A power consumption management method of an electric energy metering box and the electric energy metering box

Abstract

The application discloses a power consumption management method of an electric energy metering box and the electric energy metering box, and relates to the technical field of electric power metering equipment and intelligent power consumption management. The application adopts an edge local processing architecture, synchronously collects multi-source data of power consumption metering, box state and environment in the box, and fuses and analyzes power consumption behavior and equipment operation state abnormity by relying on a built-in research and judgment model after data purification and preprocessing. The application sets multi-stage state grading response strategies, realizes differentiated local early warning, loop control and remote data uploading, and constructs a full-closed-loop fine management system of operation and maintenance rectification and model iteration. Corresponding equipment adopts a modular independent structure, and does not need to rely on the material characteristics of the shell.

Description

Technical Field

[0001] This invention relates to the field of power metering equipment and intelligent power management technology, and more specifically to a power management method for an power metering box and the power metering box itself. Background Technology

[0002] Electricity metering boxes are core equipment for electricity metering, line protection, and electricity consumption management for end users in power systems. They are widely used in various electricity consumption scenarios, including residential buildings, industrial and commercial plants, and public buildings. Currently, existing electricity metering boxes and their supporting electricity management technologies have many technical deficiencies, resulting in a low overall level of intelligent and refined management.

[0003] In existing technologies, electricity management in electricity metering boxes mostly relies on single electricity metering data for statistics and simple over-limit warnings. The monitoring dimensions are limited, only enabling electricity consumption statistics and simple overcurrent protection, failing to combine the box's operating status, internal environmental parameters, and equipment operation behavior for comprehensive analysis. It cannot promptly and accurately identify hidden abnormal electricity usage and equipment malfunctions such as unauthorized wiring, electricity theft from open covers, aging and overheating lines, and illegal opening and operation. This results in problems such as delayed anomaly identification, high false alarm and missed alarm rates, and inability to trace the source, easily leading to excessive power line losses, equipment burnout, and electrical safety accidents.

[0004] Meanwhile, existing smart metering devices have strict limitations on the materials used for their enclosures, relying heavily on the electrical and thermal conductivity of metal casings for sensing, monitoring, and grounding protection. This makes them unsuitable for metering boxes with new insulating enclosures made of plastics or composite materials, resulting in poor device versatility and limited applicability. Furthermore, current electricity management models primarily rely on centralized backend data processing, leading to large data transmission volumes and high response delays. This hinders rapid on-site decision-making and real-time control, lacks a closed-loop mechanism for operation and maintenance, and results in low efficiency in rectifying anomalies. Consequently, these systems fall short of meeting the current demands for intelligent, refined, and safe terminal electricity management in power systems.

[0005] In summary, existing technologies suffer from technical defects such as limited monitoring dimensions, low accuracy in anomaly detection, poor equipment adaptability, delayed control and management response, and lack of a closed-loop operation and maintenance system.

[0006] Therefore, proposing an electricity management method for an electricity metering box and an electricity metering box to solve the difficulties existing in the prior art is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of this, the present invention provides an electricity management method for an electricity metering box and an electricity metering box, which solves the technical problems in the prior art such as single dimension of electricity monitoring, low accuracy of anomaly identification, limited adaptability of shell material, delayed control response, and lack of closed-loop operation and maintenance. It realizes multi-dimensional integrated analysis, on-site intelligent decision-making, and closed-loop refined electricity management, thereby improving the safety and intelligence level of electricity management and control at power terminals.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for managing electricity consumption using an electricity metering box includes the following steps: S1. Synchronously collect three types of raw operating data corresponding to the electricity metering box: user-side electricity metering data, box operating status data, and box internal environmental parameters. S2. The edge processing module built into the electricity metering box is used to clean and preprocess the various raw operating data collected, remove invalid interference data, form a standardized dataset and complete local caching. S3. Based on the preset judgment model, perform integrated analysis on the standardized dataset to simultaneously complete the judgment of abnormal user electricity consumption behavior and the judgment of abnormal metering box equipment operation status. S4. Based on the integrated analysis results, divide the operation status into multiple levels, and for different levels of operation status, execute corresponding local early warning, data uploading and power circuit control strategies. S5. Based on the abnormal data received by the backend platform, generate operation and maintenance tasks. After completing the operation and maintenance rectification, combine the operation and maintenance results to iteratively optimize and judge the model parameters, and dynamically update the user's electricity management tags to achieve refined closed-loop electricity management.

