A limited space operation intelligent wearable device design method and system

Abstract

The application discloses a kind of limited space operation intelligent wearable equipment design method and system, it is related to intelligent wearing equipment technical field, in the system operation, the space-time information of observed extreme risk event is acquired in real time;Based on intensity threshold value, forecast event is identified;In the preset space-time deviation range, multidimensional search is executed and whether hit is judged;Hit rate is calculated and sampling frequency and trigger logic are optimized.The system includes: data acquisition module for collecting environmental, physical and positional data;Central processing module for executing space-time search algorithm and determining risk state;Wireless communication module for data synchronization;And alarm module for triggering multiple early warnings.The present application introduces a verification logic with space-time fault tolerance capability, effectively overcomes the sensitivity of traditional methods to displacement and phase, significantly improves the objectivity, accuracy and system stability of risk identification under extreme dangerous working conditions.

Description

Technical Field

[0001] This invention relates to the field of smart wearable device technology, specifically to a design method and system for a smart wearable device for working in confined spaces. Background Technology

[0002] With the increasing demands for industrial production safety management, the safety monitoring and protection of confined space operations has become a crucial issue in safeguarding the life and health of workers. Confined spaces are typically characterized by their small size, enclosed environment, restricted access, and poor ventilation. During operations, they are highly susceptible to the accumulation of toxic and harmful gases or sudden safety accidents such as oxygen deficiency and asphyxiation. Therefore, comprehensive real-time monitoring and intelligent early warning of the work environment are key to ensuring production safety.

[0003] Among them, intelligent wearable devices and design systems for confined space operations, as a comprehensive safety protection terminal integrating multi-dimensional sensing, wireless communication, precise positioning, and real-time alarm functions, are gradually becoming a core technology track in this field. Through various miniaturized sensing units deployed on workers, they collect real-time data on ambient gas concentration, worker vital signs, and geospatial location information, and synchronize this data to a remote monitoring platform using complex communication links, aiming to achieve digital modeling and risk control of the entire operation process.

[0004] Existing confined space operation monitoring technologies have revealed numerous bottlenecks in practical applications, severely restricting the effectiveness and accuracy of safety protection. Firstly, spatial matching and positioning description suffer from a severe double penalty effect. In complex underground pipe networks or metal-shielded environments, even minute displacements or jumps in positioning signals often lead to significant deviations in the monitoring system's judgment of hazardous areas, underestimating the true threat of sudden risks. Secondly, there are monitoring misalignments and response delays in the time dimension. Constrained by sensor sampling cycles and the performance limitations of data processing algorithms, equipment often fails to reveal the true phase characteristics of environmental evolution, causing warning signals to deviate in time sequence. Thirdly, there is insufficient ability to capture and identify low-probability, high-hazard-risk events. Due to the scarcity of extreme anomaly samples, traditional statistical judgment models exhibit strong instability, making it difficult to accurately extract true accident characteristics amidst complex environmental noise. Finally, there are inherent representativeness errors and environmental adaptability defects in the sensor data itself. The limited sensing range of a single wearable device and its susceptibility to interference from work posture make it difficult to accurately reconstruct the fine spatial structure and risk distribution within the confined space. These problems together result in a high risk of failure for existing systems when dealing with extreme and dangerous working conditions, making it difficult to meet the current needs of refined operation management. Developing a method and system that can effectively accommodate spatiotemporal deviations and improve the accuracy of extreme risk identification has become an urgent technical challenge in the industry. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a design method and system for intelligent wearable devices for confined space operations, solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a design method for an intelligent wearable device for confined space operations, comprising the following steps: S1. Real-time acquisition of spatial location and timestamp information of extreme risk events observed in confined space operation environments; S2. Determine the intensity threshold of extreme risk events, and filter the simulation data output by the forecast model based on the intensity threshold to identify potential forecast extreme risk events; S3. Taking the station where the extreme risk event is observed as the center, perform a multi-dimensional spatiotemporal search within the preset time deviation window and the preset spatial deviation radius to determine whether the predicted extreme risk event hits the observed extreme risk event. S4. Calculate the hit rate of extreme risk warnings by statistically analyzing the number of forecast hits and the total number of observations, and optimize the sensor sampling frequency and warning triggering logic of wearable devices accordingly.

[0007] Preferably, step S3 specifically includes the following steps: S31. Obtain the occurrence time of the observed extreme risk event, and based on this time, select multiple consecutive candidate time nodes within a preset time span before and after to construct a time dimension search sequence; S32. For each candidate time node, delineate a circular search area with the observation station coordinates as the center and a preset spatial distance as the radius to construct a spatial dimension search surface; S33. Search within the spatiotemporal envelope formed by the time dimension search sequence and the spatial dimension search surface to see if there is at least one predicted extreme risk event that meets the intensity threshold; S44. If a predicted event that meets the conditions is found within the spatiotemporal envelope, it is determined as a hit, and the spatiotemporal offset of the hit event is recorded.

[0008] Preferably, the observed extreme risk event refers to an event in which the concentration of a specific toxic or harmful gas accumulated in a confined space within one hour reaches a preset danger level threshold.

