Wireless speed regulation control method and system of star flash module in computer cooling fan

Abstract

The invention discloses a wireless speed regulation control method and system of a star flash module in a computer cooling fan, and relates to the technical field of wireless communication, and the wireless speed regulation control method comprises the following steps: initializing the rotating speed of the cooling fan, outputting an initial rotating speed control instruction, sending the initial rotating speed control instruction to a star flash communication module, and executing preset rotating speed starting of the initial rotating speed control instruction; working condition time sequence data tracking acquisition of the CPU is carried out; after temperature trend prediction, output of a reference temperature prediction curve, dynamic compensation calibration, output of a compensation temperature prediction curve, heat dissipation rotation speed matching and output of a preset rotation speed instruction are carried out, starting and ending timestamps and the preset rotation speed instruction are packaged as a first SLE protocol frame through a star flash adaptation driver, and the first SLE protocol frame is sent to a star flash communication module; and the rotating speed dynamic calibration of the cooling fan is triggered. The technical problem that in the prior art, fan speed regulation cannot be precisely and dynamically adjusted according to the real-time working condition of a CPU is solved, and the technical effects that the rotating speed of the cooling fan is precisely controlled, and the cooling efficiency is improved are achieved.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, specifically to a wireless speed control method and system for using a StarSpark module in a computer cooling fan. Background Technology

[0002] With the continuous improvement of computer processor performance and the increasing complexity of application scenarios, cooling fans play a crucial role in maintaining stable system operation. However, with the increase in CPU power consumption and frequent fluctuations in operating load, the speed control of cooling fans faces higher requirements. Traditional fan speed control methods mostly rely on wired connections to the motherboard and fixed threshold temperature control strategies, which suffer from problems such as large response latency, inaccurate adjustment, and insufficient noise control, making it difficult to meet the needs of modern computers for efficient heat dissipation and low-noise operation. Summary of the Invention

[0003] This application provides a wireless speed control method and system for StarShine modules in computer cooling fans, which addresses the technical problems in the prior art where fan speed control relies on wired connections, has large response delays, and cannot be accurately and dynamically adjusted according to the real-time operating conditions of the CPU.

[0004] In view of the above problems, this application provides a wireless speed control method and system for using the StarShine module in a computer cooling fan.

[0005] A first aspect of this application provides a method for wireless speed control of a StarSpark module in a computer cooling fan, the method comprising: After the computer starts up, the host terminal initializes the cooling fan speed by matching the running load vector according to the startup timestamp and outputs an initial speed control command. The host terminal sends the initial speed control command to the StarSpark communication module on the fan end through the StarSpark low-latency wireless link, driving the cooling fan to start at the preset speed of the initial speed control command. The host terminal uses the StarSpark adapter driver to call the StarSpark protocol to track and collect CPU operating condition timing data, wherein the operating condition timing data includes timing temperature data, timing load data, and timing ambient temperature. The host terminal performs temperature trend prediction based on the timing temperature data and timing load data, outputs a baseline temperature prediction curve, and then performs dynamic compensation calibration based on the timing ambient temperature, outputting a compensated temperature prediction curve, wherein the compensated temperature prediction curve is associated with start and end timestamps. The host terminal matches the cooling fan speed according to the compensated temperature prediction curve, outputs a preset speed command, and then encapsulates the start and end timestamps and the preset speed command into a first SLE protocol frame through the StarSpark low-latency wireless link, sending it to the StarSpark communication module to trigger the dynamic calibration of the cooling fan speed.

[0006] A second aspect of this application provides a wireless speed control system for a StarSpark module in a computer cooling fan, the system comprising: The system includes a speed initialization module, used to initialize the cooling fan speed on the host side after computer startup by matching the running load vector according to the startup timestamp, and outputting an initial speed control command; a drive module, used by the host side to send the initial speed control command to the StarSpark communication module on the fan side via the StarSpark low-latency wireless link, driving the cooling fan to execute the preset speed start of the initial speed control command; and a data tracking and acquisition module, used by the host side to call the StarSpark protocol through the StarSpark adapter driver to track and acquire CPU operating condition timing data, wherein the operating condition timing data includes timing temperature data, timing load data, and timing ambient temperature; and compensation. The calibration module is used by the host to predict temperature trends based on time-series temperature data and time-series load data, output a reference temperature prediction curve, and then perform dynamic compensation calibration based on the time-series ambient temperature to output a compensated temperature prediction curve. The compensated temperature prediction curve is associated with start and end timestamps. The dynamic calibration module is used by the host to match the cooling fan speed according to the compensated temperature prediction curve, output a preset speed command, and then encapsulate the start and end timestamps and the preset speed command into a first SLE protocol frame via the StarSpark low-latency wireless link, which is then sent to the StarSpark communication module to trigger dynamic calibration of the cooling fan speed.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: After the computer boots up, the host terminal initializes the cooling fan speed by matching the running load vector according to the boot timestamp and outputs an initial speed control command. The host terminal sends the initial speed control command to the StarSpark communication module on the fan end via the StarSpark low-latency wireless link, driving the cooling fan to execute the preset speed start of the initial speed control command. The host terminal uses the StarSpark adapter driver to call the StarSpark protocol to track and collect CPU operating condition timing data, wherein the operating condition timing data includes timing temperature data, timing load data, and timing ambient temperature. The host terminal performs temperature trend prediction based on the timing temperature data and timing load data, outputs a baseline temperature prediction curve, and then performs dynamic compensation calibration based on the timing ambient temperature, outputting a compensated temperature prediction curve, wherein the compensated temperature prediction curve is associated with start and end timestamps. The host terminal matches the cooling fan speed according to the compensated temperature prediction curve, outputs a preset speed command, and then encapsulates the start and end timestamps and the preset speed command into a first SLE protocol frame via the StarSpark low-latency wireless link, sending it to the StarSpark communication module to trigger dynamic speed calibration of the cooling fan. This invention addresses the technical problems of existing technologies, such as fan speed control relying on wired connections, large response delays, and the inability to make precise dynamic adjustments based on real-time CPU operating conditions. By introducing a StarFlash low-latency wireless link and a temperature trend prediction and dynamic compensation calibration mechanism based on time-series data, it achieves the technical effects of precise control of cooling fan speed, reducing system noise, and improving heat dissipation efficiency. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic flowchart illustrating the wireless speed control method of the Star Flash module in a computer cooling fan provided in an embodiment of this application; Figure 2 This is a schematic diagram of the wireless speed control system structure of the StarShine module in a computer cooling fan, as provided in an embodiment of this application.