[0009] Optionally, the electricity metering data in S1 includes real-time current, real-time voltage, active power, reactive power, and cumulative electricity consumption; the enclosure operation status data includes enclosure opening / closing status, door lock status, and internal circuit continuity status; and the environmental parameter data includes enclosure temperature and ambient humidity.

[0010] Optionally, the data cleaning and preprocessing in S2 specifically includes: The original data is subjected to noise reduction, filtering, and outlier removal. After preprocessing, standardized econometric datasets and state datasets are generated, and a local caching mechanism is preset to achieve data retention.

[0011] Optionally, the integrated intelligent analysis in S3 includes electricity consumption behavior analysis and equipment status analysis; Electricity consumption behavior analysis includes user electricity load fluctuation analysis, time period electricity consumption matching degree analysis, and abnormal electricity consumption behavior identification. Equipment status assessment includes assessment of line overheating faults, assessment of equipment aging, and assessment of unauthorized opening and non-compliant operations.

[0012] Optionally, specific implementation methods for electricity consumption behavior analysis include: establishing a user-specific electricity load curve based on the user's historical electricity consumption data, dividing peak, flat, and off-peak electricity consumption periods and setting corresponding normal electricity load threshold ranges; comparing the real-time electricity load with the corresponding time period threshold ranges in real time, judging the condition of load exceeding the threshold and continuing for a preset duration as an abnormal load, and combining the box opening and closing records with electricity consumption data to identify unauthorized box opening, sudden load changes after opening the box, and electricity theft behavior such as unauthorized connection of lines.

[0013] Optionally, specific implementation methods for equipment status assessment include: preset internal temperature thresholds and line voltage drop thresholds, and real-time monitoring of internal temperature and line voltage drop data; determining that the internal temperature continuously exceeds the threshold and is accompanied by voltage fluctuations as line overload or line aging fault; determining that illegal opening and closing of the enclosure without background authorization records as abnormal operation, and simultaneously recording the opening and closing time, equipment number, and abnormal operating condition data for source tracing.

[0014] Optionally, the multi-level operating status in S4 includes three control levels: normal, general abnormal, and severe abnormal. The corresponding response strategies are as follows: under normal status, metering data and equipment status data are uploaded to the power backend platform on a regular basis; under general abnormal status, the abnormality type is marked locally, the data ledger is retained, and the early warning information is pushed to the operation and maintenance terminal, without performing power outage operations; under severe abnormal status, local audible and visual early warnings are triggered, abnormal details, location and traceability data are uploaded, and the abnormal power circuit is locked and restricted.

[0015] Optionally, general anomalies include short-term load fluctuations, slight exceedances of temperature and humidity, and single harmless opening operations; serious anomalies include continuous overload power consumption, overheating of lines, multiple illegal openings, and suspected electricity theft. In the event of serious anomalies, circuit current limiting protection will be activated immediately, and the back-end platform will monitor the abnormal status in real time until the fault is eliminated.

[0016] Optionally, the closed-loop operation and maintenance optimization in S5 specifically includes: the back-end platform automatically generates and intelligently dispatches operation and maintenance work orders based on abnormal data, uploads the inspection results after the operation and maintenance is completed, the edge processing module synchronously iterates and updates the judgment model parameters, and dynamically updates the user's electricity consumption classification label based on historical electricity consumption data.

[0017] An electricity metering box, implementing the electricity management method of an electricity metering box as described in any of the above claims, includes: a box body, a metering component, a sensing component, an edge processing component, a communication component, and an early warning and protection component; The metering components are fixedly installed inside the enclosure and are used to collect real-time electricity metering data from the user side. The sensing components include an opening / closing sensor, a temperature sensor, and a humidity sensor. The opening / closing sensor is installed at the matching position of the cabinet door frame to detect the opening / closing status of the cabinet in real time. The temperature sensor and humidity sensor are deployed in the wiring area inside the cabinet to collect environmental parameters inside the cabinet. The edge processing component is connected to the metering component and the sensing component respectively. It has built-in data preprocessing program, power consumption judgment model and fault judgment model to realize data preprocessing, multi-dimensional data fusion judgment and local control decision-making. The communication component is connected to the edge processing component and adopts 4G / 5G and NB-IoT dual communication modes to realize data interaction between the power metering box and the power back-end platform and operation and maintenance terminal; The early warning and protection components include an audible and visual early warning module and a loop current limiting protection module, which respectively receive control signals from the edge processing component to realize local early warning and abnormal loop protection and control.

