Multi-redundancy flexible power supply MCU control method and system

Abstract

The invention relates to a multi-redundancy flexible power supply MCU control method and system, and the method comprises the following steps: carrying out the real-time working voltage sampling detection of an MCU, and obtaining 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 characteristic matrix; performing dynamic threshold division on the MCU state characteristic matrix to obtain a multi-stage power supply switching criterion; performing power supply mode real-time scheduling on the MCU based on the multi-stage power supply switching criterion to obtain an optimal power supply configuration scheme; executing distribution on the optimal power supply configuration scheme to obtain a real-time power supply control instruction; and cooperatively controlling the multi-redundant power supply system to stably supply power to the MCU based on the real-time power supply control instruction, thereby solving the technical problems that a traditional power supply strategy usually depends on a fixed threshold value for switching, is lack of flexibility and accuracy and cannot effectively cope with a real-time changing working state.

Description

Technical Field

[0001] The present invention relates to the technical field of microcontrollers, and in particular to a multi-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 MCU are also increasing, especially in complex environments and mission-critical application scenarios. Ensuring the continuous and stable operation of MCU has become an urgent problem to be solved. Traditional power supply solutions often adopt a single or simple redundant design, which is difficult to meet the dual requirements of high reliability and high performance. This has prompted the research on more intelligent, flexible and reliable power supply control methods to adapt to changing working conditions and ensure that MCU can achieve optimal performance in various environments.

[0003] However, in practical applications, 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 operating 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, lack flexibility and precision, and cannot effectively respond to real-time changes in working conditions. On the other hand, as MCU functions continue to increase, its power consumption mode has 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, although the introduction of multiple redundant power supply systems improves the reliability of power supply, it also increases the complexity of the system. How to effectively manage and coordinate these redundant power supply resources so that they can respond quickly and provide support when needed is a difficult point in current research. Especially in distributed systems, the design of a collaborative mechanism is crucial to achieve 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 status of the MCU is of great significance to improving the reliability and efficiency of the entire system. Summary of the invention

[0005] The main purpose 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 accuracy, and cannot effectively cope with real-time changing working conditions.

[0006] To achieve the above object, the present invention provides a multiple redundant flexible power supply MCU control method, which is applied to a multiple redundant power supply system, wherein the multiple redundant power supply system is electrically connected to the MCU, and comprises the following steps: Performing real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence; Performing a multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix; Dynamically threshold the MCU state feature matrix through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion; Based on the multi-level power supply switching criterion, the power supply mode of the MCU is scheduled in real time to obtain an optimal power supply configuration solution; The optimal power supply configuration scheme is executed and allocated through a distributed collaborative mechanism to obtain a real-time power supply control instruction; Based on the real-time power supply control instruction, the multiple redundant power supply systems are collaboratively controlled to provide stable power supply to the MCU.

[0007] Furthermore, the real-time working voltage sampling detection is performed on the MCU to obtain a working voltage sampling sequence, including: The operating voltage of the MCU is collected by a multi-channel synchronous sampling circuit to obtain an original voltage signal set; 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; The optimized voltage characteristic vector is time-aligned and normalized by a dynamic time warping algorithm to obtain a working voltage sampling sequence.

[0008] Furthermore, the multi-dimensional working state evaluation of the MCU is performed 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 nonlinear mapping transformation on the voltage fluctuation feature tensor to obtain a voltage dynamic characteristic set; Extracting time-frequency domain features of the voltage dynamic characteristic set by multi-layer wavelet packet decomposition to obtain a voltage quality feature vector, and comprehensively evaluating the multiple redundant power supply systems based on the voltage quality feature vector to obtain a state scoring matrix of the multiple redundant power supply systems; Mapping the working state of the MCU based on the multi-dimensional feature of the state scoring 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; 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; A timing 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.

[0009] Furthermore, the MCU state feature matrix is ​​dynamically thresholded by the adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion, including: Perform multi-dimensional feature stratification and decoupling on the MCU state feature matrix to obtain a state feature component set, and dynamically optimize the weight of the state feature component set through an adaptive weight allocation mechanism to obtain an adaptive weight coefficient matrix; Performing feature importance evaluation based on the adaptive weight coefficient matrix to obtain a feature importance ranking table, performing multi-level threshold analysis on the feature importance ranking table to obtain a threshold candidate set, and performing dynamic partition mapping based on the threshold candidate set to obtain a power supply status partition matrix; Based on the power supply state partition matrix, multi-level threshold optimization and calibration are performed to obtain a threshold optimization sequence, and hierarchical decision rules are 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; The power supply switching rule set is cross-validated and integrated in multiple dimensions to obtain a multi-level power supply switching criterion, wherein the multi-level power supply switching criterion includes a voltage derating factor, a power consumption compensation factor and a task priority.

[0010] Furthermore, the real-time scheduling of the power supply mode of the MCU based on the multi-level power supply switching criterion to obtain the optimal power supply configuration scheme includes: 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 a power supply mode candidate solution; Performing a multi-objective evaluation on the candidate power supply mode schemes through a hierarchical analysis method to obtain a power supply scheme evaluation matrix, and prioritizing the power supply scheme evaluation matrix to obtain a power supply scheme priority sequence; Constructing multi-dimensional constraint conditions based on the power supply scheme priority sequence to obtain a power supply scheduling constraint set, and dynamically dividing the power supply scheduling constraint set to obtain power supply scheduling boundary conditions; A multi-objective optimization solution is 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 candidate configuration scheme group, and a multi-dimensional comprehensive evaluation is performed on the candidate configuration scheme group to obtain an optimal power supply configuration scheme; wherein the optimal power supply configuration scheme includes power switching timing, voltage regulation parameters and power consumption balancing strategy.