[0010] Explanation of reference numerals in the attached diagram: Speed ​​initialization module 11, drive module 12, data tracking and acquisition module 13, compensation and calibration module 14, dynamic calibration module 15. Detailed Implementation

[0011] This application provides a wireless speed control method and system for computer cooling fans using a StarSpark module. It addresses the technical problems in existing technologies, such as fan speed control relying on wired connections, large response delays, and the inability to make precise dynamic adjustments based on real-time CPU conditions. By introducing a StarSpark low-latency wireless link and a temperature trend prediction and dynamic compensation calibration mechanism based on time-series data, it achieves the technical effects of precise control of cooling fan speed, reduced system noise, and improved heat dissipation efficiency.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0014] Example 1, as Figure 1 As shown, this application provides a wireless speed control method for a StarSpark module in a computer cooling fan, the method comprising: Step S100: After the computer starts up, the host side matches the running load vector according to the startup timestamp to initialize the cooling fan speed and outputs the initial speed control command.

[0015] In this embodiment, after the computer is powered on and started, the host first obtains the startup timestamp synchronously via the StarFlash protocol and matches the corresponding startup response interval based on the startup timestamp. Subsequently, the host calls historical load timing data to perform inertial matching and outputs an inertial load pattern corresponding to the startup phase. During this process, the host interactively obtains initial process characteristics and generates an initial load pattern based on these characteristics. After integrating the initial load pattern, the inertial load pattern, and the startup response interval, the host constructs a running load vector representing the startup condition and loads this running load vector into a pre-built resource scheduling model to execute speed control decisions, generating initial speed control commands to drive the initialization of the cooling fan speed.

[0016] Furthermore, in the method provided in the application embodiments, the host side initializes the cooling fan speed by matching the running load vector according to the startup timestamp and outputs an initial speed control command, and also includes: After synchronizing the startup timestamp via the StarFlash protocol, the startup response interval is matched based on the startup timestamp; historical load timing data is called to perform inertial matching of the startup timestamp, and the inertial load pattern is output; after interactively obtaining the initial process characteristics, the initial load pattern is mapped based on the initial process characteristics; the running load vector is constructed based on the initial load pattern, the inertial load pattern, and the startup response interval; the running load vector is loaded into the resource scheduling model for speed control decision-making, and the initial speed control command is output.

[0017] In this embodiment, after the computer starts up, the host first uses a time synchronization method to synchronize the startup timestamp via the StarScan protocol. The StarScan protocol provides a low-latency communication mechanism, enabling timestamp alignment with millisecond-level precision. Using the startup timestamp as the base time information for the boot phase, after synchronization, the host continues to use a range matching method, comparing the startup timestamp with a preset time range library to quickly locate and confirm the startup response range corresponding to the boot load.

[0018] Next, the host calls the stored historical load time series data and performs comparison using the inertial matching method. In this process, the startup timestamp is used as a reference, and the trend similarity is calculated to align the current startup time series with the historical load curve segments one by one, identify the historical data segments similar to the current startup scenario, and output the inertial load pattern.

[0019] The host then uses a process feature acquisition method to interact with the boot process and obtain initial process features. These initial process features include information about the earliest started program, such as driver loading, background initialization tasks, and security detection tasks. Subsequently, a feature mapping method is used to standardize the collected initial process features, transforming them into a unified data format suitable for load modeling, and generating an initial load pattern.

[0020] Subsequently, a runtime load vector is constructed based on the initial load pattern, the inertial load pattern, and the startup response interval. In this process, the host first integrates the immediate task demands provided by the initial load pattern with the historical operational patterns provided by the inertial load pattern, and then introduces the startup response interval as a time constraint. These three types of information are uniformly encoded into multi-dimensional numerical parameters, including temperature change trends, task load intensity, and time distribution range. For example, the initial load pattern might show a high load, the inertial load pattern a medium load, and the startup response interval a range of 0 to 10 seconds. The host then transforms these data into numerical dimensions and combines them into a runtime load vector.

[0021] Finally, the running load vector is loaded into the resource scheduling model for speed control decisions. In this process, the running load vector is loaded into the resource scheduling model, where P sample load vectors are first matched, and a corresponding similarity confidence weight is assigned to each sample load vector. Then, P sets of sample speed control commands corresponding to the sample load vectors are retrieved, and the instruction domain intersection of these sample speed control commands is solved. Finally, cosine similarity weighted aggregation is performed based on the similarity confidence weights to generate the initial speed control command.

[0022] Furthermore, in the method provided in the application embodiment, before loading the running load vector into the resource scheduling model for speed control decision-making and outputting the initial speed control command, it further includes: Multiple sample load vectors are output by combining and enumerating load vectors; multiple sample load vectors are used as search conditions to locally search multiple candidate speed instruction sets; multiple candidate speed instruction sets are filtered by reuse frequency weighting based on timing decay coefficients to output multiple sets of sample speed control instructions; the multiple sample load vectors and multiple sets of sample speed control instructions are associated and stored to construct the resource scheduling model.