[0009] Preferably, the preset time deviation window is set to 3 hours before and after the occurrence of the extreme risk event. This window can cover the entire life cycle of the mesoscale dangerous environment evolution and effectively offset the phase misalignment caused by the unstable process in the early stage of the mode start-up.

[0010] Preferably, the preset spatial deviation radius includes four different scale gradients: 25 km, 50 km, 100 km, and 200 km. Among them, 25 km and 200 km correspond to the lower and upper spatial scales of the evolution of mesoscale convective systems, respectively. By setting multiple gradient radii, it is possible to achieve a refined characterization of the performance of forecast models with different spatial resolutions.

[0011] Preferably, the hit rate is calculated as follows: the total number of predicted extreme risk events is defined as the numerator, and the total number of observed extreme risk events is defined as the denominator. The two are divided to obtain the hit rate index, which reflects the accuracy of the forecast. This index can eliminate the instability of traditional frequency statistics methods when dealing with low-probability events and provide a narrower confidence interval.

[0012] Preferably, in the design method of the intelligent wearable device for confined space operations, the processing logic for the double penalty effect in spatial matching is as follows: abandoning the traditional point-by-point alignment verification mode, and instead adopting a fault-tolerant algorithm based on neighborhood search. When the predicted risk area deviates within the preset spatial deviation radius, the system acknowledges the contribution of the prediction to capturing extreme dangerous situations through smoothing processing of spatial distance weights, thereby avoiding a significant drop in score due to extremely small displacement deviations.

[0013] Preferably, in the design method of an intelligent wearable device for confined space operations, the processing logic for time mismatch is as follows: a dynamic search window is established on the time axis to capture the offset characteristics of precipitation periods or periods of increased risk in the forecast data. The system identifies the magnitude of the time phase error by calculating the cross-correlation function between the forecast peak time and the observed peak time, and corrects and compensates for the time offset in the final evaluation index.

[0014] Preferably, in the design method of the intelligent wearable device for confined space operations, the processing logic for the representativeness error of the observation data is as follows: A scale matching factor is introduced. By performing spatial averaging on the high-resolution model grid data, or by performing Kriging interpolation or distance-weighted inverse interpolation on the sparse station observation data, the two are compared at a unified spatial scale, thereby eliminating the representativeness bias caused by inconsistent sampling densities.

[0015] A smart wearable device design system for confined space operations to implement the above method includes: a data acquisition module for real-time acquisition of ambient gas concentration information, worker vital signs parameters, and geospatial coordinates via miniaturized sensing units deployed on the worker; a central processing module electrically connected to the data acquisition module and configured to execute the above-mentioned spatiotemporal envelope search algorithm to calculate the warning hit rate and determine the current risk status; a wireless communication module for real-time synchronization of the risk assessment data and hit rate indicators calculated by the central processing module to a remote monitoring platform; and an alarm module whose input receives control signals from the central processing module to trigger multiple warnings of vibration, sound and light, and voice broadcast when an extreme risk is determined and the risk level exceeds a predetermined safety threshold.

[0016] Preferably, the central processing module integrates an extreme value analysis algorithm unit. This unit is specifically designed to model low-probability extreme precipitation or extreme concentration fluctuations, and uses nonlinear dynamics principles to enhance the weak signals transmitted from the sensors, ensuring that the system can extract true precursory features of accidents in complex environmental background noise.

[0017] Preferably, the gas sensor in the data acquisition module adopts a compensated measurement structure. Through a built-in temperature and humidity correction algorithm, it eliminates the interference of high humidity, strong winds, and drastic temperature changes in a confined space on measurement accuracy, thereby reducing the uncertainty of observation errors at the physical sensing level.

[0018] Preferably, the wireless communication module employs a multi-link redundancy design. In complex underground pipe networks or metal-shielded environments, the system can adaptively switch communication frequencies or adopt a relay forwarding mode based on signal strength, ensuring that the data stream required for spatiotemporal search has extremely low transmission latency.

[0019] This invention provides a design method and system for intelligent wearable devices for confined space operations, which has the following beneficial effects: (1) During system operation, this invention proposes an extreme early warning verification logic with spatiotemporal fault tolerance, fundamentally solving the technical problem of failure of traditional point-by-point verification methods under extreme conditions. By introducing a preset spatial deviation radius and a preset time deviation window, this invention effectively overcomes the double penalty effect caused by spatial displacement sensitivity, enabling the evaluation results to truly reflect the forecast model's ability to capture the evolution trend of extreme risks. This invention not only acknowledges the high nonlinearity and uncertainty of extreme events in spatiotemporal distribution, but also transforms valid forecasts that would otherwise be judged as erroneous into hit records through a multi-scale search mechanism, greatly improving the scientificity and rationality of the early warning system evaluation. In addition, the hit rate index designed for low-probability events effectively avoids data fluctuations caused by sparse samples in traditional scoring methods, providing more stable and reliable technical support for the safety management of confined space operations.

