A safe power distribution system based on deep integration of stacked busbars and power metering boxes

Abstract

This invention discloses a safe power distribution system based on the deep integration of stacked busbars and power metering boxes, belonging to the field of power distribution network metering and protection. Specifically, it includes a stacked busbar module, a multi-source data measurement module, a feature analysis and extraction module, a main control analysis and decision-making module, a safety execution module, and a power metering box integrating all modules. The system replaces the multi-strand wire connections in traditional metering boxes with stacked busbar modules, forming a seamless, self-shielded, low-inductance main circuit path, achieving highly reliable electrical connection integration. Through a collaborative mechanism of multi-source data synchronous acquisition, feature cross-analysis and extraction, multi-dimensional risk assessment, trend prediction, and dynamic decision-making via a rule engine, it addresses the problems of weak state perception, simple risk assessment, and rigid decision-making mechanisms in traditional power metering boxes. This upgrades traditional metering boxes into intelligent, safe power distribution nodes that integrate electrical connection, multi-source perception, safety analysis, dynamic decision-making, and precise execution.

Description

Technical Field

[0001] This invention relates to the field of power distribution network metering and protection equipment technology, and more specifically, to a safe power distribution system based on the deep integration of stacked busbars and power metering boxes. Background Technology

[0002] As a key piece of equipment at the end of the distribution network, the electricity metering box undertakes basic functions such as electricity metering, load distribution and line protection. With the rapid development of new power systems, the widespread access of distributed energy, the rapid growth of electric vehicle charging load, and the continuous improvement of users' requirements for power supply reliability, higher technical requirements are being placed on the electricity metering box at the end of the distribution network.

[0003] Traditional electricity metering boxes mainly suffer from the following technical defects: First, it has a single function and low integration: it only serves as an installation carrier for electricity meters and circuit breakers. The internal structure is messy with many connection points, which can easily lead to increased contact resistance due to loose or oxidized connections, causing local overheating or even fire accidents. Second, the ability to perceive the state is weak: it is unable to monitor the transient changes of electrical parameters such as current, voltage, and leakage current of the line in real time, and it is even more unable to extract characteristic parameters that can characterize the health status of the line, making it difficult to detect latent faults such as poor contact and insulation aging in a timely manner. Third, the risk assessment logic is simple: it cannot combine multi-source information to make a multi-dimensional comprehensive risk assessment of the current operating status of the line, nor can it predict the future risk evolution trend based on historical data; Fourth, the decision-making mechanism is rigid: it lacks dynamic adaptability and cannot take preventive control measures in advance based on environmental changes or load forecast results.

[0004] To address the aforementioned issues, this invention provides a safe power distribution system based on the deep integration of stacked busbars and power metering boxes. It achieves high integration of electrical connections through stacked busbar technology, comprehensive perception of line status through multi-source synchronous acquisition technology, acquisition of characteristic parameters representing line health status through feature analysis and extraction technology, proactive safety warnings through multi-dimensional risk assessment and trend prediction, and dynamic matching of safe power distribution strategies through a rule engine. Ultimately, it upgrades traditional metering boxes into intelligent, safe power distribution nodes at the end of the line, integrating electrical connections, multi-source perception, safety analysis, dynamic decision-making, and safety execution. Summary of the Invention

[0005] The purpose of this invention is to solve the above-mentioned technical problems and provide a safe power distribution system based on the deep integration of stacked busbars and power metering boxes compared with the prior art.

[0006] The objective of this invention can be achieved through the following technical solution: a safe power distribution system based on the deep integration of laminated busbars and power metering boxes, comprising: The electricity metering box is internally divided into an inlet compartment, a metering compartment, and an outlet compartment. The stacked busbar module is located in the incoming line compartment and serves as the main circuit carrier. Its input end is used to connect to the incoming power supply, and its output end is used to connect to an outgoing branch. The multi-source data measurement module, located in the metering room, is used to collect multi-source signals reflecting the operating status of the power distribution system in real time, process the multi-source signals, and generate a spatiotemporally synchronized sampling data sequence. The multi-source signals include electrical quantity signals, thermal signals, and environmental status signals. The feature analysis and extraction module, located in the metrology room, has a built-in feature analysis and extraction algorithm. It is used to receive the sampled data sequence and perform multi-channel feature analysis based on the sampled data sequence to extract multi-source feature parameters, including electrical feature parameters, thermal feature parameters and environmental feature parameters that characterize the safety status of the line. The main control analysis and decision-making module, located in the metering room, has a built-in risk judgment logic matching library and rule engine. It is used to: perform multi-dimensional risk judgment on the current operating status of power distribution lines based on multi-source feature parameters and preset risk judgment logic, and generate multi-level risk warning signals. Based on historical time-series data of multi-source characteristic parameters and prediction models, future risks of the power distribution network are predicted, and future risk evolution curves are generated. By combining multi-level risk warning signals, future risk evolution curves, and rule engines, safe power distribution strategy instructions are dynamically matched and generated. The safety execution module, located in the outgoing line room, is used to receive risk warning signals and safety power distribution strategy instructions, and to execute corresponding safety protection operations.

