CN118604681B - Short circuit diagnosis method and system based on 4G Internet of things - Google Patents

Abstract

The invention relates to the technical field of line fault monitoring, in particular to a short circuit diagnosis method and a short circuit diagnosis system based on a 4G Internet of things, wherein the short circuit diagnosis method based on the 4G Internet of things comprises the following steps: obtaining circuit monitoring data by using a short circuit diagnosis system, and obtaining node monitoring data of different nodes of the circuit according to the circuit monitoring data; establishing a route current analysis model by combining the short circuit diagnosis system and the node monitoring data so as to obtain current quantity analysis results of different nodes; analyzing the connection condition of the circuit according to the current amount analysis result and the node monitoring data; constructing a current amplitude prediction model according to the current amount analysis result and the node monitoring data so as to obtain current amplitudes of different nodes; and analyzing a circuit fault position based on the current amplitude, and starting a circuit emergency scheme by combining the circuit fault position. The invention acquires data through the short circuit diagnosis system, can accurately reflect the state of each node in the circuit, and provides accurate judgment basis for line fault diagnosis.

Description

Short circuit diagnosis method and system based on 4G Internet of Things

Technical Field

The present invention relates to the technical field of line fault monitoring, and in particular to a short circuit diagnosis method and system based on 4G Internet of Things.

Background Art

Traditional line fault monitoring methods rely on cumbersome manual inspection processes and periodic inspection arrangements. Existing diagnostic methods are not only inefficient, but also limited by time and labor costs. It is difficult to capture sudden or potential short-circuit faults, especially when facing complex problems such as aging and insufficient capacity of the line, it is even more difficult to accurately determine the specific location of the fault. With the development of mobile Internet technology and the integration of intelligent perception, high-precision recognition and computing technology, the Internet of Things can build an Internet network that enables various information sensing devices to seamlessly access the Internet and realize real-time data collection, transmission and intelligent processing. In order to optimize the existing line maintenance and diagnosis methods and improve maintenance efficiency and response speed, there is an urgent need for an intelligent short-circuit fault troubleshooting method based on the Internet of Things technology, which can monitor the changes in the line in real time, use analysis algorithms for intelligent analysis, and accurately identify and locate short-circuit hidden dangers. It not only shortens the time interval from fault discovery to processing, reduces the maintenance cost and time loss caused by short-circuit faults, but also improves the overall work efficiency and stability of the power system.

Summary of the invention

In view of the shortcomings of existing methods and the needs of practical applications, in order to solve the problems of low efficiency, insufficient accuracy, and difficulty in finding and locating fault points in current short-circuit diagnosis methods, the present invention integrates Internet of Things technology, intelligent detection equipment and mathematical algorithms, realizes real-time monitoring of line status, can identify potential short-circuit hazards, improve the intelligence level of line short-circuit diagnosis, and help to achieve efficient and accurate short-circuit fault detection and positioning. On the one hand, the present invention provides a short-circuit diagnosis method based on 4G Internet of Things, which includes: obtaining circuit monitoring data using a short-circuit diagnosis system, and obtaining node monitoring data of different nodes of the circuit based on the circuit monitoring data; establishing a route current analysis model in combination with the short-circuit diagnosis system and the node monitoring data to obtain current analysis results of different nodes; analyzing the connection of the circuit according to the current analysis results and the node monitoring data; constructing a current amplitude prediction model based on the current analysis results and the node monitoring data to obtain the current amplitude of different nodes; analyzing the circuit fault location based on the current amplitude, and starting the circuit emergency plan in combination with the circuit fault location. The short-circuit diagnostic system of the present invention can monitor the operating status of the circuit in real time. Once abnormal data is detected, the analysis module is immediately triggered to achieve a rapid response to the short-circuit fault, which helps to reduce human errors and delays and improve the processing efficiency and quality of line faults.

Optionally, the circuit monitoring data is obtained by using the short-circuit diagnostic system, and the node monitoring data of different nodes of the circuit are obtained based on the circuit monitoring data, including: performing node segmentation processing on the detection circuit based on the short-circuit diagnostic system and the circuit monitoring data to obtain different nodes of the detection circuit; and obtaining node monitoring data of different nodes of the circuit based on the different nodes and the circuit monitoring data. The present invention performs node segmentation processing on the detection circuit, which can divide a complex circuit system into multiple relatively independent nodes, thereby realizing detailed monitoring of the circuit state, which helps to improve the accuracy and reliability of the monitoring results.

Optionally, the establishment of a route current analysis model in combination with the short-circuit diagnostic system and the node monitoring data includes: constructing a current positive phase sequence calculation formula and a current negative phase sequence calculation formula based on the short-circuit diagnostic system and the node monitoring data; and obtaining a route current analysis model in combination with the current positive phase sequence calculation formula and the current negative phase sequence calculation formula. The present invention constructs calculation formulas for positive phase sequence and negative phase sequence, and calculates the positive phase sequence and negative phase sequence of current separately, which can more accurately reflect the actual changes in current, thereby improving the accuracy of the analysis results.