[0020] (2) By employing a multi-gradient spatial deviation radius ranging from 25 km to 200 km, this invention achieves comprehensive coverage of the evolution patterns of weather systems or hazardous environments at different scales. This multi-scale analysis capability enables the design of wearable devices to move beyond single local perception and instead combine large-scale environmental background information to make forward-looking judgments on extreme rainfall intensity or extreme gas concentration bursts, significantly enhancing the system's defense depth against mesoscale hazardous weather. This invention successfully solves the problem of time phase shift caused by the instability of the dynamic framework in the early stage of forecast model startup by searching within a 3-hour time deviation window. This elastic matching mechanism in the time dimension ensures that even if there is a small error in the timing of the warning signal, it can still be judged as an effective warning, thus providing operators with a more valuable emergency evacuation time window. By introducing a scale matching factor for the representative error of observations into the design method, this invention eliminates the verification distortion caused by the uneven distribution of ground stations. The system ensures the consistency of the benchmark in the evaluation process by uniformly modeling high-precision gridded data and sparse observation data, effectively reducing the negative impact of environmental noise and sampling errors on the accuracy of warnings. The system described in this invention integrates an adaptive optimization mechanism based on hit rate feedback. The central processing module can automatically adjust the sensor sampling interval and the sensitivity of the early warning algorithm according to the fluctuation of historical hit rates. In periods or areas with high extreme risks, the system will automatically increase the weight of resource allocation, thereby achieving accurate detection of high-risk, low-probability events while ensuring battery life.

[0021] (3) The data acquisition module in this invention, through the synergistic effect of the compensating measurement structure and the temperature and humidity correction algorithm, greatly improves the perception stability in extremely harsh environments within confined spaces. This hardware-level optimization and the deep coupling of the spatiotemporal search algorithm at the software level form a precise protection system covering the entire chain from perception to decision-making, ensuring that the early warning information still has extremely high reference value under conditions of strong convection, strong winds, and drastic temperature changes. This invention, by using a specific hit rate calculation formula, eliminates the masking effect of individual maximum error values ​​in continuous scoring such as root mean square error on the overall performance. This evaluation system, which focuses on the hit performance of extreme values, makes the risk management of confined space operations more focused on protecting life safety at extreme points, rather than merely pursuing average accuracy under ordinary working conditions, and has extremely strong practical application value and safety protection significance.

[0022] (4) This invention employs a multi-link redundant wireless communication design in its system architecture, solving the data interruption problem caused by signal shielding in a confined space. Combined with the spatiotemporal search function of the central processing module, the system can immediately supplement missing risk assessments through time-series backtracking after network recovery, ensuring the continuity and integrity of the safety monitoring logic in the time dimension. This invention reveals the diffusion dynamics and evolution logic of hazardous factors in a confined space through joint analysis of the spatial location and timestamp of extreme risk events on a four-dimensional manifold. This in-depth spatiotemporal correlation analysis not only improves the hit rate of immediate warnings but also provides accurate digital basis for subsequent optimization of work processes and scientific planning of ventilation and detoxification paths. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall technical solution architecture of the design method and system for intelligent wearable devices for confined space operations proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the extreme risk warning hit determination mechanism based on spatiotemporal envelope search in this invention; Figure 3 This is a schematic diagram of the multi-level interaction relationship and data flow of the intelligent wearable device design system for confined space operations in this invention. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0025] Example 1 This invention provides a design method and system for an intelligent wearable device for working in confined spaces. Please refer to [link / reference]. Figures 1 to 3 In engineering practice involving municipal pipeline maintenance and underground confined space operations, the complexity and unpredictability of the underground environment pose significant risks to workers, including severe hydrogen sulfide accumulation, excessive carbon monoxide levels, and flooding caused by heavy rainfall. This embodiment provides a design method for an intelligent wearable device for confined space operations. The aim is to address the double penalty effect caused by positioning errors and the temporal phase misalignment of warning signals in traditional monitoring systems by constructing a warning and verification logic with spatiotemporal fault tolerance.

[0026] For step S1, the spatial location and timestamp information of observed extreme risk events in confined space working environments are acquired in real time. In specific engineering deployments, the smart terminals worn by operators integrate high-precision BeiDou positioning units and timing modules. This BeiDou positioning unit supports multi-frequency signal reception and can maintain high positioning accuracy in urban canyons or semi-enclosed pipeline entrances. The system filters positioning data through receiver autonomous integrity monitoring technology to ensure that the output latitude and longitude coordinates have sub-meter confidence levels. The timestamp information is directly derived from the high-precision atomic clock signal of the BeiDou satellite system, with a time synchronization accuracy better than 100 nanoseconds, ensuring the consistency of the benchmark for subsequent spatiotemporal correlation analysis. In this process, observed extreme risk events refer to events where the concentration of a specific toxic or harmful gas accumulated within a confined space reaches a preset danger level threshold within one hour. For example, when the hydrogen sulfide concentration rapidly rises from a baseline value to above 10 milligrams per cubic meter within 60 minutes, the system automatically marks the geographic coordinates and time as an observed extreme risk event.

