A remote overload protection control method and system for intelligent circuit breaker and storage medium

Abstract

This application relates to a remote overload protection control method, system, and storage medium for intelligent circuit breakers, belonging to the field of overload protection technology. The remote overload protection control method includes: collecting the status operation data of the intelligent circuit breaker, judging and filtering risk status data in combination with a preset overload risk threshold, and transmitting it to the cloud; receiving and classifying the risk status data to obtain the overload risk level, generating corresponding cloud control commands, and pushing early warning notifications; executing corresponding intelligent circuit breaker protection operations according to the cloud control commands, and generating execution feedback notifications; managing and analyzing risk status data, early warning notifications, and execution feedback notifications, constructing a time-series evidence chain, and optimizing the corresponding overload thresholds; and avoiding the risk of protection failure due to communication interruption through a dual mechanism of cloud-based main protection and local emergency protection, thereby reducing the recurrence rate of faults.

Description

Technical Field

[0001] This application relates to the field of overload protection technology, and in particular to a remote overload protection control method, system and storage medium for intelligent circuit breakers. Background Technology

[0002] In low-voltage power distribution systems, overload protection of circuit breakers is a core function to ensure the safety of electrical equipment and prevent line burnout or fire accidents. Traditional circuit breaker overload protection relies entirely on local hardware and mainly employs the following two methods: Thermal relay protection: This method uses the deformation of a metal strip due to heat to trigger a mechanical trip, thus achieving overload protection. This method has a long response time (usually several seconds to tens of seconds), and the protection threshold is fixed, making it impossible to dynamically adjust according to the load type (such as inductive load or capacitive load), resulting in poor adaptability. At the same time, it lacks any remote data interaction capability, requiring maintenance personnel to conduct on-site inspections to detect overload faults, leading to extremely low fault handling efficiency.

[0003] Electromagnetic overload protection: It uses the electromagnetic force generated by the electromagnetic coil under overload current to drive the tripping mechanism. Although the response speed is slightly faster than thermal relay, it still depends on local hardware parameters, cannot remotely monitor the overload status, and requires manual on-site reset after a fault. It cannot meet the needs of unattended scenarios (such as remote base stations and industrial workshop power distribution rooms).

[0004] Existing patents disclose a smart circuit breaker control method, device, system, and readable storage medium. The method includes establishing and storing the correlation between various load information and monitoring parameter information; acquiring load information based on user input and / or automatic identification, and retrieving corresponding monitoring parameter information based on the correlation; collecting corresponding monitoring parameter values ​​based on the acquired monitoring parameter information; comparing the above monitoring parameter values ​​with their corresponding fault thresholds, and controlling the opening and closing of the circuit breaker according to the comparison result. The fault threshold is set by user input, or loaded into the load information and extracted from it, or automatically generated based on the monitoring parameter values ​​corresponding to specific monitoring parameter information and a set algorithm. By matching different monitoring parameters and fault thresholds to different load devices, the smart circuit breaker can be adapted to different load devices, exhibiting strong applicability and high reliability.

[0005] The existing technical solutions mentioned above have the following drawbacks: 1. Traditional cloud platform architecture can only realize data upload and cannot make intelligent diagnosis and hierarchical protection decisions based on the cloud; the communication link is single, and if the local wireless module fails, the remote intervention capability will be completely lost. After overload occurs, manual judgment is still required to determine whether to perform protection operation, and automated remote protection cannot be achieved. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this application is to provide a remote overload protection control method, system, and storage medium for intelligent circuit breakers. This method employs a dual mechanism of cloud-based main protection and local emergency protection: when cloud communication is normal, precise control is achieved through cloud-based mechanisms; when communication is interrupted, local emergency protection is triggered, thus avoiding the risk of protection failure due to communication interruption and reducing the incidence of recurring failures.

[0007] This was achieved using the following technical solutions: In a first aspect, this application provides a remote overload protection control method for intelligent circuit breakers, comprising: Collect the status and operation data of the smart circuit breaker, combine it with the preset overload risk threshold to judge and filter the risk status data, and transmit it to the cloud; Receive and classify risk status data, obtain the overload risk level, generate corresponding cloud control instructions, and push early warning notifications; Execute the corresponding smart circuit breaker protection operation according to the cloud control command, and generate execution feedback notification; Manage and analyze risk status data, early warning notifications, and execution feedback notifications; construct a time-series evidence chain; and optimize the corresponding overload thresholds.

[0008] By adopting the above technical solution, a sliding window collects real-time status data such as circuit breaker current, voltage, and temperature. Risk data is filtered using a preset overload threshold and uploaded to the cloud. An LSTM model is used to analyze timing characteristics and perform GBDT-based hierarchical diagnosis, generating overload risk levels and corresponding control commands (such as automatic tripping or early warning pushes). After executing protection operations, the results are fed back. Finally, risk data, early warning records, and execution feedback are integrated to construct a timing evidence chain. A dynamic threshold optimization algorithm achieves closed-loop iterative optimization of the overload threshold. Through machine learning-driven real-time diagnosis and dynamic feedback mechanisms, the accuracy of fault prediction is significantly improved, and the cost of manual inspection is reduced.

