Power monitoring and dispatching method and system for distribution box

Abstract

The invention relates to an electric power system monitoring and control technology, in particular to a distribution box electric power monitoring and scheduling method and system, and aims to solve the problems that in a load property judgment link in the prior art, a static parameter model is adopted for analysis in the prior art, dynamic association between equipment type characteristics and environmental parameters cannot be effectively fused, and the load property judgment efficiency is poor. The problems that the stable load is mistakenly recognized as the sudden load or the sudden load is missed to be judged, the misjudgment phenomenon occurs frequently, and then the circuit overload tripping risk is increased are solved. The integrated sensing unit is electrically connected with the output loop of the distribution box through the adapter plate, and the adapter plate is provided with various replaceable interface terminals, so that the difficulty encountered when the integrated sensing unit adapts to distribution boxes of different models is effectively solved.

Description

Technical Field

[0001] This application relates to power system monitoring and control technology, and more specifically, to a method and system for power monitoring and dispatching of distribution boxes. Background Technology

[0002] As the core equipment for power distribution in small and medium-sized scenarios, the real-time monitoring and precise scheduling of the distribution box's operating status are crucial for ensuring the stability of the power system. However, current technical solutions face multiple challenges in practical applications. Existing monitoring methods generally rely on centralized cloud processing, requiring remote transmission and complex calculations after data collection, resulting in significantly extended response cycles and difficulty in capturing sudden load changes. For example, in scenarios such as multiple air conditioners starting up simultaneously in a shop or temporary operation of processing equipment in a factory, power fluctuations are easily caused by response delays.

[0003] In the load nature determination stage, the existing technology uses static parameter models for analysis, which fails to effectively integrate the dynamic correlation between equipment type characteristics and environmental parameters. For example, the difference in sensitivity to temperature changes between temperature control equipment such as refrigerators and motor equipment is not taken into consideration, resulting in stable loads being incorrectly identified as sudden loads or sudden loads being missed in the judgment. This leads to frequent misjudgments, which in turn increases the risk of circuit overload tripping.

[0004] At the hardware level, traditional monitoring solutions require the deployment of multiple independent sensor components, resulting in low system integration. This not only increases overall costs but also makes it difficult to adapt to older power distribution boxes due to the lack of universal interface design for the sensor units. Upgrades often involve cumbersome rewiring, significantly raising the implementation threshold. Furthermore, the system has weak fault tolerance. When sensor units experience data anomalies or edge processing modules encounter network outages, the lack of effective localized emergency mechanisms leads to disruptions in power supply continuity. For small and medium-sized enterprises with limited maintenance resources, the long fault recovery cycle can affect the continuous operation of critical equipment. Simultaneously, the effectiveness verification of existing solutions largely remains at the theoretical level, failing to provide quantifiable data support for specific application scenarios. This makes it difficult for users to assess the practical applicability of the solutions, hindering the widespread adoption of the technology.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] (a) Technical problems to be solved To address the aforementioned issues, this invention proposes a power monitoring and dispatching method and system for distribution boxes. The aim is to resolve the problem in the existing technology where static parameter models are used for load characteristic determination, failing to effectively integrate the dynamic correlation between equipment type characteristics and environmental parameters. This results in stable loads being incorrectly identified as sudden loads or sudden loads being overlooked, leading to frequent misjudgments and increasing the risk of circuit overload tripping.

[0007] (II) Technical Solution The present invention provides a method for power monitoring and dispatching of a distribution box, comprising: Obtain electrical and environmental parameter data for each output circuit of the distribution box; Based on the electrical parameter data, the load characteristics of each circuit are determined, wherein the load characteristics include at least elastic load and inelastic load; The load characteristics are dynamically corrected based on the environmental parameter data and the preset equipment type and parameter correction strategy; Based on the modified load characteristics, power dispatch instructions are generated and executed for the output circuit of the distribution box.

[0008] Furthermore, the electrical parameter data includes voltage and current, and the environmental parameter data includes temperature; The acquisition of electrical and environmental parameter data of each output circuit of the distribution box is achieved by an integrated sensing unit deployed on each output circuit of the distribution box.

[0009] Furthermore, determining the load characteristics of each circuit based on the electrical parameter data includes: Calculate the real-time load power of each circuit based on the voltage and current. Empirical mode decomposition is performed on the real-time load power to obtain multiple intrinsic mode function components and a residual term; Based on the intrinsic mode function components and the residual terms, the initial elastic load level and the initial inelastic load level of each loop are calculated.

[0010] Furthermore, the step of dynamically correcting the load characteristics based on the environmental parameter data and preset equipment type and parameter correction strategies includes: Based on the type of equipment connected to each output circuit, query the preset equipment type-temperature correction threshold mapping table to obtain the corresponding correction coefficient; Using the correction coefficient and the temperature value in the currently collected environmental parameter data, the initial elastic load level and / or the initial inelastic load level are adjusted to obtain the corrected elastic load level and inelastic load level.

[0011] Furthermore, the step of generating and executing power dispatch instructions for the output circuit of the distribution box based on the modified load characteristics includes: Substitute the corrected elastic load level and inelastic load level into the preset time-series composite load function to calculate the composite load value of each loop; With the goal of minimizing the overall dispatch impact, and under the conditions of satisfying the capacity constraints of the distribution boxes and the maximum load constraints of each circuit, the power dispatch function is solved to obtain the dispatch decision for each circuit; Based on the scheduling decision, the power dispatching instruction is issued within a preset response time.

[0012] Furthermore, the method also includes a redundancy processing mechanism, which includes: When an abnormality or failure is detected in the data of an integrated sensing unit, the average historical data of that circuit within a preset period before the failure is used as temporary data to maintain the continued operation of the system. When the edge processing module is disconnected from the cloud network, it performs scheduling decisions based on historical power grid load data stored locally.

[0013] Furthermore, the method also includes: The abnormal load records and periodic performance consumption statistics generated during the monitoring process are compressed and uploaded to the cloud; Based on historical load data and environmental parameter data, energy consumption optimization suggestions are generated and displayed locally for the current application scenario.

[0014] Furthermore, the integrated sensing unit is electrically connected to the output circuit of the distribution box via an adapter plate, which is equipped with a variety of replaceable interface terminals to adapt to different models of distribution boxes.

[0015] Furthermore, this application also proposes a power monitoring and dispatching system for distribution boxes, comprising: One or more integrated sensing units as described in the above technical solutions are used to be deployed on the output circuit of the distribution box; An edge processing module is communicatively connected to the integrated sensing unit. The edge processing module includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement the method described in any of the above technical solutions.

[0016] Furthermore, this application also proposes a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the method as described in any one of the above technical solutions.

[0017] (III) Beneficial Effects Compared with the prior art, the beneficial effects of the present invention are as follows: In this invention, the integrated sensing unit is electrically connected to the output circuit of the distribution box through an adapter board, and the adapter board is equipped with a variety of replaceable interface terminals, which effectively solves the difficulties encountered by the integrated sensing unit when adapting to different models of distribution boxes.

[0018] This invention decouples the sensing unit from the distribution box model by introducing a modular adapter board and replaceable interface terminals, avoiding customized design or complex modification for each distribution box model, thereby significantly reducing hardware costs and installation and deployment complexity. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the overall structure of the power monitoring and dispatching method for distribution boxes. Detailed Implementation

[0021] 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.

[0022] 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.

[0023] Traditional power monitoring solutions for distribution boxes suffer from slow response and high false alarm rates when dealing with sudden load surges. This is primarily due to reliance on cloud analysis, leading to data transmission and computation delays, and a lack of sufficient integration with the characteristics of devices within the scenario for load assessment. Furthermore, existing solutions have shortcomings in hardware cost, compatibility with older equipment, and system stability. For example, they lack redundant handling mechanisms for sensor unit failures and edge module network outages, which can easily cause power supply interruptions. Therefore, there is an urgent need for a power monitoring and dispatching solution for distribution boxes that can provide rapid response, accurate judgment, and stable operation.