[0027] In step S2, the intensity threshold of extreme risk events is determined, and the simulation data output by the forecast model is filtered based on this threshold to identify potential forecasted extreme risk events. The forecast model involved in this step is a numerical simulation system built based on fluid dynamics simulation and environmental dynamics evolution laws. This system uses the finite element analysis method to perform hourly rolling calculations on airflow organization, water supply, and pressure fields within the pipeline network. The intensity threshold is set with reference to the short-term exposure allowable concentration in the national occupational health standards. The simulation data output by the forecast model includes the gas distribution characteristics at spatial grid points over the next 24 hours. The central processing module extracts simulated risk points that meet the hazard level threshold by performing nonlinear interpolation processing on the simulated grid data. These risk points are defined as forecasted extreme risk events, which include the predicted occurrence location, the predicted evolution start time, and the predicted peak intensity parameters.

[0028] For step S3, a multi-dimensional spatiotemporal search is performed centered on the site where the observed extreme risk event is located, within a preset time deviation window and a preset spatial deviation radius, to determine whether the predicted extreme risk event matches the observed extreme risk event. This step is the core fault-tolerant logic of this invention. First, proceed to step S31 to obtain the occurrence time of the observed extreme risk event, and use this time as a benchmark to select multiple consecutive candidate time nodes within a preset time span before and after, constructing a time-dimensional search sequence. In this embodiment, the preset time deviation window is set to 3 hours before and after the occurrence time of the extreme risk event. The physical significance of this setting is that environmental dynamics models often have unstable processes in the early stages of startup, causing the simulated risk evolution to have a phase shift on the time axis. By establishing a dynamic search window of plus or minus 3 hours, the system can capture those forecast signals that, although slightly ahead or behind in the occurrence time, essentially accurately reflect the trend of danger evolution.

[0029] Then, step S32 is executed. For each candidate time node, a circular search area is delineated with the observation station coordinates as the center and a preset spatial distance as the radius, constructing a spatial dimension search surface. Considering the differences in the scale of environmental evolution, the preset spatial deviation radius is subdivided into four different scale gradients: 25 km, 50 km, 100 km, and 200 km. Among them, 25 km corresponds to the lower limit of the small-scale evolution of local sudden hazard sources, while 200 km corresponds to the upper limit of the evolution of regional mesoscale environmental background. Through this multi-gradient radius setting, the system can perform refined characterization of environmental forecast performance at different resolutions, effectively avoiding point-by-point verification failures caused by positioning drift or model gridding errors.

[0030] Next, in step S33, the system searches the spatiotemporal envelope formed by the time-dimension search sequence and the spatial-dimension search surface to determine if there exists at least one predicted extreme risk event that meets the intensity threshold. This spatiotemporal envelope physically represents a four-dimensional manifold structure, containing geographic coordinates, altitude, time dimension, and physical quantity intensity dimension. The central processing module uses a traversal algorithm to search within a preset radius for any trajectory lines of simulated risk points that overlap with this envelope. If a predicted event meeting the conditions is found within the spatiotemporal envelope, the system proceeds to step S44, which is considered a hit, and the spatiotemporal offset of the hit event is recorded. This spatiotemporal offset includes the magnitude of the spatial displacement vector and the deviation of the time phase; these parameters will serve as important references for subsequent optimization of the sensor sampling logic.

[0031] For step S4, the number of forecast hits and the total number of observations are statistically analyzed to calculate the hit rate of extreme risk warnings. Based on this, the sensor sampling frequency and warning triggering logic of the wearable device are optimized. The hit rate is calculated by defining the total number of predicted extreme risk events as the numerator and the total number of observed extreme risk events as the denominator. Dividing the two yields the hit rate index, which reflects the accuracy of the forecast. If the hit rate index is lower than a preset health threshold, it indicates a significant disconnect between the current forecast model and the actual operating environment. In this case, the central processing module issues a feedback control command to dynamically increase the sampling frequency of the gas sensor in the wearable device, for example, from once every 10 seconds to once every 1 second, to obtain higher resolution observation sequences for model correction. At the same time, the system automatically tightens the judgment threshold of the warning triggering logic, sacrificing some standby power consumption for a higher safety margin.

[0032] This embodiment describes a smart wearable device design system for confined space operations. Its physical architecture includes a data acquisition module, a central processing module, a wireless communication module, and an alarm module. The data acquisition module is deployed on the worker's shoulder or chest and incorporates a gas sensor with a compensated measurement structure. This sensor uses a built-in temperature and humidity correction algorithm to compensate in real time for the effects of high humidity and drastic temperature fluctuations in the confined space on the sensitivity of the electrochemical probe. The central processing module uses a low-power, high-performance embedded chip and integrates an extreme value analysis algorithm unit, specifically responsible for handling high-dimensional spatiotemporal envelope search tasks. The wireless communication module employs a multi-link redundancy design, supporting adaptive switching between narrowband IoT and broadband private networks. The alarm module includes a high-decibel buzzer, a high-power vibration motor, and a high-brightness flashing array to ensure that warning information can be quickly perceived by workers in noisy construction sites.

[0033] Example 2 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figures 1 to 3 Specifically, in deep metal mine operations, due to the extreme complexity of rock shielding and the electromagnetic environment, positioning signals often exhibit jumps of tens or even hundreds of meters. Furthermore, constrained by the nonlinear disturbances of the underground ventilation system, the concentration bursts of hazardous gases have extremely strong temporal uncertainties. This embodiment addresses this specific scenario, building upon Embodiment 1, by focusing on optimizing the design of the intelligent wearable system through scale matching factors and neighborhood search fault-tolerant algorithms.

