem of early warning for complex disasters caused by multiple risk sources, including leaks, pressure, ignition sources, earthquakes, flooding, temperature, and wind speed. The following implementation methods are based on the technical solutions in the claims, with further technical details supporting their feasibility.

[0042] The monitoring system deploys multiple sensors within the facility to form a three-dimensional monitoring network. Hydrogen concentration monitors are installed 5 to 10 centimeters below the workshop roof, while dedicated probes are positioned 20 to 30 centimeters above hydrogen production equipment, pipelines, and storage tanks, covering key areas from the space to the equipment itself. Fire and smoke detectors are installed at dual points, covering both the roof area and providing enhanced monitoring in densely populated equipment areas. Pressure change sensors are directly mounted on the surface of high-pressure hydrogen equipment using threaded fasteners to sense pressure fluctuations in real time. The monitoring system periodically captures data: hydrogen concentration data is sampled every second, pressure data every 5 seconds, and temperature data every 10 seconds. External risk data is received in real time via a dedicated interface; seismic intensity data is connected to the seismic monitoring network, and wind speed data is accessed through the meteorological early warning platform. All locally captured data and external data are synchronized with GPS clocks and timestamped, generating a structured dataset with time-series markers and source labels, such as data items labeled "Local Sensor Rooftop Hydrogen Monitor" or "External Data Seismic Intensity," ensuring data traceability.

[0043] The communication system employs a layered transmission strategy to ensure real-time data transmission. Sensors collecting hydrogen concentration and pressure data transmit via a wired industrial ring network with a ring fiber optic topology, and each node is equipped with dual network cards for redundant connections. Sensors collecting fire source signals and wind speed data transmit via an industrial wireless mesh network, supporting both 2.4GHz and 5GHz dual-band transmission and multi-hop routing. The network interface board at the control center is equipped with dual-channel receiving units. Wired channel data uses a zero-buffered pass-through queue, directly parsed by a dedicated processor; wireless channel data uses a buffered queue with a packet loss retransmission request mechanism for verification and reassembly. Both channels' data are converted to a standard format and then uniformly encapsulated before being forwarded to the data processing device, preventing data loss or delay.

[0044] The data processing unit normalizes the structured dataset, using a minimum-maximum normalization algorithm to map various risk data to a zero-to-one range. Initial weight coefficients are assigned based on historical accident frequencies and the severity scores of consequences according to industry safety standards, forming a seven-dimensional initial weight vector. When dynamically adjusting weights using the entropy weight method, the standard deviation and information entropy of the data within each monitoring period are calculated; entropy weight is inversely proportional to information entropy. In the weighted harmonic mean, entropy weight accounts for 70% of the weight, and the initial weight coefficient accounts for 30%, generating a fused weight vector. The dot product of the normalized data vector and the fused weight vector is input into a three-layer neural network-based risk probability prediction model. The model is trained using five years of historical data and outputs a comprehensive risk probability value. Multi-level warning thresholds are divided into safe, warning, and alert ranges. The model's built-in contribution analysis branch calculates the contribution of each data dimension to the comprehensive risk probability value using a gradient backpropagation algorithm. Risk data types with a contribution exceeding 0.4 are marked as dominant risk sources, enhancing the accuracy of assessing complex disaster chains.

[0045] The early warning display device dynamically visualizes risks within a 3D virtual factory environment at the control center. The scene is built on a Building Information Model (BIM) system, with each device having a unique identifier. Upon receiving an early warning command, the system locates and zooms in on the target device model. The model's surface color changes using the HSL color space algorithm: green corresponds to a safe state, yellow to a warning state, and red to an alarm state. The information panel employs a multi-window layout. The left window displays the curve of the fused weight vector change, the middle window scrolls through the comprehensive risk probability value calculation log, and the right window displays the contribution analysis results. The log refreshes every 5 seconds and is arranged in reverse order. The warning level and dominant risk source identifier in the early warning command are used to generate a risk evolution scenario simulation animation. Hydrogen leaks are simulated with gray particles to represent diffusion, and fires are simulated with red particles to represent spread. The animation duration is dynamically adjusted according to the warning level.

