Low-power-consumption semiconductor communication method based on Internet of Things

Abstract

The invention relates to the technical field of Internet of Things communication, and provides a low-power-consumption semiconductor communication method based on Internet of Things, which comprises the following steps: acquiring temperature, electromagnetic interference intensity and signal attenuation coefficient data through a multi-mode sensor node, and generating an original environment data set; performing dynamic noise classification on the original environment data set, dividing noise levels and marking noise types; performing hierarchical processing on the data by adopting a multi-stage self-adaptive noise reduction algorithm to generate a preprocessed data set; extracting dynamic characteristic parameters of a communication channel, constructing an energy efficiency prediction model, and dynamically calculating a combination of optimal transmitting frequency, power and modulation order; generating a multi-system modulation signal, superposing a dynamic coding strategy, and adjusting the symbol block length and the error correction code density; and the receiving end analyzes the signal quality index, feeds back the signal quality index to the transmitting end to optimize the parameters and updates the energy efficiency prediction model. According to the invention, the energy efficiency ratio and the anti-interference capability of the Internet of Things communication system can be improved.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things communication technologies, and more specifically, to a low-power semiconductor communication method based on the Internet of Things. Background Art

[0002] In the context of the rapid development of the Internet of Things, low-power communication technology has become a key requirement. Traditional communication systems usually adopt fixed communication parameters and coding strategies, making it difficult to adapt to complex and changing electromagnetic environments. For example, in scenarios with high temperatures, strong electromagnetic interference, or severe signal attenuation, the energy efficiency and reliability of the communication system will significantly decrease. In the prior art, although some methods attempt to cope with interference by adjusting the transmission power or adopting simple filtering algorithms, these methods often cannot dynamically adapt to environmental changes, resulting in increased energy consumption or elevated bit error rates. In addition, traditional communication systems lack an effective adaptive adjustment mechanism when facing sudden interference, making it difficult to optimize energy efficiency while ensuring communication quality.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: First, it is impossible to adjust communication parameters in real time according to the dynamic changes of environmental noise, resulting in low communication efficiency; second, there is a lack of refined noise reduction strategies for different noise levels, making it difficult to effectively cope with complex electromagnetic interference; third, it is difficult to balance energy efficiency optimization and anti-interference ability in existing communication systems, and it is impossible to achieve efficient and reliable communication in complex environments. Summary of the Invention

[0004] The present invention provides a low-power semiconductor communication method based on the Internet of Things, including: S1: Periodically collect temperature data, electromagnetic interference intensity data, and signal attenuation coefficient of the target area through a multi-modal sensor node to generate an original environmental data set; S2: Dynamically classify the original environmental data set according to the dynamic changes of electromagnetic interference intensity data and temperature data, divide the noise level, and mark the noise type; S3: Based on the noise level and type, use a multi-stage adaptive noise reduction algorithm to perform hierarchical processing on the original environmental data set to generate a preprocessed data set; S4: Extract dynamic characteristic parameters of the communication channel from the preprocessed data set, including signal instantaneous frequency offset, channel coherence time, and interference pulse density; S5: According to the dynamic characteristic parameters, construct an energy efficiency prediction model of the semiconductor module, and dynamically calculate the combination of the optimal transmission frequency, power, and modulation order; S6: Generate a multi-level modulation signal based on the optimal combination, and superimpose a dynamic coding strategy, and adjust the symbol block length and error correction code density according to the channel coherence time; S7: The receiving end parses the signal quality indicators in real time, feeds them back to the transmitting end to trigger parameter iterative optimization, and synchronously updates the weight factors in the energy efficiency prediction model.

[0005] Further, the dynamic noise classification in step S2 includes: S2-1: According to the joint distribution of the temperature data and the electromagnetic interference intensity define the piecewise function of the noise level :

[0006] where is the temperature threshold, and are the electromagnetic interference intensity classification thresholds, is the attenuation coefficient threshold; S2-2: For different noise levels, assign corresponding noise reduction algorithm priorities, and the anti-pulse interference algorithm is preferentially enabled for level 3.

[0007] Further, the multi-stage adaptive noise reduction algorithm in step S3 includes: S3-1: For the data with noise level 1, use local mean filtering based on a sliding window, and the window length satisfies:

[0008] where is the current electromagnetic interference intensity; S3-2: For the data with noise level 2, use frequency domain notch filtering to suppress interference in a specific frequency band, and the notch center frequency is determined by the following formula:

[0009] where is the reference frequency, is the frequency offset amplitude, is the interference period; S3-3: For the data with noise level 3, enable the pulse interference cancellation algorithm to compensate for the distorted waveform through time domain signal reconstruction.

[0010] Further, the construction of the energy efficiency prediction model in step S5 includes: S5-1: Define the energy consumption cost function , which includes the transmit power , the frequency switching overhead and the bit error rate penalty term :

[0011] Among them, , , are weight coefficients, is the frequency adjustment amount, is the signal-to-noise ratio; S5-2: Solve through constrained optimization to obtain the parameter combination with the minimum value, and the constraint conditions include the power consumption upper limit and the bit error rate threshold .

[0012] Furthermore, the dynamic coding strategy described in step S6 includes: S6-1: Dynamically adjust the symbol block length according to the channel coherence time :

[0013] Among them, is the maximum allowable length, is the reference symbol rate; S6-2: Calculate the error correction code redundancy based on the interference pulse density :

[0014] Among them, is the reference redundancy, is the density threshold; S6-3: Adopt an asymmetric Turbo code structure to enhance the forward error correction ability during high interference periods.