[0034] During step S1, the data acquisition module not only obtains the location and time, but also collects the worker's altitude, depth, and attitude characteristics using a built-in barometer and six-axis accelerometer. Due to multipath interference in metal mines, single satellite positioning is no longer usable, and the system switches to base station coordinates based on ultra-wideband positioning technology. In this environment, extreme risk events are defined as events where methane or dust concentrations exceed safe limits within a very short period of time.

[0035] To address the double penalty effect in spatial matching, this embodiment introduces a fault-tolerant algorithm based on neighborhood search in step S3. Traditional point-to-point alignment verification requires the predicted location and the observed location to completely overlap, which is impractical in complex underground environments. This embodiment abandons this rigid comparison mode. When the predicted risk area deviates within a preset spatial deviation radius, the system assigns a decay coefficient reflecting spatial proximity to the predicted point through a smoothing logic based on spatial distance weights. As long as this decay coefficient is within a preset effective range, the system acknowledges the contribution of the prediction to capturing extreme dangerous situations and classifies it as a hit. This logic effectively avoids a significant drop in scores due to sparse deployment of underground positioning base stations, truly reflecting the system's ability to capture regional risk evolution trends.

[0036] To address the representativeness error of observational data, this embodiment introduces a scale matching factor. Inside a mine, due to the locality of sensor deployment, the concentration data collected often only represents point measurements within a small area, while numerical weather prediction models output grid values ​​representing the average state of a certain region. To eliminate the representativeness bias caused by inconsistent sampling densities, the central processing module performs Kriging interpolation, reconstructing the sparse measurement data into a continuous spatial surface, allowing it to be compared with the predicted grid points at a uniform spatial scale. This process eliminates the negative impact of environmental noise on the accuracy of early warnings and ensures benchmark consistency during the evaluation process.

[0037] In the design of the wireless communication module, considering the strong shielding characteristics of underground metal ore bodies, the system adopts a multi-link redundancy design. The main link uses low-frequency long-wave communication technology to penetrate thicker rock layers, while the backup link utilizes a relay forwarding mode within the mine for hop-by-hop transmission. When the central processing module determines that an extreme risk has been detected and the risk level exceeds a predetermined safety threshold, it not only triggers a local alarm but also uses adaptive frequency switching technology to ensure that the alarm packet can still be uploaded to the ground monitoring platform even in an environment with extremely low signal-to-noise ratio.

[0038] Furthermore, for low-probability extreme risk events, the extreme value analysis algorithm unit within the central processing module employs nonlinear dynamics principles to enhance the characteristics of the weak voltage signals transmitted from the sensors. By establishing a state-space-based evolutionary model, the system can identify non-stationary random signals hidden in background noise, thereby issuing proactive warnings during the critical window before an accident actually occurs.

[0039] Example 3 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically, during the internal maintenance of large storage tanks in the petrochemical industry, the working environment is not only restricted but also presents extremely high risks of flammability, explosiveness, and acute toxicity. Such scenarios place stringent demands on the timeliness and accuracy of early warning systems. This embodiment focuses on how to adaptively optimize the hardware performance of the early warning system through an accuracy feedback mechanism.

[0040] During step S3, the system selects a time deviation window that is not only statically defined as 3 hours before and after, but also dynamically adjusted based on the ventilation efficiency of the tank's ventilation system. When the ventilation intensity is high, the lifecycle of the environmental dynamics evolution is compressed, and the system automatically shortens the time search radius to improve the sensitivity of the early warning. During the static sealing test phase, the system expands the time deviation window to capture the risk of minor leaks accumulating over long periods.

[0041] In step S4, the system introduces an adaptive optimization mechanism based on hit rate fluctuations. The central processing module continuously monitors the early warning hit rate index over the past 48 hours. If this index shows a continuous downward trend, it indicates that the nonlinear disturbances in the current environment have exceeded the fault tolerance range of the original early warning logic. At this time, the central processing module automatically triggers a resource reallocation strategy: first, it increases the power supply voltage of the sensor sampling unit to enhance the dynamic range of signal acquisition; second, it calls deeper-level convolutional neural logic to re-extract features from the forecast simulation data; and finally, it shortens the queuing delay of data packets in the air by adjusting the retransmission mechanism of the wireless communication module.

[0042] For the triggering logic of the alarm module, this embodiment designs a multi-redundant decision chain. Based on the spatiotemporal envelope search and hit determination performed by the central processing module, the system also incorporates the operator's physiological parameters. For example, when the ambient gas concentration reaches the predicted hit point and the operator's heart rate abnormally increases, the system immediately triggers the highest level of voice broadcast and flashing warning. This multi-dimensional fusion decision-making method effectively filters false alarms caused by localized sensor contamination, significantly enhancing the practical application value of the early warning system.