Optionally, the route current analysis model satisfies the following relationship:

in, Represents the detection node The positive phase sequence component of Indicates the monitoring location number, Indicates the node identification number. Represents the detection node The current value, Represents the detection node The comprehensive voltage value, Indicates the equivalent resistance of the short-circuit diagnostic system. Indicates the circuit thermal effect index, Indicates the total resistance of the short-circuit diagnostic system components;

The current negative phase sequence calculation formula satisfies the following relationship:

in, Represents the detection node The negative phase sequence component of Indicates the monitoring location number, Indicates the node identification number. Represents the detection node The current value, Represents the detection node The comprehensive voltage value, Indicates the equivalent resistance of the short-circuit diagnostic system. It represents the thermal effect index. The present invention calculates the positive phase sequence and negative phase sequence current components respectively, which can more accurately describe the actual situation of the current in the circuit, and can more carefully capture the changing characteristics of the current in different phases, which can further ensure the credibility of the analysis results.

Optionally, the establishment of a route current analysis model in combination with the short-circuit diagnosis system and the node monitoring data to obtain current analysis results of different nodes includes: obtaining positive phase sequence component results and negative phase sequence component results of different nodes using the route current analysis model; and obtaining current analysis results of different nodes based on the positive phase sequence component results and the negative phase sequence component results. After obtaining the current analysis results of different nodes, the present invention can further analyze the distribution and change trend of the current in the circuit, providing accurate guidance for subsequent line diagnosis and maintenance work.

Optionally, the current amplitude prediction model is constructed based on the current analysis result and the node monitoring data, including: obtaining the current phase difference of different detection nodes based on the node monitoring data. The current phase difference of the present invention is an important parameter reflecting the current distribution and flow characteristics in the circuit, so that the real-time state of the circuit can be more comprehensively understood.

Optionally, the constructing of the current amplitude prediction model based on the current analysis result and the node monitoring data includes: constructing the current amplitude prediction model based on the current phase difference and the current analysis result. The present invention uses the current phase difference and the current analysis result as key parameters of the prediction model, which can fully reflect the dynamic change characteristics of the current in the circuit, thereby improving the prediction accuracy, which is of great significance for the stable operation and fault prevention of the power system.

Optionally, the current amplitude prediction model satisfies the following relationship:

in, Represents the detection node The current amplitude, Represents the detection node The current value, Represents the detection node The current value, Represents the detection node Current phase difference. The prediction model of the present invention comprehensively considers the current values and current phase differences of adjacent nodes, and can more comprehensively reflect the distribution and flow characteristics of the current in the circuit, which is conducive to improving the reliability of the prediction results.

Optionally, the circuit fault location is analyzed based on the current amplitude, and the circuit emergency plan is started in combination with the circuit fault location, including: obtaining the current amplitude of the detection node through the current amplitude prediction model; analyzing the circuit fault location in combination with the current amplitude and the circuit monitoring data; planning and starting the circuit emergency plan in combination with the circuit fault location, the current amplitude and the short-circuit diagnosis system. After determining the circuit fault location, the present invention can formulate a more scientific and reasonable emergency maintenance plan in combination with the current amplitude, the short-circuit diagnosis system and the specific situation of the circuit, so as to effectively handle the fault, shorten unnecessary downtime and maintenance time, and improve the efficiency of emergency response.

In the second aspect, in order to efficiently execute the short-circuit diagnosis method based on 4G Internet of Things provided by the present invention, the present invention also provides a short-circuit diagnosis system based on 4G Internet of Things, the device includes a power control module, a data monitoring module, a data analysis module, a remote control module and an intelligent alarm module, the power control module data monitoring module, the data analysis module, the remote control module and the intelligent alarm module are interconnected to execute the short-circuit diagnosis method based on 4G Internet of Things as described in the first aspect of the present invention. The short-circuit diagnosis system based on 4G Internet of Things of the present invention has a compact structure and stable performance, and can stably execute the short-circuit diagnosis method based on 4G Internet of Things provided by the present invention, thereby improving the overall applicability and practical application capability of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a short circuit diagnosis method based on 4G Internet of Things of the present invention;

FIG2 is a schematic diagram of different detection nodes of a circuit to be detected in a short circuit diagnosis method based on 4G Internet of Things of the present invention;

FIG3 is a structural diagram of a short circuit diagnosis system based on 4G Internet of Things of the present invention.

DETAILED DESCRIPTION

The specific embodiments of the present invention will be described in detail below. It should be noted that the embodiments described herein are only for illustration and are not intended to limit the present invention. In the following description, a large number of specific details are set forth in order to provide a thorough understanding of the present invention. However, it is obvious to those of ordinary skill in the art that these specific details do not need to be adopted to implement the present invention. In other examples, in order to avoid confusing the present invention, known circuits, software or methods are not specifically described.

Throughout the specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in conjunction with the embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "in one embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily all refer to the same embodiment or example. In addition, particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combination and/or subcombination. In addition, it should be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes and that the figures are not necessarily drawn to scale.

Please refer to Figure 1. In view of the problems of low efficiency, limited accuracy and difficulty in locating fault points in existing short-circuit diagnosis methods, the present invention combines Internet of Things technology, intelligent detection equipment and mathematical algorithms to diagnose and detect lines, which helps to achieve real-time monitoring of line status, timely identification of potential short-circuit hazards and accurate positioning of fault locations. The present invention provides a short-circuit diagnosis method based on 4G Internet of Things, which includes the following steps:

Furthermore, the short-circuit diagnosis system based on 4G Internet of Things of the present invention mainly includes a power control module, a data monitoring module, a data analysis module, a remote control module and an intelligent alarm module.