[0015] Furthermore, the parameter iterative optimization described in step S7 includes: S7-1: The receiving end extracts signal quality indicators, including the bit error rate , the signal-to-noise ratio and the delay jitter ; S7-2: Adjust the weight coefficients in the energy efficiency prediction model according to the deviation degree between and

[0016] Among them, and are preset thresholds; S7-3: If , then recalculate , , in the energy consumption cost function.

[0017] Furthermore, the update formula for the weight coefficient in step S7-3 is as follows:

[0018]

[0019]

[0020] wherein, and and are convergence rate control parameters.

[0021] Furthermore, it further includes step S8: S8: Periodically evaluate the energy efficiency ratio of the semiconductor module , and the calculation formula is:

[0022] wherein, is the statistical period; S8-2: If decreases by more than the threshold for N consecutive periods, then switch to the backup communication protocol and reset the energy efficiency prediction model.

[0023] Furthermore, the backup communication protocol includes: S9-1: Lock the transmission frequency within the anti-interference frequency band and prohibit dynamic frequency hopping; S9-2: Adopt differential phase shift keying modulation and fix the symbol block length as ; S9-3: Enable the anti-saturation circuit and force the power to be reduced to the preset safety value .

[0024] Furthermore, the extraction method of the interference pulse density in step S4 includes: S10-1: Perform time-domain envelope detection on the preprocessed data set to identify pulses with an amplitude exceeding the threshold ; S10-2: Count the number of pulses within a unit time, and obtain it through moving window smoothing:

[0025] wherein, is the window length, is the sampling point index within the window.

[0026] The above embodiments of the present invention have at least the following beneficial effects: The low-power semiconductor communication method of the present invention can effectively address communication challenges in complex environments. Through dynamic noise classification and multi-stage adaptive noise reduction algorithms, it can flexibly adjust the noise reduction strategy according to the level and type of environmental noise, thereby achieving high-quality data transmission under different interference conditions. At the same time, the energy efficiency prediction model constructed based on dynamic characteristic parameters can calculate the optimal combination of transmission frequency, power, and modulation order in real time, further optimizing the energy consumption performance of the communication system. In addition, the dynamic coding strategy can adjust the symbol block length and error correction code density according to the channel coherence time, enhancing the anti-interference ability and error correction performance of the system and ensuring the reliability of communication.

[0027] In addition, the present invention can also achieve iterative optimization of parameters through the signal quality feedback mechanism at the receiving end, further enhancing the adaptive ability and energy efficiency performance of the system. The design of periodically evaluating the energy efficiency ratio and switching to the backup communication protocol can ensure the stability of communication under extreme conditions, avoiding communication interruptions caused by sudden interference or performance degradation, thereby providing a more efficient, reliable, and energy-saving communication solution for Internet of Things devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown by way of illustration and not limitation, wherein: Figure 1 FIG. is a schematic flowchart of a low-power semiconductor communication method based on the Internet of Things provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0029] The principles and spirit of the present invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and not to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to convey the scope of the present invention fully to those skilled in the art.

[0030] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: completely hardware, completely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0031] It should be noted that any number of elements in the drawings is for illustration and not limitation, and any naming is only for distinction and does not have any limiting meaning.

[0032] The following is a reference to Figure 1 , Figure 1 which is a schematic flowchart of a low-power semiconductor communication method based on the Internet of Things provided by an embodiment of the present invention. As Figure 1 shown, a low-power semiconductor communication method 100 based on the Internet of Things includes: S1: Periodically collect temperature data, electromagnetic interference intensity data, and signal attenuation coefficient of the target area through a multi-modal sensor node to generate an original environmental data set; S2: Dynamically classify the original environmental data set according to the dynamic changes of the electromagnetic interference intensity data and temperature data, divide the noise level and mark the noise type; S3: Based on the noise level and type, use a multi-stage adaptive noise reduction algorithm to perform hierarchical processing on the original environmental data set to generate a preprocessed data set; S4: Extract dynamic characteristic parameters of the communication channel from the preprocessed data set, including signal instantaneous frequency offset, channel coherence time, and interference pulse density; S5: According to the dynamic characteristic parameters, construct an energy efficiency prediction model of the semiconductor module, and dynamically calculate the combination of the optimal transmission frequency, power, and modulation order; S6: Generate a multi-level modulation signal based on the optimal combination, and superimpose a dynamic coding strategy, and adjust the symbol block length and error correction code density according to the channel coherence time; S7: The receiving end analyzes the signal quality index in real time, feeds it back to the transmitting end to trigger parameter iterative optimization, and synchronously updates the weight factor in the energy efficiency prediction model.

[0033] It should be noted that in the present invention, first, the temperature data, electromagnetic interference intensity data, and signal attenuation coefficient of the target area are periodically collected through a multi-modal sensor node to generate an original environmental data set. The multi-modal sensor node refers to a device integrated with multiple sensors, which can measure multiple physical quantities simultaneously. Here, the temperature data is used to reflect the thermal state of the environment, the electromagnetic interference intensity data is used to evaluate the interference degree of electromagnetic signals in the environment, and the signal attenuation coefficient is used to measure the loss of signals during transmission. These data together constitute the original environmental data set, providing a basis for subsequent noise classification and processing.