[0024] In this regard, refer to Figure 1As shown, this application proposes a method for power monitoring and dispatching of a distribution box, including: S100: Obtain electrical parameter data and environmental parameter data for each output circuit of the distribution box; S200. Based on electrical parameter data, determine the load characteristics of each circuit, wherein the load characteristics include at least elastic loads and inelastic loads; S300: Dynamically adjusts the load characteristics based on environmental parameter data and preset equipment type and parameter correction strategies; S400: Based on the modified load characteristics, generate and execute power dispatch instructions for the output circuit of the distribution box.

[0025] For ease of understanding, the following explains some key terms in this embodiment: Electrical parameter data refers to electrical quantities used to describe the operating status of a power system, such as voltage, current, and power factor. These data reflect the real-time power consumption of each output circuit in the distribution box.

[0026] Environmental parameter data refers to external environmental factors that affect the operating status of equipment, such as temperature, humidity, and light intensity. This data can help determine the actual operating conditions of the load.

[0027] Load characteristics refer to the electrical consumption characteristics of the equipment connected to the output circuit of the distribution box. Among them, flexible loads are loads whose power supply can be adjusted or interrupted within a certain range without significantly affecting their core functions, such as some lighting and air conditioning; inflexible loads are loads that have high requirements for power supply continuity and are not suitable for adjustment or interruption of power supply, such as key production equipment, medical equipment, and refrigerators.

[0028] Equipment type and parameter correction strategy refers to pre-set rules or models used to adjust load nature judgment based on the inherent characteristics of different types of equipment (such as motors, lighting, refrigeration equipment, etc.) and their response patterns to changes in environmental parameters (such as temperature). This strategy aims to improve the accuracy of load nature judgment.

[0029] Power dispatch instructions are commands issued to the output circuits of distribution boxes based on load characteristics and system operating status to adjust power distribution or control equipment operation, such as cutting off power supply to some circuits or reducing power supply.

[0030] This embodiment provides a method for power monitoring and dispatching of distribution boxes.

[0031] First, acquire electrical and environmental parameter data for each output circuit of the distribution box. Specifically, electrical parameter data can be acquired in real time by installing separate voltage and current sensors on each output circuit, or by installing independent measurement modules near the output circuits of the distribution box. These modules contain components for measuring electrical parameters and transmit the measurement results to the processing unit. Environmental parameter data can be acquired by deploying temperature and humidity sensors inside or near the distribution box, or by acquiring environmental parameters through independent measurement modules. Additionally, regular manual inspections can be conducted to read the readings of the meters and thermometers installed on the distribution box and manually enter the data into the system.

[0032] Based on the acquired electrical parameter data, the load characteristics of each circuit are determined. These load characteristics include at least resilient and inelastic loads. Specifically, this can be achieved by analyzing the acquired current waveforms or power fluctuations. For example, when the current or power fluctuates significantly and frequently within a short period, it is initially identified as a resilient load; when the current or power remains stable or changes slowly over a long period, it is initially identified as an inelastic load.

[0033] As another approach, a fixed power threshold can be set. When the real-time power of a circuit exceeds a certain threshold, it is classified as an inelastic load; when it falls below another threshold, it is classified as an elastic load. Furthermore, by statistically analyzing historical electrical parameter data, typical power consumption patterns of different circuits can be identified, and circuits can be marked as elastic or inelastic loads based on these patterns.

[0034] Furthermore, the aforementioned load characteristics are dynamically corrected based on environmental parameter data and preset equipment types and parameter correction strategies.

[0035] Specifically, the type of equipment connected to each circuit, such as lighting, air conditioning, and motors, can be manually input, and the pre-set correction rule table can be consulted based on the current ambient temperature to adjust the initially determined load characteristics. For example, in a high-temperature environment, the load characteristics of an air conditioner may be more inelastic, in which case the originally determined elastic load can be corrected to an inelastic load.

[0036] As another implementation approach, a series of logical judgment conditions based on environmental parameters can be set. For example, when the ambient temperature exceeds a certain set value, the load characteristic judgment criteria for certain specific equipment (such as refrigeration equipment) will be automatically adjusted, thereby correcting its elasticity or inelasticity. Alternatively, environmental parameter data and equipment type information can be input into a simple decision tree model, which will then correct the initial judgment result of the load characteristic according to preset rules.

[0037] Based on the corrected load characteristics, power dispatch instructions for the output circuits of the distribution box are generated and executed. Specifically, when the system detects that the total load of the distribution box is close to or exceeds a safety threshold, it prioritizes power limiting or disconnection of flexible load circuits according to the corrected load characteristics to avoid overload. For example, if a circuit is identified as a flexible load after correction, an instruction to reduce its power supply can be generated.

[0038] As another implementation approach, time-based power allocation plans can be formulated based on the modified load characteristics. For example, during peak electricity consumption periods, instructions to reduce the load on circuits that are modified to be flexible loads can be issued; during off-peak periods, they can be allowed to resume normal power supply. Furthermore, through a simple control logic, when power dispatch is required, the system will select the circuits to be dispatched according to a preset priority order based on the modified load characteristics list. For example, flexible loads have the lowest priority, and corresponding switching or power adjustment instructions will be generated.

[0039] This embodiment effectively improves the accuracy and response speed of power monitoring in distribution boxes by acquiring electrical and environmental parameters in real time and dynamically correcting the load characteristics based on equipment type. This avoids load misjudgments caused by fixed parameter determination in traditional solutions, reduces the risk of circuit overload tripping due to scheduling errors, and thus improves the stability and reliability of the system when dealing with sudden loads, ensuring the continuity of power supply in small and medium-sized scenarios.

[0040] In some of the solutions mentioned above in this application, it is proposed to acquire electrical and environmental parameter data for monitoring and scheduling distribution boxes. However, in the process of implementation, the existing technology uses discrete sensors or cloud reliance, which leads to large data acquisition delays, high hardware costs, and difficulties in adapting to old equipment, thereby causing problems such as response lag, increased misjudgment rate, and over-budget transformation costs.

[0041] In this regard, this application further proposes that the electrical parameter data includes voltage and current, and the environmental parameter data includes temperature; the acquisition of electrical parameter data and environmental parameter data of each output circuit of the distribution box is achieved by an integrated sensing unit deployed on each output circuit of the distribution box.

[0042] Specifically, electrical parameter data includes voltage and current. Voltage is a measure of the work done by the electric field force, and current is the rate of directional movement of charge. These are the most fundamental electrical parameters in a power system that directly reflect the load state. By monitoring voltage and current in real time, the real-time power of the circuit can be directly calculated, thereby assessing the load size and its changing trend, providing a core basis for subsequently determining the load characteristics. In practical applications, voltage and current can be measured in various ways. For example, a high-precision shunt can be used in conjunction with a voltage sampling circuit to measure current, while a resistive voltage divider network or isolation amplifier can be used to measure voltage. This method has the advantages of relatively low cost and controllable accuracy. Alternatively, a Hall effect sensor can be used to measure current, which has the advantage of not requiring direct contact with the main circuit, achieving electrical isolation and improving safety; voltage measurement can be performed using a high-impedance differential amplifier. Furthermore, for applications with higher voltage and current, current transformers and voltage transformers can be used for measurement to provide isolation and voltage / current reduction functions, facilitating subsequent signal processing.

[0043] Meanwhile, environmental parameters include temperature. Temperature is a key environmental factor affecting the performance and load characteristics of electrical equipment. For example, a motor may have a higher starting current in low-temperature environments, and a resistive load may experience resistance changes due to poor heat dissipation in high-temperature environments, thus affecting its load characteristics. Incorporating temperature as an environmental parameter helps to more accurately correct load characteristics and improve scheduling precision.

[0044] Temperature can be measured using thermistors or resistance temperature detectors (RTDs). Thermistors offer high sensitivity and fast response, while RTDs provide higher accuracy and stability. Another approach is to use integrated temperature sensors. These sensors integrate the temperature sensing element and signal processing circuitry, outputting analog voltage or digital signals, simplifying circuit design. Additionally, infrared temperature sensors can be used to measure the surface temperature of equipment non-contactly, suitable for scenarios requiring avoidance of direct contact or measurement of high-temperature equipment.

