Remote control system of molded case circuit breaker with edge computing

Abstract

The present application relates to the technical field of power distribution and its monitoring, and discloses a molded case circuit breaker remote control system with edge computing, which is used to solve the problem that real-time data processing delay cannot be effectively optimized in traditional methods; the present application is composed of a data acquisition module, an edge preprocessing module, a delay optimization scheduling module, a remote communication interface module, a control execution module and a state monitoring module; first, current, voltage and temperature parameters are collected on the circuit breaker body, noise filtering, adaptive compression and priority-based queue scheduling are performed on the edge node, and bidirectional communication is carried out with the remote server through encrypted wireless or wired links, the remote instruction is converted into a local breaking and closing control sequence, and meanwhile, local protection criteria, communication state monitoring and operation log recording are combined, so that fast protection action and fine state perception of the power distribution circuit are realized, and the overall performance is improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution and monitoring technology, specifically to a remote control system for molded case circuit breakers with edge computing. Background Technology

[0002] Molded case circuit breakers (MCCBs) are important protective devices in low-voltage power distribution systems, used to disconnect or close circuits to prevent overloads, short circuits, and other faults. With the development of the Internet of Things (IoT) and smart grids, integrating edge computing technology into the remote control system of MCCBs has become an industry trend. This system processes sensor data locally at edge nodes to achieve real-time monitoring, fault diagnosis, and remote operation, aiming to reduce reliance on cloud transmission and improve response speed. However, existing technologies still have significant shortcomings in terms of real-time data processing latency, causing the system to fail to meet the instantaneous requirements of power protection under high load or network fluctuation environments. For example, CN115346845A discloses a miniature intelligent low-voltage circuit breaker based on energy internet technology. This circuit breaker integrates a data acquisition module and a communication interface, supporting remote monitoring and intelligent protection functions. Through the energy internet platform, it realizes the status monitoring and remote disconnection operation of the circuit breaker. The advantage of this solution is that it improves the intelligence level of the system, but its data processing mainly relies on cloud collaboration. When multiple devices collaborate, the data transmission and analysis process is prone to millisecond-level latency. Especially when processing massive amounts of sensor data, it cannot achieve sub-millisecond-level response, resulting in delayed fault diagnosis and increasing the risk of power outages. Similarly, CN104115250A discloses a converter circuit breaker and its control method. In this solution, the circuit breaker achieves incremental protection by inserting a sliding resistor and supports remote control of circuit arrangement. The solution emphasizes dynamic adjustment and remote indication of the circuit, but the data processing still adopts the traditional centralized mode. It lacks efficient optimization algorithms at the edge. When the network fluctuates or the load is high, the latency problem of real-time data processing becomes prominent, and it cannot effectively cope with instantaneous overload scenarios, thus affecting the reliability and safety of the system. While the aforementioned existing technologies have made progress in remote control, none have effectively solved the problem of optimizing real-time data processing latency. Specifically, in edge computing environments, insufficient optimization of the data acquisition, analysis, and decision-making links leads to excessively long system response delays, failing to meet the high real-time requirements of smart grids. This not only reduces the protection efficiency of circuit breakers but may also cause safety hazards. Therefore, a remote control system for molded case circuit breakers that can effectively optimize real-time data processing latency is needed to improve overall performance. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a remote control system for molded case circuit breakers with edge computing, which solves the problem that real-time data processing delays cannot be effectively optimized in traditional methods.

[0004] To achieve the goal of improving overall performance mentioned in the background section, the present invention provides the following technical solution: A remote control system for molded case circuit breakers with edge computing includes: Data acquisition module: Collects the current, voltage and temperature parameters of the circuit breaker and converts the collected data into digital signals; Edge preprocessing module: performs preliminary filtering and compression of the collected data locally, and performs real-time data aggregation through edge nodes; Delay optimization scheduling module: dynamically allocates computing resources and prioritizes high-priority data packets, and uses queue management and prediction algorithms to adjust data processing paths; Remote communication interface module: Establishes a wireless communication link with an external server and performs low-latency remote command transmission and feedback based on the optimized data stream; Control execution module: Receives remote commands and drives the breaking or closing mechanism of the molded case circuit breaker, while integrating local edge decision control logic; Status monitoring module: continuously monitors the system's operating status and records parameter changes, providing data backtracking support for the stable operation of the overall power distribution circuit.

[0005] In a preferred embodiment, the current parameters, voltage parameters, and temperature parameters of the circuit breaker are collected, and the collected data are converted into digital signals, including: The data acquisition module is connected to the molded case circuit breaker body, and Hall current sensors are set on the phase line path to output analog signals of the current in each phase. A voltage sensor consisting of a resistor divider and a parallel capacitor is installed between the input and output terminals to convert the main circuit voltage into an analog signal suitable for sampling. A thermistor or semiconductor temperature sensor is installed near the moving and stationary contacts and the arc-extinguishing chamber of the circuit breaker, and connected to the interface circuit through a shielded wire to output a temperature analog signal. It has built-in signal conditioning circuitry and a multi-channel analog-to-digital converter to amplify and filter analog signals of current, voltage and temperature, quantize them into digital data, and output them through a serial communication bus.

[0006] In a preferred embodiment, the collected data undergoes preliminary filtering and compression locally, and real-time data aggregation is performed via edge nodes, including: It is connected to the data acquisition module via a serial communication bus, periodically reads the digital signal stream carrying time stamps and check fields, and writes the digital signal into the local buffer; The threshold filtering logic is set up to remove outliers by comparing adjacent differences in voltage and current sequences with a preset threshold. The temperature sequence is monitored by a sliding window to monitor the temperature rise rate, and samples judged to be abnormal are smoothed based on thermal inertia criteria. The adaptive group compression logic is set up to generate statistical characteristics of current, voltage and temperature according to fixed sampling segments when the running state is stable. When the parameter change rate exceeds the preset change threshold, the group window is automatically shortened. The grouped statistical features, along with timestamps, parameter type identifiers, and verification information, are encapsulated into structured data packets. These data packets are then output sequentially via a buffer queue. Simultaneously, threshold trigger events and bus error events are logged.

[0007] In a preferred embodiment, dynamically allocating computing resources and prioritizing high-priority data packets includes: It connects to the edge preprocessing module via shared memory or buffer queues to receive structured data packets and perform resource and priority scheduling; The module runs on a real-time operating system kernel, which includes a task scheduler and a memory management unit. The task scheduler sets the current overload alarm task to high priority and the normal status reporting task to medium and low priority. The memory management unit allocates independent buffers for tasks of different priorities and controls memory usage through buffer level thresholds. Data packets are retrieved at fixed time slice periods and distributed to the internal scheduling queue according to the priority identifier of the data packets; The queue length of the internal scheduling queue is monitored, and the rate of new data writing is controlled based on the comparison between the queue length and the preset queue length threshold.