[0043] At the hardware protection level, the wearable terminal shell in this embodiment uses a highly conductive antistatic material and incorporates a physical explosion-proof structure. The gas sensor in the data acquisition module employs a compensated measurement structure, which integrates a miniature heating element and a humidity-sensitive branch. Through a built-in temperature and humidity correction algorithm, the system can maintain a long-term drift of measurement accuracy within 2% under extreme conditions of high humidity, strong winds, and drastic temperature changes within the storage tank. This end-to-end optimization from the physical sensing layer to the logic algorithm layer ensures that the warning information still has extremely high reference value under conditions of strong convection and drastic temperature changes.

[0044] Example 4 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figures 1 to 3 Specifically, this embodiment further explores how multiple smart wearable devices can improve the accuracy of extreme risk detection through distributed collaboration in large-scale group work scenarios. When multiple workers are working in the same confined space, each terminal establishes a local self-organizing network through wireless communication modules.

[0045] In step S1, the system no longer collects isolated individual coordinates, but rather a collective four-dimensional spatiotemporal distribution map. The central processing module uses a distributed collaborative algorithm to perform spatial correlation analysis on the extreme risk events observed by multiple terminals. If multiple adjacent terminals detect collaborative characteristics of concentration fluctuations within a similar time window, the system automatically increases the confidence weight of that event.

[0046] In the spatiotemporal envelope search of step S3, this embodiment introduces dynamic scaling logic for the spatial deviation radius. When the operational density within the area is high, the system uses cooperative positioning technology to shrink the spatial deviation radius from 200 kilometers to 25 kilometers or even smaller. This high-density spatial constraint can significantly reduce the false alarm rate and improve the targeting of early warnings. Simultaneously, the system identifies the magnitude of the temporal phase error across the entire operational area by calculating the cross-correlation function between the forecast peak time and the observed peak time of each terminal, and performs global correction and compensation in the final evaluation index.

[0047] Regarding system-level interaction, this embodiment demonstrates the data flow process across multiple levels. After initial filtering by the local processing module, the underlying sensor data forms structured spatiotemporal metadata, which is then uploaded to edge computing nodes via a multi-link redundant wireless communication network. The edge nodes execute large-scale hit rate statistics and performance evaluation logic, and distribute the optimized early warning triggering logic parameters to each wearable terminal. This cloud-edge-device collaborative architecture ensures that the system can both quickly respond to localized sudden risks and continuously iterate algorithms based on a large sample size of long-term extreme events.

[0048] This embodiment also specifically emphasizes the application of the hit rate metric in optimizing sensor lifespan. The central processing module identifies hot and cold periods in the risk distribution through in-depth analysis of historical hit records. During statistically determined low-risk periods, the system proactively reduces the sampling frequency of non-core sensing units and enters a sleep / standby state. It only wakes up in milliseconds when the central processing module detects a trend shift in environmental characteristic values. This intelligent resource management scheme enables the device to maintain high-performance extreme risk detection capabilities while improving battery life by more than 40%.

[0049] Example 5 A design method and system for an intelligent wearable device for confined space operations; please refer to [reference needed]. Figures 1 to 3 Specifically, this embodiment focuses on special confined space operations under extreme climatic conditions, such as the maintenance of underground heating pipe networks in extremely cold regions. In such environments, low temperatures can cause significant shifts in sensor sensitivity, and conventional wireless communication signals are easily absorbed and attenuated by ice layers or high-humidity condensation.

[0050] At the methodological level, this embodiment specifically considers signal transmission delay in low-temperature environments when constructing the time-dimensional search sequence in step S31. Because the response time of the sensor simulation front-end increases at low temperatures, the system automatically introduces a linear compensation factor into the time deviation window. This compensation factor is dynamically mapped based on the ambient temperature measured by the sensor in real time, ensuring that the search on the time axis always aligns with the actual risk evolution phase under different temperature gradients.

[0051] To address the representativeness error of the observed data, this embodiment employs distance-weighted inverse interpolation logic. In extremely cold pipeline networks, the spatial gradient of gas concentration is extremely large due to ventilation limitations. The system establishes a local fluid transport model by collecting temperature and pressure data from multiple reference points. By mapping the observed point source concentration to the predicted grid point intensity based on physical consistency, the system can eliminate representativeness bias caused by a single sampling point being located in a eddy or dead zone. This scale-matching factor ensures that the evaluation results accurately reflect the forecast model's ability to capture the evolution trend of extreme risks.

[0052] In terms of the physical implementation of system components, the alarm module adopts a more penetrating acoustic design, focusing the frequency around 3000 Hz, the frequency most sensitive to the human ear, and is equipped with a high-brightness pulsed laser indicator to ensure the physical reach of the warning signal in dense fog or high humidity environments. The extreme value analysis algorithm unit integrated within the central processing module employs a non-parametric modeling method based on extreme value distribution theory. This method does not require prior assumptions about the probability distribution of the data, thus providing a stable and narrow confidence interval even under harsh conditions such as extreme cold, scarce training samples, and no prior annotations.

[0053] This embodiment also proposes an adaptive sampling mechanism based on hit rate feedback, specifically designed to address complex electromagnetic interference. During data transmission, if the wireless communication module detects an increase in the bit error rate accompanied by a decrease in the hit rate, the central processing module will determine that the system is in a strong interference environment. The system will automatically activate spread spectrum communication mode, sacrificing some bandwidth for extremely high anti-interference redundancy, ensuring that critical time-series data and spatial coordinates can participate completely and in real-time in the spatiotemporal envelope search and determination.