[0011] Furthermore, the optimal power supply configuration scheme is executed and allocated through a distributed coordination mechanism to obtain a real-time power supply control instruction, including: Perform multi-dimensional parameter analysis and decomposition on the optimal power supply configuration scheme 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; An execution sequence is generated for the power supply control timing table through a distributed collaborative mechanism to obtain a control instruction sequence matrix, and a timing consistency check is performed on the control instruction sequence matrix to obtain an instruction execution constraint set; Based on the instruction execution constraint set, multi-dimensional conflict detection and resolution are performed on the control instruction sequence matrix to obtain an optimized instruction sequence, and execution priority is divided for the optimized instruction sequence to obtain a hierarchical execution plan; Multi-dimensional execution resource allocation is performed 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 adjustment control instructions and stability assurance control instructions.

[0012] Furthermore, 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: 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 map; Constructing multi-dimensional constraint conditions for the conflict propagation topology graph to obtain a conflict resolution rule set, and reordering the control instruction sequence matrix based on the conflict resolution rule set to obtain an instruction execution optimization solution; The instruction execution optimization scheme is cross-validated in multiple dimensions to obtain an instruction optimization evaluation matrix, and a feasibility analysis and multi-objective collaborative optimization are performed on the instruction optimization evaluation matrix to obtain an optimized instruction sequence.

[0013] The present invention also provides a multiple redundant flexible power supply MCU control system, which is applied to a multiple redundant power supply system, wherein the multiple redundant power supply system is electrically connected to the MCU, and comprises: A sampling module is used to perform real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence; An evaluation module, used for performing a 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, used to perform dynamic threshold partitioning on the MCU state feature matrix through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion; A scheduling module, used to perform 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 solution; An allocation module, used to allocate the execution of the optimal power supply configuration scheme through a distributed coordination mechanism to obtain a real-time power supply control instruction; A control module is used to collaboratively control the multiple redundant power supply systems to provide stable power supply to the MCU based on the real-time power supply control instruction.

[0014] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0015] 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 any of the above-mentioned methods are implemented.

[0016] The multi-redundant flexible power supply MCU control method provided by the present invention comprises the following steps: real-time working voltage sampling detection of the MCU to obtain a working voltage sampling sequence; multi-dimensional working state evaluation of the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix; dynamic threshold division of the MCU state feature matrix to obtain a multi-level power supply switching criterion; 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; execution allocation of the optimal power supply configuration scheme to obtain a real-time power supply control instruction; based on the real-time power supply control instruction, collaborative control of the multi-redundant power supply system to provide stable power supply to the MCU solves the technical problem that the traditional power supply strategy usually relies on a fixed threshold for switching, lacks flexibility and accuracy, and cannot effectively cope with the real-time changing working state. The real-time scheduling of the power supply mode of the MCU based on the multi-level power supply switching criterion is realized, and the optimal power supply configuration scheme can be obtained. This helps to improve energy utilization efficiency, while ensuring that the MCU can obtain a stable power supply under various load conditions, and improves the technical effect of the performance and stability of the overall system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the steps of a method for controlling a multiple redundant flexible power supply MCU in one embodiment of the present invention; Figure 2 It is a structural block diagram of a multiple redundant flexible power supply MCU control device in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] like Figure 1 As shown, Figure 1 It is a schematic diagram of the steps of a multiple redundant flexible power supply MCU control method in one embodiment of the present invention; In one embodiment of the present invention, a method for controlling a multiple redundant flexible power supply MCU is provided, which is applied to a multiple redundant power supply system, wherein the multiple redundant power supply system is electrically connected to the MCU, and comprises the following steps: Step S1, performing real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence.

[0021] Specifically, the MCU is sampled and tested for working voltage in real time to obtain a working voltage sampling sequence. This process is the basic link of the entire multi-redundant flexible power supply MCU control method. Specifically, in order to achieve this step, it is necessary to introduce a high-precision voltage sampling module in the power supply circuit of the MCU. The 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 MCU may be affected by factors such as load changes, ambient temperature fluctuations or power supply noise during operation, its working voltage may fluctuate instantaneously or drift for a long time. Therefore, these subtle changes can be captured through real-time sampling, thereby 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 the actuator. If the power supply voltage fluctuates abnormally, it may cause data processing errors or control failures, and real-time working voltage sampling detection can detect these problems in time 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 also be used to pre-process 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 status assessment and power supply mode scheduling, but also helps engineers quickly locate problems and optimize system design in practical applications.

[0022] Step S2: performing a multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix.

[0023] Specifically, the MCU is evaluated in a multi-dimensional working state based on the working voltage sampling sequence to obtain the MCU state feature matrix. This process is a key step to convert the voltage data collected in real time into a data that can fully reflect the operating state of the MCU. Specifically, by analyzing 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, etc. These parameters together constitute the core content of the MCU state feature matrix. In order to achieve this goal, it is first necessary to segment the working voltage sampling sequence, and extract key features by combining the 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 robot arm on the production line. If the power supply voltage fluctuates frequently, it may cause the movement accuracy of the robot arm to decrease or even shut down. Through the multi-dimensional working state evaluation, it can be comprehensively judged whether the voltage fluctuation exceeds the safety range, and further analyze its impact on the MCU performance. In addition, in order to improve the accuracy of the evaluation, the feature weights can be dynamically adjusted in combination with historical data or preset thresholds to generate 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 system design, and ensure stable operation of the MCU in complex environments.

[0024] Step S3, dynamically thresholding the MCU state feature matrix through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion.

