Intelligent perception type integrated electric energy metering box and control method thereof

Abstract

The application discloses an intelligent perception type integrated electric energy metering box and a control method thereof. The method comprises the following steps: acquiring electrical topology information, space region information, perception node information and execution node information in the box, and establishing a multi-dimensional mapping model representing the corresponding relationship among electrical nodes, space regions, perception nodes and execution nodes; collecting multi-source perception data and mapping the multi-source perception data to corresponding electrical nodes and space regions to obtain a state fingerprint in the box; establishing or updating a health baseline according to historical operation data, and determining a target health baseline corresponding to a current load state and a current environment state; determining abnormal deviation information according to the deviation of the state fingerprint in the box from the target health baseline; determining an abnormal cause-effect chain in combination with the multi-dimensional mapping model, and determining a target control strategy according to the abnormal cause-effect chain to control the action of a corresponding execution node; and correcting the abnormal cause-effect chain and / or the target control strategy according to feedback perception data after the action.

Description

Technical Field

[0001] This application relates to the field of power metering equipment technology, and in particular to an intelligent sensing integrated power metering box and its control method. Background Technology

[0002] With the development of power distribution automation, electricity consumption information collection, and the intelligentization of low-voltage power distribution equipment, electricity metering boxes, as important terminal equipment connecting the power supply side and the user side, not only undertake basic functions such as electricity metering, line access, switch protection, and data communication, but are also increasingly required to possess capabilities such as operational status monitoring, anomaly early warning, and safety protection. Especially in application scenarios such as residential communities, commercial complexes, industrial park power distribution, and outdoor centralized metering, electricity metering boxes are constantly in a energized state. The operating status of their internal wiring terminals, switching devices, busbar connections, metering modules, and box sealing structures all directly affect metering reliability and electricity safety. Therefore, improving the ability of electricity metering boxes to perceive their internal operating status and safety risks has become an important direction for the intelligent development of low-voltage power distribution equipment.

[0003] Existing electricity metering boxes typically function primarily as electricity metering and on / off control systems. Some smart metering boxes are further equipped with detection modules for temperature, humidity, door magnets, smoke, water immersion, and residual current to monitor environmental or electrical anomalies within the box. However, in practical applications, these detection modules mostly rely on independent data acquisition and single-item threshold judgments, lacking a unified correlation between the detected data and making it difficult to accurately reflect the relationship between anomalies and specific electrical components, spatial areas, or load conditions. For example, an increase in terminal temperature inside the box can be caused by various factors such as normal heavy loads, poor heat dissipation, loose contacts, or aging connections. Relying solely on fixed temperature thresholds can easily lead to false alarms or delayed alarms. Environmental anomalies such as humidity, condensation, and water immersion can further induce leakage, short circuits, or decreased insulation performance, but relying solely on environmental detection results makes it difficult to promptly assess their impact on electrical safety. Furthermore, existing metering boxes typically rely on reporting alarms or directly cutting off power after an anomaly occurs. This control strategy is relatively crude and makes it difficult to implement targeted measures based on the type, location, and scope of the anomaly. This can easily lead to inaccurate anomaly location, low maintenance efficiency, and significant impacts on normal power circuits during anomaly handling.

[0004] Therefore, in the operation monitoring and safety control of intelligent sensing integrated power metering boxes, the weak correlation of multi-source sensing data, insufficient accuracy in identifying abnormal locations and causes, and low level of refinement of abnormal control strategies have become urgent problems to be solved. Summary of the Invention

[0005] This application provides an intelligent sensing integrated power metering box and its control method, aiming to solve the problems of weak correlation of multi-source sensing data, insufficient accuracy in identifying abnormal locations and causes, and low refinement of abnormal control strategies in the operation monitoring and safety control of intelligent sensing integrated power metering boxes.

[0006] Firstly, a control method for an intelligent sensing integrated power metering box, the method comprising: The electrical topology information, spatial area information, sensing node information, and execution node information of the power metering box are obtained. Based on the electrical topology information, spatial area information, sensing node information, and execution node information, a multi-dimensional mapping model is established. The multi-dimensional mapping model is used to characterize the correspondence between electrical nodes, spatial areas, sensing nodes, and execution nodes. Collect multi-source sensing data from the power metering box, and map the multi-source sensing data to the corresponding electrical nodes and spatial regions according to the multi-dimensional mapping model to obtain the box's internal status fingerprint; Based on the historical operating data of the power metering box, a health baseline corresponding to the electrical node is established or updated, and a target health baseline corresponding to the current load state and current environmental state is determined based on the state fingerprint of the box. The health baseline is used to characterize the normal state range of the electrical node under the corresponding load state and environmental state. Based on the deviation between the in-box status fingerprint and the target health baseline, abnormal deviation information is determined; Based on the abnormal deviation information and the multidimensional mapping model, the abnormal causal chain of the power metering box is determined, and the abnormal causal chain includes the abnormal source node, the abnormal type, and the affected node. The target control strategy is determined based on the abnormal causal chain, and the corresponding execution node actions are controlled according to the target control strategy; Obtain feedback perception data after the execution node's action, and correct the abnormal causal chain and / or the target control strategy based on the feedback perception data.

[0007] Optionally, in the above scheme, establishing a multi-dimensional mapping model based on the electrical topology information within the enclosure, the spatial region information, the sensing node information, and the execution node information includes: Based on the electrical topology information inside the box, the multiple electrical nodes inside the power metering box and the connection relationships between each electrical node are determined, thus obtaining the electrical topology relationship; Based on the spatial area information, the spatial area to which each electrical node belongs within the power metering box is determined, and the spatial relationship between the nodes is obtained; Based on the sensing node information, the detection object and detection range corresponding to each sensing node are determined, and the sensing mapping relationship is obtained; Based on the execution node information, the control object and scope of each execution node are determined, and the execution mapping relationship is obtained; The multidimensional mapping model is established based on the electrical topology, the node spatial relationship, the perception mapping relationship, and the execution mapping relationship.

[0008] Optionally, in the above scheme, the step of collecting multi-source sensing data within the energy metering box and mapping the multi-source sensing data to corresponding electrical nodes and spatial regions according to the multi-dimensional mapping model to obtain the box's internal status fingerprint includes: Collect electrical operation data, environmental status data, box status data, and execution node status data from the power metering box to obtain raw sensing data; The original sensing data is processed for time synchronization to obtain synchronized sensing data; Based on the perception mapping relationship, the synchronous perception data is associated with the corresponding electrical nodes and spatial regions to obtain node status data; Based on the electrical topology and the spatial relationship of the nodes, the node status data is combined to obtain the in-box status fingerprint.

[0009] Optionally, in the above scheme, establishing or updating the health baseline corresponding to the electrical node based on the historical operating data of the electricity metering box includes: Valid historical data are obtained by filtering historical state data that meet the normal operating conditions from the historical operating data. Based on the load status and environment status corresponding to the valid historical data, the valid historical data is grouped to obtain multiple status group data; Based on the range of state changes of the same electrical node in each state group data, determine the normal state range of the electrical node under the corresponding load state and environmental state. Based on the normal state range, generate or update the health baseline corresponding to the electrical node.

