Integrated Management Methods and Systems for Base Station Power Consumption

Abstract

This invention discloses a comprehensive management method and system for base station power consumption, belonging to the field of base station power consumption. The method includes: topological mapping of the access relationships of AC / DC equipment within the base station to form a hierarchical power consumption association list; dynamic clustering parsing of the list to generate a time-series evolution chain reflecting state changes and remote control history; constructing a device differentiation identification model based on the time-series evolution chain, determining abnormal metering factors and outputting candidate correction events; labeling the corresponding areas of the candidate events as local anomalies, and generating a correction reference group by fusing multi-source information such as meter readings and sensor data; dynamically iteratively correcting the global metering results based on the correction reference group, and generating intelligent management instructions by combining the corrected load sharing ratio and branch authorization strategy. The system includes corresponding functional modules. This invention can systematically solve the problems of unclear base station power consumption topology, inaccurate metering, and extensive management, achieving high-precision metering and automated intelligent control.

Description

Technical Field

[0001] This application pertains to the field of base station power consumption, and specifically relates to a comprehensive management method and system for base station power consumption. Background Technology

[0002] Currently, the power supply system of communication base stations needs to provide power to various devices, including core equipment of operators and extended service equipment such as surveillance cameras. These devices are installed in a variety of complex locations, distributed in different scenarios such as indoors, outdoors, on towers, and at the base of towers, and their power supply methods include both AC and DC. In the actual construction and operation process, due to the different times when the equipment is connected to the network and the different levels of construction standards, there are often problems such as messy wiring and even misaligned connections, making it difficult to clarify the actual topology of the power supply lines.

[0003] For metering the electricity consumption of equipment, existing technologies typically target DC loads by installing Hall coil-type branch metering devices on the power lines and transmitting data back through the base station's environmental monitoring system for electricity allocation. For AC loads, the methods used are to estimate based on the rated power of the equipment or to install electricity meters. Among these, the installed electricity meters are divided into traditional meters that require manual periodic readings and smart meters that can remotely transmit data.

[0004] In the aforementioned existing technical solutions, the DC-side Hall coil metering method is susceptible to interference from the complex electromagnetic environment on site. Furthermore, after equipment additions or removals or line modifications, it is often impossible to adjust or reinstall the metering device in a timely and flexible manner, leading to inaccurate or even completely invalid metering data. Meanwhile, AC-side metering methods are either too coarse, relying on fixed rated power consumption estimates that fail to reflect the dynamic energy consumption during actual equipment operation, or constrained by the cost and coverage of meter deployment, particularly the limited adoption of smart meters. Traditional meters rely on manual meter reading, resulting in poor data real-time performance. The common consequence of these problems is insufficient overall accuracy of base station electricity metering, making it impossible to support refined cost allocation and energy efficiency management. Simultaneously, the lack of accurate and real-time branch power consumption data also hinders the realization of remote intelligent control based on precise load analysis.

[0005] Therefore, there is an urgent need for a solution that can systematically improve measurement accuracy and achieve intelligent integrated management. Summary of the Invention

[0006] To address the aforementioned issues, this application provides a comprehensive management method for base station power consumption, which has the advantage of enabling high-precision metering of the overall power consumption of the base station.

[0007] This application provides a comprehensive power management method for base stations, including: The topological mapping of the equipment access relationships between AC and DC power supply terminals within the base station is performed to form a hierarchical power consumption association list; Dynamic clustering and parsing are performed on the electricity consumption association list to generate a time-series evolution chain reflecting the access status and remote control status; Based on the time-series evolution chain, a differentiated identification model for equipment power consumption is constructed to determine abnormal metering factors and output candidate correction events; The equipment and power supply channels corresponding to the candidate correction events are marked as local metering anomaly areas, and multi-source monitoring data within the local metering anomaly areas are integrated to generate a correction reference group; The global electricity metering results are dynamically iteratively corrected based on the calibration reference group, and the base station electricity management instructions are generated by combining the load sharing ratio after metering correction with the intelligent DC power distribution unit circuit authorization strategy.