[0009] This application is further configured to: collect the status operation data of the intelligent circuit breaker, combine it with a preset overload risk threshold to judge and filter the risk status data, and transmit it to the cloud, including: The status operation data of the intelligent circuit breaker is collected to obtain the line current and status temperature; Based on the acquisition frequency, the line current and temperature are filtered and denoised to obtain noise-free current and interference-free temperature. Based on the acquisition timestamp, the noiseless current and the interference-free temperature are correlated in a time sequence to obtain the current-temperature tuple; The current-temperature tuple is compared and judged based on the preset overload risk threshold. If neither of them reaches the corresponding overload risk threshold, the current current-temperature tuple is determined to be normal state data, and the normal state data is transmitted to the cloud according to the preset monitoring cycle. If the temperature data in the current-temperature tuple reaches the overload risk threshold, but the current data does not, the current temperature data is corrected based on the ambient temperature to obtain the corrected temperature. If the corrected temperature does not reach the overload risk threshold, the corrected current-temperature tuple is determined to be normal state data, and the normal state data is transmitted to the cloud according to the monitoring cycle; otherwise, the current current-temperature tuple is marked as risk state data, switched to the warning state, and immediately transmitted to the cloud. If the current data in the current-temperature tuple reaches the overload risk threshold, but the temperature data does not, or both reach the corresponding overload risk threshold, then the current current-temperature tuple is determined to be in a risk state, switched to the warning state, and immediately transmitted to the cloud.

[0010] By adopting the above technical solution, the line current and temperature of the intelligent circuit breaker are collected in real time. After filtering, noise reduction, and time-series correlation, a current-temperature tuple is formed. Combined with a preset overload risk threshold, a two-parameter collaborative judgment is performed: when either the current or temperature reaches the threshold, it is marked as risk data and immediately uploaded to the cloud; only when the temperature exceeds the limit is an ambient temperature correction algorithm introduced to avoid false alarms and ensure that the warning status is triggered only when there is a real overload. By integrating multiple filtering and dynamic correction technologies, accurate risk identification and graded response are achieved while ensuring data reliability, thereby improving the real-time performance and accuracy of overload warnings.

[0011] This application is further configured to: receive and classify risk status data, obtain overload risk levels, generate corresponding cloud control commands, and push early warning notifications, including: The intelligent circuit breaker is verified and connected according to the communication protocol, the time interval between adjacent verifications is calculated, and the results are compared and judged in conjunction with the monitoring cycle. If the time interval is less than the monitoring cycle, it is determined that the current data volume has not reached the preset limit threshold, and a temporary transmission channel is established to receive the risk status data. If the time interval is equal to the monitoring period, it is determined that the current data volume has reached the limit threshold, and the original transmission channel is started to receive normal state data. If the time interval is greater than the monitoring cycle, the current smart circuit breaker is determined and marked as an abnormal operating state, and reconnection verification is performed according to the polling cycle until the current smart circuit breaker actively sends a connection request to the cloud. The received risk status data is normalized to obtain standard risk data. Combined with historical operating data and circuit breaker parameters, real-time current, real-time temperature, number of periodic overloads, load fluctuation curve and overload duration are extracted. If the real-time current and real-time temperature do not reach the corresponding safety trigger threshold but continue to rise, or the number of cycle overloads reaches the trigger limit, or the peak value of the load fluctuation curve is greater than or equal to the safety limit, then the current overload risk level is determined to be a level one overload, and an audible and visual warning command is generated. If the real-time temperature is greater than or equal to the corresponding safety trigger threshold, or the real-time current is greater than or equal to the corresponding safety trigger threshold and the overload duration is greater than or equal to the limit duration, or the peak value of the load fluctuation curve is greater than or equal to the safety limit and shows an exponential growth state, then the current overload risk level is determined to be a level two overload, and a tripping circuit breaker command is generated. Based on the distribution time of the audible and visual warning command or the circuit breaker tripping command, the feedback time of the intelligent circuit breaker is calculated by difference to obtain the execution feedback time. If the execution feedback time is greater than or equal to the execution timeout time, the current cloud control command will be resent and an early warning notification will be pushed.

[0012] By adopting the above technical solution, the connection status classification of the intelligent circuit breaker (temporary channel / original channel / polling reconnection) is realized by comparing the dynamic verification interval and monitoring cycle. After normalizing the received risk status data, multi-dimensional parameters such as real-time current, temperature, number of periodic overloads, load fluctuation curve and duration are integrated. Threshold combination and trend analysis algorithms (such as fluctuation peak exponential growth detection) are used to accurately determine the first-level audible and visual warning or the second-level tripping circuit breaker command. A closed-loop control is formed based on the timeout retransmission mechanism of the execution feedback time. The cloud load is reduced by adaptive transmission strategy, and the real-time performance and accuracy of overload risk identification are improved by combining multi-parameter fusion diagnosis.

[0013] This application is further configured to: execute corresponding intelligent circuit breaker protection operations according to cloud control commands, and generate execution feedback notifications, including: If the cloud control command is an audible and visual warning command, the built-in audible and visual warning device of the smart circuit breaker will be activated, and the warning execution status will be fed back to the cloud at fixed time intervals. If the cloud control command is a tripping command, the mechanical tripping mechanism inside the smart circuit breaker will be driven to complete the tripping operation, and the tripping success / failure will be reported back to the cloud. The system monitors the execution process of cloud control commands. If execution fails, a fault code is generated and fed back to the cloud and pushed to the user.

[0014] By adopting the above technical solution, based on the instruction type branch processing algorithm, when an audible and visual warning instruction is received, the circuit breaker's built-in audible and visual warning device is driven and the status is fed back at fixed intervals. When a tripping instruction is received, the mechanical tripping mechanism is triggered and the operation result is fed back. At the same time, a fault code is generated when the execution fails through a real-time monitoring mechanism and pushed to the cloud and the user. The real-time performance and reliability of protection operation are significantly improved through automated fault diagnosis and multiple feedback mechanisms, and the cost of manual intervention is reduced.