[0023] In this embodiment, the existing load data is first arranged and combined in multiple dimensions using a combination enumeration method. Key parameters such as CPU temperature and ambient temperature are divided into different ranges and combined sequentially to obtain multiple sample load vectors.

[0024] Next, multiple sample load vectors are used as search criteria, and a similarity comparison method is employed to find corresponding records in the local data repository. The similarity comparison is calculated based on cosine similarity, comparing the sample load vector to be retrieved with each historical load vector stored in the repository. Taking cosine similarity as an example, the degree of similarity is measured by calculating the angle between the vectors; the closer the value is to 1, the higher the similarity. When the similarity reaches a preset threshold, the set of speed control commands corresponding to that historical record is retrieved. In this way, multiple candidate speed command sets associated with the sample load vectors are retrieved from the local data repository.

[0025] Subsequently, a timing decay coefficient is introduced to perform frequency-weighted filtering of multiple candidate speed command sets. The timing decay coefficient is a preset parameter used to adjust the weighting of the effectiveness of candidate command sets over time. Specifically, the more frequently a candidate speed command appears recently, the higher its weight is assigned. As the time interval increases, this weight gradually decreases according to the decay function. For example, command sets that have been called multiple times within the last three months will be given higher priority in the filtering, while command sets that appeared only once a year ago will be weakened or even eliminated. After this filtering, multiple sets of sample speed control commands are output.

[0026] Finally, the sample load vectors mentioned above are associated and stored with their corresponding sample speed control commands. In this process, a mapping relationship is established between each sample load vector and one or more sets of speed control commands, and this mapping table is stored in the resource repository. Through this storage and mapping, a resource scheduling model is ultimately constructed.

[0027] Furthermore, in the method provided in the application embodiment, loading the running load vector into the resource scheduling model for speed control decision-making and outputting the initial speed control command further includes: The running load vector is loaded into the resource scheduling model, and P sample load vectors are matched and output, wherein the P sample load vectors have P similarity confidence weights; P sets of sample speed control commands of the P sample load vectors are retrieved; after solving the instruction domain intersection of the P sets of sample speed control commands, cosine similarity weighted aggregation is performed according to the P similarity confidence weights, and the initial speed control command is output.

[0028] In this embodiment, the running load vector is loaded into the resource scheduling model and then a matching operation is performed. During this process, a cosine similarity calculation method is used to compare the running load vector with historical vectors stored in the resource scheduling model, selecting the P sample load vectors with the highest similarity. Each sample load vector is assigned a similarity confidence weight based on its similarity value. This weight is obtained by normalizing the similarity calculation result and reflects the closeness between the sample load vector and the running load vector, thus obtaining P sample load vectors with similarity confidence weights.

[0029] After obtaining P sample load vectors, the corresponding P sets of sample speed control instructions are retrieved. The sample speed control instructions are fan speed control schemes stored in the resource scheduling model for specific sample load vectors.

[0030] Next, the instruction domain intersection problem is solved for the P sets of sample speed control commands. By comparing the speed ranges of different instruction sets, the common parts are extracted, and the differentiated or conflicting parts are eliminated. For example, when the effective ranges of the three sets of commands are 2000 to 3000 rpm, 2200 to 3200 rpm, and 2100 to 2900 rpm, the interval obtained by the intersection problem is 2200 to 2900 rpm, which is taken as the common range of multiple commands. Finally, cosine similarity weighted aggregation is performed based on P similarity confidence weights. In this process, the instruction domain intersection result is used as the basis, and each set of sample speed control commands is weighted according to its similarity confidence weight, so that the samples that are closer to the running load vector play a greater role in the aggregation process. For example, when the similarity confidence weights of the three samples are 0.8, 0.6, and 0.4, the weighted aggregation result tends to favor the speed scheme corresponding to the sample with the highest similarity, but at the same time retains the minor corrections provided by other samples. After weighted aggregation, a control result that conforms to the characteristics of the operating load vector is finally generated and output as the initial speed control command.

[0031] Step S200: The host sends the initial speed control command to the StarSignal communication module on the fan end through the StarSignal low-latency wireless link, driving the cooling fan to start at the preset speed of the initial speed control command.

[0032] In this embodiment, the host sends an initial speed control command via the StarSignal low-latency wireless link. This initial speed control command is encapsulated into a second SLE protocol frame by the StarSignal adapter driver and transmitted to the StarSignal communication module on the fan side via the StarSignal RF module using GFSK modulation. Upon receiving the protocol frame, the StarSignal communication module performs a CRC check. If the check passes, it outputs a PWM signal through the PWM interface to drive the fan motor, thereby executing the preset speed start of the initial speed control command. If the check fails, it returns NAK feedback to the host via the StarSignal bidirectional link, triggering the command retransmission mechanism.

[0033] Furthermore, in the method provided in the application embodiment, the host end sends the initial speed control command to the StarSignal communication module on the fan end via the StarSignal low-latency wireless link, driving the cooling fan to execute the preset speed start of the initial speed control command, and further includes: The StarShine adapter driver encapsulates the initial speed control command into a second SLE protocol frame and sends it to the StarShine communication module via the StarShine radio frequency module using GFSK modulation. Upon receiving the second SLE protocol frame, the StarShine communication module performs a CRC check. If the check passes, it outputs a PWM signal through the PWM interface to drive the fan motor and execute the preset speed start of the initial speed control command. If the check fails, it returns NAK feedback to the host via the StarShine bidirectional link, triggering a retransmission of the initial speed control command.