[0034] Specifically, the multi-modal sensor node may include a temperature sensor, an electromagnetic induction sensor, a signal strength detector, etc. The temperature sensor is used to measure the ambient temperature, and its measurement range can be selected according to the actual application scenario. For example, in an industrial environment, it may be necessary to measure temperatures up to several hundred degrees Celsius, while in a smart home scenario, only room temperature range may need to be measured. The acquisition of electromagnetic interference intensity data can be achieved through an electromagnetic induction sensor, and its measured frequency range can be adjusted according to the communication frequency band of the target application. For example, in a wireless communication scenario, frequency bands from several hundred megahertz to several gigahertz may need to be concerned. The signal attenuation coefficient can be measured by a signal strength detector, and its calculation method is usually determined by comparing the signal strengths at the transmitter and the receiver, reflecting the loss degree of the signal during transmission.

[0035] Preferably, in order to improve the accuracy and reliability of data acquisition, a calibration mechanism can be introduced into the sensor node. For example, the temperature sensor can be periodically calibrated at zero point to eliminate the drift error that may be brought by long-term use; for the electromagnetic induction sensor, it can be calibrated in an environment with known interference intensity to ensure its measurement accuracy. In addition, the calculation of the signal attenuation coefficient can be corrected in combination with environmental factors. For example, in a high-humidity environment, the signal attenuation may increase, so the calculation formula or reference value of the attenuation coefficient needs to be adjusted according to the actual environmental conditions.

[0036] In some embodiments, the dynamic noise classification in step S2 includes: S2-1: According to the temperature data and the electromagnetic interference intensity of the joint distribution, define the piecewise function of the noise level :

[0037] wherein, is the temperature threshold, and are the electromagnetic interference intensity classification thresholds, is the attenuation coefficient threshold; S2-2: For different noise levels, assign corresponding noise reduction algorithm priorities, and the algorithm for anti-pulse interference is preferentially enabled for level 3.

[0038] It should be noted that dynamic noise classification classifies the collected original environmental data according to the joint distribution of temperature data and electromagnetic interference intensity, divides the noise level, and marks the noise type. The core of this process lies in setting thresholds to distinguish different noise levels, thereby providing a basis for subsequent noise reduction processing. The temperature threshold is a reference value for judging whether the environmental temperature is abnormal, the electromagnetic interference intensity classification threshold is used to distinguish different intensities of electromagnetic interference, and the attenuation coefficient threshold is used to judge whether the signal is severely attenuated during transmission. The division of the noise level directly affects the selection of subsequent noise reduction algorithms and the setting of priorities.

[0039] Specifically, the temperature threshold can be set according to the normal operating temperature range of the environment where the sensor node is located. For example, in an indoor environment, the normal temperature range may be from 15°C to 30°C, so the temperature threshold can be set to 30°C. If the temperature exceeds this value, it is considered that the environmental temperature is abnormal and may affect communication. The electromagnetic interference intensity classification threshold is used to distinguish different intensities of electromagnetic interference. For example, the first threshold can be set to 10 dBμV, indicating slight interference; the second threshold is set to 30 dBμV, indicating severe interference. When the electromagnetic interference intensity exceeds the second threshold, the anti-pulse interference algorithm is further judged whether to be enabled in combination with the signal attenuation coefficient. The signal attenuation coefficient reflects the loss degree of the signal during transmission, and its threshold can be set according to the design requirements of the communication link. For example, in wireless communication, the threshold of the signal attenuation coefficient may be set to 0.5 dB per meter, indicating that the signal attenuation is relatively severe.

[0040] Preferably, in order to perform noise classification more accurately, the threshold can be dynamically adjusted according to the actual application scenario. For example, in an industrial environment, due to more electromagnetic interference sources, the electromagnetic interference intensity classification threshold can be appropriately reduced to detect interference more sensitively. In addition, for the processing of noise level 3, in addition to preferentially enabling the anti-pulse interference algorithm, other noise reduction algorithms can also be combined for collaborative processing. For example, when the signal attenuation coefficient is high, in addition to time-domain signal reconstruction, a frequency-domain filtering algorithm can also be introduced to further improve the noise reduction effect.

[0041] In some embodiments, the multi-stage adaptive noise reduction algorithm described in step S3 includes: S3-1: For the data with noise level 1, use local mean filtering based on a sliding window, and the window length satisfies:

[0042] where, is the current electromagnetic interference intensity; S3-2: For the data with noise level 2, use frequency-domain notch filtering to suppress interference in a specific frequency band, and the notch center frequency Determined by the following formula:

[0043] where is the reference frequency, is the frequency offset amplitude, is the interference period; S3-3: For data with a noise level of 3, enable the impulse interference cancellation algorithm to compensate for the distorted waveform through time-domain signal reconstruction.

[0044] It should be noted that the multi-stage adaptive noise reduction algorithm is a method for hierarchically processing the collected original environmental data according to the noise level. This method uses different noise reduction algorithms to specifically process data with different noise levels, thereby improving the purity of the data and the communication quality. Specifically, data with a noise level of 1 uses local mean filtering based on a sliding window, data with a noise level of 2 uses frequency-domain notch filtering, and data with a noise level of 3 enables the impulse interference cancellation algorithm. The selection and application of these algorithms are based on the analysis and optimization of different noise characteristics to ensure efficient noise reduction in a complex environment.

