Data transmission method and system for confined space and electronic equipment

Abstract

The invention provides a data transmission method and system for a confined space and electronic equipment. According to the data transmission method provided by the invention, a self-adaptive transmission mechanism based on real-time link quality is introduced, so that the problem of unreliable data transmission caused by signal attenuation in a limited space is effectively solved. The method has the beneficial effects that the transmission reliability of the most critical security data is preferentially guaranteed when personnel are in the most dangerous area by intelligently switching from the full-quantity data transmission mode to the simplified core data transmission mode when the link quality is deteriorated, so that the data transmission delay, failure and error code risks are remarkably reduced. Meanwhile, according to the method, the bandwidth is fully utilized to transmit total data when the link is good, the monitoring continuity is maintained with the minimum necessary data volume when the link is poor, and the optimal balance between efficient utilization of communication resources and safety monitoring real-time performance is achieved.

Description

Technical Field

[0001] This invention relates to the technical field of communication well inspection, and more specifically to a data transmission method, system, and electronic device for confined spaces. Background Technology

[0002] A confined space safety monitoring system is a monitoring system used to monitor the internal conditions of closed or semi-closed hazardous environments (pipelines, storage tanks, underground wells). It relies on the fixed installation of various sensors (e.g., gas detectors, temperature and humidity sensors) inside the space and the transmission of monitoring data (gas concentration, temperature) to an external monitoring center via wired means to achieve centralized monitoring of environmental parameters and alarms for exceeding limits.

[0003] In related technologies, existing wireless data transmission schemes suffer from the defect that signals attenuate sharply with increasing distance in confined spaces with deep depths and rich metal structures. This leads to a severe deterioration in the reliability of the transmission of critical safety monitoring data from personnel to the monitoring terminal when they are in the most dangerous areas, resulting in technical problems such as data transmission delays, failures, or high error rates. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned technical deficiencies and provide a data transmission method, system, and electronic device for confined spaces. This addresses the technical problem that in confined spaces with deep depths and rich metal structures, existing wireless data transmission schemes suffer from a sharp decline in signal attenuation with increasing distance. This leads to a severe deterioration in the reliability of the transmission of critical safety monitoring data from personnel in the most dangerous areas to the monitoring terminal, resulting in data transmission delays, failures, or high error rates.

[0005] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a data transmission method for confined spaces, applied to a portable terminal, the method comprising: The link quality parameters of the wireless communication link between the portable terminal and the monitoring terminal are monitored in real time; wherein the monitoring terminal is configured outside the confined space, while the portable terminal is carried into the confined space by personnel. The link quality parameters are compared with a preset quality threshold. Based on the comparison results, a data transmission mode is determined: the data transmission mode includes a first data transmission mode and a second data transmission mode; wherein, the first data transmission mode is used to collect and send full security data frames including a first data set to the monitoring terminal, and the second data transmission mode is used to collect and send simplified core data frames including a second data set to the monitoring terminal; wherein, the data volume of the second data set is less than the data volume of the first data set. Data transmission is performed based on a defined data transmission pattern.

[0006] Furthermore, the step of real-time monitoring of the link quality parameters of the wireless communication link between the portable terminal and the monitoring terminal includes: During data transmission intervals or through preset probe frames, the link quality parameters of the wireless communication link are periodically acquired; wherein the link quality parameters include at least one of the following: received signal strength indication, signal-to-noise ratio, signal-to-interference-plus-noise ratio, link budget, bit error rate, or packet reception rate.

[0007] Further, the step of comparing the link quality parameters with a preset quality threshold includes: Compare one or more link quality parameters obtained from monitoring with their respective preset quality thresholds; Based on the comparison results, a decision signal for mode selection is generated; wherein the decision signal is used to indicate that the current link quality level meets the requirements of a first data transmission mode or a second data transmission mode.

[0008] Furthermore, the step of determining the data transmission mode based on the comparison results includes: When the link quality parameter is higher than or equal to the preset quality threshold, the first data transmission mode is selected; When the link quality parameter is lower than the preset quality threshold, the second data transmission mode is selected.

[0009] Furthermore, when the second data transmission mode is selected, the step of performing data transmission based on the determined data transmission mode includes: For at least one target core security parameter, multiple samples are taken within a preset time window to obtain a set of sampled data. For the sampled data set, a preset data fault tolerance process is performed to determine the final transmission value of the target core security parameter; The final transmitted value is incorporated into the second data set, and a simplified core data frame is generated based on the second data set; Transmit the simplified core data frame.

[0010] Furthermore, the step of performing preset data fault tolerance processing on the sampled data set to determine the final transmission value of the target core security parameter includes: The sampled data set is cleaned to remove abnormal sampled values ​​that are outside the preset reasonable range determined by the physical characteristics of the core security parameters, so as to obtain a valid data set. The effective dataset is clustered based on a preset clustering algorithm to identify the data cluster that best represents the stable measurement state in the time dimension as the densest cluster. The cluster center value of the most dense cluster is determined as the final transmission value of the core security parameter.

[0011] Further, the step of cleaning the sampled data set to remove abnormal sampled values ​​that fall outside a preset reasonable range determined by the physical characteristics of the core security parameters, thereby obtaining a valid data set, includes: Based on the preset reasonable range determined by the physical characteristics of the core security parameters, sampled values ​​that exceed the preset reasonable range are removed from the sampled data set to obtain a preliminary screening set; Based on the preliminary screening set, its statistical distribution characteristics are calculated; Based on the statistical distribution characteristics, a dynamic filtering range is determined, wherein the threshold value of the dynamic filtering range is within the preset reasonable range; Based on the dynamic filtering range, sampled values ​​that do not conform to the dynamic filtering range are removed from the preliminary filtering set to obtain the effective data set.

[0012] Furthermore, the step of performing cluster analysis on the effective data set based on a preset clustering algorithm to identify the data cluster that best represents the stable measurement state in the time dimension as the densest cluster includes: The preset reasonable range of the core security parameters is evenly divided into K consecutive numerical intervals, where K is an integer greater than 1; The numerical intervals that each data point falls into in the valid data set are statistically analyzed, and the data points within each numerical interval are counted. The numerical interval with the most data points is identified as the densest cluster; wherein, the cluster center value of the densest cluster is determined by calculating the arithmetic mean or median of all data points within the numerical interval.