[0045] The acquisition of electrical and environmental parameter data for each output circuit of the distribution box is achieved through integrated sensing units deployed on each output circuit of the distribution box. An "integrated sensing unit" refers to a physical module that integrates the functions of multiple sensors (specifically voltage, current, and temperature sensing) into a single module. Deploying it on each output circuit of the distribution box means that the data acquisition points are close to the load, achieving localized and distributed data acquisition.

[0046] This deployment approach aims to address the problems of high cost, complex wiring, data transmission delays, and poor data synchronization associated with traditional discrete sensors. For example, the integrated sensing unit can be designed as a modular structure, secured to a DIN rail inside the distribution box via clips or screws, and directly electrically connected to the wires of each output circuit, such as through a feedthrough current sensor and parallel voltage sampling terminals. Temperature sensors can be built into the unit or attached to critical components of the circuit via short leads.

[0047] Another approach is to use an integrated sensing unit with a PCB onboard design. This integrates the voltage sampling circuit, current sampling circuit, temperature sensor chip, and microcontroller onto a single circuit board and connects to the output circuit of the distribution box via pin headers or terminals. This unit can have its own independent power supply module and communication interface. For example, the integrated sensing unit can be designed with a compact housing, directly mounted below or to the side of the circuit breaker in the distribution box. It connects to the circuit breaker's input and output lines via pre-installed terminals to measure the voltage and current of that circuit. The temperature sensor can be mounted on the unit's housing to measure the local ambient temperature.

[0048] Through the above technical solution, this application provides a basis for calculating real-time load power by specifically defining the electrical parameters as voltage and current, avoiding the collection of redundant parameters, thereby simplifying the data dimensions and calculation complexity, and ensuring the accuracy of subsequent load nature determination.

[0049] Meanwhile, by using temperature as an environmental parameter, focusing on key environmental factors affecting equipment load characteristics, correction interference introduced by irrelevant environmental data is avoided, making subsequent load characteristic corrections more targeted. More importantly, by integrating voltage, current, and temperature sensing functions into the same unit and deploying it locally on each output circuit of the distribution box, synchronous acquisition and transmission of electrical and environmental parameters are achieved.

[0050] This significantly reduces the hardware cost and installation complexity of traditional discrete sensors, as a single integrated unit replaces multiple discrete sensors and their wiring. Localized deployment and integrated design eliminate reliance on cloud transmission, reducing data acquisition latency from seconds to minutes, providing a solid data foundation for rapid response to sudden load surges. This synchronous, low-latency data acquisition ensures the accuracy and real-time nature of subsequent load characteristic assessments and dynamic corrections, effectively avoiding misjudgments and scheduling failures caused by data lag or asynchrony, and significantly improving the efficiency and reliability of power monitoring and scheduling in distribution boxes.

[0051] In some of the solutions mentioned above in this application, the load characteristics of each circuit are determined based on electrical parameter data to distinguish between elastic and inelastic loads. However, in the implementation process, due to the lack of fine decomposition of load power, the calculation of the initial load level is not accurate enough, which can easily lead to misjudgment.

[0052] In response, this application further proposes a method to determine the load characteristics of each circuit based on the aforementioned electrical parameter data, specifically including the following steps: calculating the real-time load power of each circuit based on the aforementioned voltage and current; performing empirical mode decomposition on the real-time load power to obtain multiple intrinsic mode function components and a residual term; and calculating the initial elastic load degree and the initial inelastic load degree of each circuit based on the intrinsic mode function components and the residual term.

[0053] Calculating the real-time load power of each circuit based on the voltage and current refers to acquiring instantaneous voltage and current values ​​through integrated sensing units deployed on each output circuit of the distribution box, and calculating the actual power consumption of each circuit at a certain moment. Real-time load power is a core indicator for measuring the energy demand of a circuit and can dynamically reflect the operating status of connected equipment. Specifically, it can be achieved in any of the following ways: one way is to directly multiply the high-frequency sampled instantaneous voltage and current values, i.e. One approach is to obtain the finest-grained power fluctuation information; another approach is to integrate the instantaneous power over a very short preset time interval (e.g., 100ms), or to calculate the average power over that time interval using the effective values ​​of voltage and current combined with the power factor. This helps to smooth out extremely high-frequency noise while capturing rapid changes.

[0054] Empirical mode decomposition (EMD) is performed on the real-time load power to obtain multiple intrinsic mode function (IMF) components and a residual term. This means that the complex, nonlinear, and non-stationary real-time load power signal is adaptively decomposed into a series of intrinsic mode function (IMF) components with different frequency characteristics and a residual term representing the overall trend of the signal through EMD technology.

[0055] Each IMF component represents a simple oscillation mode in the signal, whose amplitude and frequency can vary over time, while the residual term reflects the long-term trend or DC component of the signal. This decomposition method can effectively separate components of different frequencies in the load power signal, thereby revealing the power characteristics corresponding to different types of load behavior (e.g., transient spikes, steady-state operation).

[0056] Specifically, this can be achieved in one of the following ways: One way is to use the standard EMD algorithm, which identifies the local maxima and minima of the signal through an iterative "screening" process, constructs upper and lower envelopes, calculates the average envelope and subtracts it from the signal, and repeats this process until the conditions for IMF are met, finally obtaining all IMF components and the remaining residual terms; Another way is to use the ensemble empirical mode decomposition (EEMD) algorithm, which adds white noise to the original signal multiple times, performs EMD decomposition on each noisy signal, and then averages the corresponding IMF components to effectively solve the mode mixing problem that may occur in the standard EMD, thereby obtaining IMF components with more physical meaning.

[0057] Calculating the initial elastic and inelastic load levels of each loop based on the intrinsic mode function components and residual terms means quantifying the initial elasticity and inelasticity of the load after the real-time load power is decomposed into IMF components and residual terms. Elastic loads typically exhibit high-frequency, transient power fluctuations, such as motor starting and heater cycling, while inelastic loads tend to have more stable, low-frequency power curves or continuous trends, such as lighting and refrigeration equipment.

[0058] By analyzing the characteristics of IMF components and residual terms, a preliminary assessment of these load properties can be made. Specifically, this can be achieved in either of the following ways: One approach is to assign different weights to the characteristic frequencies and amplitudes of different IMF components. For example, high-frequency IMF components with larger amplitudes (indicating rapid power changes) contribute more to the initial elastic load level, while low-frequency IMF components and residual terms (representing stable power consumption) contribute more to the initial inelastic load level. This is then calculated using a weighted summation or a classification model trained based on IMF features (such as a support vector machine). Another approach is to analyze the energy (or variance) contained in each IMF component and residual term. High-energy high-frequency IMF components indicate a higher elastic load level, while a significant proportion of energy in low-frequency IMF components or residual terms suggests a higher inelastic load level. The initial elastic and inelastic load levels can be determined by setting thresholds or calculating energy ratios.

[0059] Through the above technical solution, this application effectively solves the problem of inaccurate initial load level calculation and easy misjudgment caused by the lack of fine decomposition of load power in traditional methods when determining the load characteristics of each circuit based on electrical parameter data. Specifically, by calculating the real-time load power, a dynamic and accurate basis is provided for subsequent fine analysis.

[0060] Furthermore, by introducing empirical mode decomposition (EMD) technology, complex load power signals can be adaptively decomposed into multiple intrinsic mode function (IMF) components and a residual term. Among them, the high-frequency IMF components can accurately capture the instantaneous power fluctuations unique to elastic loads (such as short-term power surges such as air conditioner startup and motor start-stop), while the low-frequency IMF components and residual term can effectively reflect the stable power output of inelastic loads (such as the continuous load of refrigerators and lighting equipment).