[0015] This application further includes: managing and analyzing risk status data, early warning notifications, and execution feedback notifications; constructing a time-series evidence chain; and optimizing the corresponding overload thresholds, including: The risk status data, early warning notifications, and execution feedback notifications are correlated in a time sequence, and combined with the overload risk level, time-series event data is generated. Based on historical operating data and circuit breaker parameters, the time-series event data is integrated and analyzed to generate a time-series evidence chain; Based on historical event data, the temporal evidence chain is backtracked in a similar manner, and the execution time of the protection operation is verified to generate threshold correction weights; Anomaly detection is performed on the time-series evidence chain based on environmental parameters, and the risk level switching ratio and the number of false trips are statistically analyzed. If the risk level jump ratio is greater than or equal to the preset jump ratio threshold, the overload risk threshold will be adjusted according to the threshold adjustment weight. If the number of false trips is greater than or equal to the preset false trip threshold, the safety trigger threshold will be adjusted according to the threshold adjustment weight.

[0016] By adopting the above technical solution, the sliding window algorithm is used to perform time-series correlation of risk status data, early warning notifications, and execution feedback notifications to generate event data. Based on historical operating data and circuit breaker parameters, a multi-source fusion time-series evidence chain is constructed. By using similar events to backtrack and verify the protection operation duration, threshold correction weights are generated. At the same time, combined with the detection of abnormal environmental parameters, the risk tripping ratio and the number of false trips are statistically analyzed. When the risk tripping ratio exceeds the preset threshold, the overload risk threshold is dynamically corrected. When the number of false trips exceeds the preset threshold, the safety trigger threshold is corrected, forming a closed-loop feedback mechanism to achieve adaptive optimization of the threshold.

[0017] This application further specifies: performing similar backtracking on the temporal evidence chain based on historical event data, verifying the execution duration of protection operations, and generating threshold correction weights, including: The time-series evidence chain is filtered based on the identification code and event type, a cluster of similar events is established, and the historical frequency weight is calculated. Extract the time of execution feedback notifications in the temporal evidence chain to obtain the warning generation time and protection completion time, and calculate the response delay time; If the response delay time is greater than the delay duration threshold, it is marked as a delayed response event, and cluster analysis is performed to obtain the response delay weight; Perform integrity verification and analysis on the chronological evidence chain, and calculate the evidence integrity weight; The threshold correction weights are generated by calculating the historical frequency weights, response delay weights, and evidence completeness weights based on the learning rate.

[0018] By adopting the above technical solution, the historical time-series evidence chain is filtered by identification code and event type to construct a cluster of similar events, and the historical frequency weight is calculated; the response delay time is obtained by extracting the time difference between early warning generation and protection completion, and the delayed events exceeding the threshold are clustered to generate response delay weights; supplemented by evidence chain integrity verification weights, the three types of weights are dynamically fused based on the learning rate to generate threshold correction weights, thereby achieving threshold adaptive optimization and significantly improving the accuracy of security response.

[0019] Secondly, this application also provides a remote overload protection and control system for intelligent circuit breakers, employing the following technical solution: A remote overload protection control system for intelligent circuit breakers, used to implement the aforementioned remote overload protection control method, includes: The cloud control platform is used to receive and classify risk status data, obtain overload risk levels, generate corresponding cloud control commands, push early warning notifications, and manage and analyze risk status data, early warning notifications and execution feedback notifications, build a time-series evidence chain and optimize the corresponding overload thresholds. The cloud control platform includes: The communication interaction module is used to establish stable cloud communication with the intelligent circuit breaker according to the communication protocol, so as to realize bidirectional transmission of status operation data and cloud control commands; The data cleaning module is used for noise filtering and format standardization of risk status data to obtain standard operating data. The diagnostic grading module is used to determine the risk status data and overload risk level based on the safety trigger threshold combined with historical operating data and circuit breaker parameters; The instruction generation module is used to generate corresponding cloud control instructions based on the overload risk level, and to attach the instruction validity period and execution feedback requirements; The storage and traceability module is used to store and trace risk status data, early warning notifications and execution feedback notifications, build a time-series evidence chain and optimize the corresponding overload thresholds; The intelligent circuit breaker is used to collect status operation data, combine it with preset overload risk thresholds to judge and filter risk status data, and execute corresponding protection operations according to cloud control instructions, generate execution feedback notifications, and transmit them to the cloud control platform. Intelligent circuit breakers include: The status acquisition module is used to collect the status operation data of the line where the smart circuit breaker is located, including current, temperature and voltage; The edge computing module is used to filter operational status data based on overload risk thresholds to obtain risk status data. The wireless communication module is used to establish a wireless connection with the cloud control platform, transmit risk status data, and receive cloud control commands. The protection execution module is used to perform audible and visual warning operations or mechanical tripping operations according to cloud control commands; The emergency protection module is used to automatically trigger the trip protection mechanism based on the emergency overload threshold when communication is interrupted. The user interaction terminal is used to push early warning notifications and execution feedback notifications, and to display historical operating data and remotely confirm cloud control commands.

[0020] By adopting the above technical solutions, risk data is initially screened at the smart circuit breaker end through edge computing (such as lightweight filtering algorithms), while the cloud uses LSTM time series analysis combined with GBDT multi-feature hierarchical algorithm to accurately diagnose overload risk level. A time series evidence chain is constructed based on dynamic weight fusion model (integrating historical frequency, response delay and other weights) to adaptively optimize threshold, forming a two-layer protection system of "fast edge response - deep cloud decision-making". The algorithm collaboration reduces the false alarm rate and improves the fault tolerance capability under complex working conditions.

