Intelligent power supply management method and system compatible with multiple communication devices

Abstract

The invention relates to the technical field of power supply management, and discloses an intelligent power supply management method and system compatible with multiple communication devices. The method comprises the following steps: acquiring power characteristic data of equipment and real-time acquisition time sequence data; performing classification identification and matching according to the power feature data to obtain an initial power supply slice configuration scheme; obtaining an adjusted power supply slice configuration scheme through statistical analysis, weight adjustment and optimization according to the time sequence data and the initial scheme; and performing coordinated management on the adjusted scheme to obtain an intelligent power supply management control instruction. According to the method, accurate matching and dynamic optimization of power supply of multiple communication devices can be realized, and the power supply efficiency and the system stability are improved.

Description

Technical Field

[0001] This invention relates to the field of power supply management technology, and in particular to an intelligent power supply management method and system compatible with multiple communication devices. Background Technology

[0002] Currently, with the rapid development of the Internet of Things (IoT) and smart devices, power supply management for multiple communication devices has become a key technical aspect of modern intelligent systems. In application scenarios such as smart buildings, industrial IoT, and smart cities, a large number of heterogeneous communication devices need to operate stably simultaneously, placing higher demands on the reliability and intelligence level of the power supply system.

[0003] In existing technologies, power management solutions typically employ a uniform power supply strategy when dealing with multiple communication devices, lacking a refined classification and management mechanism and failing to differentiate treatment based on the characteristics of different devices. Furthermore, these solutions lack real-time responsiveness in resource allocation, making it difficult to adjust power supply strategies promptly according to changes in device operating status, resulting in inefficient resource configuration. For example, in a system containing routers, sensors, and cameras, routers require continuous power, sensors have low power consumption but are numerous, while cameras have significant peak power requirements. Traditional solutions struggle to simultaneously meet these differentiated needs and achieve optimal resource allocation.

[0004] In summary, existing technologies suffer from low overall power supply efficiency due to the inability of power supply strategies to accurately match equipment needs and dynamically allocate resources. Summary of the Invention

[0005] This invention provides an intelligent power supply management method and system compatible with multiple communication devices to solve the problem of low power supply efficiency.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides an intelligent power supply management method compatible with multiple communication devices, comprising: Acquire the power characteristic data of the equipment, classify and identify it to obtain the equipment operation type data; Based on the device operation type data, a power supply slicing scheme is matched to obtain an initial power supply slicing configuration scheme; The time series data of each device in the initial power supply slice configuration scheme are collected in real time and statistically analyzed to obtain a statistical characteristic curve. If the value range of the statistical characteristic curve exceeds the preset statistical characteristic threshold, the power allocation weight is adjusted to obtain a new power allocation weight coefficient. The new power allocation weight coefficients are optimized to obtain the optimal power allocation strategy; Based on the optimal power allocation strategy, the power upper limit and priority of the initial power supply slice configuration scheme are adjusted to obtain the adjusted power supply slice configuration scheme. The adjusted power supply slice configuration scheme is coordinated and managed to obtain intelligent power supply management and control commands.

[0007] In one optional implementation, the step of acquiring the power characteristic data of the device and classifying and identifying it to obtain device type data includes: The device identification, basic power consumption value, peak power range and power consumption cycle mode of the device are collected in real time, and the data are integrated to obtain an initial dataset; The basic power consumption values, peak power range, and power consumption cycle patterns in the initial dataset are normalized and integrated to obtain power characteristic data. The power characteristic data is clustered and grouped to obtain the device clustering results; By combining the preset device type mapping table, the device clustering results are matched with device types to obtain device operation type data.

[0008] In one optional implementation, the step of matching power supply slicing schemes based on the device operating type data to obtain an initial power supply slicing configuration scheme includes: Based on the device operation type data, perform operation type matching to obtain the corresponding type of slice template library; Based on the corresponding type of slice template library, the power value is matched with the device operation type data to obtain the precise power supply slice parameters; The power supply slice parameters for all types of precise power are combined to obtain an initial power supply slice configuration scheme.

[0009] In one optional implementation, the real-time acquisition of time-series data of each device in the initial power supply slice configuration scheme is performed and statistical analysis is conducted to obtain a statistical characteristic curve. If the value range of the statistical characteristic curve exceeds a preset statistical characteristic threshold, the power allocation weight is adjusted to obtain a new power allocation weight coefficient, including: The power consumption data stream, voltage fluctuation data stream, and load change data stream of each device in the initial power supply slice configuration scheme are collected in real time and integrated to obtain time series data; For the time series data, the mean and variance are calculated using a sliding window algorithm to obtain the statistical characteristic curve; If the range of the statistical characteristic curve exceeds the preset statistical characteristic range threshold, the device is marked as abnormal, and a list of abnormal devices is obtained. Obtain the actual power requirements of the list of abnormal devices; The power allocation weight coefficients of the abnormal device list are adjusted using a linear programming algorithm based on the actual power demand to obtain new power allocation weight coefficients.

[0010] In one optional implementation, optimizing the new power allocation weight coefficients to obtain the optimal power allocation strategy includes: The new power allocation weighting coefficients are adjusted to obtain the optimal power allocation weighting coefficients; Based on the optimal power allocation weight coefficient, the equipment operating parameters are updated to obtain the optimal power allocation strategy.

[0011] In one optional implementation, adjusting the power upper limit and priority of the initial power supply slice configuration scheme according to the optimal power allocation strategy to obtain the adjusted power supply slice configuration scheme includes: Based on the optimal power allocation strategy, obtain the current power demand data of each device; If the current power demand data exceeds the preset power quota threshold, the upper limit of the threshold of the initial power supply slice configuration scheme is adjusted to obtain the first power quota threshold. Based on the first power quota threshold, the scheduling priority is adjusted to obtain the first priority scheduling sequence; Based on the first priority scheduling sequence, the configuration parameters of the initial power supply slice configuration scheme are modified to obtain the adjusted power supply slice configuration scheme.

