A blast furnace regional coal gas leakage safety monitoring system and method based on multi-modal perception and intelligent analysis

Abstract

The present application relates to the technical field of industrial safety production monitoring, in particular to a blast furnace area coal gas leakage safety monitoring system and method based on multi-modal perception and intelligent analysis, comprising the following steps: S100, real-time acquisition of concentration data of multiple coal gas alarm instruments in the blast furnace area; when any of the coal gas alarm instruments triggers a concentration alarm threshold, the historical concentration gradient of the alarm instrument and the concentration change rate of the adjacent node in the downwind direction are extracted, and a space-time correlation data fusion verification model is used to confirm whether a real coal gas leakage occurs; the present application effectively eliminates false alarms caused by single sensor drift or interference through the multi-point space-time correlation data fusion verification model; combined with the micro-environment corrected Gaussian plume model and the spatial Kriging interpolation algorithm, the present application realizes the accurate reconstruction of the three-dimensional dynamic risk field under the air flow disturbance and physical obstruction; further, the present application maps the two-dimensional target detection to the three-dimensional physical space and fuses the human body skeleton key point posture features.

Description

Technical Field

[0001] This invention relates to the field of industrial safety production monitoring technology, specifically to a blast furnace area gas leakage safety monitoring system and method based on multimodal perception and intelligent analysis. Background Technology

[0002] In the blast furnace ironmaking process, gas (mainly composed of carbon monoxide, hydrogen, etc.) is widely distributed as a byproduct in the blast furnace body, tuyere platform, and gas pipelines. Because carbon monoxide is extremely toxic and highly flammable and explosive, real-time monitoring and emergency response to its leaks are crucial for ensuring safe industrial production. However, existing blast furnace area gas leak monitoring technologies face the following three main technical challenges in practical engineering applications:

[0003] First, existing gas monitoring and early warning systems primarily rely on threshold triggering mechanisms from single sensors, lacking multi-dimensional spatiotemporal cross-verification capabilities. The blast furnace environment is extremely complex; irreversible aging and drift of sensor components or transient strong electromagnetic interference can easily trigger sudden changes in single-point values. Existing systems directly trigger plant-wide alarms upon receiving high-concentration signals from a single point, failing to consider the hydrodynamic diffusion patterns of leaked gas to calculate the dynamic lag time between the current node and its downstream neighbor at specific wind speeds and distances. Furthermore, they cannot link historical concentration gradients and concentration change rates of spatially related nodes for verification, resulting in a persistently high false alarm rate and severely depleting on-site emergency resources.

[0004] Second, existing systems exhibit static and two-dimensional characteristics in delineating leak hazard zones, lacking the ability to dynamically reconstruct three-dimensional areas based on microenvironmental meteorological factors. Current solutions typically define a static warning radius centered on the sensor that triggers the alarm, at a fixed physical distance. This static delineation method completely ignores the decisive influence of real-time microenvironmental wind speed and direction on the gas diffusion direction in the blast furnace area. Furthermore, it fails to incorporate the spatial physical obstruction resistance formed by the densely interwoven pipeline network and large equipment at the blast furnace site into the calculation model, resulting in a severe geometrical deviation between the static warning boundary set by the system and the actual dynamic diffusion boundary of the gas.

[0005] Third, existing video surveillance linkage mechanisms are limited to rudimentary two-dimensional position tracking, failing to accurately identify hidden dangers in specific high-risk industrial scenarios. Carbon monoxide poisoning often manifests clinically as instantaneous muscle weakness (falls) or loss of consciousness (abnormal stagnation) caused by central nervous system damage. Existing visual algorithms primarily rely on conventional two-dimensional target bounding boxes for motion detection, unable to accurately map two-dimensional pixel coordinates to a three-dimensional leak field containing depth information, and lacking the ability to analyze key points of the human skeleton and three-dimensional spatial posture. This prevents the system from logically distinguishing whether a person is in a normal squatting maintenance position or in an abnormal fall or unconscious stagnation due to gas poisoning, easily delaying the optimal rescue time for high-risk personnel. Furthermore, in subsequent rescue command issuance, the lack of intergenerational coupling calculations with the high-concentration gas diffusion field prevents the planning of safe routes to avoid high-concentration leaked gas for rescue personnel. Summary of the Invention

[0006] To address the problems existing in the above-mentioned background technology, the present invention provides the following technical solution:

[0007] This invention provides a method for monitoring gas leakage safety in blast furnace areas based on multimodal perception and intelligent analysis, including the following steps: S100, real-time acquisition of concentration data from multiple gas alarms in the blast furnace area; when any of the gas alarms triggers the concentration alarm threshold, extracting the historical concentration gradient of the alarm and the concentration change rate of adjacent nodes downwind, and using a spatiotemporal correlation data fusion verification model to confirm whether a real gas leakage has occurred.

[0008] S200. If a real gas leak is confirmed, the real-time micro-environmental wind speed in the blast furnace area and the current concentration value of each gas alarm are obtained. The gas diffusion boundary is calculated and a dynamic risk field is generated. The safe zone, warning zone and danger zone are delineated in real time according to the dynamic risk field.

[0009] S300: Simultaneously retrieve the visual monitoring video stream of the corresponding area of ​​the leak, obtain the position coordinates of the personnel in the dynamic risk field in real time through the deep learning target detection algorithm, and extract the posture features of the personnel by the human skeleton key point detection algorithm to determine whether the personnel are in an abnormal fall or lingering state caused by poisoning.

[0010] S400. Based on the concentration level of the gas leak, the location area of ​​the personnel in the dynamic risk field, and the posture characteristics, match and execute the corresponding graded emergency response strategy; wherein, when it is determined that the personnel in the danger zone are in the abnormal fall or lingering state, the system triggers an extremely dangerous alarm and automatically plans a rescue route to avoid the high concentration diffusion path based on the dynamic risk field.

[0011] Furthermore, the downwind adjacent nodes are determined by a pre-constructed three-dimensional sensor topology map of the blast furnace area; the three-dimensional sensor topology map records the spatial three-dimensional coordinates and physical connectivity of gas diffusion of all gas alarms deployed on the blast furnace body, each platform and gas pipeline drainer;

[0012] The steps for extracting the historical concentration gradient of the alarm device specifically include:

[0013] Construct a sliding time window with the trigger point of the concentration alarm threshold as the cutoff time;

[0014] Multiple consecutive discrete concentration sampling values ​​of the alarm device within the sliding time window are obtained according to a preset sampling frequency;

[0015] The continuous discrete concentration sampling values ​​are fitted with time-series features using the least squares method, and the absolute value of the slope of the fitted output is used as the historical concentration gradient.