[0021] Thirdly, this application also provides a storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the remote overload protection control method for intelligent circuit breakers as described above.

[0022] In summary, the beneficial technical effects of this application are as follows: The intelligent circuit breaker achieves hierarchical connection status by comparing dynamic verification intervals and monitoring cycles. After normalizing the received risk status data, it integrates multi-dimensional parameters such as real-time current, temperature, number of periodic overloads, load fluctuation curves, and duration. It uses threshold combination and trend analysis algorithms to accurately determine the first-level audible and visual warning or the second-level tripping and circuit breaking command, and forms a closed-loop control based on the timeout retransmission mechanism of the execution feedback duration. It reduces the cloud load through adaptive transmission strategies and improves the real-time performance and accuracy of overload risk identification by combining multi-parameter fusion diagnosis. The system employs a dual mechanism of cloud-based primary protection and local emergency protection: when cloud communication is normal, it relies on precise cloud control; when communication is interrupted, local emergency protection is triggered to avoid the risk of protection failure due to communication disconnection. At the same time, full data traceability facilitates subsequent fault analysis and reduces the recurrence rate of faults. No large-scale hardware modifications to existing circuit breakers are required; simply integrating edge preprocessing modules and wireless communication modules is sufficient to connect to the cloud platform, resulting in low modification costs. Furthermore, cloud-based policy optimization can reduce unnecessary tripping operations and lower the equipment malfunction rate. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall process of the remote overload protection and control method in this application; Figure 2 This is a flowchart illustrating step S2 in the remote overload protection control method of this application; Figure 3 This is a flowchart illustrating step C of the remote overload protection and control method in this application; Figure 4 This is a schematic diagram of the remote overload protection control system in this application. Detailed Implementation

[0024] The present application will be further described in detail below with reference to the accompanying drawings.

[0025] Reference Figure 1 This application discloses a remote overload protection control method for intelligent circuit breakers, comprising: S1: Collect the status operation data of the smart circuit breaker, combine it with the preset overload risk threshold to judge and filter the risk status data, and transmit it to the cloud; S2: Receive and classify risk status data, obtain the overload risk level, generate corresponding cloud control commands, and push early warning notifications; S3: Executes the corresponding smart circuit breaker protection operation according to the cloud control command and generates an execution feedback notification; S4: Manage and analyze risk status data, early warning notifications, and execution feedback notifications; construct a time-series evidence chain; and optimize the corresponding overload thresholds.

[0026] The implementation principle of this embodiment is as follows: real-time collection of intelligent circuit breaker operation data, combined with preset overload thresholds to screen risk states and upload them to the cloud, and after graded diagnosis to obtain the risk level, control commands and early warning notifications are generated; the cloud commands trigger the circuit breaker to perform protection operations and provide feedback on the execution results. At the same time, the system integrates risk data, early warnings and operation feedback to construct a time-series evidence chain, and dynamically optimizes the overload threshold based on data analysis to achieve closed-loop predictive maintenance and adaptive protection.

[0027] Preferably, step S1 includes: The status operation data of the intelligent circuit breaker is collected to obtain the line current and status temperature; Based on the acquisition frequency, the line current and temperature are filtered and denoised to obtain noise-free current and interference-free temperature. Based on the acquisition timestamp, the noiseless current and the interference-free temperature are correlated in a time sequence to obtain the current-temperature tuple; The current-temperature tuple is compared and judged based on the preset overload risk threshold. If neither of them reaches the corresponding overload risk threshold, the current current-temperature tuple is determined to be normal state data, and the normal state data is transmitted to the cloud according to the preset monitoring cycle. If the temperature data in the current-temperature tuple reaches the overload risk threshold, but the current data does not, the current temperature data is corrected based on the ambient temperature to obtain the corrected temperature. If the corrected temperature does not reach the overload risk threshold, the corrected current-temperature tuple is determined to be normal state data, and the normal state data is transmitted to the cloud according to the monitoring cycle; otherwise, the current current-temperature tuple is marked as risk state data, switched to the warning state, and immediately transmitted to the cloud. If the current data in the current-temperature tuple reaches the overload risk threshold, but the temperature data does not, or both reach the corresponding overload risk threshold, then the current current-temperature tuple is determined to be in a risk state, switched to the warning state, and immediately transmitted to the cloud.

[0028] Preferably, refer to Figure 2 Step S2 includes: The intelligent circuit breaker is verified and connected according to the communication protocol, the time interval between adjacent verifications is calculated, and the results are compared and judged in conjunction with the monitoring cycle. If the time interval is less than the monitoring cycle, it is determined that the current data volume has not reached the preset limit threshold, and a temporary transmission channel is established to receive the risk status data. If the time interval is equal to the monitoring period, it is determined that the current data volume has reached the limit threshold, and the original transmission channel is started to receive normal state data. If the time interval is greater than the monitoring cycle, the current smart circuit breaker is determined and marked as an abnormal operating state, and reconnection verification is performed according to the polling cycle until the current smart circuit breaker actively sends a connection request to the cloud. The received risk status data is normalized to obtain standard risk data. Combined with historical operating data and circuit breaker parameters, real-time current, real-time temperature, number of periodic overloads, load fluctuation curve and overload duration are extracted. If the real-time current and real-time temperature do not reach the corresponding safety trigger threshold but continue to rise, or the number of cycle overloads reaches the trigger limit, or the peak value of the load fluctuation curve is greater than or equal to the safety limit, then the current overload risk level is determined to be a level one overload, and an audible and visual warning command is generated. If the real-time temperature is greater than or equal to the corresponding safety trigger threshold, or the real-time current is greater than or equal to the corresponding safety trigger threshold and the overload duration is greater than or equal to the limit duration, or the peak value of the load fluctuation curve is greater than or equal to the safety limit and shows an exponential growth state, then the current overload risk level is determined to be a level two overload, and a tripping circuit breaker command is generated. Based on the distribution time of the audible and visual warning command or the circuit breaker tripping command, the feedback time of the intelligent circuit breaker is calculated by difference to obtain the execution feedback time. If the execution feedback time is greater than or equal to the execution timeout time, the current cloud control command will be resent and an early warning notification will be pushed.

