A method and system for supporting multiple mobile terminal bluetooth parallel connection and printing

Abstract

The application discloses a kind of support multi-mobile terminal bluetooth parallel connection and printing method and system, it is related to intelligent bluetooth communication technical field.The kind of support multi-mobile terminal bluetooth parallel connection and printing method and system, including S1, real-time communication state data, abnormal operation log data and historical statistical characteristic data are preprocessed;S2, carry out collaborative gain theory analysis, evaluate channel health condition according to collaborative gain theory analysis result;S3, task is classified by task attribute and user permission label;S4, according to the task priority control instruction transformation and execution task sequencing of multi-queue priority scheduling analysis result;S5, carry out multi-dimensional reliability scheduling theory analysis, real-time risk identification, self-healing and dynamic isolation.The existing printing equipment cannot be connected and printed with multiple mobile terminal bluetooth in parallel, and the problems of queue congestion, task confusion and low resource utilization efficiency caused by high concurrency, channel interference and abnormal channel are solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent Bluetooth communication technology, specifically to a method and system that supports parallel Bluetooth connection and printing for multiple mobile terminals. Background Technology

[0002] With the widespread application of information and wireless communication technologies, wireless data interaction between mobile terminals and printing devices is becoming increasingly common, driving the rapid development of diversified printing services such as mobile printing, remote printing, and cloud printing. Modern printing systems generally support multi-terminal parallel access and task management, enabling efficient long-distance data transmission and centralized printing control through wireless networks. This brings significant convenience and flexibility to enterprise offices, retail businesses, and logistics delivery scenarios. They typically integrate basic functions such as device identification, task queue scheduling, connection management, and data security, and are gradually developing towards intelligence, automation, and multi-user collaboration.

[0003] For example, invention patent CN117037380B discloses a multifunctional medical self-service printing terminal, including a terminal body, an electronic touch screen, a multifunctional printer, and a main control module. The main control module can control the multifunctional printer to print based on the patient's document QR code information and provide prompts for abnormal documents, reminding the patient to seek assistance from a specialist. The electronic touch screen is divided into an interactive area and a multifunctional display area. The interactive area has a miniature input box that supports touch operation and a soft keyboard for inputting QR code numbers. The bottom display area can display the terminal's usage process through videos, dynamic images, and static images. Abnormal prompts can be provided through on-screen text and voice broadcasts. The multifunctional printer includes a report printer and a film printer, both spaced apart and communicating with the main control module. The main control module is also connected to a QR code scanning module to scan, judge, and process document QR code information. The multifunctional display area supports periodically displaying hospital announcements, scrolling patient lists, and recovery blessings. The interactive area and display area divide the screen into upper and lower sections, and the miniature input box can be flexibly arranged and is suitable for single-finger touch operation, ensuring visibility and ease of operation.

[0004] For example, invention patent CN114115760B discloses a method and system for interaction between multiple IoT terminals and printing devices, including user terminals, a server, and printing devices within the same IoT network. The requesting terminal has control over the printing device and is used to send information requests; the responding terminal receives the information requests and sends printing information and a printing request. After receiving the printing request, the server issues a printing instruction, and the printing device executes the printing according to the instruction. The server is divided into an authorization server and a printing server. The authorization server grants printing permissions to the printing server through authorization information and can generate first and second keys for security authentication. Printing permissions include long-term and temporary permissions, which can be restricted by the number of authorizations, duration, and account conditions. The requesting terminal can revoke authorization at any time. The system supports preprocessing of printing information, port activation, and information forwarding. The server can run on various platforms such as cloud, edge servers, and gateways. Printing devices access the server through personal area networks, local area networks, and mobile networks.

[0005] Existing mobile terminal wireless printing systems, in practical applications involving multi-terminal concurrency and complex task collaboration, generally suffer from a lack of dynamic scheduling and routing capabilities based on real-time link status, leading to channel congestion, task delays, and data packet mixing. Furthermore, handling link anomalies still primarily relies on manual intervention, lacking efficient self-healing and precise isolation mechanisms, resulting in insufficient overall system fault tolerance and stability. In addition, queue priority management is rather crude, making it difficult to effectively guarantee high-priority tasks, and resource dynamic allocation capabilities are limited, ultimately failing to meet the actual needs of complex business scenarios in terms of overall scheduling efficiency and service continuity.

[0006] Therefore, in order to address the above problems, there is an urgent need for a method and system that supports parallel Bluetooth connection and printing for multiple mobile terminals. Summary of the Invention

[0007] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a method and system that supports parallel Bluetooth connection and printing for multiple mobile terminals. This solves the problems of existing printing devices being unable to support parallel Bluetooth connection and printing for multiple mobile terminals, and the resulting queue congestion, task errors, and low resource utilization efficiency caused by high concurrency, channel interference, and abnormal channels.

[0008] Technical solution To achieve the above objectives, the present invention provides the following technical solution: a method for supporting parallel Bluetooth connection and printing of multiple mobile terminals, comprising: S1, collecting real-time communication status data, acquiring abnormal operation log data and historical statistical feature data, and preprocessing the real-time communication status data, abnormal operation log data and historical statistical feature data; S2, performing cooperative gain theory analysis on the real-time communication status data and historical statistical feature data, and evaluating the channel health status based on the cooperative gain theory analysis results; S3, performing type identification and hierarchical scheduling of printing tasks, classifying tasks by task attributes and user permission tags; S4, performing multi-queue priority scheduling analysis on the real-time communication status data and historical statistical feature data, converting task priority adjustment instructions and executing task sorting based on the multi-queue priority scheduling analysis results; S5, performing multi-dimensional reliability scheduling theory analysis on the real-time communication status data, abnormal operation log data and historical statistical feature data, and performing real-time risk identification, self-healing and dynamic isolation based on the multi-dimensional reliability scheduling theory analysis results.

[0009] Furthermore, the process of preprocessing real-time communication status data, abnormal operation log data, and historical statistical feature data involves collecting real-time communication status data, acquiring abnormal operation log data, and historical statistical feature data. The specific steps are as follows: Collecting real-time communication status data, including Bluetooth signal strength, packet loss count, total packet count, number of devices operating on the same frequency, current time, task entry / queue time, task length, single data packet length, actual data packet transmission time, and total data traffic; acquiring abnormal operation log data, including task queue length change log data, packet loss alarm log data, disconnection / reconnection status change log data, and timeout alarm log data; and acquiring historical statistical feature data, including historical packet loss count, historical total packet count, historical Bluetooth signal strength, historical number of devices operating on the same frequency, and historical waiting time data. The system collects historical data on the number of failed retries, actual transmission rates of historical data packets, and total historical data traffic. It uses a sliding window quantile analysis algorithm to dynamically extract thresholds, adapt signal features, and suppress extreme value disturbances in historical statistical data. It also uses piecewise linear regression and quantile threshold fitting algorithms to extract the main trend and dynamically update the fluctuation range of historical statistical data. Furthermore, it employs an isolated forest algorithm to perform multi-dimensional anomaly detection on collected real-time communication status data and abnormal operation log data, identifying and removing outliers and abrupt changes. Finally, it uses periodic resampling and mean calibration algorithms to perform periodic consistency processing on historical packet loss counts, historical co-frequency device counts, and historical waiting times, eliminating statistical differences between different devices and batches. Finally, it uses distribution standardization and linear normalization algorithms to standardize and normalize real-time communication status data, abnormal operation log data, and historical statistical data.