[0007] Furthermore, the multi-source data measurement module includes: The electrical quantity acquisition unit is used to acquire electrical quantity signals, including analog current signals, analog voltage signals, and analog leakage current signals. Thermal status acquisition unit, used to acquire thermal signals, including line temperature analog signals; The environmental status acquisition unit is used to acquire environmental status signals, including simulated environmental temperature signals, simulated environmental humidity signals, and simulated smoke concentration signals. The signal conditioning and conversion unit is used to filter, amplify, and perform analog-to-digital conversion on electrical quantity signals, thermal signals, and environmental state signals, respectively, to generate sampled data sequences, including: instantaneous current value sequences, instantaneous voltage value sequences, and instantaneous leakage current value sequences generated based on electrical quantity signal conversion; instantaneous line temperature value sequences generated based on thermal signal conversion; and environmental state parameter sequences generated based on environmental state signal conversion.

[0008] Furthermore, the multi-source data measurement module also includes a spatiotemporal alignment unit, which is used to provide a unified time reference for each acquisition channel using a precise time protocol. It generates a hardware timestamp at the moment when the data from each acquisition channel leaves the analog-to-digital converter, and interpolates and resamples the sequences of other sampling frequencies using the highest sampling frequency as the reference time axis to generate a time-aligned multi-source data matrix.

[0009] Furthermore, the electrical characteristic parameters include current safety characteristic parameters, voltage safety characteristic parameters, and leakage current safety characteristic parameters. The current safety characteristic parameters include current load factor, the voltage safety characteristic parameters include overvoltage amplitude, and the leakage current safety characteristic parameters include effective value of leakage current, insulation health index, resistive component of leakage current, and high-frequency pulse energy of leakage current. Thermal characteristic parameters include the line temperature rise rate and contact resistance index, while environmental characteristic parameters include the comprehensive index of ambient temperature and humidity and the rate of change of smoke concentration.

[0010] Furthermore, the process of conducting multi-dimensional risk assessment of the operating status of power distribution lines includes: The overload risk of the power distribution line is determined based on the current load factor and the line temperature rise rate, and the overload risk level is assessed to generate the corresponding overload risk warning signal. Based on the overvoltage amplitude, the overvoltage stress risk of the power distribution line is determined, the overvoltage risk level is assessed, and the corresponding overvoltage risk warning signal is generated. Based on the contact resistance index, the risk of poor contact in the power distribution line is determined, the risk level of poor contact is assessed, and a corresponding risk warning signal for poor contact is generated. Based on the insulation health index and the resistive component of leakage current, the insulation aging of the power distribution line is determined, the insulation aging risk level is assessed, and a corresponding insulation aging risk warning signal is generated. Partial discharge is determined based on leakage current high-frequency pulse energy of the distribution circuit, and the partial discharge risk level is assessed to generate a corresponding partial discharge risk warning signal. Environmental risks of power distribution lines are assessed based on the comprehensive index of ambient temperature and humidity and the rate of change of smoke concentration, and the level of environmental disaster risk is evaluated to generate corresponding environmental impact risk warning signals.

[0011] Furthermore, the generation process of the future risk evolution curve includes: Historical time-series data of current load rate are obtained, and historical time-series data of the comprehensive environmental temperature and humidity index within the same historical period are obtained as related influencing factors. A multivariate time series prediction model is used to generate a load prediction curve for a future preset period. Historical time-series data of insulation health index and resistive component of leakage current are obtained, and a lifetime prediction model is used to generate a prediction curve of the remaining insulation lifetime for a future preset period. Historical time-series data of contact resistance index and line temperature rise rate are obtained, and a contact condition deterioration trend prediction model is used to generate a contact condition deterioration trend curve.

