Power monitoring method for distribution box

Abstract

The invention relates to an electric power system monitoring technology, in particular to an electric power monitoring method of a distribution box, and solves the problems of unstable data transmission of edge nodes and disconnection between load scheduling and equipment health in a distributed distribution box through collaborative operation of a plurality of technical links. According to the method, the health state evaluation model is constructed by fusing the load data and the circuit impedance data, and a dynamic priority scheduling and feedback regulation mechanism is realized in combination with the transmission quality and the load demand, so that the technical problems of unstable data transmission of edge nodes and disjunction between load scheduling and the equipment health state are effectively solved; the method has the advantages that the reliability and safety of power monitoring of the distribution box are improved, dynamic power dispatching and transmission parameter feedback adjustment based on the circuit health state are realized, the overload risk is effectively prevented, and the service life of equipment is prolonged.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system monitoring, in particular to a power monitoring method of a distribution box. BACKGROUND

[0002] In the distributed distribution box application scenario, such as industrial parks, multi-storey commercial building clusters, and remote area photovoltaic supporting distribution boxes, there are two core technical problems that are intertwined with each other. First, the stability of edge node data transmission is a significant problem. Specifically, because the edge output circuits such as workshop corner devices and rooftop air conditioner circuits are far away from the main controller and are in a complex electromagnetic interference environment, when using traditional wired connection or conventional wireless communication technology (such as WiFi, ordinary LoRa) for load data transmission, data packet loss and transmission delay frequently occur, which leads to the fact that power dispatching decisions cannot be responded in a timely manner, thereby causing the risk of circuit overload.

[0003] The load dispatching strategy is seriously out of line with the device health status. The existing scheme only dispatches according to the load data and fails to take into account the device health status of the output circuit, such as hidden dangers such as aging of circuit breaker contacts and loss of wire insulation layer. When there is a potential health problem in the circuit, such as abnormal increase in contact impedance, the system still mechanically allocates power according to the fixed rated load, which not only aggravates the health problem, such as overheating at a place with too large impedance, but also can lead to a decrease in device operation reliability and a lag in fault warning mechanism.

[0004] Overall, the existing technology cannot effectively solve the problems of reliability of edge node data transmission and dynamic linkage of load dispatching and device health, resulting in double defects in real-time monitoring accuracy and device safe operation life of the distributed distribution box. These problems are particularly prominent in the daily operation and maintenance of the power distribution system in industrial areas and remote areas.

[0005] In view of the above problems, the existing technology needs to be improved. SUMMARY

[0006] (I) Technical problems to be solved The purpose of the present application is to provide a power monitoring method of a distribution box, a computing device and a non-transitory machine-readable storage medium, which has the advantages of improving the reliability and safety of power monitoring of the distribution box, realizing dynamic power dispatching and transmission parameter feedback adjustment based on the circuit health status, effectively preventing overload risk and prolonging the service life of the device.

[0007] (II) Technical solutions In a first aspect, the present application provides a power monitoring method of a distribution box, and the technical solution is as follows: Collecting load data and circuit impedance data of a plurality of output circuits in the distribution box; Based on the load data and the circuit impedance data, a health state evaluation model is constructed to output a health risk coefficient and a load tolerance threshold of each output circuit; Transmission quality data and load demand data of each output circuit are acquired; Based on the transmission quality data, the load demand data, and the health risk coefficient, dynamic priority scheduling is performed to allocate power; Based on the scheduling result and the transmission state, feedback adjustment is performed on the transmission parameters and the load allocation.

[0008] Further, the application also proposes that acquiring the load data and the circuit impedance data comprises: The load data of each output circuit is acquired through an LPWAN adaptive frequency hopping module, wherein the LPWAN adaptive frequency hopping module automatically switches channels based on a real-time channel quality detection algorithm; The circuit impedance data of each output circuit is acquired through an EIS sensor, wherein the EIS sensor injects an alternating current signal into the circuit and acquires impedance spectrum data.

[0009] Further, the application also proposes that acquiring the load data and the circuit impedance data further comprises: The LPWAN adaptive frequency hopping module and the EIS sensor share power supply and clock synchronization to ensure that the timestamp deviation of the load data and the circuit impedance data is less than a preset threshold.

[0010] Further, the application also proposes that constructing the health state evaluation model comprises: Load features are extracted from the load data, including the proportion of inelastic load and the load fluctuation frequency; Impedance features are extracted from the circuit impedance data, including the impedance value at the characteristic frequency point; Using a gradient boosting tree algorithm, the load features and the impedance features are used as input to output the health risk coefficient and the load tolerance threshold.

[0011] Further, the application also proposes that performing dynamic priority scheduling comprises: Based on the transmission quality data, the load demand data, and the health risk coefficient, each output circuit is divided into high priority, medium priority, or low priority; Power is allocated according to the priority, wherein high-priority circuits are prioritized for power supply, the load allocation of medium-priority circuits does not exceed the proportion of the load tolerance threshold, and low-priority circuits are reduced or suspended for power supply.

[0012] Further, the application also proposes that the transmission quality data includes signal-to-noise ratio, and dividing each output circuit into priority comprises: When the signal-to-noise ratio is greater than or equal to the first threshold, the load demand is greater than the preset percentage of the current load, and the health risk coefficient is less than the second threshold, it is classified as high priority; When the signal-to-noise ratio is between the third and fourth thresholds, the load demand is less than or equal to the preset percentage of the current load, and the health risk coefficient is between the fifth and sixth thresholds, it is classified as medium priority; When the signal-to-noise ratio is less than the seventh threshold or the health risk coefficient is greater than the eighth threshold, it is classified as low priority.

[0013] Furthermore, this application also proposes feedback adjustment of transmission parameters and load distribution, including: When the number of times the LPWAN adaptive frequency hopping module of the output circuit triggers frequency hopping exceeds the frequency threshold, the load distribution of the output circuit is reduced. When the health risk coefficient of the output circuit exceeds the risk threshold, the transmission priority of the output circuit is increased.

[0014] Furthermore, this application proposes that the load tolerance threshold be dynamically adjusted based on the current impedance state, instead of a fixed rated load.