[0029] Preferably, step S3 includes: If the cloud control command is an audible and visual warning command, the built-in audible and visual warning device of the smart circuit breaker will be activated, and the warning execution status will be fed back to the cloud at fixed time intervals. If the cloud control command is a tripping command, the mechanical tripping mechanism inside the smart circuit breaker will be driven to complete the tripping operation, and the tripping success / failure will be reported back to the cloud. The system monitors the execution process of cloud control commands. If execution fails, a fault code is generated and fed back to the cloud and pushed to the user.

[0030] Preferably, step S4 includes: A: Perform time-series correlation on risk status data, early warning notifications, and execution feedback notifications, and combine them with overload risk levels to generate time-series event data; B: Integrate and analyze the timing event data based on historical operating data and circuit breaker parameters to generate a timing evidence chain; C: Perform similar backtracking on the time-series evidence chain based on historical event data, verify the execution duration of protection operations, and generate threshold correction weights; D: Detect anomalies in the time-series evidence chain based on environmental parameters, and statistically analyze the risk level switching ratio and the number of false trips; If the risk level jump ratio is greater than or equal to the preset jump ratio threshold, the overload risk threshold will be adjusted according to the threshold adjustment weight. If the number of false trips is greater than or equal to the preset false trip threshold, the safety trigger threshold will be adjusted according to the threshold adjustment weight.

[0031] Preferably, refer to Figure 3 Step C includes: C1: Filter the time-series evidence chain based on the identification code and event type, establish a cluster of similar events, and calculate the historical frequency weight; C2: Extract the time of execution feedback notification in the temporal evidence chain to obtain the warning generation time and protection completion time, and calculate the response delay time; If the response delay time is greater than the delay duration threshold, it is marked as a delayed response event, and cluster analysis is performed to obtain the response delay weight; C3: Verify and analyze the integrity of the chronological evidence chain, and calculate the evidence integrity weight; C4: Calculate the historical frequency weight, response delay weight, and evidence completeness weight based on the learning rate to generate threshold correction weights.

[0032] The implementation principle of this embodiment is as follows: Line current and temperature data from the intelligent circuit breaker are collected. After filtering, denoising, and time-series correlation to construct a current-temperature tuple, the overload risk threshold is dynamically compared (introducing an ambient temperature correction mechanism). Normal data is transmitted according to the monitoring cycle, or risk data is immediately uploaded to the cloud. The cloud switches between temporary and original transmission channels to receive data based on communication verification results. Multi-dimensional features (such as load fluctuation curves and overload duration) are extracted by combining historical parameters. Graded instructions (audible and visual warnings or circuit breaker tripping) are generated through dual threshold criteria (safety trigger threshold and behavioral trend), and execution feedback is monitored. Finally, the risk data, warning notifications, and feedback results are correlated time-series. Historical frequency weights, response delay weights, and evidence completeness weights are calculated using back-tracking clustering of similar events. Threshold correction weights are generated through learning rate fusion, achieving adaptive optimization of the overload threshold and safety trigger threshold, significantly improving the accuracy of circuit breaker fault prediction and system reliability.

[0033] Reference Figure 4 A remote overload protection control system for intelligent circuit breakers, applied to a fault detection method, includes: The cloud control platform is used to receive and classify risk status data, obtain overload risk levels, generate corresponding cloud control commands, push early warning notifications, and manage and analyze risk status data, early warning notifications and execution feedback notifications, build a time-series evidence chain and optimize the corresponding overload thresholds. The cloud-based control platform includes: The communication interaction module is used to establish stable cloud communication with the intelligent circuit breaker according to the communication protocol, so as to realize bidirectional transmission of status operation data and cloud control commands; The data cleaning module is used for noise filtering and format standardization of risk status data to obtain standard operating data. The diagnostic grading module is used to determine the risk status data and overload risk level based on the safety trigger threshold combined with historical operating data and circuit breaker parameters; The instruction generation module is used to generate corresponding cloud control instructions based on the overload risk level, and to attach the instruction validity period and execution feedback requirements; The storage and traceability module is used to store and trace risk status data, early warning notifications and execution feedback notifications, build a time-series evidence chain and optimize the corresponding overload thresholds; The intelligent circuit breaker is used to collect status operation data, combine it with preset overload risk thresholds to judge and filter risk status data, and execute corresponding protection operations according to cloud control instructions, generate execution feedback notifications, and transmit them to the cloud control platform. Intelligent circuit breakers include: The status acquisition module is used to collect the status operation data of the line where the smart circuit breaker is located, including current, temperature and voltage; The edge computing module is used to filter operational status data based on overload risk thresholds to obtain risk status data. The wireless communication module is used to establish a wireless connection with the cloud control platform, transmit risk status data, and receive cloud control commands. The protection execution module is used to perform audible and visual warning operations or mechanical tripping operations according to cloud control commands; The emergency protection module is used to automatically trigger the trip protection mechanism based on the emergency overload threshold when communication is interrupted. The user interaction terminal is used to push early warning notifications and execution feedback notifications, and to display historical operating data and remotely confirm cloud control commands.

