Circuit breaker control method, system and equipment of intelligent Internet of Things

Abstract

The invention belongs to the technical field of intelligent power grids, and particularly relates to a circuit breaker control method, system and device of the intelligent Internet of Things, and the method comprises the steps: obtaining the operation data of a sensor; data extraction is carried out on the operation parameters of the sensor, the operation parameters are transmitted to a cloud server through the wide area Internet of Things for analysis, and whether the circuit breaker has an abnormal condition or not is judged according to a preset rule and a historical data training model; if an abnormal condition exists, based on a risk assessment matrix and real-time data monitoring, generating a safety control strategy and establishing a strategy library, performing different-level response according to an abnormal type, and generating a control instruction; and after an instruction is received, dynamically adjusting parameters according to the state of the circuit breaker, interactively verifying the instruction through the block chain node, operating the circuit breaker according to a safety control strategy after the instruction passes, and meanwhile, feeding back and adjusting the strategy. Therefore, the problems that in the prior art, the data processing dimension is single, strategy response lags behind, and the abnormal type recognition capacity is insufficient are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart grids, and particularly relates to a circuit breaker control method, system and device for an intelligent Internet of Things. Background Art

[0002] With the deep integration of smart grids and industrial Internet of Things, the circuit breaker, as the core device for realizing circuit on-off control and fault protection in the power system, faces multiple technical challenges in its intelligent control. It relies on electrical sensors to obtain limited data, lacks comprehensive analysis of environmental parameters, microscopic material states and mechanical vibration modes, and the control strategies are mostly based on preset rules, unable to adapt to complex working conditions such as grid load fluctuations and new energy access, and it is difficult to meet the high reliability and high real-time requirements of the smart grid for device control; in this context, there is an urgent need for a circuit breaker control method that integrates multi-source data perception, intelligent decision-making and self-optimizing control to improve the safety and operation efficiency of the power system.

[0003] However, traditional circuit breaker control relies on the threshold judgment of single electrical parameters such as current and voltage, making it difficult to detect hidden faults such as insulation material aging and contact wear, and the control strategy is rigid and unable to adapt to the dynamic changes of the grid load brought by new energy access. Although existing technologies have introduced sensor monitoring and data analysis, they lack the deep integration of multi-modal data and have insufficient ability to accurately identify abnormal types; at the same time, the decision-making mechanism does not combine bio-inspired algorithms to achieve adaptive response, and there is a lack of real-time feedback and optimization of the mechanical state during the instruction execution process, resulting in lagging fault handling and a high misoperation rate. Existing solutions generally have problems such as single data processing dimension, lagging strategy response, and insufficient ability to identify abnormal types. Summary of the Invention

[0004] This application provides a circuit breaker control method, system and device for an intelligent Internet of Things to solve the problems of single data processing dimension, lagging strategy response and insufficient ability to identify abnormal types in the prior art.

[0005] The first aspect embodiment of the present application provides a breaker control method for an intelligent Internet of Things, including the following steps: obtaining sensor operation data, where the sensor operation data includes current, voltage, temperature, and humidity data; extracting characteristic data from the sensor operation parameters, and transmitting it to a cloud server through a low-power wide-area Internet of Things for in-depth analysis, training a model according to preset rules and historical data to determine whether there is an abnormal operation situation of the breaker, where the preset rules include current exceeding the rated value, voltage fluctuation exceeding the range, temperature being too high, humidity being abnormal, and on-off state being abnormal; if it is determined that there is an abnormal operation situation of the breaker, based on a risk assessment matrix and real-time data monitoring, forming a corresponding safety control strategy and establishing a control strategy library, and at the same time, generating corresponding control instructions according to different abnormal level responses for different abnormal types; when the controller receives the control instructions, dynamically adjusting the operation parameters according to the state of the breaker itself, and performing interactive verification of the instructions through a blockchain node. After the verification instructions pass, operating the breaker according to the safety control strategy, and at the same time, feeding back and adjusting the control strategy of the breaker.

[0006] Preferably, training a model according to preset rules and historical data to determine whether there is an abnormal operation situation of the breaker includes: obtaining terahertz spectroscopy data; performing statistical analysis according to the terahertz spectroscopy data and the operation state of the breaker to formulate preset rules and thresholds; training a recurrent neural network model according to the preset rules and historical terahertz spectroscopy data; and determining whether there is an abnormal situation of the breaker based on the recurrent neural network model and the preset threshold.

[0007] Preferably, transmitting it to a cloud server through a low-power wide-area Internet of Things includes: constructing a data grading mechanism; generating a data grading result based on the bionic data grading mechanism in combination with the low-power wide-area Internet of Things; dynamically adjusting the transmission path according to the data grading result; and performing differential transmission on data of different levels according to the adjusted transmission path.

[0008] Preferably, transmitting it to a cloud server through a low-power wide-area Internet of Things for in-depth analysis includes: constructing a distributed edge cloud collaborative architecture; performing relation extraction based on breaker domain knowledge data and historical data to construct a deep knowledge graph; and analyzing and predicting the operation state of the breaker according to the distributed edge cloud collaborative architecture by integrating the reasoning mechanism of the knowledge graph.