[0012] Furthermore, the process of dynamically matching and generating safe power distribution strategy instructions includes: dynamically matching multi-level risk warning signals and future risk evolution curves with a preset rule engine; Based on the overload protection rules, if an overload risk warning signal is generated and the load prediction curve shows that the current load rate will exceed the load threshold within a preset period of time, a corresponding load interruption command will be generated according to the overload risk level. Based on the overvoltage handling rules, if an overvoltage risk warning signal is generated, a corresponding voltage limiting protection command is generated according to the overvoltage risk level. Based on the contact failure handling rules, if a contact failure risk warning signal is generated and the contact status deterioration trend curve shows that the expected time to reach the warning contact deterioration threshold is less than the preset first duration threshold, a corresponding current reduction instruction is generated according to the contact failure risk level. Based on the insulation aging treatment rules, if an insulation aging risk warning signal is generated, or if the insulation remaining life prediction curve shows that the expected time to reach the warning remaining life threshold is less than the preset second duration threshold, a corresponding aging treatment instruction will be generated according to the insulation aging risk. Based on the partial discharge handling rules, if a partial discharge risk warning signal is generated, a corresponding handling instruction is generated according to the partial discharge risk level. Based on the rules for handling environmental disasters, if an environmental impact risk warning signal is generated, corresponding handling instructions will be generated according to the specific environmental risk type and risk level.

[0013] Furthermore, the stacked busbar module, integrated in the incoming line chamber, includes multiple composite conductor layers and an insulation layer located between adjacent conductor layers. The stacked busbar module is manufactured using a seamless integrated molding process, forming a low-inductance main circuit path with a self-shielded structure. The safety execution module is installed in the outgoing line room and includes multiple intelligent circuit breakers that are directly plugged into the stacked busbar module. The intelligent circuit breaker integrates a control driver and receives safety power distribution strategy instructions from the main control analysis and decision module to execute corresponding opening, closing, current limiting, or alarm operations.

[0014] Compared with the prior art, the advantages of this invention are: 1. This invention replaces the multi-strand wires in the traditional metering box with a laminated busbar module. It adopts a multi-layer composite conductor and insulation layer integral molding process to form a jointless, self-shielded, low-inductance main circuit path. Compared with the traditional solution, by introducing laminated busbar technology, the power metering box is upgraded from a traditional installation carrier to an intelligent terminal unit that integrates electrical connection, data acquisition and safety protection, effectively eliminating contact hazards and providing self-shielding anti-interference.

[0015] 2. This invention also uses a multi-source data measurement module to synchronously collect electrical, thermal, and environmental signals and perform spatiotemporal alignment to form raw sampling data. The feature analysis and extraction module transforms the raw sampling data into quantitative features with clear physical meaning, such as current load rate, insulation health index, and contact resistance index. The main control analysis and decision-making module performs multi-dimensional current risk assessment and future risk trend prediction, and combines a rule engine to dynamically match safe power distribution strategies. This addresses the problems of weak state perception, simple risk assessment, and rigid decision-making mechanisms in traditional solutions, achieving a leap from passive protection to active safety protection and significantly improving the intelligence level of the power distribution network end. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system modules of the present invention; Figure 2 This is a flowchart of the system modules of the present invention. Detailed Implementation

[0017] 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. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] Example 1: This invention discloses a safe power distribution system based on the deep integration of laminated busbars and power metering boxes. Please refer to [link / reference]. Figures 1-2 It includes an energy metering box, a stacked busbar module, a multi-source data measurement module, a feature parsing and extraction module, a main control analysis and decision-making module, and a safety execution module; The electricity metering box is internally divided into an inlet room, a metering room, and an outlet room.

[0019] The stacked busbar module is located in the inlet chamber and serves as the main circuit carrier. It is integrated into the inlet chamber and includes multiple composite conductor layers and an insulating layer between adjacent conductor layers. It is manufactured by a seamless integrated molding process. Its input end is connected to the incoming power supply in the inlet chamber, and its output end extends to the metering chamber and the outlet chamber respectively to connect an outlet branch, forming a low-inductance main circuit path with a self-shielded structure. The stacked busbar module is integrally formed by multiple composite conductors and insulation layers, transforming the multi-strand wire connection in the traditional metering box into a stacked rigid connection. This eliminates the contact resistance and loosening hazards caused by multiple contact points, improves the reliability of electrical connections from a physical structure perspective, provides standardized hard connection interfaces for smart circuit breakers, provides modular power supply interfaces, enables plug-and-play functionality, and lays the physical foundation for modular architecture and prefabricated installation.

[0020] The multi-source data measurement module, feature parsing and extraction module, and main control analysis and decision-making module are integrated and installed in the metering room, while the safety execution module is installed in the outgoing line room.