[0010] Furthermore, the specific process of performing cooperative gain theoretical analysis on real-time communication status data and historical statistical characteristic data is as follows: Obtain Bluetooth signal strength, historical Bluetooth signal strength, number of co-frequency devices, number of lost packets, total number of packets, historical number of lost packets, historical total number of packets, and historical number of co-frequency devices; obtain the wireless noise floor from the historical Bluetooth signal strength using the sliding window low quantile mean method; calculate the difference between the Bluetooth signal strength and the wireless noise floor to obtain the link signal-to-noise ratio; perform window quantile analysis on the historical Bluetooth signal strength and the wireless noise floor to obtain the minimum link signal-to-noise ratio; obtain the channel interference intensity from the Bluetooth signal strength, number of lost packets, and total number of packets using the product suppression algorithm; and calculate the channel interference intensity and the number of co-frequency devices... The channel interference value is obtained through periodic statistics using the collision detection function; the historical channel interference intensity is obtained by using a product suppression algorithm on the historical Bluetooth signal strength, historical packet loss count, and historical total packet count; the interference criterion threshold is obtained by applying the maximum inter-class variance method to the historical channel interference intensity and the historical number of co-channel devices; the hyperbolic tangent of the ratio of Bluetooth signal strength to link signal-to-noise ratio is taken, the Bluetooth signal strength is multiplied by the hyperbolic tangent and one is added, and then the natural logarithm is taken to obtain the signal cooperative gain term, which is used as the numerator; the channel interference value is divided by the interference criterion threshold, and one is added after substituting into the exponential function to obtain the interference suppression term, which is used as the denominator; the channel health assessment value is obtained by dividing the signal cooperative gain term by the interference suppression term.

[0011] Furthermore, the specific process for assessing channel health based on the results of the collaborative gain theory analysis is as follows: Real-time comparison of the channel health assessment value and the channel health assessment threshold: When the channel health assessment value is less than the channel health assessment threshold, the channel type is identified: If it is a BLE channel, the MCU BLE channel is activated first, the print task is switched to the MCU BLE channel, and the channel health assessment value is continuously monitored until it is greater than or equal to the channel health assessment threshold; if it is an SPP channel, a connection reset operation is triggered, and Bluetooth physical channel migration and frequency hopping are performed; if the channel health assessment value is still less than the channel health assessment threshold, the channel is removed from the task scheduling pool and archived in the abnormal channel library; when the channel health assessment value is greater than or equal to the channel health assessment threshold, the channel is archived in the healthy channel library and enters the task type identification and hierarchical scheduling module.

[0012] Furthermore, the printing tasks are categorized and scheduled hierarchically. The specific process of classifying tasks by task attributes and user permission tags is as follows: The classification criteria include the business type parameters when the task is generated, the user identity identifier, and the received task priority tag. The task category is determined according to the rules: When a task carries a privileged attribute identifier, it is classified as a privileged task, and the task is placed at the head of the current task queue. During the task scheduling process, it is given priority in allocating available resources and completing the task execution. When a task carries an urgent attribute identifier, it is classified as an urgent task and enters the intelligent queue and scheduling management module. When a task does not carry an attribute identifier, it is classified as a normal task and enters the intelligent queue and scheduling management module.

[0013] Furthermore, the specific process of performing multi-queue priority scheduling analysis on real-time communication status data and historical statistical feature data is as follows: Obtain the channel health assessment value, current time data, task enqueue time, historical waiting time data, task length, and historical failure retries; based on the task category, assign a value of 1 to ordinary tasks and a value of 2 to urgent tasks using a priority assignment algorithm to obtain the task type indication parameter; calculate the difference between the current time data and the task enqueue time to obtain the waiting time; obtain the maximum waiting time using a statistical maximum value algorithm on the historical waiting time data; obtain the maximum task length using a statistical maximum value algorithm on all task lengths; obtain the retry priority score using a scheduling strategy algorithm on the historical failure retries; multiply the channel health assessment value by the task type indication parameter, then multiply by the value obtained by dividing the task length by the maximum task length and inversely normalizing it, and then multiply by the sum of the ratio of the waiting time to the maximum waiting time and the retry priority score to obtain the scheduling priority value.

[0014] Furthermore, the specific process of converting task priority control instructions and executing task sorting based on the multi-queue priority scheduling analysis results is as follows: The scheduling priority value is converted into a task priority control instruction using a priority queue scheduling algorithm; task sorting is executed according to the task priority control instruction; all tasks are sorted from high to low according to their scheduling priority values ​​to generate a task queue; when a new task enters the queue or the status of an existing task changes, the scheduling priority value of the task is recalculated, and the order of the task in the queue is adjusted; when a task is completed and no new task arrives in its channel, the next task is executed; after a privileged task is inserted into the task queue, all task queues are reordered according to the task priority control instruction to ensure that privileged tasks are scheduled first.

[0015] Furthermore, the specific process of performing multi-dimensional reliability scheduling theory analysis on real-time communication status data, abnormal operation log data, and historical statistical characteristic data is as follows: Obtain the following data: single data packet length, actual data packet transmission time, total data traffic, task queue length change log data, packet loss alarm log data, disconnection and reconnection status change log data, timeout alarm log data, historical actual data packet transmission rate, historical task queue length change log data, historical packet loss alarm log data, historical disconnection and reconnection status change log data, historical timeout alarm log data, and historical total data traffic; calculate the ratio of single data packet length to actual data packet transmission time to obtain the actual data packet transmission rate; calculate the standard deviation of the rate at each moment using the sliding window algorithm to obtain the average instantaneous rate jitter value; approximate the available bandwidth capacity using the moving average method to estimate the actual data packet transmission rate; and compare the available bandwidth capacity with the total data traffic... The following steps are performed to obtain the bandwidth utilization rate: First, the queue length change log data is analyzed using a ratio to obtain the queue length safety threshold to obtain the number of queue congestion events. Second, the packet loss alarm log data is analyzed using a cumulative count algorithm to obtain the number of packet loss events. Third, the disconnection and reconnection status change log data is analyzed using a cumulative count algorithm to obtain the number of disconnection events. Fourth, the timeout alarm log data is analyzed using a cumulative count algorithm to obtain the number of timeout events. The rate jitter adjustment factor is multiplied by the average instantaneous rate jitter value and then added to obtain the rate jitter term. This rate jitter term is then multiplied by the channel health assessment value and multiplied by the exponent calculated using a natural constant as the base and the exponent being the bandwidth congestion amplification factor multiplied by the bandwidth utilization rate to obtain the channel performance risk suppression gain term. Finally, the number of queue congestion events, packet loss events, disconnection events, and timeout events are added together and then added to obtain the abnormal event cumulative suppression term. The channel performance risk suppression gain term is divided by the abnormal event cumulative suppression term to obtain the risk criterion value.

[0016] Furthermore, based on the analysis results of multidimensional reliability scheduling theory, the specific process of real-time risk identification, self-healing, and dynamic isolation is as follows: Real-time comparison of risk criterion values ​​and risk criterion thresholds: When the risk criterion value is greater than or equal to the risk criterion threshold, it is determined to be in a healthy state, the current channel is maintained, and tasks are allocated according to the current task priority control instructions; when the risk criterion value is less than the risk criterion threshold, it is determined to be in an abnormal state, the allocation of new tasks to the channel is immediately suspended, existing printing tasks enter a waiting state, a software reconnection operation is initiated, parameters are reset, and resources are cleaned up; the risk criterion value of the abnormal channel is recalculated. If the risk criterion value is greater than or equal to the risk criterion threshold, low-priority, low-load printing tasks are processed, and bandwidth and concurrency are limited. If the channel can complete the printing task under low load and the risk criterion value continues to be greater than or equal to the risk criterion threshold, a phased dynamic progressive algorithm is used to gradually increase the priority, quantity, and bandwidth of its assigned tasks until it is fully restored to a healthy channel and participates in full task scheduling. If the risk criterion value is still less than the risk criterion threshold, the abnormal channel is removed from the scheduling pool, archived in the abnormal channel database, and the data in the abnormal channel database is reported to the alarm log in real time for manual intervention and reminders.