[0010] Optionally, in the above scheme, determining the target health baseline corresponding to the current load state and the current environmental state based on the in-box state fingerprint, and determining abnormal deviation information based on the deviation between the in-box state fingerprint and the target health baseline, includes: Based on the internal state fingerprint, determine the current load state, current environmental state, and current node state of the target electrical node; Based on the current load state and the current environment state, determine the target health baseline corresponding to the target electrical node from the health baseline; The current node status is compared with the target health baseline to obtain the node deviation result; Based on the node deviation results, the abnormal deviation information corresponding to the target electrical node is determined.

[0011] Optionally, in the above scheme, determining abnormal deviation information based on the deviation between the in-box state fingerprint and the target health baseline includes: Based on the internal state fingerprint of the enclosure, determine the temperature data, ambient temperature data, and load current data of the target connection node; Based on the temperature data, the ambient temperature data, and the load current data, the load normalized temperature rise characteristics of the target connection node are determined. The load-normalized temperature rise characteristic is compared with the target health baseline corresponding to the target connection node to obtain the temperature rise deviation result; Based on the temperature rise deviation results, the contact anomaly deviation information corresponding to the target connection node is determined.

[0012] Optionally, in the above scheme, determining abnormal deviation information based on the deviation between the in-box state fingerprint and the target health baseline includes: Based on the internal state fingerprint, determine the humidity state, condensation state, water immersion state of the target space area, and the residual current state of the electrical nodes corresponding to the target space area. Based on the target health baseline corresponding to the target spatial area, deviations from the humidity state, condensation state, and water immersion state are determined to obtain the environmental risk state. Based on the target health baseline corresponding to the electrical node, the deviation of the residual current state is judged to obtain the electrical risk state; Based on the environmental risk status and the electrical risk status, determine the environmental induced anomaly deviation information corresponding to the target spatial area.

[0013] Optionally, in the above scheme, determining the abnormal causal chain of the energy metering box based on the abnormal deviation information and the multidimensional mapping model includes: Based on the abnormal deviation information, determine the target node that has experienced abnormal deviation and the deviation type corresponding to the target node; Based on the multidimensional mapping model, the upstream node, downstream node, and adjacent spatial region corresponding to the target node are determined; Based on the state change sequence and state change correlation of the target node, the upstream node, the downstream node, and the adjacent spatial region, the anomaly source node and the anomaly propagation path are determined; The anomaly causal chain is determined based on the anomaly source node, the anomaly propagation path, and the deviation type.

[0014] Optionally, in the above scheme, determining the target control strategy based on the abnormal causal chain and controlling the corresponding execution node actions according to the target control strategy includes: Based on the abnormal causal chain, determine the abnormality level and impact range corresponding to the abnormal source node; Based on the anomaly source node, the anomaly level, and the scope of influence, candidate execution nodes that can act on the anomaly source node and / or the affected nodes are determined from the multidimensional mapping model. Based on the principle of minimum impact, a target execution node is determined from the candidate execution nodes, and a target control strategy is determined based on the target execution node; The target execution node actions are controlled according to the target control strategy to handle the abnormal source node and / or the affected node abnormally. The principle of minimum impact includes: when safety conditions are met, priority should be given to heat dissipation, dehumidification, alarm, interlocking or branch circuit isolation; when the anomaly involves the main incoming line, busbar, smoke, leakage current level reaches the preset emergency level or branch circuit isolation is ineffective, the main circuit power outage control should be implemented.

[0015] Secondly, an intelligent sensing integrated power metering box includes a box body, a metering unit, a multi-source sensing unit, an execution unit, an edge control unit, and a communication unit. The enclosure contains multiple spatial regions, and multiple electrical nodes are provided inside the enclosure, with at least some of the electrical nodes located within the corresponding spatial regions; The metering unit is used to collect the metering data of the power metering box; The multi-source sensing unit is used to collect multi-source sensing data from the power metering box. The execution unit is used to execute corresponding control actions according to control instructions; The communication unit is used for data communication with an external management platform; The edge control unit is connected to the metering unit, the multi-source sensing unit, the execution unit, and the communication unit, respectively. The edge control unit is used for: The electrical topology information, spatial area information, sensing node information, and execution node information of the power metering box are obtained. Based on the electrical topology information, spatial area information, sensing node information, and execution node information, a multi-dimensional mapping model is established. The multi-dimensional mapping model is used to characterize the correspondence between electrical nodes, spatial areas, sensing nodes, and execution nodes. Based on the multi-dimensional mapping model, the multi-source sensing data is mapped to the corresponding electrical nodes and spatial regions to obtain the in-box state fingerprint; Based on the historical operating data of the power metering box, establish or update the health baseline corresponding to the electrical node, and determine the target health baseline corresponding to the current load state and the current environmental state based on the state fingerprint of the box. Based on the deviation between the in-box status fingerprint and the target health baseline, abnormal deviation information is determined; Based on the abnormal deviation information and the multidimensional mapping model, the abnormal causal chain of the power metering box is determined, and the abnormal causal chain includes the abnormal source node, the abnormal type, and the affected node. The target control strategy is determined based on the abnormal causal chain, and the corresponding execution node in the execution unit is controlled according to the target control strategy. Obtain feedback perception data after the execution node's action, and correct the abnormal causal chain and / or the target control strategy based on the feedback perception data.

[0016] Compared with the prior art, this application has at least the following beneficial effects: This application, based on further analysis and research of existing technical problems, recognizes that existing technologies suffer from weak correlation of multi-source sensing data, insufficient accuracy in identifying abnormal locations and causes, and low refinement of abnormal control strategies in the operation monitoring and safety control of intelligent sensing integrated energy metering boxes. By acquiring the internal electrical topology, spatial area, sensing node, and execution node information of the energy metering box, and establishing a multi-dimensional mapping model to characterize the correspondence between electrical nodes, spatial areas, sensing nodes, and execution nodes, the collected multi-source sensing data is no longer treated as isolated data for judgment, but can be mapped to specific electrical nodes and spatial areas, forming an internal state fingerprint, thus providing a structured data foundation for abnormal location identification. Furthermore, based on this, a health baseline corresponding to the electrical nodes is established or updated using historical operating data, and a target health baseline corresponding to the current load state and current environmental state is determined according to the internal state fingerprint, ensuring that the current state operates within matching conditions. Deviation judgment is performed under certain conditions to avoid misjudgment caused by relying solely on fixed thresholds. Further, based on the deviation, abnormal deviation information is determined, and a multi-dimensional mapping model is used to identify the abnormal causal chain, including the abnormal source node, abnormal type, and affected nodes. This transforms the abnormal judgment from "whether it is abnormal" to "where the abnormality occurs, what type of abnormality it is, and which nodes it affects"—a more comprehensive identification process. Subsequently, a target control strategy is determined based on the abnormal causal chain, and the corresponding execution node actions are controlled. The abnormal causal chain and / or target control strategy are then corrected using feedback sensing data after execution. This allows the control actions to be continuously adjusted based on the abnormal identification results and control feedback. This improves the correlation utilization of multi-source sensing data, enhances the accuracy of abnormal location and cause identification, and improves the refinement of abnormal control strategies. This addresses the problems of weak correlation of multi-source sensing data, insufficient accuracy of abnormal location and cause identification, and low refinement of abnormal control strategies in the operation monitoring and safety control of intelligent sensing integrated power metering boxes. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the control method for an intelligent sensing integrated power metering box provided in one embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] In one embodiment, such as Figure 1As shown, a control method for an intelligent sensing integrated energy metering box is provided. This method can be executed by an edge control unit within the energy metering box, or by the edge control unit in collaboration with an external management platform. The edge control unit can be a microcontroller, an embedded processor, an edge computing module, or a control circuit with data acquisition, data processing, and control output capabilities.