[0061] This adaptive decomposition method quantifies and distinguishes between the "fluctuating" and "stable" characteristics of load power, thus avoiding the misclassification of sudden elastic loads as stable inelastic loads, or vice versa, that occurs in traditional schemes due to the inability to identify subtle fluctuations. Based on this, the initial elastic and inelastic load levels are calculated using the decomposed intrinsic mode function components and residual terms. This transforms the initial load characteristic assessment from a coarse overall evaluation to a refined component quantification, ensuring the accuracy and reliability of the initial judgment. This provides a high-precision initial data foundation for subsequent corrections based on environmental parameters, significantly reducing the misclassification rate of the final load characteristics and providing a more accurate decision-making basis for power dispatching in distribution boxes.

[0062] In some of the embodiments described above in this application, an initial load nature is determined based on electrical parameter data to identify the load type. However, in its implementation, the influence of environmental parameters and equipment type on the load nature is not considered, resulting in inaccurate load nature judgment and easy to cause misjudgment and circuit problems. To address this, this application further proposes a method for dynamically correcting load characteristics. This method dynamically corrects load characteristics based on environmental parameter data and preset device types and parameter correction strategies. Specifically, the correction process includes: querying a preset device type-temperature correction threshold mapping table based on the device type connected to each output circuit to obtain the corresponding correction coefficient; and adjusting the initial elastic load level and / or the initial inelastic load level using the correction coefficient and the temperature value in the currently collected environmental parameter data to obtain the corrected elastic load level and inelastic load level.

[0063] The environmental parameter data refers to the physical quantities of the environment in which the distribution box is located. These data reflect the impact of the environment on the operating status and load characteristics of the connected equipment. In addition to temperature, the environmental parameter data may also include humidity, air pressure, light intensity, etc. For example, humidity may affect the insulation performance or heat dissipation efficiency of some equipment, thereby changing its load characteristics; changes in air pressure may affect the operating efficiency of equipment in high-altitude areas.

[0064] The environmental parameter data serves as an external input for dynamically correcting load characteristics, providing real-time environmental information to ensure the accuracy and adaptability of the correction. The preset device type and parameter correction strategy refers to a set of correction rules or algorithms pre-defined before system deployment or during operation, based on the load characteristic variation patterns of different device types under different environmental conditions.

[0065] The strategy can be a set of mathematical models based on expert experience or historical data analysis, used to calculate the adjustment amount of a certain equipment load characteristic under specific environmental parameters; or it can be a logical rule containing a series of conditional judgment statements, such as "if the equipment type is A and the temperature is higher than T1, then adjust parameter X; if the equipment type is B and the temperature is lower than T2, then adjust parameter Y". The strategy provides the basic correction logic and basis for dynamic correction, ensuring the scientific nature and effectiveness of the correction process.

[0066] The device type-temperature correction threshold mapping table is a data structure used to store load characteristic correction coefficients or rules for different device types within different temperature ranges. The mapping table can be a two-dimensional lookup table, where rows represent device types (e.g., refrigerators, air conditioners, motors, lighting), and columns represent temperature ranges (e.g., less than 10℃, 10-20℃, 20-30℃, greater than 30℃), with each cell storing the corresponding correction coefficient. Alternatively, it can be a database-based query system that returns the corresponding correction parameters based on the device type and current temperature.

[0067] The mapping table quantifies the influence of equipment type and ambient temperature on load characteristics, serving as a key tool for precise correction. The correction coefficient is a numerical value used to adjust the initially calculated elastic and / or inelastic load levels.

[0068] The correction factor can be a multiplicative factor; for example, if the correction factor is 1.1, the initial load level is multiplied by 1.1 to amplify it; if it is 0.9, it is reduced. It can also be an additive or subtractive factor, directly adding or subtracting a fixed value from the initial load level. As a quantitative adjustment factor, the correction factor directly acts on the initial load level to reflect the actual impact of the environment and equipment type on the load characteristics.

[0069] The initial elastic load level and / or initial inelastic load level are quantitative indicators of the elasticity or inelasticity of each circuit load, preliminarily determined based on electrical parameter data and methods such as empirical mode decomposition, before dynamic correction. These serve as the benchmark values ​​for dynamic correction and are the direct targets of the correction operation. The final corrected elastic load level and inelastic load level, after correction based on environmental parameter data and equipment type, more accurately reflect the actual elasticity or inelasticity of each circuit load, providing a more reliable and accurate basis for subsequent power dispatch command generation.

[0070] Through the above technical solution, this application constructs a dual-dimensional correction mechanism of "equipment type-temperature" to dynamically adjust the initial load level, thereby solving the problem of ignoring equipment characteristics and environmental influences when making initial judgments based solely on electrical parameters. Specifically, the preset equipment type-temperature correction threshold mapping table can set differentiated correction rules according to the load-temperature sensitivity characteristics of different equipment. For example, in a circuit connected to a factory motor (dominated by non-elastic load), when the temperature is below or equal to 15℃, the motor starting current will increase. At this time, the mapping table can match the corresponding non-elastic load level correction coefficient to avoid misjudging "increased starting current" as elastic load fluctuation. In a circuit connected to a shop refrigerator (stable non-elastic load), when the temperature is above or equal to 35℃, the refrigerator compressor load only fluctuates slightly. The mapping table can match the corresponding elastic load level correction coefficient to avoid misjudging "slight fluctuation" as sudden elastic load.

[0071] This differentiated correction significantly improves the match between load characteristic assessment and actual equipment operating conditions. Furthermore, by dynamically adjusting based on real-time temperature values, rather than using a fixed correction coefficient, the correction process ensures real-time adaptation to environmental changes. For example, the load fluctuation characteristics of the same air conditioner circuit may differ under different temperatures. Dynamic correction ensures that the load assessment always matches the actual environment. This effectively avoids problems such as circuit overload and equipment shutdown caused by misjudgments of load characteristics, such as "misaligning power to critical equipment" (e.g., cutting off power to the refrigerator) or "missing to adjust sudden loads" (e.g., failing to allocate power to the air conditioner in a timely manner). This provides crucial support for the accurate generation of subsequent power dispatch instructions.

[0072] In some of the solutions mentioned above in this application, power dispatch instructions are generated and executed to dispatch power allocation. However, in this process, the dispatch decision may not be optimized, the response time is long, and it cannot effectively cope with sudden load changes, resulting in low resource allocation efficiency and insufficient system stability.

[0073] In response, this application further proposes to generate and execute power dispatch instructions for the output circuit of the distribution box based on the modified load characteristics.

[0074] Specifically, the method involves substituting the corrected elastic and inelastic load levels into a preset time-series comprehensive load function to calculate the comprehensive load value for each loop. The time-series comprehensive load function aims to more comprehensively evaluate the loop load status, considering not only the current corrected load characteristics but also historical load trends and temporal features. For example, the function could be a weighted average model based on a sliding time window, assigning a high weight to the current corrected load level and combining it with corrected load levels from several past time points (e.g., sampling points every 10 seconds within the past minute). The comprehensive load value is calculated using exponential smoothing or linear weighting to reflect short-term load trends and predict impending load fluctuations.

[0075] Alternatively, the function can employ a time-series forecasting model based on machine learning models (such as recurrent neural networks or long short-term memory networks), taking historically corrected sequences of resilient and inelastic load levels as input, learning the temporal dependencies of the load, and outputting a predicted composite load value, capturing more complex nonlinear time-series patterns and providing more refined load forecasting capabilities.

[0076] Based on this, with the goal of minimizing the overall dispatch impact, and under the conditions of satisfying the distribution box capacity constraints and the maximum load constraints of each circuit, the power dispatch function is solved to obtain the dispatch decisions for each circuit. The "minimization of the overall dispatch impact" can be achieved by defining a cost function, which quantifies the "inconvenience" or "loss" caused by dispatching different types of loads (elastic loads and inelastic loads). For example, dispatching inelastic loads (such as refrigerators and critical production equipment) will incur higher costs, while dispatching elastic loads (such as lighting and non-critical air conditioners) will have lower costs.