[0034] The implementation principle of this embodiment is as follows: Real-time current / temperature data is collected at the edge of the intelligent circuit breaker and risk screening is performed based on the overload threshold. After receiving the data through the communication interaction module, the cloud platform filters and normalizes the data through the data cleaning module. The diagnostic grading module combines historical data and circuit breaker parameters to perform multi-dimensional risk analysis (such as load fluctuation curve and overload duration) to generate grading instructions. Then, the instruction generation module issues audible and visual warnings or tripping control commands. After the protection execution module performs the linkage operation, it feeds back the status to the cloud. The storage and traceability module integrates risk data, warning notifications and execution feedback to build a time-series evidence chain. Finally, the overload threshold is optimized through a dynamic weighting algorithm to form a closed-loop management of "perception-diagnosis-control-optimization", which significantly improves the accuracy of power grid fault prediction and adaptive protection capabilities. Example

[0035] The cloud-based control platform integrates multiple core modules. Among them, the communication and interaction module establishes stable cloud communication with the smart circuit breaker based on the protocol, realizing bidirectional transmission of uplink data and downlink commands; the data cleaning module performs noise filtering and format standardization on the overload-related data (such as current, temperature, etc.) uploaded by the terminal, eliminating invalid data; the diagnostic grading module builds a diagnostic model based on historical load data and equipment rated parameters, classifying overload status into two levels: "Level 1 Warning" and "Level 2 Trip"; the command generation module generates corresponding control commands (warning commands or trip commands) according to the overload level, and adds the command validity period and execution feedback requirements; the storage and traceability module stores all overload data, diagnostic results, control commands and execution records, supporting historical data query and fault tracing.

[0036] The intelligent circuit breaker comprises multiple functional modules. The status acquisition module collects overload-related parameters such as current, temperature, and voltage of the circuit breaker's line in real time. The edge preprocessing module performs preliminary judgment on the collected data locally (e.g., whether it exceeds the "overload risk threshold") to reduce the amount of invalid data uploaded. The wireless communication module establishes a wireless connection with the cloud control platform and is responsible for transmitting data and receiving instructions. After receiving instructions from the cloud, the protection execution module performs "audible and visual warning" (level one overload) or "mechanical tripping" (level two overload) operations. The emergency protection module serves as a backup mechanism. If cloud communication is interrupted, it automatically triggers tripping protection when the local system detects an "emergency overload threshold" (e.g., the current exceeds the rated value by a certain percentage).

[0037] The user interaction terminal is presented in the form of an APP or web page. Its functions include pushing overload warnings and trip notifications to users, supporting users to view historical data, and remotely confirming the "closing and resetting" command. Example

[0038] The status acquisition module of the intelligent circuit breaker collects line current and temperature data in real time. The edge preprocessing module filters the data to remove instantaneous interference data and compares it with the locally preset "overload risk threshold" (such as the current reaching a certain percentage of the rated value). If the "overload risk threshold" is not reached, the data is only uploaded to the cloud according to the preset period. If the "overload risk threshold" is reached, the local audible and visual warning is immediately triggered, and the "risk data + warning status" is uploaded to the cloud first.

[0039] The cloud control platform receives terminal data through a communication protocol. After the data cleaning module completes the data standardization, the overload diagnosis and classification module performs multi-dimensional diagnosis, including real-time data (whether the current and temperature exceed the "safety threshold"), historical data (the number of overload risks and load fluctuation trends in the past hour), and equipment parameters (circuit breaker rated current and allowable short-term overload duration). The final output classification result is as follows: Level 1 overload (warning) indicates that the "overload risk threshold" has been reached but has not exceeded the "safety threshold", indicating a potential overload hazard; Level 2 overload (tripping) indicates that the "safety threshold" has been exceeded or the short-term overload duration has exceeded the allowable value, requiring immediate power outage protection.

[0040] The cloud-based control command generation module generates corresponding commands based on the classification results: for Level 1 overload, a "continuous audible and visual warning" command is generated, and a warning notification (including current data and risk reasons) is pushed to the user through the user interaction terminal; for Level 2 overload, an "immediate trip" command is generated, with an "execution timeout" appended to the command. If the terminal does not provide feedback on the execution result within the timeout period, the cloud automatically resends the command and pushes a "trip notification + fault data" to the user at the same time.

[0041] After receiving instructions from the cloud, the wireless communication module of the intelligent circuit breaker executes the operation according to the instructions: when executing the first-level warning, the audible and visual warning device is activated, and the "warning execution status" is fed back to the cloud every 30 seconds; when executing the second-level trip, the mechanical tripping mechanism is driven to complete the tripping, and the "tripping success / failure" result is fed back to the cloud immediately after tripping; if the instruction execution fails (such as mechanical jamming), the terminal automatically uploads the "fault code" to the cloud, and the cloud simultaneously pushes the "manual maintenance notice" to the user.

[0042] The cloud-based storage and traceability module records the entire process of "data collection - diagnostic results - instruction content - execution feedback", forming a complete data chain. At the same time, it regularly analyzes historical data and optimizes parameters such as "overload risk threshold" and "emergency overload threshold" to improve the accuracy of subsequent diagnoses.

