Control method for safety isolation and linkage protection of underground dangerous goods and storage medium

Abstract

The invention relates to a safety isolation and linkage protection control method for underground dangerous goods and a storage medium. According to the method, firstly, multi-source data collected by multiple types of underground dangerous goods areas are acquired, fusion analysis is performed on the multi-source data, and a risk monitoring result of each type of underground dangerous goods area is obtained; and if the risk monitoring result is that leakage exists, determining a risk type and a predicted diffusion path based on the multi-source data so as to dynamically select a target isolation mode. And then, according to the risk level and the risk type, performing protection operation on the underground dangerous article area with leakage according to a set priority. After the protection operation is executed, the isolation and protection operation is stopped based on the contact instruction and the monitored environment parameters. According to the method, self-adaptive control of safety isolation and linkage protection of underground dangerous goods storage is formed, and the risk blocking efficiency of underground dangerous goods storage and the timeliness of isolation protection measures are improved.

Description

Technical Field

[0001] The present application relates to the field of safety protection technology, and in particular to a control method and storage medium for the safe isolation and linkage protection of underground hazardous materials. Background Art

[0002] Traditional protection for underground hazardous materials storage facilities is primarily based on physical isolation and fire prevention regulations, such as reducing the risk of common-mode failures through fire partitioning, flame-retardant cables, and redundant equipment layouts. Traditional underground hazardous materials safety protection methods rely on the independent operation of various subsystems, such as lightning warnings, electronic fences, and video surveillance, to implement only single-parameter threshold alarms or local linkages, such as alarm and pop-up screen linkage. This leads to one-sided risk assessments and confusing response priorities. Furthermore, traditional protection methods rely on fixed physical barriers, such as explosion-proof walls, or single environmental controls, such as ventilation systems, resulting in low risk blocking efficiency. Furthermore, after completing the alarm and initial disposal, manual secondary intervention is relied upon, which can easily lead to secondary disasters due to operational delays. Therefore, traditional hazardous materials safety protection methods are inefficient in risk blocking, resulting in a higher risk of hazardous materials storage. Summary of the Invention

[0003] The purpose of this application is to provide a control method and storage medium for the safe isolation and linkage protection of underground hazardous materials, so as to solve the problems of low safety and slow risk processing speed in traditional underground hazardous materials storage.

[0004] To achieve the above objectives, the present application provides, in a first aspect, a control method for safe isolation and linkage protection of underground hazardous materials, comprising: Acquire multi-source data collected from various types of underground hazardous materials areas; Performing fusion analysis on the multi-source data to obtain risk monitoring results for each type of underground hazardous materials area, the risk monitoring results including risk levels; If the risk monitoring result indicates that there is a leakage, the risk type and the predicted diffusion path are determined based on the multi-source data to dynamically select a target isolation mode; Performing protective operations on the underground hazardous materials area where the leak occurs according to the risk level and the risk type and the set priority; In response to the environmental parameters of the underground hazardous materials area returning to the set parameter threshold and receiving a release instruction, the isolation and protection operations of the underground hazardous materials area are stopped.

[0005] A second aspect of the present application provides a computer-readable storage medium, in which a program is stored. The program can be loaded by a processor and execute the above-mentioned control method for safe isolation and linkage protection of underground hazardous materials.

[0006] The beneficial effects of this application are: This application first obtains multi-source data collected from various types of underground hazardous materials areas, performs a fusion analysis on the multi-source data, and obtains risk monitoring results for each type of underground hazardous materials area. By fusing multi-source data, the risk status of the underground hazardous materials area can be assessed more comprehensively and accurately, and the risk level can be obtained, reducing the one-sidedness brought by the single parameter assessment. If the risk monitoring result is that there is a leak, the risk type and predicted diffusion path are determined based on the multi-source data to dynamically select the target isolation mode, which can realize intelligent switching of multiple isolation modes, improve the flexibility and effectiveness of isolation measures, and maximize the blocking of the spread of leaked substances. Then, according to the risk level and risk type, protective operations are performed on the underground hazardous materials area where the leak exists according to the set priority, ensuring that when multiple risks coexist, more urgent and important risks are handled first, reducing protection failures caused by operational conflicts, and effectively controlling risk accidents in a timely manner. After the protective operation is executed, based on the contact instructions and the monitored environmental parameters, the isolation and protective operations are stopped, and normal operation is gradually restored, reducing downtime and economic losses caused by risk accidents. Therefore, based on the monitored multi-source data, this application forms an adaptive control of safe isolation and linkage protection of underground hazardous materials storage through a closed-loop logic of dynamic isolation, orderly protection, and timely resumption of operation, thereby improving the risk blocking efficiency of underground hazardous materials storage and the timeliness of isolation and protection measures.