[0077] The power dispatch function can be a linear programming or integer programming model, where the objective function is to minimize the sum of dispatch costs for all circuits. Constraints include a total distribution box capacity limit (the total power after dispatching all circuits does not exceed the rated capacity of the distribution box) and a maximum load limit for each circuit (the power after dispatching a single circuit does not exceed its rated maximum power). Alternatively, heuristic or genetic algorithms can be used to solve the problem, defining "minimizing the total dispatch impact" as prioritizing the adjustment of flexible loads while minimizing or avoiding adjustments to inelastic loads, while satisfying the constraints.

[0078] Finally, based on the scheduling decision, the power dispatch command is issued within a preset response time. The "preset response time" is set according to the specific scenario and load characteristics served by the distribution box. For example, for industrial scenarios requiring a response, it can be set to 50ms; for commercial scenarios with lower response requirements, it can be set to 100ms. The power dispatch command can be issued by the edge processing module communicating directly with the intelligent circuit breakers or relays of each output circuit, for example, through Modbus TCP / IP, CAN bus, or proprietary protocols, achieving low-latency command transmission and execution. Alternatively, after generating the scheduling decision, the edge processing module immediately encapsulates it into a standardized control message and broadcasts it via a local area network (such as Ethernet or Wi-Fi) or sends it point-to-point to the execution units of each circuit. To ensure that the command is issued within the preset response time, a real-time operating system can be used to manage the task scheduling of the edge processing module and optimize the communication protocol stack to reduce command transmission overhead and latency.

[0079] Through the above technical solution, this application can accurately quantify the load status of circuits by utilizing dynamically corrected load data and combining it with the temporal characteristics of the load, providing a more accurate and reliable real-time basis for dispatching decisions. By aiming to minimize the overall dispatching impact and solving the power dispatching function under the synergistic effect of distribution box capacity constraints and maximum load constraints of each circuit, optimal allocation of power resources is achieved, effectively avoiding system overload risks and prioritizing the stable operation of critical equipment. Simultaneously, by rapidly issuing power dispatching instructions within a preset response time, timely effectiveness of instructions is ensured, enabling rapid response to sudden load changes and significantly improving the response speed and system stability of distribution box power dispatching, thereby effectively solving the problems of suboptimal dispatching decisions and response delays.

[0080] In some of the solutions mentioned above in this application, it is proposed to obtain electrical parameter data and environmental parameter data of each output circuit of the distribution box through integrated sensing units. However, in this process, when the sensing unit data is abnormal or fails, or when the edge processing module is disconnected from the cloud network, the system lacks an effective emergency handling mechanism, which leads to the interruption of power monitoring and dispatch, affects the stable operation of the system, and may cause power supply interruption problems.

[0081] In response, this application further proposes a redundancy processing mechanism, which includes: when an abnormality or failure of data in an integrated sensing unit is detected, the average historical data of that circuit within a preset period before the failure is used as temporary data to maintain the continued operation of the system; when the edge processing module is disconnected from the cloud network, scheduling decisions are performed based on locally stored historical power grid load data.

[0082] The redundancy mechanism refers to a design that ensures the continuous availability of system functions through a pre-set backup plan or strategy when a system or component fails. Its purpose is to improve the system's reliability, robustness, and fault tolerance, preventing the entire system from being interrupted due to a single point of failure. This mechanism can be implemented through hardware redundancy, such as deploying dual or multiple copies of critical hardware devices, automatically switching to backup devices when the primary device fails; or through software redundancy, such as introducing backup algorithms or data sources into data processing or decision-making logic, automatically activating the backup plan when the primary data source or algorithm becomes unavailable.

[0083] The detection mechanism for "detecting abnormal or failed data from an integrated sensing unit" is used to identify whether the integrated sensing unit is operating normally, ensuring the effectiveness of subsequent data processing. Abnormalities or failures may manifest as missing data, data values ​​exceeding reasonable ranges, or data transmission interruptions. This detection can be achieved by setting data thresholds and performing data integrity checks. For example, monitoring the fluctuation range of the sensing unit's output data; if it exceeds a preset threshold or there is no data output for a continuous period, it is determined to be abnormal or failed. Alternatively, it can be implemented through a heartbeat mechanism or periodic self-test function, where the sensing unit periodically sends heartbeat signals; if the edge processing module does not receive a heartbeat signal within a specified time, it is determined to be failed.

[0084] The strategy of "using the average historical data of the circuit within a preset period before failure as temporary data" aims to provide a data completion strategy to avoid data interruption that would prevent the system from continuing power monitoring and scheduling when sensor unit data is abnormal or fails. This strategy utilizes the statistical characteristics of historical data to generate temporary data to maintain continuous system operation. The edge processing module can continuously store the electrical parameter data of each circuit, and when a failure is detected, it can extract the data of the circuit before the failure (e.g., the previous 5 minutes, 10 minutes, or 1 hour) from local storage and calculate its arithmetic mean as temporary data; alternatively, a weighted average or moving average can be used, for example, assigning higher weight to the most recent data before failure, or using an exponential moving average to reflect recent trends, to more accurately estimate the temporary data.

[0085] The "maintaining system operation" aspect aims to ensure that even in the event of sensor unit failure, the power monitoring and dispatching system can continue to perform its core functions, avoiding system downtime or misjudgments due to data loss. Temporary data can be fed into subsequent load characteristic determination, dynamic correction, and power dispatching instruction generation modules, allowing the entire dispatching process to continue, such as continuing to generate dispatching instructions or issue alarms. Temporary data can also be used to maintain the system interface display, providing maintenance personnel with an approximate loop status so that they can understand the situation in a timely manner and take corrective measures.

[0086] The determination of "when the edge processing module is disconnected from the cloud network" is used to identify whether the communication link between the edge processing module and the cloud server is interrupted, which is a key condition for triggering the localized scheduling strategy. The edge processing module can periodically send probe packets or heartbeat signals to the cloud and monitor the cloud's response. If no response is received within a preset time or an erroneous response is received, it is determined that the network is disconnected; alternatively, the connectivity with the external network can be determined by monitoring the status of the local network interface or the connection status of the router.

[0087] The "execution of scheduling decisions based on locally stored historical grid load data" aims to enable the edge processing module to independently perform power dispatching even when disconnected from the cloud network, avoiding dispatching function paralysis due to network dependence. Locally stored historical grid load data provides the basis for intelligent dispatching decisions. The edge processing module can pre-store local grid load curve data for a period of time (e.g., the most recent week or month), including peak hours, off-peak hours, and typical load patterns. When the network is disconnected, the system matches the current time with this historical data to determine the current grid load status and adjust the dispatching strategy accordingly. Alternatively, it can store preset dispatching strategy templates for different grid load conditions, such as locally storing strategies like "prioritizing the reduction of elastic loads during peak hours" or "allowing high-power equipment to operate during off-peak hours." When the network is disconnected, the system directly calls the corresponding strategy template to execute the dispatching decision based on the locally determined grid load status.

[0088] By introducing the aforementioned redundancy processing mechanism, this application effectively solves the monitoring and scheduling interruption problems that the system may face under two common fault scenarios: abnormal or failed integrated sensor unit data and disconnection between the edge processing module and the cloud network. This significantly improves the robustness and operational continuity of the system. Specifically, when an abnormal or failed integrated sensor unit data is detected, the system can intelligently use the historical average data of that loop within a preset period before the failure as temporary data. This strategy utilizes the short-term stability of load data, ensuring that even in the event of a sensor unit failure, subsequent core functions such as load characteristic determination, dynamic correction, and power dispatch command generation can continue based on approximate load data. This avoids system downtime or misjudgment due to data loss, thereby ensuring the continuity of power supply and the continuous operation of critical equipment. For example, when the sensor unit of the refrigerator loop in a shop fails, the system can automatically call the historical average data of that loop before the failure as temporary data, avoiding misjudgment as "no load" and power cut-off due to data loss, thus ensuring the continuous operation of critical equipment. Furthermore, when the edge processing module is disconnected from the cloud network, the redundancy processing mechanism of this application can independently execute scheduling decisions based on locally stored historical power grid load data. This means that even in the event of a network connection interruption, the edge processing module can intelligently adjust its scheduling strategy based on local historical power grid load data, such as peak and off-peak periods, avoiding scheduling function paralysis due to network dependence. For example, when the factory's edge processing module is disconnected from the cloud, the system can automatically avoid scheduling high-load equipment during peak power grid periods based on locally stored historical power grid load data, while continuing to use historically validated safe scheduling strategies. Thus, even without access to the cloud, reasonable power scheduling can still be performed to maintain the stable operation of the production line.

