A wearable monitoring method and system based on UWB technology

Abstract

The application discloses a kind of based on UWB technology's wearable monitoring method and system, through the signal strength detection mechanism of pre-established, obtain the fluctuation data in signal coverage range, get the preliminary evaluation result of signal connection stability;According to the preliminary evaluation result of signal connection stability, using hierarchical screening mode, for the area of signal intensity lower than preset threshold value is prioritized, obtain the equipment connection path needing optimization, determine the signal enhancement demand of key node;Through the analysis of signal enhancement demand of key node, using signal optimization algorithm, for the connection path between equipment is dynamically adjusted, obtain optimized signal transmission link;If signal transmission link is adjusted still there is interruption risk, then for connection interruption recovery mechanism is activated, through the quick switching of spare path, obtain stable spare connection channel.The application effectively improves the stability and reliability of equipment connection in dynamic environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of health monitoring, and particularly discloses a wearable monitoring method and system based on UWB technology. BACKGROUND

[0002] In modern society, the importance of personnel safety and health monitoring is self-evident, especially in precise positioning and real-time monitoring in special environments, which is directly related to life safety and management efficiency. However, the current related solutions often face the problem of insufficient adaptability, especially in complex environments or dynamic scenarios, existing methods are difficult to cope with changing needs, especially in the flexibility of device and environment interaction and data continuity guarantee, often appear fault or failure.

[0003] In-depth analysis of this field can find that the core challenge is concentrated on how to realize the stable connection and seamless switching of devices in dynamic environment. Especially in the case of limited signal coverage or interference, the connection stability of devices and service nodes becomes the primary problem. When the signal strength decreases or the environment changes, the device cannot adjust the connection object in time, resulting in data transmission interruption or positioning accuracy decline. And this unstable connection further aggravates the difficulty of data synchronization, because in the process of switching service nodes, the connection of historical data and real-time data often appears delay or loss, affecting the continuity and reliability of monitoring.

[0004] Therefore, how to ensure the stable connection of devices and service nodes in dynamic environment, and realize the seamless synchronization of data in the switching process, becomes the key problem to improve the reliability of monitoring system. This research will focus on this problem, explore how to guarantee the continuity of connection and the integrity of data through reasonable trigger conditions and switching mechanism under signal fluctuation or environmental change, and provide more reliable technical support for safety and health monitoring in special scenarios. SUMMARY

[0005] The present application provides a wearable monitoring method and system based on UWB technology, which aims to solve at least one of the defects in the prior art.

[0006] One aspect of the present application relates to a wearable monitoring method based on UWB technology, comprising the following steps:

[0007] Through the pre-established signal strength detection mechanism, the connection state of the device in the dynamic environment is collected in real time, the fluctuation data in the signal coverage range is obtained, and the preliminary evaluation result of the signal connection stability is obtained;

[0008] According to the preliminary evaluation result of signal connection stability, a hierarchical screening method is adopted to prioritize the areas with signal intensity lower than the preset threshold, obtain the device connection path that needs to be optimized, and determine the signal enhancement requirement of the key node;

[0009] By analyzing the signal enhancement requirement of the key node, a signal optimization algorithm is adopted to dynamically adjust the connection path between devices, and an optimized signal transmission link is obtained;

[0010] If there is still a risk of interruption after adjusting the signal transmission link, the connection interruption recovery mechanism is activated, and a stable backup connection channel is obtained through rapid switching of the backup path;

[0011] According to the activation of the backup connection channel, the role switching efficiency between devices is monitored in real time, the dynamic change data of role allocation is obtained, and potential delay problems in the role switching process are determined;

[0012] By analyzing the potential delay problems in the role switching process, a data synchronization accuracy verification method is adopted to optimize the information consistency guarantee in the switching process, and the data integrity state after synchronization is obtained;

[0013] According to the feedback of the data integrity state, the overall evaluation of system operation reliability is updated, and the collaboration task allocation result is obtained through dynamic adjustment of multi-device collaboration capability;

[0014] Through continuous tracking of the collaboration task allocation result, the real-time monitoring capability is strengthened, the operation data of the device in complex environment is obtained, and the final system optimization configuration is determined.

[0015] Further, through the pre-established signal strength detection mechanism, the connection state of the device in the dynamic environment is collected in real time, the fluctuation data within the signal coverage range is obtained, and the preliminary evaluation result of signal connection stability is obtained.

[0016] Through the pre-established signal strength detection mechanism, the connection state of the device in the dynamic environment is collected in real time, and the fluctuation data within the signal coverage range is obtained.

[0017] The fluctuation data is denoised by using a data filtering tool to obtain a preliminarily sorted signal fluctuation data set.

[0018] Further, according to the preliminary evaluation result of signal connection stability, a hierarchical screening method is adopted to prioritize the areas with signal intensity lower than the preset threshold, obtain the device connection path that needs to be optimized, and determine the signal enhancement requirement of the key node.

[0019] According to the area data with a signal strength lower than a preset threshold, a hierarchical screening tool is used for classification processing to obtain a target area with a higher priority, and a region set that needs to be focused on is obtained;

[0020] For a device connection path in the region set that needs to be focused on, a path analysis tool is used for multi-dimensional comparison to judge fluctuation monitoring data of the device connection path, and if the fluctuation value exceeds a preset range, a key path segment is determined as an object to be optimized;

[0021] By positioning the key nodes of the key path segment, signal coverage range data between the key nodes is obtained, and whether there is an insufficient coverage node is judged in combination with region division logic to obtain a specific position that needs signal enhancement;

[0022] According to the specific position that needs signal enhancement, a signal allocation tool is used to dynamically adjust resources, and the connection path of the specific position that needs signal enhancement is optimized and configured to determine a final signal enhancement scheme.

[0023] Further, by analyzing the signal enhancement demand of the key nodes, a signal optimization algorithm is used to dynamically adjust the connection path between devices, and the steps of obtaining an optimized signal transmission link include:

[0024] According to signal strength data, the distribution of key nodes is classified and processed to obtain a target area with a signal strength lower than a preset threshold from the node distribution, and a specific position with insufficient coverage is determined in combination with region division logic;

[0025] For the specific position with insufficient coverage, a path analysis tool is used to perform multi-dimensional comparison on the device connection path, and when the fluctuation of the device connection path exceeds a preset range, a path segment that needs to be dynamically adjusted is obtained, and the link stability of the path segment is judged;

[0026] By adjusting the resource allocation of the path segment with lower link stability, a signal allocation tool is used to sort the node priorities to obtain at least one target node that needs signal optimization, and a node set to be processed is obtained;

[0027] According to the node set to be processed, a signal enhancement tool is used to dynamically adjust the transmission link, and the connection path of each target node is reconfigured in combination with signal optimization logic to determine an optimized link distribution.

[0028] Further, if there is still a risk of interruption after the signal transmission link is adjusted, the connection interruption recovery mechanism is activated, and the steps of obtaining a stable backup connection channel through rapid switching of a backup path include:

[0029] According to the link state of the signal transmission link, a path monitoring tool is used to detect the current connection channel in real time for potential signal interruption risk, target nodes with path stability and transmission efficiency lower than a preset threshold are obtained, and the node range that needs to activate the connection interruption recovery mechanism is determined;

[0030] For the determined node range that needs to activate the connection interruption recovery mechanism, at least one pre-established path redundancy scheme is obtained through a backup path database, and a path comparison tool is used to sort the switching speed and stability of the backup path to obtain a priority path option suitable for dynamic switching;

[0031] If the stability of the priority path option is higher than the current link state, a path switching tool is used to quickly adjust the connection channel of the target node to obtain a new backup connection channel;

[0032] According to the new backup connection channel, a link verification tool is used to detect the overall state of node connection, and the path redundancy configuration is re-adjusted for the area where the transmission efficiency does not meet the standard, and the final stable signal transmission link is determined.

[0033] Further, according to the enablement of the backup connection channel, the role switching efficiency between devices is monitored in real time, and dynamic change data of role allocation is obtained. The steps of determining potential delay problems in the role switching process include:

[0034] According to the enablement of the backup connection channel, a channel state monitoring tool is used to continuously track the connection efficiency, from which real-time feedback data of role allocation is obtained, delay risk areas existing in the switching process are determined, and position information of potential problem nodes is obtained;

[0035] For the position information of the potential problem nodes, historical data change records related to role allocation are extracted from a pre-established allocation adjustment database, a comparison tool is used to classify the influencing factors of switching delay, and key parameters affecting efficiency evaluation are determined;

[0036] If the key parameters exceed a preset threshold, a delay optimization tool is used to dynamically adjust the connection efficiency of the backup connection channel to obtain adjusted channel state data;

[0037] According to the adjusted channel state data, a risk judgment tool is used to perform secondary verification on the delay risk, and the allocation strategy is re-adjusted for the still existing risk area to obtain the final role switching efficiency optimization configuration.