[0008] Furthermore, the tiered electricity consumption association list includes: Collect the access information of base station power distribution boxes, switching power supplies and intelligent DC power distribution units, and classify them into AC power supply groups, DC traditional power supply groups and intelligent branch power supply groups according to the power supply method of the equipment; Create a corresponding spatial index label for each group of devices; Logically hierarchical and layered power consumption association lists are generated based on equipment type, power supply mode, and intelligent circuit authorization strategy interface.

[0009] Furthermore, the access information of the intelligent DC power distribution unit also includes the allocation department information and usage permission information for each branch.

[0010] Furthermore, generating the time-series evolution chain includes: extracting features from all power connection records and output status of the intelligent DC power distribution unit in the power consumption association list, and extracting the electrical parameters, access timestamps and branch authorization information of the connection points; By combining historical power connection records and remote control operation logs, the access points are classified using an aggregation and clustering method. The classified access points are then sorted in time sequence to generate state nodes for each power consumption status change event. A chain structure is constructed through the connection relationship between nodes to form a time-series evolution chain.

[0011] Furthermore, constructing a differentiated identification model for equipment power consumption includes: The rated power consumption parameters of the devices corresponding to each node in the time-series evolution chain are compared with the real-time sampled current and voltage data to generate power consumption deviation and abnormal fluctuation range. Deviations and fluctuation ranges are grouped by equipment category and intelligent DC power distribution unit, and a differentiated hierarchical model is established. In the differentiated hierarchical model, a backup power configuration strategy and a branch authorization strategy are introduced. Threshold ranges are set for critical equipment, non-critical equipment, and each branch output to form a differentiated identification model.

[0012] Furthermore, the multi-source monitoring data integrated within the local metering anomaly area includes at least meter readings, branch sensor data, intelligent DC power distribution unit monitoring information, and manual inspection results.

[0013] Furthermore, the generation of base station power consumption management instructions also incorporates prepaid management strategies, which include: Calculate the cumulative electricity consumption of a specified department or branch based on the load sharing ratio after metering correction; When the cumulative electricity consumption reaches the warning threshold and / or overdraft threshold associated with its prepaid amount, a corresponding warning prompt instruction or remote power cut-off instruction is generated and executed.

[0014] Furthermore, the base station power management commands include at least one of the following: remote power-on command, remote power-off command, load adjustment command, and early warning notification command.

[0015] This application also provides a base station power consumption integrated management system, including: The topology mapping module is used to perform topological mapping of the equipment access relationships between AC and DC power supply terminals within the base station, forming a hierarchical power consumption association list; The clustering parsing module is used to perform dynamic clustering parsing on the electricity consumption association list and generate a time-series evolution chain that reflects the access status and remote control status. The difference identification module is used to build a difference identification model for equipment power consumption based on the time-series evolution chain, determine abnormal metering factors and output candidate correction events; The anomaly calibration module is used to calibrate the equipment and power supply channels corresponding to the candidate correction events as local metering anomaly areas, and to integrate multi-source monitoring data within the local metering anomaly areas to generate a correction reference group. The instruction generation module is used to dynamically iteratively correct the global electricity metering results based on the correction reference group, and generate base station electricity management instructions by combining the load sharing ratio after metering correction with the intelligent DC distribution unit circuit authorization strategy.

[0016] This application also provides an electronic device, which includes at least one processor and at least one memory, the memory being data-connected to the processor, wherein the memory stores instructions executable by at least one processor, the instructions being executed by at least one processor to enable at least one processor to perform any of the methods described above.

[0017] This application also provides a computer-storable medium storing computer instructions, which, when executed by a processor, specifically perform the steps of any of the methods described above.

[0018] This application also provides a computer program product, including computer instructions, which, when executed by a processor, specifically perform the steps of any of the methods described above.

[0019] Compared with the prior art, this application has the following advantages: To address the issues of chaotic power connection relationships and unclear topology among equipment within base stations, this application employs a technical approach of topological mapping of equipment access relationships and generating a hierarchical power consumption association list. This transforms the originally messy physical wiring into a structured digital relationship map, thereby clearly and systematically reflecting the connection attribution between all equipment and the power supply end, laying a reliable data foundation for subsequent refined management.