[0046] On-site control devices execute automatic disaster mitigation operations based on early warning commands. The risk and response mapping rule base uses a tree structure to store device linkage sequences, with first-level nodes corresponding to early warning levels and second-level nodes corresponding to the dominant risk source identifiers. For example, the "hydrogen leak + red alert" combined trigger sequence immediately activates the designated exhaust device, delays for 3 seconds to start the backup ventilation system, and simultaneously sends a command to cut off power to non-essential equipment. Control signals are transmitted via the Modbus TCP protocol, including device address codes, function codes, and data fields. The adaptive optimization device continuously records early warning metadata, including data vectors, fusion weight vectors, early warning commands, device linkage sequences, and risk data change trajectories. An evaluation function with disaster mitigation efficiency and risk suppression speed as optimization objectives drives a reinforcement learning algorithm to iteratively optimize the sequences and model parameters in the rule base, achieving system self-improvement.

[0047] In hydrogen production plant applications, when the system detects a synergistic risk of abnormal seismic intensity and rising hydrogen concentration, contribution analysis identifies the earthquake as the dominant risk source, triggering a coordinated response that cuts off power to equipment and activates exhaust equipment, effectively preventing secondary disasters caused by pipeline rupture. The implementation of this invention creates a closed loop from data acquisition to intelligent response through the collaboration of multiple devices, enhancing the safety and security capabilities of hydrogen energy facilities.

[0048] When a hydrogen explosion prevention monitoring, early warning, and control system for multiple risk sources is implemented in a hydrogen production plant, monitoring devices are deployed at key locations within the plant area. Hydrogen concentration monitors are installed 5-10 cm below the workshop roof, and dedicated probes are positioned 20-30 cm above the hydrogen production equipment and delivery pipelines. Fire and smoke detectors cover the roof and areas with dense equipment. Pressure change sensors are directly fixed to the surface of high-pressure hydrogen equipment. The monitoring devices periodically collect hydrogen concentration, pressure, and temperature data, sampling hydrogen concentration every second, pressure every 5 seconds, and temperature every 10 seconds. Simultaneously, they receive real-time seismic intensity data from a seismic monitoring network and wind speed data from a meteorological platform via a dedicated interface. All local and external data are timestamped using GPS clock synchronization technology, generating structured datasets with source tags, such as "local sensor rooftop hydrogen monitor" or "external data seismic intensity." The communication device uses a wired industrial ring network to transmit hydrogen concentration and pressure data, and a wireless mesh network to transmit fire source signals and wind speed data. A dual-channel receiving unit processes the data stream and forwards it to the control center. The data processing unit normalizes the dataset, assigns initial weights based on historical accident frequencies, dynamically adjusts the weights using the entropy weight method, and then inputs the fused weight vector and the dot product of the normalized data into the risk probability prediction model, outputting a comprehensive risk probability value. When the probability value exceeds the warning threshold, the model contribution analysis branch identifies the dominant risk source; for example, if the contribution of earthquake intensity anomalies exceeds 0.4, a red warning command is generated. The warning display device locates the target equipment model in the 3D virtual factory scene, switches its surface color to red, and displays the fused weight vector and contribution analysis log on a scrolling information panel. Simultaneously, it pushes dynamic messages containing risk evolution animations to on-site mobile terminals. The on-site control device calls the risk and response mapping rule base and executes the equipment linkage sequence for the "earthquake + red warning" combination: immediately cut off the power supply, start the exhaust equipment, and prevent hydrogen accumulation caused by pipeline rupture. This process achieves collaborative monitoring and automatic response of multiple risk sources, addressing the shortcomings of single leak monitoring.