[0038] Further, by analyzing the potential delay problems in the role switching process, a data synchronization accuracy verification method is used to optimize the information consistency guarantee in the switching process, and the steps of obtaining the data integrity state after synchronization completion include:

[0039] Receiving real-time data of role assignment by data collection tools, the real-time data being generated during role switching process;

[0040] Continuously tracking switching delay according to real-time data, and obtaining dynamic feedback data of delay monitoring;

[0041] Extracting historical records corresponding to dynamic feedback data through pre-established synchronization accuracy database, checking information consistency by comparison tools, judging whether there is deviation in synchronization process, and obtaining preliminary results of data checking;

[0042] If the preliminary results of data checking show that the deviation exceeds the preset threshold, dynamically adjusting the data synchronization process by using synchronization optimization tools, and obtaining adjusted synchronization accuracy data;

[0043] According to the adjusted synchronization accuracy data, using state verification tools to perform secondary verification on the integrity state, and determining the guarantee level of information consistency in the role switching process.

[0044] Further, according to the feedback of data integrity state, the overall evaluation of system running reliability is updated, and the steps of obtaining the collaboration task assignment result through dynamic adjustment of multi-device collaboration capability include:

[0045] Receiving feedback information carrying data integrity state identifier, the feedback information being generated by state monitoring tools during running process;

[0046] Triggering reliability evaluation instruction to multi-device collaboration module according to abnormal identifier in feedback information, and generating basic evaluation data containing device processing capability by multi-device collaboration module according to reliability evaluation instruction;

[0047] Obtaining dynamic adjustment instruction with device load status and collaboration capability parameters, the dynamic adjustment instruction being generated by load balancing algorithm after receiving basic evaluation data;

[0048] Judging whether the device load status exceeds the preset threshold, if the device load status exceeds the preset threshold, re-planning the collaboration task by using task assignment engine, classifying the to-be-processed tasks according to priority according to collaboration capability parameters, and obtaining preliminary task assignment scheme;

[0049] According to the preliminary task assignment scheme, performing rationality checking by using integrity verification tools, if the checking result reaches the preset standard, generating the final collaboration task assignment result.

[0050] Further, through continuous tracking of the collaboration task assignment result, the real-time monitoring capability is strengthened, the running data of the device in complex environment is obtained, and the steps of determining the final system optimization configuration include:

[0051] receiving execution state information carrying a cooperative task allocation identifier, the execution state information being generated by a task tracker during monitoring;

[0052] triggering a monitoring enhancement instruction to a data collector according to an exception identifier in the execution state information, the data collector generating deep sampling data containing an environmental complexity parameter according to the monitoring enhancement instruction;

[0053] obtaining a comprehensive data set with device running indicators and cooperative response times, the comprehensive data set being generated by a configuration generator after receiving the deep sampling data;

[0054] judging whether the cooperative efficiency between devices is lower than a preset threshold according to the comprehensive data set, and if the cooperative efficiency is lower than the preset threshold, simulating and testing a candidate configuration scheme by using a stability verification tool, adaptively checking the optimized configuration according to a response time parameter in a test result, and obtaining a final device running optimized configuration.

[0055] Another aspect of the present application relates to a wearable monitoring system based on UWB technology for executing the above-mentioned wearable monitoring method based on UWB technology, comprising:

[0056] a preliminary evaluation result acquisition module configured to collect the connection state of the device in a dynamic environment in real time by using a pre-established signal strength detection mechanism, obtain fluctuation data within a signal coverage range, and obtain a preliminary evaluation result of signal connection stability;

[0057] a signal enhancement requirement determination module configured to determine the signal enhancement requirement of the key node by using a hierarchical screening method to prioritize the areas with signal strength lower than a preset threshold and obtaining the device connection path to be optimized according to the preliminary evaluation result of signal connection stability;

[0058] a signal transmission link acquisition module configured to obtain the optimized signal transmission link by using a signal optimization algorithm to dynamically adjust the connection path between devices according to the signal enhancement requirement of the key node;

[0059] a backup connection channel acquisition module configured to activate the connection interruption recovery mechanism to obtain a stable backup connection channel by quickly switching the backup path if the signal transmission link is still at risk after adjustment;

[0060] a potential delay problem judgment module configured to obtain the dynamic change data of role allocation by monitoring the role switching efficiency between devices in real time according to the activation of the backup connection channel, and judging the potential delay problem in the role switching process;

[0061] Data integrity state acquisition module, for analyzing potential delay problems in role switching process, using data synchronization accuracy checking method, optimizing information consistency guarantee in switching process, acquiring data integrity state after synchronization completion;

[0062] Collaborative task allocation result acquisition module, for updating overall evaluation of system operation reliability according to feedback of data integrity state, acquiring collaborative task allocation result through dynamic adjustment of multi-device collaboration capability;

[0063] System optimization configuration determination module, for strengthening real-time monitoring capability through continuous tracking of collaborative task allocation result, acquiring operation data of device in complex environment, determining final system optimization configuration.

[0064] The beneficial effects obtained by the present application are:

[0065] The wearable monitoring method and system based on UWB technology provided by the present application evaluate connection stability by real-time acquisition of signal strength data, prioritize regions below threshold value, and determine key node signal enhancement requirements. Signal optimization algorithm is used to dynamically adjust connection path, and backup connection channel is activated when interruption risk occurs. Meanwhile, role switching efficiency is monitored, potential delay problems are analyzed, and information consistency guarantee is optimized. Based on data integrity state feedback, system reliability evaluation is updated, and multi-device collaboration capability is dynamically adjusted. Through continuous tracking of task allocation result, real-time monitoring capability is strengthened, complex environment operation data is acquired, and final optimization configuration is determined. The present application can effectively improve the stability and reliability of device connection in dynamic environment, optimize multi-device collaboration efficiency, and adapt to complex environment requirements. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 The flowchart of an embodiment of the wearable monitoring method based on UWB technology of the present application is shown.

[0067] Figure 2 The functional block diagram of an embodiment of the wearable monitoring system based on UWB technology of the present application is shown.

[0068] REFERENCE SIGNS:

[0069] 10, preliminary evaluation result acquisition module; 20, signal enhancement requirement determination module; 30, signal transmission link acquisition module; 40, backup connection channel acquisition module; 50, potential delay problem judgment module; 60, data integrity state acquisition module; 70, collaborative task allocation result acquisition module; 80, system optimization configuration determination module. DETAILED DESCRIPTION

[0070] For better understanding of the above technical solutions, the above technical solutions will be described in detail below in conjunction with the drawings and specific embodiments of the specification.

[0071] As Figure 1 shown, the first embodiment of the present application proposes a wearable monitoring method based on UWB technology, comprising the following steps:

[0072] Step S100, through the pre-established signal strength detection mechanism, the connection state of the device in the dynamic environment is collected in real time, the fluctuation data in the signal coverage range is obtained, and the preliminary evaluation result of the signal connection stability is obtained.

[0073] Through the pre-constructed signal strength detection mechanism (such as fixed sampling frequency signal strength collection algorithm, multi-node comparison logic, etc.), the wireless connection state (such as UWB, Bluetooth, Wi-Fi signal connection link) of the device in the dynamic environment (such as human movement, obstacle change, electromagnetic interference, etc. Scene) is continuously collected in real time, and the strength fluctuation data (such as signal strength value curve with time, fluctuation amplitude, abnormal jump times, etc.) in the signal coverage range is obtained, and the stability of the signal connection is preliminarily quantified. The process and results of the judgment based on these data.

[0074] Step S200, according to the preliminary evaluation result of the signal connection stability, using a hierarchical screening method, the priority of the area with signal strength lower than the preset threshold is sorted, the device connection path that needs to be optimized is obtained, and the signal enhancement requirement of the key node is determined.

[0075] Based on the preliminary evaluation result of the signal connection stability, through the pre-set hierarchical screening method (such as according to the signal attenuation degree, regional importance, influence range, etc. Dimension division level), the priority of the area with signal strength lower than the preset threshold is sorted, the device connection path that needs to be optimized (such as the weak link of signal transmission) is identified, and the signal enhancement requirement of the key node (such as signal relay point, device connection interface) in these paths is determined.

[0076] Step S300, by analyzing the signal enhancement requirement of the key node, using the signal optimization algorithm, the connection path between the devices is dynamically adjusted, and the optimized signal transmission link is obtained.

