Software development and updating system for wearable biological monitoring equipment

Abstract

The invention discloses a wearable biological monitoring equipment-oriented software development and updating system, which relates to the technical field of wearable intelligent equipment, and comprises a performance baseline acquisition module for acquiring current hardware configuration data and software version information of equipment, determining an initial constraint condition of function dynamic modification, obtaining an equipment performance baseline, and updating the equipment performance baseline; the function boundary judgment module is used for extracting updating frequency and storage space occupation data from historical firmware updating records, judging the matching degree of firmware updating capacity and new demand suitability and determining the range boundary of an extensible function; according to the wearable biological monitoring equipment-oriented software development and updating system, dynamic expansion and optimization of functions can be realized while the performance and the service life of the equipment are ensured, and the adaptability and the use efficiency of the equipment are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wearable intelligent devices, and in particular to a software development and updating system for wearable biological monitoring devices. Background Art

[0002] Amid the rapid development of intelligent technology, wearable devices, as a vital bridge connecting humans with the digital world, play an irreplaceable role in health monitoring, fitness tracking, personalized healthcare, and real-time data analysis. As users' demands for functional flexibility and long-term reliability continue to rise, wearable devices face increasingly complex technical challenges, particularly in device lifecycle management and the dynamic evolution of functionality.

[0003] Existing technologies mainly respond to the diversity of user needs through hardware preset functions or subsequent firmware updates. Among them, hardware design generally adopts a fixed architecture, which has limited flexibility in function expansion and is difficult to adapt to changing usage scenarios and personalized needs; and although firmware updates provide a certain degree of function upgrade capabilities, they are often constrained by device storage space and computing resources, limiting the breadth and depth of function iteration. In addition, there is currently a lack of effective mechanisms for unified understanding and classification of user software modification requirements, making it difficult for developers to accurately obtain user intentions and make targeted responses, further affecting the efficiency and effectiveness of system upgrades. In actual applications, the lack of flexibility in dynamic function adjustment, the dual impact of device life on hardware aging and software update mismatch, and the lack of a mechanism to understand software modification requirements have become key issues restricting the further intelligent and personalized development of existing wearable devices. Summary of the Invention

[0004] The purpose of the present invention is to provide a software development and update system for wearable biological monitoring devices to solve the problems existing in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a software development and update method for wearable biomonitoring devices, comprising:

[0006] The performance baseline acquisition module obtains the device's current hardware configuration data and software version information, determines the initial constraints for dynamic function modification, and obtains the device performance baseline;

[0007] The function boundary judgment module extracts update frequency and storage space usage data from historical firmware update records, determines the degree of match between the firmware update capability and the adaptability of new requirements, and determines the scope and boundaries of the expandable functions;

[0008] The intent analysis module obtains user usage data and log records of software modification intentions, performs cluster analysis on the intentions, and obtains the priority ranking of new demand adaptability;

[0009] The solution generation module uses a preset threshold to evaluate the difficulty of implementing dynamic function modification. If the difficulty is lower than the threshold, it generates a corresponding software upgrade matching solution and obtains a dynamic modification instruction set.

[0010] The lifespan assessment module extracts characteristic parameters of hardware aging from equipment runtime data, calculates the potential space for extending service life, and obtains a lifespan optimization adjustment strategy;

[0011] The resource optimization module obtains version compatibility data for the current software upgrade, compares and analyzes the interaction between firmware update capabilities and the impact of hardware aging, and determines an optimized allocation plan to address resource waste.

[0012] The configuration reallocation module reallocates resources with insufficient computing power and limited storage space, generates a runtime configuration that supports the adaptability of new requirements, and obtains an execution plan for device function expansion;

[0013] The stability analysis module extracts the performance change trend after dynamic function modification from real-time monitoring data, analyzes and determines the stability of service life extension and software upgrade matching, and obtains the final equipment optimization parameters.

[0014] Preferably, the performance baseline acquisition module acquires the current hardware configuration data and software version information of the device, determines the initial constraint conditions for dynamic function modification, and obtains the device performance baseline including:

[0015] Obtain device information and obtain a preliminary data set by parsing the hardware configuration and software version. Determine the design limit range by analyzing the correspondence between the hardware configuration and computing power in the preliminary data set. Use a linear regression algorithm to obtain a performance baseline value based on the design limit range and computing power. Extract constraints from the performance baseline value. Determine the dynamic adjustment direction by comparing the software version with the functional modification requirements. If the dynamic adjustment direction exceeds the design limit range, use a preset threshold to filter and obtain the adjusted constraints. Based on the adjusted constraints and device information, use a decision tree algorithm to determine the implementation path of the functional modification. Generate a dynamic adjustment plan based on the implementation path and the performance baseline value to determine the final functional configuration.

[0016] Preferably, the function boundary judgment module extracts update frequency and storage space occupancy data from historical firmware update records, judges the degree of compatibility between the firmware update capability and the new requirements, and determines the scope boundary of the expandable function, including:

[0017] The original data of update frequency and storage space are obtained from historical records, and time series analysis is used to determine the changing trend of update frequency. By comparing the changing trend with the new requirements, it is judged whether the update frequency meets the adaptation requirements. The historical distribution of storage space and occupancy data is obtained, and the correlation between the two is analyzed to obtain the dynamic changes of occupancy data. If the dynamic changes of occupancy data exceed the preset threshold, the upper limit of the range boundary is determined by the remaining amount of storage space. Based on the correspondence between firmware updates and capability analysis, regression analysis is used to determine the adaptability of the current firmware update to the new requirements. According to the superposition results of the adaptation degree and the range boundary, the specific boundary value of scalability is determined. The matching degree between the scalability boundary value and the output of the correlation analysis is used to determine the long-term trend of firmware updates and the new requirements.

[0018] Preferably, the intention analysis module obtains log records of user usage data and software modification intentions, performs cluster analysis on the intentions, and obtains a priority ranking of new requirement adaptability, including:

[0019] Obtain log records from the system, extract user data and modification intentions, and obtain the original data set. Perform cluster analysis on the modification intentions in the original data set using a preset classification algorithm to obtain a set of intent categories. For the set of intent categories, use statistical tools to calculate the frequency of occurrence of each category to obtain a frequency distribution result. If the frequency of a category in the frequency distribution result exceeds the preset threshold, it will be marked as a high-frequency intention to obtain a high-frequency intention list. Based on the high-frequency intention list, analyze the degree of match between each intention and the new requirement to obtain an adaptability score set. By arranging the adaptability score set in descending order, determine the priority ranking of the new requirement and obtain the final sorting result. Obtain the final sorting result, record and store it through the system, and output it to the specified database.

[0020] Preferably, the solution generation module uses a preset threshold to evaluate the difficulty of implementing dynamic modification of functions. If the difficulty is lower than the threshold, a corresponding software upgrade matching solution is generated, and the obtained dynamic modification instruction set includes:

[0021] The order of function modification is determined by priority sorting, and a sorted function list is obtained. A preset threshold is used to perform difficulty assessment on the sorted function list to obtain a difficulty assessment result. If the difficulty assessment result is lower than the preset threshold, a dynamic implementation plan is generated for the function modification to obtain an implementation plan set. Software upgrade requirements are extracted from the implementation plan set to generate a matching solution set. A dynamic instruction prototype is constructed based on the matching solution set to obtain an instruction prototype set. The dynamic implementation plan is optimized through the instruction prototype set to generate a dynamically modified instruction set. The evaluation result is verified for the dynamically modified instruction set to obtain a final instruction set.