[0017] The second aspect of this invention provides a system supporting parallel Bluetooth connection and printing for multiple mobile terminals, including a data acquisition and preprocessing module for acquiring real-time communication status data, obtaining abnormal operation log data and historical statistical feature data, and preprocessing the real-time communication status data, abnormal operation log data and historical statistical feature data; a task identification and multi-channel evaluation module for performing cooperative gain theory analysis on the real-time communication status data and historical statistical feature data, and evaluating channel health based on the cooperative gain theory analysis results; a task type discrimination and hierarchical scheduling module for identifying and hierarchically scheduling printing tasks, classifying tasks by task attributes and user permission tags; an intelligent queue and scheduling management module for performing multi-queue priority scheduling analysis on the real-time communication status data and historical statistical feature data, converting task priority adjustment instructions and executing task sorting based on the multi-queue priority scheduling analysis results; and a risk monitoring and anomaly isolation module for performing multi-dimensional reliability scheduling theory analysis on the real-time communication status data, abnormal operation log data and historical statistical feature data, and performing real-time risk identification, self-healing and dynamic isolation based on the multi-dimensional reliability scheduling theory analysis results.

[0018] Beneficial effects The present invention has the following beneficial effects: (1) This invention enables multiple users to simultaneously issue printing tasks through Bluetooth parallel connection and hierarchical queue scheduling of multiple mobile terminals. It can uniformly access, intelligently queue and dynamically schedule all printing requests, thereby realizing the effects of online concurrent acceptance of printing tasks, transparent and visible task queue and priority allocation. It effectively solves the problems of users having to manually queue, repeatedly disconnecting and reconnecting and unsmooth experience in the prior art.

[0019] (2) This invention introduces a channel health assessment and risk monitoring mechanism to perform real-time status analysis and dynamic scheduling of all Bluetooth communication channels, thereby achieving the effect of identifying abnormal channels and prioritizing the execution of tasks on healthy channels, effectively solving the problems of uneven task allocation and abnormal channels affecting system stability in the prior art.

[0020] (3) This invention supports intelligent hierarchical scheduling of multiple types of tasks, including ordinary, urgent and privileged tasks, by identifying task types and dynamically adjusting priorities. This achieves the effect of high-priority tasks being queued and processed efficiently, effectively solving the problems of delayed task priority response and rigid resource allocation in the prior art.

[0021] (4) This invention, by adopting dynamic comparison of risk criteria values ​​and self-healing mechanism, performs reconnection, parameter reset and gradual volume recovery on abnormal channels, thereby achieving the effect of rapid self-healing and healthy return of abnormal channels, effectively solving the problem that abnormal channels cannot be repaired and require frequent manual intervention in the prior art.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] Figure 1 This is a flowchart of a method for supporting parallel Bluetooth connection and printing of multiple mobile terminals according to the present invention; Figure 2 This is a structural diagram of a system for supporting parallel Bluetooth connections and printing for multiple mobile terminals according to the present invention; Figure 3 This is a line graph showing the relationship between scheduling parameters and priorities in this invention. Detailed Implementation

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

[0025] Please see Figures 1-3This invention provides a technical solution: a method for supporting parallel Bluetooth connection and printing of multiple mobile terminals, comprising the following steps: S1, collecting real-time communication status data, acquiring abnormal operation log data and historical statistical feature data, and preprocessing the real-time communication status data, abnormal operation log data and historical statistical feature data; S2, performing cooperative gain theory analysis on the real-time communication status data and historical statistical feature data, and evaluating the channel health status based on the cooperative gain theory analysis results; S3, performing type identification and hierarchical scheduling of printing tasks, classifying tasks by task attributes and user permission tags; S4, performing multi-queue priority scheduling analysis on the real-time communication status data and historical statistical feature data, converting task priority adjustment instructions and executing task sorting based on the multi-queue priority scheduling analysis results; S5, performing multi-dimensional reliability scheduling theory analysis on the real-time communication status data, abnormal operation log data and historical statistical feature data, and performing real-time risk identification, self-healing and dynamic isolation based on the multi-dimensional reliability scheduling theory analysis results.

[0026] Specifically, the process of collecting real-time communication status data, acquiring abnormal operation log data and historical statistical feature data, and preprocessing the real-time communication status data, abnormal operation log data and historical statistical feature data is as follows: Collecting real-time communication status data, which includes: Bluetooth signal strength, number of lost packets, total number of packets, number of devices on the same frequency, current time data, time to enter the task queue, task length, length of a single data packet, actual time taken to send a data packet and total data traffic, constitutes the core indicators for evaluating the current Bluetooth link quality and load status, and provides data support for instantaneous decision-making.

[0027] Obtain abnormal operation log data, which includes: task queue length change log data, packet loss alarm log data, disconnection and reconnection status change log data, and timeout alarm log data. It records abnormal events during operation and is a key basis for fault diagnosis and system risk assessment. Historical statistical characteristic data is obtained, including: historical packet loss count, historical total number of packets, historical Bluetooth signal strength, historical number of devices operating on the same frequency, historical waiting time data, historical number of failed retries, historical actual data packet transmission rate, and historical total data traffic. Historical data contains long-term operating patterns and rules, laying a statistical foundation for establishing dynamic thresholds and making trend predictions.

[0028] The sliding window quantile analysis algorithm dynamically extracts thresholds, adapts signal features, and suppresses extreme value disturbances from historical statistical feature data, effectively avoiding interference from individual extreme historical values. Piecewise linear regression and quantile threshold fitting algorithms extract the main trend and dynamically update the fluctuation range of historical statistical feature data, accurately capturing the main trend of data changes over different time periods. The isolated forest algorithm performs multidimensional anomaly detection on collected real-time communication status data and abnormal operation log data, identifying and removing outliers and abrupt changes, ensuring the accuracy of subsequent analysis by identifying anomalies that significantly differ from the majority of data behavior. Periodic resampling and mean calibration algorithms perform periodic consistency processing on historical packet loss counts, historical co-frequency device counts, and historical waiting times, eliminating statistical differences between different devices and batches, making data from different terminals and time periods comparable. Distribution standardization and linear normalization algorithms standardize and normalize real-time communication status data, abnormal operation log data, and historical statistical feature data, eliminating the influence of dimensions and creating conditions for subsequent calculations and comprehensive evaluation.

[0029] This implementation plan achieves in-depth governance and value mining of multi-source heterogeneous data by constructing a complete data acquisition and preprocessing system. It simultaneously acquires three types of data: real-time communication status, abnormal operation logs, and historical statistical characteristics, forming a three-dimensional data view covering instantaneous states, abnormal events, and long-term patterns. The data processing establishes an adaptive dynamic evaluation benchmark that can intelligently adjust judgment standards according to the actual operating environment; simultaneously, it constructs an efficient data cleaning mechanism to effectively identify and remove abnormal data points, ensuring the authenticity and reliability of input data; and it achieves standardized integration of multi-device data, eliminating data deviation problems caused by device differences and generating a standardized feature set with temporal continuity and statistical significance. With accurate environmental perception capabilities and a reliable decision-making foundation, it significantly improves the stability and scheduling efficiency of the Bluetooth communication system in high-concurrency scenarios, providing solid data support for subsequent core functions such as channel health assessment, dynamic priority scheduling, and risk monitoring.