[0034] In this embodiment, the StarShine adapter driver first performs an encapsulation operation. By using the protocol frame encapsulation method, the initial speed control command is processed according to the format of the StarShine communication protocol to generate a second SLE protocol frame containing a frame header, data segment and check field.

[0035] Next, the second SLE protocol frame is transmitted to the fan end via the StarScan RF module. During this process, GFSK modulation (Gaussian Frequency Shift Keying) is used to map the digitized command data into a carrier frequency offset signal, which is then transmitted via the StarScan low-latency wireless link. This process also transmits the GFSK modulated signal to the StarScan communication module.

[0036] After the StarScan communication module receives the second SLE protocol frame, it first performs a CRC check on the command data in the second protocol frame. The CRC check verifies the consistency between the transmitted data and the check field using the cyclic redundancy method, determining whether the command data is complete and error-free during transmission. If the CRC check result is correct, it indicates that the initial speed control command transmission is reliable, and the execution phase can begin.

[0037] When the CRC check passes, the StarFlash communication module outputs a PWM signal to the fan motor via the PWM interface. This PWM signal adjusts the duty cycle through pulse width modulation, thereby controlling the motor's drive voltage and current, enabling the cooling fan to start at the preset speed according to the parameters set in the initial speed control command, ensuring that the fan can quickly enter the target speed range during the startup phase.

[0038] If the CRC check fails, it indicates that the command data was lost or corrupted during transmission. In this case, the StarShine communication module returns a NAK feedback to the host via the StarShine bidirectional link. This feedback is a negative acknowledgment signal used to notify the host of transmission failure. After receiving the NAK feedback, the host again calls the StarShine adapter driver to re-encapsulate the command, regenerates the second SLE protocol frame, and sends it through the StarShine RF module to ensure that the initial speed control command can be correctly received and executed by the fan.

[0039] Step S300: The host end calls the StarSpark protocol through the StarSpark adapter driver to collect CPU operating condition timing data, wherein the operating condition timing data includes timing temperature data, timing load data and timing ambient temperature.

[0040] Furthermore, the method provided in the application embodiments also includes: The StarSpark communication module at the fan end tracks and collects the timing ambient temperature through the ADC interface, and then transmits it back to the host end via the StarSpark bidirectional link. The host end uses the StarSpark adapter driver to call the StarSpark protocol to collect system-level interface data of the CPU and obtain the timing temperature data and timing load data.

[0041] In this embodiment, the host computer establishes a communication channel with the CPU by calling the StarSpark protocol through the StarSpark adapter driver, which is used to perform operating condition timing data tracking and acquisition. The operating condition timing data includes timing temperature data, timing load data, and timing ambient temperature.

[0042] During data acquisition, the StarScan communication module on the fan end tracks and acquires the ambient temperature over time via the ADC interface. The ADC interface, or analog-to-digital converter, converts the analog temperature signal acquired by the external temperature sensor into a digital value, facilitating processing and transmission by the StarScan communication module. After acquisition, the ambient temperature over time is transmitted back to the host computer in real time via the StarScan bidirectional link.

[0043] Meanwhile, the host computer continues to invoke the StarSpark protocol through the StarSpark adapter driver to access the CPU's system-level interface to obtain timing temperature data and timing load data. The timing temperature data reflects the CPU's temperature changes at different times, characterizing the chip's heat dissipation status. The timing load data reflects the CPU's computational usage at various times, used to measure the chip's computing power requirements and power consumption levels. By collecting these three types of data, the host computer ultimately obtains operating timing data covering both the CPU's internal state and external environmental conditions.

[0044] Step S400: The host terminal performs temperature trend prediction based on time-series temperature data and time-series load data, outputs a baseline temperature prediction curve, and then performs dynamic compensation calibration based on the time-series ambient temperature to output a compensated temperature prediction curve, wherein the compensated temperature prediction curve is associated with start and end timestamps.

[0045] In this embodiment, the host computer uses an LSTM-based temperature trend prediction method to perform multi-scale processing and modeling on time-series temperature and load data, generating a baseline temperature prediction curve reflecting the CPU's heat variation trend. Then, it compares the time-series ambient temperature with a predefined ambient temperature threshold. When the temperature is within the threshold range, dynamic power management is triggered; when the temperature deviates from the threshold range, the baseline temperature prediction curve is corrected based on the deviation vector, ultimately outputting a compensated temperature prediction curve. The compensated temperature prediction curve is associated with start and end timestamps to ensure that the prediction results can guide fan speed adjustment within a specific time interval.

[0046] Furthermore, in the method provided in the application embodiment, after the host terminal performs temperature trend prediction based on time-series temperature data and time-series load data, outputs a reference temperature prediction curve, and then performs dynamic compensation calibration based on the time-series ambient temperature to output a compensated temperature prediction curve, it further includes: A lightweight temperature trend prediction model is constructed based on an LSTM model. The time-series temperature data and time-series load data are multi-scale sliding segmented to obtain multiple time window segments. These time window segments are loaded into the temperature trend prediction model to obtain multiple initial time-varying temperature data. The multiple initial time-varying temperature data are weighted and aggregated to output the baseline temperature prediction curve. An ambient temperature threshold is predefined, and the deviation of the time-series ambient temperature is compared using this threshold. If the time-series ambient temperature falls within the ambient temperature threshold, dynamic power consumption management is triggered. If the time-series ambient temperature deviates from the ambient temperature threshold, dynamic compensation calibration of the baseline temperature prediction curve is performed based on the deviation vector, and the compensated temperature prediction curve is output.