[0007] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of a control method for safe isolation and linkage protection of underground hazardous materials provided in an embodiment of the present application; Figure 2 This is a structural diagram of a control system for safe isolation and linkage protection of underground hazardous materials provided in an embodiment of the present application. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0010] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly specifying the number of the technical features indicated. Therefore, features specified as "first" or "second" may explicitly or implicitly include one or more of the described features. In the description of this application, "plurality" means two or more, unless otherwise specifically qualified. In this application, the word "exemplary" is used to mean "serving as an example, illustration, or illustration." Any embodiment described in this application as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is provided to enable anyone skilled in the art to implement and use the present application. In the following description, details are listed for illustrative purposes. It should be understood that one of ordinary skill in the art will recognize that the present application can be implemented without these specific details. In other instances, well-known structures and processes are not described in detail to avoid obscuring the description of this application with unnecessary detail. Therefore, this application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0011] Underground hazardous materials are potentially dangerous items that, due to their chemical, physical, or biological properties, could pose a hazard to personnel, the environment, and facilities. Examples include items with flammable, explosive, toxic, corrosive, and radioactive properties. While standards for the prevention of gunpowder and explosives storage facilities emphasize physical isolation and the deployment of explosion-proof equipment, these approaches rely on static designs and lack dynamic response capabilities.

[0012] Traditional approaches to emergency coordination and automated control often rely on pre-set interlocking logic for partial automation. Hazardous chemical companies then trigger alarm signals through the linkage of personnel positioning systems and food monitoring. Some chemical safety systems employ optimization algorithms to simulate emergency response plans. However, these operations rely on manual initiation or single-point triggering mechanisms, lacking deep collaboration with real-time monitoring data and making it difficult to achieve millisecond-level precision control. Furthermore, protocol differences and data silos hinder efficient coordination between the storage, warning, and control subsystems for underground hazardous materials.

[0013] For example, in oil depot safety management, traditional solutions typically utilize lightning early warning monitoring systems. These systems use atmospheric electric field meters to detect electric field changes in real time, integrate with data processing hosts to analyze lightning risks, trigger the automatic raising of lifting rods to guide lightning currents underground, and simultaneously activate an audible and visual alarm system. In explosives depot management, electronic fences and video surveillance systems utilize a linked host to implement intrusion alarms and pop-up notifications. However, each subsystem typically operates independently, providing only single-point alarms or localized responses, lacking multi-parameter fusion risk assessment and dynamic isolation mechanisms across facilities. Furthermore, while some systems integrate dual prevention mechanisms and intelligent video analysis, enabling real-time risk monitoring and alarm triggering via 3D maps, their isolation measures are still limited to fixed barriers or single environmental controls, lacking adaptive isolation path adjustment and full-process closed-loop control between explosives and oil depots. Traditional technologies often rely on preset thresholds to trigger responses, lack dynamic decision-making capabilities based on self-learning from historical data, and lack mature cross-system collaborative priority control.

[0014] Furthermore, traditional distributed control systems can only execute preset instructions and are unable to dynamically deduce the evolution of risks. Furthermore, traditional solutions often focus on a single scenario during the facility operation and maintenance phase, lacking the ability to manage the entire lifecycle, from design and planning to construction, maintenance, and disposal. This results in fragmented protection capabilities. These issues collectively limit the level of proactive protection for underground hazardous materials storage in extreme scenarios.

[0015] To address the fragmented response mechanisms, static isolation measures, and lack of closed-loop control in traditional underground hazardous materials storage protection methods, the present embodiment achieves intelligent safety protection between underground pyrotechnic and fuel facilities by constructing a multi-facility environmental parameter fusion analysis, dynamic isolation, and full-process closed-loop control system. Specifically, a multi-parameter collaborative risk assessment mechanism is first established, integrating multi-source data such as temperature, humidity, gas concentration, and vibration. A dynamic decision-making model is used to generate cross-system collaborative response instructions to address the issue of chaotic response priorities. Secondly, an adaptive isolation strategy is designed to dynamically switch between physical isolation (such as retractable explosion-proof walls) and environmental isolation (such as directional negative pressure ventilation) based on the type of leaked material and the predicted diffusion path, improving risk blocking efficiency. Furthermore, a full-process closed-loop control system called "monitoring-response-auto-closure" is implemented, automatically executing energy cutoff, leakage zone closure, and coordinated protection operations after an accident is triggered, eliminating delays caused by manual secondary intervention and reducing secondary disasters.