[0025] Specifically, the MCU state feature matrix is ​​dynamically divided by threshold through an adaptive weight allocation mechanism 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 characteristic parameters of multiple dimensions, which reflect the operating state of the MCU under different working conditions, and the role of the adaptive weight allocation mechanism is to dynamically adjust the weight of each characteristic parameter according to the current actual needs and environmental changes, so as to ensure that the evaluation result can more accurately reflect the true state of the MCU. For example, in an industrial automation scenario, when the MCU is responsible for controlling high-precision equipment on a production line, voltage fluctuations may have a significant impact on equipment performance, so it is necessary to give a higher weight to the voltage fluctuation amplitude, while in other scenarios, the duration of the instantaneous voltage drop may be more critical, which requires dynamic adjustment of the weight to adapt to different needs. In order to achieve this goal, a machine learning algorithm or a rule-based optimization method can be used to combine the real-time collected working voltage sampling sequence and historical data to calculate the optimal weight allocation scheme. Subsequently, by comparing the weighted characteristic parameters with the preset dynamic threshold, 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 complex and changing working environments in practical applications, ensuring that the MCU is always in the best power supply state, thereby improving the stability and reliability of the entire system.

[0026] Step S4: Based on the multi-stage power supply switching criteria, the power supply mode of the MCU is scheduled in real time to obtain an optimal power supply configuration solution.

[0027] Specifically, the power supply mode of the MCU is scheduled in real time based on the multi-level power supply switching criterion to obtain the optimal power supply configuration scheme. This process aims to dynamically adjust the power supply strategy according to the current working state of the MCU and its needs to ensure the maximization of system performance and energy efficiency. First, using the multi-level power supply switching criterion obtained in the previous step, the system can identify the optimal 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 of the working voltage sampling sequence, and based on the multi-level power supply switching criterion, it can be determined whether it is currently necessary to switch from a low-power mode to a 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 loss between different power supply modes, thereby avoiding energy waste caused by unnecessary mode switching. In specific implementation, various possible power supply configurations can be simulated by a preset algorithm model, and the optimal solution can be selected in combination with actual operating parameters. In addition, this real-time scheduling of the power supply mode is not limited to a simple switch of a single threshold, but adopts a hierarchical and multi-dimensional strategy, so that even under complex working conditions, the MCU can always be in the most suitable power supply state. For example, in the robotic arm control system on the production line, when faced with sudden task peaks or environmental interference, the system can respond quickly and adjust to the optimal power supply configuration to ensure the accuracy and response speed of the robotic arm's movements, thereby improving the efficiency and stability of the entire production line. In this way, whether in normal operation or extreme conditions, the efficient and stable operation of the MCU and even the entire system can be effectively guaranteed.

[0028] Step S5, executing and distributing the optimal power supply configuration solution through a distributed coordination mechanism to obtain a real-time power supply control instruction.

[0029] Specifically, the distributed collaborative mechanism is used to execute and distribute the optimal power supply configuration scheme to obtain real-time power supply control instructions. This process is a key step to ensure that the MCU can operate stably in a complex and dynamically changing environment. First, based on the optimal power supply configuration scheme determined in the previous step, the system needs to convert this strategy into specific execution actions and distribute it to the various components of the multiple redundant power supply system. In this process, the distributed collaborative mechanism plays a core role, which allows effective information exchange and task coordination between different power supply units. For example, in the 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 collaborative mechanism can accurately calculate the power size, switching timing and other parameters that each power module should provide according to the optimal power supply configuration scheme, and generate corresponding real-time power supply control instructions. These instructions not only take into account the current workload, 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, in order to improve the response speed and decision-making accuracy, the mechanism usually uses advanced communication protocols and algorithm models to optimize the transmission efficiency of information flow. For example, when the robotic arm control system encounters a sudden peak in tasks, the distributed coordination mechanism can quickly adjust the working status of each power module and provide the required power support in a timely manner to avoid system performance degradation due to insufficient power supply. In this way, even in the face of complex working conditions, the MCU can always obtain a stable and efficient power supply, thereby improving the reliability and stability of the entire system. This flexible and efficient power management method is essential to ensure the continuous and stable operation of mission-critical systems.

[0030] Step S6: based on the real-time power supply control instruction, collaboratively control the multiple redundant power supply systems to provide stable power supply to the MCU.

[0031] 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.

[0032] In a specific embodiment, the real-time working voltage sampling detection of the MCU to obtain a working voltage sampling sequence includes: The operating voltage of the MCU is collected by a multi-channel synchronous sampling circuit to obtain an original voltage signal set; 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; The optimized voltage characteristic vector is time-aligned and normalized by a dynamic time warping algorithm to obtain a working voltage sampling sequence.

