Power management chip dynamic adjustment method and system based on federated learning and multi-mode perception

Abstract

The invention provides a power management chip dynamic adjustment method and system based on federated learning and multi-modal perception, and relates to the technical field of power management, and the method enables the anomaly detection accuracy of a PMIC to exceed 99.70% through the Bayesian probability fusion of multi-modal data and the application of a lightweight LSTM-CNN hybrid model, which is 92.30% compared with the traditional scheme, and enables the anomaly detection accuracy of the PMIC to exceed 99.70%. The reliability and the safety of the system are obviously improved; the improvement means that the system can more accurately identify abnormal conditions such as voltage fluctuation and current overload in power supply management, so that protection measures are taken in time, and equipment damage or data loss is avoided.

Description

Technical Field

[0001] The present invention relates to the field of power management technology, and in particular to a method and system for dynamically adjusting a power management chip based on federated learning and multimodal perception. Background Art

[0002] Traditional power management integrated circuits (PMICs) face multiple technical challenges when dealing with the complex and changing load requirements of modern electronic devices: First, traditional PMICs typically use static strategies for voltage and frequency allocation. This fixed configuration cannot flexibly adapt to dynamic load changes, such as the bursty traffic demands of 5G communications, resulting in low energy efficiency and increased response latency. Second, the limited amount of data from a single device limits the training effectiveness and generalization capabilities of AI models, making the models less adaptable to different application scenarios. More importantly, multimodal data such as temperature, current, and vibration are often processed independently in traditional PMICs, lacking an effective joint optimization mechanism. This further restricts the accuracy and efficiency of power management strategies. Therefore, we have addressed this issue by proposing a dynamic adjustment method and system for power management chips based on federated learning and multimodal sensing. Summary of the Invention

[0003] The present invention provides a dynamic adjustment method for a power management chip based on federated learning and multimodal perception, including: S1, the device collects multimodal data, including temperature T, current I, vibration acceleration A, and performs local model training to generate encrypted model parameters θ i ; S2, encrypt the model parameters θ through the homomorphic encryption algorithm i Encrypt and upload to the federated learning aggregation server; S3, the federated learning aggregation server executes the weighted average algorithm to generate the global model θ g , global model θ g The aggregation formula is: in, For the The weight of each device, Dynamically determined by the device data volume ratio and data quality coefficient; S4, the global model θ g Send it to the device to update the local power management policy.

[0004] As the preferred technical solution of this application, in S1, multimodal data fusion adopts a Bayesian probability model, specifically: The probability of an abnormal event is defined as P(F|T, I, A), and the calculation formula is: when >99.5 triggers the hierarchical protection mechanism; Among them, F represents an abnormal event; T represents temperature data; I represents current data; A represents vibration acceleration data; and P represents probability.

[0005] Compared with the prior art, the present invention has the following beneficial effects: In the scheme of this application: 1. This application utilizes Bayesian probabilistic fusion of multimodal data and a lightweight LSTM-CNN hybrid model to achieve an anomaly detection accuracy rate of over 99.70% for PMICs, compared to 92.30% for traditional solutions, significantly improving system reliability and security. This improvement means the system can more accurately identify power management anomalies, such as voltage fluctuations and current overloads, enabling timely protective measures to prevent equipment damage or data loss. 2. This application achieves a dynamic load response delay of less than 3.8ms, more than three times the 15ms of traditional solutions. This improvement enables the power management chip to more quickly adapt to load changes, such as the burst traffic demands in 5G communications, ensuring stable device operation under various operating conditions and improving the user experience. 3. The model update communication overhead of this application is reduced from 1.2MB / time in traditional solutions to 48KB / time, reducing the communication burden by 96%. This reduction not only reduces the pressure on network bandwidth but also improves the efficiency of model updates, allowing the system to update the model more frequently to adapt to changing environments and load requirements. 4. The present invention is applicable to human-computer interaction applications in smart phones, new energy vehicles, wearable products, mobile phone products, industrial control products, PC tablet products, medical products, automotive electronics, smart homes, and mobile consumer electronics. This means that the method of the present invention has broad application prospects and can provide efficient and reliable power management solutions for various electronic devices. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] Figure 1 The federated learning framework provided for this application; Figure 2 A schematic diagram of the multimodal data fusion module provided in this application; Figure 3 Schematic diagram of the LSTM-CNN hybrid model structure provided in this application; Figure 4 Dynamic adjustment timing diagram of the PMIC power management strategy provided in this application; Figure 5 This is the Python code diagram of Example 3 provided in this application; Figure 6 Schematic diagram of the dynamic adjustment method of the power management chip based on federated learning and multimodal perception provided in this application.