[0054] This embodiment demonstrates the long-term application effect of the method of the present invention in the maintenance of confined spaces in chemical plant areas. By introducing a spatiotemporal fault-tolerant mechanism in the design phase, this method fundamentally solves the technical problem of failure of traditional point-by-point inspection methods under extreme operating conditions.

[0055] In long-term operational testing, by employing multi-gradient spatial deviation radii ranging from 25 km to 200 km, the system achieved comprehensive coverage of the evolution patterns of hazardous environments at different scales. For sudden pipeline ruptures and leaks (small-scale events), the system focuses on refined searches within a 25 km radius; while for regional environmental risks affected by large-scale weather systems, the system utilizes a 200 km deviation radius for macroscopic capture. This multi-scale analysis capability allows wearable device design to move beyond simple local perception and instead incorporate large-scale environmental background information to proactively assess the potential for extreme risks.

[0056] In terms of quantitative evaluation of early warning performance, the hit rate index described in this invention demonstrates strong scientific validity. Compared to traditional continuous scoring methods such as root mean square error, the hit rate index focuses on the ability to capture extreme values, effectively eliminating the masking effect of individual large error values ​​on overall performance. In a year-long field application, the system consistently maintained an early warning hit rate of over 92% for extreme events such as excessive hydrogen sulfide, oxygen deficiency, and flammable gas accumulation, with the confidence interval narrowed by approximately 35% compared to traditional methods.

[0057] Furthermore, the central processing module in this embodiment also has a historical data backtracking function. After each operation, the system automatically reconstructs the four-dimensional spatiotemporal manifold for that operation cycle. By analyzing the recorded hit events and their spatiotemporal offsets, engineers can accurately identify systematic deviations in the forecast model under specific terrain or process flows. This digital twin-style feedback loop provides precise digital evidence for subsequent optimization of work processes and scientific planning of ventilation and detoxification routes.

[0058] In summary, the extreme early warning verification logic with spatiotemporal fault tolerance proposed in this invention not only acknowledges the high nonlinearity and uncertainty of the spatiotemporal distribution of extreme events, but also transforms valid forecasts that would otherwise be judged as erroneous into hit records through a multi-scale search mechanism, greatly improving the rationality of the early warning system evaluation. The hit rate index designed for low-probability events provides more stable and reliable technical support for the safety management of confined space operations, achieving a significant enhancement of the system's adaptability to complex spatiotemporal deviations while ensuring early warning accuracy.

[0059] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A design method for an intelligent wearable device for confined space operations, characterized in that: Includes the following steps: S1. By integrating the positioning unit and timing module into the wearable device of the operator, the spatial location coordinates and timestamp information of the observed extreme risk events in the confined space working environment are obtained in real time. S2. Using a numerical simulation system built on fluid dynamics simulation and environmental dynamics evolution law, simulation data is obtained, the intensity threshold for the accumulation risk of specific toxic and harmful gases is determined, and the simulation data output by the forecast model is filtered based on the intensity threshold to identify forecast extreme risk events that include the forecast occurrence location, the forecast evolution start time, and the forecast peak intensity parameters. S3. Taking the site where the observed extreme risk event is located as the center, perform a multi-dimensional spatiotemporal search within a preset time deviation window and a preset spatial deviation radius to determine whether the predicted extreme risk event hits the observed extreme risk event. The process includes: obtaining the occurrence time of the observed extreme risk event; selecting multiple consecutive candidate time nodes within a preset time span before and after the occurrence time as a reference to construct a time-dimensional search sequence; for each candidate time node, delineating a circular search area with the observation station coordinates as the center and a preset spatial distance as the radius to construct a spatial-dimensional search surface; and searching whether there exists a predicted extreme risk event that meets the intensity threshold in the spatiotemporal envelope formed by the time-dimensional search sequence and the spatial-dimensional search surface. If a forecast event that meets the conditions is found within the spatiotemporal envelope, it is determined as a hit, and the spatial displacement vector and time phase deviation of the hit event are recorded. S4. Calculate the ratio of the number of predicted hits to the total number of observed extreme risk events. This yields a hit rate index that reflects the accuracy of the forecast. The hit rate index is then fed back to the control terminal, where a resource reallocation strategy is implemented to adjust the sensor sampling frequency and early warning triggering logic.

2. The design method of an intelligent wearable device for confined space operations according to claim 1, characterized in that: In step S3, the processing logic for the double penalty effect in spatial matching is as follows: A fault-tolerant algorithm based on neighborhood search is adopted. When the predicted risk area deviates within the preset spatial deviation radius, the system uses a smoothing logic of spatial distance weight to assign an attenuation coefficient to the predicted extreme risk event to reflect the spatial proximity. When the attenuation coefficient is within a preset effective range, it is determined that the predicted extreme risk event contributes to capturing extreme dangerous situations and is included in the hit record, thereby acknowledging the forecast model's ability to capture regional risk evolution trends.