[0033] Specifically, the process of sampling and detecting the working voltage of the MCU in real time and obtaining the working voltage sampling sequence is an important part of realizing an efficient and stable multiple redundant flexible power supply control method. First, the working voltage of the MCU is collected through a multi-channel synchronous sampling circuit. This process ensures that the working voltage state of the MCU can be fully captured from multiple angles to obtain 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, the voltage acquisition of a single channel may not fully reflect the actual working state of the MCU. Therefore, the multi-channel synchronous sampling method can obtain richer information from different dimensions and provide a solid data foundation for subsequent processing. Next, based on the preset Kalman filter fusion, the original voltage signal set is subjected to noise suppression and signal reconstruction to obtain an optimized voltage feature vector. Kalman filtering, as a powerful recursive filtering algorithm, can estimate the state variables of the system in the presence of noise. In this application scenario, due to the adverse factors such as electromagnetic interference that may exist in the production environment, the collected original voltage signal contains a lot of noise, which not only affects the accuracy of the data, but also may mislead the subsequent state evaluation. Therefore, the use of a Kalman filter fusion can effectively remove these noises and reconstruct a more accurate voltage signal. For example, when the robot arm performs high-precision operations, any slight voltage fluctuation may cause execution errors. The optimized voltage feature vector after Kalman filtering can more accurately reflect the actual working voltage of the MCU, providing a guarantee for the stable operation of the system. Subsequently, the optimized voltage feature vector is time-series aligned and normalized by the Dynamic Time Warping (DTW) algorithm 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 with time and task requirements, direct comparison or analysis of unprocessed voltage signals may have large deviations. 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, which is convenient for subsequent multi-dimensional working state evaluation. For example, when the production line encounters an emergency and requires the robot arm to adjust its position quickly, the workload of the MCU will increase instantly, resulting in drastic fluctuations 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 the data collected under other normal working conditions, thereby providing the system with more accurate power supply mode scheduling suggestions.In summary, by sampling and detecting the real-time working voltage of the MCU, and then processing it in turn through the multi-channel synchronous sampling circuit, Kalman filter fusion, and dynamic time warping algorithm, the final working voltage sampling sequence can fully and accurately reflect the actual working status of the MCU under various working conditions. This meticulous data processing process not only improves the quality of the data, but also provides strong support for the subsequent power supply strategy formulation, so that the entire system can maintain efficient and stable operation 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 a stable and sufficient power supply, avoiding system performance degradation or failure due to insufficient power supply, and greatly improving the reliability and efficiency of the entire industrial automation system.

[0034] In a specific embodiment, the multi-dimensional working state evaluation of the MCU is performed based on the working voltage sampling sequence to obtain the 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 nonlinear mapping transformation on the voltage fluctuation feature tensor to obtain a voltage dynamic characteristic set; Extracting time-frequency domain features of the voltage dynamic characteristic set by multi-layer wavelet packet decomposition to obtain a voltage quality feature vector, and comprehensively evaluating the multiple redundant power supply systems based on the voltage quality feature vector to obtain a state scoring matrix of the multiple redundant power supply systems; Mapping the working state of the MCU based on the multi-dimensional feature of the state scoring 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; 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; A timing 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.

[0035] Specifically, the MCU is evaluated in a multi-dimensional working state based on the working voltage sampling sequence to obtain the MCU state feature matrix. This process is one of the key steps to achieve intelligent power supply management. First, the working voltage sampling sequence is decomposed and reconstructed in a multi-dimensional manner. This method can deeply mine the rich information contained in the voltage signal, obtain the voltage fluctuation feature tensor, and perform nonlinear mapping transformation on the voltage fluctuation feature tensor, so as to extract the voltage dynamic feature set that can reflect the operating characteristics of the MCU. For example, in a robotic arm control system in an industrial automation scenario, due to the complex tasks and changing environment, the working voltage of the MCU is not only affected by load changes, but also may fluctuate due to external interference. By carefully analyzing and processing these fluctuation features, the performance of the MCU under different working states can be captured more accurately. Next, the voltage dynamic feature set is extracted in the time and frequency domain by multi-layer wavelet packet decomposition, in order to further refine the feature representation of the voltage signal and obtain the voltage quality feature vector. As an efficient signal processing technology, wavelet packet decomposition can provide finer resolution in the time and frequency domain, which is particularly important for analyzing rapidly changing voltage signals. Taking high-precision control on the production line as an example, when the MCU needs to quickly adjust the position of the robot arm according to the sensor data, any slight voltage change may cause execution errors. Using wavelet packet decomposition technology, more valuable detail information can be extracted from the voltage dynamic characteristic set to form a voltage quality feature vector. Then, based on the voltage quality feature vector, the multiple redundant power supply system is comprehensively evaluated to obtain a state scoring matrix of the multiple redundant power supply system. This scoring matrix not only reflects the health of the power supply system, but also provides an important basis for the subsequent optimization of the power supply strategy. On this basis, the working state of the MCU is mapped based on the multi-dimensional feature of the state scoring matrix to obtain the MCU working state feature set, and the MCU working state feature set is hierarchically decomposed to obtain the MCU performance parameter matrix. This process involves simplifying the complex MCU working state into a form that is easy to understand and analyze. Through hierarchical decomposition, the various performance indicators of the MCU, such as power consumption and response speed, can be clearly displayed. For example, when processing sudden production tasks, the MCU needs to complete a large amount of calculations and data transmission in a short time, and its power consumption and response speed become key factors. By analyzing the MCU working state feature set, potential problems can be discovered in time and corresponding measures can be taken. Subsequently, the MCU is dynamically identified based on the MCU performance parameter matrix to obtain the MCU operation feature vector, and the state space reconstruction and feature extraction are performed based on the MCU operation feature vector to obtain the MCU initial state feature matrix. This step aims to comprehensively describe the operating characteristics of the MCU from multiple dimensions, and the state space reconstruction helps to reveal the behavior mode of the MCU under different working conditions.For example, when facing 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 to provide support for real-time scheduling. Finally, the MCU initial state feature matrix is ​​subjected to time series correlation analysis to obtain the MCU state evolution sequence, and the MCU state evolution sequence is subjected to multi-dimensional feature fusion and optimization to obtain the MCU state feature matrix. Time series 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 interference, the state evolution sequence of the MCU can reflect its coping ability. Through multi-dimensional feature fusion and optimization, information from different aspects can be integrated to form a feature matrix that fully reflects the working state of the MCU, including the voltage stability degree vector, the power consumption fluctuation degree vector, and the 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 to ensure the efficient and stable operation of the MCU and even the entire industrial automation system. In short, through the multi-dimensional evaluation of the MCU working state, the reliability and flexibility of the system can be significantly improved to meet the needs of modern industry for high-performance control.