[0020] To address the core defect of inaccurate metering of DC and AC equipment, this application adopts a dynamic clustering analysis to generate a time-series evolution chain. The system can continuously track the equipment access status and remote control history, providing a complete time-series context for electricity consumption behavior analysis. Based on this, the differentiated identification model can accurately capture abnormal deviations in equipment power consumption and determine abnormal metering factors. Furthermore, by fusing multi-source monitoring data such as electricity meters, sensors, and inspection records to generate a correction reference group, and dynamically iteratively correcting the global metering results, this series of technical features work together to systematically correct errors caused by environmental interference, equipment changes, or estimation methods, achieving high-precision metering of the overall electricity consumption of the base station.

[0021] To address the lack of intelligent management capabilities in existing technologies, this application, based on accurate metering data, combines the metered-corrected load sharing ratio with strategies such as branch authorization and prepaid management to automatically generate and execute diverse management commands, including remote power-on, power-off, and early warning. This transforms base station power management from a manual mode to a proactive, precise, and automated intelligent control mode, effectively eliminating the risks of arrears and illegal electricity use, significantly improving operation and maintenance efficiency and energy utilization, and ensuring the stable and economical operation of base station facilities.

[0022] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of a base station power consumption management method provided according to an embodiment of this application; Figure 2 This is a block diagram of the functional units of a base station power consumption integrated management system provided according to an embodiment of this application. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] This invention is mainly applied to power consumption scenarios with multiple types, multiple loads, and distributed access characteristics, such as communication base stations, data center computer rooms, and distributed energy sites. In these scenarios, there are a large number of AC and DC power consumption devices, which are installed in scattered locations, have different network access times, and have complex and frequently changing power connection relationships. In the existing technology, local metering is mainly carried out through independent metering devices (such as Hall sensors and smart meters), or by relying on manual recording and estimation. It is difficult to systematically grasp the global, dynamic, and accurate power consumption topology and energy consumption data, resulting in large metering errors, extensive management, and delayed response to anomalies. It cannot support refined energy allocation, remote intelligent control, and energy efficiency optimization.

[0027] This invention provides a closed-loop management system and method that combines hardware and software, which can realize the digitization and visualization of power topology, the dynamic correction and high precision of metering data, and the automation and intelligence of management commands, thereby significantly improving operational efficiency, reducing energy consumption costs and ensuring power safety.

[0028] Please see Figure 1 The base station power consumption management method provided in this embodiment of the invention specifically includes: S1. Perform topological mapping of the equipment access relationships between AC and DC power supply terminals within the base station to form a hierarchical power consumption association list.

[0029] This step forms the basis for building a digital power consumption model. Specifically, the system collects information from all power supply terminals within the base station via data interfaces, including circuit breakers in the AC distribution box, output terminals of the DC switching power supply, and each branch output of the intelligent DC48V box. For each interface, the collected information includes the unique identifier of the connected device, device type, rated power, power supply method (AC or DC), and the physical or logical installation location of the device.

[0030] Specifically, for intelligent DC48V boxes, it is also necessary to collect the preset service attributes of each branch, such as the allocation department, the level of access permission, and the remote control interface identifier.

[0031] Subsequently, the system performs structured processing on the information from all power supply terminals: S1-1. Based on the nature and controllability of the power supply, all access points are divided into AC power supply groups from ordinary switching power supplies, DC traditional power supply groups, and DC intelligent branch groups from intelligent DC48V boxes.

[0032] S1-2. Attach the spatial location index collected in the previous step to each access point.

[0033] S1-3. Based on multiple dimensions such as the functional importance of the equipment (core equipment / auxiliary equipment), the power supply department, and the expected stability of the access, a recursive logical layering is carried out, such as forming a hierarchical relationship like "core network equipment layer - intelligent branch 1 - department A".

[0034] The final electricity consumption association list generated in this step is a structured database or list that clearly, completely, and hierarchically presents a panoramic view of which device in the base station, in what way, where it is connected, and to whom it belongs, providing an accurate and unified underlying data model for all subsequent advanced analyses.

[0035] S2. Perform dynamic clustering parsing on the electricity consumption association list to generate a time-series evolution chain reflecting the access status and remote control status.

[0036] This step aims to enable the system to perceive dynamic changes and trace historical states, specifically: S2-1. The system periodically reads the real-time operating characteristics of each access point in the list, including current, voltage, power factor and switching status.