[0008] In a preferred embodiment, queue management and prediction algorithms are used to adjust the data processing path, including: The number of data packets and their priority percentages are statistically analyzed within a sliding window, and time slices are allocated to the main processor and coprocessor units according to the load status. A hierarchical queue structure is established, and data packets enter the corresponding queue according to their priority. The task scheduler first checks the high-priority queue and allocates processor time slices to high-priority tasks. If necessary, it interrupts low-priority tasks to reallocate processor time slices. The system monitors the length of the scheduling queue. When the load threshold is exceeded, load detection is performed and a processing path is selected. After switching, the data with scheduling fields is output to the remote communication interface module in a structured format, and the rate is adjusted by temporarily storing data packets in the output queue.

[0009] In a preferred embodiment, establishing a wireless communication link with an external server includes: It connects to the delay optimization scheduling module through the input queue and receives structured data packets output by the delay optimization scheduling module; The module is built on a communication board, which integrates an RF transceiver to send and receive signals, an integrated baseband processor to parse and reassemble frame structures, an integrated protocol processing unit to encapsulate and decapsulate messages, and sets up a high-speed cache to temporarily store data packets to be sent and received. The data packet is written to the input queue. The remote communication interface module retrieves the data packet from the input queue, encapsulates it into a network message, appends a session identifier and sequence number to the message header, and encrypts the network message according to the established encrypted tunnel.

[0010] In a preferred embodiment, low-latency remote command transmission and feedback are performed based on the optimized data stream, including: Configure the physical access method by scanning available network access points to select the target access point and initiating a handshake with the server to complete identity authentication and heartbeat cycle negotiation; Monitor signal quality and round-trip time, compare signal quality and round-trip time with preset thresholds, adjust network access method according to comparison results, and switch to backup channel or backup access method when switching conditions are met; The command message is decrypted and the command type and parameters are parsed. An acknowledgment message with the corresponding sequence number is generated and sent back to the server. The running data is encapsulated into an uplink message and sent. The main send buffer is used to cache data packets to be sent. If no acknowledgment is received, the corresponding data packet is retrieved from the retransmission buffer and resent. The decrypted instructions are then output in the internal structured format.

[0011] In a preferred embodiment, the mechanism for receiving remote commands and driving the breaking or closing of the molded case circuit breaker includes: It connects to the remote communication interface module via a dedicated control bus, receives internal control messages sent by the remote communication interface module, parses the instructions in the messages and generates an execution sequence to drive the circuit breaker to open or close. The control execution module is built on the driver board. The driver board is equipped with an electromagnetic coil drive circuit to control the tripping mechanism and a solid-state relay drive circuit to control the auxiliary circuit. After the pre-charge check, it outputs a pulse current to the tripping coil to complete the disconnection action. The status of the position sensor is read to confirm that the circuit breaker has completed its operation, and the generated control result data packet is output to the status monitoring module. In a preferred embodiment, local edge decision control logic is also integrated, including: It receives operating parameters and alarm flags provided by the data acquisition module and the edge preprocessing module, and judges overload and short circuit conditions according to the preset local protection criteria. When the remote control command is inconsistent with the local protection criteria, the local protection action is executed first. The system monitors heartbeat messages from the remote communication interface module to determine the communication status. If no heartbeat message is received for a preset number of consecutive heartbeat cycles, it switches to local autonomous mode, locks the current circuit breaker operating status, and performs protection control operations only based on locally acquired parameters. After communication is restored, the remote status and local operating status are synchronized. After synchronization is completed, the reception and execution of remote control commands are resumed.

[0012] In a preferred embodiment, continuous monitoring of the system's operating status and recording of parameter changes provide data backtracking support for the stable operation of the overall power distribution circuit, including: It connects to the control execution module through a feedback interface to receive control result data packets and operating parameters; The system uses embedded memory to record monitoring logs with timestamps, organizes and manages monitoring parameters by dividing them into data blocks according to time or event, and merges similar sampling points through compression strategies. Establish a multi-level index structure, locate monitoring data blocks according to information time range and event type, and decompress the data blocks according to parameter category and output the time series of the corresponding parameters; Calculate stability metrics, and push alarm information to the upstream module when the metrics exceed the limit.

[0013] Compared with the prior art, the present invention provides a remote control system for molded case circuit breakers with edge computing, which has the following advantages: 1. This invention introduces a priority-based queue management and predictive scheduling mechanism at edge nodes to hierarchically queue and proactively allocate alarm data and control commands, enabling emergency services to receive higher scheduling priority locally. It also strengthens the security and transmission reliability of remote sessions by configuring encrypted tunnels, access mode switching, and retransmission mechanisms on the communication link side. Furthermore, it sets up local protection criteria and communication heartbeat monitoring at the execution end, allowing local autonomous logic to initiate disconnection control when communication is abnormal or when remote commands are inconsistent with local criteria. Finally, a status monitoring module performs long-term recording and trend analysis of key operating parameters, providing data support for parameter adjustment and fault diagnosis. This effectively alleviates the problems of strong cloud dependence, large end-to-end latency, and insufficient reliability in network fluctuation scenarios found in the background technology, and solves the problem of ineffective optimization of real-time data processing latency in traditional methods.

[0014] 2. This invention performs refined processing on collected data through local noise filtering, adaptive group compression, and a discrimination strategy based on the rate of change threshold, preserving key details with diagnostic value before and after a fault. The delay optimization scheduling module applies structured labeling and hierarchical output strategies to different business data. The remote communication interface module and the status monitoring module work together to complete the integrated recording of operation logs, trend information, and action results. This expands the circuit breaker from the traditional point-based remote signaling form to a data closed loop covering the entire process and all parameters. It provides a quantifiable and traceable data foundation for parameter setting optimization, equipment health assessment, and long-term operating status assessment of the power distribution system, thereby alleviating the shortcomings of the background technology, such as single monitoring dimensions, insufficient historical data accumulation, and high dependence on human experience in fault analysis. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the remote control system for a molded case circuit breaker with edge computing according to the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Example 1: Figure 1 A remote control system for molded case circuit breakers with edge computing is presented, including: Data acquisition module: Collects the current, voltage and temperature parameters of the circuit breaker and converts the collected data into digital signals; Edge preprocessing module: performs preliminary filtering and compression of the collected data locally, and performs real-time data aggregation through edge nodes; Delay optimization scheduling module: dynamically allocates computing resources and prioritizes high-priority data packets, and uses queue management and prediction algorithms to adjust data processing paths; Remote communication interface module: Establishes a wireless communication link with an external server and performs low-latency remote command transmission and feedback based on the optimized data stream; Control execution module: Receives remote commands and drives the breaking or closing mechanism of the molded case circuit breaker, while integrating local edge decision control logic; Status monitoring module: continuously monitors the system's operating status and records parameter changes, providing data backtracking support for the stable operation of the overall power distribution circuit.