[0015] Secondly, this application also provides a computing device, as follows: At least one processor; and The memory stores instructions that, when executed by at least one processor, cause the at least one processor to perform the power monitoring method for the aforementioned distribution box.

[0016] Thirdly, this application also provides a non-transitory machine-readable storage medium, as follows: It stores executable instructions, which, when executed, cause the machine to perform the power monitoring method of the aforementioned distribution box.

[0017] (III) Beneficial Effects Compared with the prior art, the beneficial effects of the present invention are as follows: (1) In this invention, a health status assessment model is constructed by integrating load data and circuit impedance data, and a dynamic priority scheduling and feedback adjustment mechanism is realized by combining transmission quality and load demand. This effectively solves the technical problems of unstable data transmission at edge nodes and the disconnect between load scheduling and equipment health status. It has the advantages of improving the reliability and safety of power monitoring of distribution boxes, realizing dynamic power scheduling and transmission parameter feedback adjustment based on circuit health status, effectively preventing overload risks and extending equipment service life. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the overall structure of the power monitoring method for the distribution box; Figure 2 This is a schematic diagram illustrating the dynamic relationship between the health risk coefficient and the load tolerance threshold. Figure 3 This is a schematic diagram illustrating the changes in power allocation under dynamic priority scheduling. Detailed Implementation

[0020] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] like Figures 1-3 As shown in the figure, this application proposes a power monitoring method for a distribution box, which includes the following specific steps: S100: Collect load data and circuit impedance data of multiple output circuits in the distribution box; Collecting load data and circuit impedance data can be understood as extracting data information reflecting the operating status and health condition of multiple output circuits in the distribution box.

[0023] Specifically, load data can be acquired using current or voltage sensors installed in the circuit, while circuit impedance data can be obtained by injecting a signal of a specific frequency into the circuit and measuring its response, for example, by using a multi-frequency AC excitation signal combined with an impedance analyzer. This data acquisition method can provide fundamental support for subsequent health status assessments.

[0024] S200: Based on load data and circuit impedance data, a health status assessment model is constructed to output the health risk coefficient and load tolerance threshold of each output circuit. Building a health status assessment model refers to processing load data and circuit impedance data through algorithms to generate quantitative indicators that characterize the health status of a circuit. Specifically, this model can use machine learning algorithms, such as support vector machines or random forests, to train and predict the input data, thereby outputting health risk coefficients and load tolerance thresholds.

[0025] As a preferred implementation, the health risk coefficient can be calculated based on the changing trend of circuit impedance, while the load tolerance threshold can be dynamically adjusted based on the comparison between historical load data and the current impedance state.

[0026] S300: Obtain transmission quality data and load requirement data for each output circuit; The process of acquiring transmission quality data and load demand data can be understood as extracting information reflecting data transmission performance and power demand from communication modules and electrical equipment, respectively. For example, transmission quality data can be obtained by monitoring the bit error rate or latency of the communication channel, while load demand data can be obtained by analyzing the operating status of electrical equipment or the power consumption plan set by the user. This data provides a basis for subsequent dynamic priority scheduling decisions.

[0027] S400 performs dynamic priority scheduling to allocate power based on transmission quality data, load demand data, and health risk coefficients; S500 adjusts transmission parameters and load distribution based on scheduling results and transmission status.

[0028] The innovation of this application lies in the fact that by integrating circuit health status assessment and transmission quality monitoring, it collaboratively solves the problems of unstable data transmission at edge nodes and the disconnect between load scheduling and equipment health in distributed distribution boxes.

[0029] Compared to existing technologies that rely solely on load data for scheduling, this embodiment introduces circuit impedance data and transmission quality data to achieve dynamic priority scheduling based on multi-dimensional information, thereby effectively avoiding overload risks caused by increased health hazards or unstable transmission.

[0030] Meanwhile, the introduction of the feedback adjustment mechanism further optimized the transmission process and load distribution, improving the overall stability of the system.

[0031] Specifically, in this disclosure, a power monitoring method for a distribution box solves the problems of unstable data transmission at edge nodes and the disconnect between load scheduling and equipment health in a distributed distribution box through the coordinated operation of multiple technical links.

[0032] First, by collecting load data and circuit impedance data from multiple output circuits in the distribution box, key information reflecting the equipment's operating status and potential health hazards is obtained. The load data reflects the actual power consumption of the circuit, while the circuit impedance data reveals hidden health problems such as contact aging and insulation layer loss, providing multi-dimensional data support for subsequent analysis.

[0033] Furthermore, a health status assessment model is constructed based on load data and circuit impedance data. This model can output the health risk coefficient and load tolerance threshold of each output circuit, thereby quantifying the health status of the circuit and dynamically adjusting the upper limit of the load to avoid exacerbating health risks caused by fixed rated load allocation.

[0034] After acquiring the transmission quality data and load demand data of each output circuit, dynamic priority scheduling is performed in conjunction with the health risk coefficient.

[0035] Specifically, transmission quality data is used to assess the stability of edge node communication, load demand data reflects actual power consumption, and the health risk coefficient provides a reference for the health status of the circuit. By integrating data from these three dimensions, priority can be assigned to each output circuit. For example, high-priority circuits are given priority in power supply, the load allocation of medium-priority circuits is limited by the health tolerance threshold, and low-priority circuits may have their load reduced or power supply suspended, thereby ensuring that power allocation meets both actual needs and circuit health status.

[0036] Based on scheduling results and transmission status, feedback adjustments are made to transmission parameters and load allocation to form a closed-loop control mechanism. For example, when the transmission parameters of an output circuit are abnormal or the health risk factor increases, the transmission process is optimized and the load is dynamically adjusted by reducing load allocation or increasing transmission priority to address scheduling lag issues caused by data packet loss or delay.

[0037] Thus, the various technical aspects work closely together. Impedance data acquisition and health assessment models provide health-dimensional input for scheduling decisions, transmission quality data guides priority allocation and feedback adjustment, and the feedback adjustment mechanism in turn optimizes the transmission process. Ultimately, this achieves coordinated operation from health monitoring to scheduling decisions and transmission optimization, effectively improving the data transmission stability and load scheduling rationality of distributed distribution boxes.