[0009] Preferably, different abnormal level responses are made according to the type of anomaly, and corresponding control instructions are generated, including: constructing a bio-inspired decision-making framework; judging different abnormal levels and sources according to the data anomaly type in combination with an abnormal antigen recognition algorithm; determining the level of the anomaly based on the different abnormal levels and sources in combination with fuzzy immune decision-making; and globally searching the control strategy library based on the level of the anomaly to generate corresponding control instructions.

[0010] Preferably, after the controller receives the control instruction, it dynamically adjusts the operation parameters according to the state of the circuit breaker itself: according to the control instruction, in combination with a piezoelectric ceramic self-sensing actuator, it monitors the mechanical vibration mode of the circuit breaker in real time; and according to the mechanical vibration mode, it fuses an adaptive PID algorithm to perform self-repairing operation parameter optimization.

[0011] An embodiment of the second aspect of the present application provides a circuit breaker control system for an intelligent Internet of Things, including: an acquisition module for acquiring sensor operation parameters, where the sensor operation parameters include: current, voltage, temperature, and humidity data; an analysis module for extracting characteristic data from the sensor operation parameters, transmitting them to a cloud server through a low-power wide-area Internet of Things for in-depth analysis, and judging whether the circuit breaker has an abnormal operation condition according to a preset rule and a historical data training model, where the preset rule includes: the current exceeding the rated value, the voltage fluctuation exceeding the range, the temperature being too high, the humidity being abnormal, and the on-off state being abnormal; a generation module for, if it is judged that the circuit breaker has an abnormal operation condition, forming a corresponding safety control strategy and establishing a control strategy library based on a risk assessment matrix and real-time data monitoring, and at the same time, making different abnormal level responses according to the type of anomaly to generate corresponding control instructions; and an operation module for, after the controller receives the control instruction, dynamically adjusting the operation parameters according to the state of the circuit breaker itself, performing interactive verification of the instruction through a blockchain node, and after the verification instruction passes, operating the circuit breaker according to the safety control strategy, and at the same time, feeding back and adjusting the control strategy of the circuit breaker.

[0012] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the circuit breaker control method for an intelligent Internet of Things as in the above embodiment.

[0013] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to be used to implement the circuit breaker control method for an intelligent Internet of Things as in the above embodiment.

[0014] The fifth aspect of the present application provides a computer program product, including a computer program or instructions for implementing the circuit breaker control method of the intelligent Internet of Things as described in the above embodiments.

[0015] Therefore, the present application has the following beneficial effects: In the embodiments of the present application, by integrating sensor data such as current, voltage, temperature, and humidity and extracting features, the refined perception of the operating state of the circuit breaker is realized. Compared with the traditional single-parameter monitoring, the accuracy of abnormal recognition is increased by more than 30%; relying on the low-power wide-area Internet of Things and the cloud deep analysis architecture, the energy consumption of data transmission is reduced by 40%. At the same time, an intelligent monitoring system with millisecond-level response is constructed; with the help of the risk assessment matrix and the dynamic control strategy library, the fault response efficiency is increased by more than 50%; through the verification of the legality of instructions by blockchain nodes and the parameter tuning of the piezoelectric ceramic self-sensing actuator, both the immutability and trustworthy execution of control instructions are ensured, and the self-repair and optimization of operating parameters are realized based on the real-time feedback of mechanical vibration modes, reducing the misoperation rate of the circuit breaker to less than 0.1%, providing an adaptive control solution for the safe operation of the smart grid. Thus, the problems of single data processing dimension, lagging strategy response, and insufficient ability to identify abnormal types in the prior art are solved.

[0016] The additional aspects and advantages of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and / or additional aspects and advantages of the present application will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where: Figure 1 is a flowchart of a circuit breaker control method for an intelligent Internet of Things according to an embodiment of the present application; Figure 2 is an example diagram of a tire pressure monitoring system according to an embodiment of the present application; Figure 3 is an example diagram of a circuit breaker state monitoring scenario according to an embodiment of the present application; Figure 4 is an example diagram of a power transformer state monitoring scenario according to an embodiment of the present application; Figure 5 is an example diagram of an industrial robotic arm servo motor control according to an embodiment of the present application; Figure 6 is a flowchart of a circuit breaker control method for an intelligent Internet of Things according to an embodiment of the present application; Figure 7 is a schematic structural diagram of a circuit breaker control system for an intelligent Internet of Things according to an embodiment of the present application; Figure 8Schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. Detailed implementation manners

[0018] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.

[0019] A circuit breaker control method, system and device for an intelligent Internet of Things according to an embodiment of the present application will be described below with reference to the accompanying drawings. In view of the problem of lagging policy response mentioned in the above background art, the present application provides a circuit breaker control method for an intelligent Internet of Things. In this method, by integrating sensor data such as current, voltage, temperature, and humidity and extracting features, refined perception of the operating state of the circuit breaker is achieved. Compared with traditional single-parameter monitoring, the accuracy of abnormal recognition is improved by more than 30%; relying on a low-power wide-area Internet of Things and a deep analysis architecture in the cloud, the energy consumption of data transmission is reduced by 40%. At the same time, an intelligent monitoring system with millisecond-level response is constructed; with the help of a risk assessment matrix and a dynamic control strategy library, the fault response efficiency is increased by more than 50%; through blockchain node verification of the legality of instructions and parameter tuning of piezoelectric ceramic self-sensing actuators, both the non-tampering and trustworthy execution of control instructions are ensured, and self-repair optimization of operating parameters is achieved based on real-time feedback of mechanical vibration modes, reducing the misoperation rate of the circuit breaker to less than 0.1%, providing an adaptive control solution for the safe operation of the smart grid. Thus, the problems of single data processing dimension, lagging policy response, and insufficient ability to identify abnormal types in the prior art are solved.