[0012] In one optional implementation, the coordinated management of the adjusted power supply slice configuration scheme to obtain intelligent power supply management control commands includes: Real-time monitoring of the instantaneous power consumption of the equipment; If the instantaneous power consumption exceeds the power quota threshold in the adjusted power supply slice configuration scheme, a power borrowing operation is performed to obtain the source slice dataset of the borrowed power. For the source slice dataset of the borrowed power, power supply slice coordination is performed, and power return operations are determined to obtain a dynamic power allocation scheme. The device responds in real time to the dynamic power allocation scheme to obtain intelligent power supply management and control commands.

[0013] Secondly, the present invention provides an intelligent power supply management system compatible with multiple communication devices, comprising: The feature recognition module is used to acquire the power feature data of the device, classify and identify it, and obtain the device operation type data. The scheme matching module is used to perform power supply slicing scheme matching based on the device operation type data to obtain an initial power supply slicing configuration scheme; The weight adjustment module is used to collect time series data of each device in the initial power supply slice configuration scheme in real time, perform statistical analysis, and obtain statistical characteristic curves. If the value range of the statistical characteristic curve exceeds the preset statistical characteristic threshold, the power allocation weight is adjusted to obtain a new power allocation weight coefficient. The strategy optimization module is used to optimize the new power allocation weight coefficients to obtain the optimal power allocation strategy. The configuration update module is used to adjust the power upper limit and priority of the initial power supply slice configuration scheme according to the optimal power allocation strategy, so as to obtain the adjusted power supply slice configuration scheme. The coordination and management module is used to coordinate and manage the adjusted power supply slice configuration scheme and obtain intelligent power supply management and control commands.

[0014] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the intelligent power supply management method for compatibility with multiple communication devices as described in any one of the above.

[0015] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform any one of the above-described intelligent power supply management methods compatible with multiple communication devices.

[0016] Compared with the prior art, the present invention has the following beneficial effects: (1) This invention uses a device power clustering algorithm to collect and generate power feature vectors based on dimensions such as the basic power consumption, peak power range, and power consumption cycle pattern of each communication device, thereby achieving automatic classification and identification of device types. Then, based on the classification results, different constant, elastic, or dynamically extended power supply slice templates are matched according to the different devices, such as continuous stable type, intermittent working type, and peak burst type. This refined matching method based on device characteristics changes the traditional "one-size-fits-all" extensive mode in power supply management, ensuring the accurate alignment between the power supply scheme and the actual needs of the equipment from the source, laying a solid foundation for subsequent dynamic resource allocation, and significantly improving the initial matching degree and rationality of the power supply strategy.

[0017] (2) This invention collects time-series data through a real-time monitoring mechanism. When the changes in the operating status of the equipment exceed a preset threshold, it activates a dynamic load balancing algorithm and a multi-objective optimization algorithm. With the dual objectives of maximizing power utilization and optimizing load balance, the power allocation weight coefficients are recalculated and optimized to obtain the system-level optimal power allocation strategy. This method enables the power supply strategy to respond to the actual operating status changes of the equipment in real time and intelligently, rather than relying on a fixed static configuration. It ensures that the allocation of power resources is not only timely but also globally optimal, thereby achieving efficient utilization of the overall system resources while ensuring the stable operation of each device.

[0018] (3) This invention introduces a power scheduling and coordination management mechanism between power slices, dynamically adjusting the power upper limit and priority parameters of each power supply slice according to the optimal power allocation strategy. Through the "borrowing" and "returning" operations of slice power, dynamic sharing of power resources between power supply slices of different devices is realized. This coordination management mechanism breaks down the fixed resource barriers between power supply slices, making the entire power supply system a flexible resource pool that can be quickly scheduled and rebalanced according to instantaneous power demand, greatly improving the flexibility, response speed and overall stability of the power supply system. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of an intelligent power supply management method compatible with multiple communication devices provided in the first embodiment of the present invention; Figure 2 This is a schematic diagram of a smart power supply management system compatible with multiple communication devices provided in the second embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Reference Figure 1 The first embodiment of the present invention provides an intelligent power supply management method compatible with multiple communication devices, comprising the following steps: S11, acquire the power characteristic data of the device, classify and identify it to obtain the device operation type data; S12, Based on the device operation type data, perform power supply slicing scheme matching to obtain an initial power supply slicing configuration scheme; S13, real-time acquisition of time series data of each device in the initial power supply slice configuration scheme, and statistical analysis to obtain statistical characteristic curves. If the value range of the statistical characteristic curves exceeds the preset statistical characteristic threshold, the power allocation weight is adjusted to obtain new power allocation weight coefficients. S14, optimize the new power allocation weight coefficients to obtain the optimal power allocation strategy; S15, According to the optimal power allocation strategy, the power upper limit and priority of the initial power supply slice configuration scheme are adjusted to obtain the adjusted power supply slice configuration scheme; S16, coordinate and manage the adjusted power supply slice configuration scheme to obtain intelligent power supply management and control instructions.

[0022] In step S11, the power characteristic data of the device is acquired and classified to obtain device type data, including: The device identification, basic power consumption value, peak power range and power consumption cycle mode of the device are collected in real time, and the data are integrated to obtain an initial dataset; The basic power consumption values, peak power range, and power consumption cycle patterns in the initial dataset are normalized and integrated to obtain power characteristic data. The power characteristic data is clustered and grouped to obtain the device clustering results; By combining the preset device type mapping table, the device clustering results are matched with device types to obtain device operation type data.

[0023] It should be noted that by deploying sensors on communication devices, key parameters of each device during operation can be collected in real time, mainly including the device's unique identifier, basic power consumption, peak power range, and power consumption cycle pattern. This multi-dimensional, real-time collected data is structured and integrated to form an initial dataset, stored in tabular form. Each row represents a complete record of a device, and the columns of the table include device identifier, basic power consumption, peak power, and power consumption cycle parameters. This provides a clear and well-organized data foundation for subsequent feature extraction and data analysis.