[0038] This application further proposes to collect load data and circuit impedance data by: collecting load data of each output circuit through an LPWAN adaptive frequency hopping module, wherein the LPWAN adaptive frequency hopping module automatically switches channels based on a channel quality real-time detection algorithm; and collecting circuit impedance data of each output circuit through an EIS sensor, wherein the EIS sensor injects an AC signal into the circuit and collects impedance spectrum data.

[0039] In practical applications, an LPWAN adaptive frequency hopping module refers to a wireless communication module with dynamic channel switching capabilities, which can be implemented using low-power wide-area network technologies such as LoRa and NB-IoT. The core function of this module is to dynamically adjust the operating frequency band according to changes in channel quality, thereby avoiding data transmission problems caused by fixed channel interference. Its purpose is to improve the stability and reliability of data transmission at edge nodes.

[0040] An EIS sensor is a sensing device that measures the impedance characteristics of a circuit by injecting an AC signal. It can be implemented using multi-frequency scanning technology or frequency domain analysis technology. The sensor is designed to capture the impedance response characteristics of a circuit at different frequencies, thereby providing high-precision input data for health status assessment.

[0041] Specifically, the above solution addresses data quality issues caused by unstable transmission at edge nodes by optimizing the data acquisition mechanism. The LPWAN adaptive frequency hopping module automatically switches channels based on a real-time channel quality detection algorithm. For example, it scans the channel signal-to-noise ratio (SNR) every 100ms. When the current channel SNR is detected to be <-12dB, it can switch to a preset backup frequency band within <50ms. This dynamic channel adjustment not only ensures the continuity of load data transmission but also provides a highly reliable transmission channel for the impedance spectrum data acquired by the EIS sensor.

[0042] The EIS sensor, by injecting a weak AC signal of 10Hz-100kHz into the circuit and collecting impedance spectrum data, overcomes the limitations of traditional health monitoring that relies solely on load data such as current surges and voltage fluctuations. It can accurately identify hidden health hazards such as contact oxidation and wire insulation aging. Furthermore, the LPWAN adaptive frequency hopping module and the EIS sensor share power supply and clock synchronization at the hardware level, ensuring a smaller timestamp deviation between load data and impedance data, thereby improving accuracy and avoiding the problem of mismatch between health risk coefficients and load conditions due to data time asynchrony. (Reference) Figure 2As shown, the solid black line represents the health risk coefficient, and the dashed gray line represents the load tolerance threshold. The health risk coefficient is calculated based on impedance characteristics collected by EIS sensors, such as the 1kHz contact impedance R1 and the 100kHz insulation impedance R2, and is used to quantify the degree of latent circuit faults. This value increases significantly when contacts age or insulation is damaged. The load tolerance threshold represents a dynamic adjustment based on the current impedance state, replacing the fixed rated load. When the health risk coefficient > 0.5, the load tolerance threshold begins to decrease linearly to prevent overload operation on aging circuits.

[0043] exist Figure 2 On the 15th day, the contact resistance R1 increased by 150%, the health risk coefficient exceeded the 0.7 threshold, and the load tolerance threshold was automatically reduced to 75% of the rated load; on the 22nd day, the insulation resistance R2 decreased by 60%, the health risk coefficient reached 0.85, the system triggered an early warning and further reduced the load tolerance threshold to 60%.

[0044] This application further proposes that the acquisition of load data and circuit impedance data also includes: enabling the LPWAN adaptive frequency hopping module and the EIS sensor to share power supply and clock synchronization, so as to ensure that the timestamp deviation of the load data and circuit impedance data is less than a preset threshold.

[0045] Specifically, shared power supply refers to providing power to both the LPWAN adaptive frequency hopping module and the EIS sensor through the same power source, aiming to eliminate voltage fluctuations and noise interference introduced by independent power supplies. In practical applications, a linear regulator can be used to control the ripple to <5mV, thereby avoiding sampling trigger time deviations between the two modules caused by ripple differences between independent power supplies. Clock synchronization refers to a hardware-level synchronization design that combines the transmission characteristics of edge nodes. It can be achieved through direct connection of clock lines at the hardware level, such as directly outputting the GPS clock or crystal oscillator clock of the LPWAN module to the EIS sensor, thereby achieving nanosecond-level clock calibration.

[0046] The preset threshold can be dynamically adjusted according to the feature extraction requirements of the subsequent health status assessment model. It is usually set to <10ms. The purpose is to ensure that the extracted features such as the proportion of inelastic load and the impedance value at the characteristic frequency point all point to the true state of the circuit at the same time point.

[0047] In detail, this solution fundamentally solves the time synchronization problem of data acquisition by integrating the power supply and clock system of the LPWAN adaptive frequency hopping module with that of the EIS sensor. The shared power supply mechanism not only eliminates voltage fluctuations and noise interference introduced by independent power supplies, but more importantly, it avoids sampling trigger time deviations caused by ripple differences between the two modules due to independent power supplies.

[0048] Building upon this, a clock synchronization design is implemented, ensuring that both load data and circuit impedance data use the same time reference source. This directly eliminates the inherent accumulated errors of independent clock systems, guaranteeing that load data and circuit impedance data are precisely timestamped at the moment of acquisition. Finally, by setting a preset threshold for timestamp deviation, the synchronization accuracy is controlled within the range required by the health status assessment model, ensuring that the extracted load characteristics and impedance characteristics strictly correspond to the circuit state at the same moment. This design is particularly suitable for high-interference environments at edge nodes, effectively avoiding misjudgments of health risks caused by data time misalignment. This supports the reliability of subsequent dynamic priority scheduling and prevents secondary damage to equipment health caused by power allocation errors.

[0049] Furthermore, this solution works seamlessly with the data acquisition stage of the aforementioned power monitoring method for distribution boxes, demonstrating significant advantages in edge node scenarios. Through a hardware-level collaborative synchronization scheme, it overcomes the limitations of traditional software calibration, moving the synchronization process from post-data transmission calibration to native synchronization during data acquisition, completely avoiding the impact of the edge transmission environment on synchronization performance. This design not only solves the problem of accurate data transmission but also ensures accurate data matching, providing a precisely matched data foundation for subsequent health status assessments.