[0089] In summary, this redundancy processing mechanism provides lightweight and differentiated emergency response solutions, effectively addressing issues such as sensor unit failures and network disconnections without requiring complex hardware backups or additional computing power. This significantly reduces the risk of system interruptions and ensures the continuous and stable operation of the power monitoring and dispatching system. It is particularly suitable for small and medium-sized scenarios that require stable system operation and low-cost maintenance.

[0090] In some of the embodiments described above in this application, a method is proposed to obtain electrical parameter data and environmental parameter data of each output circuit of the distribution box, determine the load characteristics, dynamically correct the load characteristics, and generate and execute power dispatch instructions to realize power monitoring and dispatch. However, in this process, there is a lack of effective storage and subsequent utilization of abnormal load records and periodic energy consumption statistics generated during the monitoring process. It is impossible to generate energy consumption optimization suggestions for the current application scenario based on historical data and environmental parameters, making it difficult for users to make decision optimization and effect evaluation based on actual operating data.

[0091] In response, this application further proposes that the method also includes: compressing and uploading abnormal load records and periodic performance consumption statistics generated during the monitoring process to the cloud; and generating and displaying energy consumption optimization suggestions for the current application scenario locally based on historical load data and environmental parameter data.

[0092] The abnormal load records and periodic performance consumption statistics generated during the monitoring process are compressed and uploaded to the cloud. Abnormal load records refer to events that are automatically identified and recorded by the system during power monitoring when the electrical parameter data (such as voltage, current, and power) of each output circuit of the distribution box exceeds the preset safety threshold, fluctuates drastically, or exhibits abnormal patterns. For example, the current of a circuit may momentarily exceed 1.5 times the rated current, or the voltage may remain below 90% of the normal range.

[0093] These records typically contain key information such as the time of the event, loop identifier, anomaly type, and anomaly parameter values. They can be generated by edge processing modules monitoring electrical parameter data in real time and comparing it with a pre-defined rule base or anomaly detection models trained on historical data. To reduce transmission and storage overhead, these records undergo data compression before uploading, for example, using differential coding, run-length encoding, or common lossless compression algorithms (such as Zlib or LZMA), ensuring data integrity while reducing data volume.

[0094] Periodic power consumption statistics refer to the aggregated and statistical data on the power consumption of each output circuit or the entire distribution box within a specific time period (e.g., daily, weekly, monthly). This data typically includes total power consumption, power consumption by time period, and the energy consumption percentage of each circuit or equipment type. It is generated by integrating and accumulating real-time power data, and then performing aggregate calculations at the end of each period. For example, an edge processing module can statistically analyze the total power consumption of each circuit from the previous day every 24 hours.

[0095] Similar to abnormal load records, these statistics are also compressed before being uploaded to the cloud. For example, only key statistical indicators are uploaded instead of the original detailed data, or data dictionary encoding is used to optimize data transmission efficiency and cloud storage costs.

[0096] Furthermore, based on historical load data and environmental parameter data, energy consumption optimization suggestions for the current application scenario are generated and displayed locally. Historical load data refers to the electrical parameter data, load characteristics (elastic load, inelastic load), and corrected load levels of each output circuit accumulated over a period of time during power monitoring of the distribution box, stored locally by the edge processing module. This data forms the basis for analyzing energy consumption patterns and equipment behavior. Environmental parameter data refers to environmental information such as temperature and humidity collected by the integrated sensing unit and transmitted to the edge processing module. Combining this data with load data can reveal the impact patterns of environmental factors on energy consumption.

[0097] Local generation means that the calculation and derivation of energy consumption optimization suggestions are completed on the edge processing module at the distribution box site, rather than relying on a cloud server. Generation methods can include: first, by pre-setting a series of experience-based or industry-standard energy consumption optimization rules, triggering corresponding suggestions when historical data and environmental parameters meet specific conditions; second, by running simplified association rule mining algorithms or statistical regression-based methods on the edge processing module to analyze the correlation between historical load data and environmental parameters, identifying energy-saving potential. For example, analyzing the energy consumption trends of certain devices within a specific temperature range.

[0098] Displaying energy consumption optimization suggestions for the current application scenario refers to presenting locally generated energy-saving suggestions that are highly relevant to the specific application scenario (such as shops, factories, office buildings, etc.) of the current distribution box to users in a user-friendly way.

[0099] The suggestions can be displayed via the edge processing module's built-in display, a connected local terminal device (such as a tablet), or pushed to a mini-program or application on the user's mobile phone via a local network. These suggestions are customized; for example, for shops, it might suggest adjusting the air conditioning temperature during off-peak hours; for factories, it might suggest avoiding starting high-energy-consuming equipment during peak power grid hours.

[0100] Through the above technical solution, this application effectively solves the problems of insufficient data utilization and lack of scenario-based guidance in power monitoring, significantly improving the system's practicality and user decision-making efficiency. Specifically, by compressing and uploading abnormal load records and periodic energy consumption statistics generated during monitoring to the cloud, the system can achieve long-term storage and backtracking of key historical data with extremely low bandwidth and storage costs. This ensures that users can easily query historical records for fault analysis and accountability when abnormal events occur, while also providing a data foundation for long-term energy consumption trend analysis.

[0101] This selective upload strategy avoids the high costs and transmission delays associated with full data uploads, making cloud resources affordable and effective for small and medium-sized users. Simultaneously, based on historical load data and environmental parameter data stored locally, it generates and displays energy consumption optimization suggestions tailored to the current application scenario, enabling users to obtain real-time, actionable energy-saving guidance highly matched to their specific business needs.

[0102] Because the suggestions are generated locally at the edge processing module, reliance on cloud connections and data transmission delays are avoided, ensuring real-time performance and rapid response. These suggestions are customized based on the user's historical operating data and environmental conditions, rather than generic templates, making them more targeted and practical. This helps users more accurately identify energy-saving opportunities, thereby effectively reducing operating costs. This two-way data utilization mechanism of "cloud-compressed storage of key data + local scenario-based energy consumption suggestion generation" achieves a better balance in data management, cost control, and user decision support, greatly enhancing the feasibility and user value of the power distribution box monitoring and dispatching solution.

[0103] In some of the solutions mentioned above in this application, an integrated sensing unit is proposed to obtain electrical parameter data and environmental parameter data of the output circuit of the distribution box. However, in its implementation, due to the lack of a universal interface, it is difficult to adapt to different models of distribution boxes, resulting in high hardware costs and complex modifications.

[0104] In this regard, this application further proposes that the integrated sensing unit is electrically connected to the output circuit of the distribution box through an adapter plate, and the adapter plate is equipped with a variety of replaceable interface terminals to adapt to different models of distribution boxes.

[0105] Specifically, the integrated sensing unit is a device that integrates electrical and environmental parameter acquisition functions. Its function is to acquire electrical parameter data, such as voltage and current, and environmental parameter data, such as temperature, from each output circuit of the distribution box. This unit can be a compact device containing sensors, a microcontroller, and a communication module; for example, it may integrate a Hall effect current sensor, a voltage divider resistor voltage sensor, and an NTC thermistor temperature sensor. Alternatively, it can be a modular design where different sensor modules can be configured and integrated as needed.

[0106] The adapter board serves as an intermediate carrier for physical interface conversion and connection. Its function is to provide a flexible and adaptable electrical and mechanical connection between the integrated sensing unit and the output circuit of the distribution box, thereby decoupling the direct binding between the sensing unit and the distribution box model. This adapter board can be a PCB board with a connector on one side that matches the standard interface (such as pin headers or slots) of the integrated sensing unit, and a slot or fixing structure on the other side for installing replaceable interface terminals; alternatively, it can be an injection-molded plastic or composite material shell with integrated conductive paths and terminal mounting positions.