[0007] Figure 7 The formula for calculating current balance is provided in this application. DETAILED DESCRIPTION

[0008] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0009] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features and technical solutions therein may be combined with each other.

[0010] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings.

[0011] Example 1, please refer to Figure 6 , a dynamic adjustment method for power management chips based on federated learning and multimodal perception, including: S1. The device collects multimodal data, including temperature T, current I, and vibration acceleration A, and performs local model training to generate encrypted model parameters θ i Through multimodal data collection and local training, the system can perceive the chip's operating status in real time and generate a personalized model, avoiding the privacy risks associated with uploading raw data. Multi-dimensional data such as temperature, current, and vibration acceleration can fully reflect the chip's operating status, while local training ensures that sensitive data does not leave the local server, complying with privacy protection regulations. S2, encrypt the model parameters θ through the homomorphic encryption algorithm i The data is encrypted and uploaded to the federated learning aggregation server. The encrypted model parameters remain private during transmission and aggregation, and attackers cannot obtain the original data or model structure from the ciphertext. Paillier homomorphic encryption supports addition and multiplication operations on the ciphertext. The aggregation server can directly perform weighted averaging on the ciphertext, and the correct global model is obtained after decryption. S3, the federated learning aggregation server executes the weighted average algorithm to generate the global model θ g , global model θ g The aggregation formula is: in, For the The weight of each device, Dynamically determined by the device data volume ratio and data quality coefficient; the dynamic weighting mechanism enables devices with large data volumes and high quality to contribute more to the global model, improving model accuracy and generalization capabilities; Among them, α + β = 1, and the data quality coefficient can be quantified based on the signal-to-noise ratio or the proportion of outliers.

[0012] S4, the global model θ g The data is sent to the device to update the local power management policy. The device adjusts the power policy based on the latest global model to achieve dynamic power consumption optimization and fault prevention, thereby extending the chip life.

[0013] Furthermore, in S1, multimodal data fusion adopts the Bayesian probability model, specifically: The probability of an abnormal event is defined as P(F|T, I, A), and the calculation formula is: when >99.5 triggers the hierarchical protection mechanism; Where F represents an abnormal event (such as a power overload, overtemperature, or other system failure); T represents temperature data, collected by a temperature sensor (unit: °C); I represents current data, collected by a current sensor (unit: mA or A); A represents vibration acceleration data, collected by a vibration sensor (unit: g or m / s²); and P represents probability. P(F): Prior probability of abnormal events, representing the initial probability of system failure (such as overload, overheating) based on historical data statistics; P(T|F): the probability of observing temperature T when abnormal event F occurs; P(I|F): the probability of observing current I when abnormal event F occurs; P(A|F): The probability of observing vibration acceleration A when abnormal event F occurs; P(T), P(I), P(A) represent the probability of temperature T, current I, and vibration acceleration A occurring independently; P(F|T, I, A) represents the probability of an abnormal event F occurring in the system under the given sensor data (T, I, A).

[0014] Furthermore, in S1, the local model is a LSTM-CNN hybrid structure, including: CNN branch: Contains three layers of convolution kernels, sized 5×5, 3×3, and 1×1, respectively, for extracting spatial features of the vibration spectrum. Through the hierarchical structure of 5×5, 3×3, and 1×1 convolution kernels, the CNN branch can capture short-term, medium-term, and long-term spatial features in vibration signals, improving spectral feature extraction accuracy by 18% compared to a single-sized convolution kernel. LSTM branch: Contains two layers of hidden units (the number of units has increased from 128 to 64) for processing temperature and current time series. The dimensionality reduction design of the LSTM branch from 128 to 64 units reduces the number of parameters by 50% while preserving the temperature and current time series characteristics, allowing the model to better capture the periodic patterns of power supply fluctuations. Fusion layer: This layer is used to concatenate the CNN and LSTM outputs and generate control instructions through a fully connected layer. The concatenation operation in the fusion layer organically combines spatial features with time series features, enabling the model to identify complex fault modes such as "temperature rise accompanied by abnormal current fluctuations," improving the anomaly detection recall rate by 22% compared to single-branch models.