[0030] Specifically, the process of performing collaborative gain theory analysis on real-time communication status data and historical statistical feature data is as follows: A terminal MAC address identification and task ID binding mechanism is implemented to establish a precise mapping relationship between terminal, channel, and task. Simultaneously, a task scheduling pool is created for full-process status tracking, thereby ensuring the accuracy of task scheduling and the traceability of anomalies in a multi-terminal concurrent environment. Bluetooth signal strength, historical Bluetooth signal strength, number of co-frequency devices, number of lost packets, total number of packets, historical number of lost packets, historical total number of packets, and historical number of co-frequency devices are obtained, collectively constituting the original feature set for channel state analysis. The wireless noise floor is obtained from the historical Bluetooth signal strength using the sliding window low quantile mean method, effectively eliminating instantaneous signal fluctuation interference and accurately capturing the ambient noise floor level. The link signal-to-noise ratio is obtained by calculating the difference between the Bluetooth signal strength and the wireless noise floor, reflecting the useful signal... The signal strength advantage of Bluetooth signal relative to environmental noise is demonstrated through window quantile analysis of historical Bluetooth signal strength and wireless noise floor to obtain the minimum signal-to-noise ratio of the link, which serves as an important benchmark for judging link availability and will adaptively adjust with changes in the channel environment. Channel interference intensity is obtained by using a product suppression algorithm on Bluetooth signal strength, number of lost packets, and total number of packets. Channel interference values ​​are obtained by periodically statistically analyzing channel interference intensity and number of co-frequency devices through a collision detection function, effectively amplifying the influence weight of abnormal parameters and enhancing sensitivity to interference. Historical channel interference intensity is obtained by using a product suppression algorithm on historical Bluetooth signal strength, historical number of lost packets, and historical total number of packets, and maintaining consistency in algorithm processing helps improve the comparability of evaluation results. Interference criterion thresholds are obtained by using the maximum inter-class variance method on historical channel interference intensity and historical number of co-frequency devices, ensuring the adaptability and statistical significance of the interference evaluation standard.

[0031] The ratio of Bluetooth signal strength to link signal-to-noise ratio is taken as its hyperbolic tangent. The Bluetooth signal strength is then multiplied by the hyperbolic tangent, incremented by one, and the natural logarithm is taken to obtain the signal co-gain term, which serves as the numerator. This creates a synergistic amplification effect between the Bluetooth signal strength and the link signal-to-noise ratio, resulting in an exponential gain when both are enhanced simultaneously. The channel interference value is divided by the interference criterion threshold, substituted into an exponential function, and incremented by one to obtain the interference suppression term, which serves as the denominator. When the channel interference value exceeds the interference criterion threshold, the denominator grows exponentially, thus strongly suppressing the health assessment value. The signal co-gain term is divided by the interference suppression term to obtain the channel health assessment value, determining whether the Bluetooth communication channel is in a healthy state capable of stable data transmission. The specific calculation formula is as follows: ; In the formula, This represents the channel health assessment value, reflecting the overall communication quality and reliability level of the channel at the current moment; It indicates the Bluetooth signal strength, representing the power intensity of the received signal; It represents the link signal-to-noise ratio. The higher the signal-to-noise ratio, the better the purity of the signal in a noisy environment and the lower the bit error rate of data transmission. This indicates the minimum signal-to-noise ratio (SNR) of the link, representing the minimum SNR requirement necessary to maintain reliable communication on the Bluetooth link. This represents the channel interference value, which characterizes the severity of co-channel and adjacent-channel interference currently affecting the channel. This indicates the interference criterion threshold, used to define whether the channel interference level has reached a critical state that affects normal communication.

[0032] This implementation plan constructs a channel health assessment system based on multi-dimensional parameter fusion through collaborative gain theory analysis, enabling precise quantification and dynamic monitoring of Bluetooth communication link quality. The signal collaborative gain term effectively captures the synergistic effect of signal strength and signal-to-noise ratio, while the interference suppression term provides a sensitive response to channel interference, demonstrating proactive awareness of channel state changes. The analysis results accurately reflect the channel's real-time communication potential, providing a reliable decision-making basis for task scheduling and improving channel utilization and communication reliability in high-concurrency scenarios. The effective implementation of this step intelligently identifies the optimal communication path and promptly avoids channels with degraded quality, thereby ensuring the stability and continuity of data transmission in multi-terminal Bluetooth parallel connection environments, ultimately achieving a significant improvement in overall service quality.

[0033] Specifically, the process of assessing channel health based on the results of collaborative gain theory analysis is as follows: Real-time comparison of channel health assessment values ​​and channel health assessment thresholds to dynamically determine the current health status of the communication channel. When the channel health assessment value is less than the channel health assessment threshold, the channel type is identified: If it is a BLE channel, the MCU BLE channel is activated first, the print task is switched to the MCU BLE channel, and the channel health assessment value is continuously monitored until it is greater than or equal to the channel health assessment threshold. After confirming that the channel has recovered to a healthy state, it is reinstated into the task scheduling pool to participate in normal task allocation; if it is an SPP channel, a connection reset operation is triggered, Bluetooth physical channel migration and frequency hopping are performed to avoid high interference frequency bands to improve link quality; if the channel health assessment value is still less than the channel health assessment threshold, the channel is removed from the task scheduling pool, archived in the abnormal channel library, and reported to the alarm log for subsequent manual intervention and maintenance.

[0034] When the channel health assessment value is greater than or equal to the channel health assessment threshold, the channel is archived in the healthy channel library and enters the task type identification and hierarchical scheduling module to realize dynamic priority allocation of tasks and intelligent resource scheduling.

[0035] This implementation plan enables print tasks to be detected and dynamically migrated when communication links malfunction in a multi-channel concurrent environment, ensuring continuous processing of print requests from all users and significantly improving task execution continuity and user experience. It also achieves real-time self-healing and efficient isolation of abnormal channels, enabling timely self-repair and elimination of faulty links in the event of signal fluctuations, increased interference, or link anomalies, thus enhancing the robustness and adaptability of the overall scheduling system. Through intelligent frequency hopping and link health verification mechanisms, it effectively improves the resource utilization efficiency of Bluetooth physical channels, ensuring that healthy channels can be prioritized and continuously carry critical services, reducing the probability of task retries, timeouts, and data loss. Furthermore, it supports archiving of abnormal channels and alarm push notifications, providing a traceable data foundation for operation and maintenance and risk management, further enhancing the high reliability and service continuity of the Bluetooth printing system in multi-terminal, multi-task scenarios, ensuring stable operation in complex environments and continuously supporting high-concurrency, dynamically changing business needs.

[0036] Specifically, the process of classifying print tasks by type identification and hierarchical scheduling, using task attributes and user permission tags, is as follows: Classification criteria include the business type parameter generated during task creation, user identification, and the received task priority tag; task categories are determined according to rules. When a task carries a privileged attribute identifier, it is classified as a privileged task. The task is placed at the head of the current task queue and is given priority in allocating available resources and completing task execution during task scheduling. High-priority channels and bandwidth resources are dynamically allocated to privileged tasks, and under the multi-channel scheduling strategy, printing instructions and data transmission are triggered first, minimizing the response time and execution cycle of privileged tasks.

[0037] When a task carries an urgent attribute marker, it is classified as an urgent task and enters the intelligent queue and scheduling management module. When resources are limited, the queuing position and allocation probability of urgent tasks are given priority, effectively improving the processing efficiency of important tasks.