[0037] S2-2. The system continuously collects two types of dynamic logs: the power-on, power-off, and migration records of the equipment itself, and all remote control operation records for the power supply end (especially the intelligent DC48V box). The two types of dynamic logs specifically include commands such as closing, opening, and power adjustment, as well as feedback on whether they were executed successfully.

[0038] S2-3. Based on the real-time and historical data in these dynamic logs, the system uses clustering algorithms, such as clustering based on the similarity of electricity consumption behavior patterns, to dynamically cluster all access points. Preferably, devices that operate stably for 24 hours can be classified into a stable cluster, air conditioners that start and stop with the temperature control strategy can be classified into a periodic fluctuation cluster, and newly installed devices or devices that have just undergone remote restart can be temporarily classified into a new / changing cluster.

[0039] The clustering results are not fixed and will be dynamically adjusted according to changes in device behavior.

[0040] S2-4. Create and maintain a time-series evolution chain for each power-consuming object (equipment or power supply branch). A time-series evolution chain is a data structure that links key state nodes in chronological order.

[0041] Each state node in the time-series evolution chain records a specific power consumption status change event. This event can be physical connection, physical disconnection, power supply channel switching, or remote successful closing, remote power outage, load change, etc. The data of the state node includes at least: a precise timestamp, event type, electrical parameters measured at the time of the event (such as current and voltage), and, if the event was triggered by remote operation, the corresponding operation command ID.

[0042] In actual operation, a time-series evolution chain describing device X may be presented as: `[T1: Device connected to branch 2] -> [T2: Remote closing of branch 2 successful] -> [T3: Current stabilizes to rated value of 1.5A] -> [T4: Current abnormally drops to 0.2A]`.

[0043] In this step, the time-series evolution chain records what happened and reveals the context of changes and possible causal relationships through the association between nodes, providing an indispensable time-series context for subsequent anomaly diagnosis and root cause analysis, thus enabling electricity monitoring to be continuous and traceable.

[0044] S3. Construct a differentiated identification model for equipment power consumption based on the time-series evolution chain, determine abnormal metering factors, and output candidate correction events.

[0045] The core of this step is to use historical behavioral patterns for intelligent initial screening of anomalies, which specifically includes the following steps: S3-1. By analyzing the data of each electricity user in its time-series evolution chain during its historical normal operation cycle, a differential identification model is established. The differential identification model defines the baseline of the electricity consumption behavior of the object under normal conditions, such as the reasonable fluctuation range of current / power, the typical daily load curve shape, and the standard response mode and duration to specific types of remote operations.

[0046] S3-2. In real-time monitoring, the system continuously compares the collected measured data (current, voltage) with the differential identification model of the corresponding object. When it is found that the measured value of an object continuously deviates from its model baseline by more than a preset threshold, such as three consecutive sampling cycles and the power deviation is >20%, the anomaly judgment process is triggered, and the temporal evolution chain of the object is immediately retrieved to analyze the event sequence in the period before the anomaly occurs, so as to help determine the possible root cause of the anomaly.

[0047] S3-3. Generate a structured candidate correction event based on the possible root causes of the anomaly. The event includes a unique identifier of the anomaly object, a quantified value of the deviation, a time window in which the anomaly occurred, and an index of the associated time-series evolution chain segment. The unique identifier may include the anomaly type, such as excessive power consumption, suspected electricity theft, metering failure, etc.

[0048] S4. Mark the equipment and power supply channels corresponding to the candidate correction events as local metering anomaly areas, and integrate multi-source data to generate a correction reference group.

[0049] S4-1. When the system receives a candidate correction event, it will take into account the power supply link where the device is located, and mark the device involved in the event, as well as the entire upstream channel that supplies power to the device (such as a specific branch of the smart DC48V box connected to the device, and the main input circuit breaker or meter corresponding to the branch) as a local metering anomaly area, so as to ensure the completeness of the problem investigation scope.

[0050] S4-2. For this calibrated area, initiate a synchronous collection and fusion analysis of multi-source data.

[0051] Specifically, the data sources retrieved include: (1) The reading of the high-precision smart meter at the main incoming line of this area; (2) Data from independent current sensors (such as Hall sensors) or embedded metering modules installed on each branch line within the area; (3) Detailed monitoring data of this specific branch circuit inside the intelligent DC48V box; (4) Recent manual on-site inspection reports or screenshots of automatic video inspections in the area.