[0018] The technical connections and implementation logic of the six modules are as follows: The data acquisition module is connected to the molded case circuit breaker body, acquiring parameters such as current, voltage, and temperature in real time, and converting analog signals into digital signals for transmission to the edge preprocessing module. The edge preprocessing module filters and compresses the acquired data locally, reducing redundancy before outputting it to the delay optimization scheduling module. The delay optimization scheduling module, based on dynamic resource allocation and priority queue management, combines predictive algorithms to adjust the data processing path, prioritizing high-priority data such as fault alarms, thereby reducing overall latency. The optimized data stream passes through the remote communication interface module, which establishes a wireless connection for low-latency bidirectional transmission with an external server, including command issuance and status feedback. The remote communication interface module transmits commands to the control execution module, which integrates local edge decision-making to drive the circuit breaker to open or close. The control execution module connects to the status monitoring module, which continuously records system parameter changes and supports data backtracking, feeding the analysis results back to the upstream module for iterative optimization. By completing the main data processing locally, reliance on the cloud is reduced, and the response speed and reliability of the power distribution system are improved.

[0019] The current, voltage, and temperature parameters of the circuit breaker are collected, and the collected data are converted into digital signals. The specific implementation is as follows: The data acquisition module is directly connected to the molded case circuit breaker body. It is used to collect current parameters, voltage parameters and temperature parameters in real time during the operation of the circuit breaker, and output digital signals that can be called by the subsequent edge preprocessing module, so that the load status and internal thermal status of the circuit breaker enter the subsequent processing link in a unified data form. The data acquisition module employs an embedded sensor array integrated within the circuit breaker body. This sensor array includes current sensors, voltage sensors, and temperature sensors. The current sensors can utilize Hall effect-based current transformers, fixedly installed on the phase path of the circuit breaker, to sense changes in the magnetic field generated by the main circuit conductors and convert this into current intensity. This non-contact detection method eliminates the need for current-limiting components in series in the main circuit, avoiding additional power consumption and heat generation, and maintains good linearity and response characteristics under short-circuit inrush current or load surges. In a three-phase AC system, current sensors are respectively installed in phases A, B, and C to achieve independent monitoring of the current in each phase. The voltage sensor can be a voltage transformer or a resistive voltage divider circuit. The voltage sensor is connected between the input and output terminals of the circuit breaker via a series resistor network, proportionally reducing the high voltage of the main circuit to a safe voltage range suitable for subsequent circuit processing. A parallel capacitor at the voltage divider node forms a low-pass filter structure to attenuate high-frequency noise and spike interference, thereby obtaining a relatively smooth line voltage or phase voltage signal. By adjusting the resistance combination of the voltage divider resistors, the data acquisition module can be adapted to low-voltage power distribution systems of different voltage levels. For example, within the operating voltage range of 220V to 690V, the measurement requirements of different scenarios can be met simply by changing the resistor ratio. Temperature sensors, such as negative temperature coefficient thermistors or semiconductor temperature sensors, are used to monitor temperature changes in key components inside the circuit breaker. These sensors are positioned near the moving and stationary contacts of the circuit breaker and outside the arc extinguishing chamber to track localized heating caused by changes in contact resistance and heat accumulation during arc extinguishing. This reflects the internal temperature rise trend of the circuit breaker during frequent breaking or long-term high-load operation. Each sensor is connected to the interface circuit inside the data acquisition module via shielded wires. The shielding layer is grounded to reduce the impact of electromagnetic interference during the breaking process on the measurement signal, ensuring the stability and anti-interference capability of the analog signal. The analog electrical signals output by the aforementioned current sensor, voltage sensor, and temperature sensor are first input to the signal conditioning circuit, which includes a preamplifier unit and a filter unit. The preamplifier unit can use an operational amplifier chip (such as OP07) to amplify the sensor output with a small amplitude, so that the signal falls within the operating range of the analog-to-digital converter. The filter unit can use a low-pass filter circuit to suppress high-frequency interference components and limit the input bandwidth, avoiding aliasing during sampling, thereby improving the stability and repeatability of the digital quantization results. The conditioned analog signal is input to an analog-to-digital converter (ADC), which can employ a 12-bit resolution multi-channel integrated chip, such as the ADS1015, to sample and quantize current, voltage, and temperature parameters channel by channel. The 12-bit resolution corresponds to 4096 quantization levels. With a properly set range, the single quantization error can be controlled within a small range, thus meeting the accuracy requirements for analyzing subtle changes in current, voltage, and temperature. The sampling frequency can be set to at least 1kHz to capture transient events such as short-term current surges, voltage fluctuations, and rapid temperature rises in the power system, avoiding the omission of key features due to excessively low sampling frequencies. The ADC sequentially acquires and quantizes data from each channel according to a predetermined sampling sequence and outputs an ordered parameter sequence through a digital interface. To ensure the independent and stable operation of the data acquisition module, its power supply can be provided by the auxiliary power supply circuit inside the circuit breaker. The auxiliary power supply circuit uses a transformer or switching power supply module coupled to the main circuit to convert the electrical energy in the main circuit into a stable DC voltage output, such as 5V or 3.3V, and then uses a voltage regulator chip, filter capacitor, and protection diode to regulate and smooth the output. The protection diode is used to clamp the voltage when the transient voltage rises abnormally to avoid damage to the downstream chips. The filter capacitor is used to suppress ripple and high-frequency noise to ensure the power supply quality of the analog-to-digital converter and operational amplifier. With this internal power supply method, the data acquisition module can operate on its own power supply when the circuit breaker is in normal closed operation, without the need for an external independent power supply. The digital signals output by the analog-to-digital converter are converged into a standardized digital signal stream through an interface circuit. The interface circuit can use a serial communication protocol, such as an I²C bus or an SPI bus, to establish a data connection with the edge preprocessing module. The data frame can include the sampled values ​​of each channel parameter, time stamps, and check codes to identify the correspondence between current, voltage, and temperature parameters within the same sampling period and to detect errors during transmission. Through the above configuration, the data acquisition module can uniformly convert the multi-source physical quantities of the circuit breaker into structured digital data.

