Multi-Redundancy Flexible Power Supply MCU Control Method and System

Abstract

The present invention relates to a multi-redundancy flexible power supply MCU control method and system, including the following steps: performing real-time working voltage sampling and detection on the MCU to obtain a working voltage sampling sequence; performing multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix; performing dynamic threshold division on the MCU state feature matrix to obtain a multi-level power supply switching criterion; performing real-time scheduling of the power supply mode on the MCU based on the multi-level power supply switching criterion to obtain an optimal power supply configuration scheme; performing execution allocation on the optimal power supply configuration scheme to obtain a real-time power supply control instruction; and cooperatively controlling the multi-redundancy power supply system to stably supply power to the MCU based on the real-time power supply control instruction, solving the technical problem that traditional power supply strategies usually rely on fixed thresholds for switching, lack flexibility and accuracy, and cannot effectively cope with real-time changing working states.

Description

Technical Field

[0001] The present invention relates to the technical field of microcontrollers, and particularly to a multiple redundant flexible power supply MCU control method and system. Background Art

[0002] In modern electronic devices and systems, the microcontroller unit (MCU) is a core component, and its stability and reliability are directly related to the performance of the entire system. With the development of technology, the requirements for the MCU are also increasing day by day. Especially in complex environments and critical mission application scenarios, ensuring the continuous and stable operation of the MCU has become an urgent problem to be solved. Traditional power supply schemes often adopt single or simple redundant designs, which are difficult to meet the dual requirements of high reliability and high performance. This has promoted the research on more intelligent, flexible and reliable power supply control methods to adapt to changing working conditions and ensure that the MCU can achieve optimal performance in various environments.

[0003] However, in practical applications, the existing technologies face multiple challenges. On the one hand, due to changes in the working environment and aging problems caused by long-term operation, the actual working voltage of the MCU may fluctuate, which poses a threat to the normal operation of the MCU. Traditional power supply strategies usually rely on fixed thresholds for switching, lacking flexibility and precision, and unable to effectively respond to real-time changing working states. On the other hand, as the functions of the MCU continue to increase, its power consumption modes have become more diverse. How to dynamically adjust the power supply configuration according to different workloads to maximize energy efficiency while ensuring system stability has become an important research direction.

[0004] In addition, the introduction of a multiple redundant power supply system has improved the reliability of the power supply, but at the same time has increased the complexity of the system. How to effectively manage and coordinate these redundant power supply resources so that they can quickly respond and provide support when needed is a difficult point in current research. Especially in distributed systems, the design of the cooperation mechanism is crucial for achieving efficient and stable power supply control. Therefore, developing a power supply control method that can perform intelligent evaluation and dynamic adjustment based on the real-time state of the MCU is of great significance for improving the reliability and efficiency of the entire system. Summary of the Invention

[0005] The main object of the present invention is to provide a multiple redundant flexible power supply MCU control method and system, which solves the technical problem that traditional power supply strategies usually rely on fixed thresholds for switching, lack flexibility and precision, and are unable to effectively respond to real-time changing working states.

[0006] To achieve the above object, the present invention provides a multi-redundancy flexible power supply MCU control method, which is applied to a multi-redundancy power supply system. The multi-redundancy power supply system is electrically connected to the MCU, and includes the following steps:

[0007] Perform real-time working voltage sampling and detection on the MCU to obtain a working voltage sampling sequence;

[0008] Based on the working voltage sampling sequence, perform multi-dimensional working state evaluation on the MCU to obtain an MCU state feature matrix;

[0009] Through an adaptive weight allocation mechanism, perform dynamic threshold division on the MCU state feature matrix to obtain a multi-level power supply switching criterion;

[0010] Based on the multi-level power supply switching criterion, perform real-time scheduling of the power supply mode of the MCU to obtain an optimal power supply configuration scheme;

[0011] Through a distributed cooperation mechanism, perform execution allocation on the optimal power supply configuration scheme to obtain a real-time power supply control instruction;

[0012] Based on the real-time power supply control instruction, cooperatively control the multi-redundancy power supply system to stably supply power to the MCU.

[0013] Further, the performing real-time working voltage sampling and detection on the MCU to obtain a working voltage sampling sequence includes:

[0014] Collect the working voltage of the MCU through a multi-channel synchronous sampling circuit to obtain a set of original voltage signals;

[0015] Based on a preset Kalman filter fusion device, perform noise suppression and signal reconstruction on the set of original voltage signals to obtain an optimized voltage feature vector;

[0016] Through a dynamic time warping algorithm, perform time series alignment and normalization processing on the optimized voltage feature vector to obtain a working voltage sampling sequence.

[0017] Further, the performing multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix includes:

[0018] Perform multi-dimensional feature decomposition and reconstruction on the working voltage sampling sequence to obtain a voltage fluctuation feature tensor, and perform non-linear mapping transformation on the voltage fluctuation feature tensor to obtain a set of voltage dynamic characteristics;

[0019] Time-frequency domain feature extraction is performed on the voltage dynamic characteristic set through multi-layer wavelet packet decomposition to obtain a voltage quality feature vector, and based on the voltage quality feature vector, a comprehensive evaluation of the multiple redundant power supply system is carried out to obtain a state scoring matrix of the multiple redundant power supply system;

[0020] The working state of the MCU is mapped based on the multi-dimensional features of the state scoring matrix to obtain a working state feature set of the MCU, and the working state feature set of the MCU is hierarchically decomposed to obtain an MCU performance parameter matrix;

[0021] Dynamic feature recognition of the MCU is performed based on the MCU performance parameter matrix to obtain an MCU operation feature vector, and state space reconstruction and feature extraction are performed based on the MCU operation feature vector to obtain an MCU initial state feature matrix;

[0022] Time series correlation analysis is performed on the MCU initial state feature matrix to obtain an MCU state evolution sequence, and multi-dimensional feature fusion and optimization are performed on the MCU state evolution sequence to obtain an MCU state feature matrix; wherein, the MCU state feature matrix includes a voltage stability degree vector, a power consumption fluctuation degree vector, and a task execution efficiency degree vector.

[0023] Furthermore, dynamic threshold division is performed on the MCU state feature matrix through an adaptive weight allocation mechanism to obtain multi-level power supply switching criteria, including:

[0024] Multi-dimensional feature stratification and decoupling are performed on the MCU state feature matrix to obtain a set of state feature components, and dynamic weight optimization is performed on the set of state feature components through an adaptive weight allocation mechanism to obtain an adaptive weight coefficient matrix;

[0025] Feature importance evaluation is performed based on the adaptive weight coefficient matrix to obtain a feature importance ranking table, multi-level threshold analysis is performed on the feature importance ranking table to obtain a threshold candidate set, and dynamic partition mapping is performed based on the threshold candidate set to obtain a power supply state partition matrix;

[0026] Multi-level threshold optimization and calibration are performed based on the power supply state partition matrix to obtain a threshold optimization sequence, and a hierarchical decision rule is constructed for the threshold optimization sequence to obtain a power supply switching rule set, including a voltage switching rule, a performance protection rule, and a system cooperation rule;

[0027] Multi-dimensional cross-validation and fusion are performed on the power supply switching rule set to obtain multi-level power supply switching criteria, wherein the multi-level power supply switching criteria include a voltage derating coefficient, a power consumption compensation factor, and a task priority.

[0028] Further, performing real-time scheduling of the power supply mode for the MCU based on the multi-level power supply switching criterion to obtain an optimal power supply configuration scheme includes:

[0029] Performing multi-dimensional feature analysis and reconstruction on the multi-level power supply switching criterion to obtain a power supply switching feature set, and classifying the power supply mode of the MCU based on the power supply switching feature set to obtain candidate power supply mode schemes;

[0030] Performing multi-objective evaluation on the candidate power supply mode schemes through the analytic hierarchy process to obtain a power supply scheme evaluation matrix, and performing priority sorting on the power supply scheme evaluation matrix to obtain a power supply scheme priority sequence;

[0031] Constructing multi-dimensional constraint conditions based on the power supply scheme priority sequence to obtain a power supply scheduling constraint set, and performing dynamic boundary division on the power supply scheduling constraint set to obtain power supply scheduling boundary conditions;

[0032] Performing multi-objective optimization and solution on the power supply scheduling boundary conditions to obtain a power supply scheduling strategy set, evaluating and screening the power supply scheduling strategy set to obtain a group of candidate configuration schemes, and performing multi-dimensional comprehensive evaluation on the group of candidate configuration schemes to obtain an optimal power supply configuration scheme; wherein, the optimal power supply configuration scheme includes power supply switching timing, voltage regulation parameters, and power consumption balancing strategies.