S1. Use the short circuit diagnosis system to obtain circuit monitoring data, and obtain node monitoring data of different nodes of the circuit based on the circuit monitoring data. The specific setting steps and implementation contents are as follows:

First, the data monitoring module in the short circuit diagnosis system is used to obtain circuit monitoring data. The specific contents are as follows:

In an optional embodiment, after confirming that the short-circuit diagnostic system has been firmly installed and is in normal working mode, the corresponding monitoring thresholds and standards are set in the data monitoring module according to the specific attributes of the target monitoring circuit, such as voltage level, current capacity, frequency and other key parameters. Then, the current sensor, voltage sensor and other monitoring equipment are accurately connected to the key monitoring points of the circuit to ensure that the data transmission link between the above sensors and the data monitoring module is unimpeded, so as to realize the timely transmission and storage of data information.

After the data monitoring module is started, it will immediately monitor the circuit status comprehensively, capture and record the core data indicators such as voltage, current, power and energy consumption in real time. The above data are mainly collected in the form of digital signals and directly transmitted to the core database of the short circuit diagnosis system for storage and management. Before the data is stored in the database, the data monitoring module will perform a series of pre-processing operations, including denoising to eliminate background noise interference, filtering operations to smooth data fluctuations, and data compression to optimize storage efficiency, so as to ensure the accuracy of subsequent data analysis and the optimization of data quality.

The data monitoring module is not limited to voltage monitoring. It can also continuously track the voltage levels of key nodes in the circuit through voltage monitoring equipment to ensure real-time monitoring of voltage data. At the same time, the current monitoring equipment records the current dynamics of each branch, which is conducive to discovering possible abnormal current surges. In addition, the application of power and energy consumption monitoring equipment enables the system to fully grasp the energy consumption status of the circuit, timely discover and point out potential energy waste or abnormal energy consumption, and provide data support for energy-saving and emission reduction measures. Finally, the addition of temperature monitoring equipment enables the system to monitor the temperature changes of the circuit in real time, prevent equipment damage or safety accidents caused by overheating, and ensure the safe and stable operation of the circuit.

Furthermore, the method for acquiring circuit monitoring data in the present embodiment is only an optional condition of the present invention. One or some other embodiments may be adjusted according to specific implementation requirements and data monitoring modules, so that the present invention can be flexibly adjusted according to different application scenarios and needs, and can better adapt to various complex circuit environments, thereby ensuring the actual operation of the short-circuit diagnosis method based on 4G Internet of Things.

In this embodiment, in order to ensure the normal operation of the short circuit diagnosis system, the power control module in the embodiment includes a power supply device, a capacitor discharge device and a corresponding power control device, and the specific contents are as follows:

The power supply device is the energy source of the short-circuit diagnosis system. The power supply device is responsible for converting the external input electrical energy into the voltage and current levels required by each module inside the system. In the embodiment, high-quality power supply devices are selected to ensure that they have the characteristics of high conversion efficiency, low noise, high stability, etc. to meet the long-term operation requirements of the system. At the same time, the power supply device also has safety functions such as overload protection and short-circuit protection to ensure that the power supply can be cut off in time under abnormal circumstances to protect the system from damage.

The above capacitor discharge equipment can be used to smooth voltage fluctuations, provide instantaneous current, etc. When the system is powered off or needs to be discharged quickly, the equipment can quickly release the electric energy stored in the capacitor in a safe way, thereby protecting the components inside the system from damage. The design of the capacitor discharge equipment needs to fully consider factors such as discharge speed, safety and stability to ensure that no secondary damage is caused to the system during the discharge process.

As the core of the power control module, the power control device is responsible for monitoring the operating status of the entire power system and adjusting the output voltage and current of the power device according to system requirements. At the same time, the power control device is linked with the capacitor discharge device to ensure that the discharge process can be started quickly.

The power control module in this embodiment provides stable and reliable power support for the short-circuit diagnosis system by integrating power supply equipment, capacitor discharge equipment and power control equipment, which not only ensures the normal operation of the short-circuit diagnosis system under various working conditions, but also improves the safety and reliability of the diagnosis system, thereby ensuring the effective implementation of the short-circuit diagnosis method.

Then, the data monitoring module performs node segmentation processing on the detection circuit based on the short-circuit diagnosis system and the circuit monitoring data to obtain different nodes of the detection circuit; and obtains node monitoring data of different nodes of the circuit according to different nodes and circuit monitoring data. The specific contents are as follows:

In the short-circuit diagnosis system, after completing the real-time monitoring and data collection of the key parameters of the circuit, the data monitoring module will further use the monitoring data for in-depth analysis and processing. One of the important processing steps is to perform node segmentation processing on the monitoring data based on the detection circuit.