[0024] For example, taking a base station, router, and switch in a communication system as an example, sensors can be configured to record the operating power of each device once per second. For instance, for "base station A," the sensor would record its device identifier, a base power consumption of approximately 50W, a peak power consumption of up to 200W, and the power consumption cycle during 24 hours of continuous operation. These data points, along with timestamps, are integrated into a structured dataset. In this dataset, each row contains complete power information for a device, such as device identifier, base power consumption, peak power consumption, and power consumption cycle. This tabular storage method ensures data clarity and traceability, greatly improving the efficiency of subsequent data processing and feature extraction.

[0025] It should be noted that after obtaining the initial dataset, in order to eliminate the influence of different physical dimensions on the subsequent algorithm model, the core dimension parameters in the dataset—namely, the basic power consumption value, peak power range, and power consumption cycle pattern—need to be normalized. The max-min normalization algorithm is used to map these data with different ranges and units into a unified, comparable interval, such as [0, 1]. After processing, the three normalized dimension parameters of each device are integrated to form a multi-dimensional power feature vector. This feature vector is the power feature data, which concisely describes the core power consumption characteristics of each device in a standardized form, providing high-quality input for subsequent cluster analysis.

[0026] For example, suppose there are two devices: base station A has a base power consumption of 50W, a peak power of 200W, and a power cycle of 24 hours; while base station B has a base power consumption of 60W, a peak power of 180W, and a power cycle of 12 hours. When normalizing the "base power consumption" dimension, if the minimum value in the sample is 50W and the maximum value is 60W, then a min-max normalization method is used, converting the value of this item for base station A to 0 and for base station B to 1. Similarly, peak power and power cycle are also normalized. Ultimately, the power characteristic data of base station A may be represented as a vector, such as [0, 1, 1]. This processing method eliminates the difference in dimensions, allowing the power characteristics of different devices to be directly compared mathematically, thereby significantly improving the accuracy of subsequent clustering algorithms.

[0027] It should be noted that after generating standardized power characteristic data, a clustering algorithm is used to group this data. The purpose of clustering is to automatically group devices with similar power consumption patterns into one category. In this embodiment, the K-Means clustering algorithm is used, which measures the similarity between the power feature vectors of each device based on the Euclidean distance between them. The algorithm iteratively groups points that are close in distance in the feature vector space into the same cluster. Each cluster formed is a device clustering result, representing a group of devices with similar characteristics in terms of basic power consumption, peak power, and power consumption cycle, thereby achieving automatic device classification.

[0028] For example, based on the normalized feature vector generated in the aforementioned steps, such as the vector of base station A being [0, 1, 1], the system initiates the K-means clustering algorithm. Assume the number of clusters, K=2, is preset according to business requirements. To illustrate the calculation process, two other devices are introduced: base station C, with a feature vector of [0.1, 0.9, 1]; and router D, with a feature vector of [0.4, 0.2, 0.5]. Since the feature vectors of base station A and base station C are very close in all dimensions, their Euclidean distance is very small. In contrast, the vector values ​​of base station A and router D are very different, and their Euclidean distance is much larger. Therefore, during the clustering process, the algorithm determines that base station A and base station C have high similarity and assigns them to the same cluster. This clustering result intuitively reflects the similarity of the device operating modes, providing a basis for subsequent differentiated management and resource allocation.

[0029] It's important to note that the device clustering results generated by the clustering algorithm are simply collections of data points. To assign practical business meaning to these collections, device type matching and identification are necessary. This step first requires analyzing the macroscopic characteristics of each cluster result, such as calculating the mean and variance of the feature vectors of all devices in the cluster. Then, these statistical characteristics are compared with a pre-established device type mapping table. This mapping table defines the typical feature vector patterns corresponding to different device types (such as continuous stable, intermittent, and peak burst types). Through matching, each cluster result can be clearly labeled with its device type, ultimately yielding the device operation type data.

[0030] For example, suppose a device group is obtained through cluster analysis, and the mean of the feature vector of the devices in this group is calculated to be [0.2, 0.8, 0.9]. Simultaneously, to measure the consistency of device characteristics within this group, its variance is calculated, resulting in a variance vector composed of the variance values ​​of each dimension, for example, [0.01, 0.02, 0.01]. This variance value is much lower than a preset stability threshold (e.g., 0.1), indicating that the devices in this group have high consistency and stability in terms of basic power consumption, peak power, and power consumption cycle. This suggests that the devices in this group have the characteristics of low basic power consumption, high peak power, and stable power consumption cycle. At this point, the system will query a preset device type mapping table, which defines a rule: "If the mean of the feature vector of a cluster is lower than 0.3 in the first dimension, higher than 0.7 in the second dimension, and higher than 0.8 in the third dimension, and the variance is lower than the threshold, then this class corresponds to 'continuously stable' devices." Through matching this rule, the system can automatically identify the cluster as a "continuously stable" device type. This mapping table-based automatic classification method reduces manual intervention, significantly improves the automation and intelligence level of equipment management, and can provide accurate input for subsequent energy consumption optimization and fault prediction.

[0031] In step S12, based on the device operation type data, a power supply slicing scheme is matched to obtain an initial power supply slicing configuration scheme, including: Based on the device operation type data, perform operation type matching to obtain the corresponding type of slice template library; Based on the corresponding type of slice template library, the power value is matched with the device operation type data to obtain the precise power supply slice parameters; The power supply slice parameters for all types of precise power are combined to obtain an initial power supply slice configuration scheme.

[0032] It's important to note that after obtaining the operating type data for each device through cluster analysis and type matching, the system executes differentiated power supply strategy matching based on this data. The core of this step is matching the most suitable power supply slice template library for each device type. The system pre-establishes multiple slice template libraries, each corresponding to a different device operating mode. Each template library is stored in a database table, with fields defining the template ID, base power, peak power, and priority. For example, for devices identified as "continuously stable," the system uses the constant power slice template library; for "intermittently operating" devices, it matches the elastic power slice template library; and for "peak burst" devices, it retrieves the dynamically expanding slice template library. This precise type matching ensures that subsequent power supply strategies are tailored to the inherent power consumption characteristics of the devices from the outset.

