Method for optimizing secure communication of Bluetooth chip equipment

Abstract

The invention relates to the technical field of Bluetooth communication, and discloses an optimization method for secure communication of Bluetooth chip equipment. The method comprises the following steps: identifying a device type identifier and a protocol version feature, extracting a hardware capability parameter and generating an initial security policy set; collecting channel data, and calculating a stability index and a dynamic noise baseline; then, screening and sorting security policies to generate an execution queue, adjusting an encryption strength threshold according to a noise baseline to perform iterative optimization, and outputting final policy configuration; and when the target Bluetooth chip establishes the communication connection, loading the final security policy configuration and monitoring the channel state in real time, and triggering a policy dynamic switching mechanism when the channel state change exceeds the preset tolerance so as to optimize the security communication effect of the Bluetooth chip equipment. According to the method, the security policy can be accurately matched with the device and the channel state, the policy is dynamically adjusted, and the security and the stability of communication of the Bluetooth chip device are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of Bluetooth communication technology, specifically to an optimization method for secure communication between Bluetooth chip devices. Background Technology

[0002] Bluetooth technology, as a short-range wireless communication technology, has been widely used in many fields due to its advantages such as low power consumption, low cost, and ease of integration. In the consumer electronics field, devices such as Bluetooth headsets, Bluetooth speakers, and smartwatches have become an indispensable part of people's daily lives. For example, people can freely listen to music and answer phone calls with Bluetooth headsets, freeing themselves from the constraints of wired headphones and enjoying a more convenient audio experience; smartwatches connect to mobile phones via Bluetooth, enabling them to receive notifications and monitor health data in real time.

[0003] In the smart home field, Bluetooth technology also plays a crucial role. Various smart appliances, such as smart light bulbs, smart sockets, and smart door locks, can achieve interconnection and remote control via Bluetooth, allowing users to control them remotely through their mobile phones. Users can use a mobile app on their way home to turn on smart light bulbs in advance via Bluetooth, welcoming them with warm lighting; or remotely control smart door locks to open the door for guests. In industrial control, Bluetooth technology is applied to production line data acquisition, wireless device control, and smart warehouse management. On production lines, Bluetooth wirelessly connects various sensors and controllers to acquire real-time data such as temperature, pressure, and humidity, avoiding the inconvenience of traditional wired connections and significantly improving production efficiency and data accuracy. In smart warehousing, Bluetooth tags and barcode scanners connect wirelessly to industrial all-in-one machines via Bluetooth, enabling real-time tracking of goods' location and status, improving warehouse management efficiency. It is evident that Bluetooth technology has permeated every corner of people's lives and industrial production, profoundly impacting the development of modern society.

[0004] Problems arising from differences in device type and protocol: With the widespread adoption of Bluetooth technology, a variety of Bluetooth devices have emerged on the market. These devices come from different manufacturers and possess different device type identifiers and protocol version characteristics. Differences in hardware design and software implementation among Bluetooth devices from different manufacturers lead to potential security vulnerabilities. Some early Bluetooth devices, due to limitations in technology at the time, may have deficiencies in encryption algorithms and authentication mechanisms, making them vulnerable to exploitation by attackers. Furthermore, different protocol versions also differ in security; older Bluetooth protocols may lack advanced security features, making them more susceptible to attacks. Developing appropriate security strategies when Bluetooth devices of different types and protocol versions communicate becomes a challenge. A single security strategy may only apply to specific types and protocol versions of devices, failing to meet the security needs of all devices. Summary of the Invention

[0005] The purpose of this invention is to provide an optimized method for secure communication of Bluetooth chip devices, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides an optimized method for secure communication of Bluetooth chip devices, the method comprising: Identify the device type identifier and protocol version characteristics in the current Bluetooth communication link, extract the hardware capability parameters of the target Bluetooth chip based on the device type identifier, and generate an initial security policy set based on the protocol version characteristics; The channel quality data and interference intensity sequence of the target Bluetooth chip during historical communication cycles are collected. The channel quality data is segmented and fitted to obtain the channel stability index. The dynamic noise baseline is calculated by combining the interference intensity sequence. A subset of candidate policies is selected based on the matching degree between hardware capability parameters and the initial security policy set. The candidate policy subsets are then prioritized using channel stability indicators to generate a policy execution queue. Based on the dynamic noise baseline, the encryption strength threshold of each security policy in the policy execution queue is adjusted. The policy execution queue is iteratively optimized according to the adjusted encryption strength threshold, and the final security policy configuration is output. When establishing a communication connection with the target Bluetooth chip, the final security policy configuration is loaded and channel status changes are monitored in real time. When the channel status changes exceed the preset tolerance, a dynamic policy switching mechanism is triggered.

[0007] Preferably, the step of extracting the hardware capability parameters of the target Bluetooth chip based on the device type identifier includes: Parse the manufacturer code and chip model fields in the device type identifier, and index the corresponding processor frequency and memory capacity from the pre-built hardware capability database; The target Bluetooth chip's currently supported encryption algorithm types and key length range are detected, and a hardware capability parameter matrix is ​​generated by combining the processor's clock speed and memory capacity. The hardware capability parameter matrix is ​​normalized to obtain a quantitative scoring vector for the hardware capability parameters.

[0008] Preferably, the step of performing segmented fitting processing on the channel quality data to obtain the channel stability index includes: The historical communication period is divided into multiple time windows of equal length, and the packet loss rate and signal strength fluctuation value of the channel quality data are extracted in each time window. Linear regression models were used to fit the packet loss rate change curves within each time window, and the slope difference of the fitted curves between adjacent time windows was calculated. The local stability coefficient for each time window is generated by weighting the signal strength fluctuation value and the slope difference. The local stability coefficients for all time windows are calculated using a moving average, and the channel stability index is output.

[0009] Preferably, the calculation of the dynamic noise baseline includes: Perform frequency domain transformation on the interference intensity sequence to extract the energy distribution spectrum of the characteristic frequency bands; Identify sudden interference pulses exceeding a preset threshold in the energy distribution spectrum, and statistically analyze the duration and interval of the sudden interference pulses. An adaptive update model for the dynamic noise baseline is established based on the ratio of duration to interval period.

[0010] Preferably, prioritizing the candidate strategy subset using channel stability indices includes: Establish a mapping table between channel stability indicators and the success rate of security policy execution; Remove strategy items with a success rate below the threshold from the mapping table in the candidate strategy subset to generate a valid strategy set; Calculate the policy priority weights based on the sensitivity of each policy item in the effective policy set to channel stability indicators; The effective policy set is arranged in descending order of policy priority weight, and a policy execution queue is generated.

[0011] Preferably, the encryption strength thresholds for each security policy in the adjustment policy execution queue include: Obtain the current fluctuation amplitude of the dynamic noise baseline, and determine the step size for adjusting the encryption strength based on the fluctuation amplitude; Select the first policy to be executed from the policy execution queue and read its initial encryption strength threshold and maximum allowed strength value; The initial encryption strength threshold is adjusted by increasing or decreasing the encryption strength adjustment step size so that the adjusted threshold does not exceed the maximum allowable strength value. Repeat the above correction operation on the remaining policy items in the policy execution queue until all policy items in the queue have been adjusted.

[0012] Preferably, the dynamic switching mechanism for the triggering strategy includes: Real-time acquisition of current channel state parameters, including instantaneous bit error rate and signal attenuation slope; The instantaneous bit error rate is compared with the dynamic noise baseline, and the primary strategy handover is initiated when the difference exceeds the first handover threshold. After the primary strategy switch, the changing trend of the signal attenuation slope is detected, and the secondary strategy switch is triggered when the changing trend continues to deteriorate. Record channel state parameters and execution policy identifiers during the policy switching process, and update historical communication cycle data.