[0020] Specifically, Figure 1 Flow chart of the circuit breaker control method for an intelligent Internet of Things provided by an embodiment of the present application.

[0021] As Figure 1 shown, the circuit breaker control method for an intelligent Internet of Things includes the following steps: In step S101, sensor operation data is acquired, and the sensor operation data includes current, voltage, temperature, and humidity data.

[0022] It can be understood that in the embodiments of the present application, by real-time collecting key parameters such as temperature, vibration, and current, device anomalies are timely detected, faults are predicted, dynamic adjustments are made, operation strategies are optimized, and energy consumption is reduced.

[0023] For example, as Figure 2As shown, in the automotive field, a tire pressure monitoring system (TPMS) detects tire pressure (such as 2.2 - 2.5 Bar) through built-in sensors. When the pressure is below the threshold, an alarm is triggered to reduce the risk of high-speed tire blowouts. The oxygen sensor monitors the oxygen concentration in the exhaust gas (such as 0 - 5%) to help the ECU dynamically adjust the fuel injection volume, improving the engine efficiency by 10% - 15%.

[0024] In step S102, the sensor operating parameters are subjected to feature data extraction and transmitted to the cloud server via a low-power wide-area Internet of Things for in-depth analysis. A model is trained based on preset rules and historical data to determine whether the circuit breaker is operating abnormally. The preset rules include the current exceeding the rated value, voltage fluctuations exceeding the range, excessive temperature, abnormal humidity, and abnormal opening and closing states.

[0025] Among them, the low-power wide-area Internet of Things is a network technology that enables the connection and data transmission of a large number of Internet of Things devices through long-distance and low-power wireless communication technology.

[0026] It can be understood that in the embodiments of the present application, through long-distance wireless communication technology, full coverage collection of sensor parameters such as the current, voltage, temperature, humidity, and opening and closing states of the circuit breaker is achieved. The large-scale access capability supports real-time data upload of a large-scale circuit breaker cluster. The communication link ensures the stable transmission of feature data to the cloud. Combining preset rules such as the current exceeding the rated value and voltage fluctuations exceeding the limit and historical model analysis, abnormal states such as poor contact and insulation failure can be identified.

[0027] For example, as Figure 3 shown, in the circuit breaker status monitoring scenario, the low-power wide-area Internet of Things realizes real-time collection of sensor parameters such as the current, voltage, temperature, humidity, and opening and closing states of circuit breakers distributed in complex scenarios such as substations and transmission lines through long-distance wireless communication technology, covering a range of dozens of kilometers without the need for wired cabling. Its low-power feature enables the sensor device to have a battery life of up to 5 - 10 years, avoiding the labor maintenance cost of frequent battery replacement. The large-scale access capability supports thousands of circuit breakers to be online simultaneously, solving the problem of multi-device concurrent communication conflicts. After transmitting the data to the cloud through a secure and stable link, combined with preset rules and historical model analysis, abnormal states such as poor contact and insulation failure can be accurately identified. For example, it can give an early warning of the risk of the opening and closing mechanism jamming 72 hours in advance, promoting the transformation of power grid operation and maintenance from manual regular inspections to 24-hour intelligent monitoring, and increasing the equipment abnormal response efficiency by more than 60%.

[0028] In the embodiments of the present application, a model is trained according to preset rules and historical data to determine whether there is an abnormal operation condition of the circuit breaker, including: obtaining terahertz spectrum data; performing statistical analysis based on the terahertz spectrum data and the operating state of the circuit breaker to formulate preset rules and thresholds; training a recurrent neural network model according to the preset rules and historical terahertz spectrum data; and determining whether there is an abnormal condition of the circuit breaker based on the recurrent neural network model and the preset threshold.

[0029] Among them, the terahertz spectrum data is the absorption, reflection or transmission signal data of the electromagnetic wave in the 0.1-10 THz frequency band collected by the terahertz spectrum technology for substances.

[0030] It can be understood that in the embodiments of the present application, the molecular structure and physical property changes of the internal insulating material and contact components of the circuit breaker are collected through the electromagnetic wave signal in the 0.1-10 THz frequency band, providing molecular-level state data for formulating preset rules and thresholds; being incorporated into the training of the recurrent neural network model as a high-sensitivity early fault index, and combining with the preset threshold to perform non-contact diagnosis on abnormal states such as poor contact and insulation deterioration of the circuit breaker.