[0022] Preferably, the life assessment module extracts characteristic parameters of hardware aging from the equipment runtime data, calculates the potential space for extending the service life, and obtains the life optimization adjustment strategy including:

[0023] The runtime data is obtained by modifying the instruction set, and characteristic parameters related to hardware aging are extracted from the runtime data to obtain a first data set. Regression analysis is used to calculate the characteristic parameter change trend based on the first data set to obtain a parameter change model. The potential impact of hardware aging on life extension is analyzed through the parameter change model to obtain a life impact space. The key interval in the potential space is judged based on the life impact space to obtain an optimized adjustment range. A corresponding adjustment strategy is generated for the optimized adjustment range to obtain a first strategy set. The instruction set is dynamically modified through the first strategy set to obtain updated runtime data. New characteristic parameters are extracted based on the updated runtime data to obtain a second data set and verify the life optimization adjustment strategy.

[0024] Preferably, the resource optimization module obtains version compatibility data matching the current software upgrade, and determines an optimization allocation solution for the resource waste problem by comparing and analyzing the interaction between firmware update capability and hardware aging effects, including:

[0025] Through the life optimization strategy, the version compatibility data of the software upgrade is obtained from the preset database to obtain the compatibility status of the current system match. Based on the obtained compatibility status, the firmware update capability and the hardware aging data are compared to determine the degree of interaction between the two. The judgment result is used to analyze the impact of the interaction through the preset threshold to determine the restriction range of hardware aging on firmware updates. For the restriction range, the distribution data of resource waste is obtained to obtain the deviation value of resource allocation. Based on the deviation value, the optimization allocation algorithm is used to adjust the strategy to determine the correction plan for resource waste. According to the correction plan, the adjusted firmware update capability is compared and analyzed to determine the improvement range of life optimization. The improvement range data is obtained. The long-term interaction trend is predicted through the machine learning model to determine the final optimization allocation plan.

[0026] Preferably, the configuration reallocation module reallocates resources with insufficient computing power and limited storage space, generates a runtime configuration that supports adaptability to new requirements, and obtains an execution plan for device function expansion, including:

[0027] The usage status of computing power and storage space is obtained through the dynamic scheduling algorithm, and the distribution of resource constraints is obtained. According to the distribution of resource constraints, the computing power and storage space are adjusted by the optimization allocation strategy to generate a preliminary allocation plan. Based on the preliminary allocation plan and the new demand, it is judged whether the adaptability meets the requirements. If not, the resources are reallocated through the dynamic scheduling algorithm to obtain the updated allocation plan. After obtaining the updated allocation plan, a runtime configuration that supports the new demand is generated to determine the integrity of the configuration. Through the runtime configuration, the expansion scope of the device function is judged to obtain the execution sequence of the function expansion. According to the execution sequence, the computing power and storage space are monitored using preset thresholds to determine the stability of the execution plan and obtain the execution plan with determined stability. The adaptability of dynamic scheduling and optimized allocation is verified through simulation operation to obtain the final execution plan.

[0028] Preferably, the stability analysis module extracts the performance change trend after the dynamic modification of the function from the real-time monitoring data, analyzes and determines the stability of the service life extension and the matching of the software upgrade, and obtains the final equipment optimization parameters including:

[0029] Real-time monitoring data is collected through sensors, and data cleaning technology is used to process outliers to obtain an initial data set after dynamic functional modification. Performance change characteristics are extracted from the initial data set, and time series analysis methods are used to decompose the change trend to obtain the periodic component of performance change. Based on the periodic component of performance change, the predicted value of service life extension is calculated, and its degree of match with the preset threshold is judged to determine the life assessment result. The operation log after the software upgrade is obtained, and the long-term stable indicator data in the log is analyzed to obtain the stability parameters of the upgraded system.

[0030] Preferably, the stability analysis module extracts the performance change trend after the dynamic modification of the function from the real-time monitoring data, analyzes and determines the stability of the service life extension and the matching of the software upgrade, and obtains the final equipment optimization parameters, which also includes:

[0031] By comparing the stability parameters with the life assessment results, the correlation between the two is judged and the adjustment direction of equipment optimization is determined. A linear regression algorithm is used to fit the relationship between real-time monitoring data and performance changes in the adjustment direction to obtain the final parameters for equipment optimization. The key weight values are extracted from the final parameters. Through iterative updates and dynamic modification strategies, the applicability of the parameters is judged to obtain the optimized operating configuration.

[0032] It can be seen from the above technical solution that the present invention has the following beneficial effects:

[0033] This software development and update system for wearable biomonitoring devices analyzes device hardware configuration, software version, and user usage data to determine the constraints and scalability of function modifications. Based on the priority of adaptability to new requirements, it evaluates the difficulty of function modifications and generates upgrade plans. At the same time, it considers the impact of hardware aging, calculates the potential for extending service life, and formulates optimization strategies. The present invention also uses a dynamic scheduling algorithm to reallocate computing and storage resources to generate a runtime configuration that supports new requirements. Finally, by analyzing the performance change trend after function modification and judging the long-term stability, the final device optimization parameters are obtained. This method can achieve dynamic expansion and optimization of functions while ensuring device performance and lifespan, thereby improving the adaptability and usage efficiency of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is the module connection diagram of the present invention. DETAILED DESCRIPTION

[0035] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0036] like Figure 1 As shown, the present invention provides a technical solution: a software development and update method for wearable biological monitoring devices, comprising:

[0037] The performance baseline acquisition module obtains the device's current hardware configuration data and software version information, determines the initial constraints for dynamic function modification, and obtains the device performance baseline;

[0038] The function boundary judgment module extracts update frequency and storage space usage data from historical firmware update records, determines the degree of match between the firmware update capability and the adaptability of new requirements, and determines the scope and boundaries of the expandable functions;

[0039] The intent analysis module obtains user usage data and log records of software modification intentions, performs cluster analysis on the intentions, and obtains the priority ranking of new demand adaptability;

[0040] The solution generation module uses a preset threshold to evaluate the difficulty of implementing dynamic function modification. If the difficulty is lower than the threshold, it generates a corresponding software upgrade matching solution and obtains a dynamic modification instruction set.

[0041] The lifespan assessment module extracts characteristic parameters of hardware aging from equipment runtime data, calculates the potential space for extending service life, and obtains a lifespan optimization adjustment strategy;

[0042] The resource optimization module obtains version compatibility data for the current software upgrade, compares and analyzes the interaction between firmware update capabilities and the impact of hardware aging, and determines an optimized allocation plan to address resource waste.

[0043] The configuration reallocation module reallocates resources with insufficient computing power and limited storage space, generates a runtime configuration that supports the adaptability of new requirements, and obtains an execution plan for device function expansion;

[0044] The stability analysis module extracts the performance change trend after dynamic function modification from real-time monitoring data, analyzes and determines the stability of service life extension and software upgrade matching, and obtains the final equipment optimization parameters.