[0013] Preferably, the initiation of the primary strategy switching includes: Select alternative policy items whose encryption strength threshold is lower than the current value from the policy execution queue; Calculate the average success rate and switching time of each alternative strategy item within the historical communication cycle; Select the alternative strategy with the highest average success rate and the shortest switching time as the primary switching target; Pause the current policy's data encryption process, load the policy configuration of the primary switching target, and reset the encryption session key.

[0014] Preferably, the triggering of the secondary policy switch includes: Locate the next policy item in the policy execution queue that is currently executing the policy; Verify the matching degree between the hardware compatibility parameters of the next priority policy item and the current channel state parameters; When the matching degree meets the preset conditions, the encryption strength threshold of the next priority policy item will be increased to the preset security level; The data encryption channel is reinitialized using the increased encryption strength threshold to complete the secondary strategy switch.

[0015] Preferably, the loading of the final security policy configuration includes: Analyze the key derivation algorithm and authentication protocol type in the final security policy configuration; The derivation parameter combination of the master key and the temporary session key is generated according to the key derivation algorithm; Establish a two-way authentication process according to the authentication protocol type, and activate the data encryption channel after successful authentication; Continuously monitor the key update cycle and authentication validity period in the encrypted channel, and initiate an automatic renewal request before expiration.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention, by identifying device type identifiers and protocol version characteristics in the current Bluetooth communication link, provides a deeper understanding of the features and security requirements of Bluetooth devices. By extracting the hardware capability parameters of the target Bluetooth chip based on the device type identifier, we can clearly understand the device's specific capabilities in computing power, storage capacity, etc. Based on this, when faced with numerous security strategies, a subset of candidate strategies can be selected according to the matching degree between the hardware capability parameters and the initial set of security strategies. This approach ensures that the selected security strategy is compatible with the device's hardware capabilities, avoiding performance degradation or security vulnerabilities caused by overly complex or simplistic security strategies. For example, for Bluetooth chips with weak hardware computing power, overly complex and computationally intensive encryption algorithms will not be selected; instead, a relatively simple security strategy suitable for its hardware capabilities will be chosen, thereby improving the security of Bluetooth communication while ensuring efficient device operation and reducing unnecessary resource consumption.

[0017] During Bluetooth communication, this invention comprehensively understands the dynamic changes of the channel by collecting channel quality data and interference intensity sequences of the target Bluetooth chip during historical communication cycles. Segmented fitting processing of the channel quality data yields channel stability indices, providing a quantitative assessment of channel stability. Calculating a dynamic noise baseline using the interference intensity sequence provides a benchmark for measuring noise levels in complex channel environments. Based on these indices and baselines, security strategies can be adjusted in real time according to channel conditions. When channel stability is poor and interference intensity is high, encryption strength is automatically increased to ensure the confidentiality and integrity of data during transmission; when channel stability is good and interference intensity is low, encryption strength is appropriately reduced to decrease computational resource consumption and improve communication efficiency. This dynamic adaptation to channel changes effectively reduces the impact of interference on Bluetooth communication, ensuring the accuracy and integrity of data transmission.

[0018] In this invention, a policy execution queue is generated by prioritizing a subset of candidate policies, making the execution of security policies more orderly and efficient. Furthermore, the encryption strength threshold of each security policy in the policy execution queue is adjusted based on a dynamic noise baseline, and the queue is iteratively optimized. This process fully considers the real-time channel status and security requirements. Under different channel conditions, the optimal security policy can be selected for execution, avoiding the limitations of fixed security policies in complex channel environments. When channel noise is high, the encryption strength threshold is adjusted promptly to select a stronger encryption policy, ensuring secure data transmission; when channel noise is low, the encryption strength threshold is reduced to minimize computational resource waste and improve communication efficiency. Through this optimization, the security and stability of Bluetooth communication are improved, enabling reliable operation in various complex environments. Attached Figure Description

[0019] Figure 1 This is a schematic diagram illustrating the working principle of the optimized method for secure communication of Bluetooth chip devices according to the present invention. Figure 2 A flowchart for extracting hardware capability parameters of a Bluetooth chip; Figure 3 A flowchart for segmented fitting of channel quality data and generation of channel stability indices; Figure 4 This is a graph showing the relationship between signal strength fluctuations and local stability coefficients. Detailed Implementation

[0020] 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.

[0021] Please see Figure 1This invention provides an optimization method for secure communication of Bluetooth chip devices. The method includes identifying the device type identifier and protocol version characteristics in the current Bluetooth communication link. The device type identifier is extracted from Bluetooth broadcast data packets or connection request frames, and the protocol version characteristics are obtained by parsing Bluetooth protocol data units. Based on the device type identifier, hardware capability parameters of the target Bluetooth chip are extracted, including processor clock speed, memory capacity, supported encryption algorithm types, and key length range. An initial security policy set is generated based on the protocol version characteristics, covering all authentication mechanisms and encryption suites supported by the protocol version. Channel quality data and interference intensity sequences of the target Bluetooth chip are collected during historical communication cycles. The channel quality data includes signal strength indicators and link quality indices, and the interference intensity sequence is collected through an RF front-end. The channel quality data is segmented and fitted to obtain a channel stability index. The segmented fitting divides the historical cycle into equal-length time periods and calculates the statistical characteristics of the quality parameters within each segment. A dynamic noise baseline is calculated based on the interference intensity sequence, reflecting the time-varying characteristics of channel background interference. A subset of candidate policies is selected based on the matching degree between hardware capability parameters and the initial security policy set. The matching degree evaluation considers the hardware resource's capacity to bear the policy execution overhead. The candidate policy subset is prioritized using channel stability indicators to generate a policy execution queue. The ranking principle is to prioritize complex policies for channels with higher stability. The encryption strength threshold of each security policy in the policy execution queue is adjusted based on the dynamic noise baseline. The direction of the encryption strength threshold adjustment is positively correlated with the change in noise level. The policy execution queue is iteratively optimized based on the adjusted encryption strength threshold, and the final security policy configuration is output. The iteration process eliminates conflicting configurations between policies. When a communication connection is established with the target Bluetooth chip, the final security policy configuration is loaded and channel state changes are monitored in real time. The monitored parameters include bit error rate and signal attenuation slope. When the channel state change exceeds a preset tolerance, a dynamic policy switching mechanism is triggered. The preset tolerance is dynamically set according to the security requirements of the application scenario.

[0022] Example 1: See Figure 2The process involves parsing the manufacturer code and chip model fields in the device type identifier. The manufacturer code is interpreted based on the identifier assigned by the Bluetooth Special Interest Group (SIG), and the chip model field is extracted from the device broadcast data packet. The corresponding processor clock speed and memory capacity are indexed from a pre-built hardware capability database, which is continuously updated with the hardware specifications of mainstream Bluetooth chips. The current supported encryption algorithm types and key length ranges of the target Bluetooth chip are detected, and the detection process calls the chip's underlying security management interface to query the algorithm support list. A hardware capability parameter matrix is ​​generated by combining the processor clock speed and memory capacity. The rows of the matrix represent different algorithm types, and the columns represent the correspondence between key length and processing speed. The hardware capability parameter matrix is ​​normalized using a min-max scaling method to map the values ​​of each dimension to a unified dimension. A quantized scoring vector for the hardware capability parameters is obtained, which is used for subsequent numerical comparisons of policy matching degrees.