Node segmentation is a key step in short circuit diagnosis. It can divide a complex circuit system into several relatively independent and easy-to-analyze sub-parts, namely nodes. Please refer to Figure 2, where numbers 1-12 represent different detection nodes in the circuit to be detected. Each node can be regarded as a unit with specific electrical characteristics in the circuit. The connection relationship between nodes can reflect the topology and operating status of the entire circuit. When performing node segmentation, the data monitoring module will first identify or set the key node positions of the circuit based on the design drawings or actual layout of the circuit.

Then, based on the key node location information and circuit monitoring data, the monitored voltage, current and other parameter data are segmented and processed to obtain the node monitoring data of different nodes in the circuit. The monitoring data between each node helps to analyze the electrical connection and mutual influence of the system, and obtain key indicators such as voltage difference and current distribution between different nodes, which helps to improve the feasibility of the short-circuit diagnosis method based on 4G Internet of Things.

Furthermore, the circuit can also be logically classified based on the analysis result data monitoring module of different nodes, and nodes with similar electrical characteristics or interdependencies can be grouped into the same group to form different node sets. The above node sets represent different functional areas or potential fault areas in the circuit, providing an important reference for subsequent fault diagnosis and location.

In the embodiment, the node segmentation processing is a dynamic adjustment process. With the continuous updating of monitoring data and the changes in the circuit operation status, the data monitoring module will evaluate the rationality of the node segmentation in real time, and adjust and optimize as needed. The above-mentioned dynamic adjustment mechanism ensures that the short-circuit diagnosis system can accurately and quickly respond to changes in the circuit, thereby improving the quality of node monitoring data and the accuracy of diagnostic results.

Furthermore, the monitoring data processing method in this embodiment is only an optional condition of the present invention. One or some other embodiments can be optimized according to the actual situation of the circuit and the situation of the data monitoring module. Different circuits have unique characteristics and operating states. Optimization according to the actual situation of the circuit can ensure that the data processing method is closer to the actual situation of the circuit, thereby improving the reliability of the short-circuit diagnosis results.

S2. Establish a route current analysis model by combining the short-circuit diagnosis system and node monitoring data to obtain the current analysis results of different nodes. The specific steps and implementation contents are as follows:

The data analysis module in the short-circuit diagnosis system constructs the current positive phase sequence calculation formula and the current negative phase sequence calculation formula based on the short-circuit diagnosis system and node monitoring data. The implementation content is as follows:

The data analysis module in the short-circuit diagnosis system uses the symmetrical component method and combines the structural characteristics of the short-circuit diagnosis system with the node monitoring data to establish the positive phase sequence and negative phase sequence calculation formulas for the current respectively. Based on this, the scientificity and accuracy of the short-circuit diagnosis results can be ensured, providing strong technical support for fault detection and stable operation of the power system.

The route current analysis model in the data analysis module in the embodiment satisfies the following relationship:

in, Represents the detection node The positive phase sequence component of Indicates the monitoring location number, Indicates the node identification number. Represents the detection node The current value, Represents the detection node The comprehensive voltage value, Indicates the equivalent resistance of the short-circuit diagnostic system. Indicates the circuit thermal effect index, Indicates the total resistance of the short-circuit diagnostic system components;

In the short-circuit diagnosis system, the positive phase-sequence component of the detection node x refers to the current component consistent with the normal phase sequence in the three-phase power system. In an ideal three-phase balanced system, the three-phase currents are symmetrical, that is, they have the same amplitude, frequency, and the phases differ by 120 degrees. However, in the case of a short circuit or other fault, the three-phase currents become asymmetrical. Detecting and analyzing the positive phase-sequence component of the current is of great significance for determining the type of fault, locating the fault point, and evaluating the impact of the fault. By comparing the relationship between the positive phase-sequence component and other components, the nature and severity of the circuit fault can be inferred, so that appropriate measures can be taken to handle the fault.

Monitoring location numbering refers to assigning specific numbers to different monitoring locations based on the segmentation results after the detection circuit is segmented into nodes, so as to facilitate distinction and management during circuit detection work, which helps to achieve accurate positioning of monitoring points and orderly management of data.

In the node segmentation processing of the detection circuit, in order to distinguish and manage each segmentation point, it is marked with a serial number. The serial number marking or node identification serial number helps to quickly locate the specific node or segmentation point in the subsequent analysis, detection or fault diagnosis, thereby improving work efficiency and accuracy.

The current value of a detection node refers to the current size passing through a selected detection node, such as a key point or branch point, which is quantified and recorded by measuring equipment in a circuit system. The current size of the detection node can be directly obtained. By monitoring the current value of each node, it helps to promptly detect faults and abnormal conditions in the circuit.

The comprehensive voltage value of the detection node refers to the voltage value of the detection node during the data monitoring time, and the voltage is allowed to be within a certain deviation range. The comprehensive voltage value of the detection node is not an average value or instantaneous value, but a comprehensive value that comprehensively considers multiple factors such as voltage stability, volatility and deviation range, which can reflect the stability of the detection node voltage.

In the short-circuit diagnosis system, there is impedance characteristic between different nodes of the circuit. The above equivalent resistance is not equivalent to the resistance of the resistor element, but is the equivalent resistance when a short circuit is formed between different nodes in the circuit, which can reflect the comprehensive parameters of the circuit characteristics.