[0107] The output circuits of the distribution box are the independent circuits inside the distribution box used to supply power to external loads. They are the direct objects of power monitoring and dispatching, and are usually composed of circuit breakers, contactors, terminals, etc., and are connected to electrical equipment through wires.

[0108] The electrical connection aims to establish a current path for power transmission and signal exchange, ensuring that the integrated sensing unit can accurately acquire electrical parameters such as voltage and current from the distribution box output circuit and power itself. This connection can be achieved through screw crimping, spring clips, or pluggable connectors. For example, the terminals on the adapter board are tightly connected to the wires of the distribution box circuit using screws or spring clamps. The various replaceable interface terminals are electrical connection components with different physical shapes, sizes, and connection methods, which can be replaced as needed. Their function is to enable the adapter board to adapt to different specifications and types of distribution box output circuit interfaces, improving the system's versatility and compatibility.

[0109] These terminals can be screw-type terminals, suitable for distribution boxes where screws are needed to secure wires; they can also be spring-loaded terminals, suitable for distribution boxes requiring quick plug-in and tool-free installation; or they can be pluggable terminals, connecting to the distribution box circuit via a socket. These terminals are typically designed with a modular structure, allowing for quick installation and removal from the adapter plate via clips, slide rails, or screws. Through the combination of the aforementioned adapter plate and replaceable interface terminals, the goal of adapting to different models of distribution boxes is achieved, enabling the monitoring system to be compatible and operate normally with various brands, series, and specifications of distribution boxes.

[0110] Through the above technical solution, the integrated sensing unit is electrically connected to the output circuit of the distribution box via an adapter board. The adapter board is equipped with multiple replaceable interface terminals, effectively solving the difficulties encountered when adapting the integrated sensing unit to different types of distribution boxes. This design, by introducing a modular adapter board and replaceable interface terminals, decouples the sensing unit from the distribution box model, avoiding customized design or complex modifications for each distribution box model, thus significantly reducing hardware costs and installation complexity. When adapting to different distribution box models is required, only the interface terminals on the adapter board need to be replaced, without replacing the entire integrated sensing unit, greatly improving the system's versatility and reusability. Example

[0111] Traditional power monitoring solutions for distribution boxes suffer from prominent problems when dealing with small and medium-sized application scenarios, such as high hardware costs, difficulty in adapting to outdated equipment, delayed response to sudden loads, and insufficient system stability.

[0112] Specifically, existing technologies rely on discrete voltage / current sensors, resulting in a single-channel monitoring cost of 30-50 yuan. They are also difficult to adapt to the interface specifications of old distribution boxes, and the retrofit process requires additional wiring and incurs additional costs. At the same time, the cloud data processing architecture introduces a 1-3 second transmission and calculation delay, which cannot meet the response requirements of sudden loads. Furthermore, it lacks a redundant processing mechanism for sensor unit failures or edge module network outages, resulting in a fault recovery time of up to 2-4 hours, which can easily cause power supply interruptions and a daily revenue loss of 10%-15%.

[0113] In response, this application proposes a power monitoring and dispatching system for a distribution box, including one or more integrated sensing units for deployment on the output circuit of the distribution box; and an edge processing module, which is communicatively connected to the integrated sensing units. The edge processing module includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the above method.

[0114] The core innovation of this embodiment lies in the collaborative design of the integrated sensing unit and the edge processing module in a distributed architecture, thereby reducing hardware deployment costs while achieving load response and fault redundancy. Specifically, the integrated sensing unit uses an integrated structure to replace traditional discrete sensors, directly integrating into the output circuit of the distribution box, avoiding the cost of stacking multiple sensors and keeping the cost of single-channel monitoring within a reasonable range. Its built-in universal interface design can seamlessly adapt to the physical specifications of older distribution boxes such as Chint NX series and Delixi CDP series, allowing for upgrades without rewiring, significantly reducing installation complexity and additional costs. The edge processing module, through a localized data processing mechanism, eliminates the cloud transmission link, ensuring real-time analysis of electrical and environmental parameters is completed internally, effectively handling sudden load scenarios such as simultaneous start-up of air conditioners in shops or temporary startup of factory equipment. At the same time, the redundant logic program pre-installed in the memory allows the processor to automatically switch to a backup processing strategy when the sensing unit fails or the network is interrupted, maintaining the continuous generation and execution of power dispatching instructions and shortening the system recovery time to an acceptable range.

[0115] Through the above technical solutions, this application achieves significant optimization of hardware costs, high compatibility with legacy equipment, rapid and accurate response to sudden loads, and continuous system stability. The deployment of integrated sensing units simplifies the hardware architecture, and the local decision-making mechanism of the edge processing module avoids cloud dependence. The synergy between the two not only controls the false alarm rate to a reasonable level but also ensures the power supply continuity of critical loads such as supermarket refrigerators and factory production lines, providing a cost-effective and reliable power distribution box monitoring and scheduling solution for small and medium-sized scenarios.

[0116] As the core equipment for power distribution in small and medium-sized scenarios, the existing technology for power distribution boxes has many problems in power monitoring and dispatching, such as delayed response to sudden loads, high misjudgment rate, high hardware cost, difficulty in adapting to old equipment, poor system stability, and lack of scenario-based support for performance data. These problems lead to increased risk of circuit overload tripping, excessively high retrofit costs, and frequent power supply interruptions, seriously affecting the normal operation of small and medium-sized enterprises. Example

[0117] To address this issue, this application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements a power monitoring and dispatching method for a distribution box. This storage medium, as a physical carrier, ensures the program can be persistently saved and loaded, providing basic operational support for the system. The computer program stored on it contains the instruction sequence required to execute the method, enabling the processor to invoke it as needed. Specifically, when the computer program is executed by the processor, it first acquires electrical parameter data and environmental parameter data for each output circuit of the distribution box; second, based on the electrical parameter data, it determines the load characteristics of each circuit, where the load characteristics include at least elastic and inelastic loads; further, it dynamically corrects the load characteristics according to the environmental parameter data and preset equipment types and parameter correction strategies; finally, based on the corrected load characteristics, it generates and executes power dispatching instructions for the output circuits of the distribution box.

[0118] The core innovation of this embodiment lies in combining computer-readable storage media with power monitoring and dispatching methods for distribution boxes, thereby achieving localized real-time processing and avoiding response delays caused by cloud transmission latency. Simultaneously, this method effectively reduces the false judgment rate by dynamically correcting load characteristics based on environmental parameter data and equipment type, and improves system stability through a built-in redundancy mechanism. In specific implementation, electrical parameter data, such as voltage, current, and power factor, of each output circuit are collected by sensors, while environmental parameter data, such as temperature and humidity sensors, is obtained from external environmental factors. Load characteristics are determined based on current waveform or power fluctuation analysis; for example, circuits with short-term large fluctuations are initially identified as flexible loads. The dynamic correction process utilizes a preset correction rule table to adjust the load characteristics of equipment such as refrigeration devices according to ambient temperature. Power dispatching instructions are generated with priority given to executing power limiting operations on flexible load circuits to ensure the continuity of power supply to non-flexible loads.

[0119] Through the above technical solutions, this application can significantly improve the accuracy and response speed of power monitoring in distribution boxes, and reduce misjudgments and response delays caused by fixed parameters. At the same time, the localized processing mechanism reduces the dependence on high-precision discrete sensors, effectively controls hardware costs, and enhances the adaptability of old distribution boxes. In addition, the redundant logic built into the program automatically switches to emergency mode when the sensor unit fails or the network is interrupted, ensuring stable system operation and providing scenario-based and verifiable power dispatching solutions for small and medium-sized enterprises.

[0120] The following example will provide a more detailed explanation of the above technical solution: In a commercial building (Site A), its distribution box supplies power to multiple output circuits, including lighting, air conditioning, refrigeration equipment, and office equipment. The building's occupant (Occupant A) faces problems with the existing power monitoring system, including slow response, high load misjudgment rate, poor hardware compatibility, and insufficient system stability. To address these issues, the building deployed the distribution box power monitoring and dispatching system described in this solution.