[0047] In this embodiment, when constructing a lightweight temperature trend prediction model based on an LSTM model, a supervised training method is first employed. Historical time-series temperature data, historical time-series load data, and historical time-series ambient temperature are used as inputs, and the actual future temperature for the corresponding time period is used as the output target. Multi-scale training samples are constructed, where the training data is divided into short-term windows (e.g., 5 seconds), medium-term windows (e.g., 30 seconds), and long-term windows (e.g., 2 minutes) using a multi-scale partitioning method, enabling the model to simultaneously learn short-term fluctuations and long-term trends. During training, the LSTM model utilizes its memory units and gating mechanisms to capture the temperature-load coupling relationship at different time scales. Through iterative optimization of parameters using a backpropagation algorithm, the predictive ability for future temperatures is gradually improved. Based on this, pruning and quantization are applied to control the parameter scale and computational load, resulting in a lightweight temperature trend prediction model.

[0048] Next, multi-scale sliding segmentation is performed on the collected time-series temperature data and time-series load data. The data is segmented and aligned according to different window lengths and fixed step sizes to obtain multiple sets of time window segments covering features at different time scales.

[0049] The aforementioned multiple time window segments are then input into the trained temperature trend prediction model for inference, and initial time-varying temperature data corresponding to the start and end times of each segment are obtained, thus yielding multiple initial time-varying temperature data.

[0050] After obtaining multiple initial time-varying temperature data, a weighted aggregation method is used to fuse them on a unified time axis. The weights are allocated based on two factors: firstly, the size of the time window, with shorter window data receiving higher weight during sudden temperature rises due to their faster response, and longer window data receiving higher weight during stable operation due to their smoother performance; secondly, the recentity of the data, with predictions closer to the current time receiving greater weight and those further away receiving less weight. For example, when the CPU experiences a sudden high load within 10 seconds, the initial time-varying temperature data corresponding to the shorter window will receive higher weight, while during long-term stable operation, the initial time-varying temperature data corresponding to the longer window will dominate the weighting process. In this way, a baseline temperature prediction curve is ultimately output.

[0051] Subsequently, an ambient temperature threshold is predefined, and a threshold determination method is used to compare the real-time collected ambient temperature with this threshold. If the ambient temperature is within the threshold range, it indicates that the heat dissipation conditions are normal, and the host triggers dynamic power management. In dynamic power management, the host uses a reference temperature prediction curve as the control basis and employs a voltage and frequency adjustment method to moderately reduce the CPU's operating frequency, thereby reducing power consumption and heat generation. At the same time, a task scheduling method is used to delay or allocate some high-load processes to low-priority execution, thereby dispersing heat generation.

[0052] If the time-series ambient temperature deviates from the ambient temperature threshold, dynamic compensation calibration of the reference temperature prediction curve is performed based on the deviation vector. In this process, the direction of deviation is first determined based on the ambient temperature threshold. If it is determined to be a high-temperature deviation, the deviation between the time-series ambient temperature and the ambient temperature threshold is calculated, forming a temperature deviation vector. Subsequently, this temperature deviation vector is used to match the corresponding temperature compensation coefficient in the compensation ratio mapping table, and this temperature compensation coefficient is used to calibrate the reference temperature prediction curve, finally outputting the corrected compensated temperature prediction curve. If the deviation direction is a low-temperature deviation, the deep sleep mechanism of the starlight communication module is triggered.

[0053] Furthermore, in the method provided in the application embodiment, if the time-series ambient temperature deviates from the ambient temperature threshold, dynamic compensation calibration of the reference temperature prediction curve is performed based on the deviation vector, and the compensated temperature prediction curve is output, further comprising: Using the ambient temperature threshold as a benchmark, the deviation direction of the time-series ambient temperature is determined; if the deviation direction is high temperature deviation, the deviation between the time-series ambient temperature and the ambient temperature threshold is calculated, and a temperature deviation vector is constructed; the temperature deviation vector is used to match the temperature compensation coefficient in the compensation ratio mapping table; the temperature compensation coefficient is used to calibrate the benchmark temperature prediction curve, and the compensated temperature prediction curve is output.

[0054] In this embodiment, an ambient temperature threshold is first used as a benchmark, and a threshold determination method is employed to determine the direction of the real-time collected ambient temperature. The currently collected ambient temperature is compared with a predefined ambient temperature threshold. If the current value is greater than the ambient temperature threshold, it is determined to be a high temperature deviation; if it is less than the ambient temperature threshold, it is determined to be a low temperature deviation.

[0055] If the deviation direction is high temperature deviation, then the deviation between the time series ambient temperature and the ambient temperature threshold is calculated, and this deviation is used as the temperature deviation vector.

[0056] The temperature deviation vector is then input into the compensation ratio mapping table for lookup matching. The compensation ratio mapping table stores the correspondence between different temperature deviation ranges and their corresponding temperature compensation coefficients. For example, when the temperature deviation vector is +5℃, the corresponding temperature compensation coefficient in the compensation ratio mapping table is 1.2. The temperature compensation coefficient is obtained by inputting the temperature deviation vector into the compensation ratio mapping table for lookup matching.

[0057] Finally, the matched temperature compensation coefficient is applied to the baseline temperature prediction curve. This involves proportionally correcting the curve at all prediction points according to the temperature compensation coefficient, making the overall trend of the curve closer to the actual operating conditions under high-temperature deviation environments. After this calibration, the compensated temperature prediction curve is output.

[0058] Furthermore, the method provided in the application embodiments also includes: If the deviation direction is a low-temperature deviation, the deep sleep mechanism of the star flash communication module is triggered.