The circuit thermal effect impact index refers to a comprehensive indicator that measures the impact of thermal effects such as resistor heating and component temperature rise on circuit performance during circuit operation. The above index involves multiple factors, including but not limited to component temperature, circuit heat dissipation capability, and component thermal stability.

The total resistance of the short-circuit diagnosis system components refers to the sum of the resistance values of all components on the short-circuit path, that is, all supporting equipment.

The current negative phase sequence calculation formula in the data analysis module satisfies the following relationship:

in, Represents the detection node The negative phase sequence component of Indicates the monitoring location number, Indicates the node identification number. Represents the detection node The current value, Represents the detection node The comprehensive voltage value, Indicates the equivalent resistance of the short-circuit diagnostic system. It represents the thermal effect index. Indicates the total resistance of the short circuit diagnosis system components.

Among them, the asymmetric components of the three phases are decomposed into three groups of symmetrical components: positive sequence, negative sequence and zero sequence. The negative sequence current component is one of the three components, representing the symmetrical component in the three-phase current that is opposite to the positive phase sequence. The correctness of the phase sequence in the power system is crucial to the rotation direction of the motor, the normal operation of the transformer and the stability of the entire system. If the phase sequence is wrong, that is, the negative phase sequence, it will lead to consequences such as equipment reversal, overheating or damage.

Furthermore, the method of establishing different node current analysis functions in the present embodiment is only an optional condition of the present invention. One or some other embodiments may be adjusted according to the analysis objectives of the short-circuit diagnosis method and the system equipment conditions. As the system operating status changes, the parameters or structure of the analysis function are adjusted in real time to adapt to different short-circuit fault modes and system conditions, thereby ensuring the effective implementation of the short-circuit diagnosis method based on 4G Internet of Things.

Then, the data analysis module combines the current positive phase sequence calculation formula and the current negative phase sequence calculation formula to obtain the route current analysis model, and its implementation content is as follows:

The route current analysis model of this embodiment adopts a dual phase sequence analysis mechanism, that is, combining the calculation formulas of the positive phase sequence and the negative phase sequence of the current to construct an analysis model that can comprehensively and accurately detect the dynamic changes of the current at each node in the circuit.

The route current analysis model is based on two complementary calculation formulas: the positive phase sequence and the negative phase sequence of the current. It can accurately analyze the current changes of all nodes in the circuit, providing a solid data foundation for subsequent fault diagnosis and system optimization.

In order to achieve a comprehensive analysis of the current characteristics in the detection circuit, a current analysis strategy with full phase coverage is adopted in this embodiment, and a dual-phase sequence analysis model including current positive phase sequence and negative phase sequence calculation formulas is constructed, which can ensure accurate analysis and evaluation of currents at different nodes, and achieve a comprehensive, accurate and efficient analysis of the current characteristics of each node in the detection circuit.

Furthermore, the method for establishing the route current analysis model in this embodiment is only an optional condition of the present invention. One or some other embodiments can be replaced according to the short-circuit detection requirements and the actual circuit conditions. In the embodiments, the model structure is flexibly adjusted according to the specific short-circuit detection requirements and the actual circuit conditions to adapt to different actual application scenarios.

S3. Analyze the connection status of the circuit according to the current analysis results and node monitoring data. The specific implementation content is as follows:

The data analysis module in the short circuit diagnosis system analyzes the connection status of the circuit based on the current analysis results and node monitoring data. The specific contents are as follows:

In an optional embodiment, the node monitoring data and the current analysis results are important information reflecting the circuit connection status. The data analysis module will combine the analysis results of the positive and negative phase sequences and the node monitoring data to comprehensively analyze the connection status of the circuit to be detected.

First, based on the positive phase-sequence analysis results, the data analysis module can analyze the positive phase-sequence component in the current or voltage signal obtained from the detection node. The above positive phase-sequence component reflects the phase relationship that the current or voltage in the circuit to be detected should have under normal circumstances. By comparing with the benchmark value established by preset or standard data or historical data, the data analysis module can evaluate whether the positive phase-sequence state of the current circuit is normal, and whether there are signs of deviation from the normal range.

Then, based on the negative phase sequence analysis results, corresponding to the above positive phase sequence analysis, the data analysis module will further analyze the negative phase sequence components. If the negative phase sequence components appear, it means that the detection nodes in the circuit have an abnormal state, including but not limited to short circuit, reverse connection and other problems. The analysis of negative phase sequence components helps to identify potential fault sources and preliminarily determine the nature and location of the fault.

Next, the detection nodes need to be analyzed in combination with the node monitoring data. The monitoring data of each node includes but is not limited to environmental parameters such as temperature and humidity, which are important for judging the working status of circuit components and predicting potential failures. The data analysis module monitors the environmental parameters of the nodes in real time and compares them with the preset safety range to evaluate the operating status of each node or possible risks.

In addition to environmental parameters, node monitoring data also includes electrical characteristic parameters such as voltage, current, and resistance. Electrical parameters can reflect the working status and actual operating performance of system circuit components. The data analysis module analyzes the changing trends and relationships between electrical characteristic parameters to reveal possible faults or anomalies in the circuit.