[0036] In a specific embodiment, the MCU state feature matrix is ​​dynamically thresholded by an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion, including: Perform multi-dimensional feature stratification and decoupling on the MCU state feature matrix to obtain a state feature component set, and dynamically optimize the weight of the state feature component set through an adaptive weight allocation mechanism to obtain an adaptive weight coefficient matrix; Performing feature importance evaluation based on the adaptive weight coefficient matrix to obtain a feature importance ranking table, performing multi-level threshold analysis on the feature importance ranking table to obtain a threshold candidate set, and performing dynamic partition mapping based on the threshold candidate set to obtain a power supply status partition matrix; Based on the power supply state partition matrix, multi-level threshold optimization and calibration are performed to obtain a threshold optimization sequence, and hierarchical decision rules are 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; The power supply switching rule set is cross-validated and integrated in multiple dimensions to obtain a multi-level power supply switching criterion, wherein the multi-level power supply switching criterion includes a voltage derating factor, a power consumption compensation factor and a task priority.

[0037] Specifically, the MCU state feature matrix is ​​dynamically divided by threshold through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion. This process is intended to ensure that the MCU can obtain stable and efficient power support in a complex and changeable working environment. First, the MCU state feature matrix is ​​multi-dimensionally layered and decoupled. This operation decomposes the complex feature matrix into multiple independent state feature component sets for 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 a variety of factors, such as voltage stability, power consumption fluctuations, and task execution efficiency. Through the detailed layering and decoupling of these state features, the specific role of each influencing factor can be captured more accurately, thereby laying the foundation for optimizing the power supply strategy. Next, the state feature component set is dynamically weighted and optimized through an adaptive weight allocation mechanism to obtain an adaptive weight coefficient matrix. The core of the adaptive weight allocation mechanism is to dynamically adjust the importance weight of each feature component according to the current working environment and needs. 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 they need to be given higher weights. This dynamic adjustment not only improves the flexibility and adaptability of the system, but also ensures that the best decision can be made under various working conditions. In addition, based on the adaptive weight coefficient matrix, the feature importance is evaluated to obtain a feature importance ranking table, and the feature importance ranking table is subjected to a multi-level threshold analysis 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. This process helps to identify which state features are most important under current conditions and set a reasonable threshold range accordingly. For example, when dealing with emergency production tasks, 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 the corresponding power supply adjustment measures are triggered when the range is exceeded. Subsequently, multi-level threshold optimization and calibration are performed based on the power supply state partition matrix to obtain a threshold optimization sequence, and the threshold optimization sequence is subjected to hierarchical decision rule construction 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 thresholds initially determined and transform them into specific power supply switching strategies. For example, when the voltage of the robot control system on the production line fluctuates abnormally, 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 the power consumption is too high; the system coordination rule ensures efficient collaboration among the components within the entire power supply system to jointly deal with 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, the power supply switching rule set is cross-validated and integrated in multiple dimensions to obtain a multi-level power supply switching criterion, wherein the multi-level power supply switching criterion includes a voltage derating factor, a power consumption compensation factor and a task priority. The multi-dimensional cross-validation process involves comprehensive testing and verification of the power supply switching rules from different angles to ensure its effectiveness and reliability in practical applications. For example, when the simulated production line encounters an emergency, 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 voltage derating factor, power consumption compensation factor and task priority makes the power supply switching strategy more flexible and intelligent. Specifically, the voltage derating factor can help moderately reduce the operating voltage of the MCU when the voltage is unstable, reduce energy consumption and ensure that basic functions are not affected; the power consumption compensation factor can appropriately increase the power supply power when the power consumption is detected to be too high to prevent the MCU from overloading; the task priority ensures that the most critical task requirements are met first when resources are limited. In this way, the entire system can maintain efficient and stable operation in a complex and changeable 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 emergencies. In summary, through in-depth analysis and optimization of the MCU state feature matrix, combined with the adaptive weight allocation mechanism and dynamic threshold division technology, the intelligence level of power supply management can be significantly improved, providing a solid guarantee for the efficient operation of modern industrial control systems.

[0038] In a specific embodiment, the real-time scheduling of the power supply mode of the MCU based on the multi-level power supply switching criterion to obtain the optimal power supply configuration scheme includes: 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 a power supply mode candidate solution; Performing a multi-objective evaluation on the candidate power supply mode schemes through a hierarchical analysis method to obtain a power supply scheme evaluation matrix, and prioritizing the power supply scheme evaluation matrix to obtain a power supply scheme priority sequence; Constructing multi-dimensional constraint conditions based on the power supply scheme priority sequence to obtain a power supply scheduling constraint set, and dynamically dividing the power supply scheduling constraint set to obtain power supply scheduling boundary conditions; A multi-objective optimization solution is 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 candidate configuration scheme group, and a multi-dimensional comprehensive evaluation is performed on the candidate configuration scheme group to obtain an optimal power supply configuration scheme; wherein the optimal power supply configuration scheme includes power switching timing, voltage regulation parameters and power consumption balancing strategy.