[0077] Based on the analysis result of the signal enhancement requirement of the key node, through the application of the targeted signal optimization algorithm (such as adaptive frequency hopping algorithm, beam forming technology, path weight dynamic allocation algorithm, etc.), the original connection path between the devices (including signal transmission route, frequency band, power configuration, etc.) is dynamically adjusted in real time, and finally the signal transmission link between the devices with more stable signal strength, stronger anti-interference ability and more optimal transmission efficiency is formed.

[0078] Step S400, if there is still a risk of interruption after the signal transmission link is adjusted, activate the connection interruption recovery mechanism, obtain a stable backup connection channel through fast switching of the backup path.

[0079] When the optimized signal transmission link still has a risk of connection interruption (such as the signal strength continuously being lower than the critical threshold, the fluctuation amplitude exceeding the stable range), by activating the pre-established connection interruption recovery mechanism, based on the preset backup path planning and fast switching protocol, intelligent switching from the main link to the backup path is completed within milliseconds, and finally a backup connection channel that can replace the main link to realize stable data transmission is formed.

[0080] Step S500, according to the activation of the backup connection channel, the role switching efficiency between devices is monitored in real time, the dynamic change data of role allocation is obtained, and potential delay problems in the role switching process are judged.

[0081] With the activation of the backup connection channel (such as activation frequency, duration, and switching times) as a reference, by monitoring the role switching efficiency between devices (such as the response speed of master-slave device identity conversion, the synchronization of data interaction), dynamic change data of role allocation (such as role conversion timestamp, state switching log, data transmission breakpoint) is collected, and based on these data, potential delay problems (such as response timeout, synchronization lag) in the role switching process are identified.

[0082] Step S600, by analyzing the potential delay problems in the role switching process, using a data synchronization accuracy verification method, the information consistency guarantee in the switching process is optimized, and the data integrity state after synchronization is obtained.

[0083] After identifying potential delay problems (such as response timeout, synchronization lag) in the role switching process, through the preset data synchronization accuracy verification method (such as timestamp alignment verification, data hash comparison, breakpoint resume verification, etc.), the information inconsistency risk (such as data loss, time sequence disorder, content deviation) caused by delay is optimized (such as adding redundant check fields, optimizing synchronization protocol timing), and finally the state evaluation result reflecting the integrity (no loss), consistency (no conflict), and timing accuracy (no disorder) of the data after switching is obtained.

[0084] Step S700, according to the feedback of the data integrity state, the overall evaluation of system operation reliability is updated, and the collaboration task allocation result is obtained through dynamic adjustment of multi-device collaboration capability.

[0085] With the feedback of data integrity status after synchronization (such as data loss rate, consistency conflict rate, timing accuracy, etc. Quantitative indicators) as the core input, the overall evaluation of system operation reliability is dynamically updated (such as correcting reliability score, adjusting risk level), and based on the updated evaluation results, the cooperation ability of multiple devices (such as data processing efficiency, communication stability, task execution accuracy) is adaptively adjusted (such as resource reallocation, role permission optimization), and finally the specific cooperation task allocation scheme and result for each device is determined.

[0086] Step S800, through continuous tracking of the cooperation task allocation result, the real-time monitoring ability is strengthened, the running data of the device in the complex environment is obtained, and the final system optimization configuration is determined.

[0087] Based on the continuous tracking data of the cooperation task allocation result, the real-time monitoring ability of the system is strengthened (such as optimizing monitoring frequency, expanding monitoring dimension), the running data of the device in the complex environment (such as strong interference, dynamic load, multi-device cooperation scene) (such as device state, task execution efficiency, environmental influence parameter, etc.) is comprehensively obtained, after multi-dimensional analysis and iterative verification, the comprehensive optimization configuration scheme that can make the system realize efficient, stable and low-consumption operation in the complex environment is determined.

[0088] Further, the wearable monitoring method based on UWB technology proposed in this embodiment, step S100 includes:

[0089] Step S110, through the pre-established signal strength detection mechanism, the connection state of the device in the dynamic environment is collected in real time, and the fluctuation data is obtained from the signal coverage range.

[0090] In the scenario of monitoring the connection state of the device in the dynamic environment, the establishment of the signal strength detection mechanism is the core. Assuming that it is aimed at the wireless network connection state of a mobile device in a city environment, the device will experience the fluctuation of signal strength as the user moves. In principle, the signal strength detection mechanism relies on the built-in receiver of the device to collect the strength data of the base station signal in real time, which is usually recorded in decibel milliwatts. Through the signal strength detection mechanism, the signal change of the device at different positions and different time periods is captured, such as the signal may drop to-90 decibels in a high-rise dense area, and may rise to-70 decibels in an open area. This real-time collection can provide the original data basis for subsequent analysis, which is helpful to identify the weak areas of signal coverage.

[0091] For fluctuation data acquisition in signal coverage, the built-in sensor of the device records the change of signal strength over time and location. Assuming that the device collects data every second, in the 10 minutes when the user moves from the subway station to the ground commercial area, 600 data points are collected. These data points will contain signal drops caused by building obstructions or dense crowds, such as signal strength may frequently be lower than -100 decibels in the subway station, and gradually recover after reaching the ground. Such fluctuation data provides rich material for subsequent processing, which is beneficial to accurately locate signal coverage problems and improve network optimization efficiency.

[0092] Step S120, using a data filtering tool to denoise the fluctuation data to obtain a preliminary sorted signal fluctuation data set.

[0093] For denoising of fluctuation data, the use of data filtering tools is crucial. In principle, denoising is to eliminate abnormal values caused by device jitter or temporary interference, and to retain the true signal change trend. Assuming that there is an abnormal point in the original data where the signal strength suddenly changes to 0 due to temporary disconnection of the device, the abnormal point can be replaced with the average value of the previous and subsequent data by using the median filtering tool, such as adjusting the mutation point 0 to a reasonable value of about -85 decibels. After such processing, the preliminary sorted signal fluctuation data set will be smoother, reflecting the true signal coverage. The benefit of this denoising is to improve data reliability, lay the foundation for subsequent signal optimization analysis, and avoid making wrong decisions due to noise misdirection.

[0094] For the denoised signal fluctuation data set, further analyze the stability of signal coverage. For example, in the above urban mobile scenario, it is found that the signal strength fluctuation range of a certain route is between -80 and -100 decibels, and the fluctuation frequency is high, indicating that the network coverage in this area is unstable. Such analysis results can be directly fed back to network operators for targeted deployment of signal enhancement devices, thereby improving user experience.

[0095] Further, the wearable monitoring method based on UWB technology proposed in this embodiment, step S200 includes:

[0096] Step S210, according to the region data with signal strength lower than the preset threshold, using a hierarchical screening tool for classification processing, obtaining a target region with higher priority, and obtaining a region set that needs to be focused on.

[0097] The high-priority region that needs to be focused on is screened by the following formula:

[0098] (1)

[0099] In formula (1), represents the target region set with higher priority, represents the first candidate region, represents the first region, represents the emergency degree score of the first region, represents the minimum threshold of the emergency degree, represents the first region, represents the impact range score of the first region, represents the minimum threshold of the impact range.

[0100] In the optimization scenario of wireless network signal coverage in urban environment, the data processing of the region with signal strength lower than the preset threshold is particularly important. Assuming that the preset threshold is -85 decibels, the region lower than this value is considered as a weak signal region. When using the hierarchical screening tool, the signal strength data is first divided by region, such as dividing the city into commercial area, residential area and transportation hub area, and then further classified according to the signal weakening degree, and the region with signal strength continuously lower than -90 decibels is selected as the target region with higher priority. The principle of this hierarchical screening is to prioritize the processing of the region with the most serious signal problem to quickly improve user experience. Assuming that there are 3 blocks in a commercial area with signal strength around -95 decibels, they can be classified into the region set for attention through the screening tool, providing a clear target for subsequent optimization.

[0101] Step S220, for the device connection path in the region set for attention, a path analysis tool is used for multi-dimensional comparison to judge the fluctuation monitoring data of the device connection path, and if the fluctuation value exceeds the preset range, the key path segment is determined as the optimization object.

[0102] The path fluctuation value is calculated by the following formula:

[0103] (2)

[0104] In formula (2), represents the path fluctuation value, represents the total number of monitoring time periods, represents the path performance index of the t-th time period, represents the average value of the path performance index. Formula (2) is used to calculate the fluctuation degree of the device connection path in the monitoring period, and the path stability is quantified by calculating the deviation of each time period performance index relative to the average value.

[0105] The key path segment score is calculated by the following formula:

[0106] (3)

[0107] In formula (3), represents the key path segment score, ​a number of dimensions representing path segments, a weight coefficient representing the dimension, an actual measurement value representing the dimension, a minimum threshold value representing the dimension, a maximum threshold value representing the dimension. Formula (3) identifies the key path segment that needs to be optimized through multi-dimensional weighted calculation.