[0020] Specifically, the edge control unit acquires the electrical topology information, spatial area information, sensing node information, and execution node information of the energy metering box. The electrical topology information represents the connection relationships between electrical nodes within the energy metering box, such as the main incoming line, metering units, busbars, switches, terminals, and outgoing branches. The spatial area information represents the division of the space within the box into incoming line areas, metering areas, switch areas, outgoing line areas, and terminal areas. The sensing node information represents the installation location, detection object, and detection range of each sensing node. The execution node information represents the control object and scope of each execution node. Based on the above information, the edge control unit establishes a multi-dimensional mapping model, creating queryable and associative data relationships between electrical nodes, spatial areas, sensing nodes, and execution nodes.

[0021] The edge control unit collects multi-source sensing data from within the energy metering box. This multi-source sensing data can include one or more of the following: electrical operation data, environmental status data, box status data, and execution node status data. Electrical operation data can include voltage, current, power, energy, power factor, residual current, and switch on / off status; environmental status data can include terminal temperature, busbar temperature, box interior temperature, humidity, condensation status, water immersion status, and smoke status; box status data can include door opening / closing status, electronic lock status, vibration status, and tilt status; execution node status data can include fan operation status, heating / dehumidification module operation status, branch switch status, main switch status, and audible / visual alarm status. The edge control unit maps this multi-source sensing data to corresponding electrical nodes and spatial regions using a multi-dimensional mapping model, obtaining an internal state fingerprint. This internal state fingerprint represents the structured operating status of the energy metering box at the current moment or within the current time window, such as the current status of a specific outgoing branch, terminal temperature rise status, humidity status of the surrounding spatial area, corresponding switch status, and the trend of status changes in adjacent areas.

[0022] The edge control unit establishes or updates the health baseline for each electrical node based on historical operating data from the power metering box. Historical operating data can be multi-source sensing data collected during the normal operation of the power metering box, or it can include normal operation samples that have been manually verified or calibrated by the platform. The health baseline characterizes the normal state range of electrical nodes under corresponding load and environmental conditions. For example, it represents the normal variation range of state quantities such as terminal temperature rise, residual current, metering deviation, and switch response time under different load current ranges, different ambient temperature ranges, and different humidity ranges. After the current box state fingerprint is formed, the edge control unit determines the current load state and the current environmental state based on the box state fingerprint and calls the target health baseline that matches the current load state and the current environmental state.

[0023] The edge control unit compares the internal status fingerprint of the enclosure with the target health baseline to determine abnormal deviation information. Abnormal deviation information can include the identifier of the node that deviated, the type of deviation, the magnitude of the deviation, the duration of the deviation, the trend of the deviation, and the corresponding spatial area. For example, if the temperature of a terminal block does not exceed a fixed alarm threshold, but is significantly higher than its target health baseline under the same load and environmental conditions, abnormal temperature rise deviation information for that terminal block can be generated. Similarly, if the humidity and condensation conditions at the bottom of the enclosure deviate, accompanied by a deviation in the residual current of the corresponding electrical node, environmentally induced abnormal deviation information can be generated.

[0024] The edge control unit determines the abnormal causal chain of the energy metering box based on abnormal deviation information and a multi-dimensional mapping model. The abnormal causal chain includes the abnormal source node, abnormal type, and affected nodes, and may also include the abnormal propagation path, the scope of abnormal influence, and the sequence of abnormal changes. Abnormal types can include contact abnormalities, overload abnormalities, leakage abnormalities, condensation abnormalities, water immersion abnormalities, smoke abnormalities, unauthorized opening abnormalities, metering abnormalities, and switch malfunction abnormalities. For example, if a terminal temperature rise deviation occurs first, followed by a temperature increase in adjacent switch areas, the abnormal source node can be identified as the corresponding wiring terminal, forming a thermal abnormality causal chain spreading from the wiring terminal to the switch area. If humidity deviation, condensation state deviation, and residual current deviation occur sequentially, an abnormal causal chain indicating a risk of leakage induced by condensation can be identified.

[0025] The edge control unit determines the target control strategy based on the abnormal causal chain and controls the corresponding execution node actions according to the target control strategy. The target control strategy may include heat dissipation, dehumidification, audible and visual alarms, remote reporting, electronic lock interlocking, branch circuit isolation, main circuit power failure, event logging, etc. The edge control unit can determine the execution nodes that can act on the abnormal source node or affected node from the multi-dimensional mapping model based on the abnormal source node, abnormality type, abnormality level, and affected nodes, and prioritize the execution nodes that have less impact on normal power circuits for control. For example, for abnormal local terminal temperature rise, heat dissipation or corresponding branch isolation is prioritized; for condensation risk, heating and dehumidification are prioritized; and for leakage current reaching the emergency level or abnormal smoke, the main circuit power failure is executed.

[0026] After executing a node action, the edge control unit continues to acquire feedback sensing data and corrects the abnormal causal chain and / or target control strategy based on the feedback sensing data. Feedback sensing data may include temperature changes, humidity changes, residual current changes, switch status changes, smoke status changes, etc., after the control action. If the target node temperature rise decreases after heat dissipation, the original control strategy can be confirmed as effective; if the temperature rise continues to increase after branch loop isolation, it can be determined that the abnormal source node may not be that branch loop, thus correcting the abnormal causal chain or increasing the abnormality level; if the switch status does not change after the disconnect command is issued, a switch failure to operate abnormality can be identified, and further alarms or main circuit protection can be executed.

[0027] Through the above implementation methods, the electricity metering box can associate multi-source sensing data with specific electrical nodes, spatial areas, and execution nodes. Based on a healthy baseline, it identifies abnormal deviations, determines the source node, type, and affected nodes through anomaly causal chains, and finally executes targeted control actions and provides feedback corrections. This improves the correlation and utilization of multi-source sensing data, enhances the accuracy of identifying anomaly locations and causes, improves the refinement of anomaly control strategies, and reduces the probability of false alarms, missed alarms, and unnecessary power outages.

[0028] In one implementation, the process of establishing a multidimensional mapping model may further include the generation process of electrical topology relationships, node spatial relationships, perception mapping relationships, and execution mapping relationships.