[0016] Furthermore, the core logic of the verification model using spatiotemporal correlation data fusion to confirm whether a real gas leak has occurred is as follows:

[0017] If the historical concentration gradient is greater than a first preset abrupt change threshold, and the concentration change rate of the downwind adjacent nodes is within the dynamic lag time... If the value exceeds the second preset mutation threshold, it is determined that a real gas leak has occurred, eliminating false alarms caused by single-point drift of the sensor.

[0018] Wherein, the dynamic lag time The calculation model is as follows:

[0019]

[0020] In the formula, The straight-line spatial distance between the alarm device and the adjacent downwind node; The angle between the real-time wind direction vector and the vector connecting the two nodes in space; This is a correction coefficient for gas diffusion resistance under the complex environment of the blast furnace area.

[0021] Furthermore, in S200, the step of calculating the gas diffusion boundary and generating a dynamic risk field specifically includes:

[0022] Obtain the three-dimensional spatial coordinates and initial release rate of the confirmed leak source;

[0023] Based on the real-time microenvironment wind speed and direction vector, a microenvironment-corrected Gaussian plume model is used to calculate the plume at any spatial point within the blast furnace area. Predicted coal gas concentration ;

[0024] Connect the set of spatial points where the predicted gas concentration is equal to the preset first-level alarm concentration threshold with contour lines to form an outer diffusion polygon.

[0025] The set of spatial points whose predicted gas concentration equals the preset secondary alarm concentration threshold are connected by contour lines to form an inner diffusion polygon; the outer diffusion polygon and the inner diffusion polygon together constitute the dynamic risk field.

[0026] Furthermore, the determination rule for delineating the safe zone, warning zone, and danger zone in real time based on the dynamic risk field is as follows:

[0027] The three-dimensional spatial region inside the inner diffusion polygon is defined as the danger zone;

[0028] The three-dimensional annular space region between the inner and outer diffusion polygons is defined as the warning zone.

[0029] The three-dimensional spatial region outside the outer diffusion polygon is defined as the safe zone;

[0030] Furthermore, the polygonal boundary of the dynamic risk field is dynamically reconstructed according to a preset refresh cycle as the real-time microenvironment wind speed and wind direction vector change.

[0031] Furthermore, the calculation using the micro-environment-corrected Gaussian plume model also includes a real-time error compensation step based on physical sensors:

[0032] Extract the current true concentration value of each gas alarm located within the coverage area of ​​the outer or inner diffusion polygon;

[0033] Calculate the predicted gas concentration at the coordinates of the gas alarm. The concentration difference between the actual concentration value and the current true concentration value;

[0034] The concentration difference is used as a compensation weight by spatial kriging interpolation algorithm. It is then substituted into the Gaussian plume model to correct the diffusion coefficient, thereby dynamically adjusting the boundary distortion of the outer and inner diffusion polygons. This eliminates the theoretical calculation errors caused by the blast furnace building structure and hot air flow disturbance on the gas diffusion model.

[0035] Furthermore, in S300, the real-time acquisition of the personnel's position coordinates in the dynamic risk field specifically involves the following process:

[0036] Obtain the two-dimensional pixel coordinates of the target person in the visual surveillance video stream. ;

[0037] The pre-calibrated intrinsic and extrinsic parameter matrices of the visual monitoring equipment are invoked, combined with the pre-stored three-dimensional elevation map of the blast furnace area;

[0038] The two-dimensional pixel coordinates are transformed using the inverse perspective projection model. Mapped to 3D world coordinates containing depth information ;

[0039] The three-dimensional world coordinates The data is input into the dynamic risk field generated by S200 for spatial comparison to determine the specific dynamic area where the person is currently located.

[0040] The specific logic for determining whether a person is in an abnormal fall due to poisoning is as follows:

[0041] The human skeleton key point detection algorithm is used to extract the key points of the neck and the center of the spine of the target person in real time, and to construct the human torso spatial vector.

[0042] Real-time calculation of the spatial relative angle between the human torso spatial vector and the horizontal ground normal vector. ;

[0043] Extract the bounding box of the target person in the video image, and calculate the aspect ratio of the width and height of the bounding box. ;

[0044] If in a continuous sliding time window Within, the relative angle of the space The angle of inclination is greater than the set critical fall angle, and the aspect ratio is greater than the set critical fall angle. If the gradient of change exceeds a preset mutation threshold, then it is jointly determined that the person has experienced an abnormal fall due to carbon monoxide poisoning.

[0045] The logic for determining the abnormal stagnation state is as follows:

[0046] Extract the target personnel in continuous The three-dimensional world coordinates in the frame monitoring image sequence ;

[0047] Calculate the target personnel in the The displacement variance of the three-dimensional coordinate motion trajectory in a frame image sequence;

[0048] If the target person's posture is identified as upright or not in a fallen state, but the variance of the three-dimensional coordinate motion trajectory displacement is continuously lower than the preset normal operation micro-motion threshold for a period of time exceeding the safety tolerance limit, and the target person's location is designated as a danger zone or warning zone within the dynamic risk field, then the person is determined to be in an abnormal state of stagnation caused by low-concentration gas hallucinations or loss of consciousness.

[0049] Furthermore, in S400, the matching and execution of the corresponding tiered emergency response strategy is driven by a preset dynamic emergency decision matrix, specifically including:

[0050] If the gas leak level is a Level 1 alarm, and all personnel are in the safe zone and in normal posture, then the Level 1 response strategy is executed: only the audible and visual alarms at the leak site are triggered, and an investigation work order is sent to the central control room.

[0051] If the gas leak level is a Level 2 alarm, or if any person is detected entering the warning zone or danger zone and is in a normal posture, then the Level 2 response strategy will be executed: the on-site audible and visual alarm will be switched to high-frequency flashing mode, a directional voice broadcast will be started to prompt evacuation, and the location and evacuation trajectory of the affected personnel will be highlighted on the electronic map of the warning screen in the central control room.

[0052] If it is determined that the personnel in the danger zone are in the abnormal fall or lingering state, the highest priority emergency response strategy will be triggered: the system will forcibly cut off the normal central control operation flow, push an emergency alarm and the three-dimensional coordinates of the poisoned personnel to the emergency command terminal, and simultaneously start the rescue route planning to avoid the high concentration diffusion path.

[0053] Furthermore, the specific path optimization steps for automatically planning rescue routes that avoid high-concentration diffusion paths based on the dynamic risk field are as follows:

[0054] The pre-stored 3D elevation map of the blast furnace area is spatially rasterized to generate a 3D spatial navigation grid.

[0055] The dynamic risk field generated by S200 is mapped onto the three-dimensional spatial navigation grid, and grid nodes in different regions are assigned directional danger values: the danger value of grid nodes in the danger zone is set to infinity or absolute prohibition value, the danger value of grid nodes in the warning zone is set to distance attenuation penalty value, and the danger value of grid nodes in the safe zone is set to normal passage value.