[0108] For device connection path analysis within the set of areas of focus, the path analysis tool is used for comparison from multiple dimensions. Assuming that a user moves from a commercial street to a subway entrance, the total path length is 2 kilometers, and the tool records the signal strength fluctuation data along the way, such as collecting data every 100 meters, resulting in 20 data points. If the fluctuation value exceeds the preset range, such as frequent jumping between -80 and -100 decibels of signal strength, and the fluctuation frequency exceeds 5 times per minute, it can be determined that this path segment is a key path segment and needs to be optimized. Such multi-dimensional comparison not only focuses on the absolute value of signal strength, but also combines fluctuation frequency and path characteristics to comprehensively evaluate connection quality.

[0109] Step S230, by positioning the key nodes of the key path segment, obtaining signal coverage range data between the key nodes, combining the area division logic, determining whether there is a node with insufficient coverage, and obtaining the specific location that needs signal enhancement.

[0110] In the positioning processing of the key nodes of the key path segment, the problem points are identified by analyzing the signal coverage range data between the nodes. Assuming that a path segment contains 5 key nodes, the tool detects the signal strength change between each node, and if it is found that the signal between two nodes drops to below -100 decibels and the continuous distance exceeds 200 meters, the area division logic is combined to determine that this is an insufficient coverage node. Such positioning processing helps to accurately find the weak link of signal coverage and provides specific location basis for subsequent optimization.

[0111] Step S240, according to the specific location that needs signal enhancement, using a signal allocation tool to dynamically adjust resources, optimizing the configuration of the connection path of the specific location that needs signal enhancement, and determining the final signal enhancement scheme.

[0112] When using signal allocation tools to dynamically adjust resources for specific locations requiring signal enhancement, configuration can be optimized based on location characteristics. For example, if a node is located in a densely populated area with severe signal obstruction, the tool can prioritize allocating more base station resources to that area. This could involve increasing the coverage density of small base stations and adjusting signal transmission direction to reduce interference. The final signal enhancement scheme will be tailored to the actual needs of the location, ensuring maximum resource utilization efficiency while improving connection stability in the area. This dynamic adjustment approach effectively addresses signal issues in complex environments, providing users with more reliable network services.

[0113] Furthermore, in the wearable monitoring method based on UWB technology proposed in this embodiment, step S300 includes:

[0114] Step S310: Based on the signal strength data, classify the distribution of key nodes, obtain target areas with signal strength below a preset threshold from the node distribution, and determine the specific locations with insufficient coverage by combining the area division logic.

[0115] Signal strength is calculated using the following formula:

[0116] (4)

[0117] In formula (4), Represents a node To the node signal strength, Represents a node The transmission power, Represents a node transmit gain, This represents the receive gain of node j. Represents a node and Path loss between This represents the path loss index. e Represents the natural constant. Indicates the environmental degradation coefficient. Represents a node and The distance between them.

[0118] In the optimization scenario of urban wireless network signal coverage, the classification processing of signal strength data is particularly important. The area with signal strength lower than the preset threshold is often the key point of user experience decline. Assuming that the preset threshold is -88 decibels, the area below this value is designated as the target area. By classifying the distribution of key nodes, the urban area can be divided into high-density population area and low-density population area, and then combined with signal strength data, the sub-area in the high-density population area with signal strength continuously lower than -92 decibels is selected as the key object of attention. The principle of this classification processing is to identify the core position of insufficient coverage first, and provide a clear direction for subsequent optimization.

[0119] Step S320, for the specific position with insufficient coverage, a path analysis tool is used to perform multi-dimensional comparison on the device connection path, and when the fluctuation of the device connection path exceeds the preset range, the path segment that needs to be dynamically adjusted is obtained, and the link stability of the path segment is judged.

[0120] For example, for the specific position with insufficient coverage, the multi-dimensional comparison of the path analysis tool starts from two aspects of signal fluctuation and path characteristics. Assuming that a user moves from a shopping center to a bus station in a high-density population area, the total path length is 1.5 kilometers, and the tool collects signal data every 80 meters, obtaining about 18 data points. If it is found that the signal strength frequently changes between -85 decibels and -98 decibels, and the fluctuation frequency reaches 6 times per minute, which exceeds the preset range, the path segment is determined as the object that needs to be dynamically adjusted. Such an analysis method can comprehensively evaluate the link stability and provide a basis for subsequent resource allocation.

[0121] Step S330, by adjusting the resource allocation of the path segment with lower link stability, a signal allocation tool is used to sort the node priority, obtain at least one target node that needs signal optimization, and obtain a node set to be processed.

[0122] In the resource allocation adjustment link, for the path segment with lower link stability, the signal allocation tool can sort the node priority. Assuming that a path segment contains 6 nodes, of which 2 nodes have signal strength continuously lower than -95 decibels, and the user flow is high, the tool will list these 2 nodes as the target nodes with the highest priority and include them in the node set to be processed. This sorting method ensures that resources are preferentially tilted to the most serious nodes, improving optimization efficiency.

[0123] Step S340, according to the node set to be processed, a signal enhancement tool is used to dynamically adjust the transmission link, and the connection path of each target node is reconfigured combined with the signal optimization logic, to determine the optimized link distribution.

[0124] For the set of nodes to be processed, the dynamic adjustment process of the signal enhancement tool combines signal optimization logic to reconfigure the link. Assuming that a target node is located in a densely built-up area, the signal is obviously blocked, and the signal enhancement tool will re-plan the connection path of the node, preferentially link to the base station with shorter distance and lower load, and adjust the signal transmission parameters to reduce interference. Such dynamic adjustment can effectively improve the connection quality in a specific area and provide users with more stable network experience.

[0125] Preferably, the wearable monitoring method based on UWB technology proposed in the embodiment comprises the following steps:

[0126] Step S410, according to the link state of the signal transmission link, for the potential signal interruption risk, the path monitoring tool is used to detect the current connection channel in real time, and the target node with path stability and transmission efficiency lower than the preset threshold is obtained, and the node range needing to activate the connection interruption recovery mechanism is determined.

[0127] In the optimization scenario of urban wireless network signal coverage, it is particularly important to monitor the potential interruption risk of the signal transmission link in real time. For link state detection, the path monitoring tool can continuously evaluate the stability of the connection channel. Assuming that the stability threshold of a signal transmission link in a certain area is 85%, and real-time detection finds that the stability of a certain path is only 72%, and the transmission efficiency is lower than the preset 5 megabits per second, the related node will be marked as a target node and included in the range of nodes needing to activate the connection interruption recovery mechanism. In this way, the problem area can be quickly locked down to provide a basis for subsequent adjustment.

[0128] Step S420, for the determined node range needing to activate the connection interruption recovery mechanism, at least one pre-established path redundancy scheme is obtained from the backup path database, and the switching speed and stability of the backup path are sorted by using the path comparison tool to obtain the priority path option suitable for dynamic switching.

[0129] For the determined node range needing to activate the connection interruption recovery mechanism, the backup path database usually stores multiple pre-planned redundancy paths. The path comparison tool will evaluate from two dimensions of switching speed and stability. Assuming that there are three backup paths in the database, the switching time of path A is 2 seconds and the stability is 90%; the switching time of path B is 3 seconds and the stability is 88%; the switching time of path C is 1.5 seconds and the stability is 92%. Through sorting, path C is selected as the priority path option due to its shorter switching time and higher stability. Such comparison and analysis ensures that the selection of switching path is more reasonable.

[0130] Step S430, if the stability of the priority path option is higher than the current link state, the connection channel of the target node is quickly adjusted by the path switching tool to obtain a new backup connection channel.

[0131] The stability evaluation value of the priority path option is calculated by the following formula:

[0132] (5)

[0133] In formula (5), represents the stability evaluation value of the priority path option, represents the total number of path evaluation parameters, represents the weight coefficient of the evaluation parameter, represents the reliability index of the parameter, represents the delay loss value corresponding to the parameter, represents a small constant to prevent the denominator from being zero.

[0134] In the process of path switching, if the stability of the priority path option is higher than the current link state, the path switching tool will quickly adjust the connection channel of the target node. Assuming that the stability of the current link is 72%, and path C is 92%, the tool will switch the connection of the target node to path C to form a new backup connection channel. This quick adjustment can effectively reduce the risk of signal interruption and provide users with a more continuous network experience.

[0135] Step S440, according to the new backup connection channel, the link verification tool is used to detect the overall state of node connection, and the path redundancy configuration is re-adjusted for the area where the transmission efficiency does not meet the standard, to determine the final stable signal transmission link.