[0016] Figure 1 This is a flow chart of a control method for safe isolation and linkage protection of underground hazardous materials provided in an embodiment of the present application. Figure 1 As shown, the control method may include steps 101-105, which are described in detail below.

[0017] Step 101: Acquire multi-source data collected from various types of underground hazardous materials areas.

[0018] Multi-source data refers to various types of environmental data, including gas concentration, temperature, humidity, pressure, and vibration. Hazardous materials areas are underground storage spaces for different types of hazardous materials. For each type of underground hazardous materials area, corresponding sensors can be deployed at key locations to collect the required monitoring data. Multi-source data can cover various potential risk factors in various underground hazardous materials areas.

[0019] Step 102: Perform fusion analysis on the multi-source data to obtain risk monitoring results for each type of underground hazardous materials area, where the risk monitoring results include risk levels.

[0020] After receiving data from multiple sources, it can be input into a fusion analysis model, such as a fuzzy logic algorithm or a machine learning model. This model dynamically calculates risk monitoring results for underground hazardous materials areas, taking into account various parameters such as gas concentration gradient, temperature rise rate, pressure change rate, and vibration intensity, and derives the corresponding risk level. By integrating data from multiple parameters, a more comprehensive and accurate assessment of the risk status of underground hazardous materials areas can be achieved, reducing the one-sidedness of single-parameter assessments. Categorizing risk monitoring results into different levels provides a clear basis for subsequent protective measures, improving the relevance and effectiveness of protective measures.

[0021] Step 103: If the risk monitoring result indicates that a leak exists, the risk type and the predicted diffusion path are determined based on multi-source data to dynamically select a target isolation mode.

[0022] Targeted isolation modes are selected for different risk types. Risk type refers to the type of risk at hand, such as gas leak risk or explosion risk. The predicted diffusion path is the predicted path of the leaked gas. When a leak is detected, further analysis of multi-source data is required to accurately identify the type of leaked material, such as oily volatile gas or explosive dust, based on data characteristics and pre-set rules or models. Simultaneously, a pre-configured computational fluid dynamics (CFD) model can be used to dynamically predict the diffusion path of the leaked material, including its direction and velocity, by combining real-time temperature gradients, airflow velocity, and spatial topology parameters. Based on the type of leaked material and the predicted diffusion path, a target isolation mode, such as physical or environmental, can be dynamically selected. This improves the flexibility and effectiveness of isolation operations and minimizes the spread of the leaked material.

[0023] Step 104: Based on the risk level and risk type, protective operations are performed on the underground hazardous materials area where the leak occurs according to the set priority.

[0024] Setting priority refers to the priority of protective operations determined based on the risk type. For example, the embodiment of the present application can perform protective operations in the order of priority of "explosion suppression> leakage blocking> fire extinguishing> alarm". In this way, the most urgent and important risks can be dealt with first, reducing protection failures caused by operational conflicts. In addition, the delay and uncertainty of manual intervention are reduced, and the timeliness and reliability of risk protection in underground hazardous materials areas are improved, so that the development of accidents can be effectively controlled in a short time. Different risk levels can correspond to different protective operations, realize the coordinated work of multiple protective measures, and effectively control risk accidents from multiple aspects.

[0025] Step 105: In response to the environmental parameters of the underground hazardous materials area returning to the set parameter threshold and receiving a release instruction, the isolation and protection operations of the underground hazardous materials area are stopped.

[0026] Setting parameter thresholds refers to the safety thresholds at which environmental parameters in underground hazardous materials areas are considered normal. A release command is a command received by the system to initiate a release operation, such as one sent by an operator. When environmental parameters return to a safe threshold, the safety risks associated with prematurely releasing protective measures are reduced. Adding manual verification of the environment further improves system security and reduces the premature removal of protective measures due to system misjudgments. While ensuring safety, timely cessation of isolation and protective operations can quickly restore normal operation in underground hazardous materials areas, minimizing downtime and financial losses caused by accidents.

[0027] Therefore, based on the monitored multi-source data, the embodiment of the present application forms an adaptive control of safety isolation and linkage protection of underground hazardous materials storage through a closed-loop logic of dynamic isolation, orderly protection, and timely resumption of operation, thereby improving the risk blocking efficiency of underground hazardous materials storage and the timeliness of isolation and protection measures.