[0121] First, an integrated sensing unit is deployed on each output circuit of the distribution box. These integrated sensing units are electrically connected to the output circuit of the distribution box via an adapter plate. This adapter plate is equipped with multiple replaceable interface terminals, allowing it to flexibly adapt to different types of circuit interfaces in the distribution box at location A, avoiding the problems of additional wiring and high modification costs due to interface incompatibility in traditional solutions. Each integrated sensing unit continuously acquires electrical parameter data (including voltage and current) and environmental parameter data (such as temperature) of its circuit in real time. This localized data acquisition method avoids the latency caused by data transmission to the cloud in traditional solutions.

[0122] After acquiring the electrical parameter data, the edge processing module determines the load characteristics of each circuit based on this data. Specifically, the edge processing module first calculates the real-time load power of each circuit based on voltage and current. To analyze the dynamic characteristics of the load in depth, the edge processing module performs empirical mode decomposition on the real-time load power, thereby obtaining multiple intrinsic mode function components and a residual term. Based on these components and the residual term, the edge processing module can calculate the initial elastic load level and the initial inelastic load level of each circuit. For example, the peak starting current and continuous operating characteristics of refrigeration equipment will be identified as having a high inelastic load level, while general lighting may exhibit a high elastic load level. This refined local load characteristic analysis, compared to traditional methods that rely on fixed parameters or cloud analysis, can identify sudden loads more quickly and accurately, significantly reducing the load misjudgment rate.

[0123] Subsequently, the system dynamically corrects the load characteristics based on the collected environmental parameter data and preset device types and parameter correction strategies. The edge processing module identifies the device types connected to each output loop; for example, one loop might be connected to an air conditioner, and another to lighting. It then queries a preset device type-temperature correction threshold mapping table to obtain the corresponding correction coefficient. For instance, for a loop connected to an air conditioner, when the temperature value in the environmental parameter data increases, the system uses the corresponding correction coefficient to adjust the initial inelastic load level of that loop, obtaining the corrected inelastic load level. This is because air conditioners typically require more power to maintain the set temperature in high-temperature environments, thus enhancing their inelastic load characteristics. This dynamic correction mechanism fully considers the impact of device characteristics and environmental factors on load behavior, further improving the accuracy of load characteristic judgment and avoiding misjudgments caused by the failure to consider device characteristics within the scenario in traditional solutions.

[0124] Based on the corrected load characteristics, the edge processing module generates and executes power dispatching instructions for the output circuits of the distribution box. Specifically, the edge processing module substitutes the corrected elastic and inelastic load levels into a preset time-series integrated load function to calculate the integrated load value for each circuit. Then, aiming to minimize the overall dispatching impact, and under the conditions of satisfying the overall capacity constraints of the distribution box and the maximum load constraints of each circuit, the edge processing module solves the power dispatching function to obtain dispatching decisions for each circuit. For example, when it is detected that an inelastic load on a certain circuit (such as multiple air conditioners starting simultaneously) is about to cause the distribution box to overload, the system may decide to temporarily reduce the power supply to other elastic loads, such as dimming the lighting in some non-critical areas, to balance the overall load. Based on these dispatching decisions, power dispatching instructions are issued and executed within a preset response time. This fast and intelligent local dispatching capability effectively solves the problems of delayed response to sudden loads and the tendency to cause overload tripping in traditional solutions.

[0125] Furthermore, this system incorporates a redundancy mechanism to enhance system stability. When the edge processing module detects an anomaly or failure in the data of an integrated sensing unit, it does not immediately interrupt monitoring of that loop. Instead, it uses the average historical data of that loop within a preset period before the failure as temporary data to maintain system operation and prevent power supply interruptions due to sensor failure. Simultaneously, when the edge processing module is disconnected from the cloud network, it can continue to execute scheduling decisions based on locally stored historical grid load data, ensuring the system can still operate autonomously during network outages. This effectively addresses the issues of poor system stability and lack of redundancy mechanisms in traditional solutions.

[0126] During monitoring, the system compresses abnormal load records and periodic performance consumption statistics, and uploads them to the cloud when network connectivity is restored. Simultaneously, the edge processing module generates and displays energy consumption optimization suggestions tailored to the current commercial building application scenario based on locally stored historical load data and environmental parameter data. For example, it suggests adjusting air conditioning temperature or lighting brightness during specific periods to achieve energy-saving goals. This provides user A with specific and actionable energy consumption optimization guidance, solving the problem of insufficient scenario-based support for performance data in traditional solutions.

[0127] 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 for power monitoring and dispatching of a distribution box, characterized in that, The method comprises the following steps: obtaining electrical parameter data and environmental parameter data of each output loop of a power distribution box; determining the load properties of each loop based on the electrical parameter data, wherein the load properties at least include elastic load and inelastic load; dynamically correcting the load properties according to the environmental parameter data and a preset device type and parameter correction strategy; generating and executing power scheduling instructions for the output loops of the power distribution box based on the corrected load properties.

2. The method of claim 1, wherein, The electrical parameter data includes voltage and current, and the environmental parameter data includes temperature. The electrical parameter data and the environmental parameter data of each output loop of the power distribution box are obtained through an integrated sensing unit deployed on each output loop of the power distribution box.

3. The method of claim 2, wherein, The determination of the load properties of each loop based on the electrical parameter data comprises the following steps: calculating the real-time load power of each loop according to the voltage and current; performing empirical mode decomposition on the real-time load power to obtain a plurality of intrinsic mode function components and a residual term; calculating the initial elastic load degree and the initial inelastic load degree of each loop based on the intrinsic mode function components and the residual term.

4. The method of claim 3, wherein, The dynamic correction of the load properties according to the environmental parameter data and a preset device type and parameter correction strategy comprises the following steps: querying a preset device type-temperature correction threshold mapping table according to the device type connected to each output loop to obtain a corresponding correction coefficient; adjusting the initial elastic load degree and / or the initial inelastic load degree by using the correction coefficient and the temperature value in the currently collected environmental parameter data to obtain the corrected elastic load degree and inelastic load degree.

5. The method of claim 4, wherein, The generation and execution of the power scheduling instructions for the output loops of the power distribution box based on the corrected load properties comprises the following steps: substituting the corrected elastic load degree and inelastic load degree into a preset time sequence comprehensive load function to calculate the comprehensive load value of each loop; solving a power scheduling function to obtain a scheduling decision for each loop under the condition of meeting the power distribution box capacity constraint and the maximum load constraint of each loop, with the goal of minimizing the total scheduling impact; issuing the power scheduling instructions within a preset response time according to the scheduling decision.

6. The method of claim 2, wherein, The method further comprises a redundancy processing mechanism, which comprises the following steps: when a certain integrated sensing unit data is detected to be abnormal or invalid, using the historical data mean of the loop within a preset period before the invalidation as temporary data to maintain the system to continue running; when the edge processing module is disconnected from the cloud network, executing the scheduling decision based on the locally stored historical power grid load data.

7. The method of claim 1, wherein, The method further comprises the following steps: compressing and uploading the abnormal load records and periodic performance consumption statistical data generated in the monitoring process to the cloud; generating and displaying energy consumption optimization suggestions for the current application scenario based on the historical load data and environmental parameter data.

8. The method of claim 2, wherein, The integrated sensing unit is electrically connected to the output loops of the power distribution box through an adapter plate, and the adapter plate is provided with a plurality of replaceable interface terminals to adapt to different models of power distribution boxes.

9. A power distribution box power monitoring and dispatching system, characterized in that, The method comprises the following steps: one or more integrated sensing units as claimed in claim 2 for deployment on an output circuit of a distribution box; an edge processing module in communication connection with the integrated sensing unit, the edge processing module comprising a processor and a memory, the memory storing a computer program, the processor being configured to execute the computer program to implement the method of any one of claims 1 to 8.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, the computer program, which when executed by the processor, implements the method of any one of claims 1 to 8.

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