[0049] In the application scenario of a hydrogen refueling station, the system focuses on environmental risk and equipment pressure monitoring. The monitoring device arranges multiple layers of sensors in the hydrogen storage tank area and the refueling area, installs a hydrogen concentration monitor below the roof, deploys pressure sensors on the surface of the tank, and a small weather station collects wind speed and temperature data in real time. The external data interface receives meteorological disaster warnings, such as storm information. The monitoring device samples environmental data at a high frequency. The wind speed data is updated every 2 seconds, and the temperature data is sampled every 5 seconds. The local data and the external warning data are aligned and integrated by a time stamp. The communication device uses priority transmission. The pressure data is sent in real time through a wired ring network, and the wind speed and temperature data are transmitted through a wireless Mesh network. The dual-channel receiving unit ensures data integrity and low latency. After the data processing device normalizes the multi-source data, the initial weight coefficient is adjusted based on the historical accident records of the hydrogen refueling station. The entropy weight method calculates the dynamic factor, which focuses on the volatility of the wind speed data. The fused weight vector is input into the risk probability prediction model to output the comprehensive risk probability value. When the contribution degree of the wind speed is significant, the model generates a yellow warning instruction, and the dominant risk source is identified as a storm. The warning display device highlights the refueling area model in the three-dimensional scene, the surface color changes to yellow, the information panel updates the contribution degree analysis result, and a concise disposal guidance animation is sent to the fixed terminal in the workshop. The on-site control device triggers a linkage sequence according to the warning level and the dominant risk source identification: automatically shut down the refueling equipment, start the safety valve to relieve pressure, and avoid equipment failure caused by a storm. The adaptive optimization device records the event meta information, and the reinforcement learning algorithm iteratively optimizes the model parameters to improve the system's adaptability to compound disasters. This deployment method realizes the dynamic assessment of multiple risk factors in the complex environment of a hydrogen refueling station, filling the blind spots in existing monitoring.

Claims

1. A hydrogen explosion-proof monitoring, early warning, and control system for multiple risk sources, characterized in that, include: The monitoring device is used to simultaneously collect data on hydrogen leakage concentration, equipment pressure, ignition source signals, seismic intensity, water level, ambient temperature and wind speed in the environment of hydrogen production, storage or refueling facilities, forming a multi-risk source dataset. A communication device, connected to the monitoring device, is used to transmit the multi-risk source dataset to the control center; A data processing device, located in the control center, is used to receive and integrate the multi-risk source dataset, make intelligent judgments by calculating the changing trends of risk indicators, and generate an early warning instruction when any type of risk data exceeds a preset threshold. The early warning display device is connected to the data processing device and is used to receive the early warning command and execute visual and audible warnings, while sending an early warning message including GPS positioning information to a designated terminal. The on-site control device is connected to the data processing device and is used to receive the early warning command and automatically execute disaster mitigation operations; the disaster mitigation operations include: starting the exhaust equipment to reduce the hydrogen concentration, triggering the fire extinguishing device to extinguish the fire, opening the safety valve to depressurize the equipment, or cutting off the power supply to the equipment to stop operation.

2. The hydrogen explosion-proof monitoring, early warning, and control system for multiple risk sources according to claim 1, characterized in that, The monitoring device collects and constructs a multi-risk source dataset, including: By deploying multiple sets of sensors in different spatial locations within the facility, hydrogen concentration, pressure, and temperature data are periodically captured; It receives earthquake intensity and wind speed data in real time from the earthquake monitoring network and meteorological early warning platform; The periodically captured local data and the real-time received external early warning data are aligned and integrated according to a unified timestamp to generate a structured dataset with time series markers and data source labels, which serves as the multi-risk source dataset.

3. The hydrogen explosion-proof monitoring, early warning, and control system for multiple risk sources according to claim 2, characterized in that, The data processing device integrates the multi-risk source dataset and performs intelligent judgment, including: Normalize the different types of data in the structured dataset to transform the original data of different units and magnitudes to the [0,1] interval; Based on the historical accident frequency and severity scores of risks such as hydrogen leakage, fire source, pressure, earthquake, waterlogging, and wind speed, an initial weight coefficient is assigned to each type of data, and the weight coefficients constitute an initial weight vector. The entropy weight method is used to perform a secondary analysis on the volatility of various data in the current monitoring period, and a dynamic adjustment factor is calculated to correct the initial weight vector to obtain the fused weight vector. The normalized data vector is multiplied by the fused weight vector, and the result is input into the risk probability prediction model trained with historical data to output a comprehensive risk probability value. The comprehensive risk probability value is compared with a multi-level early warning threshold. When the comprehensive risk probability value falls into different threshold ranges, an early warning instruction of the corresponding level is generated.