[0028] In step 101, corresponding sensors are first deployed at key locations in various types of underground hazardous materials areas, and multiple sensor data transmitted by these sensors are acquired. Key locations may include hazardous materials storage areas, pipeline interfaces, and vents. Sensors may include gas sensors, vibration sensors, pressure sensors, and temperature and humidity sensors. Gas sensors are used to detect combustible gas concentrations, oxygen content, and dust concentrations. Temperature and humidity sensors are used to monitor changes in ambient temperature and humidity. Pressure sensors are used to detect abnormal pipeline pressure. Vibration sensors are used to detect abnormal vibrations caused by explosions or leaks. This sensor data can be transmitted in real time to a central control platform using industrial-grade IoT communication protocols, ensuring the timeliness and reliability of data transmission.

[0029] In order to eliminate noise interference and unify the dimensions of multi-source data, multiple sensor data can be filtered to eliminate noise interference, and then the filtered sensor data can be mapped from their respective dimensions to a unified numerical range and normalized to obtain multi-source data.

[0030] As an example, you can select an appropriate filtering algorithm, such as Kalman filtering, sliding average filtering, and median filtering, based on the characteristics and noise type of the sensor data. For example, for gas concentration data, you can use Kalman filtering to process the random noise of the sensor, for vibration data, you can use median filtering to remove impulse noise, and for temperature and humidity data, you can use sliding average filtering to smooth the data.

[0031] Then, you can determine the normalization range and select a uniform numerical interval as the target range, such as [0, 1] or [-1, 1]. For each sensor data point, calculate the maximum and minimum values, or determine a fixed normalization parameter based on historical data. Map the filtered sensor data from their respective dimensions to a uniform numerical interval, for example, using a normalization formula, and perform normalization to obtain multi-source data in the same dimension.

[0032] Noise interference can cause unusual fluctuations in sensor data, leading to misjudgments. Filtering effectively removes noise and outliers from sensor data, making it smoother and more stable. This improves data quality and reliability, ensuring more accurate risk assessments and decision-making. Furthermore, different sensor data often have different dimensions and numerical ranges. Direct fusion analysis can lead to certain data being overly large, dominating the data and compromising the accuracy of the fusion results. Therefore, normalization can map all sensor data to a uniform numerical range, eliminating differences in dimensions and numerical ranges and ensuring that data can participate in fusion analysis fairly.

[0033] In step 102, multi-source data can be input into a fuzzy logic algorithm or a machine learning model. Fuzzy logic algorithms process data using fuzzy rules and fuzzy reasoning. They can effectively handle the ambiguity and uncertainty of data and are suitable for processing data with high ambiguity and uncertainty. Machine learning models learn the characteristics and patterns of data through training data, enabling more accurate risk prediction. Examples include random forests, support vector machines, and neural networks. Depending on the application scenario and data characteristics, a fuzzy logic algorithm or a machine learning model can be selected as the analysis model for multi-source data.

[0034] Then, based on historical risk data and real-time environmental data, the parameter weights of multi-source data are adjusted. By analyzing historical risk data, we can understand the importance of different parameters in different risk scenarios. Historical risk data can include past risk incident records, sensor data, and the associated amounts of risk incidents. Real-time data reflects the current environmental conditions and may affect the importance of different parameters. Therefore, the parameter weights of multi-source data can be dynamically adjusted in combination with real-time environmental data. For example, if historical data shows that gas concentration plays a dominant role in fire incidents, the weight of the gas concentration parameter is increased. If the real-time environment shows that the humidity in the current area is high, affecting the accuracy of the gas sensor, the weight of the gas concentration parameter is decreased. Through this dynamic adjustment mechanism, the system can automatically adjust the parameter weights of different multi-source data to adapt to environmental changes.

[0035] Based on the adjusted parameter weights of the multi-source data, a risk index is output for the predicted risk type. The risk index is a quantitative indicator that comprehensively reflects the current risk situation. For example, if the risk index is a value between 0 and 1, a higher value indicates a greater risk. Finally, the risk level of the predicted risk type is determined based on the risk index. The risk level is a range defined by the risk index. Categorizing risks into different levels provides a clear basis for subsequent risk management, allowing appropriate protective measures to be implemented based on the risk level, reducing manual intervention and improving the level and efficiency of automated operations.

[0036] In step 103 , the risk type and the predicted diffusion path may be determined based on the multi-source data and the spatial topological parameters of the underground hazardous materials area where the risk type is located.

[0037] As an example, the type of leaked substance may be determined based on gas data, vibration data, and temperature and humidity data in the multi-source data.

[0038] The following describes how to determine the type of leaked material, taking the leakage of oily volatile gas and explosive dust as examples.