[0033] For example, in an intelligent monitoring system, there is a server that needs to run continuously, a sensor that periodically uploads data, and a camera that activates an infrared illuminator when a moving object is detected. The server is identified as "continuously stable," and the system then matches it with a "constant power slice template library." The sensor is identified as "intermittently operating," and is matched with a "flexible power slice template library." The camera, due to its sudden power spikes, is identified as "peak burst type," and is thus matched with a "dynamically expanding slice template library."

[0034] It should be noted that after selecting the corresponding slice template library, the system further utilizes the specific power data of the device for refined parameter matching to generate power supply slice parameters tailored to that device. A set of power supply slice parameters mainly includes a power upper limit and priority parameters. This process varies depending on the device type: for continuously stable devices, the system uses a static configuration method with a fixed power upper limit and corresponding priority. For intermittently operating devices, the system configures a variable power upper limit based on the device's operation and sleep cycles. For peak-burst devices, the system continuously monitors the device's real-time power consumption. Once a sharp increase in power demand is detected and exceeds a preset peak threshold (set based on five times the device's average power consumption), the system immediately dynamically expands the power upper limit and increases the scheduling priority to ensure its burst demand. Ultimately, a precise set of power supply slice parameters is generated for each device.

[0035] For example, a communication server is classified as "continuously stable," with a stable power requirement of 300W. The system retrieves a fixed power allocation scheme of 300W from the constant power slice template library and generates a set of power supply slice parameters for it. The upper limit of power is statically configured to 300W, and a fixed scheduling priority is assigned based on its business importance. An edge computing node is classified as "intermittently operating," running for 30 minutes every 2 hours with a peak power of 150W. The slice parameters generated by the system from the elastic power slice template library based on this cycle will include a dynamic upper limit power scheme: the upper limit is adjusted to 150W during operation and reduced to 20W during sleep. An IoT gateway is classified as "peak burst type," with a normal power consumption of 100W, but potentially reaching 1000W during burst data transmission. The system monitors its power consumption in real time. When it detects that the power value rapidly increases from 100W to exceed the peak threshold (such as 500W) in a short period of time, the system will respond immediately and generate a high-priority parameter scheme for it: its power limit is dynamically expanded to 1000W, and its scheduling priority is also improved to ensure that its peak power supply demand can be met first when resources are scarce.

[0036] In step S13, time-series data of each device in the initial power supply slice configuration scheme are collected in real time and statistically analyzed to obtain a statistical characteristic curve. If the value range of the statistical characteristic curve exceeds a preset statistical characteristic threshold, the power allocation weight is adjusted to obtain a new power allocation weight coefficient, including: The power consumption data stream, voltage fluctuation data stream, and load change data stream of each device in the initial power supply slice configuration scheme are collected in real time and integrated to obtain time series data; For the time series data, the mean and variance are calculated using a sliding window algorithm to obtain the statistical characteristic curve; If the range of the statistical characteristic curve exceeds the preset statistical characteristic range threshold, the device is marked as abnormal, and a list of abnormal devices is obtained. Obtain the actual power requirements of the list of abnormal devices; The power allocation weight coefficients of the abnormal device list are adjusted using a linear programming algorithm based on the actual power demand to obtain new power allocation weight coefficients.

[0037] It should be noted that this step is achieved through high-precision sensors deployed at the power input terminals of each device. These sensors continuously collect three key operating parameters of the device at a preset sampling frequency, forming continuous data streams of power consumption, voltage fluctuation, and load change. These data are then integrated and stored as time-series data with timestamps. This approach ensures data integrity and real-time performance, providing a reliable data foundation for subsequent dynamic analysis and status assessment.

[0038] For example, in an Industrial Internet of Things (IIoT) scenario, to monitor a critical piece of equipment, a sensor is set to collect data 10 times per second. This means that the sensor will record 10 power values ​​(in watts), 10 voltage values ​​(in volts), and 10 load percentages per second. This frequently collected data is stored in a time-series format.

[0039] It should be noted that, in order to extract meaningful trends from massive amounts of time-series data and determine the operating status of equipment, the system employs a sliding window algorithm for statistical analysis. This algorithm aggregates power consumption data within a preset time window, calculating its mean and variance. As the time window slides forward across the entire dataset with a preset step size, the system successively calculates the statistical characteristics of each window. Connecting these continuous statistical values ​​forms a statistical characteristic curve that reflects the long-term operating trend of the equipment.

[0040] For example, the system processes a sequence containing one hour of device operation data. The sliding window size is set to 10 minutes, with a step size of 1 minute. The algorithm first calculates the average power and standard deviation of the first 10 minutes of data (e.g., 6000 data points). Then, the window slides forward 1 minute, calculating the statistical characteristics of the data from the 2nd to the 11th minute. This process is repeated until the window has traversed the entire one-hour dataset. This yields a series of continuously varying average power and variance values. Plotting these values ​​chronologically creates the statistical characteristic curve of the device within that hour, smoothing out short-term noise and clearly showing the macroscopic trend of the device's operation.

[0041] It should be noted that the system presets statistical characteristic range thresholds for each device type, including the mean range and variance range. The system compares the generated statistical characteristic curve with these preset thresholds in real time. Once any value on the curve, whether the mean or variance, exceeds the normal range, the system determines that the device has experienced an operational anomaly at the corresponding time point. At this time, the system immediately marks the device as an anomaly, records its unique identifier, the timestamp of the anomaly, and the specific parameters exceeding the limits, and adds it to a dynamically updated list of anomaly devices.

[0042] For example, suppose the normal operating threshold for a device is set as follows: average power range of 450-550W, with a variance upper limit of 30 watts. At a certain moment, the system calculates using a sliding window that the device's average power is 580 watts and the variance is 40 watts. Since both values ​​exceed the preset threshold range, the system immediately triggers the anomaly detection mechanism, marking the device (e.g., ID "DEV-001") as abnormal and adding it to the list of abnormal devices requiring immediate handling.