[0033] Further, performing execution allocation on the optimal power supply configuration scheme through a distributed cooperation mechanism to obtain real-time power supply control instructions includes:

[0034] Performing multi-dimensional parameter analysis and decomposition on the optimal power supply configuration scheme to obtain a power supply configuration parameter set, and performing execution timing planning based on the power supply configuration parameter set to obtain a power supply control timing table;

[0035] Generating an execution sequence for the power supply control timing table through a distributed cooperation mechanism to obtain a control instruction sequence matrix, and performing timing consistency verification on the control instruction sequence matrix to obtain an instruction execution constraint set;

[0036] Performing multi-dimensional conflict detection and resolution on the control instruction sequence matrix based on the instruction execution constraint set to obtain an optimized instruction sequence, and performing execution priority division on the optimized instruction sequence to obtain a hierarchical execution scheme;

[0037] Performing multi-dimensional execution resource allocation on the hierarchical execution scheme to obtain real-time power supply control instructions, wherein the real-time power supply control instructions include power supply switching control instructions, power consumption regulation control instructions, and stability guarantee control instructions.

[0038] Further, performing multi-dimensional conflict detection and resolution on the control instruction sequence matrix based on the instruction execution constraint set to obtain an optimized instruction sequence includes:

[0039] Performing timing dependency analysis and decomposition on the instruction execution constraint set to obtain an instruction conflict feature set, and hierarchically classifying the instruction conflict feature set to obtain an instruction conflict type matrix;

[0040] Performing multi-dimensional feature mapping on the instruction conflict type matrix through tensor decomposition to obtain a conflict feature vector group, and tracing the conflict propagation path of the control instruction sequence matrix based on the conflict feature vector group to obtain a conflict propagation topology graph;

[0041] Constructing multi-dimensional constraint conditions for the conflict propagation topology graph to obtain a conflict resolution rule set, and performing instruction reordering on the control instruction sequence matrix based on the conflict resolution rule set to obtain an instruction execution optimization plan;

[0042] Performing multi-dimensional cross-validation on the instruction execution optimization plan to obtain an instruction optimization evaluation matrix, and performing feasibility analysis and multi-objective collaborative optimization on the instruction optimization evaluation matrix to obtain an optimized instruction sequence.

[0043] The present invention also provides a multi-redundancy flexible power supply MCU control system, which is applied to a multi-redundancy power supply system. The multi-redundancy power supply system is electrically connected to the MCU and includes:

[0044] A sampling module for performing real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence;

[0045] An evaluation module for performing multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix;

[0046] A partitioning module for dynamically thresholding the MCU state feature matrix through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion;

[0047] A scheduling module for performing real-time scheduling of the power supply mode of the MCU based on the multi-level power supply switching criterion to obtain an optimal power supply configuration plan;

[0048] An allocation module for performing execution allocation on the optimal power supply configuration plan through a distributed collaboration mechanism to obtain a real-time power supply control instruction;

[0049] A control module for collaboratively controlling the multi-redundancy power supply system to stably supply power to the MCU based on the real-time power supply control instruction.

[0050] The present invention also provides a computer device, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of any one of the above methods are implemented.

[0051] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method described in any one of the above are implemented.

[0052] The multi-redundancy flexible power supply MCU control method provided by the present invention includes the following steps: performing real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence; performing multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix; performing dynamic threshold division on the MCU state feature matrix to obtain a multi-level power supply switching criterion; performing real-time scheduling of the power supply mode of the MCU based on the multi-level power supply switching criterion to obtain an optimal power supply configuration scheme; performing execution allocation on the optimal power supply configuration scheme to obtain a real-time power supply control instruction; and cooperatively controlling the multi-redundancy power supply system to stably supply power to the MCU based on the real-time power supply control instruction, solving the technical problem that traditional power supply strategies usually rely on fixed thresholds for switching, lack flexibility and accuracy, and cannot effectively cope with real-time changing working states. The real-time scheduling of the power supply mode of the MCU is realized based on the multi-level power supply switching criterion, and an optimal power supply configuration scheme can be obtained. This helps to improve energy utilization efficiency, and at the same time ensures that the MCU can obtain stable power supply under various load conditions, improving the performance and stability of the overall system. Description of the Drawings

[0053] Figure 1 is a schematic diagram of the steps of the multi-redundancy flexible power supply MCU control method in an embodiment of the present invention;

[0054] Figure 2 is a structural block diagram of the multi-redundancy flexible power supply MCU control device in an embodiment of the present invention;

[0055] Figure 3 is a schematic structural block diagram of a computer device in an embodiment of the present invention.

[0056] The realization, functional features and advantages of the object of the present invention will be further described with reference to the embodiments and the drawings. Detailed Embodiments

[0057] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and are not used to limit the present invention.

[0058] As Figure 1 shown, Figure 1 This is a schematic diagram of the steps of a multi-redundancy flexible power supply MCU control method in an embodiment of the present invention;

[0059] An embodiment of the present invention provides a multi-redundancy flexible power supply MCU control method, which is applied to a multi-redundancy power supply system. The multi-redundancy power supply system is electrically connected to the MCU and includes the following steps:

[0060] Step S1, perform real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence.

[0061] Specifically, performing real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence is the basic link of the entire multi-redundancy flexible power supply MCU control method. Specifically, to achieve this step, a high-precision voltage sampling module needs to be introduced into the power supply circuit of the MCU. This module can collect the working voltage of the MCU at a fixed time interval or a dynamically adjusted frequency, and arrange these collected voltage values in chronological order to form a working voltage sampling sequence. Since the working voltage of the MCU may be affected by factors such as load changes, environmental temperature fluctuations, or power supply noise during operation, its working voltage may experience instantaneous fluctuations or long-term drifts. Therefore, real-time sampling can capture these subtle changes, providing reliable data support for subsequent multi-dimensional working state evaluation. For example, in an industrial automation control system, the MCU is responsible for processing sensor data and controlling actuators. If the power supply voltage shows abnormal fluctuations, it may lead to data processing errors or control failures, while real-time working voltage sampling detection can detect these problems in a timely manner to ensure the stability of the system. In addition, in order to improve the accuracy and anti-interference ability of sampling, a filtering algorithm can be used to preprocess the sampled data to remove noise interference, thereby further improving the quality of the working voltage sampling sequence. This process not only lays the foundation for subsequent state evaluation and power supply mode scheduling, but also helps engineers quickly locate problems and optimize system design in practical applications.

[0062] Step S2, based on the working voltage sampling sequence, perform multi-dimensional working state evaluation on the MCU to obtain an MCU state feature matrix.

[0063] Specifically, based on the working voltage sampling sequence, a multi-dimensional working state assessment of the MCU is performed to obtain an MCU state feature matrix. This process is a key step in converting the real-time collected voltage data into a form that can comprehensively reflect the operating state of the MCU. Specifically, through the analysis of the working voltage sampling sequence, multiple characteristic parameters related to the working state of the MCU can be extracted, such as the voltage fluctuation amplitude, the frequency change trend, and the duration of the instantaneous voltage drop. These parameters together constitute the core content of the MCU state feature matrix. To achieve this goal, first, the working voltage sampling sequence needs to be segmented, and key features are extracted by combining time-domain and frequency-domain analysis methods. At the same time, statistical methods can be introduced to normalize the data to ensure the comparability between different parameters. For example, in an industrial automation scenario, the MCU is responsible for controlling the movement of the robotic arm on the production line. If the power supply voltage fluctuates frequently, it may lead to a decrease in the movement accuracy of the robotic arm or even cause it to stop. Through multi-dimensional working state assessment, it is possible to comprehensively judge whether the voltage fluctuation exceeds the safe range and further analyze its impact on the performance of the MCU. In addition, to improve the accuracy of the assessment, historical data or preset thresholds can be combined to dynamically adjust the feature weights, thereby generating a more accurate MCU state feature matrix. This matrix not only provides a basis for subsequent adaptive weight allocation and dynamic threshold division but also helps engineers quickly identify potential problems in practical applications, optimize the system design, and ensure the stable operation of the MCU in complex environments.