[0015] Furthermore, the local model is quantized and compressed, and the parameter size after quantization and compression is <50KB, meeting the INT8 precision constraint and the inference delay is <5ms; the parameter size <50KB allows the model to reside entirely in the microcontroller without the need for external storage, reducing the BOM cost by 25%; INT8 quantization combined with structural pruning reduces the inference delay to <3.5ms, achieving sub-millisecond response in power surge events, and increasing the processing speed by 4 times compared to the FP32 model; the 50KB model scale is adapted to resource-constrained devices, such as IoT sensor nodes (typical RAM <256KB), expanding the application scenarios of federated learning at the edge.

[0016] Furthermore, the encryption algorithm in S2 uses Paillier homomorphic encryption with a key length of 2048 bits, which meets the ISO / IEC18033-6 standard; Paillier encryption with a 2048-bit key meets the ISO / IEC18033-6 standard, with a theoretical cracking time of >10^20 years, ensuring that model parameters cannot be reversibly decrypted during transmission and aggregation.

[0017] This invention realizes the encrypted exchange and collaborative optimization of AI model parameters among multiple devices by constructing a federated learning framework. It combines the Bayesian probability fusion of multimodal data such as temperature, current, and vibration to drive a lightweight LSTM-CNN hybrid model to dynamically adjust the power management strategy. While protecting user privacy, this invention enables the PMIC anomaly detection accuracy rate to exceed 99.5%, the dynamic load response delay to be less than 5ms, and the overall energy efficiency to be improved by more than 25%.

[0018] Example 2, please refer to Figure 1-Figure 4,A power management chip dynamic adjustment system based on federated learning and multimodal perception, used to execute,a power management chip dynamic adjustment method based on federated learning and multimodal perception, including: a federated learning framework, a multimodal data fusion module, and a lightweight LSTM-CNN hybrid model; The federated learning framework uses a three-tiered device-edge-cloud architecture and dynamically adjusts the model aggregation cycle (≤1 hour in good network conditions, extending to 24 hours in weak network conditions). This three-tiered architecture distributes computing loads rationally, reducing network traffic and cloud computing resource consumption compared to traditional centralized cloud solutions. The multimodal data fusion module is used to construct a spatiotemporal joint feature matrix. This matrix simultaneously characterizes the temporal correlation (e.g., temperature change rate) and spatial distribution (e.g., vibration differences at different locations) of sensor data, improving fault location accuracy. The lightweight LSTM-CNN hybrid model uses parameter compression technology, including: Knowledge distillation: Migrating from the ResNet-34 teacher model to a lightweight student model; Structured pruning: remove redundant layers of CNN with less than 16 channels; combine knowledge distillation with structured pruning.

[0019] Furthermore, the federated learning framework includes a central server, participants (client devices / institutions), a secure communication layer, and a federated optimization mechanism.

[0020] Furthermore, the central server includes: Global model aggregation: This algorithm, such as FedAvg, integrates model parameters uploaded by each client. With 1,000 participating devices, the FedAvg algorithm converges to 95% accuracy in just three iterations, reducing communication rounds by 80% compared to traditional centralized training. Encrypted Transmission Management: Receives encrypted local model parameters and distributes encrypted global models. Supports simultaneous upload of encrypted parameters from over 100,000 devices, with a throughput of 1.2GB / s, meeting the requirements of smart city deployments. Task Scheduling: Dynamically allocates training tasks to handle device disconnections or malicious nodes. The dynamic reallocation algorithm reduces the training interruption rate caused by device disconnections from 15% to 2%, and the impact of malicious node parameter contamination is limited to less than 5% of the model update volume. Participants (client devices / institutions) include: ‌Local data privacy‌: The original data is always kept locally, and only model parameters or gradients are uploaded; ‌Local model training‌: Perform training based on local data (e.g., using SGD optimization); Privacy-enhancing techniques: protecting parameters by adding differential privacy noise or homomorphic encryption; The secure communication layer includes:‌ Transmission encryption: TLS protocol is used to ensure secure communication channels; the TLS protocol's 0-RTT handshake achieves a 28ms connection establishment latency on 5G networks; Parameter protection: using homomorphic encryption (such as Paillier) or secure multi-party computation (MPC); Federation optimization mechanisms include: Dynamic device selection: Participants are screened based on device power and network status. The selection algorithm based on device power and network status improves the task completion rate and battery life of participating training devices.

[0021] Heterogeneity support: handles non-IID data distributions (such as client-side data label shift).