[0043] It's important to note that after generating a list of anomalous devices containing one or more units, the system must first obtain the actual power requirements of these anomalous devices for accurate resource reallocation. The system directly queries the sensors deployed on these anomalous devices. The sensors return the instantaneous power consumption of the device at or after it was marked as anomalous. This real-time power demand data is a crucial input for subsequent weight adjustments in the linear programming algorithm, ensuring that resource allocation decisions are based on the latest and most accurate needs of the devices.

[0044] For example, suppose the list of abnormal devices includes device A, device B, and device C. The system will immediately send data request commands to the controllers or sensors of these three devices. The data fed back by the sensors shows that device A's current actual power demand is 130 kW, device B's is 150 kW, and device C's is 110 kW. The system then obtains this accurate, real-time power demand data, preparing for the next step of calculation.

[0045] It should be noted that this step is the core of dynamic load balancing. The system takes the actual power demand of the abnormal device list as input, starts a linear programming algorithm, uses the total power capacity of the entire power supply system as a constraint, and allocates power according to priority, where priority refers to the priority parameter in the initial power supply slice configuration scheme. The specific implementation steps are as follows: First, set decision variables, assigning weight coefficients to the power allocated to each abnormal device. Then, establish an objective function to maximize the system's "benefits" while satisfying constraints, i.e., prioritizing the power demand of high-priority devices. Set the constraint as the total power capacity of the entire power supply system. Finally, input the above objective function and constraints into the linear programming solver. Through optimization calculations, a new power allocation weight coefficient is determined for each device in the list. This new coefficient reflects the degree to which the system tilts power resources towards that device under the current abnormal state.

[0046] For example, suppose the total power capacity of the system is 1000 kW, and the real-time demands of abnormal devices A, B, and C are 130, 150, and 110 kW, respectively, while the total demand of other normal devices in the system is 650 kW. The priority order is A > C > B, and the priority weights assigned to A, B, and C are 3, 1, and 2, respectively. A linear programming algorithm, under the constraint of a total capacity of 1000 kW, might output the following new power allocation weight coefficients: assigning a weight of 1.0 to device A, a weight of 0.73 to device B, and a weight of 1.0 to device C, while maintaining the weight coefficient of normal devices at 1.0. In this way, the algorithm optimizes the power allocation of abnormal devices while ensuring system safety.

[0047] In step S14, the new power allocation weight coefficients are optimized to obtain the optimal power allocation strategy, including: The new power allocation weighting coefficients are adjusted to obtain the optimal power allocation weighting coefficients; Based on the optimal power allocation weight coefficient, the equipment operating parameters are updated to obtain the optimal power allocation strategy.

[0048] It should be noted that while the new power allocation weight coefficients calculated based on priorities in the previous stage ensured the needs of critical equipment and system safety, they may not be the globally optimal solution for overall system efficiency. Therefore, this step introduces the NSGA-II algorithm to refine the initial allocation scheme. The core of this algorithm is to simultaneously optimize two interrelated objectives: first, maximizing power utilization, i.e., ensuring the allocated power closely matches the actual needs of the equipment to reduce energy waste; and second, optimizing load balance, i.e., avoiding situations where some equipment is fully loaded while others are underloaded, striving to balance the load rate or demand satisfaction (i.e., weight coefficients) of each equipment, thereby improving the stability and health of the entire system. The specific implementation steps of the NSGA-II algorithm are as follows: the initial allocation scheme is used as the "parent generation," and an initial "population" containing multiple possible allocation schemes is generated through slight perturbations. By simulating crossover, mutation, and selection operations in biological evolution, new "offspring" allocation schemes are continuously generated iteratively. In each generation, the algorithm evaluates and ranks all solutions based on the two objectives mentioned above (total power and variance), selecting the "non-dominated solution"—that is, no other solution can optimize one objective without sacrificing the other. Ultimately, the algorithm's output is not a single solution, but a "Pareto Optimal Front." This front contains all the optimal "compromise" solutions: some have extremely high power utilization but slightly poor balance, while others have excellent balance but sacrifice a small amount of total power. The system can select the most suitable solution from this front based on a preset final decision preference (e.g., prioritizing stability or efficiency), and the corresponding weight coefficient is the final optimal power allocation weight coefficient.

[0049] For example, suppose that after initial allocation, there are three abnormal devices A, B, and C with the same priority, with demands of 100kW, 100kW, and 100kW respectively. The initial allocation scheme is: A is allocated 100kW (weight 1.0), B is allocated 100kW (weight 1.0), and C is allocated only 50kW (weight 0.5) due to insufficient resources. The initial scheme has a total power of 100 + 100 + 50 = 250kW, a load balance of (1.0, 1.0, 0.5) weight coefficients, a large variance, and a highly unbalanced system load. At this point, the NSGA-II algorithm is activated, aiming to "maximize the reduction of variance while maintaining the total power as much as possible." After iterative optimization, the algorithm may select a new scheme from the Pareto front: adjusting the allocation of A to 90kW (weight 0.9), the allocation of B to 90kW (weight 0.9), and the allocation of C to 70kW (weight 0.7). The total power utilization remains unchanged, and the new weighting coefficients are (0.9, 0.9, 0.7), whose variance is much smaller than that of the initial solution (1.0, 1.0, 0.5). Ultimately, the system sacrifices some satisfaction of devices A and B, significantly improving the satisfaction of device C, thus greatly reducing the satisfaction gap among the three. Although this solution is not perfect for A and B, it is a better global configuration scheme for the stability and health of the entire system. This new set of weighting coefficients (0.9, 0.9, 0.7) is the final output.

[0050] It should be noted that the optimal power allocation weighting coefficients are merely a set of ideal parameters within the system. To translate them into a practical optimal power allocation strategy, these parameters must be applied to the physical devices. The specific implementation process involves determining the optimal weighting coefficients and converting them into specific, executable controller instructions for each device. Examples include setting new power limits, adjusting inverter output frequencies, or changing load switching states. These instructions are then sent to the front-end controllers of each device via standard industrial communication protocols, updating their operating parameters in real time, thereby ensuring the optimized allocation strategy takes effect at the physical level.