[0059] In this embodiment, when the deviation direction is determined to be a low-temperature deviation, a sleep control command is sent to the StarSpark communication module at the fan end via the StarSpark low-latency wireless link, triggering its deep sleep mechanism. During this process, the StarSpark communication module first receives and parses the sleep control command encapsulated by the StarSpark adapter driver. Then, it significantly reduces power consumption by shutting down the RF transmitting unit, lowering the clock frequency, and cutting off power to some peripherals, thereby entering a low-power deep sleep state.

[0060] Step S500: The host end performs heat dissipation speed matching according to the compensated temperature prediction curve, outputs a preset speed command, and encapsulates the start and end timestamps and the preset speed command into a first SLE protocol frame through the StarShine adapter driver. The frame is then sent to the StarShine communication module through the StarShine low-latency wireless link to trigger dynamic speed calibration of the cooling fan.

[0061] In this embodiment, the host first performs heat dissipation speed matching based on the compensated temperature prediction curve. This process uses a lookup table matching method, comparing the predicted temperature points in the compensated temperature prediction curve with a preset speed mapping table one by one, determining the corresponding target speed value based on the temperature range, and thus generating a preset speed command containing the target speed data. For example, when the compensated temperature prediction curve predicts a temperature of 70°C in a certain period, a control result of 3000 RPM is obtained through mapping table matching and written into the preset speed command.

[0062] Upon receiving the preset speed command, the host computer invokes the StarShine adapter driver to perform data encapsulation. Using a protocol frame encapsulation method, the preset speed command and its corresponding start and end timestamps are combined according to the protocol format and written into the control and check fields, ultimately generating the first SLE protocol frame that meets communication requirements. This first SLE protocol frame is then transmitted via the StarShine low-latency wireless link. This process uses GFSK modulation to modulate the protocol frame data, and the StarShine RF unit performs signal transmission, ensuring stable transmission of the protocol frame to the StarShine communication module at the fan end.

[0063] Upon receiving the first SLE protocol frame, the StarScan communication module on the fan side executes the protocol parsing step. This module first decodes the first SLE protocol format and performs CRC verification. After confirming the data is correct, it extracts the preset speed command and start / end timestamps. Subsequently, the StarScan communication module converts the command content into a PWM signal via the PWM interface, driving the fan motor's speed control circuit to ensure the fan operates at the preset speed within a specified time interval. Through this process, the fan completes dynamic speed calibration based on the compensated temperature prediction curve, thereby achieving precise control over heat dissipation efficiency and energy consumption.

[0064] In summary, the embodiments of this application have at least the following technical effects: After the computer boots up, the host terminal initializes the cooling fan speed by matching the running load vector according to the boot timestamp and outputs an initial speed control command. The host terminal sends the initial speed control command to the StarSpark communication module on the fan end via the StarSpark low-latency wireless link, driving the cooling fan to execute the preset speed start of the initial speed control command. The host terminal uses the StarSpark adapter driver to call the StarSpark protocol to track and collect CPU operating condition timing data, wherein the operating condition timing data includes timing temperature data, timing load data, and timing ambient temperature. The host terminal performs temperature trend prediction based on the timing temperature data and timing load data, outputs a baseline temperature prediction curve, and then performs dynamic compensation calibration based on the timing ambient temperature, outputting a compensated temperature prediction curve, wherein the compensated temperature prediction curve is associated with start and end timestamps. The host terminal matches the cooling fan speed according to the compensated temperature prediction curve, outputs a preset speed command, and then encapsulates the start and end timestamps and the preset speed command into a first SLE protocol frame via the StarSpark low-latency wireless link, sending it to the StarSpark communication module to trigger dynamic speed calibration of the cooling fan. This invention addresses the technical problems of existing technologies, such as fan speed control relying on wired connections, large response delays, and the inability to make precise dynamic adjustments based on real-time CPU operating conditions. By introducing a StarFlash low-latency wireless link and a temperature trend prediction and dynamic compensation calibration mechanism based on time-series data, it achieves the technical effects of precise control of cooling fan speed, reducing system noise, and improving heat dissipation efficiency.

[0065] Example 2, based on the same inventive concept as the wireless speed control method of the Star Flash module in the computer cooling fan in the previous examples, such as... Figure 2 As shown, this application provides a wireless speed control system for a StarSpark module in a computer cooling fan. The system and method embodiments in this application are based on the same inventive concept. The system includes: The speed initialization module 11 is used to initialize the cooling fan speed by matching the running load vector according to the startup timestamp after the computer starts, and output an initial speed control command; the drive module 12 is used by the host to send the initial speed control command to the StarSpark communication module on the fan end through the StarSpark low-latency wireless link, driving the cooling fan to start at the preset speed of the initial speed control command; the data tracking and acquisition module 13 is used by the host to call the StarSpark protocol through the StarSpark adapter driver to track and acquire CPU operating condition timing data, wherein the operating condition timing data includes timing temperature data, timing load data, and timing ambient temperature; supplement The compensation calibration module 14 is used by the host to predict the temperature trend based on time-series temperature data and time-series load data, output a reference temperature prediction curve, and then perform dynamic compensation calibration based on the time-series ambient temperature to output a compensation temperature prediction curve. The compensation temperature prediction curve is associated with start and end timestamps. The dynamic calibration module 15 is used by the host to match the cooling fan speed according to the compensation temperature prediction curve, output a preset speed command, and then encapsulate the start and end timestamps and the preset speed command into a first SLE protocol frame through the StarSpark low-latency wireless link, and send it to the StarSpark communication module to trigger the dynamic calibration of the cooling fan speed.