4. The hydrogen explosion-proof monitoring, early warning, and control system for multiple risk sources according to claim 3, characterized in that, The data processing device also generates early warning commands in the following ways: In the risk probability prediction model, a contribution analysis branch is set up; Through the contribution analysis branch, the contribution of each dimension in the current data vector to the comprehensive risk probability value is calculated retrospectively. Identify at least one risk data type with the highest contribution and mark the risk data type as the dominant risk source identifier of the early warning instruction.

5. The hydrogen explosion-proof monitoring, early warning, and control system for multiple risk sources according to claim 4, characterized in that, The on-site control device automatically performs disaster mitigation operations, including: Read the warning level and the primary risk source identifier from the warning instruction; The system invokes a predefined risk and response mapping rule base, which stores the device linkage sequences corresponding to combinations of different warning levels and dominant risk source identifiers; Based on the equipment linkage sequence, control signals are sent sequentially or synchronously to the exhaust equipment, fire extinguishing device, safety valve, or power controller to execute disaster mitigation operations that are precisely matched with the combination.

6. The hydrogen explosion-proof monitoring, early warning, and control system for multiple risk sources according to claim 3, characterized in that, The calculation of dynamic adjustment factors using the entropy weight method includes: Calculate the information entropy of each type of data within the current monitoring period; Determine the entropy weight for each type of data based on the information entropy; The dynamic adjustment factor is calculated by weighting the entropy weight and the initial weight coefficient into a harmonic average. In the weighted harmonic average, the weight of the entropy weight is greater than the weight of the initial weight coefficient.

7. The hydrogen explosion-proof monitoring, early warning, and control system for multiple risk sources according to claim 1, characterized in that, The early warning display device provides visual and audible alerts, including: In the 3D virtual scene interface of the control center, locate and zoom in to display the real-time 3D model of the equipment or area associated with the warning command; The surface color of the real-time 3D model is switched to a preset warning color according to the level of the warning instruction; On the information panel of the three-dimensional factory virtual scene interface, the fusion weight vector, the log of the comprehensive risk probability value calculation process, and the contribution analysis results from the data processing device are displayed in a scrolling manner.

8. The hydrogen explosion-proof monitoring, early warning, and control system for multiple risk sources according to claim 2, characterized in that, The communication device transmits the multi-risk source dataset to the control center, including: The sensors in the monitoring device that collect hydrogen concentration and pressure are configured with the first priority and transmitted via a wired industrial ring network. The sensors that collect fire source signals and wind speed data are configured with a second priority and transmitted via an industrial wireless mesh network. A dual-channel receiving unit with different queue caching strategies is set at the network interface board of the control center; The dual-channel receiving unit enables a zero-buffered pass-through queue for data transmitted via the wired industrial ring network and enables a buffered queue with a packet loss retransmission request mechanism for data transmitted via the industrial wireless mesh network. The data stream processed by the dual-channel receiving unit is then forwarded to the data processing device.

9. The hydrogen explosion-proof monitoring, early warning, and control system for multiple risk sources according to claim 4, characterized in that, The early warning display device sends early warning messages to designated terminals, including: Extract the warning level and the primary risk source identifier from the warning instruction; Based on the mapping relationship between the warning level and the dominant risk source identifier, a dynamic message including an animation of the risk evolution situation is generated; The dynamic message is pushed to the terminal group determined by the warning level; the terminal group includes at least on-site mobile terminals, workshop fixed terminals and remote emergency platforms.

10. The hydrogen explosion-proof monitoring, early warning, and control system for multiple risk sources according to claim 5, characterized in that, Also includes: An adaptive optimization device is used to continuously record metadata about early warning events; The metadata includes at least the data vector at the time of triggering, the fusion weight vector, the generated warning command, the executed device linkage sequence, and the subsequent risk data change trajectory. Establish an evaluation function with disaster reduction efficiency and risk mitigation speed as joint optimization objectives; Using reinforcement learning algorithms, with the metadata as training data, the parameters of the device linkage sequence in the risk and response mapping rule base and the risk probability prediction model are iteratively optimized until the evaluation function converges to the optimal state.

Images