[0051] For example, the system determines that the optimal power allocation for device X is 124.2 kW (weight 0.92). The resource scheduling module immediately generates a controller instruction, such as an MQTT message stating "Set the power limit of target device ID 'X' to 124.2 kW". This instruction is sent to the power controller of device X via the industrial network. After receiving and parsing the instruction, the controller immediately adjusts its internal control logic to limit the device's operating power to within 124.2 kW.

[0052] In step S15, according to the optimal power allocation strategy, the power upper limit and priority of the initial power supply slice configuration scheme are adjusted to obtain the adjusted power supply slice configuration scheme, including: Based on the optimal power allocation strategy, obtain the current power demand data of each device; If the current power demand data exceeds the preset power quota threshold, the upper limit of the threshold of the initial power supply slice configuration scheme is adjusted to obtain the first power quota threshold. Based on the first power quota threshold, the scheduling priority is adjusted to obtain the first priority scheduling sequence; Based on the first priority scheduling sequence, the configuration parameters of the initial power supply slice configuration scheme are modified to obtain the adjusted power supply slice configuration scheme.

[0053] It's important to note that this step is fundamental to dynamic resource allocation. After executing the optimal power allocation strategy, the system immediately and continuously acquires real-time power demand data from each device via sensors. This uninterrupted data acquisition forms a closed-loop feedback mechanism, ensuring that the system always makes judgments and adjustments based on the latest and most accurate device status. This allows for rapid response to any new changes and avoids decision-making delays or resource misallocation caused by outdated data.

[0054] For example, in a data center's power management system, even after completing one round of power optimization allocation, the system will immediately begin a new round of data collection. For instance, it will acquire real-time power readings through sensors deployed on server racks, cooling units, and network switches, such as detecting that a server's current power requirement is 500W, while the cooling system's requirement is 2500W.

[0055] It should be noted that the system compares the current power demand data of each device with the preset power quota threshold for that device. This threshold represents the upper limit of the power for normal operation of the device. When the actual demand of a device exceeds this threshold, it indicates that the device is under high load or critical task status and requires more power resources. To this end, the system uses a preset, inherent adjustment coefficient for that device (such as the ratio of the maximum instantaneous power provided by the device manufacturer to the recommended stable operating power) to dynamically increase the power quota of its corresponding power supply slice. That is, the new power quota = power quota * adjustment coefficient, generating a higher first power quota threshold.

[0056] For example, suppose a data center server has a preset power quota threshold of 450W. Real-time data collected by the system shows that its current demand has climbed to 500W, exceeding the preset threshold. The system determines that the server is handling a high-load task and therefore immediately adjusts the configuration of its power slice with a preset adjustment factor of 1.25, increasing its power quota limit from the original 800W to 1000W. This new 1000W limit is the adjusted first power quota threshold.

[0057] It should be noted that while increasing power quotas, the system also simultaneously adjusts the scheduling priority parameters of the device. A device with excessive power demand is generally considered more important at the current moment, and therefore its priority will be increased by one level accordingly. After updating the priorities of one or more devices, the system immediately applies a hybrid weighted priority sorting algorithm to all power supply slices, generating a new, ordered scheduling list. This list is the first-priority scheduling sequence, which defines the order in which each power supply slice receives power supply when resources are scarce.

[0058] For example, when the aforementioned server's power quota is increased due to exceeding demand limits, its priority is also adjusted from "normal" to "high". Assume the data center has three power supply slices with the following priority and weight configurations: Server cluster: priority increased from "normal" to "high" (base score 1000), business weight 50; Cooling system: maintains "normal" priority (base score 500), but due to its criticality to the overall environment, its business weight is set to a higher 80; Network device: maintains "normal" priority (base score 500), business weight 30. The algorithm calculates the scheduling score for each slice: Server cluster score is 1000 + 50 = 1050, Cooling system score is 500 + 80 = 580, Network device score is 500 + 30 = 530. Based on the scheduling scores sorted from high to low, the system generates a new first-priority scheduling sequence: "1. Server cluster (1050 points) -> 2. Cooling system (580 points) -> 3. Network device (530 points)". This sequence clearly reflects the algorithm's logic: the server cluster takes the lead due to its "high" priority; among devices with "normal" priority, the cooling system, which has a higher weight, takes precedence over network devices. This ensures that, in any power shortage situation, the server cluster's power supply will receive the highest priority guarantee.

[0059] It should be noted that this step is the final execution stage of the dynamic adjustment. The system uses all the new power cap values ​​and updated priority scheduling sequences generated in the preceding steps to modify the core power supply slice configuration scheme of the system. Specifically, this involves updating the two key configurations—the power cap value and priority parameters—for each power supply slice in the system's central configuration library. After this series of modifications is completed, a complete and effective "adjusted power supply slice configuration scheme" is formed, which will serve as the latest guiding strategy for the current operation of the power system.

[0060] For example, after the above adjustments are made, the power supply slice configuration scheme in the system is updated. Specifically, the configuration parameters for the "Server Cluster" slice are modified to: power limit 5000W, priority parameter 1 (highest). The parameters for the "Cooling System" slice may be: power limit 3000W, priority 2. The parameters for the "Network Device" slice are: power limit 2000W, priority 3. This set of updated configuration schemes, containing the latest parameters for all slices, constitutes the final adjusted power supply slice configuration scheme.

[0061] In step S16, the adjusted power supply slice configuration scheme is coordinated and managed to obtain intelligent power supply management control commands, including: Real-time monitoring of the instantaneous power consumption of the equipment; If the instantaneous power consumption exceeds the power quota threshold in the adjusted power supply slice configuration scheme, a power borrowing operation is performed to obtain the source slice dataset of the borrowed power. For the source slice dataset of the borrowed power, power supply slice coordination is performed, and power return operations are determined to obtain a dynamic power allocation scheme. The device responds in real time to the dynamic power allocation scheme to obtain intelligent power supply management and control commands.