[0136] For the new backup connection channel, the link verification tool will detect the overall state of node connection to ensure that the transmission efficiency meets the expectation. Assuming that the transmission efficiency of a certain area after switching still does not meet the standard of 5 megabits per second, the tool will re-adjust the path redundancy configuration, which may be by increasing intermediate nodes or optimizing signal forwarding mode to improve efficiency. The finally determined signal transmission link will reach a stable state after multiple verifications. This repeated detection and adjustment process can guarantee the overall quality of the link.

[0137] Further, the wearable monitoring method based on UWB technology proposed in the embodiment comprises the following steps:

[0138] Step S510, according to the enabling condition of the standby connection channel, the connection efficiency is continuously tracked by using the channel state monitoring tool, the real-time feedback data of the role assignment is obtained, the delay risk area existing in the switching process is judged, and the position information of the potential problem node is obtained.

[0139] In the optimization scenario of urban wireless network signal coverage, it is particularly important to continuously track the enabling condition of the standby connection channel. The channel state monitoring tool collects related data of connection efficiency in real time, such as transmission rate and delay time, to judge the delay risk area that may exist in the switching process. Assuming that the average delay time of a certain connection is 200 milliseconds after the standby connection channel in a certain area is enabled, and the preset normal range is within 100 milliseconds, then this area will be marked as a potential problem node, and the position information will be recorded for subsequent analysis.

[0140] Step S520, according to the position information of the potential problem node, the historical data change record of the role assignment is extracted from the pre-established assignment adjustment database, the influencing factors of switching delay are classified by using the comparison tool, and the key parameters affecting the efficiency evaluation are determined.

[0141] According to the position information of the potential problem node, the historical data change record of the role assignment is extracted from the pre-established assignment adjustment database. Assuming that the switching records of this area in the past month are stored in the assignment adjustment database, it is found that the delay problem is mostly related to the overload of a certain relay node. The comparison tool will classify the factors affecting the switching delay into three categories: load, distance and signal strength, and determine that the load proportion is the key parameter. If the load proportion exceeds the preset threshold, such as 70%, it indicates that the role assignment of the node needs to be adjusted to share the pressure.

[0142] Step S530, if the key parameter exceeds the preset threshold, the connection efficiency of the standby connection channel is dynamically adjusted by using the delay optimization tool, and the adjusted channel state data is obtained.

[0143] The dynamic adjustment amount is calculated by the following formula:

[0144] (6)

[0145] In formula (6), represents the dynamic adjustment amount, represents the adjustment coefficient of the delay optimization tool, represents the actual value of the key parameter, represents the preset threshold, The function ensures that the adjustment is triggered only when exceeds .

[0146] The adjusted channel connection efficiency is calculated by the following formula:

[0147] (7)

[0148] In formula (7), represents the adjusted channel connection efficiency, represents the original connection efficiency of the backup connection channel, represents the time delay introduced by the delay optimization tool, represents the reference time parameter.

[0149] The adjusted channel state data is calculated by the following formula:

[0150] (8)

[0151] In formula (8), represents the updated channel state data, represents the original channel state data, represents the state update coefficient, is the dynamic adjustment amount, The function ensures that the state data is non-negative.

[0152] For the case where the key parameters exceed the threshold, the delay optimization tool will dynamically adjust the connection efficiency of the backup connection channel. Suppose through tool analysis, it is found that after transferring part of the data flow to the adjacent node, the load ratio is reduced to 50%, and the delay time is shortened to 120 milliseconds, and the adjusted channel state data will be updated in real time. This dynamic adjustment can effectively alleviate the node pressure and ensure the smoothness of data transmission.

[0153] Step S540, according to the adjusted channel state data, using the risk judgment tool to carry out secondary verification on the delay risk, re-adjusting the allocation strategy for the still existing risk area, and obtaining the final role switching efficiency optimization configuration.

[0154] Based on the adjusted channel state data, the risk judgment tool will carry out secondary verification on the delay risk. Suppose the delay time of a certain area has improved, but still does not meet the standard of within 100 milliseconds, the risk judgment tool will identify the still existing risk area and re-adjust the allocation strategy, such as increasing the participation of backup nodes, and finally form the optimization configuration of role switching efficiency. This secondary verification and strategy adjustment can further improve the stability of the channel and provide more reliable network support for users. Through multi-level monitoring, analysis and optimization, the whole process forms a closed loop mechanism, ensuring the efficient operation of the signal transmission link.

[0155] Further, the wearable monitoring method based on UWB technology proposed in this embodiment includes the following steps:

[0156] In step S610, the real-time data assigned by the role is received by using the data collection tool. The real-time data is generated during the role switching process.

[0157] In the scenario of urban wireless network signal coverage optimization, the data collection tool receives the role assignment data by real-time monitoring of the state changes of each base station node. For example, when a certain master node needs to transfer part of the connection to a standby node due to excessive load, the data collection tool will record the timestamp, data packet sequence number and transmission path information during the transfer process. These real-time data contain the complete trajectory of role switching, providing basic data support for subsequent analysis.

[0158] In step S620, the switching delay is continuously tracked according to the real-time data, and dynamic feedback data of delay monitoring is obtained.

[0159] The continuous tracking mechanism of switching delay obtains dynamic feedback data by setting a fixed sampling interval. Assuming that the system collects delay data every 50 milliseconds, when the delay of a certain connection is detected to suddenly jump from the normal 80 milliseconds to 180 milliseconds, the tracking tool will immediately mark this abnormal change. Dynamic feedback data not only contains the delay value itself, but also records the trend and fluctuation amplitude of delay change, which is of great significance to judge the stability of network state.

[0160] In step S630, the history record corresponding to the dynamic feedback data is extracted by using the pre-established synchronization accuracy database, the information consistency is verified by using the comparison tool, whether there is deviation in the synchronization process is judged, and the preliminary result of data verification is obtained.

[0161] The synchronization accuracy database stores the historical records of all role switching events in the past three months. When the system needs to verify the current synchronization state, the comparison tool will extract the historical data under similar scenarios from the database for comparison. For example, the role switching delay of a certain area is 150 milliseconds, while the historical record shows that the average delay of the area under the same time period and load condition is 120 milliseconds, and the difference is 30 milliseconds. If the preset deviation threshold is 25 milliseconds, the current state is determined to have synchronization deviation.

[0162] In step S640, if the preliminary result of data verification shows that the deviation exceeds the preset threshold, the synchronization optimization tool is used to dynamically adjust the data synchronization process, and the adjusted synchronization accuracy data is obtained.

[0163] The new synchronization accuracy data obtained after dynamic adjustment is calculated by the following formula:

[0164] (9)

[0165] In formula (9), represents the adjusted synchronization accuracy, represents the unadjusted synchronization accuracy, represents the system noise variance, represents the reliability variance of the adjustment process.

[0166] When the preliminary results of data verification show that the deviation exceeds the preset threshold, the synchronization optimization tool starts the dynamic adjustment mechanism. The synchronization optimization tool first analyzes the root cause of the deviation. If it is caused by the increase of delay due to the high load of a certain relay node, the synchronization optimization tool will automatically redistribute part of the data flow to the adjacent node with lighter load. During the adjustment process, the system will monitor the new synchronization accuracy data in real time to ensure the correctness of the adjustment direction.

[0167] Step S650, according to the adjusted synchronization accuracy data, the state verification tool is used to carry out secondary check on the integrity state, and the guarantee level of information consistency in the role switching process is determined.

[0168] When the state verification tool carries out secondary check on the integrity state, a multi-dimensional verification strategy is adopted. In addition to verifying the delay index, the signal strength, transmission rate and error rate and other key parameters will also be checked. Assuming that after dynamic adjustment, the delay of a certain area is reduced to 110 milliseconds, but the signal strength is still low, the state verification tool will continue to mark this area as a node that needs attention, and suggest further optimization measures. This multi-level checking and adjustment mechanism can effectively guarantee the reliability of information consistency in the role switching process. Through the closed-loop process of real-time data collection, historical comparison and analysis, dynamic optimization and adjustment and secondary state verification, the system can timely find and solve various problems in the synchronization process, and ensure the continuity and stability of network service.

[0169] Further, the wearable monitoring method based on UWB technology provided in the embodiment comprises the following steps:

[0170] Step S710, receiving feedback information carrying data integrity state identification, the feedback information is generated by the state monitoring tool during operation.

[0171] The state monitoring tool generates feedback information by continuously scanning the running parameters of each base station device in the city wireless network. When the data transmission error rate of a certain base station rises from the normal 0.05% to 0.3%, the monitoring tool will embed an abnormal identification code in the feedback information, and record the specific error type and occurrence frequency. Such feedback information not only contains the current abnormal state, but also carries the historical running track and performance change trend of the device, providing a comprehensive data basis for subsequent reliability evaluation.