[0023] The parsing of vendor codes relies on the vendor identifier allocation list maintained by the Bluetooth Special Interest Group (SIG), while the chip model field is extracted from device information exchange frames during the Bluetooth connection establishment process. The pre-build phase of the hardware capability database integrates publicly available chip specification documents and laboratory test reports; the processor clock speed index value is read from the database and converted to the unified unit, megahertz. Encryption algorithm type detection is achieved by sending Bluetooth Security Manager commands, and key length range detection uses a progressive heuristic method. The hardware capability parameter matrix generation algorithm dynamically adjusts the matrix size, determining the number of rows based on the number of detected algorithms. The processor clock speed parameter affects the processing speed-related columns in the matrix, and the memory capacity parameter determines the number of keys that can be stored in the matrix. The minimum and maximum values ​​for normalization processing are statistically obtained from historical database records, and the weighting coefficients of the quantization scoring vector are set according to the security requirements of the application scenario. The accuracy of vendor code parsing is verified through checksums, and the chip model field parsing considers version variations. The indexing operation of the hardware capability database supports fuzzy matching, and the processor clock speed parameter value considers the chip's dynamic frequency adjustment characteristics. The encryption algorithm type detection list covers all algorithms defined by the Bluetooth standard, and the key length range detection records the specific length values ​​supported by the chip. The hardware capability parameter matrix is ​​designed as a sparse matrix, and the mapping between processor clock speed and encryption algorithm execution speed is based on benchmark test data. The relationship between memory capacity and key storage size is calculated using the security context size, and the normalized parameter range is updated periodically.

[0024] Device type identifier parsing is performed during the Bluetooth connection initialization phase, and the vendor code parsing rules handle multi-byte identifiers. Chip model field parsing handles variable-length fields, and the pre-construction process of the hardware capability database includes data cleaning and verification. Processor frequency index values ​​are converted to floating-point numbers, and memory capacity index values ​​are aligned to standard units. Encryption algorithm type detection commands use opcodes specified by the Bluetooth Security Management Protocol, and the key length range detection process records the key length at failure points. The hardware capability parameter matrix generation algorithm optimizes the matrix filling order, and the processor frequency parameter is represented as a relative value in the matrix. Memory capacity parameters are mapped to storage dimensions in the matrix, and scaling parameters are dynamically adjusted during normalization. The device type identifier parsing process integrates an error handling mechanism, and vendor code parsing supports extended vendor codes defined by the Bluetooth SIG. Chip model field parsing handles the mapping between abbreviated and full model names, and the hardware capability database index operations record query logs. The processor frequency index value is verified for reasonableness, and the memory capacity index considers the chip's memory partitioning. Encryption algorithm type detection results are cached for a period of time, and key length range detection adapts to dynamic chip capability adjustments. The hardware capability parameter matrix is ​​generated and updated in real time, with the processor clock speed parameter's influence factors weighted within the matrix. The relationship between memory capacity and algorithm complexity is defined using a lookup table, and the normalized output values ​​are rounded to a specified decimal place.

[0025] The device type identifier parsing performance is optimized using parallel parsing technology, and the hardware capability database uses a multi-level index structure. Timestamps are appended to the index values ​​of processor frequency and memory capacity, and batch detection is used for encryption algorithm type detection. Binary search is used for key length range detection, and the storage format of the hardware capability parameter matrix is ​​optimized to row-column storage. Incremental update algorithms are used for normalization calculations, and vectorized operations are supported for generating quantization score vectors. The module for parsing the device type identifier is loosely coupled to other modules in the Bluetooth protocol stack, and the parsing rules for vendor code are configurable. The chip model field is parsed using model aliases, and the hardware capability database is distributed across multiple nodes. A confidence score is appended to the processor frequency index value, and the memory capacity index value includes the measurement error range. Encryption algorithm type detection commands are transmitted encrypted, and the integrity of key length range detection results is verified. Detailed steps are logged for generating the hardware capability parameter matrix, and a parameter range alarm mechanism is implemented for normalization processing. Device type identifier parsing is completed during the Bluetooth pairing phase, and vendor code parsing is compatible with both Bluetooth Low Energy and Bluetooth Classic modes. The chip model field parsing supports future Bluetooth version expansion, and the hardware capability database index API provides both synchronous and asynchronous interfaces. Processor clock speed index values ​​are uniformly expressed in Hertz, and memory capacity index values ​​are standardized to integer bytes. Encryption algorithm type detection covers national standard algorithms, and key length range detection records historical detection results. The serialized storage of the hardware capability parameter matrix supports saving and restoring the matrix state, and normalized parameters are persistently stored.

[0026] The input data for parsing the device type identifier comes from the Bluetooth host controller interface. Vendor code parsing handles multi-vendor collaborative devices. Chip model field parsing verifies field validity, and the hardware capability database index operation audit logs. The processor frequency index value caching strategy is adjustable, and the memory capacity index value source is marked. Encryption algorithm type detection results are bound to digital certificates, and key length range detection environments are isolated. Access control of the hardware capability parameter matrix restricts access to authorized modules, and normalized processing monitors computing resources. The output structure for device type identifier parsing is standardized, and vendor code parsing supports offline mode. Chip model field parsing undergoes internationalization, and the hardware capability database index results return detailed information. Processor frequency index value precision is controlled, and memory capacity index values ​​are aligned to memory page boundaries. Encryption algorithm type detection results are formatted, and key length range detection results are statistically distributed. A visualization tool for the hardware capability parameter matrix is ​​used for debugging, and normalized historical data comparison displays parameter change trends. Unit tests for the device type identifier parsing module cover various boundary conditions, and vendor code parsing error codes are defined in detail. Chip model field parsing performance benchmarking is performed, and the hardware capability database index query is optimized. The test includes: processor clock speed index value verification test; memory capacity index value consistency check; encryption algorithm type detection security test; key length range detection stress test; concurrent access control of hardware capability parameter matrix; numerical stability analysis of normalized processing; and verification of the accuracy of quantized scoring vector generation using standard test vectors.

[0027] Example 2: See Figure 3The historical communication cycle is divided into multiple time windows of equal length, with the window length adaptively adjusted based on the amount of communication data. Within each time window, the packet loss rate and signal strength fluctuation value of the channel quality data are extracted. The packet loss rate is statistically derived from the acknowledgment mechanism of the transport layer, and the standard deviation of the received signal strength indication is calculated from the signal strength fluctuation value. A linear regression model is used to fit the packet loss rate variation curve within each time window, and the least squares method is used to estimate the curve parameters. The slope difference between the fitted curves of adjacent time windows is calculated, and the slope difference is measured by Euclidean distance. A local stability coefficient for each time window is generated based on the weighted sum of the signal strength fluctuation value and the slope difference; the weighting coefficients are obtained through training with historical data. A moving average is calculated for the local stability coefficients of all time windows, outputting the channel stability index. A frequency domain transformation is performed on the interference intensity sequence, using a Fast Fourier Transform algorithm to convert the time-domain signal into a frequency-domain representation. The energy distribution spectrum of the characteristic frequency bands is extracted; these characteristic frequency bands correspond to the 2.4GHz band where the Bluetooth channel is located and its harmonic frequencies. The system identifies sudden interference pulses exceeding a preset threshold in the energy distribution spectrum. This threshold is dynamically calibrated based on the ambient noise baseline. The duration and interval of each sudden interference pulse are statistically analyzed, with the duration recording the time span during which the pulse exceeds the threshold. Based on the ratio of duration to interval, an adaptive update model for the dynamic noise baseline is established.