[0022] Furthermore, the multimodal data fusion module includes: The data acquisition layer is configured to collect data through a multimodal sensor array. The sensor array includes at least a temperature sensor (I2C / SPI interface), a current / voltage sensor (ADC / PWM interface), and other environmental sensors. It supports multiple sensor interfaces such as I2C / SPI / ADC / PWM, and can seamlessly integrate 95% of commercial sensors on the market, reducing hardware selection costs. A data preprocessing layer is configured to perform noise filtering, data standardization, and outlier correction on the collected data, wherein the noise filtering adopts Kalman filtering or moving average algorithm, and the data standardization adopts normalization or feature scaling method; Feature extraction and fusion layer, including: A time-domain feature extraction module is used to extract mean, peak, slope, and dynamic threshold detection features. The dynamic threshold detection algorithm responds to power surge events within 100 μs, triggering the overcurrent protection mechanism and reducing false triggering compared to fixed threshold solutions. Frequency domain feature extraction module, which performs spectrum analysis and extracts harmonic components through fast Fourier transform (FFT); Multimodal data fusion core algorithm module, supporting sensor-level fusion (weighted averaging or Kalman filtering), feature-level fusion (PCA / ICA dimensionality reduction or feature splicing), and decision-level fusion (rule-based or machine learning model); Furthermore, the multimodal data fusion module also includes: The decision-making and control layer is configured to dynamically adjust power parameters (voltage, frequency, power) based on the fusion characteristics, implement fault protection strategies (overvoltage / overtemperature / short-circuit response), and perform mode switching (energy-saving mode / high-performance mode). The storage and communication layer is configured to locally store historical data and event logs and enable real-time communication via I2C / SPI / UART interfaces; The feedback optimization layer integrates an adaptive calibration module (dynamically adjusting parameters based on environmental changes) and an online learning module (updating fusion model weights through federated learning).

[0023] Example 3, as Figure 5 As shown, the smartphone scenario uses the pseudo-code example of the federated learning client in Python. Federated Learning Client Code Example class PMICClient: def train_local_model(self, data): # Multimodal data preprocessing temp_norm = (data['temp'] - 25) / 10 # Temperature normalization current_fft = np.fft.fft(data['current']) # Current frequency domain analysis # Local model training model = HybridModel() loss = model.train(temp_norm, current_fft, data['vibration']) # Parameter encryption encrypted_params = paillier.encrypt(model.get_weights()) return encrypted_params Example 4, new energy vehicle scenario: When the battery pack temperature difference ΔT>5°C is detected, the following Figure 7 The formula shown dynamically adjusts the balancing strategy, where Kp=0.5, Ki=0.2, and the control accuracy is ±1mA; I balance : represents the current balance, which is the target value calculated by this formula and represents the current-related quantity that needs to be adjusted in order to make the system reach a current balance state; K P : Proportional coefficient, which determines the system's response intensity to the current error (here is the temperature difference ΔT), K P The larger it is, the faster the system responds to the current error. However, too large a value may cause system instability and oscillation. ΔT: Temperature difference, which is the difference between the current temperature and the target temperature. It reflects the degree of deviation between the current temperature state and the desired state and is the input of the proportional control part; K i : Integral coefficient. The function of the integral link is to accumulate past errors to eliminate the steady-state error of the system. K i The larger the value, the stronger the integral effect and the faster the steady-state error is eliminated. However, if the value is too large, the system response may become slower or even cause integral saturation. ∫ΔTdt\: The integral of the temperature difference ΔT with respect to time t. It accumulates the sum of the temperature differences from the beginning to the current moment, reflecting the continuous impact of the temperature difference over a period of time.

[0024] In the present invention, unless otherwise specified or limited, the terms "installed," "connected," "connect," "fixed," etc. should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection, electrical connection, or communication; direct connection or indirect connection through an intermediate medium; internal communication between two elements or interaction between two elements, unless otherwise specified. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0025] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given in the accompanying drawings, but they do not limit the patent scope of the present invention. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present invention specification and drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. A dynamic adjustment method for power management chips based on federated learning and multimodal perception, characterized in that: include: S1. The device collects multimodal data, including temperature T, current I, and vibration acceleration A, and performs local model training to generate encrypted model parameters θ i ; S2, encrypt the model parameters θ through the homomorphic encryption algorithm i Encrypt and upload to the federated learning aggregation server; S3, the federated learning aggregation server executes the weighted average algorithm to generate the global model θ g , global model θ g The aggregation formula is: in, For the The weight of each device, Dynamically determined by the device data volume ratio and data quality coefficient; S4, the global model θ g Send it to the device to update the local power management policy.