[0029] Specifically, the edge control unit determines multiple electrical nodes within the energy metering box and their connection relationships based on the internal electrical topology information, thus obtaining the electrical topology relationship. Electrical nodes may include the main incoming node, metering nodes, busbar nodes, switch nodes, terminal block nodes, and outgoing branch nodes. Connection relationships may include upstream connections, downstream connections, parallel branch connections, phase line correspondences, and protection circuit correspondences. For example, the main incoming node sequentially connects to the metering node, switch node, and multiple outgoing branch nodes, with one outgoing branch node connected to the user-side line via a corresponding terminal block.

[0030] The edge control unit determines the spatial region within the energy metering box for each electrical node based on spatial region information, thus obtaining the node spatial relationships. Spatial regions can be divided based on the box structure, functional compartments, component installation locations, or heat and moisture propagation characteristics. For example, the box can be divided into an inlet area, a metering area, a switch area, an outlet area, and a terminal area. Node spatial relationships indicate which spatial region a given electrical node is located in, or which adjacent spatial regions a given electrical node simultaneously interacts with for heat diffusion or moisture migration.

[0031] The edge control unit determines the detection object and detection range corresponding to each sensing node based on the sensing node information, thus obtaining a sensing mapping relationship. Sensing nodes may include temperature sensors, humidity sensors, condensation detection modules, water immersion detection modules, smoke detection modules, residual current acquisition modules, door status detection modules, vibration detection modules, and switch status detection modules, etc. The sensing mapping relationship indicates which electrical node or spatial area the data collected by the sensing node should be associated with. For example, a temperature sensor installed near the first outgoing terminal corresponds to the first outgoing terminal node, a water immersion detection module located at the bottom of the enclosure corresponds to the space area at the bottom of the enclosure, and a residual current acquisition module located in the main incoming circuit corresponds to the main incoming node.

[0032] The edge control unit determines the controlled object and scope of action corresponding to each execution node based on the execution node information, thus obtaining the execution mapping relationship. Execution nodes may include fans, heating and dehumidification modules, electronic locks, audible and visual alarms, branch switches, main switches, etc. The execution mapping relationship is used to indicate which electrical nodes or spatial areas a certain execution node can control. For example, the first branch switch corresponds to the first outgoing branch node, the fan corresponds to the heat dissipation range of the switch area and terminal area, the heating and dehumidification module corresponds to the dehumidification range of the metering area and terminal area, and the main switch corresponds to the main incoming node and all its downstream branch circuits.

[0033] The edge control unit establishes a multi-dimensional mapping model based on electrical topology relationships, node spatial relationships, perception mapping relationships, and execution mapping relationships. This multi-dimensional mapping model can be stored in the edge control unit in the form of a relational table, graph structure, node matrix, configuration file, or database, or it can be distributed by an external management platform and updated locally. Through this model, the edge control unit can look up the corresponding electrical nodes and spatial regions based on specific perception data, and it can also query candidate execution nodes that can act on an anomaly source node.

[0034] Through the above implementation methods, the multidimensional mapping model not only records sensor positions but also simultaneously establishes electrical connection relationships, spatial positional relationships, detection correspondence relationships, and control action relationships, enabling sensing data, anomaly localization, and execution control to be completed under the same associated model. This improves the problem of data fragmentation between multiple detection modules, providing an accurate data foundation for subsequent in-box state fingerprint generation, anomaly causal chain identification, and target control strategy determination.

[0035] In one implementation, the process of generating the in-box state fingerprint may further include raw sensing data acquisition, time synchronization, node association, and state combination.

[0036] Specifically, the edge control unit collects electrical operation data, environmental status data, cabinet status data, and execution node status data within the power metering box to obtain raw sensing data. This raw sensing data can be collected according to a preset collection cycle, or triggered when events such as door opening, sudden changes in residual current, rapid temperature increases, or switch actions are detected. Electrical operation data can be collected by the metering unit, current acquisition module, voltage acquisition module, residual current acquisition module, and switch status detection module; environmental status data can be collected by temperature sensors, humidity sensors, condensation detection modules, water immersion detection modules, and smoke detection modules; cabinet status data can be collected by door magnets, electronic lock feedback modules, vibration sensors, or tilt sensors; and execution node status data can be fed back from execution nodes such as fans, heating and dehumidification modules, branch switches, main switches, and audible and visual alarms.

[0037] Since the sampling periods and response times of different sensing nodes may differ, the edge control unit performs time synchronization processing on the raw sensing data to obtain synchronized sensing data. Time synchronization processing may include timestamp marking, sampling period alignment, time window aggregation, and data truncation before and after abnormal events. For electrical operation data with high sampling frequency, mean, peak, rate of change, or RMS processing can be performed within the target time window; for state quantity data, the state change time and duration can be determined within the target time window.

[0038] The edge control unit associates synchronous sensing data with corresponding electrical nodes and spatial areas based on the sensing mapping relationship, obtaining node status data. Node status data can include voltage, current, power, energy, residual current, temperature rise, and switch status for each electrical node, and can also include ambient temperature, humidity, condensation status, water immersion status, and smoke status for each spatial area. For example, data from a temperature sensor is associated with its corresponding terminal node, data from a humidity sensor is associated with its terminal area, and a switch status feedback is associated with its corresponding branch switch node.

[0039] The edge control unit combines node state data based on electrical topology and node spatial relationships to obtain an internal state fingerprint. The internal state fingerprint can be a set of node states at the current moment or a sequence of states within a time window. It records not only the current state of individual nodes but also the state relationships between nodes, such as temperature rise differences between adjacent nodes, upstream and downstream current differences, deviations between total incoming power and branch power, and humidity trends in adjacent areas.

[0040] Through the above implementation method, the raw sensing data is transformed into an in-box state fingerprint with electrical topology and spatial location attributes after time synchronization and node association, avoiding the problem of difficulty in associating different acquisition channels. This provides structured state data for subsequent health baseline matching, anomaly deviation judgment, and anomaly causal chain identification, improving the completeness and accuracy of anomaly identification.

[0041] In one implementation, the process of establishing or updating a health baseline may further include screening effective historical data, grouping states, determining the range of normal states, and generating the baseline.

[0042] Specifically, the edge control unit filters historical status data that meets normal operating conditions from historical operational data to obtain valid historical data. Normal operating conditions may include: the cabinet door is closed, the electronic lock is functioning normally, no alarm events such as smoke, water immersion, condensation, leakage, overload, or metering anomalies have occurred, the switch is stable, communication is normal, voltage and current are within allowable operating ranges, and there are no records of manual maintenance or abnormal resets within the corresponding time period. By filtering for normal operating conditions, data from fault states, maintenance states, or abnormal recovery states can be avoided from being mistakenly used as normal samples.

[0043] The edge control unit groups the valid historical data according to the load and environmental states corresponding to the valid historical data, resulting in multiple state group data. Load states can be divided according to current magnitude, power magnitude, load rate, power consumption period, or load variation; environmental states can be divided according to the ambient temperature, humidity, season, weather influence, or temperature and humidity conditions of the space. For example, historical state data can be divided into multiple state group data such as low load normal temperature group, medium load high temperature group, and high load high humidity group.