[0020] The collected data undergoes initial filtering and compression locally, and real-time data aggregation is performed through edge nodes. Specifically, this is implemented as follows: It is directly connected to the data acquisition module to receive the digital signal stream output by the aforementioned analog-to-digital converter at the edge, perform preliminary filtering and compression processing locally, generate structured preprocessed data, and use the preprocessed data as input to the delay optimization scheduling module; The edge preprocessing module is embedded in the edge computing node on the circuit breaker side. The edge computing node is built on a microcontroller architecture, such as using an ARM Cortex-M series processor as the core computing unit, and onboard SRAM as a local buffer for temporary storage of digital sampling sequences of current, voltage, and temperature. The power management circuit shares the auxiliary power supply path with the circuit breaker body, providing a stable 3.3V DC voltage through a voltage regulator module, and setting a low-power standby mode. When no new data is detected within a preset time window, the main frequency is automatically reduced or some peripherals are turned off to reduce energy consumption. The module adopts a board-type structure and can be installed inside the molded case circuit breaker housing. It forms a heat dissipation channel with the housing through a heat sink or heat-conducting plate to improve the working stability in high-temperature environments. Regarding the data interface, the edge preprocessing module interfaces with the data acquisition module through the aforementioned serial communication interface, acting as a bus host. It periodically reads data frames from each channel according to a predetermined sampling sequence, and parses the sampled values, timestamps, and check fields. The module first performs an integrity check on the received data based on the parity check bit or checksum carried in the data frame. For data frames that fail the check, they are discarded or marked as invalid and not included in subsequent analysis to avoid misjudgment caused by transmission errors. Data that passes the check is written to the local buffer in chronological order for subsequent filtering and compression processing. In the initial filtering stage, the edge preprocessing module performs noise removal and anomaly smoothing operations on the voltage, current, and temperature time series within the buffer. For voltage and current signals, the module traverses the sampling points in chronological order, calculates the difference between adjacent or set window sampling points, and when the difference exceeds a preset threshold, the current sampling point is considered noise interference and replaced with the average or interpolation result of the adjacent valid sampling points to smooth the waveform and maintain the continuity of the sequence. The preset threshold can be comprehensively calibrated based on the rated variation range of the corresponding parameter, the working range recommended in the equipment manual, and the statistical fluctuation range of historical operating data, so that the set threshold can effectively remove obvious abnormal peaks without masking rapid changes under normal operating conditions. For temperature signals, the filtering logic incorporates the thermal inertia characteristic of temperature changes. The module calculates the rate of temperature change within a sliding time window. Only when the rate of temperature rise at multiple consecutive sampling points exceeds the empirical upper limit within a short period of time is the corresponding sampling point considered abnormal and smoothed. For single short-term jumps, the original data is retained to more accurately reflect the temperature rise trend. The filtering order can prioritize the voltage and current channels, followed by the temperature channel, to ensure that the cleanliness requirements of core power parameters are met first. After noise filtering, the edge preprocessing module performs compression and aggregation on the time series of each channel to reduce data volume and extract key features. The module groups the time series according to a fixed number of sampling points, for example, in groups of 100 sampling points. Within each group, it calculates the average, maximum, minimum, and slope of voltage and current, and uses these statistical results as representative features of the time period to replace the original point-by-point sampling data. This significantly reduces the amount of data while retaining representative information for determining the operating status. Furthermore, to adapt to the periodic characteristics of the 50Hz AC power system, the grouping boundary can be aligned with the full cycle of voltage or current, for example, using one or several power frequency cycles as the grouping unit, to avoid waveform distortion caused by cross-cycle aggregation. Furthermore, the compression group length can be adaptively adjusted. When the edge preprocessing module detects that the rate of change of current or voltage continuously exceeds the preset change threshold within a short time window, or the rate of temperature increase increases significantly, it determines that the current stage is a high dynamic change stage. At this time, the number of sampling points in each group is automatically reduced, for example, to 20 sampling points per group, in order to improve the time resolution and retain more details. When the operating state is stable and the rate of change is lower than the preset change threshold for a longer period of time, the group length is restored to improve the compression ratio. The preset change threshold can be set according to the statistical distribution of the parameter change rate under normal operating conditions of the device, such as the slope range of the change of current, voltage and temperature under typical load curves, so as to suppress redundant data while ensuring sensitive response to abnormal changes, and achieve a balance between compression ratio and information fidelity. After filtering and compression, the data is output by the edge preprocessing module in a standardized format for subsequent modules to parse and call. The output data can be encapsulated in JSON or other structured data formats, and each data packet must contain at least a timestamp, parameter type label, group statistics, and verification information. A buffer queue is configured at the module output end. When the delay optimization scheduling module cannot immediately read the data, the preprocessing results are first queued in the buffer queue to avoid data loss due to short-term congestion. At the same time, the edge preprocessing module can log information on key events such as filtering threshold triggering, compression mode switching, and bus transmission errors, providing a basis for subsequent operation analysis and fault tracing. Through the above configuration, the edge preprocessing module completes local closed-loop processing from raw digital sampling to preprocessed data stream on the circuit breaker side.

[0021] Dynamically allocate computing resources and prioritize high-priority data packets, and use queue management and prediction algorithms to adjust data processing paths. Specifically, the implementation is as follows: The latency optimization scheduling module and the edge preprocessing module are deployed in the same edge computing node and connect to each other through shared memory or buffer queues. The module is used to receive structured data packets output by the edge preprocessing module locally and perform computing resource allocation, priority scheduling and data processing path adjustment on this basis, so that the overall processing process remains latency controllable and queues orderly when data volume fluctuates and events occur. The latency optimization scheduling module runs on top of the real-time operating system kernel, which supports multi-task concurrency and priority-based task scheduling. The kernel includes a task scheduler and a memory management unit. The task scheduler prioritizes tasks such as current overload alarms and circuit breaker control commands, while prioritizing tasks like routine status reporting and historical data processing. The memory management unit allocates independent buffers for tasks of different priorities to store data packets written from the edge preprocessing module. It controls memory usage through a buffer level threshold; when the buffer usage approaches this threshold, it triggers current limiting or an alarm to prevent memory overflow during peak data periods. As needed, the latency optimization scheduling module can also call coprocessing units on edge nodes to handle computationally intensive analysis or feature calculation tasks. The main processor and coprocessing units are interconnected via an internal bus. In terms of data integration, the edge preprocessing module writes filtered and compressed structured data packets into a shared buffer queue. Each data packet includes at least arrival time, parameter type label, and initial priority marker. The latency optimization scheduling module, acting as the reader, retrieves data packets from the buffer queue at fixed time slice intervals and distributes them to the internal scheduling queue based on the priority identifier in the packet header. The queue management logic continuously monitors the current queue length and compares it with a preset queue length threshold. When the queue length approaches the threshold, the write rate can be temporarily reduced or the writing of non-critical data can be paused. The queue length threshold can be set based on the available memory capacity of the edge node and the target maximum queuing latency, using system design and test statistics to ensure reasonable queue occupancy without packet loss. In terms of dynamically allocating computing resources, the latency optimization scheduling module evaluates the input data stream within the sliding time window, counts the number of data packets arriving per unit time, the proportion of data packets of each priority level, and the current processing thread occupancy rate to form a description of the system load status. Based on the load status, it adjusts the resource allocation strategy. Under low load, it allows low-to-medium priority tasks to obtain more processing time slices, freeing up some buffers for background tasks. Under high load, it increases the proportion of time slices allocated to high-priority processing threads, limits the concurrency of low-priority tasks, and enables coprocessing units to share computing tasks as needed. Furthermore, the latency optimization scheduling module can utilize historical traffic curves to construct trend judgment logic. When it detects a continuous upward trend in the data packet arrival rate over multiple consecutive time slices, it preemptively expands the buffer quota for high-priority tasks or preemptively wakes up coprocessing units to mitigate queuing delays during upcoming peak periods. To prioritize high-priority data packets, the delay optimization scheduling module establishes a hierarchical queue structure, including at least a high-priority queue, a medium-priority queue, and a low-priority queue. When a data packet is written, it automatically enters the corresponding queue based on its priority identifier. For example, fault alarm packets caused by current or voltage exceeding a safety threshold enter the high-priority queue, remote control command packets enter the medium-priority queue, and normal operating status reporting packets enter the low-priority queue. During scheduling, the task scheduler first checks the high-priority queue. If the queue is not empty, it selects the data packet to be processed from the high-priority queue and allocates a processor time slice. If necessary, it can interrupt the currently executing low-priority task and temporarily redirect resources to the high-priority task. After the high-priority queue is cleared, the execution of medium- and low-priority tasks resumes, thus ensuring priority response to urgent tasks in multi-task parallel scenarios. Regarding data processing path adjustment, the latency optimization scheduling module monitors the length of each scheduling queue, average queuing time, and coprocessing unit occupancy in real time. It also counts the actual number of data packets processed per second and the average queuing time within a fixed time window. When any of these indicators exceeds a preset load threshold, the path adjustment process is initiated. This load threshold can be set based on the system's average throughput and allowable latency under normal operating conditions, through offline monitoring and statistical analysis, ensuring that the conditions triggering path adjustment match the actual carrying capacity of the equipment. The path adjustment process includes load detection, candidate path selection, and switchover confirmation. When the load exceeds limits, some computationally intensive tasks can be migrated to coprocessing units for execution, or simplified processing procedures can be used for non-critical data to shorten single-packet processing time. After the switchover is completed, the queue status and processing results before and after the switchover are compared to ensure that there is no data loss or duplicate processing. After scheduling and path adjustment, the data is output to the remote communication interface module by the delay optimization scheduling module in a structured encapsulation format. The module adds scheduling-related fields to the original data packets, including the final priority label, scheduling timestamp, and processing path identifier, to indicate the priority category and processing channel traversed by the data packet during the scheduling process. The output end is configured with an output queue. When the remote communication interface module is temporarily unavailable or has insufficient processing capacity, the delay optimization scheduling module temporarily stores the scheduled data packets in the output queue and dynamically adjusts the internal processing rate according to the output queue level to prevent data accumulation at edge nodes. At the same time, the delay optimization scheduling module logs key events such as high-priority preemption, load threshold triggering, path adjustment, and output congestion.