[0013] Secondly, the present invention provides a data transmission system for confined spaces, comprising: A portable terminal is configured to monitor in real time the link quality parameters of the wireless communication link between the portable terminal and a monitoring terminal; wherein the monitoring terminal is located outside a confined space, while the portable terminal is carried into the confined space by a person; the link quality parameters are compared with a preset quality threshold; based on the comparison result, a data transmission mode is determined: wherein the data transmission mode includes a first data transmission mode and a second data transmission mode; wherein the first data transmission mode is used to collect and send full-volume secure data frames including a first data set to the monitoring terminal, and the second data transmission mode is used to collect and send simplified core data frames including a second data set to the monitoring terminal; wherein the data volume of the second data set is less than the data volume of the first data set; based on the determined data transmission mode, data transmission is performed; The monitoring terminal is configured to receive full security data frames or simplified core data frames transmitted by the portable terminal.

[0014] Thirdly, the present invention provides an electronic device, comprising: a memory, and one or more processors communicatively connected to the memory; the memory stores instructions executable by the one or more processors, the instructions being executed by the one or more processors to cause the one or more processors to implement the method described above.

[0015] Beneficial effects: The data transmission method provided by this invention effectively solves the problem of unreliable data transmission caused by signal attenuation in confined spaces by introducing an adaptive transmission mechanism based on real-time link quality. Its beneficial effect lies in intelligently switching from a first data transmission mode of full data transmission to a second data transmission mode of streamlined core data transmission when link quality deteriorates. This prioritizes the reliability of transmission of the most critical safety data when personnel are in the most dangerous areas, thereby significantly reducing data transmission delays, failures, and error risks. Simultaneously, this method fully utilizes bandwidth to transmit full data when the link is good, and maintains monitoring continuity with the minimum necessary data volume when the link is poor, achieving an optimal balance between efficient use of communication resources and real-time security monitoring. Attached Figure Description

[0016] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is one of the flowcharts illustrating a data transmission method for confined spaces provided in an embodiment of the present invention; Figure 2 This is a second schematic flowchart of a data transmission method for confined spaces provided in an embodiment of the present invention; Figure 3 This is a block diagram of a data transmission system for confined spaces provided in an embodiment of the present invention; Figure 4 This is a block diagram of an electronic device used in an embodiment of the present invention. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0018] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0019] In related technologies, personnel safety monitoring is a crucial link in ensuring the safety of workers in confined spaces with deep, metal structures, such as communication wells, urban underground utility tunnels, and railway tunnels. These typical environments have significant physical characteristics: long structural depth, internal metal pipes or supports, and relatively enclosed spaces. These physical characteristics together constitute a special wireless signal propagation environment, with extremely significant shielding and multipath attenuation effects on wireless signals.

[0020] In related technologies, in order to achieve real-time monitoring of the life safety status of workers (downhole maintenance personnel) in the aforementioned confined space, it is often necessary to reliably transmit the gas concentration data (oxygen concentration, combustible gas concentration) and vital sign data (heart rate, blood oxygen) of the personnel to a monitoring center located outside the space (above the well).

[0021] Currently, relevant technical solutions mainly rely on two types of architectures: First, deploying fixed wired or wireless sensor networks. While this solution can achieve area coverage, the low frequency of inspections in such spaces leads to extremely low utilization of fixed facilities and poor cost-effectiveness. Second, using portable detectors carried by operators. Although this solution is flexible, its function is usually limited to local audible and visual alarms and cannot remotely transmit data to external monitoring personnel, creating monitoring blind spots.

[0022] To address the challenge of long-distance transmission for portable devices, related technologies have introduced transmission schemes based on low-power wide-area wireless technologies such as LoRa. The system environment for this scheme can include: a portable terminal carried by the operator (integrating sensors, a LoRa communication module, a microcontroller, and a power supply), and a mobile monitoring terminal held by the monitoring personnel outside the well (acting as a LoRa receiver). Under ideal channel conditions, this scheme can achieve data backhaul. However, in the specific operating environment of deep, confined spaces rich in metallic structures, this technology suffers from a serious drawback: as personnel delve deeper into the work area, the wireless communication link between the portable terminal and the monitoring terminal experiences rapid and irregular signal attenuation due to the complex spatial structure.

[0023] Understandably, the data transmission strategies employed by these technologies are static or blind; regardless of changes in link quality, portable terminals typically attempt to transmit data at a fixed cycle and format (e.g., continuously sending data frames containing all parameters). When personnel are in the deepest, most dangerous areas with the worst signal, the inherent flaws of this transmission strategy become glaringly apparent: First, the probability of successfully transmitting larger data frames under poor channel conditions is significantly reduced, leading to data transmission failures or losses; second, even if data frames are partially delivered, they are highly susceptible to data distortion or unusable due to bit errors; finally, retransmission mechanisms further exacerbate data transmission delays. As a result, at critical moments when personnel are in the most dangerous locations, the most crucial safety information cannot be reliably and in real-time transmitted to the monitoring terminal, rendering remote monitoring ineffective at the most critical moment and posing a fatal security risk.

[0024] The core concept of this invention lies in resolving the aforementioned contradiction by dynamically and adaptively selecting different data transmission modes based on real-time monitoring of the wireless communication link quality on the portable terminal side and the quality assessment results. Specifically, when the link quality is good, full-content secure data frames containing rich information are transmitted; while when the link quality deteriorates, the system intelligently switches to transmitting extremely simplified core secure data frames, prioritizing the reliable transmission of the most critical life safety information at the expense of non-critical data.

[0025] like Figure 1 , Figure 2 and Figure 3 As shown, this embodiment provides a data transmission method for confined spaces, applied to a portable terminal. The method includes: Step S12: Monitor the link quality parameters of the wireless communication link between the portable terminal and the monitoring terminal in real time; wherein the monitoring terminal is configured outside the confined space, while the portable terminal is carried into the confined space by personnel.

[0026] In this embodiment, the portable terminal is connected to the monitoring terminal. The portable terminal is carried into the confined space by personnel. It collects gas concentration data through its built-in gas sensor and vital sign data through its built-in vital sign sensor. The collected gas concentration data and vital sign data are then transmitted to the monitoring terminal through the built-in first wireless communication module.

[0027] like Figure 3 As shown, in one possible and specific implementation, the portable terminal may include: The sensor module may further include a gas sensor and a vital signs sensor. Specifically, the gas sensor can be used to detect and collect ambient gas concentration data in a confined space in real time, such as the concentrations of methane, hydrogen sulfide, carbon monoxide, and oxygen, and can employ a sensor module based on electrochemical or infrared principles.