[0056] Using the rescue starting point as the initial node and the location coordinates of the person who has fallen or is stranded abnormally as the target node, an improved method is adopted. The (A-Star) heuristic pathfinding algorithm is used for path search; wherein the comprehensive cost function of the pathfinding algorithm is:

[0057]

[0058] In the formula, For nodes The comprehensive value of the product; From the starting node to the node The actual physical distance traveled; For the node The estimated Euclidean distance cost to the target node; For nodes The corresponding risk value in the dynamic risk field;

[0059] Output comprehensive value The shortest connecting path is chosen as the final rescue route;

[0060] When the critical response strategy is triggered, the tiered emergency response strategy also mandates automated physical intervention steps for the blast furnace's bottom-level equipment:

[0061] Extract the installation coordinates of the gas alarm that triggered the alarm;

[0062] Traverse the industrial control database to locate the upstream gas pipeline drain shut-off valve closest to the installation coordinates and the corresponding dust collector box emergency exhaust fan in the area.

[0063] The system sends priority control commands down the industrial bus to automatically close the upstream gas pipeline drain shut-off valve to cut off the leak source, and starts the dust collector emergency exhaust fan at full power to forcibly change the local microenvironment airflow, thereby actively compressing the diffusion boundary of the dynamic risk field.

[0064] On the other hand, a blast furnace area gas leakage safety monitoring system based on multimodal perception and intelligent analysis includes:

[0065] The data perception and verification module is used to acquire the concentration data of multiple gas alarms in the blast furnace area in real time. When any of the gas alarms triggers the concentration alarm threshold, the historical concentration gradient of the alarm and the concentration change rate of the downwind adjacent node are extracted, and the spatiotemporal correlation data fusion verification model is used to confirm whether a real gas leak has occurred.

[0066] The dynamic risk field construction module is used to obtain the real-time micro-environmental wind speed in the blast furnace area and the current concentration value of each gas alarm after the data perception and verification module confirms that a real gas leak has occurred, calculate the gas diffusion boundary and generate a dynamic risk field, and delineate the safe zone, warning zone and danger zone in real time according to the dynamic risk field.

[0067] The personnel status recognition module is used to synchronously retrieve the visual monitoring video stream of the corresponding area of ​​the leak, obtain the position coordinates of the personnel in the dynamic risk field in real time through the deep learning target detection algorithm, and extract the posture features of the personnel by using the human skeleton key point detection algorithm to determine whether the personnel are in an abnormal fall or lingering state caused by poisoning.

[0068] The graded emergency response and rescue module is used to match and execute corresponding graded emergency response strategies based on the concentration level of the gas leak, the location area of ​​the personnel in the dynamic risk field, and the posture characteristics. Specifically, when it is determined that the personnel in the danger zone are in an abnormal fall or lingering state, an extremely dangerous alarm is triggered, and a rescue route that avoids the high concentration diffusion path is automatically planned based on the dynamic risk field.

[0069] Beneficial effects

[0070] This invention effectively eliminates false alarms caused by drift or interference from a single sensor by fusing multi-point spatiotemporal correlation data to verify the model. Combining a micro-environment-corrected Gaussian plume model with spatial Kriging interpolation, it achieves accurate reconstruction of the three-dimensional dynamic risk field under airflow disturbances and physical obstruction. Furthermore, by mapping two-dimensional target detection to three-dimensional physical space and integrating the posture features of key points in the human skeleton, it can accurately identify abnormal falls or lingering states caused by carbon monoxide poisoning. Finally, it links the underlying hardware physical intervention mechanism with improvements that incorporate spatial risk cost values. The pathfinding algorithm enables a closed-loop, tiered emergency response system, from accurately detecting authenticity, dynamically defining boundaries, identifying high-risk personnel conditions, to automatically planning evacuation and rescue routes. This significantly improves the accuracy of response and the safety of rescue operations in blast furnace area gas leak incidents. Attached Figure Description

[0071] Figure 1 This is the main flowchart of the method of the present invention;

[0072] Figure 2 This is a block diagram of the overall modular structure of the system of the present invention;

[0073] Figure 3 This is a flowchart of the spatiotemporal correlation data fusion verification sub-process of the present invention;

[0074] Figure 4 This is a flowchart of the dynamic risk field generation and region delineation sub-process of the present invention;

[0075] Figure 5 This is a flowchart of the personnel status accurate recognition and posture determination sub-process of the present invention;

[0076] Figure 6 For the graded emergency response and improvement of this invention Path planning sub-flowchart. Detailed Implementation

[0077] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0078] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but includes other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0079] The present invention will now be described in further detail with reference to the accompanying drawings:

[0080] Example:

[0081] like Figure 1-6 As shown, a method for safety monitoring of blast furnace area gas leakage based on multimodal sensing and intelligent analysis includes the following steps:

[0082] S100: Real-time acquisition of concentration data from multiple gas alarms within the blast furnace area; when any gas alarm triggers the concentration alarm threshold, extract the historical concentration gradient of that alarm and the concentration change rate of adjacent nodes downwind, and use spatiotemporal correlation data fusion to verify the model to confirm whether a real gas leak has occurred.

[0083] S200. If a real gas leak is confirmed, the real-time micro-environmental wind speed in the blast furnace area and the current concentration value of each gas alarm are obtained. The gas diffusion boundary is calculated and a dynamic risk field is generated. The safe zone, warning zone and danger zone are delineated in real time based on the dynamic risk field.

[0084] S300 synchronously retrieves the visual monitoring video stream of the corresponding leak area, obtains the position coordinates of personnel in the dynamic risk field in real time through deep learning target detection algorithm, and extracts the posture features of personnel by human skeleton key point detection algorithm to determine whether personnel are in an abnormal fall or lingering state caused by poisoning.

[0085] S400: Based on the concentration level of the gas leak, the location and posture characteristics of personnel in the dynamic risk field, match and execute the corresponding graded emergency response strategy; among them, when it is determined that personnel in the danger zone are in an abnormal fall or lingering state, the system triggers an extremely dangerous alarm and automatically plans a rescue route to avoid the high concentration diffusion path based on the dynamic risk field.

[0086] Furthermore, the specific implementation process of step S100 is as follows:

[0087] Real-time concentration data from multiple gas alarms within the blast furnace area are acquired. When any gas alarm triggers the concentration alarm threshold, the historical concentration gradient of that alarm and the concentration change rate of adjacent nodes downwind are extracted. The spatiotemporal correlation data fusion verification model is used to confirm whether a real gas leak has occurred.