[0045] Specifically, for data with a noise level of 1, using local mean filtering based on a sliding window is a simple and effective method. The length of the sliding window can be dynamically adjusted according to the current electromagnetic interference intensity to ensure the filtering effect. For example, when the electromagnetic interference intensity is small, the window length can be short to reduce the computational complexity; when the interference intensity is large, the window length can be appropriately increased to better smooth the noise. For data with a noise level of 2, frequency-domain notch filtering is a targeted noise reduction method that can effectively suppress interference in a specific frequency band. The calculation of the notch center frequency is based on the reference frequency, frequency offset amplitude, and interference period, and these parameters can be set according to the interference characteristics in the actual communication environment. For data with a noise level of 3, the impulse interference cancellation algorithm compensates for the distorted waveform through time-domain signal reconstruction and can effectively cope with sudden impulse interference to ensure the integrity of the signal.

[0046] Preferably, for the implementation of the multi-stage adaptive noise reduction algorithm, refinement or alternative solutions can be provided at some key points. For example, in the data processing with a noise level of 1, the length of the sliding window can be optimized according to the actual application scenario. For a low-frequency signal environment, the window length can be set to be shorter to reduce the impact on signal details; while for a high-frequency signal environment, the window length can be appropriately increased to better smooth the high-frequency noise. In the data processing with a noise level of 2, in addition to frequency-domain notch filtering, an adaptive filter can also be combined to dynamically adjust the filtering parameters according to the interference characteristics monitored in real time, further improving the noise reduction effect. For the data processing with a noise level of 3, in addition to the impulse interference cancellation algorithm, a deep learning algorithm can also be introduced to identify and reconstruct the distorted waveform by training the model, so as to more accurately eliminate the impulse interference.

[0047] In some embodiments, the construction of the energy efficiency prediction model in step S5 includes: S5-1: Define the energy consumption cost function , including the transmit power , the frequency switching overhead and the bit error rate penalty term :

[0048] Among them, , , are weight coefficients, is the frequency adjustment amount, is the signal-to-noise ratio; S5-2: Solve through constrained optimization to obtain the parameter combination that minimizes , and the constraint conditions include the power consumption upper limit and the bit error rate threshold .

[0049] It should be noted that the construction of the energy efficiency prediction model is achieved by defining the energy consumption cost function, which comprehensively considers the transmit power, the frequency switching overhead, and the bit error rate penalty term. The purpose of the energy consumption cost function is to minimize the energy consumption of the communication system by optimizing the combination of these parameters, while satisfying the constraint conditions of the power consumption upper limit and the bit error rate threshold. Among them, the weight coefficients are used to balance the importance of different parameters in the energy consumption cost, and the frequency adjustment amount and the signal-to-noise ratio are the key factors affecting the energy consumption. Through constrained optimization, the optimal combination of transmit frequency, power, and modulation order can be dynamically calculated, so as to achieve the efficient operation of the communication system.

[0050] Specifically, the construction of the energy cost function involves several key parameters. Transmit power is the primary source of energy consumption in communication systems and directly affects device power consumption. Frequency switching overhead refers to the additional energy consumption incurred when switching transmit frequencies during communication and is typically related to the hardware characteristics of the communication device. The bit error rate penalty is designed to ensure communication quality. When the bit error rate is high, the energy cost increases, prompting the system to optimize parameters to reduce the bit error rate. The weight coefficient can be set according to actual application requirements. For example, in power-sensitive applications, the weight of transmit power can be increased; in scenarios with high communication quality requirements, the weight of the bit error rate penalty can be increased. In addition, the frequency adjustment refers to the range of variation of the transmit frequency, and the signal-to-noise ratio is the ratio of signal strength to noise strength, reflecting the quality of the communication environment.

[0051] To further optimize the energy efficiency prediction model, key points can be refined or alternative solutions provided. For example, the weight coefficients can be dynamically adjusted based on the priorities of different application scenarios. For battery-powered IoT devices, a greater emphasis can be placed on power consumption optimization, with the weight of transmit power appropriately increased. For industrial control systems with extremely high communication quality requirements, the weight of the bit error rate penalty can be increased. Furthermore, the constraints can be adjusted based on the actual device performance and application scenario. For example, the power consumption cap can be flexibly set based on the device's battery capacity and usage scenario; the bit error rate threshold can be optimized based on the requirements of the communication protocol. When solving for the optimal parameter combination, in addition to traditional optimization algorithms, machine learning algorithms can also be introduced to further improve the model's accuracy and adaptability by learning and analyzing large amounts of real-world data.

[0052] In some embodiments, the dynamic encoding strategy in step S6 includes: S6-1: Based on channel coherence time Dynamically adjust symbol block length :

[0053] in, is the maximum allowed length, is the reference symbol rate; S6-2: Based on interference pulse density Calculating error-correcting code redundancy :

[0054] in, is the baseline redundancy, is the density threshold; S6-3: Uses an asymmetric Turbo code structure to enhance forward error correction capabilities during periods of high interference.