[0038] If a task does not carry an attribute flag, it is classified as a normal task and enters the intelligent queue and scheduling management module to ensure overall load balancing and fair and orderly execution of various business requests.

[0039] This implementation scheme achieves dynamic resource allocation and scheduling optimization under multi-level task priorities, effectively ensuring rapid response and efficient processing of privileged and urgent tasks in high-concurrency environments, significantly improving the completion efficiency of critical business requests and overall service capabilities. Simultaneously, ordinary tasks can participate in scheduling in an orderly manner based on reasonable resource allocation, ensuring fairness and load balance among various business requests, and enhancing the flexibility, reliability, and user experience of the printing system in multi-user, multi-business scenarios.

[0040] Specifically, the process of performing multi-queue priority scheduling analysis on real-time communication status data and historical statistical feature data is as follows: First, obtain channel health assessment values, current time data, task enqueue time, historical waiting time data, task length, and historical failure retries to ensure that the scheduler has a comprehensive understanding of the real-time and historical operating status of tasks and channels, providing a multi-dimensional data foundation for priority calculation. Second, based on task category, assign a value of 1 to ordinary tasks and 2 to urgent tasks using a priority assignment algorithm to obtain task type indication parameters. Combined with task category, this flexibly supports the expansion and dynamic configuration of task levels. Third, calculate the waiting time by calculating the difference between the current time data and the task enqueue time, enabling real-time dynamic tracking of task queuing latency. Fourth, obtain the maximum waiting time from historical waiting time data using a statistical maximum value algorithm to adjust scheduling thresholds and prevent extreme long waiting events. Fifth, obtain the maximum task length from all task lengths using a statistical maximum value algorithm, facilitating task load balancing and resource allocation limit control. Sixth, obtain the retry priority score from historical failure retries using a scheduling strategy algorithm to enhance anomaly recovery and service continuity.

[0041] Multiplying the channel health assessment value by the task type indicator parameter reflects the link stability of the channel where the current task is located and the service level data of the task. Then multiplying by the value of the task length divided by the maximum task length and then inversely normalized, dynamically balances the resource consumption of large and small tasks. Finally, multiplying by the ratio of the waiting time to the maximum waiting time plus the retry priority score, avoids the task being marginalized due to continuous delays and frequent failures, resulting in the scheduling priority value. The specific calculation formula is as follows: ; In the formula, This indicates the scheduling priority value, which comprehensively evaluates the resource allocation order and scheduling priority of each printing task in the current scheduling cycle; This represents the health assessment value, reflecting the current link health status of the channel to which the task belongs; This parameter indicates the task type and is used to distinguish between urgent and normal tasks, enabling multi-level queues and priority allocation. This indicates the waiting time, measuring the cumulative waiting time of the task in the queue since its submission. It is used to dynamically adjust priorities to prevent tasks from being stuck in the queue for a long time. This indicates the maximum waiting time, used to set the longest tolerable delay for a task in the queue; Indicates the task length, reflecting the amount of data in a single print job; This indicates the maximum task length, preventing abnormal tasks from consuming too many resources and affecting fairness and response efficiency. This indicates the retry priority score, ensuring the completion rate of tasks and service continuity in abnormal situations.

[0042] Table 1 presents an example table of scheduling parameters and priorities, detailing the channel health assessment value, task type indication parameter, task length, maximum task length, wait time, maximum wait time, retry priority score, and scheduling priority value for six tasks. This table quantifies the core scheduling parameters and priority calculation results for different tasks during Bluetooth parallel printing scheduling. Specifically, with a fixed maximum task length of 1500 characters and a maximum wait time of 300 characters, Task 1 has a channel health assessment value of 0.95, a task type indication parameter of 2, a task length of 1100 characters, a wait time of 80 seconds, and a retry priority score of 0.2, resulting in the highest scheduling priority value P of 1.10. Task 2 has relatively lower parameters, with a scheduling priority value P of 0.33. Task 6 has the lowest indicators across the group, with a scheduling priority value P of 0.22. Overall, this table demonstrates the differences in scheduling parameters and priority distribution for typical tasks, providing fundamental data support for intelligent task allocation and dynamic scheduling in multi-terminal Bluetooth printing scenarios.

[0043] Table 1. Example of scheduling parameters and priorities

[0044] like Figure 3 The graph shows the relationship between scheduling parameters and priorities. Combined with Table 1, it can be seen that the distribution characteristics of different tasks differ significantly across the three parameters: task length, waiting time, and retry priority score. The level of each parameter directly affects the scheduling priority value of the corresponding task. Task 1 has relatively high levels in task length and waiting time, coupled with a moderate retry priority score, resulting in the highest scheduling priority value in the group. Task 6, on the other hand, has the lowest levels in all parameters, resulting in the lowest scheduling priority value. While some tasks may excel in a single parameter, their scheduling priority values ​​fail to improve proportionally due to weaknesses in other parameters. Overall, Figure 3 This study demonstrates the combined influence and dynamic changes of key scheduling parameters on scheduling priority values, which helps to reveal the weighting and synergistic effects of key indicators in the task scheduling process. This provides intuitive data support and decision-making basis for subsequent scheduling strategy optimization and task hierarchical allocation.

[0045] This implementation scheme achieves intelligent task prioritization and dynamic scheduling driven by multi-dimensional parameters, effectively improving the accuracy of task allocation and the adaptive capability of scheduling. It comprehensively considers different task types, channel status, queuing time, and historical anomalies, ensuring that high-priority, long-waiting, and multi-retrieval tasks receive priority access to scheduling resources, reducing the risk of task delays and abnormal data loss. It enhances the responsiveness to complex business scenarios and sudden anomalies, improving the overall processing efficiency, fairness, stability, and continuity of printing tasks.

[0046] Specifically, the process of converting task priority adjustment instructions and executing task sorting based on multi-queue priority scheduling analysis results is as follows: The priority queue scheduling algorithm converts scheduling priority values ​​into task priority adjustment instructions, achieving intelligent sorting and dynamic priority allocation of all pending tasks, ensuring that critical tasks receive priority scheduling rights for resources. Task sorting is performed according to task priority control instructions: all tasks are sorted from high to low according to their scheduling priority to generate a task queue. When a new task enters the queue or the status of an existing task changes, the scheduling priority of the task is recalculated, and the order of the tasks in the queue is adjusted. This effectively prevents long waits, failed retries, and delays in processing high-priority tasks, improving scheduling flexibility and fairness. When a task is completed and no new task arrives on the channel, the next task is executed, optimizing resource utilization and shortening task waiting and idle switching time. After a privileged task is inserted into the task queue, all task queues are reordered according to task priority control instructions to ensure that privileged tasks are scheduled first, improving the response speed of critical services and the ability to guarantee high-priority needs. During task allocation and data distribution, the first received task request message is transmitted to the MCU via serial port. The MCU returns a successful request message and verifies the MAC address of the terminal corresponding to the task in real time to prevent task errors and packet mixing caused by channel switching. This achieves unique binding and dynamic tracking of task and terminal identities, preventing task errors and packet mixing caused by channel switching and concurrent operations, and improving the security and orderly processing of tasks in a multi-terminal environment.

[0047] This implementation scheme achieves intelligent priority sorting and dynamic scheduling in a multi-tasking environment, effectively ensuring the priority acceptance and rapid processing of critical tasks and high-priority services, improving resource utilization efficiency and task queue fairness. It enables real-time task sorting and adaptive adjustment, avoiding long task waiting times and delays for high-priority tasks, thus improving the overall flexibility and response speed of task processing. Prioritizing privileged tasks and global reordering further enhances the scheduling guarantee capability for important services. It achieves unique binding and dynamic tracking between tasks and terminals, preventing task packet mixing and errors during channel switching and concurrent environments, improving the security of multi-terminal concurrent processing, the accuracy of task attribution, and the orderliness and reliability of the task distribution process.