[0045] This approach focuses on ensuring operational stability and functional flexibility for wearable biomonitoring devices. First, a performance baseline acquisition module collects the device's current hardware configuration parameters (such as CPU frequency, memory capacity, and sensor response frequency) and software version information to establish a set of performance benchmarks that serve as a constraint for subsequent dynamic functional modifications and system tuning. This performance baseline not only reflects the device's current capability boundaries but also provides initial input for bandwidth and resource allocation for functional upgrades. The functional boundary determination module, based on a historical firmware update database, extracts resource usage changes (such as ROM usage growth rate and OTA transmission latency) and update frequency trends associated with each update. By constructing a multidimensional mapping model, it quantifies the firmware's iterative capabilities and assesses its compatibility with current demand changes, ultimately defining a reasonable and safe functional expansion boundary. The intent analysis module leverages natural language processing and user behavior modeling to mine software modification intentions from user operation logs, usage frequency, and function call paths. Clustering algorithms (such as K-means and DBSCAN) are then used to categorize different user needs into a representative set of demand intentions. Next, the priority ranking of each requirement is calculated based on user group preferences and device adaptability, providing a structured reference for update decisions. Upon receiving high-priority intents, the solution generation module employs static analysis and resource demand prediction models to quantify the complexity of each dynamic modification operation. If the assessed operational difficulty (e.g., code refactoring requirements, interface rebinding overhead, etc.) falls below a preset threshold, the software upgrade path construction process is triggered, generating a structured dynamic modification instruction set that supports module hot swapping, version switching, and incremental updates. The lifespan assessment module extracts hardware aging-related parameters from runtime signals such as system load, power management data, and thermal sensor responses. By establishing device aging curves and performance degradation models, it predicts the service life extension capability under the current state, providing guidance for software-level functional load reduction or energy consumption optimization strategies. The resource optimization module integrates version compatibility information with hardware status feedback to construct an interaction map, identify resource bottlenecks (e.g., unused service processes, unreleased memory resources), and design a resource reallocation solution that includes memory compression, task scheduling optimization, and cache cleanup. Based on the aforementioned optimization recommendations, the configuration reallocation module employs heuristic scheduling or genetic algorithms to rebuild the operating configuration under resource-constrained conditions, prioritizing low-priority tasks or non-critical functions over high-demand adaptive operations, thereby ensuring the smooth implementation of the equipment expansion plan. Finally, the stability analysis module continuously monitors the equipment's operating status, analyzing power consumption fluctuations, response time changes, and system load trends caused by dynamic modifications. It then constructs a long-term joint performance-lifespan evaluation model to ensure a positive feedback loop between software upgrades and equipment stability and lifespan optimization. Ultimately, it outputs comprehensive optimized parameters for the equipment for maintenance or the next round of development.

[0046] This method can effectively improve the software flexibility and maintainability of wearable biomonitoring devices during practical applications, enabling on-demand dynamic updates through multi-dimensional analysis of user behavior and device status. It also improves system functional scalability while ensuring that hardware performance boundaries are not breached. By introducing a service life assessment and resource optimization mechanism, it significantly reduces the performance burden and energy waste associated with updates, improving the overall life cycle of the device and user satisfaction. The modular update decision-making mechanism also improves development efficiency and compatibility control capabilities, facilitating responses to changing demands for device products across different life cycles and user scenarios.

[0047] For example, a wearable heart rate monitoring wristband for elderly users initially supported only static heart rate monitoring and scheduled data synchronization. However, when users frequently activated exercise mode and repeatedly requested sleep respiratory rate monitoring within the app, the intent analysis module identified "exercise monitoring" and "nighttime breathing awareness" as the most representative new requirements. The performance baseline acquisition module determined that the device currently had 8MB of remaining storage and a CPU idle rate of approximately 40%, meeting the basic requirements for dynamic expansion. The functional boundary assessment module, based on previous OTA upgrade history, assessed the average firmware update frequency to be once every 45 days, with an average resource usage increase of 2MB, and determined that this requirement could be implemented within the safety boundary. Based on the evaluation results of functional module encapsulation and data flow reconstruction, the solution generation module confirmed that the difficulty of functional modification was below the set threshold, triggering the integration of the sleep monitoring module and optimizing the sensor sampling frequency. Simultaneously, the lifespan assessment module detected a rapid decline in the device's battery health and, through a power consumption control strategy, set the nighttime monitoring frequency to once every 10 minutes, balancing functional requirements with lifespan. Ultimately, the stability analysis module confirmed that the system response delay did not change significantly after the update, and the power consumption increase was controlled within 3%, verifying the safety and practicality of the upgrade strategy.

[0048] The performance baseline acquisition module obtains the device's current hardware configuration data and software version information, determines the initial constraints for dynamic function modification, and obtains the device performance baseline, including:

[0049] Obtain device information and obtain a preliminary data set by parsing the hardware configuration and software version. Determine the design limit range by analyzing the correspondence between the hardware configuration and computing power in the preliminary data set. Use a linear regression algorithm to obtain a performance baseline value based on the design limit range and computing power. Extract constraints from the performance baseline value. Determine the dynamic adjustment direction by comparing the software version with the functional modification requirements. If the dynamic adjustment direction exceeds the design limit range, use a preset threshold to filter and obtain the adjusted constraints. Based on the adjusted constraints and device information, use a decision tree algorithm to determine the implementation path of the functional modification. Generate a dynamic adjustment plan based on the implementation path and the performance baseline value to determine the final functional configuration.

[0050] This implementation transforms the performance baseline acquisition process into a quantifiable and predictable structured process by introducing data analysis and algorithmic models. First, the module collects hardware configuration information (such as the main processor model, memory capacity, and battery specifications) and the current firmware or application version from the device to form a preliminary data set. This data set reflects the device's basic operating capabilities and historical software environment, providing a static context for functional adaptation. Next, by mapping the hardware configuration to its performance output, the device's constraints in terms of processing power, storage load, and other aspects are identified. This process uses a linear regression algorithm to model performance under different hardware parameters, generating representative performance baseline values. Constraints are then extracted from these values to serve as a reference for dynamic functional adjustments. When the software requires a new functional modification, the system compares the required load characteristics with the existing software version status to derive the required adjustment direction. If the derived dynamic adjustment direction exceeds the design limit range defined by the original regression model, a preset threshold filtering mechanism is activated to eliminate overloaded paths and redefine a set of adjusted constraints. The system then uses a decision tree algorithm to comprehensively determine the available functional modules, resource distribution status, and upgrade options to generate the optimal functional modification path. Finally, by combining the performance baseline and function implementation path output by the regression model, a safe and efficient dynamic adjustment plan is constructed to obtain the final functional configuration, ensuring that the modified system can run smoothly under the original hardware architecture.

[0051] This implementation significantly improves the responsiveness and accuracy of performance constraints during device software modification. By introducing linear regression algorithms and decision tree models, efficient modeling of complex hardware-software interactions is achieved, making performance predictions more accurate. Compared with traditional estimation methods based on manual rules, this method supports automatic judgment of design constraints and reasonable adjustment directions, and can effectively avoid system crashes or resource waste caused by overloading of modification solutions. Its structured path generation mechanism also improves the success rate of modification execution, making it suitable for various wearable devices with limited hardware resources and has strong versatility and reliability.

[0052] For example, a user requested real-time blood oxygen monitoring for a smart sports wristband with multi-sensor collaborative monitoring capabilities to support high-intensity mountaineering. The system analyzed the current device configuration using the performance baseline acquisition module and identified a low- to medium-power main control chip with 1MB of memory and running software version v2.1. Preliminary data revealed that the device previously supported only periodic heart rate sampling and acceleration monitoring, resulting in resource constraints. Using a linear regression algorithm to model the concurrent processing capabilities of sensors in previous versions, the system determined that the blood oxygen function could only sample once every 10 seconds under the existing architecture. The system further compared this requirement with the functional support boundaries of version v2.1 and determined that the real-time sampling frequency exceeded the design limit. This triggered a preset threshold filtering mechanism, adjusting the sampling period to 20 seconds and introducing a dynamic resource scheduling mechanism. Finally, a decision tree algorithm determined that this function should be deployed in conjunction with other sensors by reducing their usage. A dynamic adjustment plan was generated, enabling the integration and configuration update of the blood oxygen monitoring function without hardware replacement, significantly improving the product's adaptability to professional sports environments.