[0028] Vital signs sensors can be integrated with sensors such as heart rate sensors and blood oxygen sensors to collect some physiological parameter data of workers, providing additional information for safety monitoring.

[0029] The first processing module is electrically connected to the sensor module and is used to control the sampling period of each sensor in the sensor module, process (filter, calculate) the raw data collected by the sensor module, and execute the data transmission method described in this invention (monitoring link quality, determining transmission mode, performing fault tolerance processing, etc.).

[0030] A first wireless communication module, connected to and controlled by the first processing module, is used to establish a wireless communication link with the monitoring terminal and transmit data. This first wireless communication module can be a LoRa module, used to send the full secure data frame or the simplified core data frame.

[0031] The first power module supplies power to the portable terminal.

[0032] The first alarm module may include a vibration motor and a high-brightness LED light. It can be triggered by receiving an alarm command from the monitoring terminal or by the first processing module (the processing module of the portable terminal) based on the locally collected ultra-high concentration value, and can issue a warning to the wearer in the form of vibration and light.

[0033] In one specific implementation, the portable terminal may be designed in physical form as a handheld device or a wearable device (e.g., fixed to a helmet or shoulder).

[0034] In one possible and specific implementation, the monitoring terminal may specifically include: The second wireless communication module is used to receive data frames sent by the portable terminal through the wireless communication link, namely the full secure data frame or the simplified core data frame.

[0035] The second processing module, connected to the second wireless communication module, is used to parse, verify, and intelligently decide on the received data frames.

[0036] It may also include: The human-machine interface module may further include a display unit, an alarm unit, and an input unit. Specifically, the display unit may be an LCD screen used to display real-time information such as the portable terminal's distance, gas concentration curve, signal strength, battery level, and system status, providing external monitoring personnel with intuitive situational awareness. The alarm unit may include a high-sound-pressure buzzer and a high-brightness LED warning light, used to issue a strong audible and visual alarm when the first processing module triggers a warning command, attracting the attention of monitoring personnel. The input unit may include physical buttons or a touchscreen for interactive operations such as system on / off, parameter settings (such as alarm thresholds), and well type selection.

[0037] The second power module can power the entire monitoring terminal. It can use a high-capacity lithium battery and is equipped with a power management integrated circuit to meet the needs of long-term outdoor operation.

[0038] In this embodiment, the confined space can be defined as a closed or semi-closed facility with a complex internal structure, restricted access, poor natural ventilation, not designed for long-term human residence, and potentially hazardous environment.

[0039] Specifically, the confined space can be an underground communication pipeline, such as a communication well, telecommunications pipeline, or fiber optic network maintenance hole. Its interior is often densely packed with metal cables, optical cables, junction boxes, and metal supports, forming an electromagnetic environment that attenuates wireless signals.

[0040] The confined space can be a power cable channel, such as a power cable well, cable tunnel, or substation cable layer. Such spaces also have abundant metallic structures and may pose a risk of accumulating harmful gases due to the aging and decomposition of insulation materials.

[0041] In this embodiment, the first wireless communication module of the portable terminal may be a LoRa (Long Range) module.

[0042] In this embodiment, the first wireless communication module of the portable terminal can also be a Low-Power Wide-Area Network (LPWAN) module. Specifically, it can be a Sigfox (ultra-narrowband technology) module, an NB-IoT (Narrowband Internet of Things) module, or a LoRaWAN (Long Range Wide-Area Network) module, etc.

[0043] In this embodiment, the first wireless communication module of the portable terminal may also be a proprietary protocol module based on FSK (Frequency-Shift Keying) modulation, or a wireless module based on CSS (Chirp Spread Spectrum) technology, etc.

[0044] In this embodiment, the link quality parameters can be represented as one or more physical layer or link layer metrics used to quantitatively evaluate the stability and reliability of a wireless communication link. Specifically, the link quality parameters may include: received signal strength indication, signal-to-noise ratio, signal-to-interference-plus-noise ratio, link budget, bit error rate, or packet reception rate.

[0045] More specifically, the Received Signal Strength Indicator (RSSI) is a parameter used to characterize the power of the received wireless signal and is a direct indicator of link distance and path loss. In confined spaces, the RSSI often decreases significantly with increasing distance between the portable terminal and the monitoring terminal, as well as with the increase in obstacles.

[0046] More specifically, the signal-to-noise ratio (SNR) is the ratio of signal power to background noise power. It directly reflects the quality of the signal; a lower SNR indicates that the signal is easily overwhelmed by noise, leading to an increased bit error rate.

[0047] More specifically, the Signal to Interference plus Noise Ratio (SINR) is a parameter that, in complex electromagnetic environments, takes into account not only background noise but also the effects of co-channel or adjacent-channel interference signals, thus providing a more comprehensive assessment of channel quality.

[0048] More specifically, the Bit Error Rate (BER) is a parameter that directly measures the reliability of data transmission, representing the proportion of received erroneous bits to the total number of transmitted bits.

[0049] More specifically, the Packet Reception Rate (PRR) is a parameter that represents the success rate of packet transmission at the link layer. A decrease in the PRR or an increase in the packet error rate directly indicates a deterioration in link quality and frequent data transmission failures.

[0050] Step S14: Compare the link quality parameters with a preset quality threshold.

[0051] In this embodiment, the preset quality threshold can be represented as one or more benchmark values ​​used to determine the quality level of the wireless communication link and trigger transmission mode switching.

[0052] In this embodiment, the preset quality threshold can be a fixed threshold. Specifically, a fixed threshold value can be preset for the corresponding link quality parameter based on theoretical calculations, simulations, or previous experimental data. For example, the preset quality threshold for RSSI (Received Signal Strength Indicator) can be set to -110dBm. When the measured RSSI is lower than this value, it is determined that the link quality has deteriorated. A fixed threshold value can be preset for each link quality parameter.

[0053] In this embodiment, to avoid frequent switching of transmission modes near the critical point, a pair of thresholds with hysteresis ranges can be preset for the same link quality parameter. These can include a mode degradation threshold (e.g., switching from the first data transmission mode to the second data transmission mode when RSSI ≤ -110dBm) and a mode recovery threshold (e.g., switching back from the second data transmission mode to the first data transmission mode only when RSSI ≥ -105dBm). The mode recovery threshold is set more favorably than the mode degradation threshold.

[0054] In this embodiment, the preset quality threshold can be a dynamic threshold. Specifically, the preset quality threshold is not fixed, but can be dynamically adjusted based on historical link quality data, device movement status, or environmental characteristics to adapt to different operating conditions or environmental changes.