[0088] In this embodiment, the system pre-builds and loads a three-dimensional sensor topology map of the blast furnace area in memory. This topology map records the three-dimensional absolute coordinates of all gas alarms deployed on the blast furnace body, tuyere platform, furnace top platform, and gas pipeline drainers using a Cartesian coordinate system. Simultaneously, a directed graph data structure is used to record the physical connectivity of gas diffusion between each sensor node. After the system is powered on, it acquires real-time concentration data from each gas alarm via a preset polling cycle through the fieldbus. When a real-time three-dimensional wind direction vector is acquired, the system uses the target gas alarm that triggers the concentration alarm threshold as the origin, and searches along the projection direction of the real-time three-dimensional wind direction vector in the three-dimensional sensor topology graph for sensor nodes with a physical connectivity level of one level and a positive dot product result. The physical address and memory mapping area of ​​these nodes are then marked as downwind adjacent nodes.

[0089] When extracting historical concentration gradients, the system constructs a sliding time window based on a background hardware timer and a circular data buffer, with the current system clock, which triggers the concentration alarm threshold, as the cutoff point. The sampling frequency of the system's analog-to-digital converter is set to a fixed parameter. Within the sliding time window, the system continuously extracts multiple discrete concentration samples from the target gas alarm, forming a set of discrete data pairs in memory. ,in For absolute sampling timestamps, This corresponds to the floating-point value of the concentration sample. The system utilizes the computing resources of the central processing unit to perform time-series feature fitting on the continuous discrete concentration sample values ​​using the least squares method to obtain linear parameters characterizing the concentration ramp-up rate, and the fitting slope. The specific calculation formula is as follows:

[0090]

[0091] In the formula, This represents the total number of valid sampling points within the sliding time window. The arithmetic mean of the timestamps. This is the arithmetic mean of the concentration samples. The system will output the fitted slope. The absolute value is extracted and temporarily stored in the system register as the historical concentration gradient.

[0092] A spatiotemporal correlation data fusion verification model is used to confirm whether a real gas leak has occurred. Its underlying execution logic is a multi-dimensional conditional joint judgment mechanism. The system first reads the temporarily stored historical concentration gradient and compares it with a first preset mutation threshold pre-stored in non-volatile memory. If the comparison result shows that the historical concentration gradient is greater than the first preset mutation threshold, the system immediately activates the coprocessor and calculates the dynamic lag time in real time based on a simplified empirical model of the one-dimensional convection-diffusion equation in fluid mechanics. The dynamic lag time characterizes the physical transport time required for leaked gas to travel from the current node to an adjacent node under specific meteorological conditions. Its specific calculation formula is as follows:

[0093]

[0094] In the formula, Defined as the three-dimensional straight-line distance between the target gas alarm that triggers the alarm and the calibrated downwind adjacent node, this distance is obtained by calculating the three-dimensional absolute coordinates of the two using the Euclidean distance formula; The scalar value of the micro-environmental wind speed, which is collected in real time by the on-site meteorological station or ultrasonic anemometer in the blast furnace area. The spatial angle between the real-time three-dimensional wind direction vector and the spatial connection vector between the two sensor nodes is shown. This is a correction coefficient for gas diffusion resistance in the complex environment of the blast furnace area. This coefficient is a calibration constant, and its initial value is obtained by numerical integration calculation based on the density of pipelines and the physical shielding area of ​​equipment on the blast furnace site.

[0095] To eliminate the nonlinear hysteresis effect of the dense pipe network and large equipment on the gas diffusion path in the blast furnace, a gas diffusion resistance correction coefficient is used. The specific steps to obtain it are as follows:

[0096] The system retrieves the pre-stored 3D sensor topology map of the blast furnace area and the associated 3D entity BIM (Building Information Modeling) data; it establishes a one-dimensional path integral coordinate system by connecting the target gas alarm to the adjacent downwind node with a straight line. The lower limit of integration The coordinate center of the target gas alarm, and the upper limit of integration. The coordinate center of the adjacent node downwind; the system extracts any coordinate point on the path based on 3D solid BIM data. Continuous function of pipe cross-section shading rate and the physical volume density function of the equipment ;

[0097] The system calls the floating-point arithmetic unit and uses the following line integral formula to calculate the gas diffusion resistance correction coefficient. :

[0098]

[0099] In the formula, The environmental fundamental damping constant related to the dynamic viscosity of the leaking gas; This is the first dimensionless weighting coefficient corresponding to the pipe cross-sectional obstruction rate; This is the second dimensionless weighting coefficient corresponding to the physical volume density of the equipment; This is the path integral infinitesimal element. Through this integral operation, the system can quantify the discrete physical obstacles at the blast furnace site into continuous airflow resistance time penalty values.

[0100] The system calculates the accurate dynamic lag time at the current moment. Then, starting from the trigger time of the target alarm, the system extracts a time span of [time range]. The system then calculates the concentration change rate of adjacent nodes downwind within this prospective observation time window. The system determines the concentration change rate of the target gas alarm if and only if it determines that the historical concentration gradient of the target gas alarm is greater than a first preset abrupt change threshold, and simultaneously determines that the concentration change rate of adjacent nodes downwind is within a certain dynamic lag time. When the internal leakage exceeds the second preset threshold, the AND gate in the system's main control logic network outputs a Boolean state value of "true," thus confirming a real gas leak at the underlying code level. If the conditional branch of any of the above data dimensions is not met, the system determines that the current alarm signal is a false alarm signal caused by aging and drift of a single sensor's physical sensing element or by transient electromagnetic interference in the industrial field. The system then resets its internal state machine and refuses to output a valid leak flag to subsequent data links.

[0101] Furthermore, the specific implementation process of step S200 is as follows:

[0102] After the data perception and verification module completes step S100 and confirms a real gas leak at the underlying code level, the system triggers and executes step S200 to calculate the gas diffusion boundary and generate a dynamic risk field. The specific implementation steps are as follows:

[0103] The system first extracts the three-dimensional absolute coordinates of the core sensor that triggered the alarm as the reference point for the leak source. ,in This refers to the effective physical height of the leak point relative to the ground. Simultaneously, the system retrieves instantaneous flow data or pipeline pressure drop data from the corresponding pipeline through the data acquisition and monitoring control system in the blast furnace area to estimate the current initial gas release rate. .

[0104] Subsequently, the system reads the real-time wind speed scalar collected by the micro-environment anemometer. And real-time wind direction vector, in three-dimensional spatial coordinate system The axis is rotated in the positive direction to a direction parallel to the real-time wind direction vector. The system calls the floating-point unit in the microprocessor and uses a three-dimensional continuous point source Gaussian plume diffusion model to calculate the diffusion of any spatial point within the blast furnace area. Predicted gas concentration at the location To ensure that the model can be executed by computer hardware, the specific analytical calculation formula for predicting gas concentration is fixed as follows:

[0105]

[0106] In the formula, For the horizontal diffusion parameters, These are the diffusion parameters in the vertical direction. The system obtains these parameters based on the current atmospheric stability level using either a lookup table method or a power function fitting method. and Distance from downwind The specific numerical value of the change.