[0043] If cloud communication is interrupted and then restored, the smart circuit breaker immediately uploads the "local protection record during the interruption" (such as whether emergency tripping was triggered) to the cloud. The cloud updates the data storage and sends a status notification to the user during the interruption to ensure that the data is not lost.

[0044] The implementation principle of this embodiment is as follows: The status acquisition module of the intelligent circuit breaker acquires operating parameters such as line current, temperature, and voltage in real time. The edge preprocessing module performs filtering and noise reduction, and threshold comparison to identify risk status data, which is then prioritized for upload to the cloud control platform. The cloud establishes a stable connection through the communication interaction module. After standardization processing by the data cleaning module, the diagnostic grading module combines real-time data, historical load records, and equipment rated parameters, employing time-series correlation and multi-dimensional threshold comparison algorithms (such as dynamic matching of real-time current / temperature with safety trigger thresholds, and analysis of periodic overload counts and fluctuation curves) to determine the risk level as "Level 1 Warning" or "Level 2 Trip." Instructions are then generated. The module generates corresponding control commands (such as audible and visual warnings or circuit breaker tripping) based on the classification results, and adds execution feedback requirements and timeout retransmission mechanisms; the storage and traceability module records the entire process data (collection, diagnosis, commands, feedback), constructs a time-series evidence chain, and dynamically optimizes the overload threshold through similar backtracking and anomaly detection algorithms (such as a fusion model based on historical frequency weight, response delay weight, and evidence integrity weight); the user interaction terminal pushes warning notifications and execution feedback, and supports remote confirmation operations; if cloud communication is interrupted, the emergency protection module automatically triggers circuit breaker tripping protection based on the local emergency overload threshold, and retransmits the records during the interruption period after communication is restored, ensuring the integrity of the data chain and the reliability of the system.

[0045] A storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement the remote overload protection control method for a smart circuit breaker as described above.

[0046] The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A remote overload protection control method for intelligent circuit breakers, characterized in that, include: Collect the status and operation data of the smart circuit breaker, combine it with the preset overload risk threshold to judge and filter the risk status data, and transmit it to the cloud; Receive and classify the risk status data to obtain the overload risk level, generate corresponding cloud control instructions, and push early warning notifications; Execute the corresponding smart circuit breaker protection operation according to the cloud control command, and generate an execution feedback notification; Manage and analyze the risk status data, the early warning notifications, and the execution feedback notifications to construct a time-series evidence chain and optimize the corresponding overload thresholds.

2. The remote overload protection control method for intelligent circuit breakers according to claim 1, characterized in that, The process of collecting the status and operation data of the intelligent circuit breaker, combining it with a preset overload risk threshold to determine and filter risk status data, and transmitting it to the cloud includes: The status operation data of the intelligent circuit breaker is collected to obtain the line current and status temperature; The line current and the state temperature are filtered and denoised according to the acquisition frequency to obtain noise-free current and interference-free temperature. Based on the acquisition timestamp, the noiseless current and the interference-free temperature are correlated in a time sequence to obtain a current-temperature tuple; The current-temperature tuple is compared and judged based on a preset overload risk threshold. If neither of them reaches the corresponding overload risk threshold, the current current-temperature tuple is determined to be normal state data, and the normal state data is transmitted to the cloud according to the preset monitoring cycle. If the temperature data in the current-temperature tuple reaches the overload risk threshold, but the current data does not, the current temperature data is corrected based on the ambient temperature to obtain the corrected temperature. If the corrected temperature does not reach the overload risk threshold, the corrected current-temperature tuple is determined to be normal state data, and the normal state data is transmitted to the cloud according to the monitoring cycle; otherwise, the current current-temperature tuple is marked as risk state data, switched to the warning state, and immediately transmitted to the cloud. If the current data in the current-temperature tuple reaches the overload risk threshold, but the temperature data does not, or both reach the corresponding overload risk threshold, then the current current-temperature tuple is determined to be the risk state data, the alert state is switched, and the data is immediately transmitted to the cloud.

3. The remote overload protection control method for intelligent circuit breakers according to claim 1, characterized in that, The process of receiving and classifying the risk status data to obtain the overload risk level generates corresponding cloud control commands and pushes early warning notifications, including: The intelligent circuit breaker is verified and connected according to the communication protocol, the time interval between adjacent verifications is calculated, and the results are compared and judged in conjunction with the monitoring cycle. If the time interval is less than the monitoring period, it is determined that the current data volume has not reached the preset limit threshold, and a temporary transmission channel is established to receive the risk status data. If the time interval is equal to the monitoring period, it is determined that the current data volume has reached the limit threshold, and the original transmission channel is started to receive normal state data. If the time interval is greater than the monitoring period, the current smart circuit breaker is determined and marked as being in an abnormal operating state, and reconnection verification is performed according to the polling period until the current smart circuit breaker actively sends a connection request to the cloud. The received risk status data is normalized to obtain standard risk data, and combined with historical operating data and circuit breaker parameters, real-time current, real-time temperature, number of periodic overloads, load fluctuation curve and overload duration are extracted. If the real-time current and the real-time temperature do not reach the corresponding safety trigger threshold but continue to rise, or the number of cycle overloads reaches the trigger limit, or the peak value of the load fluctuation curve is greater than or equal to the safety limit, then the current overload risk level is determined to be a level one overload, and an audible and visual warning command is generated. If the real-time temperature is greater than or equal to the corresponding safety trigger threshold, or the real-time current is greater than or equal to the corresponding safety trigger threshold and the overload duration is greater than or equal to the limit duration, or the peak value of the load fluctuation curve is greater than or equal to the safety limit and shows an exponential growth state, then the current overload risk level is determined to be a level two overload, and a tripping and circuit breaking command is generated. Based on the distribution time of the audible and visual warning command or the circuit breaker tripping command, the feedback time of the intelligent circuit breaker is calculated by difference to obtain the execution feedback duration; If the execution feedback time is greater than or equal to the execution timeout time, the current cloud control command will be resent and an early warning notification will be pushed.