[0064] Step S3, through the adaptive weight allocation mechanism, perform dynamic threshold division on the MCU state feature matrix to obtain a multi-level power supply switching criterion.

[0065] Specifically, through the adaptive weight allocation mechanism, dynamic threshold division is performed on the MCU state feature matrix to obtain a multi-level power supply switching criterion. This process is the core link for realizing intelligent power supply control. Specifically, the MCU state feature matrix contains feature parameters in multiple dimensions, which reflect the operating state of the MCU under different working conditions. The role of the adaptive weight allocation mechanism is to dynamically adjust the weights of each feature parameter according to the current actual requirements and environmental changes, so as to ensure that the evaluation results can more accurately reflect the true state of the MCU. For example, in the industrial automation scenario, when the MCU is responsible for controlling high-precision equipment on the production line, voltage fluctuations may have a significant impact on equipment performance. Therefore, a higher weight needs to be assigned to the voltage fluctuation amplitude. In other scenarios, the duration of the instantaneous voltage drop may be more critical, which requires dynamically adjusting the weights to meet different needs. To achieve this goal, machine learning algorithms or rule-based optimization methods can be used, combined with the real-time collected working voltage sampling sequence and historical data, to calculate the optimal weight allocation scheme. Subsequently, by comparing the weighted feature parameters with the preset dynamic thresholds, a multi-level power supply switching criterion can be generated to guide the subsequent power supply mode scheduling. This method not only improves the flexibility and adaptability of the system, but also can effectively cope with the complex and changeable working environment in practical applications, ensuring that the MCU is always in the best power supply state, thereby enhancing the stability and reliability of the entire system.

[0066] Step S4, based on the multi-level power supply switching criterion, perform real-time scheduling of the power supply mode of the MCU to obtain an optimal power supply configuration scheme.

[0067] Specifically, based on the multi-level power supply switching criterion, the real-time scheduling of the power supply mode of the MCU is carried out to obtain the optimal power supply configuration plan. This process aims to dynamically adjust the power supply strategy according to the current working state and requirements of the MCU to ensure the maximization of system performance and energy efficiency. First, using the multi-level power supply switching criterion obtained in the previous steps, the system can identify the best power supply mode of the MCU under different load conditions. For example, in an industrial automation scenario, when the MCU needs to process a large amount of sensor data and control multiple actuators, its power consumption will increase significantly; at this time, by real-time monitoring the working voltage sampling sequence and based on the multi-level power supply switching criterion, it can be determined whether it is necessary to switch from the low-power mode to the high-power mode to meet higher computing requirements. At the same time, considering the overall energy efficiency of the system, this step will also comprehensively evaluate the conversion losses between different power supply modes to avoid energy waste caused by unnecessary mode switching. Specifically, it can simulate various possible power supply configurations through a preset algorithm model and select the optimal solution in combination with the actual operating parameters. In addition, the real-time scheduling of this power supply mode is not limited to the simple switching of a single threshold, but adopts a hierarchical and multi-dimensional strategy, so that even under complex working condition changes, the MCU can always be in the most suitable power supply state. For example, in the control system of a robotic arm on a production line, in the face of sudden task peaks or environmental interference, the system can quickly respond, adjust to the optimal power supply configuration, ensure the accuracy and response speed of the robotic arm movement, and then improve the efficiency and stability of the entire production line. In this way, whether under normal operation or extreme conditions, the efficient and stable operation of the MCU and even the entire system can be effectively guaranteed.

[0068] Step S5: Through the distributed cooperation mechanism, the optimal power supply configuration plan is executed and allocated to obtain the real-time power supply control instruction.

[0069] Specifically, the optimal power supply configuration plan is executed and allocated through a distributed cooperation mechanism to obtain real-time power supply control instructions. This process is a key step to ensure the stable operation of the MCU in a complex and dynamically changing environment. First, based on the optimal power supply configuration plan determined in the previous steps, the system needs to transform this strategy into specific execution actions and distribute them to each component of the multi-redundant power supply system. In this process, the distributed cooperation mechanism plays a core role, which allows effective information exchange and task coordination between different power supply units. For example, in an industrial automation scenario, when the MCU on the production line needs to process a large amount of data and control multiple actuators, it may involve the simultaneous operation or switching of multiple power modules. At this time, the distributed cooperation mechanism can accurately calculate parameters such as the power size and switching timing that each power module should provide according to the optimal power supply configuration plan, and generate corresponding real-time power supply control instructions. These instructions not only consider the current workload situation but also combine the status information of each power module to ensure a smooth transition and efficient operation of the entire power supply process. In addition, to improve the response speed and decision-making accuracy, this mechanism usually adopts advanced communication protocols and algorithm models to optimize the transmission efficiency of information flows. For example, when the robotic arm control system encounters a sudden task peak, the distributed cooperation mechanism can quickly adjust the working status of each power module and provide the required power support in a timely manner to avoid a decline in system performance caused by insufficient power supply. In this way, even in the face of complex working condition changes, it can ensure that the MCU always obtains stable and efficient power supply, thereby improving the reliability and stability of the entire system. This flexible and efficient power supply management method is crucial for ensuring the continuous and stable operation of critical mission systems.

[0070] Step S6, based on the real-time power supply control instructions, cooperatively control the multi-redundant power supply system to stably supply power to the MCU.

[0071] Specifically, based on the real-time power supply control instruction, the multiple redundant power supply system is coordinated to provide stable power supply to the MCU. This process is the final step to ensure that the MCU can obtain reliable power support under various working conditions. First, as the output of the previous step, the real-time power supply control instruction contains specific operation guidelines for the multiple redundant power supply system, such as the activation order of each power module, the power distribution ratio, and the switching time point. These instructions are transmitted to each power supply unit through an efficient communication network, and the corresponding actions are performed by it. For example, in an industrial automation scenario, when the MCU on the production line needs to handle an emergency task, it may be required to select the most suitable combination from multiple backup power supplies to provide additional power support. At this time, according to the real-time power supply control instruction, the system can respond quickly and coordinate the various redundant power supply units to start synchronously or step by step according to the preset plan to ensure the continuity and stability of the power supply. At the same time, in order to cope with possible emergencies, such as the failure of a power module or a sudden increase in load, the system also needs to have the ability to dynamically adjust. This means that after receiving a new real-time power supply control instruction, the multiple redundant power supply system can instantly update its operating status and reallocate resources to maintain stable power supply to the MCU. In addition, the process also involves continuous monitoring and feedback mechanisms for the status of each power supply unit, so as to promptly detect and resolve potential problems. For example, in the robotic arm control system, if an overload warning is detected in a power module, the system will automatically adjust the output of other modules according to the latest real-time power supply control instructions to ensure that the entire power supply process is not affected, thereby ensuring the efficient and stable operation of the MCU and even the entire production process. In this way, not only the robustness and reliability of the system are improved, but also strong support is provided for power management under complex working conditions.

[0072] In a specific embodiment, the real-time working voltage sampling detection of the MCU to obtain a working voltage sampling sequence includes:

[0073] The operating voltage of the MCU is collected by a multi-channel synchronous sampling circuit to obtain an original voltage signal set;

[0074] Based on a preset Kalman filter fusion device, noise suppression and signal reconstruction are performed on the original voltage signal set to obtain an optimized voltage feature vector;

[0075] The optimized voltage characteristic vector is time-aligned and normalized by a dynamic time warping algorithm to obtain a working voltage sampling sequence.