[0039] For oily volatile gas leaks, the system first obtains methane concentration and temperature rise rate from multi-source data. If the concentration exceeds a set methane concentration for longer than a first time, and the temperature rise rate is lower than the set temperature rise rate, the leak is determined to be an oil volatile gas leak. The first time is a threshold for determining whether the methane concentration exceeds the specified value within a safe range, for example, 30 seconds. The set temperature rise rate is a threshold for determining whether the temperature rise rate is within a safe range, for example, 0.5°C / s.

[0040] For explosive dust leaks, you can first obtain the dust concentration and vibration energy of the set frequency band from multi-source data. If the time that the dust concentration exceeds the set dust concentration is longer than the second time, and the increase in the vibration energy ratio of the set frequency band exceeds the set increase, the type of leaked material is determined to be an explosive dust leak. The set frequency band refers to a specific frequency band, the set dust concentration refers to the threshold for determining whether the dust concentration exceeds the standard, and the second time is the threshold for determining whether the duration of the dust concentration exceeding the standard is within a safe range, for example, it can be 60s. The set increase is the threshold for determining whether the increase in the vibration energy ratio is within a safe range, such as 10%.

[0041] Then, based on the type of leaked material, the risk category is determined, which can include gas leak risk and explosion risk. Temperature gradients, airflow velocities, and spatial topological parameters of underground hazardous material areas derived from multi-source data are input into a computational fluid dynamics model to derive a predicted diffusion path for the leaked material. For example, this can include the diffusion direction and velocity of the leaked material, and output the coordinates of high-level areas.

[0042] Next, based on the risk type and predicted diffusion path, a physical isolation mode or an environmental isolation mode is selected as the initial isolation mode for the underground hazardous materials area. The initial isolation mode refers to the isolation mode that is initially selected.

[0043] For example, if the risk is a gas leak, environmental isolation mode is selected as the initial isolation mode. Based on the predicted diffusion path, a directional negative pressure ventilation system is used to direct the leaked gas to a harmless treatment area, or inert gas injection is dynamically controlled to dilute the oxygen concentration. If the risk is an explosion, physical isolation mode is selected as the initial isolation mode. The hydraulically driven retractable explosion-proof wall is activated. Based on the predicted diffusion path, the wall is controlled to adjust its expansion width according to the diffusion speed to block the propagation path of the explosion shock wave or flame.

[0044] The initially selected isolation mode may be insufficient. Therefore, the initial isolation mode needs to be verified and, if the isolation mode fails the verification, the initial isolation mode needs to be corrected to obtain the target isolation mode. Physical isolation needs to meet the vibration attenuation rate threshold (for example, the vibration intensity reduction after isolation is ≥90%), and environmental isolation requires the concentration reduction rate to meet the standard. If the standard is not met, the correction strategy is automatically triggered: if physical isolation is insufficient, nitrogen is added and the backup explosion-proof wall is activated; if environmental isolation fails, the fan blade angle and negative pressure value are increased until the verification indicators meet the safety requirements.

[0045] Therefore, for the risk of explosion, if the vibration intensity attenuation rate is less than the set vibration attenuation rate, the first correction strategy is triggered, inert gas is added and the backup explosion-proof wall is activated until the vibration intensity attenuation rate is greater than or equal to the set vibration attenuation rate. The set vibration attenuation rate is the threshold for judging whether the physical isolation meets the isolation requirements. For the risk of gas leakage, if the concentration drop rate of the leaked gas is less than the set concentration drop rate, the second correction strategy is triggered, the fan power of the negative pressure ventilation system is adjusted to form a directional negative pressure area, and the flow rate of the inert gas is controlled until the concentration drop rate of the leaked gas is greater than or equal to the set concentration drop rate. The set concentration drop rate is the threshold for judging whether the environmental isolation meets the isolation requirements. Finally, the corrected initial isolation mode is used as the target isolation mode.

[0046] Take the oil depot leakage scenario as an example. Assume that the initial parameter is the diffusion velocity v d =2.8 m / s, gas concentration change rate ΔC / Δt=8%LEL / s. Select to start the environmental isolation mode. Fan power calculation: P = 7.5 × (C max / 100%LEL) = 7.5 × 0.8 = 6 kW. Equipment adjustment was performed based on the CFD model output airflow deflection angle θ = 12°, adjusting the fan outlet angle to 12°. Verification and correction of the effect showed that after 5 seconds, the concentration drop rate δ = 35% (below the 40% threshold), triggering a dynamic correction: increasing the fan blade angle to 17° and raising the negative pressure to -65 Pa. Finally, the correction result was a secondary verification concentration drop rate δ = 48% (>40%), confirming successful isolation.