[0066] Furthermore, the system is also used to implement the following functions: After synchronizing the startup timestamp via the StarFlash protocol, the startup response interval is matched based on the startup timestamp; historical load timing data is called to perform inertial matching of the startup timestamp, and the inertial load pattern is output; after interactively obtaining the initial process characteristics, the initial load pattern is mapped based on the initial process characteristics; the running load vector is constructed based on the initial load pattern, the inertial load pattern, and the startup response interval; the running load vector is loaded into the resource scheduling model for speed control decision-making, and the initial speed control command is output.

[0067] Furthermore, the system is also used to implement the following functions: Multiple sample load vectors are output by combining and enumerating load vectors; multiple sample load vectors are used as search conditions to locally search multiple candidate speed instruction sets; multiple candidate speed instruction sets are filtered by reuse frequency weighting based on timing decay coefficients to output multiple sets of sample speed control instructions; the multiple sample load vectors and multiple sets of sample speed control instructions are associated and stored to construct the resource scheduling model.

[0068] Furthermore, the system is also used to implement the following functions: The running load vector is loaded into the resource scheduling model, and P sample load vectors are matched and output, wherein the P sample load vectors have P similarity confidence weights; P sets of sample speed control commands of the P sample load vectors are retrieved; after solving the instruction domain intersection of the P sets of sample speed control commands, cosine similarity weighted aggregation is performed according to the P similarity confidence weights, and the initial speed control command is output.

[0069] Furthermore, the system is also used to implement the following functions: The StarShine adapter driver encapsulates the initial speed control command into a second SLE protocol frame and sends it to the StarShine communication module via the StarShine radio frequency module using GFSK modulation. Upon receiving the second SLE protocol frame, the StarShine communication module performs a CRC check. If the check passes, it outputs a PWM signal through the PWM interface to drive the fan motor and execute the preset speed start of the initial speed control command. If the check fails, it returns NAK feedback to the host via the StarShine bidirectional link, triggering a retransmission of the initial speed control command.

[0070] Furthermore, the system is also used to implement the following functions: A lightweight temperature trend prediction model is constructed based on an LSTM model. The time-series temperature data and time-series load data are multi-scale sliding segmented to obtain multiple time window segments. These time window segments are loaded into the temperature trend prediction model to obtain multiple initial time-varying temperature data. The multiple initial time-varying temperature data are weighted and aggregated to output the baseline temperature prediction curve. An ambient temperature threshold is predefined, and the deviation of the time-series ambient temperature is compared using this threshold. If the time-series ambient temperature falls within the ambient temperature threshold, dynamic power consumption management is triggered. If the time-series ambient temperature deviates from the ambient temperature threshold, dynamic compensation calibration of the baseline temperature prediction curve is performed based on the deviation vector, and the compensated temperature prediction curve is output.

[0071] Furthermore, the system is also used to implement the following functions: Using the ambient temperature threshold as a benchmark, the deviation direction of the time-series ambient temperature is determined; if the deviation direction is high temperature deviation, the deviation between the time-series ambient temperature and the ambient temperature threshold is calculated, and a temperature deviation vector is constructed; the temperature deviation vector is used to match the temperature compensation coefficient in the compensation ratio mapping table; the temperature compensation coefficient is used to calibrate the benchmark temperature prediction curve, and the compensated temperature prediction curve is output.

[0072] Furthermore, the system is also used to implement the following functions: If the deviation direction is a low-temperature deviation, the deep sleep mechanism of the star flash communication module is triggered.

[0073] Furthermore, the system is also used to implement the following functions: The StarSpark communication module at the fan end tracks and collects the timing ambient temperature through the ADC interface, and then transmits it back to the host end via the StarSpark bidirectional link. The host end uses the StarSpark adapter driver to call the StarSpark protocol to collect system-level interface data of the CPU and obtain the timing temperature data and timing load data.

[0074] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0075] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0076] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A wireless speed control method for a star-flash module in a computer cooling fan, characterized in that, The method includes: After the computer starts up, the host side matches the running load vector according to the startup timestamp to initialize the cooling fan speed and outputs the initial speed control command. The host sends the initial speed control command to the StarSignal communication module on the fan end via the StarSignal low-latency wireless link, driving the cooling fan to start at the preset speed of the initial speed control command; The host terminal uses the StarSpark adapter driver to call the StarSpark protocol to track and collect CPU operating condition timing data, wherein the operating condition timing data includes timing temperature data, timing load data, and timing ambient temperature. The host terminal performs temperature trend prediction based on time-series temperature data and time-series load data, outputs a baseline temperature prediction curve, and then performs dynamic compensation calibration based on the time-series ambient temperature to output a compensated temperature prediction curve. The compensated temperature prediction curve is associated with start and end timestamps. The host end performs heat dissipation speed matching according to the compensated temperature prediction curve, outputs a preset speed command, and then encapsulates the start and end timestamps and the preset speed command into a first SLE protocol frame through the StarSpark low-latency wireless link, which is then sent to the StarSpark communication module to trigger dynamic speed calibration of the cooling fan.

2. The wireless speed control method for the star-flash module in a computer cooling fan as described in claim 1, characterized in that, The host initializes the cooling fan speed by matching the running load vector based on the startup timestamp and outputs an initial speed control command. The method includes: After synchronizing the startup timestamp via the StarFlash protocol, the startup response interval is matched based on the startup timestamp; Invoke historical load time series data to perform inertial matching of the start timestamp and output the inertial load pattern; After obtaining the initial process characteristics through interaction, the initial load pattern is mapped based on the initial process characteristics; The operating load vector is constructed based on the initial load pattern, the inertial load pattern, and the startup response interval; The running load vector is loaded into the resource scheduling model to make speed control decisions, and the initial speed control command is output.