[0107] To achieve automated and accurate acquisition of the current atmospheric stability level, the system retrieves data from the first temperature sensor of the meteorological station on the blast furnace top platform. and data from the second temperature sensor at the ground-based wind tunnel platform meteorological station. And read the vertical physical height difference between the two platforms. The system calculates the vertical temperature lapse rate. Simultaneously, combined with real-time wind speed scalars collected by micro-environment anemometers The input is fed into the preset Pasquale-Gifford stability classification decision tree, and the current atmospheric stability level (such as A to F) is automatically output, thereby eliminating the input lag and error caused by manual subjective judgment.

[0108] Considering the dense physical obstructions such as dust removal pipes and platform grids in the blast furnace area, as well as the disturbance of hot airflow from molten iron, the purely theoretical Gaussian plume model will inevitably have boundary calculation errors. Therefore, the system performs a real-time error compensation step based on physical sensors:

[0109] The system extracts data located within the diffusion influence range. The current actual concentration sampling value of each gas alarm is denoted as set. The system calculates the residual concentration error between the theoretically predicted concentration and the actual sampled concentration at each sensor coordinate. :

[0110]

[0111] Next, the system uses ordinary spatial kriging interpolation to reconstruct the error field of the entire three-dimensional space. The system first bases on... Using the error residual data, fit the experimental semivariance function. This is used to characterize the autocorrelation of spatial errors. Subsequently, for any unknown node in space... The system obtains the weight coefficients of each sampling point by solving the Kriging equations for unbiased optimal estimation. And calculate the compensation error of the node. :

[0112]

[0113] After obtaining the full-space error field, the system inversely superimposes the compensation error into the theoretical model to obtain the final calibrated concentration field after microenvironment correction. :

[0114]

[0115] Based on the above final calibration concentration field The system uses a moving cube algorithm or a two-dimensional contour extraction algorithm to retrieve all spatially discrete points in a three-dimensional spatial matrix whose concentration values ​​equal to a preset secondary alarm concentration threshold (high-risk threshold), and then fits and stitches these points together to form a closed inner diffusion polygon. Similarly, the system retrieves all spatially discrete points whose concentration values ​​equal to a preset primary alarm concentration threshold (low-warning threshold), and fits and stitches these points together to form a closed outer diffusion polygon. The outer and inner diffusion polygons together constitute the dynamic risk field.

[0116] The system defines regional attributes in real time based on the spatial topology of the dynamic risk field: the three-dimensional spatial region enclosed inside the inner diffusion polygon is marked as a danger zone in memory; the three-dimensional spatial region in the ring-shaped isolation space between the boundary of the inner diffusion polygon and the boundary of the outer diffusion polygon is marked as a warning zone; and the three-dimensional spatial region at infinity outside the outer diffusion polygon is marked as a safe zone.

[0117] To ensure the real-time nature of emergency response, the system has a built-in hardware timer that reacquires meteorological parameters and sensor data according to a preset refresh cycle (e.g., every 2.0 seconds) and repeatedly executes the above-mentioned spatial error interpolation and polygon reconstruction steps, so that the boundary of the dynamic risk field can adapt to the ever-changing airflow and leakage conditions at the blast furnace site in a realistic and dynamic way, just like an amoeba.

[0118] Furthermore, the specific implementation process of step S300 is as follows:

[0119] After the dynamic risk field construction module completes the three-dimensional spatial dynamic delineation of the safety zone, warning zone, and danger zone within the blast furnace area, the system synchronously triggers the personnel status recognition module to execute step S300. The core of this step is to accurately map the pixel features of personnel in the two-dimensional monitoring field of view to the aforementioned three-dimensional dynamic risk field and perform posture recognition to prevent poisoning and fainting. The specific implementation process is as follows:

[0120] The system uses RTSP or ONVIF protocols to retrieve digital video streams from high-definition visual monitoring devices covering the area corresponding to the gas leak in real time. The system's front-end graphics processing unit (GPU) decodes the video stream frame by frame and inputs the current video frame into a pre-trained deep learning object detection network to extract the two-dimensional bounding box of the target person and the two-dimensional pixel coordinates of the center contact point of their feet. Since the video feed only contains two-dimensional planar information, the system retrieves the intrinsic parameter matrix of the visual monitoring equipment, which was pre-calibrated and stored in non-volatile memory during the installation and commissioning phase. and includes rotation matrix With translation vector The extrinsic parameter matrix. Combined with a pre-loaded 3D elevation map function for the blast furnace area. The system utilizes a perspective projection inverse transformation physical model for dimensionality reduction and inverse solution. The specific spatial ray transformation formula is as follows:

[0121]

[0122] In this perspective projection inverse transformation process, the system first multiplies the pixel coordinates by the inverse of the intrinsic parameter matrix. The system generates a normalized spatial ray vector in the camera coordinate system. Then, it uses an extrinsic parameter matrix to transform this ray vector to the world coordinate system and solves for the spatial ray and the 3D elevation map function using Newton's iteration method. The spatial intersection points are used to obtain the three-dimensional world coordinates of the personnel, which contain absolute depth information. The system inputs the calculated 3D world coordinates into the dynamic risk field polygon set generated in step S200 to perform point inclusion testing, thereby absolutely determining the specific dynamic area (safe zone, warning zone, or danger zone) where the personnel are currently located on a 3D physical scale.

[0123] While acquiring the spatial location of personnel, in order to accurately identify abnormal falls or lingering states caused by blast furnace gas (carbon monoxide) poisoning, the system further crops the image region containing the target person and feeds it into the human skeleton key point detection algorithm module (such as HRNet based on a top-down architecture). The system extracts the coordinates of key points in the neck from the human body's topology in real time. Coordinates of key points at the center of the spine And construct a human torso spatial vector representing the posture of the human trunk in a spatial rectangular coordinate system. The system reads the preset horizontal ground normal vector. The spatial relative angle between the human torso spatial vector and the ground normal vector is calculated in real time using the dot product formula of spatial vectors. :

[0124]

[0125] The system also extracts the bounding boxes output by the aforementioned target detection network and calculates their width. With height Aspect Ratio Considering the complex dust interference at the blast furnace site and the high-frequency jitter noise generated by the camera's mechanical micro-vibration in the image detection frame, the system introduces an exponential moving average (EMA) filtering algorithm to adjust the aspect ratio before calculating the gradient. The time-series signal is smoothed and denoised. The specific smoothing formula is as follows:

[0126]

[0127] In the formula, The aspect ratio after smoothing at the current moment. This is the original aspect ratio observation value at the current moment. This is the smoothed value from the previous time step. This is a smoothing factor parameter based on the dynamic adaptive adjustment of blast furnace dust concentration. The system then continuously calculates the smoothed aspect ratio. Regarding time The first derivative is used as the gradient of change.