[0021] The multi-source data measurement module, located in the metering room, is electrically connected to the stacked busbar module. It is used to collect multi-source signals reflecting the operating status of the power distribution system in real time, process the multi-source signals, and generate a spatiotemporally synchronized sampling data sequence. The multi-source signals include electrical quantity signals, thermal signals, and environmental status signals. Specifically, the multi-source data measurement module includes: The electrical quantity acquisition unit is rigidly electrically connected to the laminated busbar module and is used to acquire electrical quantity signals, including current analog signals, voltage analog signals and leakage current analog signals. The leakage current analog signal is acquired through a residual current transformer and characterizes the insulation status of the line to ground. The thermal state acquisition unit is integrated into the key connection point of the laminated busbar module and is used to acquire thermal signals, including line temperature analog signals. The sampling unit is rigidly electrically connected to the laminated busbar to minimize the transmission path of analog signals and avoid external electromagnetic interference introduced by traditional wire connections. The environmental status acquisition unit is used to acquire environmental status signals, including simulated environmental temperature signals, simulated environmental humidity signals, and simulated smoke concentration signals. The signal conditioning and conversion unit is connected to the electrical quantity acquisition unit, the thermal state acquisition unit, and the environmental state acquisition unit. It is used to filter, amplify, and perform analog-to-digital conversion on the electrical quantity signals, thermal signals, and environmental state signals, respectively, to generate a sampled data sequence. The sampled data sequence includes: a sequence of instantaneous current values, a sequence of instantaneous voltage values, and a sequence of instantaneous leakage current values ​​generated based on the electrical quantity signal conversion; a sequence of instantaneous line temperature values ​​generated based on the thermal signal conversion; and a sequence of environmental state parameters generated based on the environmental state signal conversion. The time-space alignment unit, connected to the signal conditioning and conversion unit, is used to provide a unified time reference for each acquisition channel using a precise time protocol. It generates a hardware timestamp the instant the data from each acquisition channel leaves the analog-to-digital converter. Using the highest sampling frequency as the reference time axis, it interpolates and resamples the sequences of other sampling frequencies to generate a time-aligned multi-source data matrix. The sampled data sequence is "pure" waveform data without any algorithmic processing, preserving all high-frequency components and transient characteristics of the signal, laying the physical foundation for subsequent accurate analysis.

[0022] The feature analysis and extraction module is located in the metrology room and is connected to the multi-source data measurement module. It has a built-in feature analysis and extraction algorithm to receive the sampled data sequence and perform multi-channel feature analysis based on the sampled data sequence to extract multi-source feature parameters, including electrical feature parameters, thermal feature parameters and environmental feature parameters that characterize the safety status of the line. The electrical feature parameters include current safety feature parameters, voltage safety feature parameters and leakage safety feature parameters. It should be noted that the feature parsing and extraction algorithm is a multi-level, multi-domain fusion computational method system, specifically including time-domain computation sub-algorithm, frequency-domain transformation sub-algorithm, correlation analysis sub-algorithm, projection analysis sub-algorithm, trend analysis sub-algorithm, and environment fusion sub-algorithm; Among them, the current safety characteristic parameters include the current load factor, which is extracted based on the instantaneous current value sequence through a time-domain calculation sub-algorithm and is used to characterize the overload risk of the line. Voltage safety characteristic parameters include overvoltage amplitude, which is extracted by time-domain computation sub-algorithm based on voltage instantaneous value sequence analysis, and is used to characterize power supply voltage quality and voltage stress risk; The leakage current safety characteristic parameters include the effective value of leakage current, insulation health index, resistive component of leakage current, and high-frequency pulse energy of leakage current, which are used to characterize the degree of insulation aging and partial discharge risk. Among them, the effective value of leakage current is extracted from the instantaneous value sequence of leakage current through a time-domain calculation sub-algorithm; the insulation health index is calculated from the ratio of the effective value of leakage current to the preset warning threshold through a time-domain calculation sub-algorithm; the resistive component of leakage current is extracted from the correlation between the instantaneous value sequence of leakage current and the instantaneous value sequence of voltage through a projection analysis sub-algorithm; and the high-frequency pulse energy of leakage current is extracted from the instantaneous value sequence of leakage current through a frequency domain transformation sub-algorithm. The thermal characteristic parameters include the line temperature rise rate and the contact resistance index. The line temperature rise rate is extracted based on the instantaneous line temperature value sequence through a trend analysis sub-algorithm, and the contact resistance index is extracted based on the instantaneous line temperature value sequence and the effective current value through a correlation analysis sub-algorithm. Environmental characteristic parameters include the comprehensive index of environmental temperature and humidity and the rate of change of smoke concentration, which are extracted based on the environmental state parameter sequence through the environmental fusion sub-algorithm and the trend analysis sub-algorithm, respectively. The feature parsing and extraction module adopts a multi-level algorithm system to transform the raw data into quantitative features with clear physical meaning, such as current load rate, overvoltage amplitude, insulation health index, and contact resistance index. Each feature parameter directly corresponds to a specific physical phenomenon (such as overload, poor contact, and insulation aging), which is convenient for operation and maintenance personnel to understand and make decisions, and avoids the uninterpretability problem of black box models.