[0050] This application further proposes a health status assessment model including: Extract load features from the load data, including the proportion of inelastic load and the frequency of load fluctuations; Impedance features are extracted from circuit impedance data, including impedance values ​​at characteristic frequency points. Using the gradient boosting tree algorithm, with load characteristics and impedance characteristics as input, the output is a health risk coefficient and a load tolerance threshold.

[0051] Specifically, load characteristics refer to key indicators reflecting the dynamic behavior of a load, which can be achieved using the proportion of inelastic loads and the load fluctuation frequency. The proportion of inelastic loads refers to the percentage of inelastic loads in the total load, which can be calculated by analyzing the ratio of the instantaneous peak value to the average value of the load current waveform. The load fluctuation frequency refers to the number of load changes per unit time, which can be obtained by performing frequency domain transformation or statistical analysis on the load data. The purpose of introducing load characteristics is to differentiate the impact of different load types on the circuit's health status and avoid the deficiency of relying solely on average load values ​​while ignoring transient impacts.

[0052] In practical applications, impedance characteristics can be understood as a key indicator reflecting the health degradation of a circuit, which can be achieved by extracting impedance values ​​at characteristic frequency points. Characteristic frequency impedance values ​​refer to impedance values ​​measured at specific frequencies. These frequencies typically correspond to the circuit's critical resonant frequency or operating frequency; for example, 1kHz corresponds to contact impedance at the contact point, and 100kHz corresponds to insulation impedance. This feature extraction method simplifies data processing complexity while accurately capturing key indicators of health degradation.

[0053] Furthermore, the gradient boosting tree algorithm is an ensemble learning method that automatically learns the nonlinear correlations and interaction effects between multi-source features through iterative training of multiple weak classifiers. This algorithm was chosen to overcome the limitations of traditional fixed-threshold methods in adapting to complex scenarios. By training the model with historical health data, the output not only reflects the current state but also incorporates the patterns of equipment degradation.

[0054] In detail, this solution extracts the proportion of inelastic loads and the frequency of load fluctuations from load data, and combines this with impedance values ​​at characteristic frequencies extracted from circuit impedance data to form a cross-dimensional feature input. This feature combination method overcomes the limitations of traditional single-dimensional evaluation and can effectively distinguish between temporary data anomalies caused by load fluctuations and permanent data anomalies caused by health degradation. For example, when the proportion of inelastic loads increases and the frequency of load fluctuations increases, if the impedance value at 1kHz increases by 20% simultaneously, it can be accurately determined that the health degradation is caused by contact wear, rather than simply a change in load.

[0055] Building upon this foundation, the gradient boosting tree algorithm learns the three-dimensional interaction relationship of "inelastic load percentage × load fluctuation frequency × impedance value at characteristic frequency point" to achieve quantitative classification of health risks and dynamic adaptation of load tolerance. When the health risk coefficient increases from 0.5 to 0.7, the load tolerance threshold automatically decreases from 90% to 70% of the rated load. This dynamic linkage mechanism ensures that power distribution meets demand without exacerbating health risks.

[0056] The above technical solution solves the problem of evaluation distortion caused by the single-dimensional feature input of traditional models, realizes the accuracy and dynamism of health status evaluation, and provides a reliable decision-making basis for subsequent dynamic priority scheduling.

[0057] This application further proposes dynamic priority scheduling, including: Based on transmission quality data, load demand data, and health risk coefficients, each output circuit is divided into high priority, medium priority, or low priority. Power is allocated according to priority, with high-priority circuits receiving priority power supply, medium-priority circuits receiving loads not exceeding the load tolerance threshold, and low-priority circuits reducing load or suspending power supply.

[0058] Specifically, transmission quality data refers to indicators reflecting the reliability of the data link, which can be implemented using signal-to-noise ratio, bit error rate, or latency. Load demand data refers to parameters capturing real-time power consumption fluctuations, which can be implemented using current demand, power demand, or load growth rate. Health risk coefficient refers to a quantitative indicator of the implicit health status of associated circuits, which can be implemented using impedance anomaly change rate, aging index, or failure probability. The purpose of introducing these features is to ensure accurate allocation of power resources in scenarios with unstable data transmission at edge nodes and dynamic changes in equipment health status through a multi-dimensional data-driven approach.

[0059] In detail, this solution achieves dynamic quantitative evaluation of output circuit priorities by comprehensively considering transmission quality data, load demand data, and health risk coefficients. Transmission quality data ensures the identification of stable circuits even during channel quality fluctuations; load demand data accurately reflects changes in power consumption; and the health risk coefficient incorporates equipment health status into the decision-making process, thus avoiding scheduling deviations caused by relying on only a single data point. For high-priority circuits, the strategy of prioritizing power supply ensures critical circuits receive continuous power during resource constraints, preventing unexpected power outages due to data latency and improving the reliability of critical system tasks.

[0060] For medium-priority circuits, the load allocation does not exceed the proportion of the load tolerance threshold. The load tolerance threshold is used as the upper limit of allocation. When the health risk coefficient changes, the load ratio is automatically constrained to avoid overload operation on circuits with health hazards, thereby suppressing the risk of overheating and overload and extending the life of the equipment.

[0061] For low-priority circuits, the strategy of reducing load or suspending power supply actively releases power resources when transmission quality is severely degraded or health risks are too high. This not only alleviates the power supply pressure on high-priority circuits but also reduces the rate of deterioration of health hazards such as insulation loss by decreasing the load input to circuits with potential problems. Figure 3 As shown, it displays the power allocation results over 24 hours using a dynamic priority scheduling algorithm based on three factors: transmission quality, load demand, and health risk coefficient. Black represents high-priority circuits, dark gray represents medium-priority circuits, and light gray represents low-priority circuits.