[0128] To eliminate interference from bending or squatting movements by blast furnace workers during normal maintenance operations, the system introduces a sliding time window-based approach. The system employs a dual-temporal mutation joint determination logic. The system continuously calculates the aspect ratio. Regarding time The first derivative (i.e., the gradient of change) is given if and only if within the time window. Within, the system determines the relative angle in space. The value exceeds the set critical fall angle (e.g., 60 degrees), and the aspect ratio... If the gradient of change is strictly greater than the preset mutation threshold (characterizing the violent physical collapse of the human body from an upright state to a horizontal state in a very short time), the system outputs a high-level signal at the logic layer to jointly determine that the person has experienced an abnormal fall caused by gas poisoning.

[0129] To address the latent dangers of early-stage carbon monoxide poisoning, such as hallucinations, confusion, or muscle rigidity, the system executes specific judgment logic for abnormal stay states. The system continuously... The set of three-dimensional world coordinates of the target person is extracted from the frame surveillance image sequence. And calculate the variance of the motion trajectory displacement of the target person's three-dimensional coordinate sequence. :

[0130]

[0131] in, For this The arithmetic mean vector of spatial positions within the frame. If the system identifies the spatial relative angle of the target person using the aforementioned attitude determination logic... Within the upright tolerance range (no fall), but the variance of the motion trajectory displacement. If the variance remains consistently below the preset normal operational micro-motion threshold (excluding normal walking and limb waving), and this extremely low variance state is maintained for a period exceeding the preset safety tolerance time limit, the system immediately performs a backtesting check on the spatial attributes of the target personnel. If the personnel's three-dimensional world coordinates are currently dynamically classified into a danger zone or warning zone within a dynamic risk field, the system's main control logic gate is triggered and closed, formally determining that the target personnel is in an abnormal loitering state. An emergency high-priority event data packet containing the personnel's unique ID and absolute coordinates is then generated and sent to subsequent processing nodes.

[0132] Furthermore, the specific implementation process of step S400 is as follows:

[0133] First, multi-dimensional Boolean logic operations are performed on the input status word. If the gas leak level is within the first-level alarm threshold range, and the system spatial comparison confirms that all personnel coordinates fall within the safe zone, and the posture characteristic status word is normal (not fallen or lingering), the system main control unit executes the first-level response strategy: triggering the audible and visual alarm relay at the leak site to close via the industrial bus, and generating a digital investigation work order through the Manufacturing Execution System (MES) interface and pushing it to the central control room. If the gas leak level reaches the second-level alarm threshold, or the spatial comparison confirms that the three-dimensional coordinates of any personnel have entered the warning zone or danger zone, but the personnel's posture characteristic status word is still normal and upright, the system main control unit executes the second-level response strategy: sending a high-frequency duty cycle drive signal to the on-site audible and visual alarm to switch it to high-frequency flashing mode, closing the power amplifier circuit of the digital audio broadcast module to play directional evacuation voice, and overlaying and rendering the dynamic red dots of the affected personnel and the estimated evacuation trajectory on the electronic elevation map of the upper computer configuration software in the central control room.

[0134] When the system's main control logic gate determines that the status word of a person in the danger zone changes to an abnormal fall or lingering state, the system triggers the highest priority emergency response strategy. At this time, the system's main thread sends a high-level interrupt signal, forcibly suspending the regular central control operation flow and pushing an emergency alarm to the emergency command terminal. Simultaneously, the system initiates a high-dimensional hazard avoidance and rescue route planning algorithm based on a dynamic risk field. The specific calculation and execution steps are as follows:

[0135] The system retrieves a pre-stored 3D elevation map of the blast furnace area and uses a spatial partitioning algorithm (such as the octree algorithm) to perform spatial rasterization, generating a 3D spatial navigation mesh composed of discrete voxel nodes of equal volume. The system maps the dynamic risk field polygon equation generated in step S200 to the coordinate system of the 3D spatial navigation mesh, assigning directional hazard values ​​to the raster nodes of each region. .

[0136] Specifically, the system assigns an infinite value to the danger value of grid nodes whose spatial coordinates are completely contained within the danger zone (the inner polygon). In the underlying pathfinding logic, it is marked as an absolutely forbidden node; the system extracts the grid nodes located within the warning zone, calculates the shortest Euclidean distance between them and the boundary of the inner polygon, and assigns a distance decay penalty value to them using an exponential decay function. The distance decay penalty value The specific computational physical mathematical model is as follows:

[0137]

[0138] In the formula, The initial penalty cost constant set for the system to be close to the boundary of the danger zone; For this grid node The shortest spatial Euclidean distance to the boundary of the inner diffusion polygon; This represents the spatial risk attenuation coefficient, whose value is positively correlated with the real-time microenvironment wind speed scalar. Through this continuous exponential decay function, the system avoids path oscillations and backtracking phenomena that occur when the pathfinding algorithm crosses the hard boundaries between the safe zone and the warning zone.

[0139] For grid nodes located within the safe zone, the system sets their hazard cost to the normal physical access constant (e.g., a value of 1).

[0140] After completing the topology construction of the cost grid, the system uses the preset nearest safety exit or medical rescue station node as the starting node and the absolute 3D coordinates of the person in an abnormal fall or stranded state as the target node, and loads the improved A-Star heuristic spatial pathfinding algorithm. When traversing the spatial grid, the core synthesis cost function of this algorithm is fixed as follows:

[0141]

[0142] In this core cost function, Characterizing grid nodes The comprehensive value of pathfinding; The representation is the movement from the starting node to the node along the explored connected mesh. The actual physical three-dimensional spatial movement distance cost; Representation from node The estimated spatial Euclidean distance heuristic cost for moving to the target node; That is, a node The risk cost value is obtained from the aforementioned dynamic risk field mapping. The system uses a priority queue data structure to iteratively update the node cost parameters in the open and closed lists, outputting the comprehensive risk value. The smallest set of three-dimensional connected node sequences. This sequence, as the optimal rescue route that perfectly avoids the high-concentration diffusion path, is encapsulated by the system in the format of a three-dimensional vector path packet and transmitted to the handheld explosion-proof terminal of the rescue personnel via a wireless transmission protocol.