[0022] Establish a wireless communication link with an external server, and perform low-latency remote command transmission and feedback based on the optimized data stream. Specifically, the implementation is as follows: The remote communication interface module and the delay optimization scheduling module are deployed in the same edge computing node, serving as a bridge unit between the two and the external server. This module receives structured data packets with attached priority tags, scheduling timestamps and processing path identifiers from the delay optimization scheduling module through the input queue inside the node, and establishes a two-way communication connection with the remote server in a wireless or wired network environment, thereby realizing the uplink transmission of local running data and the downlink reception of remote control commands. The remote communication interface module is built on a dedicated communication board, which integrates an RF transceiver, a baseband processor, and a network protocol processing unit. The RF transceiver is responsible for transmitting and receiving RF signals, the baseband processor performs physical layer frame structure parsing and reassembly, and the protocol processing unit implements message encapsulation and decapsulation for TCP / IP, UDP, or other upper-layer protocol stacks. The board is equipped with a high-speed cache RAM to temporarily store data packets to be sent and received but not yet parsed data packets, avoiding data loss during short transmission intervals. To improve electromagnetic compatibility, sensitive components such as RF and baseband are arranged in a metal shielded cavity, isolated from the high-voltage conductors and arc-extinguishing area inside the molded case circuit breaker, and grounding design reduces the interference of arc discharge on the communication circuit. In terms of interfacing with the delay optimization scheduling module, the remote communication interface module receives the data packets output by the module through an internal input queue. These data packets contain the service load, priority field, and scheduling information. The input queue can adopt a circular buffer structure, which is written by the delay optimization scheduling module and retrieved by the remote communication interface module according to the order of the read pointer. After retrieving the data packets, the interface module encapsulates the service load into the payload of the network packet and adds a session identifier and sequence number field to the packet header so that the remote server can reassemble and verify the integrity of the packet. Regarding external network connectivity, the remote communication interface module can be configured with different physical access methods according to the application scenario. These include accessing a wired local area network via an Ethernet controller, or accessing a wireless network via an integrated Wi-Fi module, cellular communication module (such as 4G / 5G), or low-power wide area network module (such as LoRa). The specific standard used can be determined through parameter configuration during device deployment. For example, in urban power distribution environments, high-bandwidth Ethernet or 5G networks are preferred, while in remote areas, low-power long-distance links such as LoRa can be enabled. Upon startup, the module first scans for available networks, selects an access point that meets the preset signal quality and latency requirements, and initiates a handshake process with the target server. The handshake process includes authentication, encryption method negotiation, and heartbeat cycle negotiation to establish a stable session. To improve link robustness, the remote communication interface module monitors indicators such as signal quality, round-trip latency, and packet loss rate of the current connection, and compares these indicators with preset signal quality and latency thresholds. When the signal quality is detected to be consistently below the corresponding threshold for a certain period of time, or the round-trip latency is consistently greater than the allowable range, the module can attempt to switch to a backup base station or access point under the same standard. In deployment modes that support multiple access standards, it can also switch to a backup access mode according to the configuration, such as switching from Wi-Fi to cellular network, or switching to a slower but more stable LoRa channel when the cellular signal is weak. The thresholds can be set after a comprehensive analysis of the network test results of the target deployment area and the service requirements for latency and reliability. In terms of secure communication, the remote communication interface module creates an encrypted tunnel when establishing a session with the server, providing end-to-end protection for uplink running data and downlink control commands. Furthermore, the module can use a symmetric encryption algorithm in conjunction with a key exchange mechanism to establish a session key. In the generation of the session key, device identifiers such as circuit breaker numbers or installation location codes can be introduced as encryption salts to improve the uniqueness and specificity of the key. Before data packets are sent, the service load and key header fields are encrypted and their integrity verified according to the session configuration. After decryption, the receiving end verifies the checksum to detect tampering or transmission errors. To reduce the risks associated with long-term key use, the session key can be rotated according to a preset time period or a data volume threshold. The data volume threshold and time period can be configured through security policies, and the upper limit of the data volume that a single session can carry and the rotation interval can be determined by combining the strength of the encryption algorithm used, the expected session duration, and the service's tolerance for leakage risks. Regarding remote command transmission and feedback, when the server issues a disconnection command, a closure command, or a parameter query request, the remote communication interface module first decrypts the command content from the received encrypted message, parsing out the command type, target circuit breaker address, and parameter fields. The parsed command is then encapsulated into an internal control message and transmitted to the control execution module through the node's internal interface. Simultaneously, the remote communication interface module generates an acknowledgment message and sends it back to the server. This acknowledgment message contains the sequence number of the corresponding command, a processing result marker, and a local timestamp, which the server uses to track the execution status of the command. For local status reporting, the module can encapsulate and upload the operating data provided by the delay optimization scheduling module according to a predetermined period or an event-triggered method, enabling the server to obtain a real-time view of the circuit breaker's current, voltage, temperature, and alarm status. Furthermore, the upload and download paths can operate independently using asynchronous queues to avoid unidirectional congestion affecting data transmission in the other direction. To cope with network fluctuations, the remote communication interface module is equipped with a multi-level buffering and retransmission mechanism. The sending side can configure a main sending buffer and a retransmission buffer. The main sending buffer sequentially caches data packets to be sent, and removes them only after successful confirmation. If no server confirmation is received within the preset retransmission waiting time, the module searches for the corresponding data packet in the retransmission buffer based on the packet sequence number and retransmits it. The retransmission waiting time can be set based on the statistical distribution of round-trip delay and the congestion level of the target network, ensuring it is neither overly sensitive nor experiences excessive waiting times during actual packet loss. The module can periodically count the number of packet losses and retransmissions within a certain time window and compare it with a preset packet loss rate threshold. When the packet loss rate exceeds this threshold, the sending rate is adjusted or the retransmission waiting time is increased to mitigate the impact of network congestion. The packet loss rate threshold can be set after statistical analysis of measured data from the target network environment and the acceptable retransmission overhead for the service. After the above processing, the remote communication interface module will output the decrypted and integrity-verified control commands to the control execution module in an internal standard format, and transmit the confirmed operation data to the remote server through the network. The module can also log key events such as link establishment, key rotation, protocol switching and retransmission triggering.