[0047] In an embodiment of the present application, the risk level may include an emergency level, a high-risk level, and an early warning level. The priority of the emergency level is greater than the high-risk level, and the priority of the high-risk level is greater than the early warning level. In step 104, if the risk level is an emergency level, the pipeline solenoid valve in the underground hazardous materials area is closed, the inert gas injection is started, and the main power supply is cut off. If the risk level is a high-risk level, the explosion-proof exhaust fan is started and the external fire-fighting system is linked to spray the fire extinguishing agent. If the risk level is an early warning level, the sound and light alarm is activated and an alarm message is sent to the monitoring terminal. In this way, when multiple instructions conflict, they can be executed in the order of priority of "suppressing explosion> blocking leakage> extinguishing fire> alarm" to ensure that key operations are responded to first.

[0048] The following takes the safe isolation and linkage protection of the underground hazardous materials area of ​​an underground oil depot and explosives depot complex as an example.

[0049] First, the system was deployed and initialized. A high-precision sensor network was initially deployed within an underground oil depot and explosives depot complex. Infrared combustible gas sensors and piezoresistive pressure sensors were installed at the flanges of the oil depot's oil pipelines. Temperature and humidity sensors and triaxial vibration sensors were installed at the explosives depot's vents. A laser dust sensor was installed at the top of the passageway between the two depots. Sensor data is transmitted in real time to the central control platform via the LoRa wireless network, with a 500-millisecond transmission cycle and a packet loss rate of less than 0.1%. The platform integrates a risk assessment engine based on the random forest algorithm and connects to actuators such as valves and fans via the OPC UA protocol to ensure seamless command transmission.

[0050] Next, an accident triggering and data collection were performed. A simulated oil pipeline rupture accident at an oil depot was performed, with an initial leak rate of 50 liters / minute and an oil-gas mixture concentration increasing at 5% LEL / second. The pressure sensor detected a pressure drop rate of 35 kPa / second in real time, while the gas sensor monitored a concentration gradient rising to 8% LEL / second. The vibration sensor data was normal. All data was uploaded to the central control platform via the LoRa network, which simultaneously recorded timestamps and device status, providing a complete data chain for subsequent analysis.

[0051] Next, risk assessment and dynamic decision-making are performed. The central platform preprocesses the raw data. A Kalman filter is used to eliminate pump and valve interference in the pressure data, and the gas concentration data is normalized to standard LEL units. Input features include gas concentration gradient, pressure drop rate, and normal temperature and humidity values. A pre-trained random forest model calculates a risk index of 85, with parameter weights assigned as 0.6 for gas concentration, 0.3 for pressure, and 0.1 for other parameters. Based on the risk level mapping rules, a secondary response command is triggered, prioritizing ventilation and valve closure.

[0052] Adaptive isolation and linkage control were then implemented. The system activated the directional negative pressure ventilation system, with axial flow fans generating 5,000 cubic meters per hour of airflow to create a negative pressure zone of -60 Pa downstream of the leak point, directing the oil and gas to the catalytic combustion unit for treatment. Simultaneously, the hydraulically driven explosion-proof walls of the passageway between the oil depot and the explosives depot were pre-deployed at a speed of 0.8 meters per second and an angle of +10 degrees to match the airflow direction. The linkage control was executed according to priority: Level 1 closed the electric ball valve of the oil pipeline; Level 2 activated the explosion-proof exhaust fan and foam fire extinguishing system; Level 3 activated the 90-decibel audible and visual alarm and the emergency broadcast loop.

[0053] Finally, the system shut down and recovered. Within 0.3 seconds of confirming the accident, a solid-state relay cut off the power to the oil depot, and the airtight gate closed at a pressure of 6 kN / m2, sealing the leak area and maintaining a negative pressure of -30 Pa. Accident data was stored on the SSD at a 10 Hz sampling rate. During the recovery phase, continuous monitoring lasted 30 minutes. After confirming that the gas concentration had dropped to 3% LEL and the temperature had stabilized at 38°C, the operator remotely authorized the system through two-factor authentication, soft-started the ventilation system, and restored power, ensuring a safe system restart.

[0054] The embodiments of the present application significantly improve the safety protection efficiency of underground pyrotechnics and oil facilities through multi-parameter fusion analysis, dynamic isolation strategy and full-process closed-loop control. The specific technical effects are as follows.

[0055] 1. Significantly improved risk response efficiency. Traditional technologies rely on single parameter threshold alarms, resulting in long response delays and high false alarm rates. The embodiment of the present application dynamically optimizes parameter weights through a multi-source data fusion model, significantly improving the accuracy of risk assessments and shortening response times to less than seconds. For example, in an oil depot leak scenario, the system can simultaneously trigger ventilation and valve closure operations, effectively reducing the scope of the leak spread and significantly improving blocking efficiency compared to traditional manual operations.