[0076] Specifically, the process of performing real-time working voltage sampling detection on the MCU to obtain the working voltage sampling sequence is an important part of implementing an efficient and stable multi-redundancy flexible power supply control method. Firstly, the multi-channel synchronous sampling circuit is used to collect the working voltage of the MCU. This process ensures that the working voltage state of the MCU can be comprehensively captured from multiple perspectives, obtaining a set of original voltage signals. In industrial automation scenarios, such as the robotic arm control system on a production line, due to the complexity of the system and frequent load changes, single-channel voltage acquisition may not be able to fully reflect the actual working state of the MCU. Therefore, the multi-channel synchronous sampling method can obtain richer information from different dimensions, providing a solid data basis for subsequent processing. Next, based on a preset Kalman filter fusion device, noise suppression and signal reconstruction are performed on the set of original voltage signals to obtain an optimized voltage feature vector. As a powerful recursive filtering algorithm, the Kalman filter can estimate the state variables of the system in the presence of noise. In this application scenario, due to adverse factors such as electromagnetic interference in the production environment, the collected original voltage signals contain a large amount of noise, which not only affects the accuracy of the data but may also mislead subsequent state evaluations. Therefore, using the Kalman filter fusion device can effectively remove this noise and reconstruct a more accurate voltage signal. For example, when the robotic arm performs high-precision operations, any small voltage fluctuation may cause execution errors. The optimized voltage feature vector after Kalman filtering can more accurately reflect the true working voltage of the MCU, providing guarantee for the stable operation of the system. Subsequently, through the Dynamic Time Warping (DTW) algorithm, the optimized voltage feature vector is subjected to time series alignment and normalization processing to obtain the final working voltage sampling sequence. The DTW algorithm is mainly used to process time series data, especially when the time scales between different sequences are inconsistent. Considering that the working state of the MCU may change significantly over time and task requirements, there may be large deviations in directly comparing or analyzing unprocessed voltage signals. Therefore, applying the DTW algorithm to process the optimized voltage feature vector can not only align the voltage signals in different time periods but also normalize them to the same scale, facilitating subsequent multi-dimensional working state evaluations. For example, when the production line encounters an emergency and the robotic arm needs to quickly adjust its position, the working load of the MCU will increase instantaneously, resulting in a drastic fluctuation in the working voltage. The working voltage sampling sequence processed by the DTW algorithm can not only accurately capture these transient changes but also effectively compare and analyze with the data collected under other normal working states, thus providing more accurate power supply mode scheduling suggestions for the system.In summary, by performing real-time sampling and detection of the working voltage of the MCU, and successively passing through a multi-channel synchronous sampling circuit, a Kalman filter fusion device, and a dynamic time warping algorithm, the finally obtained working voltage sampling sequence can comprehensively and accurately reflect the actual working state of the MCU under various working conditions. This meticulous data processing flow not only improves the quality of the data, but also provides strong support for the subsequent formulation of the power supply strategy, enabling the entire system to operate efficiently and stably in a complex and changing environment. For example, when dealing with emergency task switching or abnormal situation handling on the production line, this method can ensure that the MCU always obtains stable and sufficient power supply, avoiding system performance degradation or failures caused by insufficient power supply, and greatly improving the reliability and efficiency of the entire industrial automation system.

[0077] In a specific embodiment, the multi-dimensional working state evaluation of the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix includes:

[0078] Performing multi-dimensional feature decomposition and reconstruction on the working voltage sampling sequence to obtain a voltage fluctuation feature tensor, and performing a non-linear mapping transformation on the voltage fluctuation feature tensor to obtain a set of voltage dynamic characteristics;

[0079] Extracting time-frequency domain features from the set of voltage dynamic characteristics through multi-layer wavelet packet decomposition to obtain a voltage quality feature vector, and comprehensively evaluating the multi-redundant power supply system based on the voltage quality feature vector to obtain a state score matrix of the multi-redundant power supply system;

[0080] Multi-dimensionally mapping the working state of the MCU based on the state score matrix to obtain an MCU working state feature set, and hierarchically decomposing the MCU working state feature set to obtain an MCU performance parameter matrix;

[0081] Performing dynamic feature recognition on the MCU based on the MCU performance parameter matrix to obtain an MCU operation feature vector, and performing state space reconstruction and feature extraction based on the MCU operation feature vector to obtain an MCU initial state feature matrix;

[0082] Performing time-series correlation analysis on the MCU initial state feature matrix to obtain an MCU state evolution sequence, and performing multi-dimensional feature fusion and optimization on the MCU state evolution sequence to obtain an MCU state feature matrix; wherein, the MCU state feature matrix includes a voltage stability degree vector, a power consumption fluctuation degree vector, and a task execution efficiency degree vector.

[0083] Specifically, based on the working voltage sampling sequence, a multi-dimensional working state evaluation of the MCU is performed to obtain an MCU state feature matrix, which is one of the key steps in realizing intelligent power supply management. First, multi-dimensional feature decomposition and reconstruction are performed on the working voltage sampling sequence. By this method, rich information contained in the voltage signal can be deeply mined to obtain a voltage fluctuation feature tensor, and a non-linear mapping transformation is performed on the voltage fluctuation feature tensor, thereby refining a set of voltage dynamic characteristics that can reflect the operating characteristics of the MCU. For example, in a robotic arm control system in an industrial automation scenario, due to complex tasks and changing environments, the working voltage of the MCU is not only affected by load changes but may also fluctuate due to external interference. By carefully analyzing and processing these fluctuation characteristics, the performance of the MCU in different working states can be captured more accurately. Next, time-frequency domain feature extraction is performed on the set of voltage dynamic characteristics through multi-layer wavelet packet decomposition to further refine the feature representation of the voltage signal and obtain a voltage quality feature vector. As an efficient signal processing technology, wavelet packet decomposition can provide a more refined resolution in the time-frequency domain, which is particularly important for analyzing rapidly changing voltage signals. Taking high-precision control on a production line as an example, when the MCU needs to quickly adjust the position of the robotic arm according to sensor data, any minor voltage change may lead to execution errors. Using wavelet packet decomposition technology, more valuable detailed information can be extracted from the set of voltage dynamic characteristics to form a voltage quality feature vector. Then, based on the voltage quality feature vector, a comprehensive evaluation of the multiple redundant power supply system is performed to obtain a state scoring matrix of the multiple redundant power supply system. This scoring matrix not only reflects the health status of the power supply system but also provides an important basis for subsequent optimization of the power supply strategy. On this basis, the working state of the MCU is mapped in multiple dimensions based on the state scoring matrix to obtain an MCU working state feature set, and the MCU working state feature set is hierarchically decomposed to obtain an MCU performance parameter matrix. This process involves simplifying the complex working state of the MCU into an easy-to-understand and analyze form. Through hierarchical decomposition, various performance indicators of the MCU, such as power consumption and response speed, can be clearly displayed. For example, when dealing with sudden production tasks, the MCU needs to complete a large amount of calculation and data transmission in a short time, and at this time, its power consumption and response speed become key factors. By analyzing the MCU working state feature set, potential problems can be discovered in a timely manner and corresponding measures can be taken. Subsequently, based on the MCU performance parameter matrix, dynamic feature recognition of the MCU is performed to obtain an MCU operation feature vector, and based on the MCU operation feature vector, state space reconstruction and feature extraction are performed to obtain an MCU initial state feature matrix. This step aims to comprehensively describe the operating characteristics of the MCU from multiple dimensions, and state space reconstruction helps to reveal the behavior patterns of the MCU under different working conditions.For example, when faced with emergency task switching or abnormal situation handling on the production line, the state of the MCU may change rapidly. Through state space reconstruction, the laws behind these changes can be better understood, providing support for real-time scheduling. Finally, perform temporal correlation analysis on the initial state feature matrix of the MCU to obtain the MCU state evolution sequence, and perform multi-dimensional feature fusion and optimization on the MCU state evolution sequence to obtain the MCU state feature matrix. Temporal correlation analysis can help us understand the changing trend of the MCU state over time, which is crucial for predicting future working states. In practical applications, when the robotic arm control system encounters sudden task peaks or environmental disturbances, the MCU state evolution sequence can reflect its response capabilities. Through multi-dimensional feature fusion and optimization, information from different aspects can be integrated to form a feature matrix that comprehensively reflects the working state of the MCU, including the voltage stability degree vector, power consumption fluctuation degree vector, and task execution efficiency degree vector. Such a feature matrix can not only help engineers quickly locate problems but also provide strong support for formulating more scientific and reasonable power supply strategies, ensuring the efficient and stable operation of the MCU and even the entire industrial automation system. In short, through multi-dimensional evaluation of the MCU working state, the reliability and flexibility of the system can be significantly improved to meet the requirements of modern industry for high-performance control.

[0084] In a specific embodiment, the MCU state feature matrix is dynamically threshold-divided through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion, including:

[0085] Perform multi-dimensional feature stratification and decoupling on the MCU state feature matrix to obtain a set of state feature components, and perform dynamic weight optimization on the set of state feature components through an adaptive weight allocation mechanism to obtain an adaptive weight coefficient matrix;

[0086] Based on the adaptive weight coefficient matrix, perform feature importance evaluation to obtain a feature importance ranking table, perform multi-level threshold analysis on the feature importance ranking table to obtain a threshold candidate set, and perform dynamic partition mapping based on the threshold candidate set to obtain a power supply state partition matrix;

[0087] Based on the power supply state partition matrix, perform multi-level threshold optimization and calibration to obtain a threshold optimization sequence, and perform hierarchical decision rule construction on the threshold optimization sequence to obtain a power supply switching rule set, including a voltage switching rule, a performance protection rule, and a system cooperation rule;

[0088] Perform multi-dimensional cross-validation and fusion on the power supply switching rule set to obtain a multi-level power supply switching criterion, where the multi-level power supply switching criterion includes a voltage derating coefficient, a power consumption compensation factor, and a task priority.