4. The remote overload protection control method for intelligent circuit breakers according to claim 1, characterized in that, The step of executing the corresponding smart circuit breaker protection operation according to the cloud control command and generating an execution feedback notification includes: If the cloud control command is an audible and visual warning command, the built-in audible and visual warning device of the smart circuit breaker will be activated, and the warning execution status will be fed back to the cloud at fixed time intervals. If the cloud control command is a tripping command, the mechanical tripping mechanism inside the smart circuit breaker will be driven to complete the tripping operation, and the tripping success / failure will be reported back to the cloud. The execution process of the cloud control commands is monitored. If the execution fails, a fault code is generated and fed back to the cloud and pushed to the user.

5. The remote overload protection control method for intelligent circuit breakers according to claim 1, characterized in that, The management and analysis of the risk status data, the early warning notification, and the execution feedback notification, constructing a temporal evidence chain, and optimizing the corresponding overload thresholds include: The risk status data, early warning notifications, and execution feedback notifications are correlated in a time sequence, and combined with the overload risk level, time-series event data is generated. The time-series event data is integrated and analyzed based on historical operating data and circuit breaker parameters to generate a time-series evidence chain. Based on historical event data, the time-series evidence chain is backtracked in the same way, and the execution time of the protection operation is verified to generate threshold correction weights. Anomaly detection is performed on the time-series evidence chain based on environmental parameters, and the risk level switching ratio and the number of false trips are statistically analyzed. If the risk level jump ratio is greater than or equal to the preset jump ratio threshold, then the overload risk threshold is adjusted according to the threshold adjustment weight. If the number of false trips is greater than or equal to the preset false trip threshold, the safety trigger threshold is adjusted according to the threshold adjustment weight.

6. The remote overload protection control method for intelligent circuit breakers according to claim 1, characterized in that, The step of performing similar backtracking on the time-series evidence chain based on historical event data, verifying the execution duration of protection operations, and generating threshold correction weights includes: The time-series evidence chain is filtered based on the identification code and event type, a cluster of similar events is established, and the historical frequency weight is calculated. Extract the time of execution feedback notifications in the temporal evidence chain to obtain the warning generation time and protection completion time, and calculate the response delay time; If the response delay time is greater than the delay duration threshold, it is marked as a delayed response event, and cluster analysis is performed to obtain the response delay weight; The integrity of the chronological evidence chain is verified and analyzed, and the integrity weight of the evidence is calculated. The threshold correction weight is generated by calculating the historical frequency weight, the response delay weight, and the evidence completeness weight based on the learning rate.

7. A remote overload protection control system for intelligent circuit breakers, used to implement the remote overload protection control method as described in any one of claims 1-6, characterized in that, include: The cloud control platform is used to receive and classify the risk status data, obtain the overload risk level, generate corresponding cloud control commands, push early warning notifications, and manage and analyze the risk status data, the early warning notifications and execution feedback notifications, construct a time-series evidence chain and optimize the corresponding overload thresholds. The intelligent circuit breaker is used to collect status operation data, combine it with preset overload risk thresholds to judge and filter risk status data, and execute corresponding protection operations according to the cloud control instructions, generate the execution feedback notification, and transmit it to the cloud control platform. The user interaction terminal is used to push the warning notification and the execution feedback notification, and to display historical operation data and remotely confirm the cloud control commands.

8. The remote overload protection control method for intelligent circuit breakers according to claim 7, characterized in that, The cloud control platform includes: The communication interaction module is used to establish stable cloud communication with the intelligent circuit breaker according to the communication protocol, so as to realize bidirectional transmission of status operation data and cloud control commands; The data cleaning module is used for noise filtering and format standardization of risk status data to obtain standard operating data. The diagnostic grading module is used to determine the overload risk level by combining the safety trigger threshold with historical operating data and circuit breaker parameters to judge the risk status data. The instruction generation module is used to generate corresponding cloud control instructions based on the overload risk level, and to attach the instruction validity period and execution feedback requirements; The storage and traceability module is used to store and trace risk status data, early warning notifications and execution feedback notifications, build a time-series evidence chain and optimize the corresponding overload thresholds.

9. The remote overload protection control method for intelligent circuit breakers according to claim 7, characterized in that, The intelligent circuit breaker includes: The status acquisition module is used to collect the status operation data of the line where the smart circuit breaker is located, including current, temperature and voltage; The edge computing module is used to filter the state operation data according to the overload risk threshold to obtain risk state data; The wireless communication module is used to establish a wireless connection with the cloud control platform, transmit the risk status data, and receive cloud control commands. The protection execution module is used to perform audible and visual warning operations or mechanical tripping operations according to the cloud control instructions; The emergency protection module is used to automatically trigger the trip protection mechanism based on the emergency overload threshold when communication is interrupted.

10. A storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the remote overload protection control method for a smart circuit breaker as described in any one of claims 1 to 6.

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