[0055] It should be noted that the core of the dynamic coding strategy lies in dynamically adjusting the symbol block length and the error correction code density according to the channel coherence time to adapt to the changes in the communication channel. The channel coherence time refers to the duration during which the channel characteristics remain relatively stable, and it directly affects the selection of the symbol block length. The longer the symbol block length, the higher the transmission efficiency, but the higher the requirement for channel stability. The error correction code density is calculated based on the interference pulse density and is used to enhance the anti-interference ability of the signal. By dynamically adjusting these parameters, the energy efficiency can be optimized while ensuring the communication quality.

[0056] Specifically, the channel coherence time is an important parameter in the communication system, which reflects the ability of the channel to remain stable over a period of time. The selection of the symbol block length needs to be dynamically adjusted according to the channel coherence time. For example, when the channel coherence time is long, the symbol block length can be appropriately increased to improve the transmission efficiency; while when the channel coherence time is short, the symbol block length needs to be shortened to avoid transmission errors caused by channel changes. The calculation of the error correction code density is based on the interference pulse density. The higher the interference pulse density, the higher the redundancy of the error correction code is required to enhance the anti-interference ability of the signal. In addition, adopting an asymmetric Turbo code structure can enhance the forward error correction ability during high-interference periods and further improve the reliability of the system.

[0057] Preferably, in order to further optimize the dynamic coding strategy, it can be refined or alternative solutions can be provided at some key points. For example, in the adjustment of the symbol block length, multiple thresholds can be set according to the specific value of the channel coherence time to achieve more refined dynamic adjustment. For the calculation of the error correction code density, in addition to the linear relationship based on the interference pulse density, a non-linear function can be introduced to better adapt to complex interference environments. In addition, the structure of the asymmetric Turbo code can be optimized according to the actual application scenario. For example, in some high-interference scenarios, the number of encoder stages can be increased or the depth of the interleaver can be adjusted to further improve the error correction ability.

[0058] In some embodiments, the parameter iterative optimization in step S7 includes: S7-1: The receiving end extracts signal quality indicators, including the bit error rate , signal-to-noise ratio and delay jitter

[0059] S7-2: According to and the deviation degree of adjust the weight coefficients in the energy efficiency prediction model:

[0060] where and is a preset threshold; S7-3: If , then recalculate , , .

[0061] It should be noted that the parameter iterative optimization is to parse the signal quality indicators in real time at the receiving end and feedback them to the transmitting end, triggering the dynamic adjustment of communication parameters. The core of this process is to dynamically update the weight coefficients in the energy efficiency prediction model according to the changes in signal quality, so as to optimize the performance of the communication system. The signal quality indicators include bit error rate, signal-to-noise ratio, and delay jitter, etc. These indicators reflect the actual operating state of the communication link. The deviation degree is the difference between the signal quality indicator and the preset threshold, which is used to measure the deviation degree between the current communication state and the ideal state. By adjusting the weight coefficients, the system can dynamically optimize the energy consumption cost function according to the actual communication environment, so as to achieve efficient and reliable communication.

[0062] Specifically, the signal quality indicators are the key parameters for evaluating the performance of the communication system. The bit error rate (BER) represents the proportion of error bits in the transmission process and is an important indicator for measuring communication reliability; the signal-to-noise ratio (SNR) reflects the ratio of the signal strength to the noise strength and directly affects the communication quality; the delay jitter represents the change in the signal transmission delay and is particularly important for communication scenarios with high real-time requirements. The deviation degree is determined by calculating the difference between the actual bit error rate and the preset bit error rate threshold, and is used to evaluate whether the current communication state deviates from the ideal value. The adjustment of the weight coefficients is based on the deviation degree. When the deviation degree is large, the system will increase the weight of the corresponding parameter to optimize the communication performance. For example, if the bit error rate deviates greatly from the preset threshold, the system will increase the weight of the bit error rate penalty term, prompting the energy consumption cost function to pay more attention to reducing the bit error rate.

[0063] Preferably, in order to further improve the efficiency and adaptability of the parameter iterative optimization, it can be refined or alternative solutions can be provided at some key points. For example, in the update of the weight coefficients, an adaptive adjustment mechanism can be introduced to dynamically adjust the update rate of the weight coefficients according to historical data and the current communication environment. For the calculation of the deviation degree, in addition to the simple difference calculation, a weighted average or sliding window mechanism can also be introduced to smooth short-term fluctuations and more accurately reflect the changes in long-term communication quality. In addition, when the system detects that the deviation degree remains high, it can trigger deeper parameter optimization, such as adjusting the transmission frequency or modulation method, rather than just updating the weight coefficients.

[0064] In some embodiments, the update formula of the weight coefficients in step S7-3 is:

[0065]

[0066]

[0067] Among them, and and are convergence rate control parameters.

[0068] It should be noted that this embodiment relates to a method for updating weight coefficients. The core lies in dynamically adjusting the weight coefficients in the energy efficiency prediction model according to the deviation degree of the signal quality index to optimize the performance of the communication system. The deviation degree refers to the difference between the actual signal quality index and the preset threshold, reflecting the deviation degree between the current state and the ideal state of the communication link. By adjusting the weight coefficients, the system can dynamically respond to changes in signal quality, thereby achieving efficient energy consumption management in complex environments. The update formula of the weight coefficients is based on the deviation degree. By introducing convergence rate control parameters, it is ensured that the adjustment process of the weight coefficients is both fast and stable.