[0055] In this embodiment, comparing the link quality parameters with preset quality thresholds can be expressed as comparing one link quality parameter with its corresponding preset quality threshold, comparing multiple link quality parameters with their corresponding preset quality thresholds, or comparing all link quality parameters with their corresponding preset quality thresholds. Specifically, a key link quality parameter (e.g., the Received Signal Strength Indicator (RSSI), which is most easily obtained and directly reflects path loss) can be compared with its corresponding preset quality threshold (or a pair of thresholds with hysteresis ranges). Alternatively, multiple link quality parameters (e.g., considering both RSSI and SNR) can be compared with their respective preset quality thresholds, and then a comprehensive judgment is made based on preset decision logic.

[0056] More specifically, the decision logic may include: The logic of AND is that the link quality is determined to be degraded and the transmission mode is triggered only when all the parameters involved in the comparison do not meet their corresponding quality requirements (for example, RSSI is lower than its preset quality threshold and SNR is lower than its preset quality threshold).

[0057] The OR logic means that if any of the parameters being compared does not meet its corresponding quality requirements (for example, RSSI is lower than its preset quality threshold or SNR is lower than its preset quality threshold), then the link quality is determined to be degraded.

[0058] More specifically, all available parameters in the link quality parameters (e.g., RSSI, SNR, BER, etc.) can be compared with their respective preset quality thresholds, and the final mode selection decision can be generated based on a preset comprehensive scoring rule or weighted decision algorithm.

[0059] Step S16: Based on the comparison results, determine the data transmission mode: wherein the data transmission mode includes a first data transmission mode and a second data transmission mode; wherein the first data transmission mode is used to collect and send full security data frames including a first data set to the monitoring terminal, and the second data transmission mode is used to collect and send simplified core data frames including a second data set to the monitoring terminal; wherein the data volume of the second data set is smaller than that of the first data set.

[0060] In this embodiment, the first data transmission mode is referred to as the full data transmission mode. Specifically, in this mode, the portable terminal controls its sensor module to collect complete security monitoring data, which constitutes the first data set.

[0061] The first data set may include: Gas concentration data, such as oxygen concentration, combustible gas concentration, carbon monoxide concentration, hydrogen sulfide concentration, etc.

[0062] Vital signs data, such as heart rate, blood oxygen saturation, body temperature, etc.

[0063] Device identification and auxiliary data, such as the portable terminal's identity, timestamp, battery level, and rough location information.

[0064] The portable terminal can encapsulate all parameters in the first data set according to a preset full data frame format to generate a full secure data frame. This full secure data frame has a complete structure and contains all usable information, but the data volume is large. Then, through the first wireless communication module, the complete full secure data frame is sent to the monitoring terminal at normal transmission power and rate.

[0065] In this embodiment, the second data transmission mode is referred to as a simplified core data transmission mode. In this mode, the portable terminal only collects the most critical parameters preset to ensure the basic life safety of personnel, forming a second data set with a very small data volume.

[0066] The second data set may include: Gas concentration data can be just oxygen concentration (the most critical indicator for maintaining life). Vital signs data can be limited to heart rate (a basic indicator reflecting cardiovascular activity).

[0067] The portable terminal can further perform preset fault-tolerant processing on the collected core parameters to improve their reliability, forming the final value to be transmitted. Then, the fault-tolerant core parameter value (or the directly collected value), along with any potentially triggered emergency alarm flags, is packaged into a minimally sized, simplified core data frame. This simplified core data frame can be designed to contain only a few to tens of bytes. Finally, through the first wireless communication module, methods such as reducing the data transmission rate (to improve receiving sensitivity) or repeated transmission can be used to prioritize the successful transmission of this simplified core data frame.

[0068] Step S18: Perform data transmission based on the determined data transmission mode.

[0069] The data transmission method provided in this embodiment effectively solves the problem of unreliable data transmission caused by signal attenuation in confined spaces by introducing an adaptive transmission mechanism based on real-time link quality. Its beneficial effect lies in intelligently switching from full data transmission mode to a simplified core data transmission mode when link quality deteriorates, prioritizing the reliability of transmission of the most critical safety data when personnel are in the most dangerous areas, thereby significantly reducing data transmission latency, failure, and error risks. Simultaneously, this method fully utilizes bandwidth to transmit full data when the link is good, and maintains monitoring continuity with the minimum necessary data volume when the link is poor, achieving an optimal balance between efficient use of communication resources and real-time security monitoring.

[0070] In some embodiments, the step of real-time monitoring of the link quality parameters of the wireless communication link between the portable terminal and the monitoring terminal includes: Step S122: During the data transmission interval or through a preset probe frame, periodically acquire the link quality parameters of the wireless communication link; wherein the link quality parameters include at least one of the following: received signal strength indication, signal-to-noise ratio, signal-to-interference-plus-noise ratio, link budget, bit error rate, or packet reception rate.

[0071] In this embodiment, the link quality can be passively monitored during the normal data transmission gaps of the portable terminal. After completing the transmission of a data frame (whether it is a full security data frame or a simplified core data frame), the portable terminal can listen for the response from the monitoring terminal. The link quality parameters can be directly extracted from the link layer acknowledgment frame returned by the monitoring terminal.

[0072] For example, in LoRa-based communication, after successfully demodulating a data packet, the receiving end (monitoring terminal) often sends an acknowledgment frame containing received signal strength indication and signal-to-noise ratio information that can be parsed by the sending end (portable terminal). By parsing these acknowledgment frames, the portable terminal can obtain the current downlink (from the monitoring terminal to the portable terminal) quality parameters and can consider them as an equivalent reflection of the uplink quality.

[0073] In this embodiment, the link quality can also be obtained through a preset probe frame. Specifically, the portable terminal can actively generate and send a small data packet, i.e., a probe frame. This probe frame carries little or no service data; its main purpose is to trigger a response from the monitoring terminal. Upon receiving this probe frame, the monitoring terminal will also return a response frame containing link quality information (this can be a standard acknowledgment frame or a response frame specifically designed for probes). The portable terminal analyzes this response frame to obtain the current link quality parameters. This method is more proactive and provides a more timely assessment of link status, but it introduces additional communication overhead.

[0074] This implementation method periodically acquires link quality parameters by utilizing data transmission intervals or dedicated probe frames. Its advantage lies in achieving continuous and real-time perception of the wireless link status without significantly increasing communication overhead and system power consumption. This provides timely and reliable decision-making basis for the accurate switching of subsequent adaptive transmission modes, thereby ensuring that the system can quickly respond to changes in channel conditions.