[0023] It receives remote commands and drives the breaking or closing mechanism of the molded case circuit breaker, while integrating local edge decision control logic. Specifically, the implementation is as follows: The control execution module and the remote communication interface module are deployed on the same edge node, serving as the execution unit in the remote control system of the molded case circuit breaker. This module receives internal control messages output by the remote communication interface module through the node's internal control interface. The control messages include at least the instruction type, the target circuit breaker address, and the execution parameter field. The control execution module parses the instructions locally and generates a specific execution sequence based on the currently collected operating parameters to drive the circuit breaker to complete the opening or closing action. In terms of connectivity, the control execution module can interface with the remote communication interface module via a dedicated control bus or board-to-board interface. The control bus can use a CAN bus or other industrial fieldbus to achieve reliable data exchange within the node. The physical wiring of the bus uses twisted-pair shielded cable to connect to the interface board inside the circuit breaker to improve electromagnetic interference resistance. When the control execution module receives a control message, it first verifies the cyclic redundancy check code in the message header to confirm the integrity of the data packet before writing the instruction content into the local execution queue. The bus side uses optocouplers and isolation power supplies to electrically isolate the signal side from the main circuit of the circuit breaker to prevent high voltage from the main circuit from entering the control circuit. In terms of hardware integration, the control and execution module is built on a dedicated driver board. This driver board is equipped with an electromagnetic coil drive circuit and a solid-state relay drive circuit, which are used to control the tripping mechanism and auxiliary contact circuit of the circuit breaker, respectively. The electromagnetic coil drive circuit is connected to the tripping coil of the circuit breaker and outputs a controlled pulse current to trigger the mechanical tripping mechanism when it receives a disconnection command. The solid-state relay drive circuit is connected to the auxiliary power supply circuit and is used to control the closing or resetting action. The driver board is equipped with a heat sink or heat fin to dissipate the heat generated by the drive components during the moment of action. The power supply is derived from the auxiliary power supply of the circuit breaker and provides a stable DC voltage after passing through multiple stages of filtering and voltage regulation circuits to ensure that the drive signal can still maintain the accuracy of amplitude and pulse width when the mains voltage fluctuates. During the remote command reception and mapping process, the control execution module first reads the command type field from the control message to identify whether the command is a disconnection command, a closure command, or a parameter adjustment command. Then, it determines whether the command should be executed by this circuit breaker based on the target address field. If the target address matches, the command is mapped to an internal execution step sequence, and the steps include at least selecting the output channel, setting the drive timing, and checking the safety interlock conditions. Furthermore, the module can use the priority tags in the upstream data packets to insert emergency commands marked with high priority into the execution queue, so that tasks such as fault disconnection can be processed first when multiple commands coexist. If the command parameters are inconsistent with the current circuit breaker configuration, the control execution module can refuse to execute and generate an error feedback code. In terms of driving the molded case circuit breaker to open or close, the control execution module controls the operation of the drive circuit according to the internal execution sequence. When the opening action is performed, the module first performs a pre-charge check on the drive power supply. After the drive voltage reaches the preset range, it outputs a pulse current of a set width to the trip coil to trigger the mechanical trip mechanism. The preset voltage range can be set with reference to the rated operating voltage of the trip coil and in combination with the tolerance requirements of the drive circuit to avoid failure due to too low voltage or overheating of the coil due to too high voltage. In order to reduce the impact of inrush current on the coil, the front end of the drive pulse can use a ramp-up or current-limiting circuit to control the rate of current change. When performing the closing action, the module controls the auxiliary circuit through a solid-state relay or relay coil to reset the circuit breaker and complete the closing. The action time is controlled within the preset millisecond-level protection action time range, which can be set according to the rated parameters of the circuit breaker and the selective coordination requirements of the power distribution system. To monitor the execution results, the control execution module can read the status of the built-in position sensor or auxiliary contact. It will only end the drive output and mark the action result as successful when a contact position signal is detected. If no contact position signal is detected, the action will be marked as abnormal and an alarm event will be generated. In terms of local edge decision fusion, the control execution module also receives real-time operating parameters and alarm flags from the data acquisition module and the edge preprocessing module, such as phase current, voltage, temperature, and local overload and short-circuit judgment results. Furthermore, the module internally sets local protection criteria corresponding to circuit breaker settings and power distribution design parameters. The overload protection threshold can be set based on the circuit breaker's rated current and allowable overload multiple, while the short-circuit criterion can be set based on the current rise slope and instantaneous current threshold. The instantaneous current threshold can be determined through calculation and experimentation, combining the power distribution system's short-circuit capacity, conductor cross-section, and upper-level protection device setting parameters. When the local system detects current or temperature exceeding the preset threshold and meeting the corresponding protection criteria, the control execution module can directly generate a local tripping sequence and trigger tripping without relying on remote command confirmation. When a conflict arises between a remote command and local protection decision, such as a remote request for closure while the local system remains in an overload alarm state, the module prioritizes local protection actions through priority arbitration logic, placing the remote command in a waiting or rejected state and explaining the reason in the feedback information. To ensure basic protection functions are maintained even in the event of communication failure, the control execution module is equipped with autonomous redundancy control logic. The module can determine the communication status by monitoring heartbeat messages from the remote communication interface module. If no heartbeat is received within a preset number of consecutive heartbeat cycles, the current mode is switched from remote collaborative mode to local autonomous mode. The heartbeat loss threshold can be determined experimentally based on the communication bus update cycle and the system's maximum allowable communication interruption time. During mode switching, the module locks the current circuit breaker operating status and protection settings, stops receiving new remote control commands, and only performs necessary disconnection protection based on locally acquired parameters. When communication is restored and the heartbeat messages remain stable for a preset period, the control execution module gradually reverts to the remote and local collaborative mode. The preset time can be set based on the heartbeat cycle and the tolerance for false recovery. After confirming that the remote and local states are consistent, the execution of remote control commands is fully enabled. Through the above execution process, after the circuit breaker action is completed, the control execution module generates a control result data packet containing the action type, trigger source (local protection or remote command), execution result, action time, and a snapshot of relevant operating parameters. This data packet is then output to the status monitoring module through an internal interface to record circuit breaker action events and operating status. Furthermore, the module can set self-check logic for key steps during execution. The self-check content includes the driving power supply voltage, coil circuit continuity, and the legality of the position feedback signal. When an abnormality is detected, alarm information is reported in a timely manner.