[0044] The edge control unit determines the normal state range of an electrical node under corresponding load and environmental conditions based on the state change range of the same electrical node in each state group data. The normal state range can be a value interval of a state variable, an allowable deviation range near the mean, a range of change rates, or a duration range. State variables can include terminal temperature rise, busbar temperature rise, residual current, metering deviation, switch response time, humidity change rate, etc. For example, for a certain terminal node, a lower normal temperature rise range can be determined in the low-load, normal-temperature group, and a higher but still normal temperature rise range can be determined in the high-load, high-temperature group.

[0045] The edge control unit generates or updates the health baseline corresponding to the electrical nodes based on the normal state range. Health baselines can be stored separately for each electrical node or jointly for both electrical nodes and spatial regions. Health baselines can be updated over time, but must continue to meet normal operating conditions during updates to prevent abnormal data from being included in the health baseline. For newly installed energy metering boxes or those with limited historical data, the factory baseline, the baseline of the same model of equipment, or the initial baseline issued by the platform can be used initially, and then gradually updated during normal operation.

[0046] Through the above implementation method, the health baseline can reflect the normal state range of the same electrical node under different load and environmental conditions, rather than using a single fixed threshold for judgment. This reduces false alarms caused by seasonal changes, load variations, or installation differences, and enables the timely detection of early anomalies that have deviated from normal operating patterns but have not yet reached the fixed alarm threshold.

[0047] In one implementation, abnormal deviation information can be obtained by matching the target health baseline and comparing node status.

[0048] Specifically, the edge control unit determines the current load status, current environmental status, and current node status of the target electrical node based on the status fingerprint within the enclosure. The target electrical node can be any of the following: main incoming node, metering node, busbar node, switch node, terminal block node, or outgoing branch node. The current load status can be determined based on the current, power, or load rate corresponding to the target electrical node; the current environmental status can be determined based on the ambient temperature, humidity, and condensation conditions of the space area where the target electrical node is located; and the current node status can be determined based on the temperature rise, residual current, metering deviation, switch status, or status change trend of the target electrical node.

[0049] The edge control unit determines the target health baseline corresponding to the target electrical node from the health baseline based on the current load state and the current environment state. If the current load state and the current environment state completely match a certain state group data, the health baseline corresponding to that state group data is called; if the current state is between adjacent state groups, the closest health baseline can be selected, or the target health baseline can be determined after smoothing based on adjacent health baselines.

[0050] The edge control unit compares the current node state with the target health baseline to obtain the node deviation result. The node deviation result can include whether there is a deviation, the direction of deviation, the magnitude of deviation, the duration of deviation, and the trend of deviation. For example, when the current temperature rises above the normal temperature rise range of the target health baseline, a node deviation result of high temperature rise can be obtained; when the current residual current is consistently higher than the normal variation range of the target health baseline, a node deviation result of high residual current can be obtained; when the current switching response time exceeds the normal response time range corresponding to the target health baseline, a node deviation result of abnormal switching response can be obtained.

[0051] The edge control unit determines the abnormal deviation information corresponding to the target electrical node based on the node deviation results. The abnormal deviation information may further include the node identifier of the target electrical node, its spatial region, deviation type, deviation level, deviation duration, and related sensing data. For cases with short duration and small deviation magnitude, it can be identified as a warning-level abnormal deviation; for cases with large deviation magnitude, continuously increasing trend, or simultaneous occurrence with other node abnormalities, it can be identified as a higher-level abnormal deviation.

[0052] Through the above implementation method, the current node status is not simply compared with a fixed alarm threshold, but rather with a target health baseline that matches the current load status and the current environmental status. This allows for a more accurate distinction between status fluctuations caused by normal load changes and status deviations caused by abnormal factors, improving the adaptability and accuracy of anomaly identification.

[0053] In one implementation, for target connection nodes such as terminals, busbar connections, switch connections, incoming line connections, or outgoing line connections, contact abnormality deviation information can be determined based on the load normalized temperature rise characteristics.

[0054] Specifically, the edge control unit determines the temperature data, ambient temperature data, and load current data of the target connection node based on the internal status fingerprint. The temperature data can be collected by a temperature sensor located near the target connection node, or by an infrared temperature detection module or a multi-point temperature acquisition module; the ambient temperature data can be collected by an ambient temperature sensor within the space area where the target connection node is located; and the load current data can be determined by a current acquisition module, metering unit, or branch acquisition module of the loop where the target connection node is located.

[0055] The edge control unit determines the load-normalized temperature rise characteristic (RTD) of the target connection node based on temperature data, ambient temperature data, and load current data. The RTD characterizes the degree of temperature rise anomaly of the target connection node after excluding or mitigating the influence of ambient temperature and load magnitude. In practice, the temperature rise state of the target connection node relative to the environment can be determined first based on the temperature and ambient temperature data, and then combined with the load current data to determine whether this temperature rise state is reasonable under the current load conditions. If the target connection node continues to heat up under low load or small load changes, or if its temperature rise level is significantly higher than that of similar connection nodes under similar load conditions, then its RTD characterization can indicate a higher risk of contact anomalies.

[0056] The edge control unit compares the load-normalized temperature rise characteristic with the target health baseline corresponding to the target connected node to obtain the temperature rise deviation result. The target health baseline can record the normal temperature rise range or normal temperature rise trend of the target connected node under different load conditions and different ambient temperatures. If the load-normalized temperature rise characteristic exceeds the normal range corresponding to the target health baseline, or the slope of the temperature rise change is consistently higher than the allowable range of the target health baseline, then a temperature rise deviation is determined to exist.

[0057] Based on the temperature rise deviation, the edge control unit determines the contact anomaly deviation information corresponding to the target connection node. This contact anomaly deviation information may include risks such as loose contact, connection aging, abnormal terminal crimping, or increased local contact resistance. For minor temperature rise deviations that have not spread to adjacent nodes, a warning message can be generated and heat dissipation or inspection prompts can be executed. For continuously increasing temperature rise deviations or abnormal smoke conditions, the anomaly level can be increased and branch circuit isolation or main circuit power-off control can be executed.

[0058] Through the above implementation method, the anomaly detection of the target connection node does not only rely on whether the temperature exceeds a fixed threshold, but also combines ambient temperature, load current, and the target health baseline to determine the normalized temperature rise characteristics of the load. This reduces false alarms caused by normal high loads and enables early identification of safety hazards such as loose contacts, aging connections, or increased local contact resistance.

[0059] In one implementation, for a target spatial region, environmentally induced abnormal deviation information can be determined based on the environmental state and the residual current state of the corresponding electrical nodes.