[0039] Specifically, the power supply mode of the MCU is scheduled in real time based on the multi-level power supply switching criterion to obtain the optimal power supply configuration scheme. 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, the multi-dimensional feature analysis and reconstruction of the multi-level power supply switching criterion are carried out. This operation converts the complex power supply switching criterion into a power supply switching feature set that is easier to understand and process, providing a basis for the subsequent power supply mode classification. For example, in the 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, by analyzing the characteristics of the multi-level power supply switching criterion, it is possible to identify which factors (such as voltage stability, power consumption fluctuations, etc.) are the most critical, and generate power supply mode candidate schemes accordingly. This meticulous analysis not only improves the accuracy of decision-making, but also provides an important basis for optimizing power supply strategies. Next, the candidate power supply mode schemes are evaluated by multi-objectives through the hierarchical analysis method to obtain a power supply scheme evaluation matrix, and the power supply scheme evaluation matrix is ​​prioritized to obtain a power supply scheme priority sequence. The analytic hierarchy process is an effective multi-criteria decision-making tool that allows for comprehensive evaluation while considering multiple conflicting objectives (such as performance, cost, reliability, etc.). For example, in a robotic arm control system, when faced with sudden task peaks or environmental interference, it may be necessary to minimize energy consumption while ensuring high-precision operation. Through the analytic hierarchy process, different weights can be assigned to each target score to form a power supply scheme evaluation matrix. Then, based on the matrix, the candidate power supply modes are prioritized to determine which scheme best meets the current needs. This step not only improves the transparency of the decision-making process, but also enables the system to respond quickly to changes and select the most suitable power supply mode. On this basis, multi-dimensional constraints are constructed based on the priority sequence of the power supply scheme to obtain a power supply scheduling constraint set, and the power supply scheduling constraint set is dynamically divided to obtain the power supply scheduling boundary conditions. This process involves setting a series of constraints according to specific working conditions, such as maximum power consumption limit, minimum voltage requirement, etc., which together constitute the power supply scheduling constraint set. For example, when a production line encounters an emergency task switch, in order to ensure that all equipment can operate normally, a reasonable upper power consumption limit and lower voltage limit must be set. By dynamically dividing the boundaries of 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 the foundation for subsequent optimization solutions. Subsequently, the power supply scheduling boundary conditions are multi-objective optimized to obtain the power supply scheduling strategy set, and the power supply scheduling strategy set is evaluated and screened to obtain a candidate configuration solution group, and the candidate configuration solution group is comprehensively evaluated in multiple dimensions to obtain the optimal power supply configuration solution.The multi-objective optimization solution process aims to find a set of power supply scheduling strategies that can satisfy all constraints and achieve optimal performance. For example, when processing complex production tasks, the MCU may need to complete a large amount of calculations and data transmission in a short period of 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 reduce energy consumption as much as possible while ensuring efficient operation can be selected. Finally, after a comprehensive evaluation in multiple dimensions, the optimal power supply configuration scheme is determined, including specific contents such as power switching timing, voltage regulation parameters and power consumption balancing strategy. These strategies not only ensure the stable operation of the MCU under various working conditions, but also effectively improve the energy efficiency of the entire system. In short, through in-depth analysis and optimization of multi-level power supply switching criteria, combined with hierarchical analysis methods and multi-objective optimization techniques, intelligent scheduling of MCU power supply modes can be achieved to ensure 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, the above steps can effectively respond to various emergencies, such as task peaks, external interference, etc., to ensure that it can respond quickly and maintain normal operation even under extreme conditions. This approach not only improves the flexibility and adaptability of the system, but also provides strong support for the successful completion of key tasks, greatly improving the reliability and efficiency of the entire industrial automation system. At the same time, by continuously optimizing the power supply configuration solution, the potential of the system can be further explored to promote technological progress and application innovation.

[0040] In a specific embodiment, the optimal power supply configuration scheme is executed and allocated through a distributed coordination mechanism to obtain a real-time power supply control instruction, including: Perform multi-dimensional parameter analysis and decomposition on the optimal power supply configuration scheme 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; An execution sequence is generated for the power supply control timing table through a distributed collaborative mechanism to obtain a control instruction sequence matrix, and a timing consistency check is performed on the control instruction sequence matrix to obtain an instruction execution constraint set; Based on the instruction execution constraint set, multi-dimensional conflict detection and resolution are performed on the control instruction sequence matrix to obtain an optimized instruction sequence, and execution priority is divided for the optimized instruction sequence to obtain a hierarchical execution plan; Multi-dimensional execution resource allocation is performed 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 adjustment control instructions and stability assurance control instructions.