[0053] The function boundary judgment module extracts update frequency and storage space usage data from historical firmware update records to determine the degree of compatibility between the firmware update capability and the new requirements. It also determines the scope and boundaries of the expandable functions, including:

[0054] The original data of update frequency and storage space are obtained from historical records, and time series analysis is used to determine the changing trend of update frequency. By comparing the changing trend with the new requirements, it is judged whether the update frequency meets the adaptation requirements. The historical distribution of storage space and occupancy data is obtained, and the correlation between the two is analyzed to obtain the dynamic changes of occupancy data. If the dynamic changes of occupancy data exceed the preset threshold, the upper limit of the range boundary is determined by the remaining amount of storage space. Based on the correspondence between firmware updates and capability analysis, regression analysis is used to determine the adaptability of the current firmware update to the new requirements. According to the superposition results of the adaptation degree and the range boundary, the specific boundary value of scalability is determined. The matching degree between the scalability boundary value and the output of the correlation analysis is used to determine the long-term trend of firmware updates and the new requirements.

[0055] This implementation aims to scientifically determine the feasibility and boundaries of device feature expansion through historical data modeling. The system first retrieves past firmware upgrade records from the device backend or OTA platform, extracting the time of each update and the corresponding storage usage increment to form a raw data sequence. Time series analysis models (such as ARIMA or exponential smoothing) are used to assess long-term trends in update frequency, determining whether system updates are slowing, accelerating, or stabilizing. This trend is then compared with upcoming functional requirements to determine whether the device's update capacity is sufficiently high. For storage usage analysis, the system performs histogram distribution statistics on the raw usage data to identify dynamic changes in space usage with version updates, and incorporates sliding window techniques to track short-term fluctuations in resource usage. If this fluctuation exceeds a pre-defined system tolerance threshold, the current remaining storage space is used as the upper bound for the expansion range to prevent system instability caused by resource overruns. To further improve the accuracy of this assessment, the system also incorporates regression analysis to build a fitting model between "firmware update capability" and "new requirement resource usage characteristics," outputting a compatibility score based on the current version. Finally, the scalability boundary value is generated by superimposing the adaptation degree score and the spatial boundary to assist in judging whether the long-term update trend can support the new functions, thereby providing a reliable basis for the access decision of the software module.

[0056] This solution combines time series and regression analysis techniques to quantitatively assess the relationship between system update capabilities and new requirements, avoiding the errors caused by traditional reliance on manual estimation or empirical judgment. It effectively provides early warning of resource shortage risks, allows for pre-planning of feature and version cadence, and improves the scientific nature of software updates and system stability. Furthermore, this method enables device manufacturers to develop customized update strategies based on the characteristics of different device models and user groups, achieving precise system-level control and resource conservation, and enhancing the software system's lifecycle management capabilities.

[0057] For example, a manufacturer of a children's smart wristband planned to add voice announcement and weather notification features to enhance the user experience. The system reviewed OTA firmware update records from the past 12 months and found that the update frequency had decreased from an average of once a month to once a quarter, while the average storage usage had increased from 1.2MB to 2.7MB. Time series analysis revealed that the current update frequency was insufficient to support frequent feature modifications, while only 4MB of storage remained. The system determined that introducing a voice module would require an additional 2.5MB of storage, exceeding the warning threshold. Regression analysis indicated that the device's firmware structure was relatively stable, with medium to weak adaptability to new features. Ultimately, the function boundary determination module generated a set of boundary values and recommended only implementing the low-resource weather text notification feature, temporarily deferring the voice announcement module. This process ensured system security for new feature deployment while maximizing the efficiency of existing resources.

[0058] The intent analysis module obtains user usage data and log records of software modification intentions, performs cluster analysis on the intentions, and obtains the priority ranking of new requirement adaptability, including:

[0059] Obtain log records from the system, extract user data and modification intentions, and obtain the original data set. Perform cluster analysis on the modification intentions in the original data set using a preset classification algorithm to obtain a set of intent categories. For the set of intent categories, use statistical tools to calculate the frequency of occurrence of each category to obtain a frequency distribution result. If the frequency of a category in the frequency distribution result exceeds the preset threshold, it will be marked as a high-frequency intention to obtain a high-frequency intention list. Based on the high-frequency intention list, analyze the degree of match between each intention and the new requirement to obtain an adaptability score set. By arranging the adaptability score set in descending order, determine the priority ranking of the new requirement and obtain the final sorting result. Obtain the final sorting result, record and store it through the system, and output it to the specified database.

[0060] This implementation utilizes user behavior logs and data mining techniques to establish an automated decision-making chain between feature requirement generation and prioritization. The system retrieves log data from user terminals or cloud servers, extracting information such as user operation paths, button click records, exception feedback, and feature call logs. It then identifies the semantic structure of feature modification or addition requests, generating a raw dataset containing user behavior and intent expressions. This dataset is then classified using a pre-defined clustering algorithm (such as K-means, hierarchical clustering, or DBSCAN density clustering) to identify user-proposed feature change intents and generate a set of clear intent categories. The system then counts the frequency of user appearances in each category and constructs a frequency distribution map. Categories with a frequency exceeding a system-defined threshold (e.g., 15% of users repeatedly mentioning them) are labeled "high-frequency intents" and a list of high-frequency intents is generated. Next, the system combines the current new requirement characteristics (e.g., functional module relevance, resource call similarity, target user group overlap, etc.) with the high-frequency intents for matching analysis, generating a set of compatibility scores based on a pre-defined model (e.g., cosine similarity, association rule analysis, or content tag intersection). The scoring results are sorted in descending order by numerical value to create a prioritized list of requirements, which serves as an important input for subsequent feature development decisions. The ranking results are structured and stored in a database on the system side to support cross-departmental sharing and subsequent version iteration management, and can be used for future demand trend forecasting or user behavior research.

[0061] This solution proactively detects changing trends in user needs and scientifically prioritizes feature development using statistics and algorithms, avoiding wasted development resources and directional deviations. Compared to traditional manual research methods, cluster analysis and adaptability scoring models provide a more timely and accurate basis for judgment. The dynamic recognition mechanism for high-frequency intent helps capture potential market hotspots or user pain points, thereby guiding rapid product iteration and improving user satisfaction. Furthermore, while ensuring the automation of the decision-making process, this solution also provides a reliable tool foundation for data-driven product strategy development.

[0062] For example, in a smart health watch mainly for elderly users, the system detected that a large number of users frequently tried to use voice control to call medication reminders during use, but failed to respond because the current version only supported touch-screen operations. Log record analysis showed that the relevant voice operation intentions appeared more than 500 times within two weeks, accounting for 28% of the total operation logs. The clustering model classified it as a "voice-assisted operation" category and marked it as a high-frequency intention. The adaptability scoring model further analyzed the correlation between this category and the "voice reminder function" and scored 0.91 (out of a full score of 1.0), which was listed as the highest priority requirement by the system. Ultimately, the ranking results were stored and synchronized to the development team platform, providing clear data support for the priority introduction of the voice reminder function in the next version, enhancing the product's service capabilities for specific user groups.

[0063] The solution generation module uses a preset threshold to evaluate the difficulty of implementing dynamic function modification. If the difficulty is lower than the threshold, a corresponding software upgrade matching solution is generated. The dynamic modification instruction set obtained includes:

[0064] The order of function modification is determined by priority sorting, and a sorted function list is obtained. A preset threshold is used to perform difficulty assessment on the sorted function list to obtain a difficulty assessment result. If the difficulty assessment result is lower than the preset threshold, a dynamic implementation plan is generated for the function modification to obtain an implementation plan set. Software upgrade requirements are extracted from the implementation plan set to generate a matching solution set. A dynamic instruction prototype is constructed based on the matching solution set to obtain an instruction prototype set. The dynamic implementation plan is optimized through the instruction prototype set to generate a dynamically modified instruction set. The evaluation result is verified for the dynamically modified instruction set to obtain a final instruction set.