[0172] Step S720: Trigger a reliability assessment instruction to the multi-device collaboration module based on the anomaly identifier in the feedback information. The multi-device collaboration module generates basic assessment data containing the device processing capabilities based on the reliability assessment instruction.

[0173] After receiving a reliability assessment command, the multi-device collaboration module performs a comprehensive capability evaluation of all relevant devices within the network coverage area. For example, when the primary base station malfunctions, the collaboration module assesses the processing capabilities of three surrounding backup base stations, including their maximum concurrent connections, signal coverage radius, and current idle resource ratio. Assuming backup base station A has a processing capacity of 8000 data packets per second, backup base station B 6500 data packets per second, and backup base station C 7200 data packets per second, these data constitute the core content of the basic assessment data.

[0174] Step S730: Obtain a dynamic adjustment instruction containing device load status and collaboration capability parameters. The dynamic adjustment instruction is generated by the load balancing algorithm after receiving basic evaluation data.

[0175] The load balancing algorithm considers the real-time load status and collaboration potential of devices when generating dynamic adjustment instructions. When the network load in a certain area reaches a high level of 85%, the load balancing algorithm calculates the load distribution differences between devices. For example, if the main base station is currently loaded at 90% while the neighboring base stations are loaded at only 45%, the system will generate adjustment instructions containing load redistribution suggestions to guide the subsequent task replanning process.

[0176] Step S740: Determine whether the device load exceeds the preset threshold. If the device load exceeds the preset threshold, the task allocation engine is used to re-plan the collaborative tasks. The tasks to be processed are prioritized according to the collaborative capability parameters to obtain a preliminary task allocation scheme.

[0177] Whether the equipment load exceeds the preset threshold is determined by the following formula:

[0178] (10)

[0179] In formula (10), This indicates the current load status of the equipment. Indicates the first The weighting coefficients of each task. Indicates the first The amount of resources used by each task. Indicates the total resource capacity of the equipment. This indicates a preset load threshold. When the load exceeds the threshold, the task will be rescheduled.

[0180] The priority classification of the to-be-processed tasks is implemented by the following formula:

[0181] (11)

[0182] In formula (11), denotes the priority score of the i-th to-be-processed task, denotes the task urgency coefficient, denotes the task importance coefficient, denotes the task complexity coefficient, , , , respectively denote the corresponding collaboration capability parameter weight factors for task priority classification.

[0183] The task allocation engine prioritizes different types of network service requests according to the collaboration capability parameters. High-priority tasks include emergency communications and critical data transmission, medium-priority tasks cover regular voice calls, and low-priority tasks include general data downloads. The allocation engine will prioritize high-priority tasks to the most stable devices according to the processing capacity and current load of each base station, ensuring the continuity of critical services.

[0184] Step S750, according to the preliminary task allocation scheme, the integrity verification tool is used for rationality checking, if the checking result reaches the preset standard, the final collaboration task allocation result is generated.

[0185] When the preliminary task allocation scheme is determined, the integrity verification tool will perform rationality checking from multiple dimensions. The verification process includes checking whether the expected load of each device after allocation is balanced, whether the task type matches the device capability, and whether the overall network performance can meet the quality of service requirements. If the amount of tasks allocated to a base station is expected to make its load exceed the 80% safety threshold, the integrity verification tool will mark this allocation scheme as unreasonable and require re-adjustment. This multi-level evaluation and verification mechanism can ensure the scientificity and executability of the collaboration task allocation. Through the complete process of real-time monitoring feedback, capability evaluation, load analysis and rationality checking, the system can quickly respond when the device is abnormal or the load fluctuates, ensuring the stable operation of the network service and the consistency of user experience.

[0186] Further, the wearable monitoring method based on UWB technology proposed in the embodiment comprises the following steps:

[0187] Step S810, receiving execution state information carrying a collaboration task allocation identifier, the execution state information being generated by a task tracker in a monitoring process.

[0188] The task tracker generates execution status information by continuously monitoring the cooperative task execution of each base station device in the urban wireless network. When a certain group of base stations is processing a data forwarding task, the task tracker records the task completion rate, response delay, and resource occupation of each base station. For example, when base station A experiences a connection interruption while executing a voice call transfer task, the task tracker embeds an abnormality identification code "ERR_CONN_LOST" in the execution status information, while recording the specific time point of the interruption and the number of affected users.

[0189] Step S820, trigger the monitoring enhancement instruction to the data collector according to the abnormality identification in the execution status information, and the data collector generates deep sampling data containing the environmental complexity parameter according to the monitoring enhancement instruction.

[0190] After receiving the monitoring enhancement instruction, the data collector performs deep data sampling on the abnormal area. The data collector not only collects the basic running parameters of the base station, but also obtains the environmental complexity parameters, including signal interference intensity, user density distribution, and network topology change. For example, when it is detected that the user access amount of a certain commercial area increases to 3.2 times of the usual amount during 9:00-11:00 am, the data collector records the signal attenuation coefficient at this time as -85 dBm, and simultaneously collects the multipath effect parameters caused by the reflection of surrounding buildings.

[0191] Step S830, obtain a comprehensive data set containing device running indicators and cooperative response time, and the comprehensive data set is generated by the configuration generator after receiving the deep sampling data.

[0192] The configuration generator comprehensively analyzes the device running indicators and cooperative response time when processing the deep sampling data. The configuration generator calculates the data transmission delay between each base station and evaluates the stability of the cooperative link. For example, when the average response times of the main base station and three cooperative base stations are 12 ms, 18 ms, and 15 ms, respectively, the configuration generator integrates these data into a comprehensive data set, and marks the link with a response time exceeding 20 ms as a potential risk point.

[0193] Step S840, according to the comprehensive data set, determine whether the cooperative efficiency between devices is lower than a preset threshold value, if the cooperative efficiency is lower than the preset threshold value, use the stability verification tool to perform simulation test on the candidate configuration scheme, and according to the response time parameter in the test result, adaptively check the optimized configuration to obtain the final device running optimization configuration.

[0194] The cooperative efficiency between devices is calculated by the following formula:

[0195] (12)

[0196] In formula (12), representing the efficiency of inter-device collaboration, representing the number of devices participating in collaboration, representing the number of collaboration tasks, representing the device weight coefficient of task j, representing the device actual time to complete task , representing the ideal completion time. When is less than the preset threshold, optimization configuration is needed.

[0197] The final device operation optimization configuration is calculated by the following formula:

[0198] (13)

[0199] In formula (13), representing the adaptability score of the final optimization configuration, , , representing the weight coefficient of different evaluation dimensions, representing the baseline response time, representing the optimized response time, representing the number of successfully executed tasks, representing the total number of tasks, representing the baseline calculation cost, representing the optimized calculation cost.

[0200] Collaboration efficiency evaluation is based on multi-dimensional indicators. The system sets the collaboration efficiency threshold to 75%, and when the actual collaboration efficiency is lower than this value, the optimization process will be triggered. For example, a certain base station group is handling emergency communication tasks, and due to the long collaboration response time, the overall efficiency has dropped to 68%. At this time, the system will automatically start the stability verification tool.

[0201] The stability verification tool will perform simulation testing on the candidate configuration scheme. The stability verification tool simulates the device running state under different load conditions and tests the stability performance of various configuration parameters.

[0202] When testing a configuration scheme that adjusts the maximum concurrent connection number of the primary base station from 5000 to 4500, the simulation results show that the average response time decreases from 22 milliseconds to 16 milliseconds, and the connection success rate increases from 92% to 97%. The adaptive verification process will analyze the response time parameter in the test results in detail. The stability verification tool checks the consistency of the performance of the configuration scheme under different time periods and load conditions. For example, an optimized configuration has a response time of 14 milliseconds under normal load, but the response time increases to 35 milliseconds under peak load. At this time, the stability verification tool determines that the adaptability of the configuration is insufficient and needs to be further adjusted.