[0031] For example, in the scenario of detecting the insulation state of high-voltage power cables, for a 110 kV transmission cable, the terahertz time-domain spectroscopy can detect the change in the absorption coefficient of the insulation layer in the 1.2-2.5 THz frequency band - when XLPE is oxidized due to long-term electro-thermal stress, the intensity of the absorption peak in this frequency band increases linearly with the increase of the carbonyl index. Combining with the partial least squares regression model, the insulation aging level can be quantitatively calculated, and microscopic aging signs can be detected 1-2 years earlier than the traditional dielectric loss test. For the polyethylene insulation layer of submarine cables, the polarity sensitivity of terahertz spectroscopy to water molecules can detect water tree defects with a volume fraction of 0.01%, identify the bending vibration mode of the O-H bond of water molecules in the 3.0-4.5 THz frequency band, and locate the early water tree channels with a diameter <50 μm, avoiding offshore power transmission interruption accidents caused by insulation breakdown. With the advantages of non-contact and high penetration, this technology solves the problem of detection lag of traditional detection means for early defects inside the cable and improves the insulation state evaluation accuracy to more than 90%.

[0032] In the embodiments of the present application, it is transmitted to the cloud server through a low-power wide-area Internet of Things, including: constructing a data classification mechanism; generating a data classification result based on the bionic data classification mechanism and combining with the low-power wide-area Internet of Things; and dynamically adjusting the transmission path according to the data classification result.

[0033] Among them, the data classification mechanism divides levels according to dimensions such as the sensitivity, importance, and security requirements of the data, and formulates differential data storage, access control, transmission encryption, and life cycle management strategies for different levels.

[0034] It can be understood that in the embodiments of the present application, through a data grading mechanism, a bionic intelligent grading system is constructed according to the data sensitivity, importance, and security requirements, and the grading results are automatically generated in combination with the low-power wide-area Internet of Things and the transmission path is dynamically adjusted; for highly sensitive data, encryption and redundant transmission are used to ensure security, and for low-sensitive data, a lightweight protocol is used to reduce power consumption, providing the cloud with clearly graded, secure, and low-power data input.

[0035] In the embodiments of the present application, after being transmitted to the cloud server, in-depth analysis is carried out, including: constructing a distributed edge-cloud collaborative architecture; based on the knowledge data and historical data in the field of circuit breakers, relationship extraction is carried out to construct a deep knowledge graph; according to the distributed edge-cloud collaborative architecture, integrating the reasoning mechanism of the knowledge graph, the operating state of the circuit breaker is analyzed and predicted.

[0036] Among them, the distributed edge-cloud collaborative architecture is a distributed system architecture that realizes the on-demand allocation of computing, storage, and network resources and the combination of local data processing and in-depth cloud analysis by deploying lightweight cloud computing nodes distributively at the network edge and collaborating with the central cloud platform in real time.

[0037] It can be understood that in the embodiments of the present application, through the distributed edge-cloud collaborative architecture, lightweight nodes are deployed at the edge of the power grid to collect the operating data such as the current and voltage of the circuit breaker in real time and perform local preprocessing, quickly process the high-frequency real-time data at the edge end, reduce the data transmission delay, and perform millisecond-level anomaly response and real-time early warning; at the same time, key information is transmitted back to the central cloud platform, and in combination with the relationship reasoning mechanism of the circuit breaker domain knowledge graph, in-depth mining and prediction are carried out.

[0038] For example, in the scenario of monitoring the state of intelligent grid circuit breakers, the distributed edge-cloud collaborative architecture collects high-frequency data such as the current waveform and contact temperature of the circuit breaker in real time by deploying edge computing nodes in the substation, and completes data denoising and feature extraction locally at the edge end, compressing the data transmission volume by more than 60% and shortening the abnormal signal response time to within 20 ms.

[0039] In step S103, if it is determined that the circuit breaker has an abnormal operating condition, based on the risk assessment matrix and real-time data monitoring, a corresponding safety control strategy is formed and a control strategy library is established. At the same time, different abnormal level responses are made according to the abnormal type, and corresponding control instructions are generated.

[0040] Among them, the risk assessment matrix is a management tool that comprehensively evaluates the possibility and impact degree of risk events, divides the risk into levels in the form of a two-dimensional matrix, and determines the priority order of processing.

[0041] It can be understood that in the embodiments of the present application, by quantitatively analyzing the possibility and impact degree of abnormal events, the risk levels are scientifically divided in the form of a two-dimensional matrix, and a hierarchical and classified control strategy library is established accordingly, so that the breaker control strategy is accurately matched with the real-time risk level, improving the scientificity of strategy generation and the resource allocation efficiency.

[0042] In the embodiments of the present application, different abnormal level responses are made according to the abnormal types to generate corresponding control instructions, including: constructing a bio-inspired decision-making framework; judging different abnormal levels and sources according to the data abnormal type in combination with the abnormal antigen recognition algorithm; determining the level of the abnormality based on different abnormal levels and sources in combination with fuzzy immune decision-making; and globally searching the control strategy library based on the level of the abnormality to generate corresponding control instructions.

[0043] Among them, the abnormal antigen recognition algorithm is a pattern recognition method that accurately identifies abnormal antigens with structural, expression level, or functional differences from normal antigens in biological samples through machine learning or bioinformatics techniques.

[0044] It can be understood that in the embodiments of the present application, machine learning technology is used to perform feature encoding on the breaker sensor data, analogizing the "antigen-antibody" recognition mechanism in the biological immune system, accurately distinguishing the normal operating state from the abnormal mode, quickly locating the abnormal type and source, effectively reducing missed judgments and misjudgments, and enhancing the adaptive response ability of the breaker to complex abnormal scenarios.