[0056] 2. Comprehensive optimization of adaptive isolation efficiency. Traditional fixed isolation measures have limited blocking efficiency in complex scenarios. The embodiment of the present application dynamically switches between physical isolation and environmental isolation, combines real-time diffusion simulation to optimize equipment parameters, and achieves multi-dimensional improvement based on dynamic mode selection and closed-loop verification mechanism, significantly optimizing risk blocking efficiency, achieving precise real-time control of equipment parameters, significantly reducing operation and maintenance costs and reducing accident handling losses, significantly reducing the risk of secondary disasters through automated control, and improving personnel safety assurance capabilities.

[0057] 3. Closed-loop control effectively reduces secondary disasters. Traditional technologies rely on manual intervention, resulting in a high risk of secondary disasters. The present embodiment utilizes fully automated control to rapidly shut down energy and seal the leaking area upon confirmation of an incident, significantly reducing the incidence of secondary disasters. For example, in the event of a pipeline rupture, the system automatically executes shutdown, fire extinguishing, and sealing operations, significantly reducing the spread of pollution and secondary losses.

[0058] 4. Outstanding economic benefits and social value. The dynamic learning model reduces ineffective actions resulting from false alarms, significantly reducing operation and maintenance costs. Rapid response and closed-loop control significantly minimize accident losses. Furthermore, the real-time positioning and emergency broadcast system improves evacuation efficiency, achieving zero casualties in the pilot area and significantly enhancing social security.

[0059] 5. Comparative Advantages of Technical Indicators: Compared with traditional technologies, the embodiments of the present application have achieved breakthrough improvements in response speed, blocking efficiency, and secondary disaster control, and the false alarm rate has been reduced to an extremely low level. The overall performance is significantly better than traditional solutions.

[0060] Figure 2 This is a schematic diagram of the structure of a control system for safe isolation and linkage protection of underground hazardous materials provided in an embodiment of the present application. Figure 2 As shown, the control system 200 may include a memory 201 and a processor 202. The memory 201 is configured to store instructions, and the processor 202 is configured to call instructions from the memory 201 and implement any of the control methods for safe isolation and linkage protection of underground hazardous materials in the embodiments of the present application when executing the instructions.

[0061] An embodiment of the present application also provides a computer-readable storage medium, which stores a program that can be loaded by a processor and execute any of the control methods for safe isolation and linkage protection of underground hazardous materials in the embodiments of the present application.

[0062] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above embodiments can be implemented by hardware or by computer program. When all or part of the functions in the above embodiments are implemented by computer program, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above functions can be implemented. In addition, when all or part of the functions in the above embodiments are implemented by computer program, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and saved in the memory of the local device by downloading or copying, or the system of the local device is updated. When the program in the memory is executed by the processor, all or part of the functions in the above embodiments can be implemented.

[0063] The above specific examples are used to illustrate the present application, which is only used to help understand the present application and is not intended to limit the present application. For those skilled in the art of the present application, based on the concept of the present application, they can also make some simple deductions, modifications or substitutions.

Claims

1. A control method for safe isolation and linkage protection of underground dangerous goods, characterized in that: include: Acquire multi-source data collected from various types of underground hazardous materials areas; Performing fusion analysis on the multi-source data to obtain risk monitoring results for each type of underground hazardous materials area, the risk monitoring results including risk levels; If the risk monitoring result indicates that there is a leakage, the risk type and the predicted diffusion path are determined based on the multi-source data to dynamically select a target isolation mode; Performing protective operations on the underground hazardous materials area where the leak occurs according to the risk level and the risk type and the set priority; In response to the environmental parameters of the underground hazardous materials area returning to the set parameter threshold and receiving a release instruction, the isolation and protection operations of the underground hazardous materials area are stopped.

2. The control method according to claim 1, characterized in that: Determining the risk type and predicting the diffusion path based on the multi-source data to dynamically select a target isolation mode includes: Determine the risk type and predict the diffusion path based on the multi-source data and the spatial topological parameters of the underground hazardous materials area where the risk type is located; Based on the risk type and the predicted diffusion path, selecting a physical isolation mode or an environmental isolation mode as an initial isolation mode for the underground hazardous materials area; The initial isolation mode is verified, and if the initial isolation mode fails the verification, the initial isolation mode is modified to obtain the target isolation mode.