[0065] This implementation establishes a complete evaluation-screening-planning-generation-verification closed loop to control the complexity and automate the dynamic function modification process. First, the system obtains the function priority ranking results output by the intent analysis module and generates a function modification list based on this ranking. Each function modification is input into the difficulty assessment module, which performs a multi-dimensional analysis based on preset thresholds and a historical modification cost model. Key reference indicators include the number of interface dependencies, predicted resource usage, coupling with the existing architecture, and potential impact on system stability. If the assessment result of a function modification falls below the set risk tolerance threshold (e.g., a complexity score less than 0.4), it is considered eligible for dynamic integration and the system enters the dynamic implementation planning phase, generating an implementation plan set containing elements such as the execution path, resource calls, and interface mapping. After demand aggregation and version matching, the system automatically generates a set of feasible software upgrade solutions. Subsequently, the system constructs dynamic instruction prototypes corresponding to each solution based on standardized templates, forming a set of instruction prototypes. This set is further optimized by the rule engine to produce a final set of dynamic modification instructions with a reasonable structure and optimal resource scheduling. To ensure the effectiveness and security of the instruction set, the system also includes a backtesting and simulation module to verify the instruction set's performance in a simulated environment, including assessments of response latency, memory leak risks, and user experience impact. The system then outputs the verified instruction set for device updates or hot-swap loading.

[0066] This approach automates and standardizes traditional manual analysis and rule development processes, significantly improving the efficiency of software feature iteration. By introducing a "feature difficulty assessment - dynamic implementation plan generation - instruction prototyping - verification and feedback" process, it ensures the rationality of the update path and system compatibility, avoiding crashes or resource conflicts caused by insufficient analysis. This approach is particularly suitable for wearable device scenarios with high update frequency and extensive customization requirements, providing secure and rapid dynamic deployment capabilities for software modules, improving terminal operational efficiency and user experience.

[0067] For example, in a smart wristband positioned for chronic disease management, users hope to add medication intake reminders and automatic message push functions. According to the intent analysis module, this function ranks among the top three priorities. The system call solution generation module automatically evaluates the modification of this function and obtains a complexity score of 0.32, which is lower than the dynamic implementation threshold of 0.5 set by the system. Therefore, the system automatically generates an implementation plan, recognizing that this function can reuse the original notification system and time scheduling module, and only needs to add a small number of API interfaces. Then, a matching upgrade plan is constructed, and a set of dynamic modification instruction prototypes are formed. After template assembly, a complete instruction set is generated. A run test is carried out in a simulation environment to confirm that the response time delay is controlled within 100ms and there is no risk of system conflict. Finally, the system automatically deploys the instruction set and successfully implements the hot update of software functions, greatly improving the user experience and product adaptability.

[0068] The lifespan assessment module extracts characteristic parameters of hardware aging from the equipment runtime data, calculates the potential space for extending the service life, and obtains the lifespan optimization adjustment strategy including:

[0069] The runtime data is obtained by modifying the instruction set, and characteristic parameters related to hardware aging are extracted from the runtime data to obtain a first data set. Regression analysis is used to calculate the characteristic parameter change trend based on the first data set to obtain a parameter change model. The potential impact of hardware aging on life extension is analyzed through the parameter change model to obtain a life impact space. The key interval in the potential space is judged based on the life impact space to obtain an optimized adjustment range. A corresponding adjustment strategy is generated for the optimized adjustment range to obtain a first strategy set. The instruction set is dynamically modified through the first strategy set to obtain updated runtime data. New characteristic parameters are extracted based on the updated runtime data to obtain a second data set and verify the life optimization adjustment strategy.

[0070] This implementation dynamically assesses the system's aging and its potential for adjustable life extension by continuously tracking key hardware status parameters during device operation. The system first activates data acquisition channels using a dynamically modified runtime instruction set to extract characteristic parameters highly correlated with hardware aging, including CPU temperature, frequency fluctuations, battery cycle counts, charging rate variations, and memory error rates, forming a first dataset. This dataset is then input into a regression analysis model (such as polynomial regression, Bayesian regression, or an LSTM time series network) to fit the time-varying trends of these parameters and establish a parameter variation model. The model results predict the path and inflection points of hardware performance degradation and further derive the device's lifespan impact space—the range of lifespans that can still be extended through system optimization under the current state. Within this lifespan impact space, the system identifies the most critical performance degradation regions, such as those where high temperatures accelerate battery aging or those where frequent reads and writes shorten memory lifespan. For these critical regions, the system generates corresponding optimization adjustment ranges, such as reducing operating frequency, extending charging intervals, and reducing memory calls, and constructs a first set of policies. This first set of policies then applies to the dynamic instruction set to adjust system parameters, updating the device's operating status in real time and generating new runtime data. The system extracts the hardware aging characteristic parameters of the second round again to form a second data set, compares the changes in the two data sets, evaluates the changing trend of the life indicators after the strategy is executed, and verifies the effectiveness of the life optimization strategy.

[0071] This method enables continuous monitoring and dynamic control of hardware aging during equipment operation, breaking through the limitations of traditional reliance on static design lifespans. By coupling regression analysis with optimization strategies, not only can the path of lifespan degradation be predicted, but real-time intervention can also be made to delay its onset, significantly extending the overall equipment lifecycle. Furthermore, a system feedback mechanism can be used to continuously optimize model parameters, enhancing the personalization and environmental adaptability of control strategies, and providing equipment manufacturers with precise, data-driven maintenance solutions.

[0072] For example, in a wearable device used for remote medical monitoring, the system discovered through operational data analysis that the main control chip maintained a high load for extended periods during the nighttime heart rate detection phase, with the CPU core temperature remaining above 60°C for extended periods. A regression model predicted that continuing at the current load level would result in a 15% decrease in processor responsiveness within two months. The system identified the "high temperature-shortened lifespan" interval and developed a first strategy set of "nighttime frequency reduction + interval sampling." After the update, the instruction set controlled the CPU's nighttime operating frequency to drop from 1.2GHz to 0.8GHz, and the heart rate sampling frequency was adjusted from once per minute to once every two minutes. Two weeks after the strategy was implemented, the second data set showed that the average core temperature had dropped to 52°C, extending the predicted lifespan by approximately three months. This verification result confirms the effectiveness of the lifespan optimization strategy, ensuring the long-term and stable operation of medical equipment.

[0073] The resource optimization module obtains version compatibility data for the current software upgrade. By comparing and analyzing the interaction between firmware update capabilities and the impact of hardware aging, it determines the optimal allocation solution for resource waste, including:

[0074] Through the life optimization strategy, the version compatibility data of the software upgrade is obtained from the preset database to obtain the compatibility status of the current system match. Based on the obtained compatibility status, the firmware update capability and the hardware aging data are compared to determine the degree of interaction between the two. The judgment result is used to analyze the impact of the interaction through the preset threshold to determine the restriction range of hardware aging on firmware updates. For the restriction range, the distribution data of resource waste is obtained to obtain the deviation value of resource allocation. Based on the deviation value, the optimization allocation algorithm is used to adjust the strategy to determine the correction plan for resource waste. According to the correction plan, the adjusted firmware update capability is compared and analyzed to determine the improvement range of life optimization. The improvement range data is obtained. The long-term interaction trend is predicted through the machine learning model to determine the final optimization allocation plan.