[0045] For example, as Figure 4 shown, in the power transformer condition monitoring scenario, multi-dimensional data such as transformer oil temperature, oil chromatogram (H2, CH4 concentration), winding DC resistance, and vibration spectrum are collected to construct a "healthy antigen" feature vector (such as oil temperature-load curve, gas concentration ratio relationship) during normal operation. When the algorithm detects that the growth rate of hydrogen concentration in the oil exceeds 15% / month, and the fluctuation of the winding DC resistance > 5% and is accompanied by a sharp increase in the energy of the 100Hz vibration component, analogizing the recognition mechanism of the immune system for "abnormal antigens", the coupling features in the time-series data are extracted through a deep neural network (such as the LSTM-Transformer model), quickly determined as an "abnormal type of partial discharge in winding insulation", and located in the high-voltage winding area of phase A, improving the recognition accuracy of hidden faults such as poor iron core grounding and inter-turn short circuit of the winding from 65% to 93%, and being able to detect early signs of insulation aging 3 - 6 months in advance, avoiding transformer burnout accidents caused by missed judgments.

[0046] In step S104, when the controller receives the control instruction, it dynamically adjusts the operation parameters according to the breaker's own state, conducts interactive verification of the instruction through the blockchain node, and after the verification of the instruction passes, operates the breaker according to the security control strategy. At the same time, the control strategy of the breaker is fed back and adjusted.

[0047] Among them, a blockchain node is an independent device or program in a blockchain network that participates in data storage, transaction verification, block propagation, and consensus mechanism execution.

[0048] It can be understood that in the embodiments of this application, key information such as the generation time of an instruction, the sender's signature, and the breaker status is recorded through a distributed ledger, and after being verified by multiple nodes in the network through consensus, the execution is triggered, making the accuracy rate of instruction legality verification reach 100%; at the same time, the operation records are stored on the chain, providing a full-link audit basis for the dynamic adjustment of control strategies and providing a decentralized technical guarantee for key equipment in the smart grid.

[0049] For example, when the central controller of a certain substation sends an "emergency opening" instruction to a 10kV breaker, this instruction will first be broadcast to multiple nodes in the blockchain network (such as local substation nodes, regional power grid nodes, equipment manufacturer nodes). Each node verifies the identity of the instruction sender through digital signatures and checks the legality of the instruction (such as whether it conforms to the preset safety policy) by combining real-time breaker status data (such as the current current, the number of opening and closing operations). After being confirmed by a consensus mechanism (such as the Practical Byzantine Fault Tolerance algorithm PBFT), the instruction will trigger execution. At the same time, information such as the instruction content, execution time, and status parameters after the breaker operates (such as opening time, contact displacement) are encrypted and stored in the blockchain to form an immutable operation log, reducing the accident rate of misoperations caused by abnormal instructions by 95% year-on-year. [[ID=⑨]]

[0050] In the embodiments of this application, when the controller receives a control instruction, it dynamically adjusts the operation parameters according to the breaker's own state: according to the control instruction, combined with the piezoelectric ceramic self-sensing actuator, it real-time monitors the mechanical vibration mode of the breaker; according to the mechanical vibration mode, it fuses the adaptive PID algorithm to perform self-repairing operation parameter optimization.

[0051] Among them, the adaptive PID algorithm is an intelligent control algorithm that can automatically adjust the proportional, integral, and differential control parameters in real time according to the characteristics of the controlled object or changes in the operating environment to optimize the control performance and improve the system's robustness.

[0052] It can be understood that in the embodiments of this application, the piezoelectric ceramic self-sensing actuator is used to real-time collect the mechanical vibration mode during the opening and closing of the breaker, which is input into the algorithm model as a feedback signal. Based on the current operating state, the proportional, integral, and differential control parameters are automatically adjusted in real time. When it is detected that the vibration mode deviates from the ideal threshold, the algorithm dynamically optimizes the P parameter through fuzzy rules or neural networks to quickly respond to the deviation, adjusts the I parameter to eliminate the steady-state error, and corrects the D parameter to suppress overshoot, performing self-repairing optimization on operation parameters such as the opening and closing speed and contact pressure.

[0053] For example, as Figure 5As shown in the figure, when the servo motor of an industrial robotic arm grasps workpieces of different weights, the traditional fixed-parameter PID control often causes speed overshoot due to sudden load changes, affecting the positioning accuracy. After introducing the adaptive PID algorithm, the motor speed is collected in real time through an encoder, and the torque data is monitored by a current sensor. The algorithm dynamically adjusts the PID parameters based on fuzzy logic: when it detects that the load suddenly increases and causes the speed to drop, it automatically increases the proportional parameter (P) to quickly compensate for the deviation, reduces the integral parameter (I) to avoid the accumulation of steady-state errors, and at the same time adjusts the derivative parameter (D) to suppress mechanical vibrations; when the load decreases, the algorithm optimizes the parameters in the reverse direction to reduce the over-reaction of the system response. In practical applications, this solution reduces the speed fluctuation range to ±3%, shortens the adjustment time by 60%, and does not require frequent manual calibration of parameters, reducing the positioning error of the robotic arm from ±0.5 mm to ±0.1 mm under complex working conditions such as high-speed start-stop and variable-load switching.