[0018] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method for electricity management using an electricity metering box and an electricity metering box, the beneficial effects of which are: 1) This invention abandons the traditional single metering data monitoring mode and integrates multi-dimensional information such as electricity metering data, cabinet opening and closing status, and internal temperature and humidity environmental parameters. Through a dedicated judgment model, it achieves comprehensive and accurate identification of electricity consumption behavior, equipment failure, and illegal operation, effectively solving the problems of false alarm, missed alarm, and identification lag in existing technologies, and accurately preventing and controlling various risks such as electricity theft, line failure, equipment aging, and illegal operation. 2) This invention adopts edge local preprocessing and intelligent decision-making, which does not rely on centralized backend computing, greatly reducing data transmission pressure and shortening anomaly response delay. It can realize early warning and flow limiting protection locally. At the same time, it constructs a closed-loop management system of "data collection - anomaly analysis - early warning response - work order dispatch - operation and maintenance rectification - model iteration", which solves the problems of fragmented operation and maintenance, lack of traceability and lack of optimization, and realizes dynamic and refined upgrade of power management. 3) This invention sets up differentiated response mechanisms for different levels of anomalies, distinguishing between minor fluctuation anomalies and high-risk fault anomalies. This avoids interference with normal power use caused by over-protection, and can quickly deal with high-risk safety hazards, taking into account both power stability and safety, and adapting to the management and control needs of various power use scenarios such as residential, industrial and commercial. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 A flowchart of an electricity metering box power management method provided by the present invention; Figure 2 This is a structural diagram of an electricity metering box provided by the present invention. Detailed Implementation

[0021] 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.

[0022] See Figure 1 As shown, this invention discloses a method for managing electricity consumption using an electricity metering box, comprising the following steps: S1. Synchronously collect three types of raw operating data corresponding to the electricity metering box: user-side electricity metering data, box operating status data, and box internal environmental parameters. S2. The edge processing module built into the electricity metering box is used to clean and preprocess the various raw operating data collected, remove invalid interference data, form a standardized dataset and complete local caching. S3. Based on the preset judgment model, perform integrated analysis on the standardized dataset to simultaneously complete the judgment of abnormal user electricity consumption behavior and the judgment of abnormal metering box equipment operation status. S4. Based on the integrated analysis results, divide the operation status into multiple levels, and for different levels of operation status, execute corresponding local early warning, data uploading and power circuit control strategies. S5. Based on the abnormal data received by the backend platform, generate operation and maintenance tasks. After completing the operation and maintenance rectification, combine the operation and maintenance results to iteratively optimize and judge the model parameters, and dynamically update the user's electricity management tags to achieve refined closed-loop electricity management.

[0023] Furthermore, the electricity metering data in S1 includes real-time current, real-time voltage, active power, reactive power, and cumulative electricity consumption; the enclosure operation status data includes enclosure opening / closing status, door lock status, and internal circuit continuity status; and the environmental parameter data includes enclosure temperature and ambient humidity.

[0024] Furthermore, the data cleaning and preprocessing in S2 specifically includes: The original data is subjected to noise reduction, filtering, and outlier removal. After preprocessing, standardized econometric datasets and state datasets are generated, and a local caching mechanism is preset to achieve data retention.

[0025] Furthermore, S3 integrates intelligent analysis, including electricity consumption behavior analysis and equipment status analysis; Electricity consumption behavior analysis includes user electricity load fluctuation analysis, time period electricity consumption matching degree analysis, and abnormal electricity consumption behavior identification. Equipment status assessment includes assessment of line overheating faults, assessment of equipment aging, and assessment of unauthorized opening and non-compliant operations.