The data analysis module combines the positive and negative phase sequence components with the node monitoring data to comprehensively analyze the circuit connection status. The data analysis module will conduct a comprehensive comparison and correlation analysis of the analysis results of the positive and negative phase sequence components with the node monitoring data. Based on this, a more comprehensive understanding of the circuit connection status, component working conditions and potential failure risks can be obtained.

Based on the results of the comprehensive analysis, the analysis module can transmit relevant analysis signals to the remote control module and the intelligent alarm module according to the analysis results, which is helpful for the subsequent judgment of the nature of the fault and the analysis of the severity, and is conducive to the intelligent alarm module to issue corresponding early warning information to guide the subsequent fault diagnosis and processing work.

Furthermore, the specific steps of analyzing the connection status of the circuit in the present embodiment are merely an optional condition of the present invention. In one or some other embodiments, the analysis method of the circuit connection status can be flexibly selected and adjusted according to the circuit detection status and the short-circuit diagnosis requirements. Different circuit types and fault modes require different analysis methods. Flexible adjustment of the analysis steps can reduce unnecessary analysis time, thereby improving the overall diagnostic efficiency of the short-circuit diagnosis method.

S4. A current amplitude prediction model is constructed based on the current analysis results and node monitoring data to obtain the current amplitudes of different nodes. The specific implementation contents are as follows:

In the data analysis module of the short-circuit diagnosis system, a current amplitude prediction model is constructed based on the above current analysis results and node monitoring data.

In an optional embodiment, the data analysis module receives the node monitoring data output by the data monitoring module, obtains the current phase difference of different detection nodes based on the node monitoring data, and constructs a current amplitude prediction model according to the current phase difference and current analysis results.

The data analysis module receives and analyzes the monitoring data of different nodes, and calculates the phase difference of the current between different detection nodes. The phase difference is an important indicator to measure the relative position of the current waveform on the time axis, and can reflect the synchronization or phase relationship between the currents of each node in the circuit.

The calculation result of the phase difference is of great significance for judging the connection status of the circuit to be tested, load distribution, and whether there are faults such as short circuit and open circuit. Under normal circumstances, the current phase in the same circuit will remain consistent or show a specific phase difference; when a short circuit fault occurs, the current phase before and after the fault point will change significantly.

Furthermore, in addition to detecting the current phase difference of the nodes, the data analysis module can also analyze the voltage data of different nodes. By comparing the voltage amplitude and phase relationship between different nodes, the accuracy of the current phase difference can be further verified, and possible voltage imbalance, voltage drop and other problems in the circuit can be revealed.

Then, a current amplitude prediction model is constructed in the data analysis module according to the current phase difference and current analysis results.

In the data analysis module, a current amplitude prediction model is constructed based on the current phase difference and the positive and negative phase sequence results of the current. The above model can predict the current amplitude of each node in the circuit, thereby providing a basis for the stable operation and daily maintenance of the circuit.

First, the received node monitoring data is cleaned to remove noise and outliers, and the current phase difference, current positive phase sequence component, negative phase sequence component and other possible related factors are used as input features. Then, it is necessary to analyze the influence of each input feature on the current amplitude and select the most representative feature for modeling. During the process, each parameter needs to be scaled, encoded or converted to ensure the effectiveness and accuracy of the model. Next, the data analysis module selects a suitable prediction model based on the attribute characteristics of the question parameters and the distribution of the data, including but not limited to linear regression, decision tree, random forest, gradient boosting tree, neural network, etc. Furthermore, considering that the current amplitude is affected by many factors and there are complex nonlinear relationships between different factors, models such as neural networks or gradient boosting trees can be selected for optimization.

In the embodiment, the current amplitude prediction model is trained using a training data set, and the model parameters are adjusted to optimize the prediction performance. On the other hand, the trained model is evaluated using a validation data set to check its performance indicators such as prediction accuracy and generalization ability, and the model is tuned according to the evaluation results, including but not limited to adjusting the model structure, parameters, feature selection, etc. In addition, cross-validation is required to further verify the stability and reliability of the model.

Finally, the trained current amplitude prediction model is deployed to the data analysis module. This module can receive the node monitoring data of the data monitoring module in real time, and use the current amplitude prediction model to predict the current amplitude of different nodes. In the embodiment, the current amplitude prediction model is constructed in the data analysis module, which can provide accurate guidance for the stable operation of the circuit and the emergency repair of faults.

The current amplitude prediction model in the data analysis module satisfies the following relationship:

in, Represents the detection node The current amplitude, Represents the detection node The current value, Represents the detection node The current value, Represents the detection node Current phase difference.

Among them, the current amplitude of the detection node refers to the maximum absolute value of the alternating current that appears instantaneously at a specific detection point in the circuit, i.e., the node. It is also half the distance from the peak to the trough in a sine wave, or the maximum value that the signal can reach in one cycle. The current amplitude is one of the important parameters that describe the characteristics of the alternating current waveform, and is of great significance for understanding the operating status of the circuit, diagnosing circuit faults, and optimizing circuit design.