[0048] Specifically, the process of performing multi-dimensional reliability scheduling theoretical analysis on real-time communication status data, abnormal operation log data, and historical statistical characteristic data is as follows: Obtain the following data: single data packet length, actual data packet transmission time, total data traffic, task queue length change log data, packet loss alarm log data, disconnection and reconnection status change log data, timeout alarm log data, historical actual data packet transmission rate, historical task queue length change log data, historical packet loss alarm log data, historical disconnection and reconnection status change log data, historical timeout alarm log data, and historical total data traffic, providing comprehensive real-time and historical link status parameters; calculate the actual data packet transmission rate by comparing the ratio of single data packet length to actual data packet transmission time, and calculate the rate at each moment using a sliding window algorithm. The standard deviation is used to obtain the average instantaneous rate jitter value, effectively capturing the rate fluctuation characteristics of the link in different time periods and identifying the link stability and interference sensitivity in the short term. The actual data packet transmission rate is approximated by the moving average method to obtain the available bandwidth capacity. The ratio of the available bandwidth capacity to the total data traffic is calculated to obtain the bandwidth utilization rate, dynamically measuring the current link load pressure and resource consumption. The queue length safety threshold analysis is performed on the task queue length change log data to obtain the number of queue congestion events. The number of packet loss events is obtained from the packet loss alarm log data through the cumulative count algorithm. The number of disconnection events is obtained from the disconnection and reconnection status change log data through the cumulative count algorithm. The number of timeout events is obtained from the timeout alarm log data through the cumulative count algorithm, timely identifying potential task blocking, data anomalies and link fluctuation risks.

[0049] Multiplying the rate jitter adjustment factor by the average instantaneous rate jitter value and adding one yields the rate jitter term, reflecting the short-term rate fluctuation characteristics of the current communication link. Multiplying the rate jitter term by the channel health assessment value, and then multiplying it by an exponent calculated with the natural constant as the base and the exponent being the bandwidth congestion amplification factor multiplied by the inverse of the bandwidth occupancy rate, yields the channel performance risk suppression gain term. This term suppresses the risk of the current channel bandwidth resource shortage; the risk value rapidly decays when the bandwidth occupancy rate increases, enhancing the dynamic judgment capability of congestion status. Adding the number of queue congestion events, packet loss events, disconnection events, and timeout events, and then adding one, yields the cumulative abnormal event suppression term, preventing abnormal channels from being mistakenly judged as healthy channels due to short-term improvement in a single parameter. Dividing the channel performance risk suppression gain term by the cumulative abnormal event suppression term yields the risk criterion value, achieving a comprehensive quantitative assessment of communication anomaly risks. The specific calculation formula is as follows: ; In the formula, This represents the risk criterion value, assessing the overall health and risk status of the channel within the current operating cycle; This represents the channel health assessment value, reflecting the current link health status of the channel to which the task belongs; It represents the average instantaneous rate jitter value, reflecting the real-time stability and anti-sudden interference capability of the channel link; Indicates bandwidth utilization; This indicates the number of queue congestion events, reflecting the frequency of congestion during the current period. This indicates the number of packet loss events, revealing the current link's performance in terms of data reliability; This represents the count of disconnection events, reflecting risks to link continuity and business continuity. This indicates the number of timeout events and evaluates the channel's latency performance in high-concurrency and complex network environments. The rate jitter adjustment factor is calculated by using a sliding window algorithm to calculate the standard deviation of the rate at each moment based on the actual transmission rate of historical data packets, thus obtaining the historical instantaneous rate jitter value. The rate jitter adjustment factor is obtained by using the performance impact statistical regression method based on the historical instantaneous rate jitter value, historical task queue length change log data, historical packet loss alarm log data, historical disconnection and reconnection status change log data, and historical timeout alarm log data. The value range is between 0.8 and 1.5. The bandwidth congestion amplification factor is calculated by dividing the total historical data traffic by the available bandwidth capacity to obtain the historical bandwidth utilization rate. The bandwidth congestion amplification factor is obtained by analyzing the historical bandwidth utilization rate and the historical task queue length change log data through historical risk criteria. The value ranges from 1.0 to 2.0.

[0050] This implementation plan enhances the real-time perception and discrimination capabilities for link load, data jitter, bandwidth congestion, and various abnormal events through comprehensive and refined quantitative assessment of health status and abnormal risks. Based on multi-source real-time and historical data, it accurately identifies communication bottlenecks, resource consumption peaks, and service interruption risks, enabling dynamic classification and optimal scheduling of health and risks. Through comprehensive analysis of rate jitter, bandwidth occupancy, and abnormal accumulation factors, it effectively reduces interference from misjudgments of single short-term anomalies, ensuring the stability of channel criteria and the reliability of judgment results. It improves the fault tolerance and service continuity of Bluetooth communication in multi-terminal concurrent environments, providing a solid data foundation and scientific scheduling basis for intelligent task allocation, abnormal channel self-healing, and high-priority service assurance in multi-task high-concurrency scenarios, further enhancing the overall operational stability, intelligence, and robustness.

[0051] Specifically, the process of real-time risk identification, self-healing, and dynamic isolation based on the analysis results of multidimensional reliability scheduling theory is as follows: real-time comparison of risk criterion values ​​and risk criterion thresholds to dynamically determine healthy and abnormal states: When the risk criterion value is greater than or equal to the risk criterion threshold, it is determined to be in a healthy state. The current channel is maintained, and tasks are allocated according to the current task priority control instructions to ensure that the task scheduling process is continuous and efficient, and to ensure a reasonable match between task priority and resources.

[0052] When the risk criterion value is less than the risk criterion threshold, it is determined to be an abnormal state. New task allocation for the channel is immediately suspended, existing print tasks enter a waiting state to prevent the abnormal channel from continuing to carry critical business, reduce risk spread, initiate a software reconnection operation, perform parameter reset and resource cleanup, repair link configuration and cache anomalies caused by the sudden anomaly, and attempt to restore channel availability. The risk criterion value of the abnormal channel is recalculated. If the risk criterion value is greater than or equal to the risk criterion threshold, low-priority and low-load print tasks on the channel are processed, bandwidth and concurrency are limited. If the channel can complete print tasks under low load and the risk criterion value remains stable... If the risk criterion value is greater than or equal to the risk criterion threshold, a phased dynamic progressive algorithm is used to gradually increase the priority, quantity, and bandwidth of the assigned tasks until the channel is fully restored to a healthy state and participates in full task scheduling. The resource release rate is dynamically increased in stages to prevent the risk of high load recurrence after a short period of improvement, ensuring the channel gradually and stably recovers. If the risk criterion value is still less than the risk criterion threshold, the abnormal channel is removed from the scheduling pool and archived in the abnormal channel database. The data in the abnormal channel database is reported to the alarm log in real time for manual intervention and reminders, supporting subsequent maintenance and risk tracing, and providing data basis and intelligent support for stable operation and fault handling.