[0062] It should be noted that when the system detects that the instantaneous power consumption of a device exceeds its allocated power quota threshold, it will determine that the device is a high-demand device and immediately initiate an inter-slice power scheduling algorithm to perform a power borrowing operation. The specific implementation steps are as follows: Analyze the operating status of other devices in the system, identify devices with current power surplus (i.e., instantaneous power consumption is lower than the quota threshold, where surplus = quota threshold - instantaneous power consumption), and designate these devices as potential power sources. Based on a preset strategy (e.g., prioritizing borrowing from the slice with the largest surplus), sort the slices according to the surplus size, determine the source slices for borrowing and the specific amount of power, forming a dataset of source slices for borrowed power.

[0063] For example, because server A's power consumption of 1200W exceeds its 1000W quota threshold, the system triggers a power borrowing operation. The scheduling algorithm detects that server B has a power surplus of 200W (1000W-800W) and server C has a power surplus of 400W (1000W-600W). To meet server A's excess 200W demand, the algorithm can decide to borrow 200W from the power supply slice of server C, which has the largest surplus. At this time, the source slice dataset contains a record of "Source: Power supply slice of server C, Quantity: 200W".

[0064] It should be noted that after determining the power borrowing scheme, the system will execute the actual power allocation through power supply slice coordination operations and establish corresponding power return rules. The specific implementation steps of the power borrowing coordination operation are as follows: When the real-time power demand of device A exceeds its current power limit, the system immediately scans all other power supply slices in the system (such as devices B and C) except for device A, and calculates the current "available redundant power" of each slice, that is, the difference between its "power limit" and "real-time power consumption". According to a preset strategy (e.g., prioritizing borrowing from the slice with the largest redundant power or the lowest priority), one or more source slices are selected. For example, if the algorithm finds that device C has the highest redundant power of 400W, it is determined to be the source slice. The system records a borrowing transaction in the log, including: {Borrower: A, Source: C, Borrowed Amount: 400W, Timestamp: ...}. The system generates intelligent control commands and issues them to the power management units of the relevant devices through the device control module.

[0065] It should be noted that the system continuously monitors the status of device A. When it detects that device A's power consumption has fallen back below its original quota threshold (1000W) for a period of time (e.g., 1 minute, to prevent power fluctuations), it automatically triggers a power return operation. The specific implementation steps of the power return coordination operation are as follows: The system queries the log to find the incomplete transaction of device A as the "borrower" and determines that it needs to return 400W of power to device C. The system issues a command to device A: "Restore original power limit" and an instruction to device C: "Unfreeze 400W power and restore original power limit". The returned 400W of power is returned to the system's shared resource pool. The system updates the borrowing transaction log, marking it as "returned", completing the closed-loop operation of this borrowing and return.

[0066] For example, after server A borrows power and passes its peak business period, its instantaneous power consumption drops to 900W, below the 1000W quota threshold. The system then triggers a power return operation, requiring server A to "return" the previously borrowed power. The coordination mechanism can redistribute this returned power to server C, restoring its original power quota, or, based on the overall system load, allocate this power to other devices as needed, for example, allocating 200W each to B and C in a 1:1 ratio. This entire cycle of borrowing and returning constitutes a dynamic power allocation scheme.

[0067] It's important to note that this step is crucial in translating the logical-level dynamic power allocation scheme into actionable actions by physical devices. The system translates high-level scheduling decisions (such as "borrow 200W from C to A") into specific, low-level intelligent power management control commands. These commands are then sent to the corresponding control modules or power management units of each device to complete real-time adjustments to power sharing between devices, ensuring that the allocation scheme can be executed quickly and accurately.

[0068] For example, when the system decides to perform power borrowing, the intelligent control command it generates might be a specific voltage adjustment command or a power quota modification command. For instance, the system might issue a command to the power management unit of server A, allowing it to temporarily increase its power limit to 1200W; simultaneously, it might issue a command to the power management unit of server C, temporarily lowering its power limit or allowing it to output power to the shared grid. In this way, the logical power allocation scheme is ensured to be responded to by the devices in real time, thereby reducing the delay of manual intervention and improving the overall operating efficiency and stability of the system.

[0069] In summary, this invention constructs a full-process intelligent power supply solution that integrates data-driven device identification with dynamic optimization based on real-time status. This solution addresses the technical problems of existing technologies, such as coarse power supply strategies that fail to accommodate differentiated needs and dynamic changes. It significantly improves the precision, response speed, and resource utilization efficiency of power supply management, ensuring the stability and efficiency of power supply for various types of communication equipment.

[0070] Reference Figure 2 The second embodiment of the present invention provides an intelligent power supply management system compatible with multiple communication devices, comprising: The feature recognition module is used to acquire the power feature data of the device, classify and identify it, and obtain the device operation type data. The scheme matching module is used to perform power supply slicing scheme matching based on the device operation type data to obtain an initial power supply slicing configuration scheme; The weight adjustment module is used to collect time series data of each device in the initial power supply slice configuration scheme in real time, perform statistical analysis, and obtain statistical characteristic curves. If the value range of the statistical characteristic curve exceeds the preset statistical characteristic threshold, the power allocation weight is adjusted to obtain a new power allocation weight coefficient. The strategy optimization module is used to optimize the new power allocation weight coefficients to obtain the optimal power allocation strategy. The configuration update module is used to adjust the power upper limit and priority of the initial power supply slice configuration scheme according to the optimal power allocation strategy, so as to obtain the adjusted power supply slice configuration scheme. The coordination and management module is used to coordinate and manage the adjusted power supply slice configuration scheme and obtain intelligent power supply management and control commands.

[0071] It should be noted that the intelligent power supply management system compatible with multiple communication devices provided in this embodiment of the invention is used to execute all the process steps of the intelligent power supply management method compatible with multiple communication devices in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0072] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a smart power management program compatible with multiple communication devices. When the processor executes the computer program, it implements the steps in the various embodiments of the smart power management method compatible with multiple communication devices described above, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the feature recognition module.