[0062] During the morning peak electricity consumption period from 6:00 AM to 8:00 AM, the load demand of high-priority circuits increases, resulting in more power allocation. From 10:30 AM to 11:30 AM, the health risk coefficient of medium-priority circuits is detected to rise to 0.65, triggering load limiting and allocating power to no more than 90% of the tolerance threshold. From 2:00 PM to 3:30 PM, low-priority circuits continuously trigger frequency hopping and have a health risk coefficient > 0.7, so the system automatically reduces the load by 50%. During the evening peak electricity consumption period from 6:00 PM to 8:00 PM, power is redistributed based on transmission quality (SNR fluctuations) and health status, with high-priority circuits ensuring power supply. After 10:00 PM, the overall load demand decreases, and the system enters a low-power monitoring mode.

[0063] This application further proposes that in the power monitoring method for the aforementioned distribution box, the transmission quality data includes the signal-to-noise ratio, and the priority classification of each output circuit includes: When the signal-to-noise ratio is greater than or equal to the first threshold, the load demand is greater than the preset percentage of the current load, and the health risk coefficient is less than the second threshold, it is classified as high priority; When the signal-to-noise ratio is between the third and fourth thresholds, the load demand is less than or equal to the preset percentage of the current load, and the health risk coefficient is between the fifth and sixth thresholds, it is classified as medium priority; When the signal-to-noise ratio is less than the seventh threshold or the health risk coefficient is greater than the eighth threshold, it is classified as low priority.

[0064] Specifically, the signal-to-noise ratio (SNR) is the ratio of signal strength to noise strength, which can be expressed in decibels (dB) and is used to quantify the stability of data transmission at edge nodes.

[0065] Load demand refers to the ratio of the actual load demand of the current circuit to the current load, which can be calculated by collecting load data in real time. The health risk coefficient is an indicator of the circuit's health status based on load and impedance characteristics, and can be generated using a gradient boosting tree algorithm. The purpose of introducing these parameters is to achieve more precise priority allocation through multi-dimensional quantitative indicators.

[0066] In detail, this scheme achieves precise and operable priority allocation by defining threshold conditions for multiple parameter combinations. Specifying the signal-to-noise ratio (SNR) as the transmission quality data directly quantifies the stability of data transmission at edge nodes. As a core indicator of wireless communication, SNR effectively reflects signal quality under electromagnetic interference, avoiding the decision-making lag caused by relying solely on vague transmission quality descriptions in traditional schemes.

[0067] When assigning high priority, a combination of conditions is used, namely, a signal-to-noise ratio greater than or equal to a first threshold, a load demand greater than a preset percentage of the current load, and a health risk coefficient less than a second threshold. This ensures that power supply is only prioritized when transmission is stable, power demand is urgent, and circuit health is good, thereby preventing the misallocation of high priority resources under conditions of poor communication quality or health hazards.

[0068] When assigning priorities, the system is based on the following conditions: the signal-to-noise ratio is between the third and fourth thresholds, the load demand is less than or equal to a preset percentage of the current load, and the health risk coefficient is between the fifth and sixth thresholds. This allows for moderate power supply in scenarios with moderate transmission quality, low demand, and moderate health risk, thus avoiding overload risks caused by fixed thresholds.

[0069] When assigning low priority, the signal-to-noise ratio is triggered when it is less than the seventh threshold or the health risk coefficient is greater than the eighth threshold. This can quickly identify circuits with severe transmission instability or excessive health risk, and promptly block potential hazards by reducing load or suspending power supply, preventing overheating and overload at impedance abnormalities.

[0070] Each condition uses a combination of multiple parameters rather than a single indicator, which significantly improves the robustness of the division. For example, high priority requires all conditions to be met simultaneously, avoiding the risk of misjudgment based solely on load demand or health risk. This effectively solves the problem of inaccurate scheduling and ensures that dynamic priority scheduling can coordinately respond to data transmission fluctuations and changes in equipment health status.

[0071] This application further proposes a specific implementation method for feedback adjustment of transmission parameters and load distribution, including the following: When the number of times the LPWAN adaptive frequency hopping module of the output circuit triggers frequency hopping exceeds the frequency threshold, the load distribution of the output circuit is reduced. When the health risk coefficient of the output circuit exceeds the risk threshold, the transmission priority of the output circuit is increased.

[0072] Specifically, an LPWAN adaptive frequency hopping module refers to a communication module that automatically switches channels based on a real-time channel quality detection algorithm. It can be implemented using low-power wide-area network technologies such as LoRa and NB-IoT. The frequency threshold refers to the preset upper limit of the number of frequency hopping attempts, which can be set to different values ​​such as 3-5 times / minute depending on the actual application scenario. Its purpose is to characterize the channel interference intensity through frequency hopping behavior. Load allocation refers to the dynamic allocation of power resources in the output circuit. This can be achieved by adjusting the power supply or limiting the number of devices using the circuit, with the aim of alleviating channel congestion.

[0073] The health risk coefficient is a numerical value representing the health status of a circuit, calculated using a health status assessment model. It can be represented as a floating-point number between 0 and 1. The risk threshold is a preset health risk warning line, which can be set to different values ​​such as 0.7-0.8 according to actual conditions, aiming to promptly detect potential circuit health problems. Transmission priority refers to the allocation level of channel resources during data transmission. It can be achieved by adjusting the channel scanning frequency or channel occupancy permissions, aiming to ensure the stable transmission of critical data.

[0074] In detail, this solution addresses the issues of unstable data transmission and load scheduling disconnect at edge nodes in distributed distribution boxes by establishing a dynamic feedback correlation between transmission stability and device health status. Regarding transmission stability, when the frequency hopping count of the LPWAN adaptive frequency hopping module exceeds the frequency threshold, it indicates that the current channel interference has exceeded the module's dynamic anti-interference capability. At this point, reducing load allocation can decrease the amount of data transmitted per unit time, alleviate channel pressure, and bring the frequency hopping count back to a normal range. This method of mitigating transmission problems through load adjustment overcomes the limitations of traditional methods that rely solely on optimizing transmission parameters to address interference.