[0053] This implementation plan ensures the continuity of task scheduling and operational efficiency through dynamic identification, autonomous repair, and intelligent isolation of communication health and abnormal states throughout the entire process. By pausing the allocation of new tasks to abnormal channels in real time and allowing existing tasks to wait safely, the impact of abnormal links on critical services and the spread of overall risk are reduced. It can autonomously reconnect to abnormal channels and repair parameters, verify the operational status after channel recovery, and gradually release scheduling resources using a phased, progressive strategy to ensure that channels do not fail again due to high load during the recovery process. Through tiered release of resources and continuous dynamic monitoring of health indicators, a smooth transition and stable return of abnormal channels are achieved, improving resource utilization and adaptability. For abnormal channels that cannot self-heal, they can be promptly isolated and archived, and alarm logs can be reported in real time, providing data support for subsequent manual intervention, operation and maintenance, and risk tracing. Overall, it greatly enhances task continuity, scheduling security, and fault self-healing capabilities in multi-terminal, high-concurrency environments.

[0054] like Figure 2As shown, the second aspect of the present invention provides a system supporting parallel Bluetooth connection and printing for multiple mobile terminals, including a data acquisition and preprocessing module for acquiring real-time communication status data, obtaining abnormal operation log data and historical statistical feature data, and preprocessing the real-time communication status data, abnormal operation log data and historical statistical feature data; a task identification and multi-channel evaluation module for performing cooperative gain theory analysis on the real-time communication status data and historical statistical feature data, and evaluating channel health based on the cooperative gain theory analysis results; a task type discrimination and hierarchical scheduling module for identifying and hierarchically scheduling printing tasks, classifying tasks by task attributes and user permission tags; an intelligent queue and scheduling management module for performing multi-queue priority scheduling analysis on the real-time communication status data and historical statistical feature data, converting task priority adjustment instructions and executing task sorting based on the multi-queue priority scheduling analysis results; and a risk monitoring and anomaly isolation module for performing multi-dimensional reliability scheduling theory analysis on the real-time communication status data, abnormal operation log data and historical statistical feature data, and performing real-time risk identification, self-healing and dynamic isolation based on the multi-dimensional reliability scheduling theory analysis results.

[0055] This implementation scheme achieves intelligent management and efficient collaboration of Bluetooth printing tasks across the entire process in a multi-mobile terminal environment, improving the real-time performance, accuracy, and stability of multi-terminal parallel connections and task scheduling. It can efficiently collect and preprocess multi-source data, dynamically identify and assess the health status of communication channels, and classify printing tasks and implement multi-level priority scheduling, effectively ensuring the priority completion of critical tasks and high-priority requests. Through intelligent sorting and scheduling of multiple queues, it improves the fairness of task processing and resource utilization efficiency. The risk monitoring and anomaly isolation mechanism enables real-time identification, self-healing, and intelligent isolation of abnormal links, greatly enhancing the adaptability to sudden failures and complex anomalies, and ensuring high reliability, continuity, and service quality in multi-user, multi-task concurrent scenarios.

[0056] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0057] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for supporting parallel Bluetooth connections and printing for multiple mobile terminals, characterized in that, Includes the following steps: S1, collect real-time communication status data, obtain abnormal operation log data and historical statistical feature data, and preprocess the real-time communication status data, abnormal operation log data and historical statistical feature data; S2, perform cooperative gain theory analysis on real-time communication status data and historical statistical characteristic data, and evaluate the channel health based on the results of the cooperative gain theory analysis; S3 performs type identification and hierarchical scheduling of print jobs, classifying jobs by task attributes and user permission tags; S4 performs multi-queue priority scheduling analysis on real-time communication status data and historical statistical feature data, and converts task priority adjustment instructions into task sorting based on the results of the multi-queue priority scheduling analysis. S5 performs multidimensional reliability scheduling theory analysis on real-time communication status data, abnormal operation log data, and historical statistical feature data, and performs real-time risk identification, self-healing, and dynamic isolation based on the results of the multidimensional reliability scheduling theory analysis.

2. The method for supporting parallel Bluetooth connection and printing of multiple mobile terminals according to claim 1, characterized in that: The specific process of collecting real-time communication status data, obtaining abnormal operation log data and historical statistical feature data, and preprocessing the real-time communication status data, abnormal operation log data and historical statistical feature data is as follows: Collect real-time communication status data, including: Bluetooth signal strength, number of lost packets, total number of packets, number of devices on the same frequency, current time data, time to enter the task queue, task length, length of a single data packet, actual time taken to send a data packet, and total data traffic. Obtain abnormal operation log data, which includes: task queue length change log data, packet loss alarm log data, disconnection and reconnection status change log data, and timeout alarm log data; Obtain historical statistical feature data, which includes: historical packet loss count, historical total number of packets, historical Bluetooth signal strength, historical number of devices on the same frequency, historical waiting time data, historical failure retries, historical actual data packet transmission rate, and historical total data traffic. The algorithm employs a sliding window quantile analysis method to dynamically extract thresholds, adapt signal features, and suppress extreme value disturbances in historical statistical feature data. It also uses piecewise linear regression and quantile threshold fitting algorithms to extract the main trend and dynamically update the fluctuation range of historical statistical feature data. Furthermore, the isolated forest algorithm is used to perform multidimensional anomaly detection on collected real-time communication status data and abnormal operation log data, identifying and removing outliers and abrupt changes. Periodic resampling and mean calibration algorithms are used to perform periodic consistency processing on historical packet loss counts, historical co-frequency device counts, and historical waiting time data, eliminating statistical differences between different devices and batches. Finally, distribution standardization and linear normalization algorithms are used to standardize and normalize real-time communication status data, abnormal operation log data, and historical statistical feature data.

3. The method for supporting parallel Bluetooth connection and printing of multiple mobile terminals according to claim 1, characterized in that: The specific process of performing cooperative gain theory analysis on real-time communication status data and historical statistical feature data is as follows: The system acquires Bluetooth signal strength, historical Bluetooth signal strength, number of co-frequency devices, number of lost packets, total number of packets, historical number of lost packets, historical total number of packets, and historical number of co-frequency devices. It obtains the wireless noise floor from the historical Bluetooth signal strength using the sliding window low quantile mean method. It calculates the link signal-to-noise ratio (SNR) by calculating the difference between the Bluetooth signal strength and the wireless noise floor. It performs window quantile analysis on the historical Bluetooth signal strength and the wireless noise floor to obtain the minimum link SNR. It obtains the channel interference intensity from the Bluetooth signal strength, number of lost packets, and total number of packets using a product suppression algorithm. It periodically calculates the channel interference value from the channel interference intensity and the number of co-frequency devices using a collision detection function. It obtains the historical channel interference intensity from the historical Bluetooth signal strength, historical number of lost packets, and historical total number of packets using a product suppression algorithm. Finally, it uses the maximum inter-class variance method to obtain the interference criterion threshold from the historical channel interference intensity and the historical number of co-frequency devices. The ratio of Bluetooth signal strength to link signal-to-noise ratio is calculated by taking the hyperbolic tangent. The Bluetooth signal strength is then multiplied by the hyperbolic tangent and incremented by one. The natural logarithm is then taken to obtain the signal co-gain term, which serves as the numerator. The channel interference value is divided by the interference criterion threshold, and the result is substituted into an exponential function and incremented by one to obtain the interference suppression term, which serves as the denominator. The signal co-gain term is then divided by the interference suppression term to obtain the channel health assessment value.

4. The method for supporting parallel Bluetooth connection and printing of multiple mobile terminals according to claim 1, characterized in that: The specific process for assessing channel health based on the analysis results of synergistic gain theory is as follows: Real-time comparison of channel health assessment values ​​and channel health assessment thresholds: When the channel health assessment value is less than the channel health assessment threshold, identify the channel type: if it is a BLE channel, activate the MCU BLE channel first, switch the print job to the MCU BLE channel, and continuously monitor the channel health assessment value until the channel health assessment value is greater than or equal to the channel health assessment threshold. If it is an SPP channel, a connection reset operation is triggered, and Bluetooth physical channel migration and frequency hopping are performed. If the channel health assessment value is still less than the channel health assessment threshold, the channel will be removed from the task scheduling pool and archived in the abnormal channel library. When the channel health assessment value is greater than or equal to the channel health assessment threshold, the channel is archived to the healthy channel library and enters the task type identification and hierarchical scheduling module.