[0075] This implementation analyzes the co-evolution of software and hardware systems to identify redundancies and conflicts in resource allocation, thereby optimizing firmware update strategies and resource allocation efficiency. First, based on the adjustment strategy generated by the lifespan assessment module, the system accesses a pre-set database to obtain version compatibility data for the current software upgrade path, confirms the device's currently supported upgrade range and compatibility modes, and constructs a version matching matrix. The system then compares this matrix with the latest hardware status data (including runtime load, battery degradation parameters, processor frequency stability, etc.) to identify the coupling strength between firmware update capabilities and hardware aging factors. Using an interaction model, it calculates whether hardware aging significantly limits certain update paths, such as whether a low battery health status hinders high-frequency I / O operations during the update process. This determination is fed into the interaction impact analysis module, which determines whether resource reallocation mechanisms should be initiated based on a set threshold (e.g., if aging impacts update efficiency by no more than 20%). If the triggering criteria are met, the system calls the resource distribution database to analyze the deviation distribution of existing system resources (such as memory usage, cache distribution, and process load) to identify areas of resource waste. Based on the deviation, the system initiates an optimization allocation algorithm (such as particle swarm optimization (PSO), simulated annealing, or reinforcement learning models) to generate a correction strategy that minimizes waste and enhances aging adaptability. After applying the strategy, the system re-measures the update capability, compares it with the original capability, calculates the lifetime optimization improvement, and uses this as model feedback. Finally, the system invokes a machine learning model (such as a random forest or LSTM sequence model) to predict the long-term evolution of resource update interaction structures based on firmware update history and hardware degradation trends. This output then produces the final optimized allocation plan, ensuring sustainable system updates and stable operation.

[0076] This approach overcomes the limitations of previous approaches, which relied on isolated update strategies and resource allocation, by precisely modeling the dynamic relationship between firmware updateability and hardware aging. By identifying resource waste and deploying intelligent optimization strategies, it not only improves software update efficiency but also significantly extends the overall lifespan of devices. This approach is particularly well-suited for the middle and later stages of a device's lifecycle, particularly those experiencing increasing aging and resource constraints. By leveraging predictive and adaptive mechanisms, it builds a dynamic, self-regulating system, enhancing system resilience and adaptability while providing scientific data support for long-term maintenance strategies.

[0077] For example, in a wearable device used for postoperative rehabilitation monitoring, the system detected that the device's battery health had dropped to 78%, and the CPU power consumption fluctuations increased, affecting the data verification process of multiple modules during the firmware upgrade. The resource optimization module called the version compatibility database to identify that there was a serious conflict between the modules in the v3.5 upgrade package that required high-frequency flash memory access and battery degradation. The system identified that the current cache usage rate was low and the resource waste rate of the background heart rate detection task was as high as 18%. Through the particle swarm optimization algorithm, task priorities and data write paths were reallocated, and some non-critical sensor calls were closed. The optimized update path shortened the upgrade time from 45 seconds to 28 seconds, while reducing system power consumption by 9%. The device is expected to operate stably for an additional 3 months. The predictive model further pointed out that this optimization can delay the system performance degradation trend by 22%, providing reliable protection for long-term operation and continuous updates.

[0078] The configuration reallocation module reallocates resources with insufficient computing power and limited storage space, generates a runtime configuration that supports the adaptability of new requirements, and obtains an execution plan for device function expansion, including:

[0079] The usage status of computing power and storage space is obtained through the dynamic scheduling algorithm, and the distribution of resource constraints is obtained. According to the distribution of resource constraints, the computing power and storage space are adjusted by the optimization allocation strategy to generate a preliminary allocation plan. Based on the preliminary allocation plan and the new demand, it is judged whether the adaptability meets the requirements. If not, the resources are reallocated through the dynamic scheduling algorithm to obtain the updated allocation plan. After obtaining the updated allocation plan, a runtime configuration that supports the new demand is generated to determine the integrity of the configuration. Through the runtime configuration, the expansion scope of the device function is judged to obtain the execution sequence of the function expansion. According to the execution sequence, the computing power and storage space are monitored using preset thresholds to determine the stability of the execution plan and obtain the execution plan with determined stability. The adaptability of dynamic scheduling and optimized allocation is verified through simulation operation to obtain the final execution plan.

[0080] This implementation enables the dynamic adaptation and secure deployment of new features in resource-constrained environments through intelligent scheduling and spatial reconfiguration of computing resources. First, the system invokes the dynamic scheduling module to collect real-time information about the device's current processor utilization, thread allocation status, storage usage, cache occupancy, and other information, creating a distribution map of resource constraints. This map visualizes resource bottlenecks (e.g., where a sensor task consumes excessive CPU time), providing a basis for developing subsequent optimization strategies. Based on the resource constraint distribution, the system invokes an optimized allocation strategy (such as a constrained optimization-based partition scheduling algorithm or a minimum load redistribution mechanism) to generate a preliminary allocation plan. This plan attempts to free up critical resources without adding new hardware by adjusting task priorities, compressing functional modules, and delaying the scheduling of non-critical processes. The system then compares the preliminary plan with the resource model required by the new requirements. If resource coverage is insufficient or conflicts are severe, a second round of resource rescheduling is triggered. The updated resource allocation plan is then used to construct the runtime configuration. The system analyzes the consistency and integrity of each configuration item (such as sensor sampling frequency, task execution period, buffer size, etc.) to ensure that it meets the device's operational constraints and expansion requirements. After the configuration passes the verification, the system will generate the execution sequence of the function expansion in sequence, clarifying the loading order and dependency of each new function. Before the plan is implemented, the system will also simulate and monitor each execution node based on preset thresholds (such as CPU occupancy not exceeding 70% and memory redundancy greater than 10%) to evaluate whether the resource status after the plan is run is stable. Finally, the overall adaptability is verified through dynamic scheduling simulation run tests in a simulated environment. If all monitoring items are qualified, the final function expansion execution plan will be output.

[0081] This approach establishes a flexible mapping between functional expansion and device resources, maximizing the utilization of computing and storage resources. Through a closed-loop scheduling-verification-correction mechanism, it ensures secure and reliable software upgrade deployment even under resource constraints. This approach is particularly suitable for low- and mid-range wearable devices with limited chip performance or storage. Furthermore, it enables personalized expansion configuration without compromising core functionality, enhancing product adaptability and flexibility for future upgrades.

[0082] For example, in a student health bracelet with only 64MB of memory, the user hopes to add a new "segmented recording of exercise periods" function, which requires high-frequency sampling and local caching of data within a specific time period. The system detects that 26MB of the current memory is occupied by the background heart rate trend analysis task, and the CPU usage remains at 82% all year round. The configuration reallocation module starts the dynamic scheduling mechanism, adjusts the heart rate analysis module to run at a low frequency at night, frees up about 8MB of memory, and allocates the high-load tasks of the exercise recording module to resource-free periods. The newly generated runtime configuration verifies through threshold monitoring that the CPU usage does not exceed 85%, and the memory margin retains 5MB. The simulation run shows that there is no conflict in function loading. The final output execution plan successfully supports the expanded deployment of new functions and improves the practicality and adaptability of the device.

[0083] The stability analysis module extracts the performance change trend after dynamic function modification from real-time monitoring data, analyzes and determines the stability of service life extension and software upgrade matching, and obtains the final equipment optimization parameters including:

[0084] Real-time monitoring data is collected through sensors, and data cleaning technology is used to process outliers to obtain an initial data set after dynamic functional modification. Performance change characteristics are extracted from the initial data set, and time series analysis methods are used to decompose the change trend to obtain the periodic component of performance change. Based on the periodic component of performance change, the predicted value of service life extension is calculated, and its degree of match with the preset threshold is judged to determine the life assessment result. The operation log after the software upgrade is obtained, and the long-term stable indicator data in the log is analyzed to obtain the stability parameters of the upgraded system.