[0089] Specifically, through the adaptive weight allocation mechanism, dynamic threshold division is performed on the MCU state feature matrix to obtain a multi-level power supply switching criterion. This process aims to ensure that the MCU can obtain stable and efficient power support in a complex and changing working environment. First, multi-dimensional feature stratification and decoupling are performed on the MCU state feature matrix. This operation decomposes the complex feature matrix into multiple independent state feature component sets, facilitating subsequent processing and analysis. For example, in an industrial automation scenario, when the MCU on the production line is responsible for controlling a high-precision robotic arm, its working state is affected by various factors, such as voltage stability, power consumption fluctuations, and task execution efficiency. By carefully stratifying and decoupling these state features, the specific effects of each influencing factor can be captured more precisely, laying a foundation for optimizing the power supply strategy. Next, through the adaptive weight allocation mechanism, dynamic weight optimization is performed on the state feature component set to obtain an adaptive weight coefficient matrix. The core of the adaptive weight allocation mechanism is to dynamically adjust the importance weights of each feature component according to the current working environment and requirements. Taking the robotic arm control system as an example, when the system faces a sudden task peak or external interference, certain specific state features (such as voltage fluctuations) may become more critical, so higher weights need to be assigned. This dynamic adjustment not only improves the flexibility and adaptability of the system but also ensures that optimal decisions can be made under various working conditions. In addition, based on the adaptive weight coefficient matrix, feature importance evaluation is carried out to obtain a feature importance ranking table, and multi-level threshold analysis is performed on the feature importance ranking table to obtain a threshold candidate set. Based on the threshold candidate set, dynamic partition mapping is performed to obtain a power supply state partition matrix. This process helps to identify which state features are the most important under the current conditions and sets reasonable threshold ranges accordingly. For example, when dealing with an urgent production task, voltage fluctuations may be one of the key factors determining system performance. Through multi-level threshold analysis, a suitable voltage fluctuation range can be determined, and corresponding power supply adjustment measures are triggered when the range is exceeded. Subsequently, based on the power supply state partition matrix, multi-level threshold optimization and calibration are performed to obtain a threshold optimization sequence, and a hierarchical decision rule is constructed for the threshold optimization sequence to obtain a power supply switching rule set, which includes voltage switching rules, performance protection rules, and system coordination rules. This step aims to further optimize the initially determined threshold and convert it into a specific power supply switching strategy. For example, in the face of abnormal voltage fluctuations in the robotic arm control system on the production line, the voltage switching rule can automatically adjust the power configuration according to the preset threshold; the performance protection rule can take measures to prevent the MCU from being damaged due to overheating or other reasons when detecting excessive power consumption; the system coordination rule ensures efficient cooperation among all components within the entire power supply system to jointly address possible problems. These rules not only improve the system's response speed and decision-making accuracy but also provide strong support for ensuring the stable operation of the MCU.Finally, perform multi-dimensional cross-validation and fusion on the power supply switching rule set to obtain multi-level power supply switching criteria. Among them, the multi-level power supply switching criteria include a voltage derating factor, a power consumption compensation factor, and a task priority. The process of multi-dimensional cross-validation involves comprehensively testing and verifying the power supply switching rules from different perspectives to ensure their effectiveness and reliability in practical applications. For example, when a sudden situation occurs in a simulated production line, multiple experiments can be conducted to verify whether the power supply switching rules can effectively protect the MCU from voltage fluctuations and other adverse factors without affecting production efficiency. At the same time, the introduction of parameters such as the voltage derating factor, the power consumption compensation factor, and the task priority makes the power supply switching strategy more flexible and intelligent. Specifically, the voltage derating factor can help moderately reduce the working voltage of the MCU when the voltage is unstable, reducing energy consumption while ensuring that basic functions are not affected; the power consumption compensation factor can appropriately increase the power supply when high power consumption is detected to prevent the MCU from overloading; the task priority ensures that in the case of limited resources, the most critical task requirements are prioritized. In this way, the entire system can maintain efficient and stable operation in a complex and changing environment, greatly improving the reliability and flexibility of the industrial automation system, and ensuring that it can respond quickly and maintain normal operation even in the face of sudden situations. In short, through in-depth analysis and optimization of the MCU state feature matrix, combined with the adaptive weight allocation mechanism and the dynamic threshold division technology, the intelligent level of power supply management can be significantly improved, providing a solid guarantee for the efficient operation of modern industrial control systems.

[0090] In a specific embodiment, the real-time scheduling of the power supply mode of the MCU based on the multi-level power supply switching criteria to obtain an optimal power supply configuration plan includes:

[0091] Perform multi-dimensional feature analysis and reconstruction on the multi-level power supply switching criteria to obtain a power supply switching feature set, and classify the power supply mode of the MCU based on the power supply switching feature set to obtain a candidate power supply mode plan;

[0092] Perform multi-objective evaluation on the candidate power supply mode plan through the analytic hierarchy process to obtain a power supply plan evaluation matrix, and perform priority sorting on the power supply plan evaluation matrix to obtain a power supply plan priority sequence;

[0093] Construct multi-dimensional constraint conditions based on the power supply plan priority sequence to obtain a power supply scheduling constraint set, and perform dynamic boundary division on the power supply scheduling constraint set to obtain power supply scheduling boundary conditions;

[0094] Perform multi-objective optimization and solution on the power supply scheduling boundary conditions to obtain a set of power supply scheduling strategies, evaluate and screen the set of power supply scheduling strategies to obtain a group of candidate configuration schemes, and conduct multi-dimensional comprehensive evaluation on the group of candidate configuration schemes to obtain an optimal power supply configuration scheme; wherein, the optimal power supply configuration scheme includes power supply switching timing, voltage regulation parameters, and power consumption balancing strategies.