[0028] The time window division algorithm considers the periodic changes in communication load, with the window length being an integer multiple of the load period. Packet loss rate extraction includes both transmission errors and timeout losses, and signal strength fluctuation calculation excludes power adjustments caused by equipment power saving. The goodness-of-fit test of the linear regression model determines whether to use a higher-order polynomial for fitting, with a goodness threshold set at 0.8. The slope difference calculation incorporates a time decay factor, assigning higher weight to differences in recent windows. The weighting coefficients of the local stability coefficient are dynamically adjusted, with increased weight for signal strength under high interference environments. The window size for the moving average calculation is configurable, and its size is negatively correlated with the stability update frequency. The sampling rate for frequency domain transformation satisfies the Nyquist criterion to avoid spectral aliasing. The energy distribution map is generated using the Welch average periodogram method to reduce variance. A minimum duration threshold is set for identifying sudden interference pulses to filter transient noise. The duration statistics are accurate to the microsecond level, and a circular buffer is used to store pulse events for interval period calculation. The adaptive update model uses an exponentially weighted moving average algorithm, with new observations having higher weights than historical values. The historical communication cycle is divided based on input data from the communication quality monitoring module, with data packet timestamps aligned to the global clock. Packet loss rate statistics are implemented using a sliding counter, which is reset when the window switches. Input data preprocessing for linear regression fitting removes obvious outliers, with outlier identification based on box plot rules. Slope difference calculation considers curve sign changes; a reversal of the sign indicates a trend reversal. The local stability coefficient generation module integrates weight learning functionality, with weights adjusted based on historical accuracy feedback. The moving average calculation employs a double-buffer mechanism: one buffer calculates the current average, while the other receives updates from new data.

[0029] Interference intensity sequences are acquired via an RF front-end analog-to-digital converter, with sampled values ​​quantized into digital signals. Preprocessing for frequency domain transformation includes windowing to reduce spectral leakage; the window type is the Hanning window. Characteristic frequency bands of the energy distribution map are divided according to Bluetooth channel planning, with energy values ​​calculated for each of the 40 channels. The burst interference pulse identification algorithm includes leading-edge detection and trailing-edge detection, with pulse width measurement accurate to the sampling period. Duration and interval statistics utilize a high-precision timer with nanosecond resolution. Time window division supports a dynamic re-division mechanism, automatically adjusting the window length when channel conditions change drastically. Packet loss rate statistics distinguish between controllable and uncontrollable packet loss; controllable packet loss includes active packet loss due to power management. When the linear regression model fails to fit, a backup fitting algorithm is activated, employing a moving average smoothing followed by difference calculation of the slope. The calculation of slope difference incorporates confidence interval estimation, based on historical difference distributions. The weighted sum calculation of local stability coefficients introduces a nonlinear transformation, using the Sigmoid function as the transformation function. The window boundary processing for moving average calculation employs mirror expansion to avoid boundary effects. Parallel computation of the frequency domain transform utilizes a multi-core processor, decomposing the transform task into multiple subtasks for parallel execution. Energy distribution maps are stored in a sparse matrix format, with zero-value regions compressed for storage. The identification of sudden interference pulses incorporates pattern matching; common interference sources such as Wi-Fi signals exhibit specific patterns. Duration statistics are grouped and aggregated based on duration intervals, with interval sizes logarithmically distributed. The adaptively updated model parameters are learned online, with the learning rate decaying over time.

[0030] The time window division is synchronized with the upper-layer application, and the window length is shortened during periods of application data bursts. Packet loss rate statistics are linked to application-layer retransmission information, and packet loss caused by retransmission is counted separately. Real-time optimization of linear regression fitting adopts recursive least squares method to avoid matrix inversion. Slope difference calculation introduces multi-window sliding comparison, with the comparison window size variable from 3 to 7. The generation of local stability coefficients adds time-related constraints, limiting the coefficient difference between adjacent windows to a certain range. The weight distribution of the moving average calculation is adjustable, supporting both uniform and Gaussian distribution modes. The input data buffer for frequency domain transformation adopts a circular buffer, and the choice of buffer length balances latency and computational overhead. The analysis of energy distribution spectrum adds spectral kurtosis calculation, with kurtosis values ​​characterizing the shape of energy distribution. The identification results of sudden interference pulses are classified and stored, based on pulse shape and frequency position. The calculation of the ratio of duration to interval period adds moving average smoothing, with the smoothing window size proportional to the pulse frequency. The initial value setting of the adaptive update model depends on historical data, and conservative default values ​​are used when initial values ​​are insufficient. The time window division is synchronized in the distributed system, with window boundaries aligned across multiple nodes. Packet loss rate statistics integrate information from multiple data sources, including link layer statistics and business layer feedback. Confidence intervals for linear regression fitting are calculated using a Student's t-distribution, with interval width related to data volume and variance. Long-term trend analysis of slope dissimilarity employs time series analysis, with trend components used to predict future dissimilarity. The generation of local stability coefficients incorporates spatial correlation, with coefficients from neighboring channels cross-referenced. Outlier robustness is improved through moving average calculation, and outlier detection is based on the absolute deviation of the median.

[0031] The acquisition and calibration of the interference intensity sequence front-end gain are performed, and the gain value is recorded for data reconstruction. The output accuracy of the frequency domain transformation is controlled, with accuracy related to the calculated word length. Compressed transmission of the energy distribution map uses differential coding, and the coding efficiency is related to the rate of change of the map. The identification of sudden interference pulses incorporates multi-threshold detection, with different thresholds corresponding to different sensitivities. Distributed aggregation of duration statistics is performed, fusing statistical values ​​from multiple acquisition points. Parameters for adaptively updated models are securely stored using secure elements as the storage medium. Metadata records for time window partitioning are maintained, including the partitioning time point and window identifier. The sampling interval for packet loss rate statistics is configurable, matching the data rate. Residual analysis of linear regression fitting is performed, and the residual distribution is used for model validation. A caching mechanism for slope difference calculation caches the most recently calculated results. The generation pipeline for local stability coefficients is optimized, with multiple windows calculating coefficients in parallel. Numerical stability of moving average calculations is improved, using the Kahan summation algorithm to reduce accumulated errors.

[0032] Hardware acceleration for frequency domain transformation utilizes a dedicated digital signal processor, with the transformation kernel embedded in the hardware. A visualization and debugging interface for energy distribution maps is provided, displaying the spectrum changes over time. Machine learning enhancement is used for identifying sudden interference pulses, with the identification model employing support vector machine classification. Real-time report generation for duration statistics is provided, with standardized report formats. Version compatibility handling is implemented for adaptive model updates, ensuring newer models are compatible with older parameter formats. Time window division is flexible and scalable, with window merging during periods of high system load. Quality assessment metrics for packet loss rate statistics include completeness and accuracy scores. Distributed computation for linear regression fitting distributes the fitting task across multiple computation nodes. Slope difference prediction model integration uses an autoregressive integral moving average model. Local stability coefficient generation supports online learning, employing stochastic gradient descent as the learning algorithm. The window size for moving average calculation is adaptively adjusted based on the data change rate. Preprocessing optimization for frequency domain transformation includes DC component removal and trend elimination. Anomaly detection in the energy distribution map is performed based on outlier analysis. A rule engine for identifying sudden interference pulses is included, with rule scripts dynamically loaded. Data compression for continuous duration statistics is performed using a dictionary-based compression algorithm. Redundant backups of the adaptively updated model are implemented, with backup models running in parallel.