[0085] This implementation analyzes the operational status of dynamically modified functions and systematically assesses their comprehensive impact on device lifespan and performance stability, ultimately forming a comprehensive optimization parameter output process. The system first collects operational data from the modified functions using various embedded sensors (such as temperature sensors, battery voltage monitors, and processor frequency trackers) to form an initial dataset. To improve data quality and modeling accuracy, the system employs anomaly detection and data cleaning algorithms (such as Z-Score filtering and local outlier factors) to address sensor errors and acquisition jitter, remove extreme values and discontinuities, and ensure data consistency and representativeness. From this cleaned data set, the system extracts multiple performance characteristics, such as response time variation, energy consumption fluctuations, and module call frequency. It then decomposes performance trends using time series analysis models (such as STL decomposition or Fourier transforms) to extract their cyclical components. These cyclical components reveal long-term patterns in the impact of dynamic function modifications on system performance. Based on this information, the system further assesses the potential for lifespan extension, calculates a predicted extension value, and compares it with a system-defined lifespan optimization threshold (e.g., an expected lifespan increase of ≥10%) to determine whether the current strategy meets the target. At the same time, the system extracts stability metrics (such as average run time without exceptions, number of restarts, and memory leak frequency) from the post-upgrade operation logs and structures them using a stability assessment model to generate a set of stability parameters for the upgraded system. Finally, the system integrates the lifespan assessment results with the stability parameters to comprehensively assess the positive or negative impact of the current software upgrade solution on the long-term operational capability of the equipment. The system then outputs a set of equipment optimization parameters to guide subsequent policy revisions or hardware replacement planning.

[0086] This method accurately assesses device operational status after dynamic function modifications, effectively mitigating the risks associated with performance fluctuations. By introducing periodic change modeling and operation log analysis, device operational trend assessment becomes more detailed and comprehensive. The extraction of system stability parameters, combined with lifespan prediction, enables intelligent upgrade value assessment and enhances device lifecycle management. The overall process is highly practical and automatable, providing a scientific basis for decision-making regarding the long-term stable operation of wearable devices.

[0087] For example, a new "automatic abnormality alarm" module was introduced in a blood glucose monitoring wristband designed for diabetics. This module periodically wakes the device and pushes notifications to the mobile app. After the feature was launched, the system collected real-time power consumption and response time data for two weeks and found that the average system response time delay after each alarm increased from 0.8 seconds to 1.2 seconds. Data cleaning and periodic analysis revealed that this delay has a clear fluctuation pattern with a 24-hour cycle, consistent with the daily wake-up frequency rhythm. Predictive analysis shows that the potential for life extension remains within an acceptable range (an 8% increase). The operation log found that the system restart rate remained below 0.2%, indicating that the functional integration did not undermine system stability. The final optimization parameters recommended extending the background residence period to reduce inefficient nighttime wake-ups, balancing functional effectiveness and system durability.

[0088] The stability analysis module extracts the performance change trend after dynamic function modification from real-time monitoring data, analyzes and determines the stability of service life extension and software upgrade matching, and obtains the final equipment optimization parameters including:

[0089] By comparing the stability parameters with the life assessment results, the correlation between the two is judged and the adjustment direction of equipment optimization is determined. A linear regression algorithm is used to fit the relationship between real-time monitoring data and performance changes in the adjustment direction to obtain the final parameters for equipment optimization. The key weight values are extracted from the final parameters. Through iterative updates and dynamic modification strategies, the applicability of the parameters is judged to obtain the optimized operating configuration.

[0090] Building on the aforementioned foundation, this implementation further enhances the accuracy and adaptability of device optimization strategies. The system obtains performance trends and stability parameters after dynamic function modification and, combined with previously generated lifespan assessment results, quantitatively analyzes the relationship between them. By cross-comparing stability parameters such as performance fluctuations, energy consumption curves, and response time changes with predicted lifespan extension values, the system determines whether performance improvements are positively impacting lifespan improvements. To clarify optimization targets, the system uses a linear regression algorithm to model the relationship between collected real-time monitoring data and various performance indicators, constructing an adjustment direction model. For example, by analyzing the correlation between CPU load and system stability parameters, it determines which performance improvement paths are most likely to extend lifespan. The model outputs a set of optimal adjustment parameters, reflecting the most effective optimized configuration under the current circumstances. From this final set of parameters, the system further extracts key weights—those parameters with the most significant impact on system performance and lifespan indicators, such as "percentage of high-temperature operation time" and "frequency of abnormal task wakeups." Based on these weights, the system optimizes the dynamic modification strategy through an adaptive iterative mechanism and adjusts the configuration logic in real time during subsequent updates. Ultimately, the module outputs a set of optimized operating configurations that take into account stability, performance, and lifespan, forming a closed-loop mechanism for personalized device tuning.

[0091] This method enables data-driven, dynamic, and evolving optimization of device systems, eliminating the reliance on static rules for parameter adjustment. The introduction of linear regression and weight extraction algorithms ensures that each optimization accurately responds to specific device states. Its iterative strategy design mechanism helps continuously improve system efficiency and slow hardware aging, significantly enhancing system adaptability and user experience consistency, providing a technical foundation for large-scale deployment and intelligent upgrades.

[0092] For example, in a health monitoring wristband designed for plateau environments, after integrating an altitude-adaptive algorithm, stability parameters indicated a slight increase in the data transmission error rate, while lifespan assessment data indicated a decrease in battery life due to ambient temperature fluctuations. Linear regression analysis revealed a negative correlation between "high-frequency sensor activation frequency" and "stability score," with a weight of 0.76. Based on this, the system reduced the sensor activation frequency and reconfigured the cache allocation logic. After two policy iterations, the transmission error rate decreased by 15% and the predicted battery life increased by approximately 9%. Based on these optimization results, the system automatically generated a new operating configuration, achieving stable operation and improved battery life in extreme plateau conditions.

[0093] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A software development and update method for wearable biological monitoring equipment, characterized in that: include: The performance baseline acquisition module obtains the device's current hardware configuration data and software version information, determines the initial constraints for dynamic function modification, and obtains the device performance baseline; The function boundary judgment module extracts update frequency and storage space usage data from historical firmware update records, determines the degree of match between the firmware update capability and the adaptability of new requirements, and determines the scope and boundaries of the expandable functions; The intent analysis module obtains user usage data and log records of software modification intentions, performs cluster analysis on the intentions, and obtains the priority ranking of new demand adaptability; The solution generation module uses a preset threshold to evaluate the difficulty of implementing dynamic function modification. If the difficulty is lower than the threshold, it generates a corresponding software upgrade matching solution and obtains a dynamic modification instruction set. The lifespan assessment module extracts characteristic parameters of hardware aging from equipment runtime data, calculates the potential space for extending service life, and obtains a lifespan optimization adjustment strategy; The resource optimization module obtains version compatibility data for the current software upgrade, compares and analyzes the interaction between firmware update capabilities and the impact of hardware aging, and determines an optimized allocation plan to address resource waste. The configuration reallocation module reallocates resources with insufficient computing power and limited storage space, generates a runtime configuration that supports the adaptability of new requirements, and obtains an execution plan for device function expansion; The stability analysis module extracts the performance change trend after dynamic function modification from real-time monitoring data, analyzes and determines the stability of service life extension and software upgrade matching, and obtains the final equipment optimization parameters.

2. A software development and update system for wearable biological monitoring devices according to claim 1, characterized in that: The performance baseline acquisition module obtains the current hardware configuration data and software version information of the device, determines the initial constraint conditions for dynamic function modification, and obtains the device performance baseline including: Obtain device information and obtain a preliminary data set by parsing the hardware configuration and software version. Determine the design limit range by analyzing the correspondence between the hardware configuration and computing power in the preliminary data set. Use a linear regression algorithm to obtain a performance baseline value based on the design limit range and computing power. Extract constraints from the performance baseline value. Determine the dynamic adjustment direction by comparing the software version with the functional modification requirements. If the dynamic adjustment direction exceeds the design limit range, use a preset threshold to filter and obtain the adjusted constraints. Based on the adjusted constraints and device information, use a decision tree algorithm to determine the implementation path of the functional modification. Generate a dynamic adjustment plan based on the implementation path and the performance baseline value to determine the final functional configuration.