[0075] Regarding equipment health issues, when the health risk coefficient exceeds the risk threshold, it indicates that the circuit has hidden potential problems requiring close monitoring. In this case, increasing the transmission priority ensures more stable data transmission for the circuit's health data, providing maintenance personnel with a more continuous health deterioration curve. Simultaneously, increasing the transmission priority does not affect load limiting strategies, enabling parallel implementation of enhanced health monitoring and safe load control.

[0076] Based on this, the two feedback adjustment mechanisms have clear scenario-specific adaptation logic. Given the different characteristics of sudden transmission fluctuations and progressive health risks at edge nodes, a two-way feedback mechanism with differentiated trigger conditions and precise response actions is designed, effectively solving the problems of delayed response and singular actions in traditional feedback adjustment. By using the frequency hopping count of the transmission module as the trigger signal for load adjustment and the health risk coefficient as the basis for adjusting transmission priority, coordinated optimization of transmission and health is achieved.

[0077] This application further proposes a load tolerance threshold that is dynamically adjusted based on the current impedance state, in order to replace a fixed rated load.

[0078] Specifically, the load tolerance threshold refers to the upper limit of the load dynamically calculated based on the real-time health status of the circuit. This can be achieved using a quantitative mapping relationship, such as establishing a correspondence between different impedance characteristics and load tolerance capabilities based on the impedance values ​​at characteristic frequency points collected by an EIS sensor. In practical applications, impedance status can be understood as a key indicator reflecting potential health hazards in the circuit. This can be achieved by analyzing impedance changes in specific frequency bands, such as the contact impedance value in the 1kHz band and the insulation impedance value in the 100kHz band. The purpose of introducing this dynamic adjustment mechanism is to ensure that the load distribution always matches the actual carrying capacity of the circuit, avoiding the risk of overload due to a fixed rated load.

[0079] In detail, this scheme achieves a shift from "static fixed" to "dynamic quantification" by deeply binding the load tolerance threshold to the circuit impedance state. In implementation, firstly, impedance values ​​at key characteristic frequency points are extracted based on impedance spectrum data collected by EIS sensors. Then, a quantification standard is established based on the influence of impedance characteristics on load tolerance at different frequency bands; for example, a 1Ω increase in the 1kHz band corresponds to a 5% reduction in the load tolerance threshold. Simultaneously, this scheme effectively complements the aforementioned health status assessment model, providing a tiered basis for threshold adjustment through health risk coefficients; for example, a risk coefficient of 0.5-0.7 corresponds to a 10%-20% reduction in the threshold, thus providing a precise upper limit for subsequent dynamic priority scheduling. This design breaks through the technical inertia of traditional fixed rated loads, enabling load allocation decisions to respond promptly to changes in circuit health status, suppressing overload risks while improving power resource utilization.

[0080] The above technical solutions not only solve the problem that fixed rated loads cannot adapt to dynamic changes in health status, but also provide key quantitative standards for the safe dispatching of the entire power monitoring system.

[0081] Example 1

[0082] In another embodiment, this application also discloses a computing device, including: At least one processor; and The memory stores instructions that, when executed by at least one processor, cause the processor to perform a power monitoring method for the distribution box.

[0083] The core innovation of this application lies in combining at least one processor and memory in a closely coordinated manner, and introducing a health status assessment model, transmission quality data, and dynamic priority scheduling mechanism, thereby solving the problems of unstable data transmission at edge nodes and the disconnect between load scheduling and equipment health in distributed power distribution box scenarios, achieving the effects of improving data transmission stability, optimizing load allocation, and extending equipment life.

[0084] In practical applications, this computing device enables the implementation of power monitoring methods for distribution boxes by providing a hardware execution environment. Specifically, at least one processor acts as the computing core, responsible for executing the instructions of the power monitoring method and processing the monitoring and scheduling tasks of the distribution box in real time; the memory stores the instructions of the power monitoring method, ensuring that the method can be stably invoked and executed. When an instruction is executed by at least one processor, it enables at least one processor to execute the power monitoring method of the distribution box. This mechanism achieves close collaboration between the processor and the memory, automating the operation of the monitoring method and responding promptly to changes in the status of the distribution box. These technical features work together, with the processor relying on the instructions in the memory for computation, and the memory supporting dynamic adjustments through instruction execution feedback, jointly ensuring the efficient operation of the power monitoring method and effectively addressing the technical challenges of unstable data transmission at edge nodes and the disconnect between load scheduling and equipment health.

[0085] Furthermore, this application proposes a power monitoring method for a distribution box, comprising: collecting load data and circuit impedance data of multiple output circuits in the distribution box; constructing a health status assessment model based on the load data and circuit impedance data to output a health risk coefficient and load tolerance threshold for each output circuit; acquiring transmission quality data and load demand data for each output circuit; performing dynamic priority scheduling based on the transmission quality data, load demand data, and health risk coefficient to allocate power; and adjusting transmission parameters and load allocation based on the scheduling results and transmission status.

[0086] Acquiring load and circuit impedance data can be understood as extracting data reflecting the operating status and health condition of multiple output circuits in a distribution box. Specifically, load data can be acquired using current or voltage sensors installed in the circuit, while circuit impedance data can be obtained by injecting a signal of a specific frequency into the circuit and measuring its response, for example, using a multi-frequency AC excitation signal combined with an impedance analyzer. This data acquisition method provides fundamental support for subsequent health status assessments.

[0087] Furthermore, constructing a health status assessment model refers to processing load data and circuit impedance data through algorithms to generate quantitative indicators characterizing the health status of the circuit. Specifically, this model can use machine learning algorithms (such as support vector machines or random forests) to train and predict the input data, thereby outputting a health risk coefficient and a load tolerance threshold. As a preferred implementation, the health risk coefficient can be calculated based on the changing trend of circuit impedance, while the load tolerance threshold can be dynamically adjusted based on the comparison between historical load data and the current impedance status.

[0088] Furthermore, the process of acquiring transmission quality data and load demand data can be understood as extracting information reflecting data transmission performance and power demand from the communication module and electrical equipment, respectively. For example, transmission quality data can be obtained by monitoring the bit error rate or latency of the communication channel, while load demand data can be obtained by analyzing the operating status of electrical equipment or the power consumption plan set by the user. This data provides a basis for subsequent dynamic priority scheduling decisions.