[0023] The main control analysis and decision-making module is located in the metering room and communicates with the feature parsing and extraction module. It has a built-in risk judgment logic matching library and rule engine, which is used to: perform multi-dimensional risk judgment on the current operating status of the power distribution line based on multi-source feature parameters and preset risk judgment logic, and generate multi-level risk warning signals. Based on historical time-series data of multi-source characteristic parameters and prediction models, future risks of the power distribution network are predicted, and future risk evolution curves are generated. By combining multi-level risk warning signals, future risk evolution curves, and rule engines, safe power distribution strategy instructions are dynamically matched and generated. The process of conducting multi-dimensional risk assessment of the current operating status of power distribution lines includes: The overload risk of the power distribution line is determined based on the current load rate and the line temperature rise rate. When the current load rate exceeds the first safety threshold and the line temperature rise rate exceeds the second safety threshold, it is determined that there is an overload risk in the current operating state. The overload risk level is assessed and a corresponding overload risk warning signal is generated. The overvoltage stress risk of the power distribution line is determined based on the overvoltage amplitude. When the overvoltage amplitude exceeds the third safety threshold, it is determined that there is an overvoltage risk in the current operating state, the overvoltage risk level is assessed, and an overvoltage risk warning signal of the corresponding level is generated. Based on the contact resistance index, the risk of poor contact in the power distribution line is determined. When the contact resistance index exceeds the fourth safety threshold of its historical baseline value and continues to exceed the first duration, it is determined that there is a risk of poor contact in the current operating state. The risk level of poor contact is assessed and a corresponding risk warning signal for poor contact is generated. Insulation aging is determined based on insulation health index and leakage current resistive component. When the insulation health index is lower than the fifth safety threshold and continues to exceed the second duration, and the leakage current resistive component shows an upward trend, it is determined that there is an insulation aging risk in the current operating state. The insulation aging risk level is assessed and a corresponding insulation aging risk warning signal is generated. Partial discharge is determined based on the leakage current high-frequency pulse energy of the distribution circuit. When the leakage current high-frequency pulse energy exceeds the sixth safety threshold, it is determined that there is a partial discharge risk in the current operating state, the partial discharge risk level is assessed, and a corresponding partial discharge risk warning signal is generated.

[0024] Environmental risk assessment of power distribution lines is conducted based on the comprehensive index of ambient temperature and humidity and the rate of change of smoke concentration. When the comprehensive index of ambient temperature and humidity exceeds the seventh safety threshold or the rate of change of smoke concentration exceeds the eighth safety threshold, it is determined that there is an environmental disaster risk in the current operating state. The environmental disaster risk level is assessed and a corresponding environmental impact risk warning signal is generated. In the process of multi-dimensional risk assessment, a risk assessment logic matching library is used. The risk assessment logic matching library has different assessment parameters preset based on different risk types, which serve as the basis for risk level classification. These parameters are used to assess overvoltage risk level, poor contact risk level, insulation aging risk level, partial discharge risk level, and environmental disaster risk level. The risk levels are divided into mild, moderate, and severe. Based on the preset risk assessment logic, multi-level risk warning signals corresponding to different risk types are generated. Overload risk warning signals, overvoltage risk warning signals, poor contact risk warning signals, insulation aging risk warning signals, partial discharge risk warning signals, and environmental impact risk warning signals are all collectively summarized into multi-level risk warning signals; The generation process of the future risk evolution curve includes: Historical time-series data of current load rate are obtained, and historical time-series data of the comprehensive environmental temperature and humidity index within the same historical period are obtained as related influencing factors. A multivariate time series forecasting model is used to generate load forecast curves for the future preset period. The multivariate time series forecasting model can adopt the seasonal difference autoregressive moving average model, which is a conventional model in the field of time series forecasting and can effectively handle load data with periodicity and trend. Historical time-series data of insulation health index and leakage current resistive component are obtained, and a lifetime prediction model is used to generate insulation remaining lifetime prediction curves for a future preset period. The insulation remaining lifetime prediction adopts a data-driven model based on degradation trajectory fitting. Historical time-series data of contact resistance index and line temperature rise rate are obtained, and a contact condition deterioration trend prediction model is used to generate a contact condition deterioration trend curve. The contact condition deterioration trend prediction model adopts a linear trend extrapolation model. The load forecast curve, the insulation remaining life forecast curve, and the contact condition deterioration trend curve are collectively summarized into the future risk evolution curve.