[0033] See Figure 4In the optimization method for secure communication of Bluetooth chip devices, the integration process of channel stability assessment and dynamic noise baseline calculation relies on multi-dimensional data fusion technology. Specifically, historical communication cycles are divided into adaptive time windows, with the window length dynamically adjusted based on the amount of communication data to match periodic load changes. Within each time window, the packet loss rate is statistically derived from the transport layer acknowledgment mechanism, distinguishing between controllable and uncontrollable packet loss. Signal strength fluctuations are obtained by calculating the standard deviation of the received signal strength indication, excluding the impact of power adjustments caused by device power saving. A linear regression model is used to fit the packet loss rate change curves within each window. The model uses the least squares method to estimate parameters, with a goodness-of-fit threshold set at 0.8; if insufficient, a higher-order polynomial backup algorithm is used. The slope difference between the fitted curves of adjacent windows is calculated, introducing Euclidean distance and a time decay factor, with higher weights given to recent window differences. A local stability coefficient is generated by combining the weighted sum of signal strength fluctuations and slope differences. The weight coefficients are dynamically adjusted through training with historical data, increasing the signal strength weight under high interference environments. The local stability coefficients of all windows are calculated using a moving average. The window size is configurable, and a double-buffer mechanism is employed to avoid boundary effects, outputting channel stability indices. Simultaneously, dynamic noise baseline calculation is achieved by frequency domain transformation of the interference intensity sequence. Before transformation, a Hanning window is applied to reduce spectral leakage, and the sampling rate satisfies the Nyquist criterion. Energy distribution maps of characteristic frequency bands are extracted, corresponding to the Bluetooth 2.4GHz channel and its harmonics, and the Welch average periodogram method is used to generate the maps. Sudden interference pulses exceeding the dynamic threshold in the map are identified, and a minimum duration threshold is set to filter transient noise. Pulse identification includes leading-edge detection and trailing-edge detection. The duration and interval period of the pulses are statistically analyzed using a high-precision timer, with duration accuracy down to the microsecond level. Based on the ratio of duration to interval period, an adaptive update model for the dynamic noise baseline is established. The model employs an exponentially weighted moving average algorithm, with new observations having higher weights than historical values.

[0034] Example 3: Establish a mapping table between channel stability indicators and security policy execution success rates. The mapping table is constructed based on historical communication data statistics. Each channel stability indicator interval corresponds to a set of policy execution success rate data. The mapping table uses a two-dimensional structure to store the correspondence between indicator values ​​and success rates. Remove policy items with success rates below a threshold from the mapping table within the candidate policy subset. The threshold is dynamically set according to the security requirements of the application scenario. The removal operation iterates through each policy item in the candidate policy subset, querying the mapping table to obtain its historical success rate. Generate a set of effective policies, containing all policy items that pass the threshold screening. The generation of the set is completed in one go using list comprehension, avoiding multiple iterations. Calculate the policy priority weight based on the sensitivity of each policy item in the effective policy set to the channel stability indicators. Sensitivity is measured by the rate of change of the policy's success rate under different stability conditions, calculated using the derivative method. Arrange the effective policy set in descending order of policy priority weight, generating a policy execution queue. The arrangement algorithm uses quicksort, with high-weight policies stored at the head of the queue.

[0035] The current fluctuation amplitude of the dynamic noise baseline is obtained by calculating the variance of the dynamic noise baseline over the most recent sampling periods using an unbiased estimation formula. The encryption strength adjustment step size is determined based on the fluctuation amplitude, using a piecewise linear function to map the fluctuation amplitude value to the step size value. The first policy to be executed is selected from the policy execution queue by reading the queue head pointer, which locates the policy configuration storage address. The initial encryption strength threshold and maximum allowed strength value of the first policy to be executed are read, accessing the policy configuration database, which uses a relational model to store strength parameters. The initial encryption strength threshold is incrementally or incrementally adjusted using the encryption strength adjustment step size. The adjustment direction is determined based on the changing trend of the dynamic noise baseline, using linear regression for trend analysis. The adjusted threshold is ensured not to exceed the maximum allowed strength value. Boundary checks are performed after each adjustment operation, truncating to the boundary value if the limit is exceeded. The above adjustment operation is repeated for the remaining policy items in the policy execution queue, using a loop structure to traverse the queue, terminating when the queue is empty.

[0036] The mapping table is constructed using a machine learning algorithm. The training features are the discretized level labels of the channel stability index, and the training objective is the statistical value of the policy execution success rate. The success rate threshold is set considering the risk of communication interruption due to policy execution failure; higher thresholds are set for high-risk applications. The threshold setting formula is: in: This indicates the final dynamic threshold used. Indicates the basic threshold. Indicates the risk coefficient. This represents the variance of historical success rates. During the generation of the effective strategy set, deduplication is performed to avoid the same strategy appearing multiple times; deduplication is based on the hash value of the strategy identifier. A time decay factor is introduced into the calculation of strategy priority weights, with more recent success rate data having higher weights; the decay factor uses an exponential function. The strategy execution queue is arranged considering the dependencies between strategies; dependent strategies are kept consecutively, and dependencies are resolved from the strategy configuration file.

[0037] The calculation window for fluctuation amplitude is synchronized with the dynamic noise baseline update cycle, and the window boundaries are aligned with the baseline update time point to ensure the timeliness of data acquisition. The piecewise linear function parameters for adjusting the encryption strength step size are determined experimentally, and the function inflection point is dynamically adjusted according to the device type to achieve personalized configuration. The selection of the first policy to be executed adds validity verification, confirming policy compatibility by querying the hardware capability database to avoid resource conflicts. The initial encryption strength threshold read operation is protected by a mutex lock to prevent multi-threaded concurrent access issues and ensure data consistency. The encryption strength threshold correction operation is executed atomically to ensure that the correction process is uninterrupted and the results take effect immediately. The maximum allowable strength value is set with both soft and hard boundaries. The soft boundary allows temporary over-limit and triggers alarms, while the hard boundary strictly limits to prevent system overload. The mapping relationship table triggers incremental updates after policy execution, and new data is smoothly integrated into the historical records. The success rate threshold is dynamically adjusted according to the network security status, and the status assessment uses a multi-indicator weighted score. The effective policy set is maintained through an LRU caching mechanism, and a fixed-size cache automatically evicts idle policies. The calculation of policy priority weights introduces a complexity factor, and the weight of complex policies is increased accordingly. The strategy execution queue supports priority preemption, allowing high-priority strategies to be inserted in the middle of the queue. Fluctuation amplitude calculations utilize spectral analysis to filter periodic interference, and the encryption strength adjustment step size is optimized based on historical performance. The first executed strategy is equipped with a failover mechanism. The initial encryption strength threshold is optimized through caching and version management, and operation logs are corrected. The maximum allowable strength value is set differently according to device specifications to ensure stable system operation.

[0038] The mapping table query is optimized using a B+ tree index, with the index field being the channel stability level. The success rate threshold is seasonally adjusted based on network load, with the adjustment cycle pre-configured. The effective policy set is stored using bitmap encoding compression, employing run-length encoding as the compression algorithm. Policy priority weights are dynamically updated based on real-time channel conditions, with a fixed update cycle. The policy execution queue uses a round-robin algorithm for multi-core load balancing. Fluctuation amplitude calculation employs pipelined processing, including three stages: data sampling, variance calculation, and result output. The encryption strength adjustment step size is implemented using fuzzy control, with fuzzy rules supporting dynamic configuration. The first policy to be executed uses a pre-loading mechanism to reduce latency. The initial encryption strength threshold is encrypted and stored using the AES algorithm. After threshold correction, its effectiveness is verified through simulation. The maximum allowable strength value is progressively increased with device performance. The mapping table uses a range sharding strategy for distributed storage. The success rate threshold is set using a collaborative filtering algorithm with reference to similar devices. A hash table is built for the effective policy set to enable fast retrieval. A visual debugging interface is provided for policy priority weights. Periodic recovery points are set in the policy execution queue for fault tolerance. Anomaly detection of fluctuation amplitude uses the isolated forest algorithm to mark outliers. The encryption strength adjustment step size is optimized for long-term returns through dynamic programming. The first execution policy monitors CPU utilization for load. The initial encryption strength threshold is optimized using a genetic algorithm. The encryption strength threshold achieves distributed consensus using the Paxos algorithm. The maximum allowed strength value is constrained by physical hardware limits. A mapping table implements a differential backup mechanism. The success rate threshold is adaptively learned using a gradient descent algorithm. The effective policy set is version-controlled using a Git model. Policy priority weights are trained collaboratively across multiple devices using federated learning. The policy execution queue uses a priority inheritance protocol to avoid priority inversion.