[0069] Specifically, the signal quality indicators include bit error rate (BER), signal-to-noise ratio (SNR), and delay jitter, etc. These indicators are used to evaluate the performance of the communication link. The bit error rate is the proportion of transmitted error bits. The signal-to-noise ratio reflects the intensity ratio of the signal to the noise, while the delay jitter represents the variation of the signal transmission delay. The deviation degree is determined by calculating the difference between the actual bit error rate or signal-to-noise ratio and the preset threshold. For example, if the actual bit error rate is higher than the preset threshold, the deviation degree will increase, indicating that the performance of the communication link needs to be optimized. The update formula of the weight coefficients adjusts the change rate of the weight coefficients by introducing convergence rate control parameters, ensuring that the system can quickly adapt to changes in signal quality during the dynamic adjustment process, and at the same time avoiding system instability caused by too fast adjustment.

[0070] Preferably, in order to further improve the flexibility and adaptability of weight coefficient update, it can be refined or alternative solutions can be provided at some key points. For example, for the convergence rate control parameter, it can be dynamically adjusted according to the actual application scenario. In a rapidly changing communication environment, the convergence rate can be appropriately increased to respond more quickly to changes in signal quality; while in a relatively stable environment, the convergence rate can be reduced to reduce unnecessary adjustments. In addition, the update of the weight coefficients can be optimized by combining historical data. For example, by introducing weighted average or sliding window mechanism, the change of the weight coefficients can be smoothed to avoid misadjustment caused by short-term fluctuations. In addition, machine learning algorithms can be introduced. By learning a large amount of historical data, the update strategy of the weight coefficients can be automatically adjusted to further improve the adaptive ability of the system.

[0071] In some embodiments, it further includes step S8: S8: Periodically evaluate the energy efficiency ratio of the semiconductor module , and the calculation formula is:

[0072] where, is the statistical period; S8-2: If the energy efficiency ratio has decreased by more than the threshold for N consecutive periods, then switch to the backup communication protocol and reset the energy efficiency prediction model.

[0073] It should be noted that this embodiment involves the periodic evaluation of the energy efficiency ratio of the semiconductor module and the mechanism of switching to the backup communication protocol when the energy efficiency decreases. The energy efficiency ratio is an important indicator for measuring the performance of a communication system, and it evaluates the efficiency of the communication system by calculating the ratio of the amount of effectively transmitted data to the total energy consumption. The statistical period refers to the time interval for evaluating the energy efficiency ratio and is used to dynamically monitor the operating state of the communication system. When the energy efficiency ratio decreases by more than the set threshold for multiple consecutive statistical periods, the system will switch to the backup communication protocol to ensure the stability and reliability of communication, and at the same time reset the energy efficiency prediction model to provide a new optimization basis for subsequent communication.

[0074] Specifically, the calculation formula for the energy efficiency ratio is the amount of effectively transmitted data divided by the total energy consumption, and then multiplied by the reciprocal of the statistical period. The amount of effectively transmitted data refers to the amount of data successfully transmitted during the communication process, usually in bits or bytes; the total energy consumption refers to the energy consumed during the communication process, usually in joules or watt-hours. The statistical period can be set according to the actual application scenario. For example, in scenarios with high real-time requirements, the statistical period can be set to the second level; while in scenarios with low real-time requirements, the statistical period can be set to the minute level or longer. The threshold of the energy efficiency ratio is used to determine whether the communication system needs to switch to the backup communication protocol, and this threshold can be set according to the actual requirements and performance indicators of the communication system. For example, if the energy efficiency ratio drops by more than 10%, it is considered that the system performance has decreased and the backup protocol needs to be switched.

[0075] Preferably, to further improve the reliability and flexibility of the system, refinement or alternative solutions can be made at some key points. For example, the statistical period can be adaptively adjusted according to the dynamic changes of the communication environment. When the communication environment is relatively stable, the statistical period can be appropriately extended to reduce the number of evaluations; while when the environment changes rapidly, the statistical period can be shortened to detect the energy efficiency decline in a timely manner. For the threshold setting of the energy efficiency ratio, it can be processed in a hierarchical manner according to different application scenarios of the communication system. For example, in scenarios with extremely high energy efficiency requirements, a lower threshold can be set to more sensitively detect the energy efficiency decline; while in scenarios with relatively low energy efficiency requirements, the threshold can be appropriately increased to avoid frequent switching of communication protocols. In addition, the switching mechanism of the backup communication protocol can also be further optimized. For example, a short test can be carried out before switching to ensure that the backup protocol can work properly, thereby improving the overall reliability of the system.

[0076] In some embodiments, the backup communication protocol includes: S9-1: Lock the transmission frequency within the anti-interference frequency band and prohibit dynamic frequency hopping; S9-2: Adopt differential phase shift keying modulation and fix the symbol block length to a preset minimum value

[0077] S9-3: Enable the anti-saturation circuit and force the power to be reduced to a preset safe value .

[0078] It should be noted that this embodiment relates to the setting of a backup communication protocol for ensuring the stability of communication when the main communication protocol fails or the energy efficiency declines. The backup communication protocol ensures that the communication system can still maintain basic communication functions in extreme environments or emergencies by measures such as locking the transmission frequency, adopting a specific modulation method, and enabling the anti-saturation circuit. Among them, the anti-interference frequency band refers to the frequency range in which the communication system can still work normally in the face of strong electromagnetic interference; differential phase shift keying (DPSK) is a common modulation method that can maintain good communication performance in an interference environment; the anti-saturation circuit is used to prevent the power amplifier from saturating and distorting due to overload, ensuring that the transmission power is within a safe range.