3. The wireless speed control method for the star-flash module in a computer cooling fan as described in claim 2, characterized in that, The method includes loading the running load vector into the resource scheduling model for speed control decision-making and outputting the initial speed control command. Prior to this, the method includes: Multiple sample load vectors are output by combining and enumerating load vectors; Using the multiple sample load vectors as search criteria, multiple candidate speed instruction sets are searched locally. Based on the timing decay coefficient, the multiple candidate speed command sets are weighted by reuse frequency to output multiple sets of sample speed control commands. The resource scheduling model is constructed by associating and storing the multiple sample load vectors and multiple sets of sample speed control commands.

4. The wireless speed control method for the star-flash module in a computer cooling fan as described in claim 3, characterized in that, The method includes loading the runtime vector into a resource scheduling model for speed control decision-making and outputting the initial speed control command. The running load vector is loaded into the resource scheduling model, and P sample load vectors are matched and output, wherein the P sample load vectors have P similarity confidence weights; Retrieve the P sets of sample speed control commands for the P sample load vectors; After solving the instruction domain intersection of the P sample speed control commands, the initial speed control command is output by cosine similarity weighted aggregation based on the P similarity confidence weights.

5. The wireless speed control method for the star-flash module in a computer cooling fan as described in claim 1, characterized in that, The host computer sends the initial speed control command to the fan's communication module via a StarSignal low-latency wireless link, driving the cooling fan to start at the preset speed specified in the initial speed control command. The method includes: The StarShine Adaptor Driver encapsulates the initial speed control command into a second SLE protocol frame and then sends it to the StarShine Communication Module via the StarShine RF Module in GFSK modulation. After receiving the second SLE protocol frame, the StarScan communication module performs a CRC check. If the verification passes, the PWM signal is output through the PWM interface to drive the fan motor and execute the preset speed start of the initial speed control command; If the verification fails, NAK feedback is returned to the host via the StarFlash bidirectional link, triggering the retransmission of the initial speed control command.

6. The wireless speed control method for the star-flash module in a computer cooling fan as described in claim 5, characterized in that, The host terminal performs temperature trend prediction based on time-series temperature data and time-series load data, outputs a baseline temperature prediction curve, and then performs dynamic compensation calibration based on the time-series ambient temperature to output a compensated temperature prediction curve. The method includes: A lightweight temperature trend prediction model is constructed based on the LSTM model; The time-series temperature data and time-series load data are divided into multiple time window segments by multi-scale sliding segmentation. The multiple time window segments are loaded into the temperature trend prediction model to obtain multiple initial time-varying temperature data. By weighted aggregation of the multiple initial time-varying temperature data, the baseline temperature prediction curve is output; A predefined ambient temperature threshold is used to compare the deviation of the time-series ambient temperature. If the timing ambient temperature falls within the ambient temperature threshold, dynamic power management is triggered; If the time-series ambient temperature deviates from the ambient temperature threshold, then the reference temperature prediction curve is dynamically compensated and calibrated based on the deviation vector, and the compensated temperature prediction curve is output.

7. The wireless speed control method for the star-flash module in a computer cooling fan as described in claim 6, characterized in that, If the time-series ambient temperature deviates from the ambient temperature threshold, then dynamic compensation calibration of the reference temperature prediction curve is performed based on the deviation vector, and the compensated temperature prediction curve is output. The method includes: Based on the ambient temperature threshold, the direction of deviation of the time-series ambient temperature is determined; If the deviation direction is high temperature deviation, then calculate the deviation between the time-series ambient temperature and the ambient temperature threshold, and construct a temperature deviation vector; The temperature deviation vector is used to match the temperature compensation coefficient in the compensation ratio mapping table; The reference temperature prediction curve is calibrated using the temperature compensation coefficient, and the compensated temperature prediction curve is output.

8. The wireless speed control method for the star-flash module in a computer cooling fan as described in claim 7, characterized in that, If the deviation direction is a low-temperature deviation, the deep sleep mechanism of the star flash communication module is triggered.

9. The wireless speed control method for the star-flash module in a computer cooling fan as described in claim 1, characterized in that, The StarSpark communication module at the fan end tracks and collects the timing ambient temperature through the ADC interface, and then transmits it back to the host end via the StarSpark bidirectional link. The host end uses the StarSpark adapter driver to call the StarSpark protocol to collect system-level interface data of the CPU and obtain the timing temperature data and timing load data.

10. A wireless speed control system for a star-flash module in a computer cooling fan, characterized in that, The system is used to execute the wireless speed control method of the Starlight module in a computer cooling fan as described in any one of claims 1-9, the system comprising: The speed initialization module is used to initialize the cooling fan speed on the host side after the computer starts up, based on the startup timestamp and the running load vector, and output the initial speed control command. The drive module is used to send the initial speed control command from the host to the StarSignal communication module on the fan end via the StarSignal low-latency wireless link, thereby driving the cooling fan to start at the preset speed of the initial speed control command. The data tracking and acquisition module is used by the host to call the StarSpark protocol through the StarSpark adapter driver to track and acquire CPU operating condition timing data, wherein the operating condition timing data includes timing temperature data, timing load data and timing ambient temperature. The compensation calibration module is used by the host to predict the temperature trend based on time-series temperature data and time-series load data, output a reference temperature prediction curve, and then perform dynamic compensation calibration based on the time-series ambient temperature to output a compensation temperature prediction curve. The compensation temperature prediction curve is associated with start and end timestamps. The dynamic calibration module is used by the host to match the cooling speed according to the compensation temperature prediction curve, output a preset speed command, and then encapsulate the start and end timestamps and the preset speed command into a first SLE protocol frame through the StarShine adapter driver. The frame is then sent to the StarShine communication module through the StarShine low-latency wireless link to trigger the dynamic calibration of the cooling fan speed.

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