[0039] The fluctuation amplitude analysis employs wavelet transform for multi-resolution analysis, and the encryption strength adjustment step size utilizes Q-learning algorithm for reinforcement learning. The first execution policy is selected using an LSTM model for prediction. The initial encryption strength threshold is protected using the Shamir secret sharing scheme. The encryption strength threshold is updated using Paillier homomorphic encryption. The maximum allowed strength value is verified through a trusted execution environment. The mapping relationship table uses a graph database to store the relationship structure. The success rate threshold achieves multi-party equilibrium through game theory. The effective policy set handles partial order relationships through topological sorting. Policy priority weights are dynamically adjusted using an attention mechanism to adjust feature attention. The policy execution queue uses an M / M / 1 model for performance analysis. Fluctuation amplitude modeling employs Caputo fractional derivatives. The encryption strength adjustment step size is optimized using stochastic gradient descent. The first execution policy is optimized using quantum computing. The initial encryption strength threshold is protected using differential privacy technology. The encryption strength threshold is distributedly stored using blockchain. The maximum allowed strength value is bound to the device fingerprint using a PUF function.

[0040] Example 4: Real-time acquisition of current channel state parameters, including instantaneous bit error rate (BER) and signal attenuation slope. The acquisition process is implemented through the physical layer monitoring module of the Bluetooth baseband chip, with the sampling frequency synchronized with the data packet transmission cycle. The instantaneous BER is compared with the dynamic noise baseline. The comparison algorithm calculates the percentage deviation of the absolute difference from the dynamic noise baseline, and the percentage deviation value is stored in a circular buffer to record historical comparison results. When the difference exceeds a first switching threshold, a primary strategy switch is initiated. The first switching threshold is set in stages according to communication reliability requirements, and the threshold parameter is stored in the strategy configuration database. After the primary strategy switch, the changing trend of the signal attenuation slope is detected. The trend analysis uses a sliding window linear fitting, with the fitting window size matched to the channel coherence time. When the changing trend continues to deteriorate, a secondary strategy switch is triggered. Continuous deterioration is determined by three consecutive sampling cycles meeting the deterioration condition, which is that the negative rate of change of the signal attenuation slope exceeds a threshold value. The channel state parameters and the executed strategy identifier are recorded during the strategy switch process. The recorded data is stored in a structured format, including a timestamp, parameter vector, and strategy identifier triplet.

[0041] The algorithm selects candidate policies from the policy execution queue whose encryption strength threshold is lower than the current value. Selection criteria include policy compatibility checks and resource availability verification. The selection results generate a candidate policy list. The average execution success rate and switching time of each candidate policy are calculated over historical communication cycles. The average execution success rate is calculated using a weighted moving average, and the switching time is the time interval from policy activation to encryption readiness. The candidate policy with the highest average execution success rate and the shortest switching time is selected as the primary switching target. The selection algorithm uses multi-objective optimization Pareto front solving, prioritizing success rate in case of conflicts. The data encryption process of the current policy is paused. The pause operation saves the encryption context state to a secure storage area, and the pause signal is transmitted via an interrupt mechanism. The policy configuration of the primary switching target is loaded, and the encryption session key is reset. The policy configuration is loaded from the policy library into memory, and the session key is generated using an enhanced key derivation function.

[0042] Referring to Table 1, the next-order policy item of the currently executed policy is located in the policy execution queue. The location algorithm traverses the queue linked list structure, and the order determination is based on the priority weight sorting result. The matching degree between the hardware compatibility parameters of the next-order policy item and the current channel state parameters is verified. The matching degree is calculated using a multi-dimensional feature cosine similarity algorithm, and the similarity threshold is dynamically adjusted. When the matching degree meets the preset conditions, the encryption strength threshold of the next-order policy item is increased to a preset security level, which corresponds to the security requirements of high-reliability application scenarios. The data encryption channel is reinitialized using the increased encryption strength threshold. The reinitialization process includes resetting the cryptographic algorithm and injecting key materials. The initialization completion signal is notified through a callback function.

[0043] Table 1: Comparison Table of Strategy Switching Threshold Parameters The instantaneous bit error rate is collected using a statistical counter from the forward error correction module. The counter value is sampled every millisecond, and the sampled values ​​are digitally filtered. The signal attenuation slope is calculated by fitting the most recent ten signal strength sampling points using the least squares method, and the fitted slope value is converted into a percentage change rate for storage. A hysteresis interval is set for the dynamic noise baseline comparison operation to avoid frequent switching caused by parameter jitter near the threshold; the hysteresis interval width is configurable. The hierarchical management of the first switching threshold corresponds to the service quality requirements of different service types. The service level is read from the configuration file, and the level classification standard is based on latency sensitivity. The trend detection algorithm adds confidence assessment; low-confidence trends require an additional verification period, and the confidence calculation is based on the fitted residuals. The criteria for determining continuous deterioration consider the device's motion state, which is assisted by inertial measurement unit data, and a motion compensation algorithm eliminates false deterioration. A checksum field is added to the data structure of the policy switching record; the checksum is used to check record integrity, and damaged records are automatically repaired. The selection process for alternative policy items performs a policy dependency check; dependent policies must simultaneously meet the selection criteria, and the dependency graph is stored as a directed acyclic graph. The average success rate is calculated by differentiating between different channel conditions. Scenario classification is based on historical channel state clustering results, and scenario labels are weighted with success rate. Handover time statistics include policy configuration loading time and key negotiation time, measured using a high-precision performance counter. The primary handover target selection algorithm implements multi-objective optimization, with a weighted summation of objective functions to generate a comprehensive score, and weight coefficients adjusted according to business requirements. Pausing the data encryption process uses atomic operations to ensure state consistency; atomic operations are implemented through hardware memory barriers, and pause points are selected at packet boundaries. The policy configuration loading process verifies digital signatures to prevent tampering; the signature algorithm uses elliptic curve digital signatures, and the certificate chain is verified for integrity. Session key reset follows forward security principles; the new key has no mathematical association with the old key, and key derivation uses temporary parameter exchange.

[0044] The positioning of the next priority strategy item implements a fast jump mechanism, with queue nodes storing forward and backward pointers, resulting in a constant-time complexity for the positioning operation. Hardware compatibility parameter verification includes processor architecture checks and instruction set support, with instruction set testing performed via feature identifier registers. The multi-dimensional features used in matching degree calculation include channel bandwidth utilization and antenna configuration parameters, with feature vector normalization eliminating the influence of dimensions. The mapping relationship of preset security levels is stored in the security policy database, with level definitions referencing industry security standards, and the standard version is updated periodically. The encryption strength threshold is increased using a step-by-step adjustment, adjusting one security level at a time, with the level interval fixed in security strength units. The re-initialization of the data encryption channel executes a two-way handshake protocol, with a handshake timeout mechanism to prevent initialization suspension; the timeout value is dynamically calculated. Instantaneous bit error rate data is collected to calibrate receiver sensitivity; sensitivity parameters are derived from a device calibration table, which is updated periodically. The calculation of the signal attenuation slope excludes the influence of adaptive antenna gain adjustment, using the gain control signal as a reference input. The hysteresis interval is set using adaptive width adjustment; the interval width is negatively correlated with the channel change rate, and the rate measurement uses derivative calculations. The business-level classification management supports dynamic reclassification. Reclassification events are triggered by changes in application-layer business types, and event notifications employ the observer pattern. The confidence assessment threshold is learned through training on historical data, with training features including the joint distribution of signal strength and bit error rate. The motion state compensation algorithm fuses multi-sensor data, including accelerometers and gyroscopes, using Kalman filtering. The record data structure is designed to support fast queries, with the query index built on the timestamp field and using a B+ tree index.