The current value of the detection node refers to the magnitude of the current passing through the node at a specific detection point in the circuit, i.e., the node. The current value can be a constant value of direct current or an instantaneous value of alternating current at a certain moment. In an AC circuit, since the magnitude and direction of the current will change over time, the current value of the detection node usually refers to the average value, effective value, peak value, or peak-to-peak value of the current at a certain moment or in a certain cycle. In practical applications, the corresponding current value can be selected to describe the current characteristics of the detection node according to the circuit conditions and monitoring requirements. For example, the effective value of the current is required in the monitoring of the power system, which can reflect the load conditions and power consumption of the circuit; in some cases of controlling the current waveform, parameters such as the instantaneous value or peak value of the current are required.

In the short-circuit diagnosis system based on 4G Internet of Things of the present invention, the data analysis module uses the route current analysis model to obtain the positive phase sequence component results and the negative phase sequence component results of different nodes; then, the data analysis module obtains the current analysis results of different nodes based on the positive phase sequence component results and the negative phase sequence component results.

The data analysis module uses the route current analysis model to obtain the positive phase sequence component results and negative phase sequence component results of different nodes. The phase sequence of current and voltage during circuit detection is an important parameter for analyzing circuit operation status and fault conditions. The positive phase sequence and negative phase sequence represent the order in which the current or voltage reaches the maximum value, which helps to understand the energy flow and phase relationship in the circuit to be detected.

The route current analysis model can calculate the current components of each node in the circuit, including positive phase sequence components and negative phase sequence components, reflecting the phase information of the current, which is helpful for understanding the overall operating status and fault conditions of the circuit to be detected.

After receiving the data, the data analysis module uses the route current analysis model to calculate the positive phase sequence component and negative phase sequence component of each node. After the calculation is completed, the data analysis module transmits the results to the remote control module. At the same time, the remote control module will display the analysis results in the form of charts, numbers, etc., which helps to understand the current distribution, phase relationship and possible fault conditions of each node in the circuit. In addition, in addition to the positive phase sequence component and the negative phase sequence component, the data analysis module can also use the effective value, peak value, power factor, etc. of the route current to comprehensively analyze the operating status of the circuit and perform fault diagnosis.

S5. Analyze the circuit fault location based on the current amplitude, and start the circuit emergency plan in combination with the circuit fault location. The specific implementation steps and contents are as follows:

In an optional embodiment, the data analysis module obtains the current amplitude of the detection node according to the current amplitude prediction model. The data analysis module calculates the current peak value of a specific detection node in the circuit at a certain moment or cycle through the current amplitude prediction model, that is, the maximum absolute value of the current.

Based on historical data and real-time monitoring information, the data analysis module accurately predicts the amplitude range of current fluctuations at the detection node, providing powerful information for circuit operation analysis. By deeply analyzing the inherent laws and changing trends of current data, it achieves accurate prediction of the current amplitude at the detection node, providing a scientific diagnostic basis for short-circuit diagnosis methods.

The remote control module will promptly receive the analysis results output by the data analysis module, and analyze the circuit fault location based on the current amplitude and circuit monitoring data. The remote control module can also intelligently plan the emergency plan based on the circuit fault location, current amplitude and short-circuit diagnosis system and automatically start the circuit emergency plan.

The remote control module has a high degree of integration and intelligent response capabilities. It can instantly receive relevant data information from the data monitoring module and the data analysis module, including but not limited to the real-time analysis results of the current amplitude, the current data of different nodes and the circuit temperature monitoring information, etc. Based on the relevant data, the remote control module can accurately identify and locate the fault point in the circuit to be detected. Furthermore, the remote control module is not limited to circuit fault identification, it can also analyze the specific location of the circuit fault, the abnormal degree of the current amplitude, and timely and intelligently plan the optimal emergency treatment plan.

Based on the system analysis results, the circuit conditions to be detected and the circuit characteristics, the circuit emergency plan is determined. Once the emergency plan is determined, the remote control module will automatically remind relevant personnel to start and execute the corresponding circuit emergency plan, thereby achieving rapid response and efficient processing of circuit faults. The above-mentioned automated emergency response mechanism not only shortens the fault processing and recovery time, but also significantly improves the overall stability and intelligence of the short-circuit diagnosis system based on 4G Internet of Things of the present invention.

The intelligent alarm module in the short-circuit diagnosis system based on 4G Internet of Things will receive the information output by the remote control module, and send out an alarm signal based on the received information to remind relevant personnel to pay attention. The specific contents are as follows:

As one of the core components of the short-circuit diagnosis system based on the 4G Internet of Things, the intelligent alarm module can receive information output by the remote control module. If it receives key indicator information such as short circuit, overload, abnormal fluctuation, etc. of the circuit to be detected, the intelligent alarm module will start immediately and send out alarm signals in a variety of ways.

The intelligent alarm module, in addition to the traditional sound and light alarm, also supports sending instant alarm notifications to a preset mobile phone number, email or mobile application via the 4G network. The notification can contain detailed fault descriptions, fault locations and necessary emergency contact information to help relevant personnel quickly understand the situation and take circuit repair measures. In the embodiment, the intelligent alarm module in the short-circuit diagnosis system based on the 4G Internet of Things contributes to the effective implementation and actual operation of short-circuit diagnosis.