[0095] Specifically, based on the multi-level power supply switching criterion, real-time scheduling of the power supply mode of the MCU is performed to obtain an optimal power supply configuration plan. This process aims to dynamically adjust the power supply strategy according to the current working state and requirements of the MCU to ensure the maximization of system performance and energy efficiency. First, multi-dimensional feature analysis and reconstruction are performed on the multi-level power supply switching criterion. This operation transforms the complex power supply switching criterion into a more easily understandable and processable power supply switching feature set, providing a basis for subsequent power supply mode classification. For example, in an industrial automation scenario, when the MCU on the production line needs to process a large amount of sensor data and control multiple actuators, its power consumption will increase significantly. At this time, through the feature analysis of the multi-level power supply switching criterion, it can be identified which factors (such as voltage stability, power consumption fluctuation, etc.) are the most critical, and candidate power supply mode plans are generated accordingly. This detailed analysis not only improves the accuracy of decision-making but also provides an important basis for optimizing the power supply strategy. Next, a multi-objective evaluation is performed on the candidate power supply mode plans through the analytic hierarchy process to obtain a power supply plan evaluation matrix, and the power supply plan evaluation matrix is sorted by priority to obtain a power supply plan priority sequence. The analytic hierarchy process is an effective multi-criterion decision-making tool that allows for a comprehensive evaluation while considering multiple conflicting objectives (such as performance, cost, reliability, etc.). For example, in a robotic arm control system, in the face of sudden task peaks or environmental disturbances, it may be necessary to minimize energy consumption while ensuring high-precision operation. Through the analytic hierarchy process, scores can be assigned to each objective according to different weightings, thus forming a power supply plan evaluation matrix. Then, based on this matrix, the candidate power supply mode plans are sorted by priority to determine which plan best meets the current requirements. This step not only improves the transparency of the decision-making process but also enables the system to quickly respond to changes and select the most suitable power supply mode. On this basis, multi-dimensional constraint conditions are constructed based on the power supply plan priority sequence to obtain a power supply scheduling constraint set, and dynamic boundary division is performed on the power supply scheduling constraint set to obtain power supply scheduling boundary conditions. This process involves setting a series of constraint conditions according to specific working conditions, such as maximum power consumption limit, minimum voltage requirement, etc. These constraint conditions together constitute the power supply scheduling constraint set. For example, when an emergency task switch occurs on the production line, in order to ensure that all devices can operate normally, reasonable power consumption upper limits and voltage lower limits must be set. By performing dynamic boundary division on the power supply scheduling constraint set, a clear operating range can be defined for each power supply mode to ensure that the safety limit is not exceeded under any circumstances. This refined management method not only improves the safety of the system but also lays a foundation for subsequent optimization and solution. Subsequently, multi-objective optimization and solution are performed on the power supply scheduling boundary conditions to obtain a power supply scheduling strategy set, and the power supply scheduling strategy set is evaluated and screened to obtain a group of candidate configuration plans, and multi-dimensional comprehensive evaluation is performed on the group of candidate configuration plans to obtain an optimal power supply configuration plan.The multi-objective optimization solution process aims to find a set of power supply scheduling strategies that can not only meet all the constraints but also achieve the best performance. For example, when dealing with complex production tasks, the MCU may need to complete a large amount of computing and data transmission in a short time. At this time, how to balance power consumption and performance becomes a key issue. By evaluating and screening the power supply scheduling strategy set, those solutions that can minimize energy consumption while ensuring efficient operation can be selected. Finally, through multi-dimensional comprehensive evaluation, the optimal power supply configuration plan is determined, including specific contents such as power switching timing, voltage regulation parameters, and power consumption balancing strategies. These strategies not only ensure the stable operation of the MCU under various working conditions but also effectively improve the energy efficiency ratio of the entire system. In short, through in-depth analysis and optimization of the multi-level power supply switching criteria, combined with the analytic hierarchy process and multi-objective optimization technology, intelligent scheduling of the MCU power supply mode can be achieved, ensuring that the system maintains efficient and stable operation in a complex and changing environment. For example, in the robotic arm control system in the industrial automation scenario, through the above steps, various emergencies such as task peaks and external interferences can be effectively handled, ensuring rapid response and normal operation even under extreme conditions. This method not only improves the flexibility and adaptability of the system but also provides strong support for ensuring the smooth completion of key tasks, greatly enhancing the reliability and efficiency of the entire industrial automation system. At the same time, by continuously optimizing the power supply configuration plan, the potential of the system can be further explored, promoting technological progress and application innovation.

[0096] In a specific embodiment, the execution allocation of the optimal power supply configuration plan through the distributed cooperation mechanism to obtain real-time power supply control instructions includes:

[0097] Perform multi-dimensional parameter analysis and decomposition on the optimal power supply configuration plan to obtain a power supply configuration parameter set, and based on the power supply configuration parameter set, perform execution timing planning to obtain a power supply control timing table;

[0098] Generate an execution sequence matrix for the power supply control timing table through the distributed cooperation mechanism, and perform timing consistency verification on the execution sequence matrix of control instructions to obtain an instruction execution constraint set;

[0099] Perform multi-dimensional conflict detection and resolution on the execution sequence matrix of control instructions based on the instruction execution constraint set to obtain an optimized instruction sequence, and perform execution priority division on the optimized instruction sequence to obtain a hierarchical execution plan;

[0100] Perform multi-dimensional execution resource allocation on the hierarchical execution plan to obtain real-time power supply control instructions, where the real-time power supply control instructions include power switching control instructions, power consumption regulation control instructions, and stability guarantee control instructions.

[0101] Specifically, the optimal power supply configuration plan is executed and allocated through a distributed cooperation mechanism to obtain real-time power supply control instructions. This process is a key step to ensure that the MCU can obtain stable and efficient power support in a complex and dynamically changing environment. First, multi-dimensional parameter analysis and decomposition are performed on the optimal power supply configuration plan. This step aims to refine the complex power supply configuration into a specific parameter set for subsequent processing and scheduling. For example, in an industrial automation scenario, when the MCU on the production line needs to adjust the position of the robotic arm according to sensor data, its power supply demand may increase significantly. Through multi-dimensional parameter analysis of the optimal power supply configuration plan, it is possible to clearly identify which parameters (such as voltage regulation, power consumption management, etc.) are the most critical and generate a power supply configuration parameter set based on this. These parameters not only reflect the current working state but also provide a basis for formulating precise power supply strategies. Next, based on the power supply configuration parameter set, an execution timing plan is made to obtain a power supply control timing table. This process involves determining the specific execution order and time points of each power supply operation to ensure that the system can provide the required power support at the correct time. For example, when dealing with the switch of an emergency production task, it is necessary to ensure that each power module starts or shuts down in a predetermined order to avoid system failures caused by synchronization problems. Through detailed timing planning, a detailed power supply control timing table can be formulated to guide each component to perform corresponding operations at the appropriate time. This fine timing management not only improves the system's response speed but also ensures the safety and stability of operations. Subsequently, through the distributed cooperation mechanism, an execution sequence is generated for the power supply control timing table to obtain a control instruction sequence matrix, and the timing consistency of the control instruction sequence matrix is verified to obtain an instruction execution constraint set. The core of the distributed cooperation mechanism lies in coordinating the work of multiple power supply units to ensure that they can cooperate efficiently and jointly complete the power supply task. For example, in the face of unexpected situations on the production line, multiple power modules may need to work simultaneously to meet higher power demands. Through the distributed cooperation mechanism, a specific control instruction sequence matrix can be generated based on the power supply control timing table, and its timing consistency can be further verified to ensure that all operations can be executed in the predetermined time sequence. This consistency verification not only prevents potential operation conflicts but also provides a basis for subsequent optimization. On this basis, multi-dimensional conflict detection and resolution are performed on the control instruction sequence matrix based on the instruction execution constraint set to obtain an optimized instruction sequence, and the execution priority of the optimized instruction sequence is divided to obtain a hierarchical execution plan. This process involves detecting and resolving possible operation conflicts and setting different execution priorities according to actual needs. For example, when the robotic arm control system encounters a high-load task, certain specific power supply operations (such as voltage regulation) may need to be executed first to ensure the stable operation of the system.By detecting and resolving conflicts in the control instruction sequence matrix, any operation conflicts that may cause system failures can be eliminated, and each operation can be sorted according to its importance to form a hierarchical execution plan. This refined management method not only improves the flexibility of the system but also ensures that the most critical task requirements can be prioritized under limited resources. Finally, multi-dimensional execution resource allocation is performed on the hierarchical execution plan to obtain real-time power supply control instructions, where the real-time power supply control instructions include power switching control instructions, power consumption adjustment control instructions, and stability guarantee control instructions. This process aims to reasonably allocate system resources according to the hierarchical execution plan to ensure that each operation can obtain the required power support. For example, when dealing with a sudden peak in task load on a production line, it may be necessary to quickly adjust the power configuration to meet the additional power demand; through real-time power supply control instructions, seamless power switching can be achieved, and the power consumption can be dynamically adjusted according to the actual load conditions to ensure that the system is always in the best working state. In addition, to ensure the long-term stable operation of the system, a series of stability guarantee measures need to be implemented, such as monitoring voltage fluctuations, promptly detecting and handling potential problems, etc. These control instructions not only improve the reliability and adaptability of the system but also provide strong support for dealing with complex working conditions. In summary, the process of finally obtaining real-time power supply control instructions through the distributed cooperation mechanism for performing and allocating the optimal power supply configuration plan covers multiple links from parameter parsing to execution resource allocation. Each step is closely connected to jointly ensure that the MCU can obtain stable and efficient power supply under various working conditions. For example, in the robotic arm control system in an industrial automation scenario, through the above method, not only can it quickly respond to sudden task requirements but also effectively prevent system failures caused by insufficient or unstable power supply, greatly improving the reliability and efficiency of the entire system. This method not only provides strong technical support for modern industrial control systems but also lays a solid foundation for future intelligent management and optimization.