[0024] Continuously monitor the system's operating status and record parameter changes to provide data backtracking support for the stable operation of the overall power distribution circuit. The specific implementation is as follows: The status monitoring module and the control execution module are deployed in the same edge node, serving as the feedback and diagnostic unit in the remote control system for molded case circuit breakers with edge computing. On the one hand, the status monitoring module receives control result data packets output by the control execution module. On the other hand, it receives operating parameters such as current, voltage, and temperature provided by the data acquisition module and the edge preprocessing module. This is used to continuously monitor the circuit breaker's operating status and changes in electrical parameters, forming a time-stamped operating record locally, providing a data foundation for fault analysis, preventive maintenance, and control strategy adjustment. In terms of connection, the status monitoring module interfaces with the control execution module through an internal feedback interface. The feedback data includes at least the contact position status, action result marker, trigger source (local protection or remote command), and execution timestamp. Auxiliary information such as drive current and coil voltage may also be included if necessary. The feedback interface can be wired using differential signal lines and shielded cables to improve electromagnetic interference resistance and adapt to the high-current, high-voltage operating environment inside the circuit breaker. The interface circuit has a built-in line status detection unit for periodically detecting the impedance of the feedback line. When the line impedance deviates from the nominal value by more than a preset deviation range, the current feedback path is marked as abnormal and automatically switched to a backup channel, thus avoiding monitoring interruption due to a single point of failure. The impedance deviation threshold can be set based on cable specifications, wiring length, and factory test results. In terms of hardware architecture, the status monitoring module can be implemented using a dedicated monitoring board. This board is equipped with embedded non-volatile memory and a runtime buffer. For example, a Flash memory chip can be configured as a circular buffer structure to record long-term runtime logs, while an EEPROM or other non-volatile memory can be used to store monitoring configuration parameters and index information. The monitoring board also features a real-time clock circuit to provide a unified time reference for each record and receives multi-source monitoring signals from the control execution module and the internal bus of the edge nodes via a multi-channel input interface. The power management section uses a low-power voltage regulator and reserves a backup battery or supercapacitor interface to maintain the continuity of the writing process and time reference for critical monitoring data in the event of an external power outage. In the continuous monitoring of the system's operating status, the status monitoring module triggers data acquisition tasks according to a preset cycle, such as reading the current current, voltage, temperature, and circuit breaker operating status every second or every few seconds, and binding them with the timestamps output by the real-time clock to form structured recording units. At the same time, the module can also be configured with an event triggering mechanism. When any monitored parameter is detected to deviate from its rated value or historical benchmark by more than a preset threshold, the sampling frequency is automatically increased, and data within the preceding and following few seconds is recorded at a higher density to obtain waveform and status information with higher time resolution in the event of an anomaly or fault. The deviation threshold can be determined through experiments and statistical analysis, taking into account the circuit breaker's rated parameters, typical operating conditions, and historical statistical fluctuation range. During the recording of parameter changes, the status monitoring module organizes and manages the collected multidimensional data in blocks. Continuous monitoring data can be divided into data blocks according to fixed time lengths or event boundaries, such as forming recording blocks in 1-minute or 5-minute units. Each data block contains a list of events, voltage and current statistics, temperature change trends, and several typical sampling points within that time period. For operating ranges that remain stable for extended periods, the module can employ a compression recording strategy. When the rate of change and fluctuation amplitude of operating parameters are both below preset thresholds, a large number of similar sampling points are merged into representative statistical values, retaining only extreme values, average values, and status change points to reduce storage usage and extend memory lifespan. The thresholds for the rate of change and fluctuation amplitude can be set offline based on the historical fluctuation statistics of the equipment under normal stable operating conditions and the acceptable data compression ratio. When providing data backtracking support, the status monitoring module responds to retrieval requests initiated by upstream modules or operation and maintenance terminals through internal query interfaces and external maintenance interfaces. The module also establishes a multi-level index structure for the recorded monitoring data, where the main index marks the corresponding data blocks according to time range and event type, and the sub-index marks the detailed trajectory within the block according to parameter category. In this way, when a query request is received regarding the changes in current and temperature within a certain fault and several minutes before and after it, the relevant data blocks can be quickly located through the main index, and then the time series of the target parameters can be decompressed and output through the sub-index for fault root cause analysis and operation status assessment. To assist in the stable operation of the overall power distribution circuit, the status monitoring module can also calculate operational stability indicators and trend indicators based on long-term recorded data, such as the long-term mean and variance of voltage deviation, temperature rise rate, and frequency of current peak occurrences. When the above cumulative indicators exceed preset limits, the status monitoring module actively pushes alarm information and corresponding statistical results to the edge preprocessing module or delay optimization scheduling module. The cumulative limits can be comprehensively determined based on the allowable voltage fluctuation range of the power distribution system, equipment insulation level, allowable temperature rise, and alarm standards set by the operation and maintenance unit. After receiving these statistical results, the edge preprocessing module or delay optimization scheduling module can adjust the filtering threshold, compression strategy, or scheduling strategy accordingly, so that the system has the ability to adaptively adjust when facing chronic degradation or trend anomalies. Through the aforementioned monitoring and feedback process, after completing data collection and analysis, the status monitoring module generates a monitoring data package containing timestamps, event types, trigger sources, snapshots of operating parameters, and statistical indicators. This data package is then provided to other edge modules via an internal interface, while the external maintenance interface supports operations and maintenance personnel in exporting historical records as needed. During operation, the status monitoring module can perform self-checks on storage write status, index integrity, and clock consistency. When a record failure or index corruption risk is detected, an alarm is immediately generated and a maintenance prompt is given, thereby ensuring the reliability and continuity of the backtracking data.

[0025] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0026] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0027] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0028] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0029] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0030] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0031] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0032] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0033] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0034] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A remote control system for molded case circuit breakers with edge computing, characterized in that, include: Data acquisition module: Collects the current, voltage and temperature parameters of the circuit breaker and converts the collected data into digital signals; Edge preprocessing module: performs preliminary filtering and compression of the collected data locally, and performs real-time data aggregation through edge nodes; Delay optimization scheduling module: dynamically allocates computing resources and prioritizes high-priority data packets, and uses queue management and prediction algorithms to adjust data processing paths; Remote communication interface module: Establishes a wireless communication link with an external server and performs low-latency remote command transmission and feedback based on the optimized data stream; Control execution module: Receives remote commands and drives the breaking or closing mechanism of the molded case circuit breaker, while integrating local edge decision control logic; Status monitoring module: continuously monitors the system's operating status and records parameter changes, providing data backtracking support for the stable operation of the overall power distribution circuit.