Furthermore, the assembly method of the short-circuit diagnostic system based on 4G Internet of Things in this embodiment is only an optional condition of the present invention. In one or some other embodiments, the specific equipment of the short-circuit diagnostic system can be adjusted and replaced according to the specific diagnostic situation and circuit detection requirements. Different circuit environments and application scenarios have different diagnostic difficulties and special requirements. By flexibly adjusting the system equipment, it can be ensured that the short-circuit diagnostic method can adapt to various complex and changeable circuit environments, thereby improving the overall applicability and practicality of the short-circuit diagnostic method.

Please refer to Figure 3. In an optional embodiment, in order to efficiently execute the short-circuit diagnosis method based on 4G Internet of Things provided by the present invention, the present invention also provides a short-circuit diagnosis system based on 4G Internet of Things, wherein the system includes a power control module, a data monitoring module, a data analysis module, a remote control module, and an intelligent alarm module, wherein the power control module, the data monitoring module, the data analysis module, the remote control module, and the intelligent alarm module are interconnected to implement the specific steps of the relevant embodiments of the short-circuit diagnosis method based on 4G Internet of Things provided by the present invention. The short-circuit diagnosis system based on 4G Internet of Things of the present invention has a complete structure, is objective and stable, and can efficiently execute the short-circuit diagnosis method based on 4G Internet of Things of the present invention, thereby improving the overall applicability and practical application capability of the present invention.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims (5)

1. A short circuit diagnosis method based on 4G Internet of Things, characterized in that the method comprises: Obtaining circuit monitoring data using a short circuit diagnosis system, and obtaining node monitoring data of different nodes of the circuit based on the circuit monitoring data; Establishing a route current analysis model in combination with the short circuit diagnosis system and the node monitoring data to obtain current analysis results of different nodes; Analyze the connection status of the circuit according to the current analysis result and the node monitoring data; Constructing a current amplitude prediction model based on the current analysis result and the node monitoring data to obtain the current amplitudes of different nodes; Analyze the circuit fault location based on the current amplitude, and initiate a circuit emergency plan in combination with the circuit fault location; The establishing of a route current analysis model in combination with the short circuit diagnosis system and the node monitoring data comprises: Constructing a current positive phase sequence calculation formula and a current negative phase sequence calculation formula according to the short circuit diagnosis system and the node monitoring data; Combining the current positive phase sequence calculation formula and the current negative phase sequence calculation formula to obtain a route current analysis model; The route current analysis model satisfies the following relationship: , in, Represents the detection node The positive phase sequence component of Indicates the monitoring location number, Indicates the node identification number. Represents the detection node The current value, Represents the detection node The comprehensive voltage value, Indicates the equivalent resistance of the short-circuit diagnostic system. Indicates the circuit thermal effect index, Indicates the total resistance of the short-circuit diagnostic system components; The current negative phase sequence calculation formula satisfies the following relationship: , in, Represents the detection node The negative phase sequence component of Indicates the monitoring location number, Indicates the node identification number. Represents the detection node The current value, Represents the detection node The comprehensive voltage value, Indicates the equivalent resistance of the short-circuit diagnostic system. It represents the thermal effect index. Indicates the total resistance of the short-circuit diagnostic system components; The constructing of the current amplitude prediction model according to the current analysis result and the node monitoring data comprises: Obtaining current phase difference values of different detection nodes based on the node monitoring data; The constructing of the current amplitude prediction model according to the current analysis result and the node monitoring data comprises: Constructing a current amplitude prediction model according to the current phase difference and the current analysis result; The current amplitude prediction model satisfies the following relationship: , in, Represents the detection node The current amplitude, Represents the detection node The current value, Represents the detection node The current value, Represents the detection node Current phase difference. 2. The short circuit diagnosis method based on 4G Internet of Things according to claim 1 is characterized in that the circuit monitoring data is obtained by using a short circuit diagnosis system, and the node monitoring data of different nodes of the circuit are obtained according to the circuit monitoring data, comprising: Performing node segmentation processing on the detection circuit based on the short-circuit diagnosis system and the circuit monitoring data to obtain different nodes of the detection circuit; Node monitoring data of different nodes of the circuit are obtained according to the different nodes and the circuit monitoring data. 3. The short circuit diagnosis method based on 4G Internet of Things according to claim 1 is characterized in that the establishing of a route current analysis model in combination with the short circuit diagnosis system and the node monitoring data to obtain current analysis results of different nodes comprises: The route current analysis model is used to obtain positive phase sequence component results and negative phase sequence component results of different nodes; The current analysis results of different nodes are obtained according to the positive phase sequence component result and the negative phase sequence component result. 4. The short circuit diagnosis method based on 4G Internet of Things according to claim 1 is characterized in that the circuit fault location is analyzed based on the current amplitude, and the circuit emergency plan is started in combination with the circuit fault location, comprising: Obtaining the current amplitude of the detection node through the current amplitude prediction model; analyzing a circuit fault location in combination with the current amplitude and the circuit monitoring data; A circuit emergency plan is planned and initiated in combination with the circuit fault location, the current amplitude and the short circuit diagnosis system. 5. A short-circuit diagnostic system based on 4G Internet of Things, characterized in that the short-circuit diagnostic system based on 4G Internet of Things includes a power control module, a data monitoring module, a data analysis module, a remote control module and an intelligent alarm module, and the system executes the short-circuit diagnostic method based on 4G Internet of Things as described in any one of claims 1-4.