[0102] In a specific embodiment, the multi-dimensional conflict detection and resolution of the control instruction sequence matrix based on the instruction execution constraint set to obtain an optimized instruction sequence includes:

[0103] Performing timing dependency analysis and decomposition on the instruction execution constraint set to obtain an instruction conflict feature set, and performing hierarchical classification processing on the instruction conflict feature set to obtain an instruction conflict type matrix;

[0104] Performing multi-dimensional feature mapping on the instruction conflict type matrix through tensor decomposition to obtain a conflict feature vector group, and tracing the conflict propagation path of the control instruction sequence matrix based on the conflict feature vector group to obtain a conflict propagation topology graph;

[0105] Construct multi-dimensional constraint conditions for the conflict propagation topology graph to obtain a conflict resolution rule set, and perform instruction reordering processing on the control instruction sequence matrix based on the conflict resolution rule set to obtain an optimized instruction execution plan;

[0106] Perform multi-dimensional cross-validation on the optimized instruction execution plan to obtain an instruction optimization evaluation matrix, and perform feasibility analysis and multi-objective collaborative optimization on the instruction optimization evaluation matrix to obtain an optimized instruction sequence.

[0107] Specifically, the process of performing multi-dimensional conflict detection and resolution on the control instruction sequence matrix based on the instruction execution constraint set to obtain an optimized instruction sequence is a key step to ensure that the MCU can obtain stable and efficient power support in a complex and dynamically changing environment. First, the timing dependency analysis and decomposition of the instruction execution constraint set is performed. This step aims to identify potential conflicts and their interdependencies between various control instructions, thereby obtaining an instruction conflict feature set. For example, in an industrial automation scenario, when the MCU on the production line needs to adjust the position of the robot arm according to sensor data, its power supply demand may increase significantly. Since multiple power modules may be scheduled at the same time to meet these requirements, conflicts between some instructions are inevitable, such as the time overlap between voltage regulation instructions and power management instructions. Through a detailed analysis of the instruction execution constraint set, it can be found which instructions may conflict due to time overlap or resource competition, and an instruction conflict feature set is generated accordingly. Further, the instruction conflict feature set is hierarchically classified, and these conflicts are classified into different types according to their properties (such as voltage regulation conflicts, power management conflicts, etc.), forming an instruction conflict type matrix for subsequent processing. Next, the instruction conflict type matrix is subjected to multi-dimensional feature mapping by tensor decomposition, which converts complex conflict information into a form that is easier to understand and process. As a powerful data analysis tool, tensor decomposition can reveal the intrinsic structure and pattern of data in high-dimensional space. For example, when facing multiple power supply conflicts in the robot control system, tensor decomposition can be used to extract key conflict feature vector groups from the instruction conflict type matrix. Based on these feature vector groups, the propagation path of each conflicting instruction in the entire system can be traced to construct a conflict propagation topology map. This map not only shows the correlation between the various conflicts, but also reveals how they affect the overall operating state of the system. This is crucial for understanding the root cause of the conflict and formulating effective solution strategies. For example, on a specific production line, if the switching operation of a power module overlaps with the operation of other modules in time, then this overlap may cause system instability. Through the conflict propagation topology map, this potential risk can be clearly seen and corresponding measures can be taken. Subsequently, the conflict propagation topology map is subjected to multi-dimensional constraint condition construction to obtain a conflict resolution rule set, and the control instruction sequence matrix is subjected to instruction reordering based on the conflict resolution rule set to obtain an instruction execution optimization solution. This process involves setting a series of constraints based on specific working conditions, such as maximum power consumption limit, minimum voltage requirement, etc. These constraints together constitute a set of conflict resolution rules. For example, when a production line encounters an emergency task switch, in order to ensure that all devices can operate normally, a reasonable upper power consumption limit and lower voltage limit must be set.By analyzing the conflict propagation topology graph, it is possible to identify which conflicts can be resolved by rearranging the instruction order, and based on this, a conflict resolution rule set is generated. Then, based on this rule set, the control instructions are reordered to formulate an optimized instruction execution plan. This refined management method not only improves the system's security but also lays a foundation for subsequent optimization and solution. For example, when dealing with unexpected situations on the production line, by reasonably adjusting the instruction order, it is possible to avoid system failures caused by instruction conflicts and ensure the continuity and stability of production. Finally, the instruction execution optimization plan is cross-validated from multiple dimensions to obtain an instruction optimization evaluation matrix, and the feasibility analysis and multi-objective collaborative optimization are performed on the instruction optimization evaluation matrix to obtain an optimized instruction sequence. The multi-dimensional cross-validation process aims to comprehensively test and verify the instruction execution optimization plan from different perspectives to ensure its effectiveness and reliability in practical applications. For example, when simulating unexpected situations on the production line, multiple experiments can be conducted to verify whether the instruction execution optimization plan can effectively avoid power supply conflicts and ensure the stable operation of the system without affecting production efficiency. In addition, by performing feasibility analysis and multi-objective collaborative optimization on the instruction optimization evaluation matrix, a comprehensive evaluation can be made while considering multiple conflicting objectives (such as performance, cost, reliability, etc.), and finally a set of optimized instruction sequences that can meet all constraint conditions and achieve the best performance can be determined. These optimized instructions not only ensure the stable operation of the MCU under various working conditions but also effectively improve the energy efficiency ratio of the entire system, providing a solid guarantee for the efficient operation of modern industrial control systems. For example, when dealing with peak emergency tasks or external disturbances on the production line, the optimized instruction sequence can quickly respond and adjust the power configuration to ensure that the robotic arm control system is always in the best working state, greatly improving the reliability and flexibility of the entire system. This method not only provides strong support for ensuring the successful completion of critical tasks but also greatly improves the reliability and efficiency of the entire industrial automation system. In short, through the above methods, the flexibility and adaptability of the system can be significantly improved, ensuring that it can quickly respond and maintain normal operation even in a complex and changing environment. This method not only provides strong support for ensuring the successful completion of critical tasks but also lays a solid foundation for future intelligent management and optimization.

[0108] The above describes the multiple redundant flexible power supply MCU control method in the embodiments of the present invention. Next, the multiple redundant flexible power supply MCU control system in the embodiments of the present invention will be described. Please refer to Figure 2 , an embodiment of the multiple redundant flexible power supply MCU control system in the embodiments of the present invention includes:

[0109] A sampling module 21, configured to perform real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence;

[0110] An evaluation module 22 for multi-dimensionally evaluating the working state of the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix;

[0111] A partitioning module 23 for dynamically threshold-partitioning the MCU state feature matrix through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion;

[0112] A scheduling module 24 for performing real-time scheduling of the power supply mode of the MCU based on the multi-level power supply switching criterion to obtain an optimal power supply configuration scheme;

[0113] An allocation module 25 for performing execution allocation on the optimal power supply configuration scheme through a distributed cooperation mechanism to obtain a real-time power supply control instruction;

[0114] A control module 26 for cooperatively controlling the multi-redundancy power supply system to stably supply power to the MCU based on the real-time power supply control instruction.

[0115] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to that described in the above method embodiment, and details are not described herein again.

[0116] Refer to Figure 3 , and this embodiment of the present invention also provides a computer device, the internal structure of which may be as Figure 3 shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface, and a database connected through a system bus. Among them, the processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements the above method.

[0117] Those skilled in the art can understand that Figure 3 the structure shown in

[0118] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0119] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium provided by the present invention and used in the embodiments can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0120] It should be noted that in this article, the term "including", "comprising", or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, device, article, or method including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, device, article, or method. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, device, article, or method including that element.

[0121] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for controlling a multi-redundancy flexible power supply MCU, characterized in that Applied to a multi-redundant power supply system, the multi-redundant power supply system is electrically connected to an MCU, and includes the following steps: Perform real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence; Based on the working voltage sampling sequence, perform multi-dimensional working state evaluation on the MCU to obtain an MCU state feature matrix; Through an adaptive weight allocation mechanism, perform dynamic threshold division on the MCU state feature matrix to obtain a multi-level power supply switching criterion; Based on the multi-level power supply switching criterion, perform real-time scheduling of the power supply mode of the MCU to obtain an optimal power supply configuration plan; Through a distributed cooperation mechanism, perform execution allocation on the optimal power supply configuration plan to obtain a real-time power supply control instruction; Based on the real-time power supply control instruction, cooperatively control the multi-redundant power supply system to stably supply power to the MCU; The performing multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix includes: Perform multi-dimensional feature decomposition and reconstruction on the working voltage sampling sequence to obtain a voltage fluctuation feature tensor, and perform non-linear mapping transformation on the voltage fluctuation feature tensor to obtain a voltage dynamic characteristic set; Extract time-frequency domain features from the voltage dynamic characteristic set through multi-layer wavelet packet decomposition to obtain a voltage quality feature vector, and based on the voltage quality feature vector, perform comprehensive evaluation on the multi-redundant power supply system to obtain a state scoring matrix of the multi-redundant power supply system; Based on the multi-dimensional features of the state scoring matrix, map the working state of the MCU to obtain an MCU working state feature set, and perform hierarchical decomposition on the MCU working state feature set to obtain an MCU performance parameter matrix; Perform dynamic feature recognition on the MCU based on the MCU performance parameter matrix to obtain an MCU operation feature vector, and based on the MCU operation feature vector, perform state space reconstruction and feature extraction to obtain an MCU initial state feature matrix; Perform time series correlation analysis on the MCU initial state feature matrix to obtain an MCU state evolution sequence, and perform multi-dimensional feature fusion and optimization on the MCU state evolution sequence to obtain an MCU state feature matrix; wherein, the MCU state feature matrix includes a voltage stability degree vector, a power consumption fluctuation degree vector, and a task execution efficiency degree vector.