[0060] Specifically, the edge control unit determines the humidity, condensation, and water immersion status of the target space area, as well as the residual current status of the corresponding electrical nodes, based on the internal status fingerprint of the enclosure. The target space area can be an incoming line area, a metering area, a switch area, an outgoing line area, a terminal area, or the bottom area of ​​the enclosure. The electrical nodes corresponding to the target space area can be determined through a multi-dimensional mapping model; for example, the terminal area corresponds to wiring terminal nodes and outgoing branch nodes, and the incoming line area corresponds to the main incoming line node and busbar nodes.

[0061] The edge control unit determines the environmental risk status by assessing deviations from the target health baseline corresponding to the target space area, including humidity, condensation, and water immersion. The target health baseline records the normal humidity range, condensation variation patterns, and normal water immersion values ​​for the target space area under different seasons, internal temperatures, and ventilation conditions. If the humidity remains above the normal range, the condensation state changes from no condensation to a tendency to condense, or the water immersion state changes from normal to abnormal, the environmental risk status can be determined.

[0062] The edge control unit determines the electrical risk status by judging the deviation of the residual current status based on the target health baseline corresponding to the electrical node. The residual current status can include the residual current value, the trend of change, and the duration. If the residual current status of the corresponding electrical node also deviates abnormally after the environmental risk status of the target space area occurs, the electrical risk status can characterize the existence of leakage risk induced by humidity, condensation, or water immersion in that space area.

[0063] The edge control unit determines environmentally induced anomaly deviation information for the target spatial area based on the environmental and electrical risk status. Environmentally induced anomaly deviation information can include condensation-induced anomalies, water immersion-induced anomalies, and high humidity-induced leakage anomalies. When only humidity deviation exists but the electrical risk status is not yet abnormal, an environmental warning can be issued. When both environmental and electrical risk statuses exist simultaneously, or when they are sequentially related in time, the anomaly level can be increased, and controls such as dehumidification, alarms, branch isolation, or main circuit power outage can be implemented.

[0064] Through the above implementation method, environmental anomalies and electrical anomalies are no longer judged independently, but are instead analyzed in a coupled manner based on the target spatial area and its corresponding electrical nodes. This allows for the early detection of the impact of humidity, condensation, or water immersion on electrical safety, and reduces false alarms caused by relying solely on humidity or water immersion signals.

[0065] In one implementation, the abnormal causal chain can be determined based on abnormal deviation information, a multidimensional mapping model, and the state change relationships of relevant nodes and spatial regions.

[0066] Specifically, the edge control unit determines the target node and its corresponding deviation type based on the abnormal deviation information. The target node can be an electrical node or a nodeized object corresponding to a spatial region. Deviation types can include temperature rise deviation, residual current deviation, humidity deviation, metering deviation, switch response deviation, and door status deviation, etc.

[0067] The edge control unit determines the upstream node, downstream node, and adjacent spatial regions corresponding to the target node based on a multi-dimensional mapping model. For electrical nodes, their upstream and downstream nodes can be determined according to electrical topology relationships. For example, the upstream node of a certain outgoing branch node can be a switch node or a busbar node, and the downstream node can be a user-side connection node. For spatial regions, adjacent spatial regions can be determined according to node spatial relationships. For example, the terminal area can be adjacent to the switch area and the bottom area of ​​the enclosure.

[0068] The edge control unit determines the anomaly source node and anomaly propagation path based on the sequence and correlation of state changes of the target node, upstream node, downstream node, and adjacent spatial regions. The sequence of state changes indicates the order in which anomalies occur at different nodes or spatial regions, while the correlation of state changes indicates whether there are synchronous changes, lagging changes, or consistent trends between different state quantities. For example, if a terminal block node first experiences a temperature rise deviation, and then the adjacent switch area experiences a temperature rise, then that terminal block node can be identified as the anomaly source node, and the adjacent spatial region can be identified as the affected area. If the metering deviation and branch current state become abnormal after the box door is abnormally opened, then the box door abnormality can be considered as one of the starting events in the anomaly causal chain. If the switch state does not change after a disconnect command is issued, then the corresponding switch node can be identified as the switch failure to operate anomaly source node.

[0069] The edge control unit determines the anomaly causal chain based on the anomaly source node, anomaly propagation path, and deviation type. The anomaly causal chain can be represented using structured data, including, for example, the anomaly source node, anomaly type, anomaly propagation path, affected nodes, affected spatial area, anomaly level, and related sensing data. The anomaly type can be determined based on the deviation type and its associated status; for example, temperature rise deviation and load normalized temperature rise anomaly correspond to contact anomaly, humidity deviation and residual current deviation correspond to environmentally induced leakage anomaly, and metering deviation and door anomaly correspond to suspected wiring or metering anomaly.

[0070] Through the above implementation methods, anomaly identification no longer focuses on whether a single detection data point is abnormal, but rather combines electrical connection relationships, spatial adjacency relationships, and state change correlations to determine the anomaly source node and its impact path. This improves the accuracy of anomaly location and cause identification, enabling maintenance personnel to more quickly determine the anomaly source and affected area.

[0071] In one implementation, the target control strategy can be determined based on the abnormal causal chain, the abnormality level, the scope of influence, and the principle of minimum impact.

[0072] Specifically, the edge control unit determines the anomaly level and impact range corresponding to the anomaly source node based on the anomaly causal chain. The anomaly level can be determined based on the anomaly type, deviation magnitude, duration, trend of change, whether there is a risk of propagation, and whether it involves personal or equipment safety. Anomaly levels can include warning level, alert level, protection level, and emergency level. The impact range can be determined based on the anomaly source node, affected nodes, anomaly propagation path, and electrical topology. For example, if the anomaly is limited to a certain outgoing branch, the impact range can be that branch circuit; if the anomaly involves the main incoming line, busbar, or multiple branches, the impact range can extend to the main line or multiple spatial areas.

[0073] The edge control unit determines candidate execution nodes that can act on the source node and / or affected nodes from a multi-dimensional mapping model based on the anomaly source node, anomaly level, and impact range. Candidate execution nodes may include fans, heating and dehumidification modules, audible and visual alarms, electronic locks, branch switches, and main switches. For example, for abnormal temperature rise in the terminal area, candidate execution nodes may include fans and corresponding branch switches; for high humidity or condensation anomalies, candidate execution nodes may include heating and dehumidification modules and fans; for unauthorized opening anomalies, candidate execution nodes may include electronic locks and audible and visual alarms; for anomalies where the leakage current level reaches a preset emergency level, candidate execution nodes may include branch switches or main switches.

[0074] The edge control unit determines the target execution node from the candidate execution nodes based on the principle of minimum impact, and then determines the target control strategy based on the target execution node. The principle of minimum impact means that, under the condition of meeting safety handling conditions, priority is given to the control actions that have the least impact on the normal power circuit. For example, if the anomaly can be suppressed by heat dissipation or dehumidification, heat dissipation or dehumidification is executed first; if the anomaly can be resolved by isolating a single branch circuit, the corresponding branch circuit is disconnected first; and the main circuit power cut-off control is only executed when the anomaly involves the main incoming line, busbar, smoke, leakage current level reaches the preset emergency level, or branch circuit isolation is ineffective.