[0052] S4-3. Strictly align the above independent data from different sources on the timeline, and then perform consistency cross-validation.

[0053] The core verification of consensus cross-validation includes: Compare the total meter reading with the sum of the readings of each branch sensor plus the theoretical line loss estimate to check for any unexplained discrepancies. By comparing the branch load monitored by the smart box with the sum of the rated loads of all registered devices under that branch, it can be determined whether there are any unauthorized devices connected.

[0054] This multi-source verification effectively identifies and eliminates false alarms caused by occasional failures of a single sensor, transient signal interference, or data transmission errors, thereby confirming real anomalies and accurately locating their possible causes, such as equipment damage, electricity theft, or inaccurate metering devices.

[0055] S4-4. The system packages all valid verification data, consistency analysis conclusions, and suggested correction coefficients (such as multiplying a sensor reading by a correction factor of 1.1) into a calibration reference set.

[0056] S5. Based on the calibration reference group, the global power metering results are dynamically iteratively corrected, and the base station power management instructions are generated by combining the corrected load sharing ratio and the intelligent DC48V branch authorization strategy.

[0057] S5-1. Using the correction coefficients confirmed in the calibration reference group, the historical data and future real-time data in the global electricity metering database are dynamically and iteratively updated and corrected.

[0058] Specifically, if a Hall sensor is found to have a systematic deviation of -10%, the system will apply correction to all metering data related to this sensor and retrospectively correct the affected energy consumption allocation history. This process continues, so that the overall metering accuracy of the system continuously improves with each anomaly discovery and correction.

[0059] S5-2: Based on the corrected and highly accurate load sharing ratios of each device and department, the system calls the integrated business strategy engine to automatically generate directly executable management commands.

[0060] Specifically, in conjunction with the prepaid management strategy, the system calculates the cumulative electricity consumption of a department in real time, and automatically generates a payment warning notification when it approaches its prepaid limit and sends it to the designated contact person; When the quota is exhausted, a remote power-off command will be automatically generated and sent to the field equipment.

[0061] Combined with the routing authorization strategy, the system monitors whether the actual load of each routing exceeds its authorized limit, and can generate alarm logs or remote load reduction commands for over-limit behavior.

[0062] In addition, the system can generate energy efficiency optimization suggestions (such as merging light load circuits) based on accurate load data, or generate an orderly sequence of remote dispatch instructions in emergency situations according to the protection level.

[0063] Through this step, this application achieves a closed loop from accurate data to intelligent execution, transforming energy management from a passive response to proactive, accurate, and automated intelligent control, greatly improving operation and maintenance efficiency and management level.

[0064] Please see Figure 2 To implement the above method, the present invention also provides a comprehensive power management system for base stations. This system is typically deployed in a regional monitoring center or cloud platform and connects to the sensing, metering, and control equipment at the base station site via a network. The system includes a topology mapping module, a clustering parsing module, a difference identification module, an anomaly calibration module, and an instruction generation module.

[0065] These modules correspond one-to-one with the steps S1 to S5 of the above method, and realize the logical transmission of data flow and control flow through software programming. They work together to complete the entire process from data acquisition to instruction generation, forming a complete intelligent management platform that integrates software and hardware.

[0066] This application also provides an electronic device, including at least one processor and at least one memory, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the business setup method based on a low-code platform as described above.

[0067] This application also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, specifically perform the steps of the method described in any of the preceding claims.

[0068] This application also provides a computer program product, including computer instructions, which, when executed by a processor, specifically perform the steps of the method described in any of the preceding claims.

[0069] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A comprehensive power management method for base stations, characterized in that, include: The topological mapping of the equipment access relationships between AC and DC power supply terminals within the base station is performed to form a hierarchical power consumption association list; Dynamic clustering parsing is performed on the electricity consumption association list to generate a time-series evolution chain reflecting the access status and remote control status; Based on the aforementioned time-series evolution chain, a differentiated identification model for equipment power consumption is constructed to determine abnormal metering factors and output candidate correction events; The devices and power supply channels corresponding to the candidate correction events are marked as local metering anomaly areas, and multi-source monitoring data within the local metering anomaly areas are fused to generate a correction reference group; Based on the aforementioned correction reference group, the global electricity metering results are dynamically iteratively corrected. Combined with the corrected load sharing ratio and the intelligent DC distribution unit circuit authorization strategy, base station electricity management instructions are generated.