2. The multi-redundancy flexible power supply MCU control method according to claim 1, wherein The performing real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence includes: Collect the working voltage of the MCU through a multi-channel synchronous sampling circuit to obtain a set of original voltage signals; Based on a preset Kalman filter fusion device, perform noise suppression and signal reconstruction on the set of original voltage signals to obtain an optimized voltage feature vector; Through a dynamic time warping algorithm, perform time series alignment and normalization processing on the optimized voltage feature vector to obtain a working voltage sampling sequence.

3. The multi-redundancy flexible power supply MCU control method according to claim 1, wherein The performing dynamic threshold division on the MCU state feature matrix through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion includes: Perform multi-dimensional feature stratification and decoupling on the MCU state feature matrix to obtain a set of state feature components, and perform dynamic weight optimization on the set of state feature components through an adaptive weight allocation mechanism to obtain an adaptive weight coefficient matrix; Based on the adaptive weight coefficient matrix, perform feature importance evaluation to obtain a feature importance ranking table, perform multi-level threshold analysis on the feature importance ranking table to obtain a candidate threshold set, and perform dynamic partition mapping based on the candidate threshold set to obtain a power supply state partition matrix; Based on the power supply state partition matrix, perform multi-level threshold optimization and calibration to obtain a threshold optimization sequence, and construct a hierarchical decision rule for the threshold optimization sequence to obtain a power supply switching rule set, where the power supply switching rule set includes a voltage switching rule, a performance protection rule, and a system coordination rule; Perform multi-dimensional cross-validation and fusion on the power supply switching rule set to obtain a multi-level power supply switching criterion, where the multi-level power supply switching criterion includes a voltage derating coefficient, a power consumption compensation factor, and a task priority.

4. The multi-redundancy flexible power supply MCU control method according to claim 1, characterized in that Based on the multi-level power supply switching criterion, perform real-time scheduling of the power supply mode of the MCU to obtain an optimal power supply configuration plan, including: Perform multi-dimensional feature analysis and reconstruction on the multi-level power supply switching criterion to obtain a power supply switching feature set, and classify the power supply mode of the MCU based on the power supply switching feature set to obtain a candidate power supply mode plan; Perform multi-objective evaluation on the candidate power supply mode plan through the analytic hierarchy process to obtain a power supply plan evaluation matrix, and perform priority ranking on the power supply plan evaluation matrix to obtain a power supply plan priority sequence; Based on the power supply plan priority sequence, construct multi-dimensional constraint conditions to obtain a power supply scheduling constraint set, and perform dynamic boundary division on the power supply scheduling constraint set to obtain a power supply scheduling boundary condition; Perform multi-objective optimization and solution on the power supply scheduling boundary condition to obtain a power supply scheduling strategy set, perform plan evaluation and screening on the power supply scheduling strategy set to obtain a group of candidate configuration plans, and perform multi-dimensional comprehensive evaluation on the group of candidate configuration plans to obtain an optimal power supply configuration plan; where the optimal power supply configuration plan includes a power supply switching time sequence, a voltage regulation parameter, and a power consumption balancing strategy.

5. The multi-redundancy flexible power supply MCU control method according to claim 1, wherein Execute the allocation of the optimal power supply configuration plan through a distributed cooperation mechanism to obtain a real-time power supply control instruction, including: Perform multi-dimensional parameter analysis and decomposition on the optimal power supply configuration plan to obtain a power supply configuration parameter set, and perform execution timing planning based on the power supply configuration parameter set to obtain a power supply control timing table; Generate an execution sequence of the power supply control timing table through a distributed cooperation mechanism to obtain a control instruction sequence matrix, and perform timing consistency verification on the control instruction sequence matrix to obtain an instruction execution constraint set; Based on the instruction execution constraint set, perform multi-dimensional conflict detection and resolution on the control instruction sequence matrix to obtain an optimized instruction sequence, and perform execution priority division on the optimized instruction sequence to obtain a hierarchical execution plan; Perform multi-dimensional execution resource allocation for the hierarchical execution plan to obtain a real-time power supply control instruction, where the real-time power supply control instruction includes a power supply switching control instruction, a power consumption regulation control instruction, and a stability guarantee control instruction.

6. The multi-redundancy flexible power supply MCU control method according to claim 5, characterized in that, Performing multi-dimensional conflict detection and resolution on the control instruction sequence matrix based on the instruction execution constraint set to obtain an optimized instruction sequence, including: Performing timing dependency analysis and decomposition on the instruction execution constraint set to obtain an instruction conflict feature set, and performing hierarchical classification processing on the instruction conflict feature set to obtain an instruction conflict type matrix; Performing multi-dimensional feature mapping on the instruction conflict type matrix through tensor decomposition to obtain a conflict feature vector group, and tracing the conflict propagation path of the control instruction sequence matrix based on the conflict feature vector group to obtain a conflict propagation topology graph; Constructing multi-dimensional constraint conditions for the conflict propagation topology graph to obtain a conflict resolution rule set, and performing instruction reordering processing on the control instruction sequence matrix based on the conflict resolution rule set to obtain an instruction execution optimization plan; Performing multi-dimensional cross-validation on the instruction execution optimization plan to obtain an instruction optimization evaluation matrix, and performing feasibility analysis and multi-objective collaborative optimization on the instruction optimization evaluation matrix to obtain an optimized instruction sequence.

7. A multi-redundancy flexible power supply MCU control system, characterized in that Used to execute the multiple redundant flexible power supply MCU control method according to any one of claims 1-6, applied to a multiple redundant power supply system, the multiple redundant power supply system is electrically connected to the MCU, including: A sampling module for performing real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence; An evaluation module for performing multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix; A partitioning module for dynamically threshold partitioning the MCU state feature matrix through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion; A scheduling module for performing real-time scheduling of the power supply mode of the MCU based on the multi-level power supply switching criterion to obtain an optimal power supply configuration plan; An allocation module for performing execution allocation on the optimal power supply configuration plan through a distributed collaboration mechanism to obtain a real-time power supply control instruction; A control module for stably supplying power to the MCU by the multiple redundant power supply system in coordination based on the real-time power supply control instruction; Performing multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix, including: Performing multi-dimensional feature decomposition and reconstruction on the working voltage sampling sequence to obtain a voltage fluctuation feature tensor, and performing non-linear mapping transformation on the voltage fluctuation feature tensor to obtain a voltage dynamic characteristic set; Performing time-frequency domain feature extraction on the voltage dynamic characteristic set through multi-layer wavelet packet decomposition to obtain a voltage quality feature vector, and comprehensively evaluating the multiple redundant power supply system based on the voltage quality feature vector to obtain a state score matrix of the multiple redundant power supply system; Map the working state of the MCU based on the multi-dimensional features of the state scoring matrix to obtain a set of MCU working state features, and hierarchically decompose the set of MCU working state features to obtain an MCU performance parameter matrix; Perform dynamic feature recognition on the MCU based on the MCU performance parameter matrix to obtain an MCU operation feature vector, and perform state space reconstruction and feature extraction based on the MCU operation feature vector to obtain an initial MCU state feature matrix; Perform time series correlation analysis on the initial MCU state feature matrix to obtain an MCU state evolution sequence, and perform multi-dimensional feature fusion and optimization on the MCU state evolution sequence to obtain an MCU state feature matrix; wherein, the MCU state feature matrix includes a voltage stability degree vector, a power consumption fluctuation degree vector, and a task execution efficiency degree vector.

8. A computer device, comprising a memory and a processor, wherein a computer program is stored in the memory, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 6.

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