[0075] In some implementations, the step of comparing the link quality parameter with a preset quality threshold includes: Step S142: Compare one or more link quality parameters obtained from monitoring with their respective preset quality thresholds.

[0076] In this embodiment, each link quality parameter selected for the decision (e.g., RSSI, SNR) can have its own corresponding preset quality threshold.

[0077] In this implementation, the real-time measured value of each link quality parameter that needs to be considered in the decision can be compared with its corresponding preset quality threshold. The comparison operation outputs a Boolean value (true / false) or a rating result.

[0078] Step S144: Based on the comparison result, generate a decision signal for mode selection; wherein the decision signal is used to indicate that the current link quality level meets the requirements of the first data transmission mode or the requirements of the second data transmission mode.

[0079] In this embodiment, one or more decision logics can be predefined. Specifically, it can be an AND decision logic, for example, generating a decision signal indicating "switch to the second data transmission mode" only when all participating link quality parameters simultaneously meet the degradation conditions (e.g., RSSI is lower than its mode degradation threshold and SNR is lower than its mode degradation threshold). It can also be an OR decision logic, for example, generating a decision signal indicating "switch to the second data transmission mode" immediately as long as any one of the participating link quality parameters meets the degradation conditions (e.g., RSSI is lower than its mode degradation threshold or SNR is lower than its mode degradation threshold).

[0080] In this embodiment, the output result of step S142 can be comprehensively judged according to the selected decision logic, and finally a clear, digital decision signal can be generated. This signal can be a simple binary flag.

[0081] This implementation method introduces a multi-parameter comparison and decision signal generation mechanism to achieve comprehensive evaluation and digital decision-making of wireless link quality. Its beneficial effect is that by comparing multiple link quality parameters with their corresponding thresholds in parallel and generating a unified mode selection decision signal based on all comparison results, it avoids the risk of misjudgment that may be caused by single-parameter evaluation and ensures the comprehensiveness and reliability of transmission mode switching decisions. This provides clear and executable instructions for adaptive transmission control and significantly improves the decision quality and stability of the system when facing complex and variable channel environments.

[0082] In some implementations, the step of determining the data transmission mode based on the comparison results includes: When the link quality parameter is higher than or equal to the preset quality threshold, the first data transmission mode is selected; When the link quality parameter is lower than the preset quality threshold, the second data transmission mode is selected.

[0083] like Figure 2 As shown, in some embodiments, when a second data transmission mode is selected, the step of performing data transmission based on the determined data transmission mode includes: Step S182: For at least one target core security parameter, perform multiple samplings within a preset time window to obtain a set of sampled data.

[0084] In this embodiment, the target core safety parameter can be oxygen concentration or toxic gas concentration. It can also be heart rate or blood oxygen saturation, etc.

[0085] Step S184: Perform preset data fault tolerance processing on the sampled data set to determine the final transmission value of the target core security parameter.

[0086] In this embodiment, the preset data fault tolerance processing can be to calculate the statistical characteristic value of the sampled data set as the final transmitted value. For example, it can calculate the arithmetic mean or the median value, etc.

[0087] In this embodiment, the preset data fault tolerance processing can be a clustering filtering algorithm, or a sliding window-based filtering algorithm, etc.

[0088] Step S186: Incorporate the final transmission value into the second data set, and generate a simplified core data frame based on the second data set.

[0089] In this embodiment, the final transmission values ​​of the core safety parameters after fault tolerance processing (e.g., oxygen concentration value, heart rate value) can be filled into a fixed-length data buffer in a preset order to form a binary representation of the second data set. Then, a frame structure is generated based on a predefined simplified core data frame protocol.

[0090] Step S188: Transmit the simplified core data frame.

[0091] This implementation method effectively improves the accuracy and reliability of core security parameters transmitted in harsh channel environments by introducing a multi-sampling and data fault-tolerance processing mechanism in the second data transmission mode. By sampling the target core security parameters multiple times and performing fault-tolerance processing, the impact of single sampling errors or instantaneous interference on data accuracy can be significantly suppressed. This ensures that the generated simplified core data frame not only has a small data volume and high transmission efficiency, but also carries optimized high-reliability security information. Ultimately, it provides dual protection for the reliable transmission of core life safety data when the link quality is poor.

[0092] In some situations, sensors may generate outliers with large amplitudes but extremely short durations due to transient environmental disturbances (e.g., dust from human activity, momentary electromagnetic pulses, or brief equipment collisions). While fixed thresholds (based on physically reasonable ranges) can eliminate outliers that clearly exceed physical limits, they are ineffective against transient disturbances that fall within reasonable ranges but deviate significantly from the true values. This can lead to a single outlier potentially contaminating the entire batch of data, and if this data is directly used for transmission, it will convey severely distorted security information to the monitoring system.

[0093] Furthermore, a more challenging situation arises from persistent and complex interference, such as cross-gas interference at specific locations in the environment, momentary drift of sensor modules, or physiological fluctuations in human vital signs caused by brief periods of stress. Such interference can cause data collected within a preset time window to form multiple clusters—one representing the true, stable measurement state, and others representing the disturbed state. Traditional averaging algorithms indiscriminately mix and calculate these different clusters of data, resulting in an erroneous "averaged" value that lies between the true value and the interference value. This obscures the true nature of the data and may even mask underlying signs of danger.

[0094] Especially when multiple interference patterns occur simultaneously—that is, the dataset contains both instantaneous outliers and "pseudo-reasonable" outliers within a reasonable range due to persistent interference—existing single-mode data processing methods are completely inadequate. These technical solutions often only filter out outliers outside the range, leaving outliers within the range, or they can only perform global smoothing, failing to accurately extract the core data that best represents the true security status from the mixed data.

[0095] Therefore, there is an urgent need in this field for a data fault-tolerant processing method that can adapt to complex interference patterns in confined spaces. This method should not only be able to remove obvious outliers, but also have the ability to intelligently identify and extract the most reliable and stable data clusters from contaminated small-scale sampled data sets.

[0096] To address the aforementioned technical problems, in some embodiments, the step of performing preset data fault tolerance processing on the sampled data set to determine the final transmission value of the target core security parameter includes: Step S1842: Perform data cleaning on the sampled data set to remove abnormal sampled values ​​that are outside the preset reasonable range determined by the physical characteristics of the core security parameters, and obtain a valid data set.