[0143] Within the same clock cycle that triggers the critical response strategy, the system executes automated intervention steps based on the underlying physical hardware in parallel. The system extracts the physical installation three-dimensional coordinates of the core gas alarm that triggered the leak alarm and uses this as the search origin to traverse and query the relational industrial control database. The system locates the network IP address of the upstream gas pipeline drain shut-off valve, which is the closest in three-dimensional linear distance and located in the same physical network, and simultaneously locks the control channel of the emergency exhaust fan equipment in the corresponding dust collector area. The system sends a highest-priority forced write command to the underlying programmable logic controller (PLC) of the corresponding equipment via PROFINET or ModbusTCP industrial Ethernet bus. This command directly drives the field pneumatic or electric actuator to automatically close the upstream gas pipeline drain shut-off valve, blocking the continuous gas leakage from the physical fluid dynamics source; simultaneously, the system sends a full-load duty cycle drive signal to start the emergency exhaust fan in the dust collector at full power. By forcibly creating a local negative pressure zone through the high-speed operation of the exhaust fan, the hydrodynamic distribution of the airflow in the microenvironment of the blast furnace is changed, thereby actively compressing and curbing the outward diffusion boundary of the dynamic risk field at the physical space level, and buying safe time and space redundancy for the rescue of poisoned personnel.

[0144] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for safety monitoring of blast furnace area gas leakage based on multimodal sensing and intelligent analysis, characterized in that, Includes the following steps: S100. Real-time acquisition of concentration data from multiple gas alarms within the blast furnace area; when any of the gas alarms triggers the concentration alarm threshold, extract the historical concentration gradient of the alarm and the concentration change rate of adjacent nodes downwind, and use the spatiotemporal correlation data fusion verification model to confirm whether a real gas leak has occurred. S200. If a real gas leak is confirmed, the real-time micro-environmental wind speed in the blast furnace area and the current concentration value of each gas alarm are obtained. The gas diffusion boundary is calculated and a dynamic risk field is generated. The safe zone, warning zone and danger zone are delineated in real time according to the dynamic risk field. S300: Simultaneously retrieve the visual monitoring video stream of the corresponding area of ​​the leak, obtain the position coordinates of the personnel in the dynamic risk field in real time through the deep learning target detection algorithm, and extract the posture features of the personnel by the human skeleton key point detection algorithm to determine whether the personnel are in an abnormal fall or lingering state caused by poisoning. S400. Based on the concentration level of the gas leak, the location area of ​​the personnel in the dynamic risk field, and the posture characteristics, match and execute the corresponding graded emergency response strategy; wherein, when it is determined that the personnel in the danger zone are in the abnormal fall or lingering state, the system triggers an extremely dangerous alarm and automatically plans a rescue route to avoid the high concentration diffusion path based on the dynamic risk field.

2. The method for safety monitoring of blast furnace area gas leakage based on multimodal sensing and intelligent analysis according to claim 1, characterized in that, The downwind adjacent nodes are determined by a pre-constructed three-dimensional sensor topology map of the blast furnace area; the three-dimensional sensor topology map records the spatial three-dimensional coordinates and physical connectivity of gas diffusion of all gas alarms deployed on the blast furnace body, each platform and gas pipeline drainer. The steps for extracting the historical concentration gradient of the alarm device specifically include: Construct a sliding time window with the trigger point of the concentration alarm threshold as the cutoff time; Multiple consecutive discrete concentration sampling values ​​of the alarm device within the sliding time window are obtained according to a preset sampling frequency; The continuous discrete concentration sampling values ​​are fitted with time-series features using the least squares method, and the absolute value of the slope of the fitted output is used as the historical concentration gradient.

3. The method for safety monitoring of blast furnace area gas leakage based on multimodal perception and intelligent analysis according to claim 2, characterized in that, The core logic of the verification model that uses spatiotemporal correlation data fusion to confirm whether a real gas leak has occurred is as follows: If the historical concentration gradient is greater than a first preset abrupt change threshold, and the concentration change rate of the downwind adjacent nodes is within the dynamic lag time... If the value exceeds the second preset mutation threshold, it is determined that a real gas leak has occurred, eliminating false alarms caused by single-point drift of the sensor. Wherein, the dynamic lag time The calculation model is as follows: In the formula, The straight-line spatial distance between the alarm device and the adjacent downwind node; The angle between the real-time wind direction vector and the vector connecting the two nodes in space; This is a correction coefficient for gas diffusion resistance under the complex environment of the blast furnace area.

4. The method for safety monitoring of blast furnace area gas leakage based on multimodal sensing and intelligent analysis according to claim 3, characterized in that, In step S200, the steps of calculating the gas diffusion boundary and generating the dynamic risk field specifically include: Obtain the three-dimensional spatial coordinates and initial release rate of the confirmed leak source; Based on the real-time microenvironment wind speed and direction vector, a microenvironment-corrected Gaussian plume model is used to calculate the plume at any spatial point within the blast furnace area. Predicted coal gas concentration ; Connect the set of spatial points where the predicted gas concentration is equal to the preset first-level alarm concentration threshold with contour lines to form an outer diffusion polygon. The set of spatial points whose predicted gas concentration equals the preset secondary alarm concentration threshold are connected by contour lines to form an inner diffusion polygon; the outer diffusion polygon and the inner diffusion polygon together constitute the dynamic risk field.

5. The method for safety monitoring of blast furnace area gas leakage based on multimodal perception and intelligent analysis according to claim 4, characterized in that, The rules for determining the safe zone, warning zone, and danger zone in real time based on the dynamic risk field are as follows: The three-dimensional spatial region inside the inner diffusion polygon is defined as the danger zone; The three-dimensional annular space region between the inner and outer diffusion polygons is defined as the warning zone. The three-dimensional spatial region outside the outer diffusion polygon is defined as the safe zone; Furthermore, the polygonal boundary of the dynamic risk field is dynamically reconstructed according to a preset refresh cycle as the real-time microenvironment wind speed and wind direction vector change.

6. The method for safety monitoring of blast furnace area gas leakage based on multimodal perception and intelligent analysis according to claim 5, characterized in that, The calculation using the micro-environment-corrected Gaussian plume model also includes a real-time error compensation step based on physical sensors. Extract the current true concentration value of each gas alarm located within the coverage area of ​​the outer or inner diffusion polygon; Calculate the predicted gas concentration at the coordinates of the gas alarm. The concentration difference between the actual concentration value and the current true concentration value; The concentration difference is used as a compensation weight by spatial kriging interpolation algorithm. It is then substituted into the Gaussian plume model to correct the diffusion coefficient, thereby dynamically adjusting the boundary distortion of the outer and inner diffusion polygons. This eliminates the theoretical calculation errors caused by the blast furnace building structure and hot air flow disturbance on the gas diffusion model.

