Ship bus duct electrical performance detection method and system

Abstract

The invention discloses a ship bus duct electrical performance detection method and system, and relates to the technical field of ship electrical appliances, and the method comprises the steps: a first detection assembly detects an initial electrical performance index of a target bus duct according to a preset static strategy, and activates a second detection assembly when the index reaches a limit value; and the edge data processor performs dynamic operation to detect real-time operation state information, analyzes the information to obtain a real-time state feature set, inputs the real-time state feature set into the electrical performance evaluation model to obtain a real-time electrical performance index, and stops detection of the target bus duct if the real-time index does not reach a limit value. The technical problems that in the prior art, ship bus duct electrical performance detection is not comprehensive and inaccurate, and real-time performance and intelligence are insufficient are solved, and the technical effects that comprehensive, real-time, accurate and intelligent detection and evaluation of the ship bus duct electrical performance are achieved, and the safety and reliability of a ship electrical system are improved are achieved.

Description

Technical Field

[0001] The present application relates to the field of ship electrical technology, and in particular to a method and system for detecting the electrical performance of a ship bus duct. Background Art

[0002] In the scenario of ship bus duct electrical performance testing, the need for accurate testing of the bus duct electrical performance is extremely critical, and the contradiction between testing resources and actual needs is becoming increasingly significant. How to efficiently and reasonably allocate testing resources to better meet the needs of comprehensive bus duct testing has become a key issue in achieving effective electrical performance testing. Traditional ship bus duct electrical performance testing is often one-sided and limited. It relies only on a single testing method or a small amount of testing equipment, and the control of the overall electrical performance of the bus duct is not comprehensive enough. There is a lack of in-depth mining and application of test data, making it difficult to accurately assess the electrical performance risk status of the bus duct under different conditions. There are irrational aspects in the allocation of testing resources, resulting in insufficient testing of certain key parts and redundant testing resources in certain parts. The formulation of testing plans is relatively simple and rigid, and cannot effectively adapt to the ever-changing actual electrical performance conditions of ship bus ducts.

[0003] At present, relevant technologies still have technical problems such as incomplete and inaccurate detection of the electrical performance of ship bus ducts, lack of real-time performance and intelligence. Summary of the Invention

[0004] The present application provides a method and system for detecting the electrical performance of a ship bus duct, which uses a first detection component to detect the initial electrical performance index of the target bus duct according to a predetermined static strategy. When the index reaches a limit, the second detection component is activated, which performs dynamic operation detection to obtain real-time operation status information. The edge data processor analyzes the real-time status feature set of the information and inputs it into the real-time electrical performance index of the electrical performance evaluation model (the model is based on neural network training). If the real-time index does not reach the limit, the target bus duct is stopped for inspection, thereby achieving the technical effect of realizing comprehensive, real-time, accurate and intelligent detection and evaluation of the electrical performance of the ship bus duct, and improving the safety and reliability of the ship's electrical system.

[0005] The present application provides a method for detecting the electrical performance of a ship bus duct, comprising:

[0006] The first detection component performs electrical performance detection on the target bus duct based on a predetermined static detection strategy to obtain a target initial electrical performance index; when the target initial electrical performance index reaches the electrical performance index limit, the second detection component is activated; the target bus duct is dynamically operated by the second detection component to obtain target real-time operation status information; the target real-time operation status information is analyzed by an edge data processor to obtain a real-time status feature set, wherein the edge data processor is communicatively connected to the second detection component; the real-time status feature set is input into an electrical performance evaluation model to obtain a target real-time electrical performance index, and the electrical performance evaluation model is an intelligent model trained based on the principle of neural network; when the target real-time electrical performance index is not within the electrical performance index limit, the target bus duct is stopped for inspection.

[0007] The present application also provides a ship bus duct electrical performance detection system, comprising:

[0008] An electrical performance index acquisition module, wherein the electrical performance index acquisition module is used by the first detection component to perform electrical performance detection on the target bus duct based on a predetermined static detection strategy to obtain a target initial electrical performance index; a detection component activation module, wherein the detection component activation module is used to activate the second detection component when the target initial electrical performance index reaches the electrical performance index limit; an operation status information acquisition module, wherein the operation status information acquisition module is used to perform dynamic operation detection on the target bus duct through the second detection component to obtain target real-time operation status information; a real-time status feature set acquisition module, wherein the real-time status feature set acquisition module is used to analyze the target real-time operation status information through an edge data processor to obtain a real-time status feature set, wherein the edge data processor is communicatively connected to the second detection component; an electrical performance index acquisition module, wherein the electrical performance index acquisition module is used to input the real-time status feature set into an electrical performance evaluation model to obtain a target real-time electrical performance index, wherein the electrical performance evaluation model is an intelligent model trained based on the principle of neural network; and a stop inspection processing module, wherein the stop inspection processing module is used to stop inspection processing on the target bus duct when the target real-time electrical performance index is not within the electrical performance index limit.

[0009] A method and system for detecting the electrical performance of a ship bus duct proposed in this application is to first detect the initial electrical performance index of the target bus duct according to a predetermined static strategy. If the index reaches the limit, the second detection component is activated, which performs dynamic operation detection to obtain real-time operation status information. The edge data processor analyzes the real-time status feature set of the information and inputs it into the real-time electrical performance index of the electrical performance evaluation model (the model is based on neural network training). If the real-time index does not reach the limit, the target bus duct is stopped for inspection, thereby achieving the technical effect of realizing comprehensive, real-time, accurate and intelligent detection and evaluation of the electrical performance of the ship bus duct and improving the safety and reliability of the ship's electrical system. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0011] Figure 1 A schematic flow chart of a method for detecting the electrical performance of a ship bus duct provided in an embodiment of the present application;

[0012] Figure 2 A schematic structural diagram of a ship bus duct electrical performance detection system provided in an embodiment of the present application.