[0079] Specifically, the anti-interference frequency band in the backup communication protocol can be selected according to the electromagnetic interference characteristics of the actual application environment. For example, in an industrial environment, a relatively clean frequency band can be selected, such as a frequency band near 2.4 GHz, to avoid common electromagnetic interference. Differential phase shift keying (DPSK) is a modulation method that carries information through the phase difference between adjacent symbols, and it has good robustness against channel noise and multipath interference. In the backup protocol, the symbol block length is fixed to a preset value, such as 1024 symbols, to simplify the encoding and decoding processes. The function of the anti-saturation circuit is to forcibly reduce the transmission power to a preset safety value, such as limiting the power within 100 mW, to avoid hardware damage or communication quality degradation caused by excessive power.

[0080] Preferably, in order to further improve the reliability and adaptability of the backup communication protocol, it can be refined or alternative solutions can be provided at some key points. For example, the selection of the anti-interference frequency band can be dynamically adjusted according to the interference intensity monitored in real time. The system can pre-store multiple alternative frequency bands and automatically switch when the main frequency band is interfered. For the modulation method, in addition to differential phase shift keying (DPSK), other modulation methods with stronger robustness can be selected according to actual needs, such as quadrature phase shift keying (QPSK). In addition, the power limit value of the anti-saturation circuit can be optimized according to the hardware characteristics of the communication device. For example, the power limit value can be appropriately increased in high-power devices to increase the communication distance while ensuring the safe operation of the device.

[0081] In some embodiments, the interference pulse density described in step S4 is extracted by the following method: S10-1: Perform time-domain envelope detection on the preprocessed data set to identify pulses whose amplitude exceeds the threshold ; S10-2: Count the number of pulses within a unit time , and obtain through sliding window smoothing:

[0082] where is the window length, is the sampling point index within the window.

[0083] It should be noted that this embodiment relates to a method for extracting the interference pulse density. The core lies in identifying and counting the density of pulse signals from the preprocessed data set through time-domain envelope detection and sliding window smoothing processing. The interference pulse density refers to the number of pulse signals appearing per unit time, which reflects the intensity and frequency of burst interference in the communication channel. Time-domain envelope detection is a signal processing technique used to identify pulses with signal amplitudes exceeding a specific threshold; the sliding window smoothing processing is used to smooth the statistical results to reduce the influence of noise and improve the accuracy of pulse density estimation.

[0084] Specifically, in the process of extracting the interference pulse density, it is first necessary to set an amplitude threshold to distinguish normal signals from interference pulses. For example, the amplitude threshold can be set to twice the average amplitude of the signal to ensure that only pulses significantly higher than the normal signal level are identified as interference. In time-domain envelope detection, the system scans the preprocessed data set to identify pulse signals with amplitudes exceeding this threshold. Subsequently, the number of pulses within a unit time is statistically processed through sliding window smoothing. The length of the sliding window can be set according to the characteristics of the signal. For example, in a high-frequency signal environment, the window length can be set to a short time period, such as 1 millisecond; while in a low-frequency signal environment, the window length can be appropriately increased. The sliding window smoothing process processes the number of pulses through weighted averaging, thereby reducing the statistical error caused by random noise.

[0085] Preferably, in order to further improve the accuracy and adaptability of interference pulse density extraction, it can be refined or alternative solutions can be provided at some key points. For example, the amplitude threshold can be adaptively adjusted according to the dynamic changes in the communication environment. The system can dynamically update the threshold by real-time monitoring of the background noise level of the signal, so as to more accurately identify interference pulses. For the sliding window smoothing process, more complex filtering algorithms, such as Gaussian filtering or median filtering, can be introduced to better suppress the influence of noise. In addition, machine learning algorithms can be combined to classify and identify pulse signals, further improving the system's adaptability to different types of interference.

[0086] The above embodiments of the present invention have the following beneficial effects: By collecting environmental data through multi-modal sensor nodes and performing dynamic noise classification, the present invention can flexibly adjust the noise reduction strategy according to the noise level and type, so as to achieve high-quality data transmission in a complex electromagnetic environment. At the same time, a multi-stage adaptive noise reduction algorithm is adopted, and local mean filtering, frequency domain notch filtering, and impulse interference cancellation algorithms are respectively applied to data with different noise levels, which can effectively reduce the impact of noise on communication and improve the purity of the signal. In addition, the energy efficiency prediction model constructed based on dynamic characteristic parameters can calculate the optimal combination of transmission frequency, power, and modulation order in real time, further optimizing the energy consumption performance of the communication system, while meeting the constraint conditions of the power consumption upper limit and the bit error rate threshold, ensuring the efficiency and reliability of communication.

[0087] Furthermore, the dynamic coding strategy can adjust the symbol block length and error correction code density according to the channel coherence time, and adopt an asymmetric Turbo code structure to enhance the anti-interference ability, so as to improve the error correction performance of the system during high-interference periods and ensure the stability of communication. The signal quality feedback mechanism at the receiving end can trigger parameter iterative optimization in real time and dynamically adjust the weight coefficients in the energy efficiency prediction model, further enhancing the adaptive ability of the system. In addition, the design of periodically evaluating the energy efficiency ratio and switching to the backup communication protocol can ensure the stability of communication under extreme conditions, avoiding communication interruptions caused by sudden interference or performance degradation, thus providing a more efficient, reliable, and energy-saving communication solution for Internet of Things devices.