[0025] The process of dynamically matching and generating safe power distribution strategy instructions includes: dynamically matching multi-level risk warning signals and future risk evolution curves with a preset rule engine; Based on the overload protection rules, if an overload risk warning signal is generated and the load prediction curve shows that the current load rate will exceed the load threshold within a preset period of time, a corresponding load interruption command will be generated according to the overload risk level. If the overload risk level is moderate, a load interruption command is generated to cut off the lowest level interruptible load. If the overload risk level is severe, a load interruption command is generated to cut off all interruptible loads. Based on the overvoltage handling rules, if an overvoltage risk warning signal is generated, a corresponding voltage limiting protection command is generated according to the overvoltage risk level. If the overvoltage risk level is mild, generate and record the overvoltage event waveform and notify the maintenance personnel to pay attention to the insulation status. If the overvoltage risk level is moderate, generate a status check instruction for the voltage limiting protection device (such as surge arrester, varistor) and notify the operation and maintenance personnel to arrange insulation testing; If the overvoltage risk level is severe, an isolation command to cut off the power supply to that branch is generated, and an emergency insulation maintenance alarm message is generated. Based on the contact failure handling rules, if a contact failure risk warning signal is generated and the contact status deterioration trend curve shows that the expected time to reach the warning contact deterioration threshold is less than the preset first duration threshold, a corresponding current reduction instruction is generated according to the contact failure risk level. If the risk level of poor contact is moderate, an instruction will be generated to reduce the current of that branch to 80% of the rated value; If the risk level of poor contact is severe, a command to cut off the power supply to that branch and to perform contact maintenance will be generated. Based on the insulation aging treatment rules, if an insulation aging risk warning signal is generated, or if the insulation remaining life prediction curve shows that the expected time to reach the warning remaining life threshold is less than the preset second duration threshold, a corresponding aging treatment instruction will be generated according to the insulation aging risk. If the insulation aging risk level is moderate, generate insulation reinforcement recommendations and increase the frequency of insulation testing (from once a month to once a week); If the risk level of insulation aging is severe, a line replacement recommendation or an emergency power outage maintenance order will be generated. Based on the partial discharge handling rules, if a partial discharge risk warning signal is generated, a corresponding handling instruction is generated according to the partial discharge risk level. If the partial discharge risk level is mild, an early warning instruction is generated to mark the branch and strengthen monitoring; If the partial discharge risk level is moderate, generate a command to reduce the load on that branch (limit the current to a preset percentage below the rated value) and arrange for insulation testing; If the partial discharge risk level is severe, an isolation command is generated for that branch, and an emergency power outage is performed for maintenance. Based on the rules for handling environmental disasters, if an environmental impact risk warning signal is generated, corresponding disposal instructions will be generated according to the specific environmental risk type and risk level. If the combined temperature and humidity index of the environment exceeds the standard and the environmental risk level is moderate, an auxiliary instruction to strengthen ventilation or dehumidification will be generated. If the combined environmental temperature and humidity index exceeds the standard and the environmental risk level is severe, an instruction to reduce the load (limit the total load to below the preset percentage of the rated value) or to cut off power to some branches will be generated. If the rate of change in smoke concentration exceeds the standard and the environmental risk level is moderate, a smoke alarm will be generated and a fire inspection order will be issued. If the rate of change in smoke concentration exceeds the standard and the environmental risk level is severe, an instruction will be generated to cut off the incoming power supply and trigger a fire alarm. The main control analysis and decision-making module performs multi-dimensional risk classification based on multi-source characteristics. It combines load forecasting, insulation life forecasting, and contact condition deterioration forecasting, and dynamically matches differentiated safety strategies through a rule engine. This upgrades the traditional post-event protection to "pre-event warning + in-event intervention", identifying potential hazards before a fault occurs and controlling risks in advance during their evolution, thus achieving true proactive safety.

[0026] The safety execution module, located in the outgoing line room, is connected to the main control analysis and decision module. It is used to receive risk warning signals and safety power distribution strategy instructions and execute corresponding safety protection operations. The safety execution module includes multiple intelligent circuit breakers that are directly plugged into the stacked busbar module. The intelligent circuit breaker integrates a control driver and receives safety power distribution strategy instructions from the main control analysis and decision module to execute corresponding opening, closing, current limiting, or alarm operations.

[0027] It should be added that the article involves comparisons of various thresholds. Thresholds, preset values, preset ranges, etc., are set for result comparison and analysis to determine whether they are good or bad. The magnitude of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be appropriately adjusted based on seasonal or common-sense influences.