[0026] Furthermore, the specific implementation methods for analyzing electricity consumption behavior include: establishing a user-specific electricity load curve based on the user's historical electricity consumption data, dividing peak, flat, and off-peak electricity consumption periods and setting corresponding normal electricity load threshold ranges; comparing the real-time electricity load with the corresponding time period threshold ranges in real time, judging the condition of load exceeding the threshold and continuing for a preset duration as an abnormal load, and combining the box opening and closing records with electricity consumption data to identify unauthorized box opening, sudden load changes after opening the box, and electricity theft behavior such as unauthorized connection of lines.

[0027] Furthermore, the specific implementation methods for equipment status assessment include: preset the internal temperature threshold and the line voltage drop threshold, and monitor the internal temperature and line voltage drop data in real time; determine the working condition where the internal temperature continuously exceeds the threshold and is accompanied by voltage fluctuation as line overload or line aging fault; determine the illegal opening and closing of the enclosure without background authorization record as abnormal operation, and simultaneously record the opening and closing time, equipment number, and abnormal working condition data for source tracing.

[0028] Furthermore, the S4 multi-level operating status includes three control levels: normal, general abnormal, and severe abnormal. The corresponding response strategies are as follows: under normal conditions, metering data and equipment status data are uploaded to the power backend platform on a regular basis; under general abnormal conditions, the abnormality type is marked locally, the data ledger is retained, and the early warning information is pushed to the operation and maintenance terminal, without performing power outage operations; under severe abnormal conditions, local audible and visual early warnings are triggered, abnormal details, location and traceability data are uploaded, and the abnormal power circuit access is locked and restricted.

[0029] Furthermore, general anomalies include short-term load fluctuations, slight exceedances of temperature and humidity, and single harmless opening operations; serious anomalies include continuous overload power consumption, overheating of lines, multiple illegal openings, and suspected electricity theft. In the event of serious abnormal conditions, circuit current limiting protection will be activated immediately, and the back-end platform will monitor the abnormal status in real time until the fault is eliminated.

[0030] Furthermore, the closed-loop operation and maintenance optimization in S5 specifically includes: the back-end platform automatically generates and intelligently dispatches operation and maintenance work orders based on abnormal data, uploads the inspection results after the operation and maintenance is completed, the edge processing module synchronously iteratively updates the judgment model parameters, and dynamically updates the user's electricity consumption classification label based on historical electricity consumption data.

[0031] and Figure 1Corresponding to the method described above, this embodiment of the invention also discloses an electricity metering box for measuring electricity consumption. Figure 1 For a detailed implementation of the method, please refer to its structure diagram. Figure 2 As shown, it includes: housing, metering components, sensing components, edge processing components, communication components, and early warning and protection components; The metering components are fixedly installed inside the enclosure and are used to collect real-time electricity metering data from the user side. The sensing components include an opening / closing sensor, a temperature sensor, and a humidity sensor. The opening / closing sensor is installed at the matching position of the cabinet door frame to detect the opening / closing status of the cabinet in real time. The temperature sensor and humidity sensor are deployed in the wiring area inside the cabinet to collect environmental parameters inside the cabinet. The edge processing component is connected to the metering component and the sensing component respectively. It has built-in data preprocessing program, power consumption judgment model and fault judgment model to realize data preprocessing, multi-dimensional data fusion judgment and local control decision-making. The communication component is connected to the edge processing component and adopts 4G / 5G and NB-IoT dual communication modes to realize data interaction between the power metering box and the power back-end platform and operation and maintenance terminal; The early warning and protection components include an audible and visual early warning module and a loop current limiting protection module, which respectively receive control signals from the edge processing component to realize local early warning and abnormal loop protection and control.

[0032] Specifically, the sensing component uses a low-power integrated sensor, requiring no housing material compatibility and independent of the housing's conductivity and thermal conductivity, independently completing data acquisition. The edge processing component locally stores the device's unique identifier and the user's power consumption profile, enabling precise tracing of abnormal events and supporting online iterative updates of model parameters. The loop current limiting protection module is connected in series in the user's power circuit and can adaptively adjust the loop current threshold based on the edge processing component's analysis results, achieving tiered current limiting and power-off protection operations.