2. The method according to claim 1, characterized in that, The resulting tiered electricity consumption association list includes: Collect the access information of base station power distribution boxes, switching power supplies and intelligent DC power distribution units, and classify them into AC power supply groups, DC traditional power supply groups and intelligent branch power supply groups according to the power supply method of the equipment; Create a corresponding spatial index label for each group of devices; Logically hierarchical and layered power consumption association lists are generated based on equipment type, power supply mode, and intelligent circuit authorization strategy interface.

3. The method according to claim 2, characterized in that, The access information of the intelligent DC power distribution unit also includes the allocation department information and usage permission information for each branch.

4. The method according to claim 1, characterized in that, The generation of the time-series evolution chain includes: extracting features from all power connection records and output status of the intelligent DC power distribution unit in the power consumption association list, and extracting the electrical parameters, access timestamps and branch authorization information of the access points; By combining historical power connection records and remote control operation logs, the access points are classified using an aggregation and clustering method. The classified access points are then sorted in time sequence to generate state nodes for each power consumption status change event. A chain structure is constructed through the connection relationship between nodes to form a time-series evolution chain.

5. The method according to claim 1, characterized in that, The differentiated identification model for the power consumption of the constructed equipment includes: The rated power consumption parameters of the devices corresponding to each node in the time-series evolution chain are compared with the real-time sampled current and voltage data to generate power consumption deviation and abnormal fluctuation range. The aforementioned deviations and fluctuation ranges are grouped according to equipment category and intelligent DC power distribution unit circuits to establish a differentiated hierarchical model; The differentiated hierarchical model introduces a backup power configuration strategy and a branch authorization strategy, and sets threshold ranges for critical equipment, non-critical equipment and each branch output to form a differentiated identification model.

6. The method according to claim 1, characterized in that, The fusion of multi-source monitoring data within the local metering anomaly area includes at least meter readings, branch sensor data, intelligent DC power distribution unit monitoring information, and manual inspection results.

7. The method according to claim 1, characterized in that, The generated base station power management instruction also incorporates a prepaid management strategy, which includes: Based on the load sharing ratio after metering correction, calculate the cumulative electricity consumption of a specified department or branch. When the cumulative electricity consumption reaches the warning threshold and / or overdraft threshold associated with its prepaid amount, a corresponding warning prompt instruction or remote power cut-off instruction is generated and executed.

8. The method according to claim 1, characterized in that, The base station power management commands include at least one of the following: remote power-on command, remote power-off command, load adjustment command, and early warning notification command.

9. A comprehensive power management system for base stations, characterized in that, include: The topology mapping module is used to perform topological mapping of the equipment access relationships between AC and DC power supply terminals within the base station, forming a hierarchical power consumption association list; The clustering parsing module is used to perform dynamic clustering parsing on the electricity consumption association list and generate a time-series evolution chain that reflects the access status and remote control status. The difference identification module is used to construct a difference identification model for equipment power consumption based on the time-series evolution chain, determine abnormal metering factors, and output candidate correction events; An anomaly calibration module is used to calibrate the equipment and power supply channels corresponding to the candidate correction events as local metering anomaly areas, and to integrate multi-source monitoring data within the local metering anomaly areas to generate a correction reference group. The instruction generation module is used to dynamically iteratively correct the global electricity metering results based on the correction reference group, and generate base station electricity management instructions by combining the load sharing ratio after metering correction with the intelligent DC distribution unit circuit authorization strategy.

10. An electronic device, characterized in that, The electronic device includes at least one processor and at least one memory, the memory being data-connected to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-8.

11. A computer-storable medium, characterized in that, The storable medium stores computer instructions, which, when executed by a processor, specifically perform the steps of the method as described in any one of claims 1-8.

12. A computer program product comprising computer instructions, characterized in that, When the computer instructions are executed by the processor, they specifically perform the steps in the method as described in any one of claims 1-8.

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