[0028] In summary, this safe power distribution system includes: a stacked busbar module, serving as the main circuit carrier; a multi-source data measurement module, electrically connected to the stacked busbar module, used to collect electrical quantities, thermal signals, and environmental status signals in real time, generating a spatiotemporally synchronized sampling data sequence; a feature analysis and extraction module, used to extract multi-source feature parameters such as current load rate, overvoltage amplitude, insulation health index, and contact resistance index from the sampling data sequence; a main control analysis and decision-making module, with a built-in rule engine, used to perform multi-dimensional risk assessment of the current operating status based on multi-source feature parameters, generate multi-level risk warning signals, predict future risks based on historical time-series data of each feature parameter, generate future risk evolution curves, and dynamically match safe power distribution strategy instructions by combining risk warning signals, risk evolution curves, and the rule engine; and a safety execution module, used to receive instructions and execute safety protection operations. This invention organically integrates various modules to achieve a leap from traditional metering boxes to intelligent and safe power distribution nodes at the end of the distribution network that integrate electrical connection, multi-source sensing, safety analysis, dynamic decision-making, and precise execution. It achieves proactive safety warnings through multi-dimensional risk assessment and trend prediction, and improves the safety and intelligence level of the distribution network end by dynamically matching differentiated protection strategies through a rule engine.

[0029] The above description is merely a preferred embodiment of the present invention; however, the scope of protection of the present invention is not limited thereto; any equivalent substitutions or modifications made by those skilled in the art within the technical scope disclosed in the present invention, based on the technical solution and its improved concept, should be covered within the scope of protection of the present invention.

Claims

1. A safe power distribution system based on deep integration of stacked busbars and power metering boxes, characterized in that: include: The electricity metering box is internally divided into an inlet compartment, a metering compartment, and an outlet compartment. The stacked busbar module is located in the incoming line compartment and serves as the main circuit carrier. Its input end is used to connect to the incoming power supply, and its output end is used to connect to an outgoing branch. The multi-source data measurement module, located in the metering room, is used to collect multi-source signals reflecting the operating status of the power distribution system in real time, process the multi-source signals, and generate a spatiotemporally synchronized sampling data sequence. The multi-source signals include electrical quantity signals, thermal signals, and environmental status signals. The feature analysis and extraction module, located in the metrology room, has a built-in feature analysis and extraction algorithm. It is used to receive the sampled data sequence and perform multi-channel feature analysis based on the sampled data sequence to extract multi-source feature parameters, including electrical feature parameters, thermal feature parameters and environmental feature parameters that characterize the safety status of the line. The main control analysis and decision-making module, located in the metering room, has a built-in risk judgment logic matching library and rule engine. It is used to: perform multi-dimensional risk judgment on the current operating status of power distribution lines based on multi-source feature parameters and preset risk judgment logic, and generate multi-level risk warning signals. Based on historical time-series data of multi-source characteristic parameters and prediction models, future risks of the power distribution network are predicted, and future risk evolution curves are generated. By combining multi-level risk warning signals, future risk evolution curves, and rule engines, safe power distribution strategy instructions are dynamically matched and generated. The safety execution module, located in the outgoing line room, is used to receive risk warning signals and safety power distribution strategy instructions, and to execute corresponding safety protection operations.

2. The safety power distribution system based on deep integration of stacked busbars and power metering boxes according to claim 1, characterized in that: The multi-source data measurement module includes: an electrical quantity acquisition unit, used to acquire electrical quantity signals, including current analog signals, voltage analog signals and leakage current analog signals; Thermal status acquisition unit, used to acquire thermal signals, including line temperature analog signals; The environmental status acquisition unit is used to acquire environmental status signals, including simulated environmental temperature signals, simulated environmental humidity signals, and simulated smoke concentration signals. The signal conditioning and conversion unit is used to filter, amplify, and perform analog-to-digital conversion on electrical quantity signals, thermal signals, and environmental state signals, respectively, to generate sampled data sequences, including: instantaneous current value sequences, instantaneous voltage value sequences, and instantaneous leakage current value sequences generated based on electrical quantity signal conversion; instantaneous line temperature value sequences generated based on thermal signal conversion; and environmental state parameter sequences generated based on environmental state signal conversion.

3. The safety power distribution system based on the deep integration of stacked busbars and power metering boxes as described in claim 2, the multi-source data measurement module further includes a spatiotemporal alignment unit, which is used to provide a unified time reference for each acquisition channel using a precise time protocol, generate a hardware timestamp at the instant when the data of each acquisition channel leaves the analog-to-digital converter, and interpolate and resample the sequences of other sampling frequencies using the highest sampling frequency as the reference time axis to generate a time-aligned multi-source data matrix.