[0033] In a specific embodiment, the details are as follows: This embodiment discloses a method for managing electricity consumption using an electricity metering box, which specifically includes the following steps: S1. Real-time data acquisition: The metering and acquisition unit collects the user's real-time current, voltage, power and cumulative power consumption every 10ms. The opening and closing status of the cabinet is monitored in real time by the opening and closing sensor. The environmental data inside the cabinet is collected in real time by the temperature and humidity sensor, realizing multi-dimensional data synchronous high-frequency acquisition.

[0034] S2. Data edge preprocessing: The edge processing module filters and reduces noise in the collected raw data, removes outliers caused by sensor jitter and electromagnetic interference, generates a standardized dataset, and caches it locally for 30 days to ensure data integrity and accuracy.

[0035] S3. Multi-dimensional integrated analysis: Retrieve the user's historical electricity consumption data from the past 3 months to generate a personalized load curve and set normal load thresholds for each time period; simultaneously, combine this with the box temperature threshold of 60℃ and the voltage drop threshold of 5% to analyze equipment status. If the user's real-time load exceeds the time period threshold for more than 5 minutes, it is determined to be an abnormal load; if the box temperature exceeds 60℃ and is accompanied by voltage fluctuations, it is determined to be line overheating and aging; if the box is detected to be open or closed without a background authorization record, it is determined to be an illegal or irregular operation.

[0036] S4. Tiered Early Warning and Response: Short-term load fluctuations and slight exceedances of temperature and humidity are judged as general anomalies, and only data logs are retained and maintenance reminders are pushed; continuous overload, high temperature inside the box, multiple unauthorized openings of the box, and suspected power theft due to sudden load changes are judged as serious anomalies, and audible and visual warnings are immediately activated, circuit current limiting protection is executed, and the anomaly location, time, and operating condition data are uploaded to the backend platform simultaneously.

[0037] S5. Closed-loop operation and maintenance management: The backend automatically generates corresponding operation and maintenance work orders based on the type of anomaly and accurately dispatches them to the operation and maintenance personnel in the jurisdiction. After the operation and maintenance is completed, the inspection record is uploaded. The edge module updates the model threshold based on the inspection data to optimize the accuracy of subsequent judgments. At the same time, the user electricity consumption classification label is updated monthly to achieve refined electricity consumption management and control.

[0038] The electricity metering box disclosed in this embodiment adopts a modular integrated design. All functional modules are installed independently and are not functionally related to the shell material. Plastic, metal, and composite material shells can all be directly adapted. The sensing component independently completes data acquisition, the edge processing module independently realizes data processing and decision-making, the communication component ensures stable data transmission, and the early warning and protection component realizes local control. The overall structure is simple, easy to install, and highly adaptable.

[0039] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0040] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing electricity consumption using an electricity metering box, characterized in that, Includes the following steps: S1. Synchronously collect three types of raw operating data corresponding to the electricity metering box: user-side electricity metering data, box operating status data, and box internal environmental parameters. S2. The edge processing module built into the electricity metering box is used to clean and preprocess the various raw operating data collected, remove invalid interference data, form a standardized dataset and complete local caching. S3. Based on the preset judgment model, perform integrated analysis on the standardized dataset to simultaneously complete the judgment of abnormal user electricity consumption behavior and the judgment of abnormal metering box equipment operation status. S4. Based on the integrated analysis results, divide the operation status into multiple levels, and for different levels of operation status, execute corresponding local early warning, data uploading and power circuit control strategies. S5. Based on the abnormal data received by the backend platform, generate operation and maintenance tasks. After completing the operation and maintenance rectification, combine the operation and maintenance results to iteratively optimize and judge the model parameters, and dynamically update the user's electricity management tags to achieve refined closed-loop electricity management.

2. The method for electricity management using an electricity metering box according to claim 1, characterized in that, The electricity metering data in S1 includes real-time current, real-time voltage, active power, reactive power, and cumulative electricity consumption; the cabinet operation status data includes cabinet opening and closing status, door lock status, and internal circuit continuity status; environmental parameter data includes cabinet temperature and ambient humidity.

3. The method for electricity management using an electricity metering box according to claim 1, characterized in that, The data cleaning and preprocessing in S2 specifically includes: The original data is subjected to noise reduction, filtering, and outlier removal. After preprocessing, standardized econometric datasets and state datasets are generated, and a local caching mechanism is preset to achieve data retention.