[0097] In this embodiment, the preset reasonable range can be an absolute safety boundary pre-set based on basic physical laws and physiological common sense. For example, the reasonable range for human survival can be preset to 15% VOL to 23.5% VOL. Below 15% is a risk of hypoxia, and above 23.5% may be an oxygen-rich environment (with a risk of deflagration). Therefore, any sampling value below 15% or above 23.5% will be considered an absolute outlier. For heart rate, the reasonable range for an adult at rest can be preset to 40 bpm to 180 bpm. Sampling points outside this range are considered physiologically impossible or extremely unstable outliers.

[0098] Step S1844: Perform cluster analysis on the effective data set based on a preset clustering algorithm to identify the data cluster that best represents the stable measurement state in the time dimension as the densest cluster.

[0099] In this embodiment, due to the limited computing power of portable terminals, the preset clustering algorithm is not a complex multidimensional clustering algorithm (e.g., K-means, K-means Clustering Algorithm), but rather an optimized, lightweight method suitable for one-dimensional data. For example, the entire preset reasonable range of the core safety parameters can be evenly divided into K consecutive, equally wide numerical intervals (bins). For instance, the reasonable range of oxygen concentration (15.0%-23.5%) can be divided into (23.5-15.0)*10=85 intervals, each 0.1% wide. Then, each data point in the valid dataset is traversed, and it is counted which numerical interval it falls into, and the data points in each numerical interval are counted. Finally, the count values ​​of all numerical intervals are compared, and the numerical interval containing the most data points is identified as the densest cluster. This densest cluster represents the state where the measured values ​​are most concentrated and stable within the sampling time window.

[0100] Step S1846: Determine the cluster center value of the densest cluster as the final transmission value of the core security parameter.

[0101] In this embodiment, for the densest cluster, the arithmetic mean or median of all data points within the numerical range can be calculated, and this value can be used as the cluster center value of the densest cluster. For example, if the densest cluster of oxygen concentration is in the range of 20.1%-20.2%, and there are 8 data points in this range, the average of these 8 points (e.g., 20.15%) is the cluster center value.

[0102] In this embodiment, the calculated cluster center value can be directly used as the final transmission value of the core security parameter.

[0103] In this implementation, significant outliers are first quickly removed by cleaning within a physically reasonable range. Then, cluster analysis is used to intelligently identify the densest cluster that best represents the stable state from the remaining data. Finally, the center value of this cluster is used as the output. This effectively resists instantaneous outliers and continuous complex interference, while accurately extracting the true and reliable core values ​​of security parameters. This completely avoids the distortion caused by averaging in traditional averaging algorithms and significantly improves the accuracy and reliability of core security data transmitted in harsh communication environments.

[0104] In related technologies, data cleaning to remove obvious outliers has become a common practice. However, relying solely on a single data cleaning operation within a fixed physical scope cannot guarantee the highest representativeness and reliability of the data values ​​ultimately used for transmission.

[0105] In some cases, even after initial screening, the dataset may still contain some pseudo-reasonable outliers that, while not exceeding physically reasonable limits, deviate significantly from the true values. For example, an oxygen sensor might show a momentary lower reading due to the brief exhalation of exhaust fumes, or a heart rate sensor might capture a few high pulses due to motion interference. If the global average is directly calculated from this initially clean dataset, these few but highly skewed outliers will, with their numerical weight, distort the final result, causing the calculated average to fail to accurately reflect the true steady-state.

[0106] Furthermore, there exists a more insidious and dangerous situation: sensors may be subject to persistent, directional interference (e.g., a contaminating gas causing a consistently slightly higher reading on a gas sensor, or a minor temperature drift in the device itself). This interference introduces a small, overall shift across the entire dataset. Traditional single-step fixed-threshold averaging completely fails to detect this because all data remains within a pre-defined, reasonable range. Directly performing a global averaging incorporates this systematic error, ultimately transmitting a "precise but incorrect" safety value, thus masking a potentially slow and dangerous trend of deterioration.

[0107] Especially when both of the aforementioned interference modes—namely, instantaneous local anomalies and persistent systemic shifts—coexist, the limitations of the existing simplistic data processing workflow (direct averaging after cleaning) become glaringly apparent. It is unable to resist the interference of local anomalies, nor does it possess the ability to identify and correct minor systemic shifts. The result is a "beautified" and "averaged" safety value provided to the monitoring center, while precisely filtering out the crucial data fluctuations and trends that best reveal the true risk status, thus creating serious hidden dangers in the most critical security decision-making process.

[0108] Therefore, there is an urgent need in this field for an advanced fault-tolerance mechanism that can further identify and eliminate the aforementioned "pseudo-reasonable outliers" and "systematic minor offsets" after data cleaning and before final value determination. This mechanism should not be satisfied with the "physical reasonableness" of the data, but should strive to intelligently extract the core subset of data that best represents the current stable and true state from the initially screened data.

[0109] To address the aforementioned technical problems, in some embodiments, the step of cleaning the sampled data set to remove abnormal sampled values ​​that fall outside a preset reasonable range determined by the physical characteristics of the core security parameters, thereby obtaining a valid data set, includes: Step S18422: Based on the preset reasonable range determined by the physical characteristics of the core security parameters, remove sampled values ​​that exceed the preset reasonable range from the sampled data set to obtain a preliminary screening set.

[0110] In this embodiment, the preset reasonable range can be an absolute safety boundary pre-set based on basic physical laws and physiological common sense. For example, the reasonable range for human survival can be preset to 15% VOL to 23.5% VOL. Below 15% is a risk of hypoxia, and above 23.5% may be an oxygen-rich environment (with a risk of deflagration). Therefore, any sampling value below 15% or above 23.5% will be considered an absolute outlier. For heart rate, the reasonable range for an adult at rest can be preset to 40 bpm to 180 bpm. Sampling points outside this range are considered physiologically impossible or extremely unstable outliers.

[0111] Step S18424: Calculate the statistical distribution characteristics based on the preliminary screening set.

[0112] In this embodiment, the statistical distribution characteristic can be a central tendency indicator. Specifically, the arithmetic mean or median of the preliminary screening set can be calculated.

[0113] In this embodiment, the statistical distribution characteristic can be an index of dispersion. Specifically, the standard deviation of the preliminary screening set can be calculated.

[0114] Step S18426: Determine the dynamic filtering range based on the statistical distribution characteristics, wherein the threshold value of the dynamic filtering range is within the preset reasonable range.