[0075] The edge control unit controls the actions of target execution nodes according to the target control strategy to handle anomalies in the source node and / or affected nodes. Control actions may include starting the fan, activating the heating and dehumidification module, outputting a local audible and visual alarm, uploading anomaly information to an external management platform, locking the electronic lock, and disconnecting the target branch switch or the main switch. While executing control actions, the edge control unit can also record the anomaly occurrence time, the source node, the anomaly type, the anomaly level, the target control strategy, and the feedback status of the execution node for subsequent maintenance and traceability.

[0076] Through the above implementation methods, anomaly handling actions can be matched with the anomaly source node, anomaly level, and scope of impact, avoiding direct power outages to the entire circuit during local anomalies and avoiding issuing only ordinary alarms during emergency anomalies. Therefore, while ensuring electrical safety, the impact of anomaly handling on normal power circuits can be reduced, improving the targeting and precision of the control strategy.

[0077] In one embodiment, the intelligent sensing integrated power metering box includes a box body, a metering unit, a multi-source sensing unit, an execution unit, an edge control unit, and a communication unit.

[0078] The enclosure houses the metering unit, multi-source sensing unit, execution unit, edge control unit, and communication unit. Multiple spatial regions are formed within the enclosure, and multiple electrical nodes are installed within it, with at least some electrical nodes located within their respective spatial regions. The spatial regions may include incoming line areas, metering areas, switching areas, outgoing line areas, and terminal areas. Electrical nodes may include main incoming line nodes, metering nodes, busbar nodes, switching nodes, terminal block nodes, and outgoing branch nodes. This division of spatial regions and electrical nodes establishes a correspondence between the internal structure of the enclosure and the data processing procedures.

[0079] The metering unit is used to collect metering data from the electricity metering box. This data may include voltage, current, energy, power, power factor, etc. The metering unit may include an electricity meter, a metering chip, a voltage sampling circuit, a current sampling circuit, or a data acquisition interface that communicates with the electricity meter. The data collected by the metering unit can be used as part of the electrical operation data and transmitted to the edge control unit.

[0080] The multi-source sensing unit is used to collect multi-source sensing data within the power metering box. The multi-source sensing unit may include one or more of the following modules: terminal temperature acquisition module, busbar temperature acquisition module, ambient temperature and humidity acquisition module, condensation detection module, water immersion detection module, smoke detection module, residual current acquisition module, box door status detection module, vibration detection module, and switch status detection module. Each sensing node in the multi-source sensing unit can be located near the corresponding electrical node or within the corresponding spatial area to acquire status data related to the electrical node or spatial area.

[0081] The execution unit is used to execute corresponding control actions according to control commands. The execution unit may include a fan, a heating and dehumidification module, an audible and visual alarm, an electronic lock, branch switches, and a main switch. The fan can be used to dissipate heat from the target space; the heating and dehumidification module can be used to reduce humidity inside the enclosure or suppress condensation; the audible and visual alarm can be used to output a local alarm; the electronic lock can be used to lock the enclosure door; the branch switch can be used to isolate the target branch circuit; and the main switch can be used to cut off the main power supply in an emergency.

[0082] The communication unit is used for data communication with an external management platform. The communication unit can employ wired or wireless communication modules, such as power line communication modules, Ethernet communication modules, cellular communication modules, narrowband IoT communication modules, and short-range wireless communication modules. The communication unit can upload in-box status fingerprints, abnormal deviation information, abnormal causal chains, target control strategies, and execution feedback information. It can also receive configuration parameters, health baseline update data, or remote control commands from the external management platform.

[0083] The edge control unit is connected to the metering unit, multi-source sensing unit, execution unit, and communication unit, respectively. The edge control unit is used to acquire electrical topology information, spatial area information, sensing node information, and execution node information within the enclosure, and to establish a multi-dimensional mapping model. It is also used to map multi-source sensing data to corresponding electrical nodes and spatial areas according to the multi-dimensional mapping model, obtaining an internal state fingerprint. Furthermore, it is used to establish or update a health baseline based on historical operating data, determine a target health baseline based on the internal state fingerprint, and determine abnormal deviation information based on the deviation between the internal state fingerprint and the target health baseline. It is also used to determine anomaly causal chains based on abnormal deviation information and the multi-dimensional mapping model, and to determine a target control strategy based on the anomaly causal chains, thereby controlling the actions of the corresponding execution nodes in the execution unit. Finally, it is used to acquire feedback sensing data after the execution node actions, and to correct the anomaly causal chains and / or the target control strategy based on the feedback sensing data.

[0084] Through the above implementation methods, the intelligent sensing integrated energy metering box integrates metering, sensing, edge analysis, execution control, and communication functions in its hardware, and achieves collaborative association between electrical nodes, spatial areas, sensing nodes, and execution nodes in data processing. This enables the energy metering box to possess the capabilities of proactive anomaly identification, anomaly location positioning, anomaly impact range judgment, and hierarchical control, thereby improving the metering box's operational safety, anomaly handling efficiency, and intelligent management level.

[0085] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

Claims

1. A control method for an intelligent sensing integrated power metering box, characterized in that, include: The electrical topology information, spatial area information, sensing node information, and execution node information of the power metering box are obtained. Based on the electrical topology information, spatial area information, sensing node information, and execution node information, a multi-dimensional mapping model is established. The multi-dimensional mapping model is used to characterize the correspondence between electrical nodes, spatial areas, sensing nodes, and execution nodes. Collect multi-source sensing data from the power metering box, and map the multi-source sensing data to the corresponding electrical nodes and spatial regions according to the multi-dimensional mapping model to obtain the box's internal status fingerprint; Based on the historical operating data of the power metering box, a health baseline corresponding to the electrical node is established or updated, and a target health baseline corresponding to the current load state and current environmental state is determined based on the state fingerprint of the box. The health baseline is used to characterize the normal state range of the electrical node under the corresponding load state and environmental state. Based on the deviation between the in-box status fingerprint and the target health baseline, abnormal deviation information is determined; Based on the abnormal deviation information and the multidimensional mapping model, the abnormal causal chain of the power metering box is determined, and the abnormal causal chain includes the abnormal source node, the abnormal type, and the affected node. The target control strategy is determined based on the abnormal causal chain, and the corresponding execution node actions are controlled according to the target control strategy; Obtain feedback perception data after the execution node's action, and correct the abnormal causal chain and / or the target control strategy based on the feedback perception data.

2. The control method for the intelligent sensing integrated power metering box according to claim 1, characterized in that, The step of establishing a multi-dimensional mapping model based on the electrical topology information within the enclosure, the spatial region information, the sensing node information, and the execution node information includes: Based on the electrical topology information inside the box, the multiple electrical nodes inside the power metering box and the connection relationships between each electrical node are determined, thus obtaining the electrical topology relationship; Based on the spatial area information, the spatial area to which each electrical node belongs within the power metering box is determined, and the spatial relationship between the nodes is obtained; Based on the sensing node information, the detection object and detection range corresponding to each sensing node are determined, and the sensing mapping relationship is obtained; Based on the execution node information, the control object and scope of each execution node are determined, and the execution mapping relationship is obtained; The multidimensional mapping model is established based on the electrical topology, the node spatial relationship, the perception mapping relationship, and the execution mapping relationship.