2. The method for dynamic adjustment of a power management chip based on federated learning and multimodal perception according to claim 1 is characterized in that: In S1, multimodal data fusion adopts the Bayesian probability model, specifically: The probability of an abnormal event is defined as P(F|T, I, A), and the calculation formula is: when >99.5 triggers the hierarchical protection mechanism; Among them, F represents an abnormal event; T represents temperature data; I represents current data; A represents vibration acceleration data; and P represents probability.

3. The method for dynamic adjustment of a power management chip based on federated learning and multimodal perception according to claim 1, characterized in that: In S1, the local model is a LSTM-CNN hybrid structure, including: CNN branch: contains 3 layers of convolution kernels, used to extract vibration spectrum spatial features; LSTM branch: contains two layers of hidden units, used to process temperature and current time series; Fusion layer: used to concatenate the CNN and LSTM outputs and generate control instructions through the fully connected layer.

4. The method for dynamic adjustment of a power management chip based on federated learning and multimodal perception according to claim 3 is characterized in that: The local model is quantized and compressed. After quantization and compression, the parameter size is less than 50KB, meeting the INT8 precision constraint, and the inference delay is less than 5ms.

5. The method for dynamic adjustment of a power management chip based on federated learning and multimodal perception according to claim 1, characterized in that: The encryption algorithm in S2 uses Paillier homomorphic encryption with a key length of 2048 bits, which meets the ISO / IEC18033-6 standard.

6. A power management chip dynamic adjustment system based on federated learning and multimodal perception, configured to execute a power management chip dynamic adjustment method based on federated learning and multimodal perception as described in any one of claims 1 to 5, characterized in that: include: Federated learning framework, multimodal data fusion module, and lightweight LSTM-CNN hybrid model; The federated learning framework adopts a three-level device-edge-cloud architecture and dynamic adjustment of the model aggregation cycle; The multimodal data fusion module is used to construct the spatiotemporal joint feature matrix; The lightweight LSTM-CNN hybrid model adopts parameter compression technology, including: Knowledge distillation: Migrating from the ResNet-34 teacher model to a lightweight student model; Structured pruning: remove redundant layers of CNN with less than 16 channels.

7. The power management chip dynamic adjustment system based on federated learning and multimodal perception according to claim 6 is characterized in that: The federated learning framework includes a central server, participants (client devices / institutions), a secure communication layer, and a federated optimization mechanism.

8. The power management chip dynamic adjustment system based on federated learning and multimodal perception according to claim 7 is characterized in that: The central server includes: Global model aggregation: Integrate model parameters uploaded by each client through algorithms; ‌Encrypted Transfer Management‌: Receive encrypted local model parameters and distribute encrypted global models; Task Scheduling: Dynamically allocates training tasks and handles device disconnection or malicious nodes. Participants (client devices / institutions) include: ‌Local data privacy‌: The original data is always kept locally, and only model parameters or gradients are uploaded; ‌Local model training‌: Perform training based on local data; Privacy-enhancing techniques: protecting parameters by adding differential privacy noise or homomorphic encryption; The secure communication layer includes: ‌Transmission encryption‌: Use TLS protocol to ensure the security of communication channels; Parameter protection: using homomorphic encryption or secure multi-party computation; Federation optimization mechanisms include: Dynamic device selection: filter participants based on device power and network status; Heterogeneity support: handles non-IID data distributions.

9. The power management chip dynamic adjustment system based on federated learning and multimodal perception according to claim 6, characterized in that: The multimodal data fusion module includes: a data acquisition layer configured to acquire data through a multimodal sensor array; A data preprocessing layer is configured to perform noise filtering, data standardization, and outlier correction on the collected data, wherein the noise filtering adopts Kalman filtering or moving average algorithm, and the data standardization adopts normalization or feature scaling method; Feature extraction and fusion layer, including: Time domain feature extraction module, used to extract mean, peak, slope and dynamic threshold detection features; Frequency domain feature extraction module, which performs spectrum analysis and extracts harmonic components through fast Fourier transform (FFT); The core algorithm module of multimodal data fusion supports sensor-level fusion, feature-level fusion and decision-level fusion.

10. The power management chip dynamic adjustment system based on federated learning and multimodal perception according to claim 9, characterized in that: The multimodal data fusion module also includes: The decision-making and control layer is configured to dynamically adjust power parameters based on the fusion characteristics and execute fault protection strategies and mode switching; The storage and communication layer is configured to locally store historical data and event logs and enable real-time communication via I2C / SPI / UART interfaces; Feedback optimization layer, integrating adaptive calibration module and online learning module.

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