[0088] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods. Among them, the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, or terminal devices such as computers, servers, mobile phones, and tablets.

[0089] The above description is only some preferred embodiments of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with (but not limited to) the technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A low-power semiconductor communication method based on the Internet of Things, characterized in that It includes the following steps: S1: Periodically collect the temperature data, electromagnetic interference intensity data, and signal attenuation coefficient of the target area through multi-modal sensor nodes to generate an original environmental data set; S2: Dynamically classify the original environmental data set according to the dynamic changes of the electromagnetic interference intensity data and temperature data, divide the noise level, and mark the noise type; S3: Based on the noise level and type, use a multi-stage adaptive noise reduction algorithm to perform hierarchical processing on the original environmental data set to generate a preprocessed data set; S4: Extract the dynamic characteristic parameters of the communication channel from the preprocessed data set, including the instantaneous signal frequency offset, channel coherence time, and interference pulse density; S5: According to the dynamic characteristic parameters, construct an energy efficiency prediction model for the semiconductor module, and dynamically calculate the combination of the optimal transmission frequency, power, and modulation order; S6: Generate a multi-level modulation signal based on the optimal combination, and superimpose a dynamic coding strategy, and adjust the symbol block length and error correction code density according to the channel coherence time; S7: The receiving end parses the signal quality index in real time, feedbacks it to the transmitting end to trigger parameter iterative optimization, and synchronously updates the weight factor in the energy efficiency prediction model.

2. The low-power semiconductor communication method based on the Internet of Things according to claim 1, wherein The dynamic noise classification described in step S2 includes: S2-1: According to the temperature data and the electromagnetic interference intensity joint distribution, define the piecewise function of the noise level as follows: ； Among them, is the temperature threshold, and is the electromagnetic interference intensity classification threshold, is the attenuation coefficient threshold; S2-2: For different noise levels, assign corresponding noise reduction algorithm priorities, and the anti-pulse interference algorithm is preferentially enabled for level 3.

3. The low-power semiconductor communication method based on the Internet of Things according to claim 1, characterized in that The multi-stage adaptive noise reduction algorithm described in step S3 includes: S3-1: For data with a noise level of 1, use local mean filtering based on a sliding window; S3-2: For data with a noise level of 2, use frequency domain notch filtering to suppress interference in specific frequency bands; S3-3: For data with a noise level of 3, enable the pulse interference cancellation algorithm to compensate for the distorted waveform through time-domain signal reconstruction.

4. The low-power semiconductor communication method based on the Internet of Things according to claim 1, wherein The construction of the energy efficiency prediction model described in step S5 includes: S5-1: Define the energy consumption cost function , including the transmission power , the frequency switching overhead and the bit error rate penalty term : ； Among them, , , are weighting coefficients, is the frequency adjustment amount, is the signal-to-noise ratio; S5-2: Solve through constraint optimization to obtain the parameter combination that minimizes the upper power consumption limit and the bit error rate threshold 5. The low-power semiconductor communication method based on the Internet of Things according to claim 1, characterized in that The dynamic coding strategy described in step S6 includes: S6-1: Dynamically adjust the symbol block length according to the channel coherence time; S6-2: Calculate the error correction code redundancy based on the interference pulse density; S6-3: Adopt an asymmetric Turbo code structure to enhance the forward error correction ability during high interference periods.

6. The low-power semiconductor communication method based on the Internet of Things according to claim 1, characterized in that The parameter iterative optimization described in step S7 includes: S7-1: The receiving end extracts signal quality indicators, including bit error rate , signal-to-noise ratio and delay jitter ; S7-2: Adjust the weight coefficient in the energy efficiency prediction model according to the deviation from to : ； Among them, and are preset thresholds; S7-3: If , then recalculate , , in the energy consumption cost function.

7. The low-power semiconductor communication method based on the Internet of Things according to claim 6, characterized in that The update formula for the weight coefficient in step S7-3 is: ； ； ； Among them, , , are convergence rate control parameters.

8. The low-power semiconductor communication method based on the Internet of Things according to claim 1, characterized in that It also includes step S8: S8-1: Periodically evaluate the energy efficiency ratio of the semiconductor module ; S8-2: If it drops by more than the threshold for N consecutive periods, switch to the backup communication protocol and reset the energy efficiency prediction model.

9. The low-power semiconductor communication method based on the Internet of Things according to claim 8, wherein The backup communication protocol includes: S9-1: Lock the transmission frequency within the anti-interference frequency band and prohibit dynamic frequency hopping; S9-2: Adopt differential phase shift keying modulation and fix the symbol block length to a preset minimum value; S9-3: Enable an anti-saturation circuit to forcibly reduce the power to a preset safety value.

10. The low-power semiconductor communication method based on the Internet of Things according to claim 1, characterized in that The extraction method of the interference pulse density described in step S4 includes: S10-1: Perform time-domain envelope detection on the preprocessed data set to identify pulses with amplitudes exceeding a preset threshold; S10-2: Count the number of pulses within a unit time and obtain the final number of pulses within a unit time through sliding window smoothing.

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