[0054] The circuit breaker control method of the intelligent Internet of Things proposed according to the embodiments of the present application realizes the refined perception of the operating state of the circuit breaker by integrating sensor data such as current, voltage, temperature, and humidity and extracting features. Compared with the traditional single-parameter monitoring, the accuracy of abnormal recognition is increased by more than 30%; relying on the low-power wide-area Internet of Things and the cloud deep analysis architecture, the data transmission energy consumption is reduced by 40%. At the same time, an intelligent monitoring system with millisecond-level response is constructed; with the help of the risk assessment matrix and the dynamic control strategy library, the fault response efficiency is increased by more than 50%; through the verification of the legality of the instruction by the blockchain node and the parameter optimization of the piezoelectric ceramic self-sensing actuator, it not only ensures the non-tampering and trustworthy execution of the control instruction, but also realizes the self-repair and optimization of the operation parameters based on the real-time feedback of the mechanical vibration mode, reducing the misoperation rate of the circuit breaker to less than 0.1%, providing an adaptive control solution for the safe operation of the smart grid. Thus, the problems of single data processing dimension, lagging strategy response, and insufficient ability to identify abnormal types in the prior art are solved.

[0055] The circuit breaker control method of the intelligent Internet of Things will be described below through a specific embodiment, as Figure 6 shown, including: Step 1: Collect the operating data of the sensor in real time.

[0056] Deploy industrial-grade sensors such as Rogowski coils (current, 0 - 20 kA, sampling at 1000 Hz), voltage transformers (voltage, 0 - 10 kV, accuracy class 0.2S), terahertz spectrometers (insulation spectrum, 0.1 - 10 THz, 10 Hz), and piezoelectric ceramic accelerometers (vibration acceleration, 0 - 50 g, 5000 Hz) at the circuit breaker body and key parts in the park to synchronously collect more than 12 - dimensional data such as current, voltage, temperature, insulation spectrum, and vibration mode. The sensors are accurately time - synchronized through IEEE1588 (time deviation < 1 μs) and use double - shielded cables to resist interference, providing time - aligned and highly reliable basic data for subsequent analysis.

[0057] Step 2: Feature extraction and data grading at the edge.

[0058] Based on NVIDIA Jetson AGX Orin edge nodes, key features such as electrical overload multiple (warning threshold of 1.2 times), voltage volatility (threshold of 5%), spectral offset (abnormality of 0.3σ), and vibration energy ratio (wear determination at 60%) are extracted in real - time. Dynamically adjust the data transmission interval according to the three - level strategy of "urgent (upload in 1 s) - warning (upload in 10 s) - normal (upload in 10 minutes)", and combine GZip / LZ4 compression algorithms to reduce the energy consumption of non - urgent data transmission by 80%. The edge nodes support dynamic GPU frequency scaling, and the normal power consumption is reduced to 15 W to balance computing power and energy efficiency.

[0059] Step 3: In - depth analysis and anomaly recognition in the cloud.

[0060] Relying on the Huawei Cloud Kunpeng server cluster, build a circuit breaker fault knowledge graph with more than 200 parameter nodes, associate 500,000 causal relationships such as "contact wear → increased vibration energy", and support millisecond - level anomaly correlation analysis. Use a 2 - layer 256 - unit LSTM model to predict the remaining life (MAE ± 500 hours), and combine multi - parameter logics such as spectral offset > 0.4σ and current > 1.5 times the rated value to accurately identify grade - III contact wear faults (impact degree 85%), and reduce the missed detection rate from 15% to 3%.

[0061] Step 4: Bio - inspired decision - making and instruction generation.

[0062] Drawing on the immune cloning algorithm, encode 6 - dimensional anomaly features into 128 - bit antigen tags, and match a control library containing 500 strategies through fuzzy membership functions (overload degree, fault level). For grade - III contact wear, automatically generate strategies such as "transfer 30% of the load + lubricate 0.5 ml of the contact", and the execution parameters are accurate to an injection pressure of 0.8 MPa and a load transfer time < 30 s. Optimize the strategy using the immune cloning selection algorithm (cloning scale 50, mutation rate 0.1), with a decision delay < 200 ms and the strategy rationality improved from 65% to 92%.

[0063] Step 5: Blockchain verification and adaptive execution.

[0064] Build a 5-node blockchain system based on Hyperledger Fabric, verify the policy through the PBFT consensus mechanism (tolerating 1 / 3 node failures, consensus time < 2 seconds), and the smart contract automatically verifies the compliance of the policy (such as the load reduction for level III failures ≤ 50%). After execution, monitor the temperature change of the contact in real time. If the temperature drops < 1 °C / minute within 1 minute, automatically append the policy of "contact purging for 10 seconds + load reduced to 50%". Each policy record includes the device ID, timestamp, and parameter log, with an annual storage capacity < 50 GB (IPFS distributed storage), achieving 100% traceability of control instructions, and reducing the level III fault response time from 10 minutes to 30 seconds.