3. The software development and update system for wearable biological monitoring devices according to claim 1, characterized in that: The function boundary judgment module extracts update frequency and storage space usage data from historical firmware update records, judges the degree of compatibility between the firmware update capability and the new requirements, and determines the scope boundary of the expandable function, including: The original data of update frequency and storage space are obtained from historical records, and time series analysis is used to determine the changing trend of update frequency. By comparing the changing trend with the new requirements, it is judged whether the update frequency meets the adaptation requirements. The historical distribution of storage space and occupancy data is obtained, and the correlation between the two is analyzed to obtain the dynamic changes of occupancy data. If the dynamic changes of occupancy data exceed the preset threshold, the upper limit of the range boundary is determined by the remaining amount of storage space. Based on the correspondence between firmware updates and capability analysis, regression analysis is used to determine the adaptability of the current firmware update to the new requirements. According to the superposition results of the adaptation degree and the range boundary, the specific boundary value of scalability is determined. The matching degree between the scalability boundary value and the output of the correlation analysis is used to determine the long-term trend of firmware updates and the new requirements.

4. The software development and update system for wearable bio-monitoring devices according to claim 1, characterized in that: The intent analysis module obtains user usage data and log records of software modification intentions, performs cluster analysis on the intentions, and obtains the priority ranking of new requirement adaptability, including: Obtain log records from the system, extract user data and modification intentions, and obtain the original data set. Perform cluster analysis on the modification intentions in the original data set using a preset classification algorithm to obtain a set of intent categories. For the set of intent categories, use statistical tools to calculate the frequency of occurrence of each category to obtain a frequency distribution result. If the frequency of a category in the frequency distribution result exceeds the preset threshold, it will be marked as a high-frequency intention to obtain a high-frequency intention list. Based on the high-frequency intention list, analyze the degree of match between each intention and the new requirement to obtain an adaptability score set. By arranging the adaptability score set in descending order, determine the priority ranking of the new requirement and obtain the final sorting result. Obtain the final sorting result, record and store it through the system, and output it to the specified database.

5. The software development and update system for wearable biological monitoring devices according to claim 1, characterized in that: The solution generation module uses a preset threshold to evaluate the difficulty of implementing dynamic function modification. If the difficulty is lower than the threshold, a corresponding software upgrade matching solution is generated. The dynamic modification instruction set obtained includes: The order of function modification is determined by priority sorting, and a sorted function list is obtained. A preset threshold is used to perform difficulty assessment on the sorted function list to obtain a difficulty assessment result. If the difficulty assessment result is lower than the preset threshold, a dynamic implementation plan is generated for the function modification to obtain an implementation plan set. Software upgrade requirements are extracted from the implementation plan set to generate a matching solution set. A dynamic instruction prototype is constructed based on the matching solution set to obtain an instruction prototype set. The dynamic implementation plan is optimized through the instruction prototype set to generate a dynamically modified instruction set. The evaluation result is verified for the dynamically modified instruction set to obtain a final instruction set.

6. The software development and update system for wearable bio-monitoring devices according to claim 1, characterized in that: The lifespan assessment module extracts characteristic parameters of hardware aging from the equipment runtime data, calculates the potential space for extending the service life, and obtains the lifespan optimization adjustment strategy including: The runtime data is obtained by modifying the instruction set, and characteristic parameters related to hardware aging are extracted from the runtime data to obtain a first data set. Regression analysis is used to calculate the characteristic parameter change trend based on the first data set to obtain a parameter change model. The potential impact of hardware aging on life extension is analyzed through the parameter change model to obtain a life impact space. The key interval in the potential space is judged based on the life impact space to obtain an optimized adjustment range. A corresponding adjustment strategy is generated for the optimized adjustment range to obtain a first strategy set. The instruction set is dynamically modified through the first strategy set to obtain updated runtime data. New characteristic parameters are extracted based on the updated runtime data to obtain a second data set and verify the life optimization adjustment strategy.

7. The software development and update system for wearable bio-monitoring devices according to claim 1, characterized in that: The resource optimization module obtains version compatibility data for the current software upgrade, and determines an optimized allocation solution for resource waste by comparing and analyzing the interaction between firmware update capabilities and the impact of hardware aging. The solution includes: Through the life optimization strategy, the version compatibility data of the software upgrade is obtained from the preset database to obtain the compatibility status of the current system match. Based on the obtained compatibility status, the firmware update capability and the hardware aging data are compared to determine the degree of interaction between the two. The judgment result is used to analyze the impact of the interaction through the preset threshold to determine the restriction range of hardware aging on firmware updates. For the restriction range, the distribution data of resource waste is obtained to obtain the deviation value of resource allocation. Based on the deviation value, the optimization allocation algorithm is used to adjust the strategy to determine the correction plan for resource waste. According to the correction plan, the adjusted firmware update capability is compared and analyzed to determine the improvement range of life optimization. The improvement range data is obtained. The long-term interaction trend is predicted through the machine learning model to determine the final optimization allocation plan.

8. The software development and update system for wearable biological monitoring devices according to claim 1, characterized in that: The configuration reallocation module reallocates resources with insufficient computing power and limited storage space, generates a runtime configuration that supports adaptability to new requirements, and obtains an execution plan for device function expansion, including: The usage status of computing power and storage space is obtained through the dynamic scheduling algorithm, and the distribution of resource constraints is obtained. According to the distribution of resource constraints, the computing power and storage space are adjusted by the optimization allocation strategy to generate a preliminary allocation plan. Based on the preliminary allocation plan and the new demand, it is judged whether the adaptability meets the requirements. If not, the resources are reallocated through the dynamic scheduling algorithm to obtain the updated allocation plan. After obtaining the updated allocation plan, a runtime configuration that supports the new demand is generated to determine the integrity of the configuration. Through the runtime configuration, the expansion scope of the device function is judged to obtain the execution sequence of the function expansion. According to the execution sequence, the computing power and storage space are monitored using preset thresholds to determine the stability of the execution plan and obtain the execution plan with determined stability. The adaptability of dynamic scheduling and optimized allocation is verified through simulation operation to obtain the final execution plan.

9. The software development and update system for wearable biological monitoring devices according to claim 1, characterized in that: The stability analysis module extracts the performance change trend after dynamic function modification from the real-time monitoring data, analyzes and determines the stability of the service life extension and software upgrade matching, and obtains the final equipment optimization parameters including: Real-time monitoring data is collected through sensors, and data cleaning technology is used to process outliers to obtain an initial data set after dynamic functional modification. Performance change characteristics are extracted from the initial data set, and time series analysis methods are used to decompose the change trend to obtain the periodic component of performance change. Based on the periodic component of performance change, the predicted value of service life extension is calculated, and its degree of match with the preset threshold is judged to determine the life assessment result. The operation log after the software upgrade is obtained, and the long-term stable indicator data in the log is analyzed to obtain the stability parameters of the upgraded system.

10. A software development and update system for wearable biological monitoring devices according to claim 9, characterized in that: The stability analysis module extracts the performance change trend after dynamic function modification from the real-time monitoring data, analyzes and determines the stability of the service life extension and software upgrade matching, and obtains the final equipment optimization parameters. By comparing the stability parameters with the life assessment results, the correlation between the two is judged and the adjustment direction of equipment optimization is determined. A linear regression algorithm is used to fit the relationship between real-time monitoring data and performance changes in the adjustment direction to obtain the final parameters for equipment optimization. The key weight values are extracted from the final parameters. Through iterative updates and dynamic modification strategies, the applicability of the parameters is judged to obtain the optimized operating configuration.

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