[0041] Specifically, the optimal power supply configuration scheme is executed and allocated through a distributed collaborative 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, the optimal power supply configuration scheme is analyzed and decomposed in multiple dimensions. 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 robot arm according to sensor data, its power supply demand may increase significantly. Through the multi-dimensional parameter analysis of the optimal power supply configuration scheme, 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 status, but also provide a basis for formulating accurate power supply strategies. Next, execution timing planning is performed based on the power supply configuration parameter set to obtain a power supply control timing table. This process involves determining the specific execution order and time point of each power supply operation to ensure that the system can provide the required power support at the right time. For example, when handling emergency production task switching, it is necessary to ensure that each power module is started or shut 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 response speed of the system, but also ensures the safety and stability of the operation. Subsequently, the power supply control timing table is executed through a distributed collaborative mechanism to generate an execution sequence to obtain a control instruction sequence matrix, and the control instruction sequence matrix is ​​subjected to a timing consistency check to obtain an instruction execution constraint set. The core of the distributed collaborative mechanism is to coordinate the work between multiple power supply units to ensure that they can collaborate efficiently and jointly complete the power supply task. For example, when facing an emergency on a production line, multiple power modules may need to work simultaneously to meet higher power requirements. Through a distributed collaborative mechanism, a specific control instruction sequence matrix can be generated according to the power supply control timing table, and a timing consistency check can be further performed on it to ensure that all operations can be executed in a predetermined time sequence. This consistency check not only prevents potential operation conflicts, but also provides a basis for subsequent optimization. On this basis, the control instruction sequence matrix is ​​subjected to multi-dimensional conflict detection and elimination based on the instruction execution constraint set to obtain an optimized instruction sequence, and the optimized instruction sequence is divided into execution priorities to obtain a hierarchical execution scheme. This process involves detecting and resolving possible operational conflicts and setting different execution priorities based on actual needs. For example, when a robotic arm control system encounters a high-load task, certain specific power supply operations (such as voltage regulation) may need to be prioritized to ensure stable operation of the system.By detecting and resolving conflicts in the control instruction sequence matrix, any operational conflicts that may cause system failures can be eliminated, and each operation can be sorted according to 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 met first when resources are limited. Finally, the hierarchical execution plan is multi-dimensionally allocated with execution resources to obtain real-time power supply control instructions, wherein the real-time power supply control instructions include power switching control instructions, power consumption adjustment control instructions, and stability assurance 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 sudden task peaks on the 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, not only can seamless power switching be achieved, but also power consumption can be dynamically adjusted according to actual load conditions to ensure that the system is always in the best working state. In addition, in order to ensure the long-term stable operation of the system, a series of stability assurance measures need to be implemented, such as monitoring voltage fluctuations, timely discovering and handling potential problems, etc. These control instructions not only improve the reliability and adaptability of the system, but also provide strong support for coping with complex working conditions. In summary, the process of executing and distributing the optimal power supply configuration scheme through a distributed collaborative mechanism and finally obtaining real-time power supply control instructions covers multiple links from parameter parsing to execution resource allocation. Each step is closely linked to ensure that the MCU can obtain a stable and efficient power supply under various working conditions. For example, in the robotic arm control system in the industrial automation scenario, the above method can not only 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 approach not only provides strong technical support for modern industrial control systems, but also lays a solid foundation for future intelligent management and optimization.

[0042] 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: 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 map; Constructing multi-dimensional constraint conditions for the conflict propagation topology graph to obtain a conflict resolution rule set, and reordering the control instruction sequence matrix based on the conflict resolution rule set to obtain an instruction execution optimization solution; The instruction execution optimization scheme is cross-validated in multiple dimensions to obtain an instruction optimization evaluation matrix, and a feasibility analysis and multi-objective collaborative optimization are performed on the instruction optimization evaluation matrix to obtain an optimized instruction sequence.

[0043] 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, it is possible to identify which conflicts can be resolved by rearranging the order of instructions, and generate a conflict resolution rule set accordingly. Then, based on the rule set, the control instructions are reordered to develop an optimized instruction execution plan. This refined management method not only improves the security of the system, but also lays the foundation for subsequent optimization solutions. For example, when dealing with emergencies on the production line, by reasonably adjusting the order of instructions, system failures caused by instruction conflicts can be avoided, ensuring the continuity and stability of production. Finally, the instruction execution optimization plan is cross-validated in multiple dimensions to obtain an instruction optimization evaluation matrix, and the instruction optimization evaluation matrix is ​​feasibility analyzed and multi-objective collaboratively optimized 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 angles to ensure its effectiveness and reliability in practical applications. For example, when simulating an emergency on a production line, multiple experiments can be conducted to verify whether the instruction execution optimization plan can effectively avoid power supply conflicts and ensure stable system operation without affecting production efficiency. In addition, by conducting 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 constraints and achieve optimal 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 emergency task peaks or external interference on the production line, the optimized instruction sequence can respond quickly, adjust the power configuration, and ensure that the robot control system is always in the best working state, greatly improving the reliability and flexibility of the entire system. This approach not only provides strong support for ensuring the smooth completion of key tasks, but also greatly improves the reliability and efficiency of the entire industrial automation system. In short, the above method can significantly improve the flexibility and adaptability of the system, ensuring that it can respond quickly and maintain normal operation even in complex and changing environments. This approach not only provides strong support for ensuring the smooth completion of key tasks, but also lays a solid foundation for future intelligent management and optimization.

[0044] The above describes the multiple redundant flexible power supply MCU control method in the embodiment of the present invention. The following describes the multiple redundant flexible power supply MCU control system in the embodiment of the present invention. Figure 2 In one embodiment of the present invention, a multiple redundant flexible power supply MCU control system includes: The sampling module 21 is used to perform real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence; An evaluation module 22, configured to perform a multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix; A division module 23, used to perform dynamic threshold division on the MCU state feature matrix through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion; A scheduling module 24, configured to perform 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 solution; An allocation module 25 is used to allocate the optimal power supply configuration scheme through a distributed coordination mechanism to obtain a real-time power supply control instruction; The control module 26 is used to collaboratively control the multiple redundant power supply systems to provide stable power supply to the MCU based on the real-time power supply control instruction.

[0045] In this embodiment, for the specific implementation of each unit in the above system embodiment, please refer to the above method embodiment, which will not be repeated here.

[0046] Reference Figure 3 The present invention also provides a computer device in an embodiment, wherein the internal structure of the computer device can be as follows: Figure 3 As 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 designed by the computer 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. When the computer program is executed by the processor, the above method is implemented.

[0047] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion 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.

[0048] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0049] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0050] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0051] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A multiple redundant flexible power supply MCU control method, characterized in that: Applied to a multiple redundant power supply system, the multiple redundant power supply system is electrically connected to the MCU, comprising the following steps: Performing real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence; Performing a multi-dimensional working state evaluation on the MCU based on the working voltage sampling sequence to obtain an MCU state feature matrix; Dynamically threshold the MCU state feature matrix through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion; Based on the multi-level power supply switching criterion, the power supply mode of the MCU is scheduled in real time to obtain an optimal power supply configuration solution; The optimal power supply configuration scheme is executed and allocated through a distributed collaborative mechanism to obtain a real-time power supply control instruction; Based on the real-time power supply control instruction, the multiple redundant power supply systems are collaboratively controlled to provide stable power supply to the MCU.