[0089] The innovation of this application lies in its integration of circuit health status assessment and transmission quality monitoring, which collaboratively solves the problems of unstable data transmission at edge nodes and the disconnect between load scheduling and equipment health in distributed distribution boxes. Compared with existing technologies that rely solely on load data for scheduling, this embodiment introduces circuit impedance data and transmission quality data to achieve dynamic priority scheduling based on multi-dimensional information, thereby effectively avoiding overload risks caused by exacerbated health hazards or transmission instability. Simultaneously, the introduction of a feedback adjustment mechanism further optimizes the transmission process and load allocation, improving the overall stability of the system.

[0090] A power monitoring method for distribution boxes addresses the issues of unstable data transmission at edge nodes and the disconnect between load scheduling and equipment health in distributed distribution boxes through the coordinated operation of multiple technical aspects. First, by collecting load and circuit impedance data from multiple output circuits within the distribution box, key information reflecting the equipment's operating status and potential health hazards is obtained. Load data reflects the actual power consumption of the circuit, while circuit impedance data reveals hidden health problems such as contact aging and insulation layer loss, providing multi-dimensional data support for subsequent analysis. Furthermore, a health status assessment model is constructed based on the load and circuit impedance data. This model outputs the health risk coefficient and load tolerance threshold for each output circuit, thereby quantifying the circuit's health status and dynamically adjusting the load limit to avoid exacerbating health hazards due to fixed rated load allocation.

[0091] After acquiring transmission quality data and load demand data for each output circuit, dynamic priority scheduling is performed by combining this data with a health risk coefficient. Specifically, transmission quality data is used to assess the stability of edge node communication, load demand data reflects actual power consumption, and the health risk coefficient provides a reference for the circuit's health status. By integrating data from these three dimensions, priority is assigned to each output circuit. For example, high-priority circuits are given priority in power supply, the load allocation of medium-priority circuits is limited by a health tolerance threshold, and low-priority circuits may have their load reduced or power supply suspended. This ensures that power allocation meets both actual demand and circuit health status.

[0092] Finally, based on the scheduling results and transmission status, feedback adjustments are made to transmission parameters and load allocation, forming a closed-loop control mechanism. For example, when the transmission parameters of an output circuit are abnormal or the health risk coefficient increases, the transmission process is optimized and the load is dynamically adjusted by reducing load allocation or increasing transmission priority to address scheduling lag issues caused by data packet loss or delay. Thus, all technical aspects work closely together: impedance data acquisition and health assessment models provide health-dimensional input for scheduling decisions; transmission quality data guides priority allocation and feedback adjustment; and the feedback adjustment mechanism, in turn, optimizes the transmission process. Ultimately, this achieves coordinated operation from health monitoring to scheduling decisions to transmission optimization, effectively improving the data transmission stability and load scheduling rationality of the distributed distribution box.

[0093] Example 2

[0094] In another embodiment, this application also discloses a non-transitory machine-readable storage medium storing executable instructions that, when executed, cause a machine to perform a power monitoring method for a distribution box.

[0095] The core innovation of this application lies in solving the problems of unstable data transmission at edge nodes and the disconnect between load scheduling and equipment health in distributed distribution box scenarios by combining non-transitory machine-readable storage media with executable instructions. Specifically, this solution provides a reliable hardware carrier to ensure persistent storage and stable invocation of instructions in complex industrial environments. Simultaneously, the executable instructions define the implementation logic of the power monitoring method for the distribution box, enabling the machine to automatically trigger the monitoring process and respond in real time to load changes and transmission status. This design effectively avoids overload risks and potential equipment health hazards caused by data transmission interruptions or scheduling delays, achieving the effects of improving monitoring accuracy and extending equipment lifespan.

[0096] In practical applications, this method involves collecting load and circuit impedance data from multiple output circuits in a distribution box, and constructing a health status assessment model based on this data to output the health risk coefficient and load tolerance threshold for each output circuit. Further, it acquires transmission quality and load demand data for each output circuit, and performs dynamic priority scheduling based on the health risk coefficient to allocate power. Finally, it adjusts transmission parameters and load allocation based on the scheduling results and transmission status.

[0097] Acquiring load and circuit impedance data can be understood as extracting data reflecting the operating status and health condition of multiple output circuits in a distribution box. Specifically, load data can be acquired using current or voltage sensors installed in the circuit, while circuit impedance data can be obtained by injecting a signal of a specific frequency into the circuit and measuring its response, for example, using a multi-frequency AC excitation signal combined with an impedance analyzer. This data acquisition method provides fundamental support for subsequent health status assessments.

[0098] Furthermore, constructing a health status assessment model refers to processing load data and circuit impedance data through algorithms to generate quantitative indicators characterizing the health status of the circuit. Specifically, this model can use machine learning algorithms (such as support vector machines or random forests) to train and predict the input data, thereby outputting a health risk coefficient and a load tolerance threshold. As a preferred implementation, the health risk coefficient can be calculated based on the changing trend of circuit impedance, while the load tolerance threshold can be dynamically adjusted based on the comparison between historical load data and the current impedance status.

[0099] Furthermore, the process of acquiring transmission quality data and load demand data can be understood as extracting information reflecting data transmission performance and power demand from the communication module and electrical equipment, respectively. For example, transmission quality data can be obtained by monitoring the bit error rate or latency of the communication channel, while load demand data can be obtained by analyzing the operating status of electrical equipment or the power consumption plan set by the user. This data provides a basis for subsequent dynamic priority scheduling decisions.

[0100] The innovation of this application lies in its integration of circuit health status assessment and transmission quality monitoring, which collaboratively solves the problems of unstable data transmission at edge nodes and the disconnect between load scheduling and equipment health in distributed distribution boxes. Compared with existing technologies that rely solely on load data for scheduling, this embodiment introduces circuit impedance data and transmission quality data to achieve dynamic priority scheduling based on multi-dimensional information, thereby effectively avoiding overload risks caused by exacerbated health hazards or transmission instability. Simultaneously, the introduction of a feedback adjustment mechanism further optimizes the transmission process and load allocation, improving the overall stability of the system.