3. The control method for the intelligent sensing integrated power metering box according to claim 2, characterized in that, The process involves collecting multi-source sensing data from within the power metering box and mapping the data to corresponding electrical nodes and spatial regions using the multi-dimensional mapping model to obtain a box-level status fingerprint, including: Collect electrical operation data, environmental status data, box status data, and execution node status data from the power metering box to obtain raw sensing data; The original sensing data is processed for time synchronization to obtain synchronized sensing data; Based on the perception mapping relationship, the synchronous perception data is associated with the corresponding electrical nodes and spatial regions to obtain node status data; Based on the electrical topology and the spatial relationship of the nodes, the node status data is combined to obtain the in-box status fingerprint.

4. The control method for the intelligent sensing integrated power metering box according to claim 1, characterized in that, The step of establishing or updating the health baseline corresponding to the electrical node based on the historical operating data of the power metering box includes: Valid historical data are obtained by filtering historical state data that meet the normal operating conditions from the historical operating data. Based on the load status and environment status corresponding to the valid historical data, the valid historical data is grouped to obtain multiple status group data; Based on the range of state changes of the same electrical node in each state group data, determine the normal state range of the electrical node under the corresponding load state and environmental state. Based on the normal state range, generate or update the health baseline corresponding to the electrical node.

5. The control method for the intelligent sensing integrated power metering box according to claim 4, characterized in that, The step of determining a target health baseline corresponding to the current load state and current environmental state based on the in-box state fingerprint, and determining abnormal deviation information based on the deviation between the in-box state fingerprint and the target health baseline, includes: Based on the internal state fingerprint, determine the current load state, current environmental state, and current node state of the target electrical node; Based on the current load state and the current environment state, determine the target health baseline corresponding to the target electrical node from the health baseline; The current node status is compared with the target health baseline to obtain the node deviation result; Based on the node deviation results, the abnormal deviation information corresponding to the target electrical node is determined.

6. The control method for the intelligent sensing integrated power metering box according to claim 1, characterized in that, The step of determining abnormal deviation information based on the deviation between the in-box state fingerprint and the target health baseline includes: Based on the internal state fingerprint of the enclosure, determine the temperature data, ambient temperature data, and load current data of the target connection node; Based on the temperature data, the ambient temperature data, and the load current data, the load normalized temperature rise characteristics of the target connection node are determined. The load-normalized temperature rise characteristic is compared with the target health baseline corresponding to the target connection node to obtain the temperature rise deviation result; Based on the temperature rise deviation results, the contact anomaly deviation information corresponding to the target connection node is determined.

7. The control method for the intelligent sensing integrated power metering box according to claim 1, characterized in that, The step of determining abnormal deviation information based on the deviation between the in-box state fingerprint and the target health baseline includes: Based on the internal state fingerprint, determine the humidity state, condensation state, water immersion state of the target space area, and the residual current state of the electrical nodes corresponding to the target space area. Based on the target health baseline corresponding to the target spatial area, deviations from the humidity state, condensation state, and water immersion state are determined to obtain the environmental risk state. Based on the target health baseline corresponding to the electrical node, the deviation of the residual current state is judged to obtain the electrical risk state; Based on the environmental risk status and the electrical risk status, determine the environmental induced anomaly deviation information corresponding to the target spatial area.

8. The control method for the intelligent sensing integrated power metering box according to claim 1, characterized in that, The step of determining the abnormal causal chain of the energy metering box based on the abnormal deviation information and the multidimensional mapping model includes: Based on the abnormal deviation information, determine the target node that has experienced abnormal deviation and the deviation type corresponding to the target node; Based on the multidimensional mapping model, the upstream node, downstream node, and adjacent spatial region corresponding to the target node are determined; Based on the state change sequence and state change correlation of the target node, the upstream node, the downstream node, and the adjacent spatial region, the anomaly source node and the anomaly propagation path are determined; The anomaly causal chain is determined based on the anomaly source node, the anomaly propagation path, and the deviation type.

9. The control method for the intelligent sensing integrated power metering box according to claim 8, characterized in that, The step of determining the target control strategy based on the abnormal causal chain and controlling the corresponding execution node actions according to the target control strategy includes: Based on the abnormal causal chain, determine the abnormality level and impact range corresponding to the abnormal source node; Based on the anomaly source node, the anomaly level, and the scope of influence, candidate execution nodes that can act on the anomaly source node and / or the affected nodes are determined from the multidimensional mapping model. Based on the principle of minimum impact, a target execution node is determined from the candidate execution nodes, and a target control strategy is determined based on the target execution node; The target execution node actions are controlled according to the target control strategy to handle the abnormal source node and / or the affected node abnormally. The principle of minimum impact includes: when safety conditions are met, priority should be given to heat dissipation, dehumidification, alarm, interlocking or branch circuit isolation; when the anomaly involves the main incoming line, busbar, smoke, leakage current level reaches the preset emergency level or branch circuit isolation is ineffective, the main circuit power outage control should be implemented.

10. An intelligent sensing integrated power metering box, characterized in that, It includes a housing, a metering unit, a multi-source sensing unit, an execution unit, an edge control unit, and a communication unit; The enclosure contains multiple spatial regions, and multiple electrical nodes are provided inside the enclosure, with at least some of the electrical nodes located within the corresponding spatial regions; The metering unit is used to collect the metering data of the power metering box; The multi-source sensing unit is used to collect multi-source sensing data from the power metering box. The execution unit is used to execute corresponding control actions according to control instructions; The communication unit is used for data communication with an external management platform; The edge control unit is connected to the metering unit, the multi-source sensing unit, the execution unit, and the communication unit, respectively. The edge control unit is used for: The electrical topology information, spatial area information, sensing node information, and execution node information of the power metering box are obtained. Based on the electrical topology information, spatial area information, sensing node information, and execution node information, a multi-dimensional mapping model is established. The multi-dimensional mapping model is used to characterize the correspondence between electrical nodes, spatial areas, sensing nodes, and execution nodes. Based on the multi-dimensional mapping model, the multi-source sensing data is mapped to the corresponding electrical nodes and spatial regions to obtain the in-box state fingerprint; Based on the historical operating data of the power metering box, establish or update the health baseline corresponding to the electrical node, and determine the target health baseline corresponding to the current load state and the current environmental state based on the state fingerprint of the box. Based on the deviation between the in-box status fingerprint and the target health baseline, abnormal deviation information is determined; Based on the abnormal deviation information and the multidimensional mapping model, the abnormal causal chain of the power metering box is determined, and the abnormal causal chain includes the abnormal source node, the abnormal type, and the affected node. The target control strategy is determined based on the abnormal causal chain, and the corresponding execution node in the execution unit is controlled according to the target control strategy. Obtain feedback perception data after the execution node's action, and correct the abnormal causal chain and / or the target control strategy based on the feedback perception data.

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