[0115] In this embodiment, the final dynamic filtering range can be determined using a preset dynamic range generation algorithm. Specifically, the central tendency indicator (arithmetic mean or median) and dispersion indicator (standard deviation) of the preliminary filtering set are first read, and then the upper and lower thresholds of the dynamic filtering range are calculated according to the following formula: Dynamic lower limit = max(preset reasonable range lower limit, central trend indicator - N × standard deviation); Dynamic upper limit = min(preset reasonable range upper limit, central trend indicator + N × standard deviation).

[0116] In the formula, N is a preset sensitivity coefficient, which can be 2 or 3, used to control the tolerance of the dynamic range. The core constraint of this calculation process is to ensure that the threshold value of the dynamic screening range is always within the preset reasonable range, that is, the lower limit of the dynamic range is not lower than the lower limit of the preset reasonable range, and the upper limit of the dynamic range is not higher than the upper limit of the preset reasonable range.

[0117] For example, for the oxygen concentration parameter, if the preset reasonable range is 15% to 23.5%, the calculated dynamic range must fall within this range. If the lower limit of the dynamic range calculated based on statistical characteristics is lower than 15%, then 15% is actually used as the lower limit of the dynamic range; if the upper limit of the dynamic range calculated is higher than 23.5%, then 23.5% is actually used as the upper limit of the dynamic range.

[0118] Step S18428: Based on the dynamic filtering range, remove sampled values ​​that do not conform to the dynamic filtering range from the preliminary filtering set to obtain the effective data set.

[0119] This implementation proposes a two-tiered progressive data cleaning architecture that constructs a static physical range initial screening and a dynamic statistical range fine screening. Based on the first round of screening based on the physical reasonable range, this implementation further generates a dynamic screening range according to the statistical distribution characteristics of the dataset. This not only effectively eliminates pseudo-reasonable outliers hidden within the reasonable range, but also identifies and resists systematic deviation interference, thereby ensuring that the final effective data set not only meets the physical reasonableness requirement, but also represents the most stable and authentic data core, providing a high-quality data foundation for subsequent processing and greatly improving the accuracy and reliability of security monitoring.

[0120] In related technologies, although the idea of ​​cluster analysis can effectively improve data reliability, conventional clustering algorithms have revealed their inadequacy when applied to the resource-constrained environment of portable terminals.

[0121] In some cases, the computing power and memory resources of the microcontroller in portable terminals are extremely limited. Directly using classic clustering algorithms (such as K-means) results in high computational complexity, requiring multiple iterations of distance calculation and cluster partitioning. This leads to significant processing latency and high power consumption when handling real-time acquired security data, failing to meet the stringent requirements of security monitoring systems for both real-time performance and low power consumption.

[0122] Furthermore, classical clustering algorithms often require pre-defined parameters (e.g., the number of clusters K in K-means). The setting of these parameters is uncertain; the optimal parameters may differ under different environments, different sensors, or different monitoring parameters. Dynamically finding suitable parameters for these algorithms within a constrained space lacking prior knowledge is itself a challenge. Inappropriate parameters can lead to clustering failures, introduce new errors, and reduce the reliability and usability of the system.

[0123] Especially when dealing with small-scale datasets with clearly defined physical boundaries, as addressed in this invention, the limitations of classical clustering algorithms become even more apparent. Their complex design aims to discover unknown patterns of arbitrary shapes within massive datasets, while the data in this application scenario has clear characteristics, a fixed scale, and a well-defined physical range. Forcibly applying these general-purpose algorithms not only wastes computational resources but also introduces unnecessary stability and reliability risks to the entire system due to their parameter sensitivity and complexity.

[0124] Therefore, there is an urgent need in this field for a clustering method specifically designed for portable security monitoring terminals. This method must have low computational complexity, well-defined parameter setting rules, and efficient processing capabilities for small-scale bounded datasets, so as to reliably achieve the technical effects of clustering analysis with extremely limited hardware resources.

[0125] To address the aforementioned technical problems, in some embodiments, the step of performing cluster analysis on the effective dataset based on a preset clustering algorithm to identify the data cluster that best represents the stable measurement state over time as the densest cluster includes: Step S18442: Divide the preset reasonable range of the core security parameters evenly into K consecutive numerical intervals, where K is an integer greater than 1.

[0126] In this embodiment, the width (step size) of each numerical interval can be obtained by dividing the total span of the entire preset reasonable range by the number of intervals K. That is: interval width = (upper limit of range - lower limit of range) / K.

[0127] In this embodiment, the K value is a design parameter, and the K value can be set to match the accuracy of the sensor and the normal fluctuation range of the parameters. For example, for an oxygen sensor with a resolution of 0.1%, the interval width can be set to 0.1% (i.e., K=85).

[0128] Step S18444: Calculate the numerical intervals that each data point in the valid data set falls into, and count the data points within each numerical interval.

[0129] In this implementation, each data point in the valid dataset can be traversed, and for each data point, its numerical value can be used to determine which range it belongs to.

[0130] Step S18446: Identify the numerical interval with the most data points as the densest cluster; wherein, the cluster center value of the densest cluster is determined by calculating the arithmetic mean or median of all data points within the numerical interval.

[0131] In this embodiment, all data points falling within the numerical range of the densest cluster can be extracted from the effective dataset to form a purer and more compact subset of data.

[0132] In this embodiment, the calculated average or median can be used as the final transmission value of the core security parameter.

[0133] This implementation method creatively provides a clustering method optimized for portable terminals by uniformly dividing the physical reasonable range of core security parameters and statistically analyzing the interval data density. This implementation method can replace the complex iterative calculations in classic clustering algorithms with interval division and counting operations with extremely low computational complexity. By setting deterministic parameters based on physical range, it completely avoids the parameter sensitivity problem of traditional algorithms. Thus, it achieves efficient and reliable clustering analysis of small-scale datasets under strictly limited hardware resources, ensuring the accuracy of core security parameter values ​​and the real-time performance of system response under the worst communication environment.

[0134] This embodiment provides a data transmission system for confined spaces, including: A portable terminal is configured to monitor in real time the link quality parameters of the wireless communication link between the portable terminal and a monitoring terminal; wherein the monitoring terminal is located outside a confined space, while the portable terminal is carried into the confined space by a person; the link quality parameters are compared with a preset quality threshold; based on the comparison result, a data transmission mode is determined: wherein the data transmission mode includes a first data transmission mode and a second data transmission mode; wherein the first data transmission mode is used to collect and send full-volume secure data frames including a first data set to the monitoring terminal, and the second data transmission mode is used to collect and send simplified core data frames including a second data set to the monitoring terminal; wherein the data volume of the second data set is less than the data volume of the first data set; based on the determined data transmission mode, data transmission is performed.