7. The method for safety monitoring of blast furnace area gas leakage based on multimodal perception and intelligent analysis according to claim 6, characterized in that, In S300, the real-time acquisition of the personnel's position coordinates in the dynamic risk field specifically involves the following process: Obtain the two-dimensional pixel coordinates of the target person in the visual surveillance video stream. ; The intrinsic and extrinsic parameter matrices of the pre-calibrated visual monitoring equipment are invoked, combined with the pre-stored three-dimensional elevation map of the blast furnace area; The two-dimensional pixel coordinates are transformed using the inverse perspective projection transformation model. Mapped to 3D world coordinates containing depth information ; The three-dimensional world coordinates The data is input into the dynamic risk field generated by S200 for spatial comparison to determine the specific dynamic area where the person is currently located. The specific logic for determining whether a person is in an abnormal fall due to poisoning is as follows: The human skeleton key point detection algorithm is used to extract the key points of the neck and the center of the spine of the target person in real time, and to construct the human torso spatial vector. Real-time calculation of the spatial relative angle between the human torso spatial vector and the horizontal ground normal vector. ; Extract the bounding box of the target person in the video image, and calculate the aspect ratio of the width and height of the bounding box. ; If in a continuous sliding time window Within, the relative angle of the space The angle of inclination is greater than the set critical fall angle, and the aspect ratio is greater than the set critical fall angle. If the gradient of change exceeds a preset mutation threshold, then it is jointly determined that the person has experienced an abnormal fall due to carbon monoxide poisoning. The logic for determining the abnormal stagnation state is as follows: Extract the target personnel in continuous The three-dimensional world coordinates in the frame monitoring image sequence ; Calculate the target personnel in the The displacement variance of the three-dimensional coordinate motion trajectory in a frame image sequence; If the target person's posture is identified as upright or not in a fallen state, but the variance of the three-dimensional coordinate motion trajectory displacement is continuously lower than the preset normal operation micro-motion threshold for a period of time exceeding the safety tolerance limit, and the target person's location is designated as a danger zone or warning zone within the dynamic risk field, then the person is determined to be in an abnormal state of stagnation caused by low-concentration gas hallucinations or loss of consciousness.

8. The method for safety monitoring of blast furnace area gas leakage based on multimodal perception and intelligent analysis according to claim 7, characterized in that, In step S400, the matching and execution of the corresponding tiered emergency response strategy is driven by a preset dynamic emergency decision matrix, specifically including: If the gas leak level is a Level 1 alarm, and all personnel are in the safe zone and in normal posture, then the Level 1 response strategy is executed: only the audible and visual alarms at the leak site are triggered, and an investigation work order is sent to the central control room. If the gas leak level is a Level 2 alarm, or if any person is detected entering the warning zone or danger zone and is in a normal posture, then the Level 2 response strategy will be executed: the on-site audible and visual alarm will be switched to high-frequency flashing mode, a directional voice broadcast will be started to prompt evacuation, and the location and evacuation trajectory of the affected personnel will be highlighted on the electronic map of the warning screen in the central control room. If it is determined that the personnel in the danger zone are in the abnormal fall or lingering state, the highest priority emergency response strategy will be triggered: the system will forcibly cut off the normal central control operation flow, push an emergency alarm and the three-dimensional coordinates of the poisoned personnel to the emergency command terminal, and simultaneously start the rescue route planning to avoid the high concentration diffusion path.

9. A method for safety monitoring of blast furnace area gas leakage based on multimodal perception and intelligent analysis according to claim 8, characterized in that, The specific path optimization steps for automatically planning rescue routes that avoid high-concentration diffusion paths based on the dynamic risk field are as follows: The pre-stored 3D elevation map of the blast furnace area is spatially rasterized to generate a 3D spatial navigation grid. The dynamic risk field generated by S200 is mapped onto the three-dimensional spatial navigation grid, and grid nodes in different regions are assigned directional danger values: the danger value of grid nodes in the danger zone is set to infinity or absolute prohibition value, the danger value of grid nodes in the warning zone is set to distance attenuation penalty value, and the danger value of grid nodes in the safe zone is set to normal passage value. Using the rescue starting point as the initial node and the location coordinates of the person who has fallen or is stranded abnormally as the target node, an improved method is adopted. The (A-Star) heuristic pathfinding algorithm is used for path search; wherein the comprehensive cost function of the pathfinding algorithm is: In the formula, For nodes The comprehensive value of the product; From the starting node to the node The actual physical distance traveled; For the node The estimated Euclidean distance cost to the target node; For nodes The corresponding risk value in the dynamic risk field; Output comprehensive value The shortest connecting path is chosen as the final rescue route; When the critical response strategy is triggered, the tiered emergency response strategy also mandates automated physical intervention steps for the blast furnace's bottom-level equipment: Extract the installation coordinates of the gas alarm that triggered the alarm; Traverse the industrial control database to locate the upstream gas pipeline drain shut-off valve closest to the installation coordinates and the corresponding dust collector box emergency exhaust fan in the area. The system sends priority control commands down the industrial bus to automatically close the upstream gas pipeline drain shut-off valve to cut off the leak source, and starts the dust collector emergency exhaust fan at full power to forcibly change the local microenvironment airflow, thereby actively compressing the diffusion boundary of the dynamic risk field.

10. A blast furnace area gas leakage safety monitoring system based on multimodal sensing and intelligent analysis, characterized in that, A method for safety monitoring of blast furnace area gas leakage based on multimodal sensing and intelligent analysis according to any one of claims 1-9, the system comprising: The data perception and verification module is used to acquire the concentration data of multiple gas alarms in the blast furnace area in real time. When any of the gas alarms triggers the concentration alarm threshold, the historical concentration gradient of the alarm and the concentration change rate of the downwind adjacent node are extracted, and the spatiotemporal correlation data fusion verification model is used to confirm whether a real gas leak has occurred. The dynamic risk field construction module is used to obtain the real-time micro-environmental wind speed in the blast furnace area and the current concentration value of each gas alarm after the data perception and verification module confirms that a real gas leak has occurred, calculate the gas diffusion boundary and generate a dynamic risk field, and delineate the safe zone, warning zone and danger zone in real time according to the dynamic risk field. The personnel status recognition module is used to synchronously retrieve the visual monitoring video stream of the corresponding area of ​​the leak, obtain the position coordinates of the personnel in the dynamic risk field in real time through the deep learning target detection algorithm, and extract the posture features of the personnel by using the human skeleton key point detection algorithm to determine whether the personnel are in an abnormal fall or lingering state caused by poisoning. The graded emergency response and rescue module is used to match and execute corresponding graded emergency response strategies based on the concentration level of the gas leak, the location area of ​​the personnel in the dynamic risk field, and the posture characteristics. Specifically, when it is determined that the personnel in the danger zone are in an abnormal fall or lingering state, an extremely dangerous alarm is triggered, and a rescue route that avoids the high concentration diffusion path is automatically planned based on the dynamic risk field.

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