2. The multiple redundant flexible power supply MCU control method according to claim 1 is characterized in that: The real-time working voltage sampling detection is performed on the MCU to obtain a working voltage sampling sequence, including: The operating voltage of the MCU is collected by a multi-channel synchronous sampling circuit to obtain an original voltage signal set; 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; The optimized voltage characteristic vector is time-aligned and normalized by a dynamic time warping algorithm to obtain a working voltage sampling sequence.

3. The multiple redundant flexible power supply MCU control method according to claim 1 is characterized in that: The multi-dimensional working state evaluation of the MCU is performed 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 nonlinear mapping transformation on the voltage fluctuation feature tensor to obtain a voltage dynamic characteristic set; Extracting time-frequency domain features of the voltage dynamic characteristic set by multi-layer wavelet packet decomposition to obtain a voltage quality feature vector, and comprehensively evaluating the multiple redundant power supply systems based on the voltage quality feature vector to obtain a state scoring matrix of the multiple redundant power supply systems; Mapping the working state of the MCU based on the multi-dimensional feature of the state scoring 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; 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; A timing 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.

4. The multiple redundant flexible power supply MCU control method according to claim 1, characterized in that: The method of dynamically dividing the MCU state feature matrix by a self-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 state feature component set, and dynamically optimize the weight of the state feature component set through an adaptive weight allocation mechanism to obtain an adaptive weight coefficient matrix; Performing feature importance evaluation based on the adaptive weight coefficient matrix to obtain a feature importance ranking table, performing multi-level threshold analysis on the feature importance ranking table to obtain a threshold candidate set, and performing dynamic partition mapping based on the threshold candidate set to obtain a power supply status partition matrix; Based on the power supply state partition matrix, multi-level threshold optimization and calibration are performed to obtain a threshold optimization sequence, and hierarchical decision rules are constructed for the threshold optimization sequence to obtain a power supply switching rule set, wherein the power supply switching rule set includes voltage switching rules, performance protection rules and system coordination rules; The power supply switching rule set is cross-validated and integrated in multiple dimensions to obtain a multi-level power supply switching criterion, wherein the multi-level power supply switching criterion includes a voltage derating factor, a power consumption compensation factor, and a task priority.

5. The multiple redundant flexible power supply MCU control method according to claim 1, characterized in that: The real-time scheduling of the power supply mode of the MCU based on the multi-level power supply switching criterion to obtain the optimal power supply configuration scheme includes: 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 a power supply mode candidate solution; Performing a multi-objective evaluation on the candidate power supply mode schemes through a hierarchical analysis method to obtain a power supply scheme evaluation matrix, and prioritizing the power supply scheme evaluation matrix to obtain a power supply scheme priority sequence; Constructing multi-dimensional constraint conditions based on the power supply scheme priority sequence to obtain a power supply scheduling constraint set, and dynamically dividing the power supply scheduling constraint set to obtain power supply scheduling boundary conditions; A multi-objective optimization solution is 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 candidate configuration scheme group, and a multi-dimensional comprehensive evaluation is performed on the candidate configuration scheme group to obtain an optimal power supply configuration scheme; wherein the optimal power supply configuration scheme includes power switching timing, voltage regulation parameters and power consumption balancing strategy.

6. The multiple redundant flexible power supply MCU control method according to claim 1, characterized in that: The step of executing and distributing the optimal power supply configuration scheme through a distributed coordination mechanism to obtain a real-time power supply control instruction includes: Perform multi-dimensional parameter analysis and decomposition on the optimal power supply configuration scheme 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; An execution sequence is generated for the power supply control timing table through a distributed collaborative mechanism to obtain a control instruction sequence matrix, and a timing consistency check is performed on the control instruction sequence matrix to obtain an instruction execution constraint set; Based on the instruction execution constraint set, multi-dimensional conflict detection and resolution are performed on the control instruction sequence matrix to obtain an optimized instruction sequence, and execution priority is divided for the optimized instruction sequence to obtain a hierarchical execution plan; Multi-dimensional execution resource allocation is performed on the hierarchical execution scheme to obtain real-time power supply control instructions, wherein the real-time power supply control instructions include power switching control instructions, power consumption adjustment control instructions and stability assurance control instructions.

7. The multiple redundant flexible power supply MCU control method according to claim 6 is characterized in that: The 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: 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 map; Constructing multi-dimensional constraint conditions for the conflict propagation topology graph to obtain a conflict resolution rule set, and reordering the control instruction sequence matrix based on the conflict resolution rule set to obtain an instruction execution optimization solution; The instruction execution optimization scheme is cross-validated in multiple dimensions to obtain an instruction optimization evaluation matrix, and a feasibility analysis and multi-objective collaborative optimization are performed on the instruction optimization evaluation matrix to obtain an optimized instruction sequence.

8. A multiple redundant flexible power supply MCU control system, characterized in that: Applied to a multiple redundant power supply system, the multiple redundant power supply system is electrically connected to the MCU, comprising: A sampling module is used to perform real-time working voltage sampling detection on the MCU to obtain a working voltage sampling sequence; An evaluation module, used for performing a 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, used to perform dynamic threshold partitioning on the MCU state feature matrix through an adaptive weight allocation mechanism to obtain a multi-level power supply switching criterion; A scheduling module, used to perform 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 solution; An allocation module, used to allocate the execution of the optimal power supply configuration scheme through a distributed coordination mechanism to obtain a real-time power supply control instruction; A control module is used to collaboratively control the multiple redundant power supply systems to provide stable power supply to the MCU based on the real-time power supply control instruction.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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