4. The safety power distribution system based on deep integration of stacked busbars and power metering boxes according to claim 2, characterized in that: The electrical characteristic parameters include current safety characteristic parameters, voltage safety characteristic parameters and leakage current safety characteristic parameters. The current safety characteristic parameters include current load factor, the voltage safety characteristic parameters include overvoltage amplitude, and the leakage current safety characteristic parameters include effective value of leakage current, insulation health index, resistive component of leakage current and high-frequency pulse energy of leakage current. Thermal characteristic parameters include the line temperature rise rate and contact resistance index, while environmental characteristic parameters include the comprehensive index of ambient temperature and humidity and the rate of change of smoke concentration.

5. The safety power distribution system based on deep integration of stacked busbars and power metering boxes according to claim 3, characterized in that: The process of conducting multi-dimensional risk assessment of the operating status of power distribution lines includes: The overload risk of the power distribution line is determined based on the current load factor and the line temperature rise rate, and the overload risk level is assessed to generate the corresponding overload risk warning signal. Based on the overvoltage amplitude, the overvoltage stress risk of the power distribution line is determined, the overvoltage risk level is assessed, and the corresponding overvoltage risk warning signal is generated. Based on the contact resistance index, the risk of poor contact in the power distribution line is determined, the risk level of poor contact is assessed, and a corresponding risk warning signal for poor contact is generated. Based on the insulation health index and the resistive component of leakage current, the insulation aging of the power distribution line is determined, the insulation aging risk level is assessed, and a corresponding insulation aging risk warning signal is generated. Partial discharge is determined based on leakage current high-frequency pulse energy of power distribution lines, and the partial discharge risk level is assessed to generate a corresponding partial discharge risk warning signal. Environmental risks of power distribution lines are assessed based on the comprehensive index of ambient temperature and humidity and the rate of change of smoke concentration, and the level of environmental disaster risk is evaluated to generate corresponding environmental impact risk warning signals.

6. The safety power distribution system based on deep integration of stacked busbars and power metering boxes according to claim 5, characterized in that: The generation process of the future risk evolution curve includes: Historical time-series data of current load rate are obtained, and historical time-series data of the comprehensive environmental temperature and humidity index within the same historical period are obtained as related influencing factors. A multivariate time series prediction model is used to generate a load prediction curve for a future preset period. Historical time-series data of insulation health index and resistive component of leakage current are obtained, and a lifetime prediction model is used to generate a prediction curve of the remaining insulation lifetime for a future preset period. Historical time-series data of contact resistance index and line temperature rise rate are obtained, and a contact condition deterioration trend prediction model is used to generate a contact condition deterioration trend curve.

7. The safety power distribution system based on deep integration of stacked busbars and power metering boxes according to claim 6, characterized in that: The process of dynamically matching and generating safe power distribution strategy instructions includes: dynamically matching multi-level risk warning signals and future risk evolution curves with a preset rule engine; Based on the overload protection rules, if an overload risk warning signal is generated and the load prediction curve shows that the current load rate will exceed the load threshold within a preset period of time, a corresponding load interruption command will be generated according to the overload risk level. Based on the overvoltage handling rules, if an overvoltage risk warning signal is generated, a corresponding voltage limiting protection command is generated according to the overvoltage risk level. Based on the contact failure handling rules, if a contact failure risk warning signal is generated and the contact status deterioration trend curve shows that the expected time to reach the warning contact deterioration threshold is less than the preset first duration threshold, a corresponding current reduction instruction is generated according to the contact failure risk level. Based on the insulation aging treatment rules, if an insulation aging risk warning signal is generated, or if the insulation remaining life prediction curve shows that the expected time to reach the warning remaining life threshold is less than the preset second duration threshold, a corresponding aging treatment instruction will be generated according to the insulation aging risk. Based on the partial discharge handling rules, if a partial discharge risk warning signal is generated, a corresponding handling instruction is generated according to the partial discharge risk level. Based on the rules for handling environmental disasters, if an environmental impact risk warning signal is generated, corresponding handling instructions will be generated according to the specific environmental risk type and risk level.

8. The safety power distribution system based on deep integration of stacked busbars and power metering boxes according to claim 7, characterized in that: The stacked busbar module is integrated in the incoming line room and includes multiple composite conductor layers and an insulation layer between adjacent conductor layers. The stacked busbar module is manufactured by a seamless one-piece molding process, forming a low-inductance main circuit path with a self-shielded structure. The safety execution module is installed in the outgoing line room and includes multiple intelligent circuit breakers that are directly plugged into the stacked busbar module. The intelligent circuit breaker integrates a control driver and receives safety power distribution strategy instructions from the main control analysis and decision module to execute corresponding opening, closing, current limiting, or alarm operations.

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