[0203] See Figure 2The embodiment also provides a wearable monitoring system based on UWB technology, which is used for executing the wearable monitoring method based on UWB technology, and comprises a preliminary evaluation result acquisition module 10, a signal enhancement requirement determination module 20, a signal transmission link acquisition module 30, a backup connection channel acquisition module 40, a potential delay problem judgment module 50, a data integrity state acquisition module 60, a cooperative task allocation result acquisition module 70 and a system optimization configuration determination module 80. The preliminary evaluation result acquisition module 10 is used for collecting the connection state of the equipment in a dynamic environment in real time through a pre-established signal strength detection mechanism, acquiring fluctuation data in a signal coverage range, and obtaining a preliminary evaluation result of signal connection stability. The signal enhancement requirement determination module 20 is used for performing priority sorting on a region with a signal strength lower than a preset threshold value in a hierarchical screening manner according to the preliminary evaluation result of signal connection stability, acquiring an equipment connection path that needs to be optimized, and determining the signal enhancement requirement of a key node. The signal transmission link acquisition module 30 is used for performing dynamic adjustment on the connection path between the equipment by analyzing the signal enhancement requirement of the key node and using a signal optimization algorithm, and acquiring an optimized signal transmission link. The backup connection channel acquisition module 40 is used for activating a connection interruption recovery mechanism if there is still an interruption risk after the signal transmission link is adjusted, acquiring a stable backup connection channel through rapid switching of a backup path. The potential delay problem judgment module 50 is used for monitoring the role switching efficiency between the equipment in real time according to the use condition of the backup connection channel, acquiring dynamic change data of role allocation, and judging a potential delay problem in a role switching process. The data integrity state acquisition module 60 is used for optimizing the information consistency guarantee in the switching process by analyzing the potential delay problem in the role switching process and using a data synchronization accuracy checking manner, and acquiring a data integrity state after synchronization is completed. The cooperative task allocation result acquisition module 70 is used for updating the overall evaluation of system operation reliability according to the feedback of the data integrity state, acquiring a cooperative task allocation result through dynamic adjustment of the multi-equipment cooperation capability. The system optimization configuration determination module 80 is used for strengthening the real-time monitoring capability by continuously tracking the cooperative task allocation result, acquiring operation data of the equipment in a complex environment, and determining a final system optimization configuration.

[0204] Compared with the prior art, the wearable monitoring method and system based on UWB technology provided by the embodiment have the beneficial effects as follows:

[0205] I. Improve the stability and reliability of signal transmission

[0206] 1) Real-time perception and precise optimization: Through the closed-loop process of "real-time signal acquisition connection state → preliminary stability assessment → hierarchical screening of low signal area → dynamic adjustment of transmission link", the weak link of signal (such as dynamic environment shielding, multipath interference area) can be targeted and located, and the transmission path can be precisely optimized through signal optimization algorithm, which can significantly reduce the fluctuation of UWB signal in wearable scene (such as human shielding, complex indoor environment), and improve the stability of signal strength and transmission quality.

[0207] 2) Strengthen the anti-interference ability: In view of the problem that UWB technology is easily affected by dynamic environmental interference (such as human movement, obstacle change), through key node signal enhancement and link dynamic adjustment, the influence of environmental noise on monitoring data can be reduced, and the signal connection quality of wearable device in dynamic scene (such as daily activities, sports) can be ensured.

[0208] II. Ensure the continuity and integrity of monitoring data

[0209] 1) Anti-interruption and rapid recovery: When there is interruption risk in signal transmission link, through "activating connection interruption recovery mechanism → quickly switching standby path", millisecond-level link switching can be realized, which can avoid the disconnection of monitoring data caused by instantaneous signal loss, and ensure the continuity of monitoring in complex environment (such as indoor multi-room movement, multi-person interaction scene).

[0210] 2) Data synchronization and integrity protection: Through "data synchronization accuracy verification → optimization of switching process information consistency", data loss or misplacement can be avoided when device role switching (such as master-slave device switching), and the time continuity and integrity of monitoring data (such as physiological parameters, motion trajectory) can be ensured, providing reliable data basis for subsequent analysis (such as health monitoring, behavior evaluation).

[0211] III. Enhance the efficiency and smoothness of multi-device cooperation

[0212] 1) Low delay of role switching: Through "real-time monitoring of role switching efficiency → judging potential delay problems", the communication protocol and cooperation logic between devices can be targeted and optimized, the role switching delay of multi-device cooperation (such as wearable device and surrounding base station, other wearable devices) can be reduced, the smoothness of cooperation process (such as data interaction, task handover) can be ensured, and the real-time demand of multi-device linkage in wearable scene can be adapted.

[0213] 2) Dynamic cooperation ability adaptation: Through "updating system reliability evaluation → dynamically adjusting multi-device cooperation ability", task allocation can be optimized according to device running data (such as signal strength, power consumption, load) in complex environment, single device overload or resource waste can be avoided, the overall efficiency of multi-device cooperation can be improved, which is especially suitable for multi-wearable device joint monitoring scene (such as whole body multi-site synchronous monitoring).

[0214] Four, the adaptability and robustness of the lifting system in complex environments

[0215] 1) Environmental adaptability enhancement: Through "continuous tracking of collaborative tasks, strengthening real-time monitoring capabilities, obtaining complex environment operation data, and determining the final system optimization configuration", the system can autonomously learn and optimize parameters in diversified complex environments (such as different indoor layouts, dynamic occlusion, electromagnetic interference scenarios), gradually improve the adaptability to environmental changes, and reduce the need for manual intervention.

[0216] 2) Robustness improvement: From signal acquisition, link optimization, interruption recovery to data synchronization, the whole process design forms a multi-layer protection mechanism of "prevention-optimization-recovery-verification", so that the system can still run stably under the harsh conditions of wearable devices (such as low power consumption constraints, human motion interference, signal occlusion), reducing the probability of failure.

[0217] Five, the practicality and user experience of optimizing wearable monitoring

[0218] 1) Continuous and stable monitoring service: Through the above mechanism, the wearable device can provide uninterrupted monitoring support in user daily activities, sleep, exercise and other scenarios, avoiding monitoring interruption or invalid data caused by signal problems, and improving user trust in the device.

[0219] 2) Lightweight and low intervention: The whole process automatically completes signal optimization, path switching, role adjustment and other operations through algorithms, without the need for user manual intervention, adapting to the core needs of "unconscious monitoring" of wearable devices, reducing user operation burden and improving use convenience.

[0220] In summary, the wearable monitoring method and system based on UWB technology provided in the embodiment significantly improves the stability, continuity, reliability and adaptability of the wearable monitoring system in dynamic complex environments through whole-link optimization of UWB signal transmission, multi-device collaboration and data management, providing core technical support for high-precision, low-intervention wearable monitoring applications (such as health monitoring, motion analysis, safety warning, etc.).

[0221] Although the preferred embodiments of the present application have been described, those skilled in the art can make further changes and modifications to these embodiments once they know the basic inventive concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A wearable monitoring method based on UWB technology, characterized in that, The method comprises the following steps: Through a pre-established signal strength detection mechanism, the connection state of the device in a dynamic environment is collected in real time, fluctuation data within the signal coverage range is obtained, and a preliminary evaluation result of signal connection stability is obtained; According to the preliminary evaluation result of signal connection stability, a hierarchical screening method is used to prioritize the areas with signal strength lower than the preset threshold, obtain the device connection path that needs to be optimized, and determine the signal enhancement requirement of the key node; By analyzing the signal enhancement requirement of the key node, a signal optimization algorithm is used to dynamically adjust the connection path between devices, and an optimized signal transmission link is obtained; If the signal transmission link still has interruption risk after adjustment, the connection interruption recovery mechanism is activated, a stable backup connection channel is obtained through rapid switching of the backup path; According to the activation of the backup connection channel, the role switching efficiency between devices is monitored in real time, the dynamic change data of role allocation is obtained, and potential delay problems in the role switching process are determined; By analyzing the potential delay problems in the role switching process, a data synchronization accuracy verification method is used to optimize the information consistency guarantee in the switching process, and the data integrity state after synchronization is completed is obtained; According to the feedback of the data integrity state, the overall evaluation of system operation reliability is updated, the dynamic adjustment of multi-device collaboration capability is obtained, and the collaboration task allocation result is obtained; Through continuous tracking of the collaboration task allocation result, the real-time monitoring capability is strengthened, the operation data of the device in a complex environment is obtained, and the final system optimization configuration is determined; If the signal transmission link still has interruption risk after adjustment, the connection interruption recovery mechanism is activated, a stable backup connection channel is obtained through rapid switching of the backup path, and the steps include: According to the link state of the signal transmission link, for the potential signal interruption risk, a path monitoring tool is used to detect the current connection channel in real time, target nodes with path stability and transmission efficiency lower than the preset threshold are obtained, and the node range that needs to activate the connection interruption recovery mechanism is determined; For the determined node range that needs to activate the connection interruption recovery mechanism, at least one pre-established path redundancy scheme is obtained through a backup path database, a path comparison tool is used to sort the switching speed and stability of the backup path, and a priority path option suitable for dynamic switching is obtained; If the stability of the priority path option is higher than the current link state, a path switching tool is used to quickly adjust the connection channel of the target node, and a new backup connection channel is obtained; According to the new backup connection channel, a link verification tool is used to detect the overall state of node connection, the path redundancy configuration is re-adjusted for the area with substandard transmission efficiency, and the final stable signal transmission link is determined.