[0101] This disclosed power monitoring method for distribution boxes solves the problems of unstable data transmission at edge nodes and the disconnect between load scheduling and equipment health in distributed distribution boxes through the coordinated operation of multiple technical aspects. First, by collecting load data and circuit impedance data from multiple output circuits in the distribution box, key information reflecting the equipment's operating status and potential health hazards is obtained. The load data reflects the actual power consumption of the circuit, while the circuit impedance data reveals hidden health problems such as contact aging and insulation layer loss, providing multi-dimensional data support for subsequent analysis.

[0102] Furthermore, a health status assessment model is constructed based on load data and circuit impedance data. This model can output the health risk coefficient and load tolerance threshold of each output circuit, thereby quantifying the health status of the circuit and dynamically adjusting the upper limit of the load to avoid exacerbating health risks caused by fixed rated load allocation.

[0103] After acquiring transmission quality data and load demand data for each output circuit, dynamic priority scheduling is performed in conjunction with a health risk coefficient. Specifically, transmission quality data is used to assess the stability of edge node communication, load demand data reflects actual power consumption, and the health risk coefficient provides a reference for the circuit's health status.

[0104] By integrating data from these three dimensions, priority can be assigned to each output circuit. For example, high-priority circuits are given priority in power supply, the load allocation of medium-priority circuits is limited by the health tolerance threshold, and low-priority circuits may be deloaded or have their power supply suspended, thereby ensuring that power allocation meets actual needs while taking into account the health status of the circuits.

[0105] Finally, based on the scheduling results and transmission status, feedback adjustments are made to the transmission parameters and load allocation to form a closed-loop control mechanism. For example, when the transmission parameters of an output circuit are abnormal or the health risk factor increases, the transmission process is optimized and the load is dynamically adjusted by reducing the load allocation or increasing the transmission priority to address scheduling lag issues caused by data packet loss or delay.

[0106] Thus, the various technical aspects work closely together. Impedance data acquisition and health assessment models provide health-dimensional input for scheduling decisions, transmission quality data guides priority allocation and feedback adjustment, and the feedback adjustment mechanism in turn optimizes the transmission process. Ultimately, this achieves coordinated operation from health monitoring to scheduling decisions and transmission optimization, effectively improving the data transmission stability and load scheduling rationality of distributed distribution boxes.

[0107] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method of power monitoring of a distribution box, characterized by, The power monitoring method of the distribution box comprises: Collecting load data and circuit impedance data of multiple output circuits in the distribution box; Based on the load data and the circuit impedance data, a health state evaluation model is constructed to output a health risk coefficient and a load tolerance threshold of each output circuit; Obtaining transmission quality data and load demand data of each output circuit; Based on the transmission quality data, the load demand data and the health risk coefficient, dynamic priority scheduling is performed to allocate power; Based on the scheduling result and the transmission state, feedback adjustment is made to the transmission parameters and load allocation.

2. The power monitoring method of an electrical distribution panel of claim 1, wherein, The collection of load data and circuit impedance data comprises: Collecting load data of each output circuit through an LPWAN adaptive frequency hopping module, wherein the LPWAN adaptive frequency hopping module automatically switches channels based on a real-time channel quality detection algorithm; Collecting circuit impedance data of each output circuit through an EIS sensor, wherein the EIS sensor injects an alternating current signal into the circuit and collects impedance spectrum data.

3. The method of claim 2, wherein, The collection of load data and circuit impedance data further comprises: Making the LPWAN adaptive frequency hopping module and the EIS sensor share power supply and clock synchronization to ensure that the timestamp deviation of the load data and the circuit impedance data is less than a preset threshold.

4. The method of claim 1, wherein, The construction of the health state evaluation model comprises: Extracting load features from the load data, the load features including a non-elastic load proportion and a load fluctuation frequency; Extracting impedance features from the circuit impedance data, the impedance features including a characteristic frequency point impedance value; Using a gradient boosting tree algorithm to input the load features and the impedance features and output the health risk coefficient and the load tolerance threshold.

5. The method of claim 1, wherein, The dynamic priority scheduling comprises: Based on the transmission quality data, the load demand data and the health risk coefficient, dividing each output circuit into high priority, medium priority or low priority; According to the priority, allocating power, wherein high priority circuits are preferentially guaranteed for power supply, the load allocation of medium priority circuits does not exceed a proportion of the load tolerance threshold, and low priority circuits are reduced in load or suspended in power supply.

6. The method of claim 5, wherein, The transmission quality data includes signal-to-noise ratio, and the division of each output circuit into priority comprises: When the signal-to-noise ratio is greater than or equal to a first threshold, the load demand is greater than a preset percentage of the current load, and the health risk coefficient is less than a second threshold, it is divided into high priority; When the signal-to-noise ratio is between a third threshold and a fourth threshold, the load demand is less than or equal to a preset percentage of the current load, and the health risk coefficient is between a fifth threshold and a sixth threshold, it is divided into medium priority; When the signal-to-noise ratio is less than a seventh threshold or the health risk coefficient is greater than an eighth threshold, it is divided into low priority.

7. The method of power monitoring of an electrical distribution panel of claim 1, wherein, The feedback adjustment to the transmission parameters and load allocation comprises: When the number of times that the LPWAN adaptive frequency hopping module of an output circuit triggers frequency hopping exceeds a frequency threshold, the load allocation of the output circuit is reduced; When the health risk coefficient of an output circuit exceeds a risk threshold, the transmission priority of the output circuit is increased.

8. The method of power monitoring of an electrical distribution panel of claim 1, wherein, The load tolerance threshold is dynamically adjusted based on the current impedance state to replace the fixed rated load.

9. A computing device comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the method of power monitoring of an electrical panel of any one of claims 1-8.

10. A non-transitory machine-readable storage medium storing executable instructions that, when executed, cause a machine to perform the method of power monitoring of an electrical panel of any one of claims 1-8.

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