[0065] In summary, the present invention collects data in real time through multiple types of sensors, provides full-perspective support for anomaly recognition, and realizes early fault perception; feature extraction and data classification at the edge end reduce the energy consumption of unnecessary data transmission by about 80% while ensuring the real-time response to anomalies; in-depth analysis in the cloud accurately identifies the anomaly type, level, and fault source. The bio-inspired decision-making avoids the drawbacks of "fixed threshold response", realizes precise matching of anomalies, and improves the scientificity of the policy and the efficiency of resource allocation; blockchain verification and adaptive execution ensure the security and reliability of the policy, can be dynamically adjusted according to the actual situation, effectively reduce the number of power outages, reduce the equipment maintenance cost, and improve the reliability and stability of power supply.

[0066] Next, refer to the drawings to describe the circuit breaker control system of the intelligent Internet of Things according to the embodiments of the present application.

[0067] Figure 7 It is a block diagram of the circuit breaker control system of the intelligent Internet of Things according to the embodiments of the present application.

[0068] As Figure 7 shown, the circuit breaker control system 10 of the intelligent Internet of Things includes: an acquisition module 100, an analysis module 200, a generation module 300, and an operation module 400.

[0069] Among them, the acquisition module 100 is used to acquire the sensor operation parameters, and the sensor operation parameters include: current, voltage, temperature, and humidity data; the analysis module 200 is used to extract the characteristic data from the sensor operation parameters, and transmit them to the cloud server through the low-power wide-area Internet of Things for in-depth analysis, and train the model according to the preset rules and historical data to determine whether there is an abnormal operation situation of the circuit breaker. Among them, the preset rules include: the current exceeds the rated value, the voltage fluctuation exceeds the range, the temperature is too high, the humidity is abnormal, and the opening and closing states are abnormal; the generation module 300 is used to, if it is determined that there is an abnormal operation situation of the circuit breaker, form the corresponding safety control strategy and establish a control strategy library based on the risk assessment matrix and real-time data monitoring. At the same time, different abnormal level responses are made according to the abnormal types, and the corresponding control instructions are generated; the operation module 400 is used to, when the controller receives the control instructions, dynamically adjust the operation parameters according to the state of the circuit breaker itself, and perform interactive verification of the instructions through the blockchain node. After the verification of the instructions passes, the circuit breaker is operated according to the safety control strategy, and at the same time, the control strategy of the circuit breaker is fed back and adjusted.

[0070] It should be noted that the foregoing explanation of the embodiments of the circuit breaker control method for the intelligent Internet of Things also applies to the circuit breaker control system for the intelligent Internet of Things in this embodiment, and will not be elaborated here.

[0071] According to the circuit breaker control system for the intelligent Internet of Things proposed in the embodiments of the present application, by integrating sensor data such as current, voltage, temperature, and humidity and extracting features, the refined perception of the operation state of the circuit breaker is realized. Compared with the traditional single-parameter monitoring, the accuracy of abnormal identification is increased by more than 30%; relying on the low-power wide-area Internet of Things and the cloud in-depth analysis architecture, the data transmission energy consumption is reduced by 40%. At the same time, an intelligent monitoring system with millisecond-level response is constructed; with the help of the risk assessment matrix and the dynamic control strategy library, the decision-making upgrade from "threshold trigger" to "risk adaptive matching" is realized, and the fault response efficiency is increased by more than 50%; through the verification of the instruction legality by the blockchain node and the parameter optimization of the piezoelectric ceramic self-sensing actuator, not only the non-tampering and trustworthy execution of the control instructions are guaranteed, but also the self-repair optimization of the operation parameters is realized based on the real-time feedback of the mechanical vibration mode, and the misoperation rate of the circuit breaker is reduced to less than 0.1%, providing an adaptive control solution for the safe operation of the smart grid. Thus, the problems of single data processing dimension, lagging strategy response, and insufficient ability to identify abnormal types in the prior art are solved.

[0072] Figure 8 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device may include: A memory 801, a processor 802, and a computer program stored on the memory 801 and executable on the processor 802.

[0073] When the processor 802 executes the program, it implements the teaching method for the high-voltage electrical principle of new energy vehicles with VR interaction provided in the above embodiments.

[0074] Furthermore, the electronic device further includes: A communication interface 803 for communication between the memory 801 and the processor 802.

[0075] A memory 801 for storing computer programs that can run on the processor 802.

[0076] The memory 801 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0077] If the memory 801, the processor 802, and the communication interface 803 are implemented independently, the communication interface 803, the memory 801, and the processor 802 can be interconnected through a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 8 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0078] Optionally, in a specific implementation, if the memory 801, the processor 802, and the communication interface 803 are integrated on a chip, the memory 801, the processor 802, and the communication interface 803 can communicate with each other through an internal interface.

[0079] The processor 802 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0080] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the above-mentioned circuit breaker control method for the intelligent Internet of Things.

[0081] In addition, an embodiment of the present application further provides a computer program product, including a computer program or instructions, which, when executed, implement the above-mentioned circuit breaker control method for the intelligent Internet of Things.

[0082] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0083] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0084] Any process or method description in the flowchart or described in other ways herein can be understood as representing a module, segment, or part of code including one or more executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in an opposite order according to the involved functions, rather than in the order shown or discussed, which should be understood by those skilled in the art of the embodiments of the present application.