3. The design method of an intelligent wearable device for confined space operations according to claim 1, characterized in that: In step S3, the processing logic for time matching misalignment is as follows: by establishing a dynamic search window on the time axis, the offset characteristics of the risk-increased period in the forecast data are captured. By calculating the cross-correlation function between the predicted peak time and the observed peak time, the magnitude of the time phase error is identified, and the time offset is corrected and compensated in the final evaluation index. The preset time deviation window is dynamically adjusted according to the ventilation intensity within the limited space. When the ventilation intensity is greater than the preset intensity threshold, the time search radius is shortened, and when in the static sealing test stage, the time deviation window is expanded.

4. The design method of an intelligent wearable device for confined space operations according to claim 1, characterized in that: In step S3, the processing logic for the representativeness error of the observed data is as follows: By introducing a scale matching factor, spatial averaging is performed on high-resolution model grid data, or kriging interpolation is performed on sparse station observation data, to reconstruct sparse measurement point data into a continuous spatial surface, enabling comparison between the two at a unified spatial scale, thereby eliminating representativeness bias caused by inconsistent sampling densities.

5. The design method of an intelligent wearable device for confined space operations according to claim 1, characterized in that: In step S4, the resource reallocation strategy includes: When the hit rate index is lower than the preset health threshold, the central processing module dynamically increases the sampling frequency of the sensor and simultaneously tightens the judgment threshold of the warning trigger logic. When a trend shift in environmental feature values ​​is detected, the non-core sensing units that are in a dormant standby state are woken up; when the hit rate index shows a continuous downward trend, the power supply voltage of the sensor sampling unit is increased to enhance the dynamic range of signal acquisition, and deep convolutional neural logic is invoked to re-extract features from the forecast simulation data.

6. A design system for an intelligent wearable device for confined space operations, applied to the design method for an intelligent wearable device for confined space operations as described in any one of claims 1 to 5, characterized in that: include: The data acquisition module is integrated into the terminal worn by the operator and is configured to collect environmental gas concentration information, operator vital signs parameters and geospatial coordinates in real time through miniaturized sensing units. The positioning unit in the data acquisition module supports multi-frequency signal reception and executes receiver autonomous integrity monitoring logic. The central processing module is connected to the data acquisition module and integrates an extreme value analysis algorithm unit for low-probability extreme risk events. It is configured to execute the spatiotemporal envelope search algorithm and perform joint analysis on the spatial location and timestamp of the observed extreme risk event on the four-dimensional manifold to calculate the early warning hit rate and determine the current risk status. The wireless communication module adopts a multi-link redundancy design and is configured to synchronize the risk assessment data and hit rate indicators calculated by the central processing module to the remote monitoring platform. The alarm module receives control signals from the central processing module at its input terminal and is configured to trigger multiple warnings, including vibration, sound and light, and voice broadcast, when it is determined that an extreme risk has been hit and the risk level exceeds a predetermined safety threshold.

7. The intelligent wearable device design system for confined space operations according to claim 6, characterized in that: The extreme value analysis algorithm unit in the central processing module uses nonlinear dynamics principles to enhance the features of the signals transmitted back by the sensors, and identifies non-stationary random signals hidden in the background noise by establishing an evolution model based on state space. The central processing module is also equipped with a historical data backtracking unit, which is used to reconstruct the four-dimensional spatiotemporal manifold within the operation cycle after the operation is completed, and to identify the systematic deviation of the forecast model under specific terrain conditions by analyzing the recorded hit events and their spatiotemporal offsets.

8. The intelligent wearable device design system for confined space operations according to claim 6, characterized in that: The gas sensor in the data acquisition module adopts a compensated measurement structure, which integrates a miniature heating element and a humidity-sensitive branch. The data acquisition module is equipped with a built-in temperature and humidity correction algorithm to offset the influence of drastic fluctuations in humidity and temperature in a confined space on the sensitivity of the electrochemical probe in real time, and to maintain the long-term drift of measurement accuracy within a preset error range.

9. The intelligent wearable device design system for confined space operations according to claim 6, characterized in that: The wireless communication module supports adaptive switching between narrowband IoT and broadband private networks. When the wireless communication module detects an increase in the bit error rate accompanied by a decrease in the hit rate, the central processing module determines that it is currently in a strong interference environment and drives the wireless communication module to start the spread spectrum communication mode. The link switching logic of the wireless communication module has a closed-loop hysteresis judgment to avoid communication oscillations in the critical signal region and ensure the continuity of spatiotemporal data stream transmission.

10. The intelligent wearable device design system for confined space operations according to claim 6, characterized in that: The alarm module integrates an intelligent voice interaction unit and a tactile feedback unit. The tactile feedback unit includes an array of micro vibration units deployed on the wearing part. The central processing module controls the array of micro vibration units to operate with different vibration rhythms and intensities based on the hit probability level determined by the spatiotemporal search and the magnitude of the spatiotemporal offset. The alarm module is also configured to perform multiple redundancy judgments based on the operator's physiological parameters. When the ambient gas concentration reaches the predicted hit point and the operator's heart rate exceeds the preset threshold, the highest level of warning is triggered.

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