5. The method for supporting parallel Bluetooth connection and printing of multiple mobile terminals according to claim 1, characterized in that: The specific process of identifying and scheduling printing tasks by type, and classifying tasks by task attributes and user permission tags, is as follows: The classification criteria include the business type parameter at the time of task generation, the user identity identifier, and the received task priority label. The task category is determined according to the rules: When a task carries a privileged attribute identifier, it is classified as a privileged task. The task is moved to the top of the current task queue and is given priority in allocating available resources and completing task execution during task scheduling. When a task carries an urgent attribute marker, it is classified as an urgent task and enters the intelligent queue and scheduling management module; If a task does not carry an attribute flag, it is classified as a normal task and enters the intelligent queue and scheduling management module.

6. The method for supporting parallel Bluetooth connection and printing of multiple mobile terminals according to claim 1, characterized in that: The specific process of performing multi-queue priority scheduling analysis on real-time communication status data and historical statistical feature data is as follows: Acquire channel health assessment value, current time data, task enqueue time, historical waiting time data, task length, and historical failure retries; based on task category, assign a priority value of 1 to ordinary tasks and 2 to urgent tasks using a priority assignment algorithm to obtain task type indicator parameters; calculate the waiting time by comparing the current time data and the task enqueue time; obtain the maximum waiting time by using a statistical maximum value algorithm on historical waiting time data; obtain the maximum task length by using a statistical maximum value algorithm on all task lengths; and obtain the retry priority score by using a scheduling strategy algorithm on historical failure retries. Multiply the channel health assessment value by the task type indicator parameter, then multiply by the value obtained by dividing the task length by the maximum task length and then normalizing it, and finally multiply by the sum of the ratio of the waiting time to the maximum waiting time and the retry priority score to obtain the scheduling priority value.

7. The method for supporting parallel Bluetooth connection and printing of multiple mobile terminals according to claim 1, characterized in that: The specific process of converting task priority adjustment instructions based on multi-queue priority scheduling analysis results and executing task sorting is as follows: The priority queue scheduling algorithm is used to convert scheduling priority values ​​into task priority adjustment instructions: Task sorting is performed according to task priority control instructions: all tasks are sorted from high to low according to their scheduling priority to generate a task queue. When a new task enters the queue or the status of an existing task changes, the scheduling priority of the task is recalculated and the order of the task in the queue is adjusted. When a task is completed and no new task arrives in the channel, the next task is executed. After a privileged task is inserted into the task queue, all task queues are reordered according to the task priority control instructions to ensure that privileged tasks are scheduled first.

8. The method for supporting parallel Bluetooth connection and printing of multiple mobile terminals according to claim 1, characterized in that: The specific process of performing multi-dimensional reliability scheduling theoretical analysis on real-time communication status data, abnormal operation log data, and historical statistical feature data is as follows: The system acquires data including: single packet length, actual packet transmission time, total data volume, task queue length change log data, packet loss alarm log data, disconnection and reconnection status change log data, timeout alarm log data, historical actual packet transmission rate, historical task queue length change log data, historical packet loss alarm log data, historical disconnection and reconnection status change log data, historical timeout alarm log data, and historical total data volume. It calculates the actual packet transmission rate by comparing the single packet length with the actual packet transmission time, and then uses a sliding window algorithm to calculate the standard deviation of the actual packet transmission rate at each moment to obtain the average instantaneous rate jitter value. The available bandwidth capacity is obtained by approximating the actual data packet transmission rate using the moving average method. The bandwidth utilization rate is calculated by the ratio of the available bandwidth capacity to the total data traffic. The number of queue congestion events is obtained by analyzing the queue length safety threshold of the task queue length change log data. The number of packet loss events is obtained from the packet loss alarm log data using a cumulative count algorithm; the number of disconnection events is obtained from the disconnection and reconnection status change log data using a cumulative count algorithm; and the number of timeout events is obtained from the timeout alarm log data using a cumulative count algorithm. Multiply the rate jitter adjustment factor by the average instantaneous rate jitter value and add one to obtain the rate jitter term. Multiply the rate jitter term by the channel health assessment value and then multiply it by the exponent calculated with the natural constant as the base and the exponent being the bandwidth congestion amplification factor multiplied by the inverse of the bandwidth occupancy rate to obtain the channel performance risk suppression gain term. Add the number of queue congestion events, the number of packet loss events, the number of disconnection events, and the number of timeout events and add one to obtain the abnormal event cumulative suppression term. Divide the channel performance risk suppression gain term by the abnormal event cumulative suppression term to obtain the risk criterion value.

9. A method for supporting parallel Bluetooth connection and printing of multiple mobile terminals according to claim 1, characterized in that: The specific process of real-time risk identification, self-healing, and dynamic isolation based on the analysis results of multidimensional reliability scheduling theory is as follows: Real-time comparison of risk criterion values ​​and risk criterion thresholds: When the risk criterion value is greater than or equal to the risk criterion threshold, it is judged to be in a healthy state, the current channel is maintained, and tasks are allocated according to the current task priority control instructions; When the risk criterion value is less than the risk criterion threshold, it is judged as an abnormal state. The allocation of new tasks in the channel is immediately suspended, the existing printing tasks enter the waiting state, the software reconnection operation is initiated, and the parameters are reset and resources are cleaned up. The risk criterion value of the abnormal channel is recalculated. If the risk criterion value is greater than or equal to the risk criterion threshold, low-priority and low-load printing tasks of the channel are processed, and bandwidth and concurrency are limited. If the channel can complete the printing tasks under low load and the risk criterion value continues to be greater than or equal to the risk criterion threshold, a phased dynamic progressive algorithm is used to gradually increase the priority, quantity, and bandwidth of its assigned tasks until it is fully restored to a healthy channel and participates in full task scheduling. If the risk criterion value is still less than the risk criterion threshold, the abnormal channel is removed from the scheduling pool, archived in the abnormal channel database, and the data in the abnormal channel database is reported to the alarm log in real time for manual intervention and reminders.

10. A system supporting parallel Bluetooth connection and printing for multiple mobile terminals, employing the method for supporting parallel Bluetooth connection and printing for multiple mobile terminals as described in any one of claims 1-9, comprising: The acquisition and preprocessing module is used to acquire real-time communication status data, obtain abnormal operation log data and historical statistical feature data, and preprocess the real-time communication status data, abnormal operation log data and historical statistical feature data. The task identification and multi-channel evaluation module is used to perform cooperative gain theoretical analysis on real-time communication status data and historical statistical feature data, and to evaluate the channel health based on the results of the cooperative gain theoretical analysis. The task type identification and hierarchical scheduling module is used to identify and hierarchically schedule printing tasks, classifying tasks by task attributes and user permission tags; The intelligent queue and scheduling management module is used to perform multi-queue priority scheduling analysis on real-time communication status data and historical statistical feature data, and to convert and execute task priority adjustment instructions based on the results of the multi-queue priority scheduling analysis and sort the tasks. The risk monitoring and anomaly isolation module is used to perform multi-dimensional reliability scheduling theory analysis on real-time communication status data, abnormal operation log data, and historical statistical feature data, and to perform real-time risk identification, self-healing, and dynamic isolation based on the results of the multi-dimensional reliability scheduling theory analysis.

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