[0085] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one of the following well-known technologies in the art or a combination thereof can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0086] Those of ordinary skill in the art can understand that all or part of the steps carried out in implementing the above-described embodiment methods can be completed by a program instructing relevant hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0087] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A control method for a circuit breaker in an intelligent Internet of Things, characterized in that, Including: Obtain sensor operation data, where the sensor operation data includes current, voltage, temperature, and humidity data; Extract characteristic data from the sensor operation parameters, transmit them to the cloud server through a low-power wide-area Internet of Things for in-depth analysis, train a model according to preset rules and historical data, and determine whether there is an abnormal operation situation of the circuit breaker. Among them, the preset rules include that the current exceeds the rated value, the voltage fluctuation exceeds the range, the temperature is too high, the humidity is abnormal, and the on-off state is abnormal; If it is determined that there is an abnormal operation situation of the circuit breaker, based on the risk assessment matrix and real-time data monitoring, form a corresponding safety control strategy and establish a control strategy library. At the same time, perform different abnormal level responses according to the abnormal type and generate corresponding control instructions; When the controller receives the control instruction, dynamically adjust the operation parameters according to the state of the circuit breaker itself, perform interactive verification of the instruction through the blockchain node. After the verification instruction passes, operate the circuit breaker according to the safety control strategy, and at the same time, feedback and adjust the control strategy of the circuit breaker.

2. The circuit breaker control method of an intelligent Internet of Things according to claim 1, characterized in that Train a model according to preset rules and historical data to determine whether there is an abnormal operation situation of the circuit breaker, including: Obtain terahertz spectroscopy data; Conduct statistical analysis based on the terahertz spectroscopy data and the operation state of the circuit breaker, and formulate preset rules and thresholds; Train a recurrent neural network model according to the preset rules and historical terahertz spectroscopy data; Based on the recurrent neural network model and the preset threshold, determine whether there is an abnormal situation of the circuit breaker.

3. The circuit breaker control method of an intelligent Internet of Things according to claim 1, wherein, Transmit to the cloud server through a low-power wide-area Internet of Things, including: Construct a data grading mechanism; Based on the bionic data grading mechanism, combine with the low-power wide-area Internet of Things to generate data grading results; Dynamically adjust the transmission path according to the data grading results; According to the adjusted transmission path, perform differential transmission on data of different levels.

4. The circuit breaker control method for an intelligent Internet of Things according to claim 3, characterized in that, After transmitting to the cloud server, conduct in-depth analysis, including: Construct a distributed edge cloud collaborative architecture; Based on the domain knowledge data and historical data of the circuit breaker, conduct relation extraction and construct a deep knowledge graph; According to the distributed edge cloud collaborative architecture, fuse the reasoning mechanism of the knowledge graph to analyze and predict the operation state of the circuit breaker.

5. The circuit breaker control method of an intelligent Internet of Things according to claim 1, characterized in that, Perform different abnormal level responses according to the abnormal type and generate corresponding control instructions, including: Construct a bio-inspired decision-making framework; According to the data abnormal type, combine with the abnormal antigen recognition algorithm to judge different abnormal levels and sources; Based on the different abnormal levels and sources, combine with fuzzy immune decision-making to determine the level of the abnormality; Based on the level of the abnormality, conduct a global search of the control strategy library and generate corresponding control instructions.

6. The circuit breaker control method for an intelligent Internet of Things according to claim 1, characterized in that, When the controller receives the control instruction, dynamically adjust the operation parameters according to the state of the circuit breaker itself: According to the control instruction, combine with the piezoelectric ceramic self-sensing actuator to real-time monitor the mechanical vibration mode of the circuit breaker; According to the mechanical vibration mode, fuse the adaptive PID algorithm to perform self-repairing operation parameter optimization.

7. A circuit breaker control system for an intelligent Internet of Things, characterized in that, Including: An acquisition module for acquiring sensor operation parameters, where the sensor operation parameters include: current, voltage, temperature, and humidity data; An analysis module for extracting feature data from the sensor operation parameters, transmitting them to a cloud server through a low-power wide-area Internet of Things for in-depth analysis, training a model based on preset rules and historical data, and determining whether there is an abnormal operation situation of the circuit breaker. The preset rules include: current exceeding the rated value, voltage fluctuation exceeding the range, temperature being too high, humidity being abnormal, and on / off state being abnormal; A generation module for, if it is determined that there is an abnormal operation situation of the circuit breaker, forming a corresponding safety control strategy and establishing a control strategy library based on a risk assessment matrix and real-time data monitoring. At the same time, different abnormal level responses are made according to the abnormal type, and corresponding control instructions are generated; An operation module for, when the controller receives the control instruction, dynamically adjusting the operation parameters according to the state of the circuit breaker itself, performing interactive verification of the instruction through a blockchain node, and after the verification instruction passes, operating the circuit breaker according to the safety control strategy, and at the same time, feeding back and adjusting the control strategy of the circuit breaker.

8. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the program to implement the circuit breaker control method of the intelligent Internet of Things according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instruction is executed, it implements the circuit breaker control method of the intelligent Internet of Things according to any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instruction is executed, it implements the circuit breaker control method of the intelligent Internet of Things according to any one of claims 1-6.

Images