2. The UWB technology-based wearable monitoring method of claim 1, wherein, The step of collecting the connection state of the device in a dynamic environment in real time through a pre-established signal strength detection mechanism, obtaining fluctuation data within the signal coverage range, and obtaining a preliminary evaluation result of signal connection stability includes: Through a pre-established signal strength detection mechanism, the connection state of the device in a dynamic environment is collected in real time, and fluctuation data is obtained from the signal coverage range; A data filtering tool is used to denoise the fluctuation data to obtain a preliminarily sorted signal fluctuation data set.

3. The UWB technology-based wearable monitoring method of claim 1, wherein, According to the preliminary evaluation result of the signal connection stability, a hierarchical screening method is used to prioritize the areas with signal strength lower than the preset threshold, to obtain the device connection path that needs to be optimized, and to determine the signal enhancement requirement of the key node, which includes: According to the area data with signal strength lower than the preset threshold, a hierarchical screening tool is used for classification processing to obtain target areas with higher priority, and a set of areas that need to be focused on is obtained; For the device connection path in the set of areas that need to be focused on, a path analysis tool is used for multi-dimensional comparison to judge the fluctuation monitoring data of the device connection path. If the fluctuation value exceeds the preset range, the key path segment is determined as the object to be optimized. By positioning the key nodes of the key path segment, the signal coverage range data between the key nodes is obtained, and whether there is a node with insufficient coverage is judged based on the region division logic to obtain the specific location that needs signal enhancement. According to the specific location that needs signal enhancement, a signal allocation tool is used to dynamically adjust the resources, and the connection path of the specific location that needs signal enhancement is optimized and configured to determine the final signal enhancement scheme.

4. The UWB technology-based wearable monitoring method of claim 1, wherein, The step of analyzing the signal enhancement requirement of the key node, using a signal optimization algorithm, and dynamically adjusting the connection path between devices to obtain an optimized signal transmission link includes: According to the signal strength data, the distribution of the key nodes is classified and processed to obtain target areas with signal strength lower than the preset threshold from the node distribution, and the specific location with insufficient coverage is determined based on the region division logic. For the specific location with insufficient coverage, a path analysis tool is used for multi-dimensional comparison of the device connection path. When the fluctuation of the device connection path exceeds the preset range, the path segment that needs to be dynamically adjusted is obtained, and the link stability of the path segment is judged. By adjusting the resource allocation of the path segment with low link stability, a signal allocation tool is used to sort the node priorities to obtain at least one target node that needs signal optimization, and a set of nodes to be processed is obtained. According to the set of nodes to be processed, a signal enhancement tool is used to dynamically adjust the transmission link, and the connection path of each target node is reconfigured based on the signal optimization logic to determine the optimized link distribution.

5. The UWB technology-based wearable monitoring method of claim 1, wherein, According to the enabling situation of the standby connection channel, the role switching efficiency between devices is monitored in real time to obtain dynamic change data of role allocation, and the step of judging potential delay problems in the role switching process includes: According to the enabling situation of the standby connection channel, a channel state monitoring tool is used to continuously track the connection efficiency to obtain real-time feedback data of role allocation, to judge the delay risk area existing in the switching process, and to obtain the location information of the potential problem node. According to the position information of the potential problem node, historical data change records related to the role assignment are extracted from a pre-established distribution adjustment database, factors affecting the switching delay are classified using a comparison tool, and key parameters affecting efficiency evaluation are determined; If the key parameters exceed the preset threshold, the connection efficiency of the standby connection channel is dynamically adjusted by a delay optimization tool, and adjusted channel state data is obtained; According to the adjusted channel state data, a risk judgment tool is used to perform secondary verification on the delay risk, and the distribution strategy is re-adjusted for the remaining risk area to obtain the final role switching efficiency optimization configuration.

6. The UWB technology-based wearable monitoring method of claim 1, wherein, The step of obtaining the data integrity state after synchronization by analyzing the potential delay problem in the role switching process and optimizing the information consistency guarantee in the switching process using a data synchronization accuracy verification method includes: Real-time data of role assignment is received using a data collection tool, and the real-time data is generated during the role switching process; The delay is continuously tracked according to the real-time data, and dynamic feedback data of delay monitoring are obtained; The historical records corresponding to the dynamic feedback data are extracted from a pre-established synchronization accuracy database, the information consistency is verified using a comparison tool, it is judged whether there is deviation in the synchronization process, and a preliminary result of data verification is obtained; If the preliminary result of data verification shows that the deviation exceeds the preset threshold, a synchronization optimization tool is used to dynamically adjust the data synchronization process, and adjusted synchronization accuracy data is obtained; According to the adjusted synchronization accuracy data, a state verification tool is used to perform secondary verification on the integrity state, and the guarantee level of information consistency in the role switching process is determined.

7. The UWB technology-based wearable monitoring method according to claim 1, wherein, The step of updating the overall evaluation of system operation reliability according to the feedback of data integrity state and obtaining the collaboration task allocation result by dynamically adjusting the multi-device collaboration capability includes: Receive feedback information carrying data integrity state identification, which is generated by the state monitoring tool during operation; Trigger the reliability evaluation instruction to the multi-device collaboration module according to the exception identification in the feedback information, and the multi-device collaboration module generates basic evaluation data containing device processing capacity according to the reliability evaluation instruction; Get dynamic adjustment instructions with device load status and collaboration capability parameters, which are generated by the load balancing algorithm after receiving the basic evaluation data; Determine whether the device load status exceeds the preset threshold, if the device load status exceeds the preset threshold, use the task allocation engine to re-plan the collaboration task, and classify the to-be-processed tasks according to the collaboration capability parameters to obtain a preliminary task allocation scheme; According to the preliminary task allocation scheme, the integrity verification tool is used for rationality verification, and if the verification result meets the preset standard, the final collaboration task allocation result is generated.

8. The UWB technology-based wearable monitoring method according to claim 1, wherein, The step of strengthening real-time monitoring capability by continuously tracking the collaboration task allocation result, obtaining operation data of devices in complex environments, and determining the final system optimization configuration includes: Receiving execution state information carrying a cooperative task allocation identifier, the execution state information being generated by a task tracker during monitoring; Triggering a monitoring enhancement instruction to a data collector according to an exception identifier in the execution state information, the data collector generating deep sampling data containing an environmental complexity parameter according to the monitoring enhancement instruction; Obtaining a comprehensive data set with device running indicators and cooperative response times, the comprehensive data set being generated by a configuration generator after receiving the deep sampling data; According to the comprehensive data set, determining whether the inter-device cooperation efficiency is lower than a preset threshold, if the cooperation efficiency is lower than the preset threshold, using a stability verification tool to simulate test the candidate configuration scheme, and according to the response time parameter in the test result, adaptively checking the optimized configuration to obtain the final device running optimization configuration.

9. A wearable monitoring system based on UWB technology for performing the wearable monitoring method based on UWB technology according to any one of claims 1 to 8, characterized in that Comprise: A preliminary evaluation result acquisition module (10) is used for collecting the connection state of the device in the dynamic environment in real time through a pre-established signal strength detection mechanism, obtaining fluctuation data in the signal coverage range, and obtaining a preliminary evaluation result of signal connection stability; A signal enhancement demand determination module (20) is used for determining the signal enhancement demand of the key node by prioritizing the areas with signal strength lower than the preset threshold according to the preliminary evaluation result of signal connection stability, obtaining the device connection path to be optimized, and determining the signal enhancement demand of the key node; A signal transmission link acquisition module (30) is used for dynamically adjusting the connection path between devices by analyzing the signal enhancement demand of the key node and using a signal optimization algorithm to obtain an optimized signal transmission link; A backup connection channel acquisition module (40) is used for activating the connection interruption recovery mechanism to obtain a stable backup connection channel through rapid switching of the backup path if the signal transmission link is still at risk after adjustment; A potential delay problem judgment module (50) is used for monitoring the role switching efficiency between devices in real time according to the activation of the backup connection channel, obtaining dynamic change data of role allocation, and judging potential delay problems in the role switching process; A data integrity state acquisition module (60) is used for optimizing the information consistency guarantee in the switching process by analyzing the potential delay problems in the role switching process and using a data synchronization accuracy verification method to obtain the data integrity state after synchronization; A cooperative task allocation result acquisition module (70) is used for updating the overall evaluation of system running reliability according to the feedback of the data integrity state, obtaining the cooperative task allocation result through dynamic adjustment of the multi-device cooperation capability; A system optimization configuration determination module (80) is used for strengthening the real-time monitoring capability by continuously tracking the cooperative task allocation result, obtaining the running data of the device in the complex environment, and determining the final system optimization configuration.

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