[0135] In this embodiment, the portable terminal is also configured to perform any of the implementation methods described in the above embodiments.

[0136] The monitoring terminal is configured to receive full secure data frames or simplified core data frames transmitted by the portable terminal.

[0137] According to an embodiment of the present invention, an electronic device is provided; please refer to... Figure 4 The electronic device in this embodiment may include one or more of the following components: a processor, a network interface, memory, non-volatile memory, and one or more application programs, wherein the one or more application programs may be stored in non-volatile memory and configured to be executed by one or more processors, and the one or more programs are configured to perform the methods as described in the foregoing method embodiments.

[0138] According to embodiments of the present invention, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a computer, causes the computer to perform the method described in any of the above embodiments.

[0139] According to embodiments of the present invention, a computer program product comprising instructions is also provided, which, when executed by a computer, cause the computer to perform a method in any of the above embodiments.

[0140] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

[0141] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0142] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0143] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0144] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A data transmission method for confined spaces, characterized in that, Applied to portable terminals, the method includes: The link quality parameters of the wireless communication link between the portable terminal and the monitoring terminal are monitored in real time; wherein the monitoring terminal is configured outside the confined space, while the portable terminal is carried into the confined space by personnel. The link quality parameters are compared with a preset quality threshold. Based on the comparison results, a data transmission mode is determined: the data transmission mode includes a first data transmission mode and a second data transmission mode; wherein, the first data transmission mode is used to collect and send full security data frames including a first data set to the monitoring terminal, and the second data transmission mode is used to collect and send simplified core data frames including a second data set to the monitoring terminal; wherein, the data volume of the second data set is less than the data volume of the first data set. Data transmission is performed based on a defined data transmission pattern.

2. The method according to claim 1, characterized in that, The step of real-time monitoring of the link quality parameters of the wireless communication link between the portable terminal and the monitoring terminal includes: During data transmission intervals or through preset probe frames, the link quality parameters of the wireless communication link are periodically acquired; wherein the link quality parameters include at least one of the following: received signal strength indication, signal-to-noise ratio, signal-to-interference-plus-noise ratio, link budget, bit error rate, or packet reception rate.

3. The method according to claim 1, characterized in that, The step of comparing the link quality parameters with a preset quality threshold includes: Compare one or more link quality parameters obtained from monitoring with their respective preset quality thresholds; Based on the comparison results, a decision signal for mode selection is generated; wherein the decision signal is used to indicate that the current link quality level meets the requirements of a first data transmission mode or a second data transmission mode.

4. The method according to claim 1, characterized in that, The step of determining the data transmission mode based on the comparison results includes: When the link quality parameter is higher than or equal to the preset quality threshold, the first data transmission mode is selected; When the link quality parameter is lower than the preset quality threshold, the second data transmission mode is selected.

5. The method according to any one of claims 1-4, characterized in that, When the second data transmission mode is selected, the step of performing data transmission based on the determined data transmission mode includes: For at least one target core security parameter, multiple samples are taken within a preset time window to obtain a set of sampled data. For the sampled data set, a preset data fault tolerance process is performed to determine the final transmission value of the target core security parameter; The final transmitted value is incorporated into the second data set, and a simplified core data frame is generated based on the second data set; Transmit the simplified core data frame.

6. The method according to claim 5, characterized in that, The step of performing preset data fault tolerance processing on the sampled data set to determine the final transmission value of the target core security parameter includes: The sampled data set is cleaned to remove abnormal sampled values ​​that are outside the preset reasonable range determined by the physical characteristics of the core security parameters, so as to obtain a valid data set. The effective dataset is clustered based on a preset clustering algorithm to identify the data cluster that best represents the stable measurement state in the time dimension as the densest cluster. The cluster center value of the most dense cluster is determined as the final transmission value of the core security parameter.

7. The method according to claim 6, characterized in that, The step of cleaning the sampled data set to remove abnormal sampled values ​​that fall outside a preset reasonable range determined by the physical characteristics of the core security parameters, and obtaining a valid data set, includes: Based on the preset reasonable range determined by the physical characteristics of the core security parameters, sampled values ​​that exceed the preset reasonable range are removed from the sampled data set to obtain a preliminary screening set; Based on the preliminary screening set, its statistical distribution characteristics are calculated; Based on the statistical distribution characteristics, a dynamic filtering range is determined, wherein the threshold value of the dynamic filtering range is within the preset reasonable range; Based on the dynamic filtering range, sampled values ​​that do not conform to the dynamic filtering range are removed from the preliminary filtering set to obtain the effective data set.

8. The method according to claim 6, characterized in that, The step of performing cluster analysis on the effective data set based on a preset clustering algorithm to identify the data cluster that best represents the stable measurement state in the time dimension as the densest cluster includes: The preset reasonable range of the core security parameters is evenly divided into K consecutive numerical intervals, where K is an integer greater than 1; The numerical intervals that each data point falls into in the valid data set are statistically analyzed, and the data points within each numerical interval are counted. The numerical interval with the most data points is identified as the densest cluster; wherein, the cluster center value of the densest cluster is determined by calculating the arithmetic mean or median of all data points within the numerical interval.

9. A data transmission system for confined spaces, characterized in that, include: A portable terminal is configured to monitor in real time the link quality parameters of the wireless communication link between the portable terminal and a monitoring terminal; wherein the monitoring terminal is located outside a confined space, while the portable terminal is carried into the confined space by a person; the link quality parameters are compared with a preset quality threshold; based on the comparison result, a data transmission mode is determined: wherein the data transmission mode includes a first data transmission mode and a second data transmission mode; wherein the first data transmission mode is used to collect and send full-volume secure data frames including a first data set to the monitoring terminal, and the second data transmission mode is used to collect and send simplified core data frames including a second data set to the monitoring terminal; wherein the data volume of the second data set is less than the data volume of the first data set; based on the determined data transmission mode, data transmission is performed; The monitoring terminal is configured to receive full security data frames or simplified core data frames transmitted by the portable terminal.

10. An electronic device, characterized in that, include: A memory, and one or more processors communicatively connected to the memory; The memory stores instructions that can be executed by the one or more processors to cause the one or more processors to implement the method as described in any one of claims 1 to 8.

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