2. The remote control system for molded case circuit breakers with edge computing according to claim 1, characterized in that, The circuit breaker's current, voltage, and temperature parameters are collected, and the collected data is converted into digital signals, including: The data acquisition module is connected to the molded case circuit breaker body, and Hall current sensors are set on the phase line path to output analog signals of the current in each phase. A voltage sensor consisting of a resistor divider and a parallel capacitor is installed between the input and output terminals to convert the main circuit voltage into an analog signal suitable for sampling. A thermistor or semiconductor temperature sensor is installed near the moving and stationary contacts and the arc-extinguishing chamber of the circuit breaker, and connected to the interface circuit through a shielded wire to output a temperature analog signal. It has built-in signal conditioning circuitry and a multi-channel analog-to-digital converter, which amplifies and filters analog signals of current, voltage and temperature, quantizes them into digital data, and outputs them through a serial communication bus.

3. The remote control system for molded case circuit breakers with edge computing according to claim 1, characterized in that, The collected data undergoes initial filtering and compression locally, and real-time data aggregation is performed through edge nodes, including: It is connected to the data acquisition module via a serial communication bus, periodically reads the digital signal stream carrying time stamps and check fields, and writes the digital signal into the local buffer; The threshold filtering logic is set up to remove outliers by comparing adjacent differences in voltage and current sequences with a preset threshold. The temperature sequence is monitored by a sliding window to monitor the temperature rise rate, and samples judged to be abnormal are smoothed based on thermal inertia criteria. The adaptive group compression logic is set up to generate statistical characteristics of current, voltage and temperature according to fixed sampling segments when the running state is stable. When the parameter change rate exceeds the preset change threshold, the group window is automatically shortened. The grouped statistical features, along with timestamps, parameter type identifiers, and verification information, are encapsulated into structured data packets. These data packets are then output sequentially via a buffer queue. Simultaneously, threshold trigger events and bus error events are logged.

4. The remote control system for molded case circuit breakers with edge computing according to claim 1, characterized in that, Dynamically allocate computing resources and prioritize high-priority data packets, including: It connects to the edge preprocessing module via shared memory or buffer queues to receive structured data packets and perform resource and priority scheduling; The module runs on a real-time operating system kernel, which includes a task scheduler and a memory management unit. The task scheduler sets the current overload alarm task to high priority and the normal status reporting task to medium and low priority. The memory management unit allocates independent buffers for tasks of different priorities and controls memory usage through buffer level thresholds. Data packets are retrieved at fixed time slice periods and distributed to the internal scheduling queue according to the priority identifier of the data packets; The queue length of the internal scheduling queue is monitored, and the rate of new data writing is controlled based on the comparison between the queue length and the preset queue length threshold.

5. The remote control system for molded case circuit breakers with edge computing according to claim 1, characterized in that, The data processing path is adjusted using queue management and prediction algorithms, including: The number of data packets and their priority percentages are statistically analyzed within a sliding window, and time slices are allocated to the main processor and coprocessor units according to the load status. A hierarchical queue structure is established, and data packets enter the corresponding queue according to their priority. The task scheduler first checks the high-priority queue and allocates processor time slices to high-priority tasks. If necessary, it interrupts low-priority tasks to reallocate processor time slices. The system monitors the length of the scheduling queue. When the load threshold is exceeded, load detection is performed and a processing path is selected. After switching, the data with scheduling fields is output to the remote communication interface module in a structured format, and the rate is adjusted by temporarily storing data packets in the output queue.

6. The remote control system for molded case circuit breakers with edge computing according to claim 1, characterized in that, Establish a wireless communication link with an external server, including: It connects to the delay optimization scheduling module through the input queue and receives structured data packets output by the delay optimization scheduling module; The module is built on a communication board, which integrates an RF transceiver to send and receive signals, an integrated baseband processor to parse and reassemble frame structures, an integrated protocol processing unit to encapsulate and decapsulate messages, and sets up a high-speed cache to temporarily store data packets to be sent and received. The data packet is written to the input queue. The remote communication interface module retrieves the data packet from the input queue, encapsulates it into a network message, appends a session identifier and sequence number to the message header, and encrypts the network message according to the established encrypted tunnel.

7. The remote control system for molded case circuit breakers with edge computing according to claim 1, characterized in that, Low-latency remote command transmission and feedback are performed based on the optimized data stream, including: Configure the physical access method by scanning available network access points to select the target access point and initiating a handshake with the server to complete identity authentication and heartbeat cycle negotiation; Monitor signal quality and round-trip time, compare signal quality and round-trip time with preset thresholds, adjust network access method according to comparison results, and switch to backup channel or backup access method when switching conditions are met; The command message is decrypted and the command type and parameters are parsed. An acknowledgment message with the corresponding sequence number is generated and sent back to the server. The running data is encapsulated into an uplink message and sent. The main send buffer is used to cache data packets to be sent. If no acknowledgment is received, the corresponding data packet is retrieved from the retransmission buffer and resent. The decrypted instructions are then output in the internal structured format.

8. The remote control system for molded case circuit breakers with edge computing according to claim 1, characterized in that, Receiving remote commands and driving the breaking or closing mechanism of the molded case circuit breaker, including: It connects to the remote communication interface module via a dedicated control bus, receives internal control messages sent by the remote communication interface module, parses the instructions in the messages and generates an execution sequence to drive the circuit breaker to open or close. The control execution module is built on the driver board. The driver board is equipped with an electromagnetic coil drive circuit to control the tripping mechanism and a solid-state relay drive circuit to control the auxiliary circuit. After the pre-charge check, it outputs a pulse current to the tripping coil to complete the disconnection action. The status of the position sensor is read to confirm that the circuit breaker has completed its operation, and the generated control result data packet is output to the status monitoring module.

9. The remote control system for molded case circuit breakers with edge computing according to claim 1, characterized in that, Simultaneously, it integrates local edge decision control logic, including: It receives operating parameters and alarm flags provided by the data acquisition module and the edge preprocessing module, and judges overload and short circuit conditions according to the preset local protection criteria. When the remote control command is inconsistent with the local protection criteria, the local protection action is executed first. The system monitors heartbeat messages from the remote communication interface module to determine the communication status. If no heartbeat message is received for a preset number of consecutive heartbeat cycles, it switches to local autonomous mode, locks the current circuit breaker operating status, and performs protection control operations only based on locally acquired parameters. After communication is restored, the remote status and local operating status are synchronized. After synchronization is completed, the reception and execution of remote control commands are resumed.

10. The remote control system for molded case circuit breakers with edge computing according to claim 1, characterized in that, Continuously monitor the system's operating status and record parameter changes to provide data backtracking support for the stable operation of the overall power distribution circuit, including: It connects to the control execution module through a feedback interface to receive control result data packets and operating parameters; The system uses embedded memory to record monitoring logs with timestamps, organizes and manages monitoring parameters by dividing them into data blocks according to time or event, and merges similar sampling points through compression strategies. Establish a multi-level index structure, locate monitoring data blocks according to information time range and event type, and decompress the data blocks according to parameter category and output the time series of the corresponding parameters; Calculate stability metrics, and push alarm information to the upstream module when the metrics exceed the limit.

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