4. The method for electricity management using an electricity metering box according to claim 1, characterized in that, S3 integrates intelligent analysis, including electricity consumption behavior analysis and equipment status analysis; Electricity consumption behavior analysis includes user electricity load fluctuation analysis, time period electricity consumption matching degree analysis, and abnormal electricity consumption behavior identification. Equipment status assessment includes assessment of line overheating faults, assessment of equipment aging, and assessment of unauthorized opening and non-compliant operations.

5. The method for electricity management using an electricity metering box according to claim 4, characterized in that, The specific implementation methods for analyzing electricity consumption behavior include: establishing a user-specific electricity load curve based on the user's historical electricity consumption data, dividing peak, flat, and off-peak electricity consumption periods and setting corresponding normal electricity load threshold ranges; comparing the real-time electricity load with the corresponding time period threshold ranges in real time, judging the condition of load exceeding the threshold and continuing for a preset duration as an abnormal load, and combining the box opening and closing records with electricity consumption data to identify unauthorized box opening, sudden load changes after opening the box, and electricity theft behavior such as unauthorized connection of lines.

6. The method for electricity management using an electricity metering box according to claim 4, characterized in that, The specific implementation methods for equipment status assessment include: setting preset temperature thresholds and line voltage drop thresholds, and monitoring the temperature inside the enclosure and line voltage drop data in real time; determining the condition where the temperature inside the enclosure continuously exceeds the threshold and is accompanied by voltage fluctuations as line overload or line aging fault; determining the illegal opening and closing of the enclosure without background authorization records as abnormal operation, and simultaneously recording the opening and closing time, equipment number, and abnormal operating condition data for source tracing.

7. The method for electricity management using an electricity metering box according to claim 1, characterized in that, The S4 multi-level operating status includes three control levels: normal, general abnormal, and severe abnormal. The corresponding response strategies are as follows: Under normal conditions, metering data and equipment status data are uploaded to the power backend platform on a regular basis; under general abnormal conditions, the abnormality type is marked locally, the data ledger is retained, and the early warning information is pushed to the operation and maintenance terminal, without performing power outage operations; under severe abnormal conditions, local audible and visual early warnings are triggered, abnormal details, location and traceability data are uploaded, and the abnormal power circuit is locked and restricted.

8. The method for managing electricity consumption using an electricity metering box according to claim 7, characterized in that, General anomalies include short-term load fluctuations, slight exceedances of temperature and humidity, and single harmless opening operations; serious anomalies include continuous overload power consumption, overheating of lines, multiple illegal openings, and suspected electricity theft. In the event of serious anomalies, circuit current limiting protection will be activated immediately, and the back-end platform will monitor the abnormal status in real time until the fault is eliminated.

9. The method for electricity management using an electricity metering box according to claim 1, characterized in that, The closed-loop operation and maintenance optimization in S5 specifically includes: the back-end platform automatically generates and intelligently dispatches operation and maintenance work orders based on abnormal data; after the operation and maintenance is completed, the inspection results are uploaded; the edge processing module synchronously iterates and updates the judgment model parameters; and at the same time, it dynamically updates the user's electricity consumption classification label based on historical electricity consumption data.

10. An electricity metering box, characterized in that... A power management method for an electricity metering box according to any one of claims 1-9 includes: a box body, a metering component, a sensing component, an edge processing component, a communication component, and an early warning and protection component; The metering components are fixedly installed inside the enclosure and are used to collect real-time electricity metering data from the user side. The sensing components include an opening / closing sensor, a temperature sensor, and a humidity sensor. The opening / closing sensor is installed at the matching position of the cabinet door frame to detect the opening / closing status of the cabinet in real time. The temperature sensor and humidity sensor are deployed in the wiring area inside the cabinet to collect environmental parameters inside the cabinet. The edge processing component is connected to the metering component and the sensing component respectively. It has built-in data preprocessing program, power consumption judgment model and fault judgment model to realize data preprocessing, multi-dimensional data fusion judgment and local control decision-making. The communication component is connected to the edge processing component and adopts 4G / 5G and NB-IoT dual communication modes to realize data interaction between the power metering box and the power back-end platform and operation and maintenance terminal; The early warning and protection components include an audible and visual early warning module and a loop current limiting protection module, which respectively receive control signals from the edge processing component to realize local early warning and abnormal loop protection and control.

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