[0045] The generation of the alternative strategy list optimizes query performance; query conditions are pushed to the database engine for execution, and the database uses indexes to optimize the query plan. The clustering algorithm for scenario classification uses K-means clustering, with the number of cluster centers determined according to the elbow rule, and features are standardized. Performance counters use calibrated clock offsets, and clock synchronization between multi-core processors is achieved through timestamp counters. The Pareto solution set generation for multi-objective optimization uses a non-dominated sorting algorithm, with the solution set size limited to the top ten best solutions. The implementation of hardware memory barriers relies on processor-specific instructions, and the instruction sequence ensures the consistency of memory access order. The certificate chain for digital signature verification starts from the root certificate, which is pre-stored in a secure storage area, and the certificate revocation list is checked periodically. Temporary exchange parameters are generated using a true random number generator, with the random number entropy source derived from hardware noise sources, and entropy quality testing is performed continuously. The pointer maintenance for the queue jump mechanism uses atomic updates, employing comparison-swap instructions to avoid concurrent modification issues. The instruction set testing coverage includes encrypted extension instructions, and extension instruction detection is achieved through feature bit scanning. The feature vector normalization method uses min-max scaling, with scaling parameters obtained statistically from the training data. The security standard version update supports a smooth transition, maintaining compatibility with the old standard policy during the transition period, and the transition period length is configurable. The security strength unit is defined based on the encryption algorithm key length, and a unit conversion table stores the equivalent strength for different algorithms. The handshake protocol is implemented using a three-way handshake process, with encrypted transmission of handshake messages and message authentication codes used for message integrity verification.

[0046] The instantaneous bit error rate acquisition system calibration uses a standard signal source, and the calibration process is completed during the factory testing phase. Calibration data is written to read-only memory. Timing alignment of signal strength sampling points uses an interpolation algorithm, with linear interpolation as the chosen method, and the sampling clock synchronized with the global clock. The adaptive adjustment algorithm for hysteresis intervals uses fuzzy control, with the channel parameter change rate as the input variable and the interval width as the output variable. Service type change detection utilizes deep packet inspection technology, with detection features including protocol header information, and the feature library is updated periodically. Jointly distributed training data collection covers typical application scenarios, including indoor, outdoor, and mobile states, with data annotation done manually. Kalman filter parameter adjustment is based on sensor accuracy indicators derived from the sensor datasheet, and the initial filter value is self-calibrated. Record query optimization implements a caching mechanism, caching hot query results in memory, with a time-driven cache invalidation strategy. Database query plan optimization uses statistical information, including a data distribution histogram, which is periodically reconstructed. The elbow rule automatically determines the optimal number of clusters, the rule indicator calculates the sum of squares within clusters, and inflection point detection uses second-order differencing. The timestamp counter is synchronized using a network time protocol, which corrects clock drift and adaptively adjusts the correction period. The non-dominated sorting algorithm is optimized using fast non-dominated sorting, reducing the sorting time complexity to O(NlogN). Access control for the secure storage area is based on hardware security domains, which isolate data from different applications; domain switching requires privileged instructions.

[0047] Example 5: Parsing the key derivation algorithm and authentication protocol type in the final security policy configuration. The key derivation algorithm field identifier is KDF_AES_256, and the authentication protocol type field value is SC_3_0. Based on the key derivation algorithm, a combination of derivation parameters for the master key and temporary session key is generated. The master key derivation uses the device's unique identifier as the input seed. A two-way authentication process is established according to the authentication protocol type, using the elliptic curve digital signature algorithm. After successful verification, the data encryption channel is activated, using AES-GCM mode. The key update cycle and authentication validity period in the encryption channel are continuously monitored, with the key update cycle set to 3600 seconds. An automatic renewal request is initiated before expiration, triggered 300 seconds prior to expiration. The key derivation algorithm is parsed to verify the digital signature; the configuration file signature uses the RSA-2048 algorithm. The authentication protocol type is parsed to check version compatibility; backward compatibility mode is enabled when the protocol version is lower than 4.0. The master key derivation parameter combination includes a key purpose identifier; keys with different purposes use different derivation paths. The derived parameters of the temporary session key are supplemented with context information, including the device type and session identifier. The two-way authentication process follows the FIPS 196 standard, and the randomness of the challenge code passes an entropy test. Activation testing of the data encryption channel verifies the encryption functionality by sending known plaintext to verify the correctness of encryption and decryption.

[0048] The key update cycle is monitored using multiple redundant timers, with the master timer being a hardware counter. Authentication validity checks are performed using both the device system clock and network time protocol. Automatic renewal requests are encrypted using the current session key, and the request message includes new key negotiation parameters. Failures in the renewal process are handled with multiple retransmissions, with exponential backoff intervals. Key switching after successful renewal uses a smooth transition, with the old and new keys used concurrently for 10 seconds before the old key is replaced. Key derivation algorithm parameter verification includes algorithm strength checks; algorithms with insufficient strength are flagged. The authentication protocol type parsing handles protocol extension fields, which indicate optional security enhancements. The hierarchical structure of the master key derivation path supports multi-level key derivation. Formal session key derivation introduces a forward security mechanism, using a new random number for each derivation. A timeout handling mechanism is implemented for the two-way authentication process; if authentication fails to complete within the timeout period, the connection is terminated. The data encryption channel activation order is parameter negotiation followed by engine initialization. Key derivation algorithm strength testing performs cryptographic test vector verification. Authentication protocol type version checks ensure that interconnected devices use the same protocol version. The master key derivation parameters are protected using secure hardware storage. Ephemeral session key derivation is logged in an audit log, containing key usage and derivation timestamps. Two-way authentication certificate verification follows the X.509 standard. Performance monitoring of the data encryption channel measures throughput and latency.

[0049] The choice of key derivation algorithm considers the device's computing power. The configuration of authentication protocol types supports the coexistence of multiple protocols. Master key storage employs distributed storage, with some key components memorized by the user. Temporary session key derivation uses a deterministic random number generator. The auxiliary channel for two-way authentication uses visual or audio channels. Key updates for the data encryption channel are proactively pushed. The implementation of the key derivation algorithm passes payment industry security certification. Parameter configuration of the authentication protocol type is protected by security policies. Master key generation is completed within a secure element. Temporary session key derivation uses a hardware cryptographic engine. Biometric integration for two-way authentication, using fingerprint or facial recognition as auxiliary authentication factors. Key updates for the data encryption channel are tied to transactions. Real-time testing of the key derivation algorithm measures algorithm execution time. Optimization of the authentication protocol type reduces handshake rounds. Master key updates are synchronized with device maintenance cycles. Temporary session key derivation considers the deterministic requirements of the control system. The certificate design for two-way authentication includes device lifecycle information. Error tolerance mechanisms for the data encryption channel ensure system reliability. In practical implementation, when the Bluetooth smart lock pairs with the mobile phone, the key derivation algorithm in the final security policy configuration is PBKDF2. The smart lock combines the MAC address and the manufacturer's certificate hash when generating the master key. The ephemeral session key derivation uses elliptic curve multiplication to calculate the shared key. Two-way authentication employs a timestamp challenge mechanism; upon successful verification, the AES-CCM encrypted channel is activated. The key update cycle is set to 3580 seconds, automatically initiating a renewal request before expiration. In medical device scenarios, the heart rate monitor uses the HMAC-BasedKDF algorithm to generate the master key. The ephemeral session key derivation undergoes 1000 iterations to enhance security. Two-way authentication uses a certificate chain verification mechanism. The encrypted channel uses the AES-128-GCM algorithm to protect patient data. Key validity monitoring employs a hardware timer to ensure continuous and secure transmission of medical data.