[0073] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0074] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0075] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0076] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0077] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0078] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0079] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A smart power supply management method compatible with multiple communication devices, characterized in that, Executed by a computer, including: Acquire the power characteristic data of the equipment, classify and identify it to obtain the equipment operation type data; Based on the device operation type data, a power supply slicing scheme is matched to obtain an initial power supply slicing configuration scheme; The time series data of each device in the initial power supply slice configuration scheme are collected in real time and statistically analyzed to obtain a statistical characteristic curve. If the value range of the statistical characteristic curve exceeds the preset statistical characteristic threshold, the power allocation weight is adjusted to obtain a new power allocation weight coefficient. The new power allocation weight coefficients are optimized to obtain the optimal power allocation strategy; Based on the optimal power allocation strategy, the power upper limit and priority of the initial power supply slice configuration scheme are adjusted to obtain the adjusted power supply slice configuration scheme. The adjusted power supply slice configuration scheme is coordinated and managed to obtain intelligent power supply management and control commands.

2. The intelligent power supply management method compatible with multiple communication devices according to claim 1, characterized in that, The process of acquiring power characteristic data of the device and classifying and identifying it to obtain device type data includes: The device identification, basic power consumption value, peak power range and power consumption cycle mode of the device are collected in real time, and the data are integrated to obtain an initial dataset; The basic power consumption values, peak power range, and power consumption cycle patterns in the initial dataset are normalized and integrated to obtain power characteristic data. The power characteristic data is clustered and grouped to obtain the device clustering results; By combining the preset device type mapping table, the device clustering results are matched with device types to obtain device operation type data.

3. The intelligent power supply management method compatible with multiple communication devices according to claim 1, characterized in that, The step of matching power supply slicing schemes based on the device operation type data to obtain an initial power supply slicing configuration scheme includes: Based on the device operation type data, perform operation type matching to obtain the corresponding type of slice template library; Based on the corresponding type of slice template library, the power value is matched with the device operation type data to obtain the precise power supply slice parameters; The power supply slice parameters for all types of precise power are combined to obtain an initial power supply slice configuration scheme.

4. The intelligent power supply management method compatible with multiple communication devices according to claim 1, characterized in that, The system collects time-series data of each device in the initial power supply slice configuration scheme in real time, performs statistical analysis, and obtains a statistical characteristic curve. If the value range of the statistical characteristic curve exceeds a preset statistical characteristic threshold, the power allocation weight is adjusted to obtain a new power allocation weight coefficient, including: The power consumption data stream, voltage fluctuation data stream, and load change data stream of each device in the initial power supply slice configuration scheme are collected in real time and integrated to obtain time series data; For the time series data, the mean and variance are calculated using a sliding window algorithm to obtain the statistical characteristic curve; If the range of the statistical characteristic curve exceeds the preset statistical characteristic range threshold, the device is marked as abnormal, and a list of abnormal devices is obtained. Obtain the actual power requirements of the list of abnormal devices; The power allocation weight coefficients of the abnormal device list are adjusted using a linear programming algorithm based on the actual power demand to obtain new power allocation weight coefficients.

5. The intelligent power supply management method compatible with multiple communication devices according to claim 1, characterized in that, The optimization of the new power allocation weight coefficients to obtain the optimal power allocation strategy includes: The new power allocation weighting coefficients are adjusted to obtain the optimal power allocation weighting coefficients; Based on the optimal power allocation weight coefficient, the equipment operating parameters are updated to obtain the optimal power allocation strategy.

6. The intelligent power supply management method compatible with multiple communication devices according to claim 1, characterized in that, The step of adjusting the power upper limit and priority of the initial power supply slice configuration scheme according to the optimal power allocation strategy to obtain the adjusted power supply slice configuration scheme includes: Based on the optimal power allocation strategy, obtain the current power demand data of each device; If the current power demand data exceeds the preset power quota threshold, the upper limit of the threshold of the initial power supply slice configuration scheme is adjusted to obtain the first power quota threshold. Based on the first power quota threshold, the scheduling priority is adjusted to obtain the first priority scheduling sequence; Based on the first priority scheduling sequence, the configuration parameters of the initial power supply slice configuration scheme are modified to obtain the adjusted power supply slice configuration scheme.

7. The intelligent power supply management method compatible with multiple communication devices according to claim 1, characterized in that, The coordinated management of the adjusted power supply slice configuration scheme to obtain intelligent power supply management and control commands includes: Real-time monitoring of the instantaneous power consumption of the equipment; If the instantaneous power consumption exceeds the power quota threshold in the adjusted power supply slice configuration scheme, a power borrowing operation is performed to obtain the source slice dataset of the borrowed power. For the source slice dataset of the borrowed power, power supply slice coordination is performed, and power return operations are determined to obtain a dynamic power allocation scheme. The device responds in real time to the dynamic power allocation scheme to obtain intelligent power supply management and control commands.

8. A smart power supply management system compatible with multiple communication devices, characterized in that, include: The feature recognition module is used to acquire the power feature data of the device, classify and identify it, and obtain the device operation type data. The scheme matching module is used to perform power supply slicing scheme matching based on the device operation type data to obtain an initial power supply slicing configuration scheme; The weight adjustment module is used to collect time series data of each device in the initial power supply slice configuration scheme in real time, perform statistical analysis, and obtain statistical characteristic curves. If the value range of the statistical characteristic curve exceeds the preset statistical characteristic threshold, the power allocation weight is adjusted to obtain a new power allocation weight coefficient. The strategy optimization module is used to optimize the new power allocation weight coefficients to obtain the optimal power allocation strategy. The configuration update module is used to adjust the power upper limit and priority of the initial power supply slice configuration scheme according to the optimal power allocation strategy, so as to obtain the adjusted power supply slice configuration scheme. The coordination and management module is used to coordinate and manage the adjusted power supply slice configuration scheme and obtain intelligent power supply management and control commands.

9. An electronic device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements a smart power management method compatible with multiple communication devices as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform a smart power management method compatible with multiple communication devices as described in any one of claims 1 to 7.

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