3. The control method according to claim 2, characterized in that: The determining the risk type and predicting the diffusion path based on the multi-source data and the spatial topological parameters of the underground hazardous materials area where the risk type is located includes: Determining the type of leaked material based on the gas data, vibration data, and temperature and humidity data in the multi-source data; Determining a risk type based on the type of the leaked substance, wherein the risk type includes gas leakage risk and explosion risk; The temperature gradient, air flow velocity and spatial topological parameters of the underground hazardous materials area in the multi-source data are input into a computational fluid dynamics model to obtain a predicted diffusion path of the leaked material.

4. The control method according to claim 3, characterized in that: The types of the leaked substances include oily volatile gas leakage and explosive dust leakage. The determining of the type of the leaked substance based on the gas data, vibration data, and temperature and humidity data in the multi-source data includes: Obtaining methane concentration and temperature rise rate from the multi-source data; If the time during which the methane concentration exceeds the set methane concentration is greater than the first time, and the temperature rise rate is lower than the set temperature rise rate, the type of the leaked substance is determined to be a leak of oil volatile gas; Obtaining dust concentration and vibration energy in a set frequency band from the multi-source data; If the time during which the dust concentration exceeds the set dust concentration is greater than the second time, and the increase in the vibration energy proportion of the set frequency band exceeds the set increase, the type of the leaked substance is determined to be explosive dust leakage.

5. The control method according to claim 2, characterized in that: The selecting, based on the risk type and the predicted diffusion path, a physical isolation mode or an environmental isolation mode as the initial isolation mode of the underground hazardous materials area includes: If the risk type is a gas leakage risk, the environmental isolation mode is selected as the initial isolation mode, and based on the predicted diffusion path, the leaked gas is directed to a harmless treatment area through a directional negative pressure ventilation system or the injection of inert gas is dynamically controlled to dilute the oxygen concentration; If the risk type is explosion risk, the physical isolation mode is selected as the initial isolation mode, the hydraulically driven retractable explosion-proof wall is started, and based on the predicted diffusion path, the explosion-proof wall is controlled to adjust the expansion width according to the diffusion speed to block the propagation path of the explosion shock wave or flame.

6. The control method according to claim 5, characterized in that: Verifying the initial isolation mode and, if the initial isolation mode fails the verification, correcting the initial isolation mode to obtain the target isolation mode, includes: In response to the explosion risk, if the vibration intensity attenuation rate is less than the set vibration attenuation rate, a first correction strategy is triggered to add inert gas and activate the backup explosion-proof wall until the vibration intensity attenuation rate is greater than or equal to the set vibration attenuation rate; In response to the gas leakage risk, if the concentration decrease rate of the leaked gas is less than the set concentration decrease rate, a second correction strategy is triggered to adjust the fan power of the negative pressure ventilation system to form a directional negative pressure area and control the flow rate of the inert gas until the concentration decrease rate of the leaked gas is greater than or equal to the set concentration decrease rate; The modified initial isolation mode is used as the target isolation mode.

7. The control method according to claim 1, characterized in that: The multi-source data collected from various types of underground hazardous materials areas includes: Deploy corresponding sensors at key locations of various types of underground hazardous materials areas; Acquiring a plurality of sensor data sent by a plurality of sensors; Eliminating noise interference from the plurality of sensor data by filtering the plurality of sensor data; Mapping the filtered sensor data from their respective dimensions to a unified numerical interval and performing normalization processing to obtain the multi-source data; Among them, the sensors include gas sensors, vibration sensors, pressure sensors and temperature and humidity sensors.

8. The control method according to claim 1, characterized in that: The multi-source data is fused and analyzed to obtain risk monitoring results for each type of underground hazardous materials area, including: inputting the multi-source data into a fuzzy logic algorithm or a machine learning model; Adjusting parameter weights of the multi-source data based on historical risk data and real-time environmental data; Outputting a risk index for the predicted risk type based on the adjusted parameter weights of the multi-source data; A risk level of the predicted risk type is determined based on a risk index of the predicted risk type.

9. The control method according to claim 1, characterized in that: The risk levels include emergency level, high risk level, and warning level. The emergency level has a higher priority than the high risk level, and the high risk level has a higher priority than the warning level. The protective operation for the underground hazardous materials area where the leak occurs is performed according to the set priority based on the risk level and the risk type, including: If the risk level is the emergency level, the pipeline solenoid valve of the underground hazardous materials area is closed, the inert gas injection is started, and the main power supply is cut off; If the risk level is the high risk level, the explosion-proof exhaust fan is started and the external fire fighting system is linked to spray the fire extinguishing agent; If the risk level is the warning level, the sound and light alarm is activated and an alarm message is sent to the monitoring terminal.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a program, which can be loaded by a processor and execute the control method for safe isolation and linkage protection of underground hazardous materials according to any one of claims 1 to 9.

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