[0050] Industrial IoT gateways use the national cryptographic algorithm SM2 for key derivation. Sensor nodes employ an identifier-based authentication protocol. Dynamic token authentication ensures real-time security of the industrial control system. Encrypted channels use lightweight algorithms to meet the needs of resource-constrained environments. In vehicle-to-everything (V2X) communication, the on-board unit generates a master key using GPS location information. Temporary session keys are bound to traffic event data to ensure key uniqueness. Digital certificate verification mechanisms safeguard V2X communication security. Key update strategies are dynamically adjusted based on message criticality. Smart home systems employ lightweight key derivation algorithms. PIN code authentication transmits verification information through an auxiliary channel. Device pairing codes are combined with network identifiers to generate master keys. Encrypted commands use efficient algorithms to ensure fast response times for smart devices. Mobile payment terminals follow the PCI standard for key management. A three-tier certificate system ensures payment transaction security. Transaction serial numbers are combined with random numbers to generate session keys. Biometric authentication enhances payment system security. Industrial control systems use dedicated encryption algorithms to meet real-time requirements. Control parameters are associated with key materials to ensure system determinism. Two-way digital certificates contain device function permission information. Lightweight encryption algorithms ensure security while meeting control timeliness requirements.

[0051] 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.

[0052] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An optimized method for secure communication in Bluetooth chip devices, characterized in that, Includes the following steps: Identify the device type identifier and protocol version characteristics in the current Bluetooth communication link, extract the hardware capability parameters of the target Bluetooth chip based on the device type identifier, and generate an initial security policy set based on the protocol version characteristics; The channel quality data and interference intensity sequence of the target Bluetooth chip during historical communication cycles are collected. The channel quality data is segmented and fitted to obtain the channel stability index. The dynamic noise baseline is calculated by combining the interference intensity sequence. A subset of candidate policies is selected based on the matching degree between hardware capability parameters and the initial security policy set. The candidate policy subsets are then prioritized using channel stability indicators to generate a policy execution queue. Based on the dynamic noise baseline, the encryption strength threshold of each security policy in the policy execution queue is adjusted. The policy execution queue is iteratively optimized according to the adjusted encryption strength threshold, and the final security policy configuration is output. When establishing a communication connection with the target Bluetooth chip, the final security policy configuration is loaded and channel status changes are monitored in real time. When the channel status changes exceed the preset tolerance, a dynamic policy switching mechanism is triggered.

2. The optimization method for secure communication of Bluetooth chip devices according to claim 1, characterized in that, The step of extracting the hardware capability parameters of the target Bluetooth chip based on the device type identifier includes: Parse the manufacturer code and chip model fields in the device type identifier, and index the corresponding processor frequency and memory capacity from the pre-built hardware capability database; The target Bluetooth chip's currently supported encryption algorithm types and key length range are detected, and a hardware capability parameter matrix is ​​generated by combining the processor's clock speed and memory capacity. The hardware capability parameter matrix is ​​normalized to obtain a quantitative scoring vector for the hardware capability parameters.

3. The optimization method for secure communication of Bluetooth chip devices according to claim 1, characterized in that, The process of segmenting and fitting the channel quality data to obtain the channel stability index includes: The historical communication period is divided into multiple time windows of equal length, and the packet loss rate and signal strength fluctuation value of the channel quality data are extracted in each time window. Linear regression models were used to fit the packet loss rate change curves within each time window, and the slope difference of the fitted curves between adjacent time windows was calculated. The local stability coefficient for each time window is generated by weighting the signal strength fluctuation value and the slope difference. The local stability coefficients for all time windows are calculated using a moving average, and the channel stability index is output.

4. The optimization method for secure communication of Bluetooth chip devices according to claim 1, characterized in that, The calculated dynamic noise baseline includes: Perform frequency domain transformation on the interference intensity sequence to extract the energy distribution spectrum of the characteristic frequency bands; Identify sudden interference pulses exceeding a preset threshold in the energy distribution spectrum, and statistically analyze the duration and interval of the sudden interference pulses. An adaptive update model for the dynamic noise baseline is established based on the ratio of duration to interval period.

5. The optimization method for secure communication of Bluetooth chip devices according to claim 1, characterized in that, The step of prioritizing the candidate strategy subset using channel stability indicators includes: Establish a mapping table between channel stability indicators and the success rate of security policy execution; Remove strategy items with a success rate below the threshold from the mapping table in the candidate strategy subset to generate a valid strategy set; Calculate the policy priority weights based on the sensitivity of each policy item in the effective policy set to channel stability indicators; The effective policy set is arranged in descending order of policy priority weight, and a policy execution queue is generated.

6. The optimization method for secure communication of Bluetooth chip devices according to claim 1, characterized in that, The encryption strength thresholds for each security policy in the adjustment policy execution queue include: Obtain the current fluctuation amplitude of the dynamic noise baseline, and determine the step size for adjusting the encryption strength based on the fluctuation amplitude; Select the first policy to be executed from the policy execution queue and read its initial encryption strength threshold and maximum allowed strength value; The initial encryption strength threshold is adjusted by increasing or decreasing the encryption strength adjustment step size so that the adjusted threshold does not exceed the maximum allowable strength value. Repeat the above correction operation on the remaining policy items in the policy execution queue until all policy items in the queue have been adjusted.

7. The optimization method for secure communication of Bluetooth chip devices according to claim 1, characterized in that, The dynamic switching mechanism for the triggering strategy includes: Real-time acquisition of current channel state parameters, including instantaneous bit error rate and signal attenuation slope; The instantaneous bit error rate is compared with the dynamic noise baseline, and the primary strategy handover is initiated when the difference exceeds the first handover threshold. After the primary strategy switch, the changing trend of the signal attenuation slope is detected, and the secondary strategy switch is triggered when the changing trend continues to deteriorate. Record channel state parameters and execution policy identifiers during the policy switching process, and update historical communication cycle data.

8. The optimization method for secure communication of Bluetooth chip devices according to claim 7, characterized in that, The initial strategy switching includes: Select alternative policy items whose encryption strength threshold is lower than the current value from the policy execution queue; Calculate the average success rate and switching time of each alternative strategy item within the historical communication cycle; Select the alternative strategy with the highest average success rate and the shortest switching time as the primary switching target; Pause the current policy's data encryption process, load the policy configuration of the primary switching target, and reset the encryption session key.

9. The optimization method for secure communication of Bluetooth chip devices according to claim 7, characterized in that, The triggering of the secondary policy switch includes: Locate the next policy item in the policy execution queue that is currently executing the policy; Verify the matching degree between the hardware compatibility parameters of the next priority policy item and the current channel state parameters; When the matching degree meets the preset conditions, the encryption strength threshold of the next priority policy item will be increased to the preset security level; The data encryption channel is reinitialized using the increased encryption strength threshold to complete the secondary strategy switch.

10. The optimization method for secure communication of Bluetooth chip devices according to claim 1, characterized in that, The loading of the final security policy configuration includes: Analyze the key derivation algorithm and authentication protocol type in the final security policy configuration; The derivation parameter combination of the master key and the temporary session key is generated according to the key derivation algorithm; Establish a two-way authentication process according to the authentication protocol type, and activate the data encryption channel after successful authentication; Continuously monitor the key update cycle and authentication validity period in the encrypted channel, and initiate an automatic renewal request before expiration.

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