[0013] Explanation of the reference numerals: electrical performance index acquisition module 10 , detection component activation module 20 , operation status information acquisition module 30 , real-time status feature set acquisition module 40 , electrical performance index acquisition module 50 , and inspection stop processing module 60 . DETAILED DESCRIPTION

[0014] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0015] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0016] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or that are inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0017] The present application provides a method for detecting the electrical performance of a ship bus duct. Figure 1 As shown, the method includes:

[0018] In step S100, the first detection component performs an electrical performance test on the target bus duct based on a predetermined static detection strategy to obtain a target initial electrical performance index.

[0019] Specifically, the predetermined static detection strategy refers to disconnecting the power supply of the target bus duct before performing an electrical performance test on the target bus duct. Based on this strategy, the first detection component selects the target bus duct, prepares relevant detection instruments, such as a non-contact voltage detector, a digital insulation resistance meter, and a digital multimeter, performs electrical performance testing, and obtains the static voltage data, insulation resistance data, and conductivity data of the target bus duct. The static voltage data represents the voltage condition of the target bus duct when the power supply is disconnected, the insulation resistance data represents the insulation performance of the target bus duct, and the conductivity data represents the conductive state of the target bus duct. Subsequently, these detection data are analyzed and processed. When the target static voltage meets the static limit constraint, the normalized static voltage data, insulation resistance data, and conductivity data are weightedly calculated to obtain the target initial electrical performance index, which reflects the electrical performance condition of the target bus duct under static testing.

[0020] In one possible implementation, the first detection component performs an electrical performance test on the target bus duct based on a predetermined static detection strategy to obtain a target initial electrical performance index. Step S100 further includes step S110, and the predetermined static detection strategy refers to disconnecting the power supply of the target bus duct before performing an electrical performance test on the target bus duct. Specifically, according to the characteristics and detection requirements of the target bus duct, the power disconnection operation is determined. The power disconnection is a key step that must be performed before performing an electrical performance test on the target bus duct. Only after this operation is completed can the test be officially started. Similarly, the predetermined static detection strategy on which the test is based is determined, that is, the specific rules and methods followed when implementing the test. A judgment criterion for the completion of the test is set. The judgment criterion for the completion of the test is that the test process is considered to be completed when the target initial electrical performance index is obtained.

[0021] In a possible implementation, step S100 further includes that the first detection component includes a non-contact voltage detector, a digital insulation resistance meter, and a digital multimeter. Specifically, the instruments included in the first detection component are clearly divided into a non-contact voltage detector, a digital insulation resistance meter, and a digital multimeter. The functions of these three instruments are recorded to form non-contact voltage detector function record data, digital insulation resistance meter function record data, and digital multimeter function record data. Among them, the non-contact voltage detector function record data is a function record corresponding to the voltage detection of the target bus duct using a non-contact voltage detector; the digital insulation resistance meter function record data is a function record corresponding to the insulation resistance detection of the target bus duct using a digital insulation resistance meter; and the digital multimeter function record data is a function record corresponding to the detection of multiple electrical parameters of the target bus duct using a digital multimeter.

[0022] The method further includes step S120, wherein the non-contact voltage detector is used to detect and obtain the target static voltage of the target bus duct. Specifically, the non-contact voltage detector is placed close to the target bus duct to begin detecting and analyzing the electric field around the bus duct. The detector collects and processes relevant data information and converts the detected electric field signal into a specific voltage value. Over time, the detector continuously optimizes and calibrates the obtained voltage data to improve accuracy and reliability. After a period of detection and analysis, the non-contact voltage detector successfully detects the target static voltage of the target bus duct and feeds this key data back to the detection personnel or the relevant data processing system for subsequent further analysis, evaluation, and decision-making.

[0023] Step S130: Detecting the target insulation resistance of the target bus duct using the digital insulation resistance meter. Specifically, the target bus duct information input by the detection terminal is received, and the insulation resistance of the target bus duct is detected using an insulation resistance detection network. Based on the detection results, a specific detection algorithm (such as a correlation detection algorithm) is used for analysis and processing. After multiple operations, the target insulation resistance of the target bus duct is obtained.

[0024] Step S140: Target conductivity of the target bus duct is obtained by testing with the digital multimeter. Specifically, target bus duct information inputted by the detection terminal is received, and the target bus duct is subjected to a conductivity detection network for conductivity detection. Based on the detection results, a specific analysis algorithm (such as a correlation analysis algorithm) is used for analysis and processing. Based on the analysis results and the conductivity requirements of the target bus duct, a corresponding optimization algorithm (such as a progressive optimization algorithm) is used for optimization and adjustment. After multiple operations, a target conductivity result that meets the conductivity requirements of the target bus duct is generated.

[0025] Step S150: When the target static voltage meets the static limit constraint, a weighted calculation is performed on the normalized target static voltage, the target insulation resistance, and the target conductivity to obtain the target initial electrical performance index. Specifically, when the target static voltage meets the static limit constraint, a weighted calculation is performed on the normalized target static voltage, the target insulation resistance, and the target conductivity to determine the weighting coefficient. The selection of the weighting coefficient should be evaluated based on the specific situation to reflect the importance of each parameter in the electrical performance evaluation and perform a weighted calculation. The normalized target static voltage, target insulation resistance, and target conductivity are multiplied by the corresponding weighting coefficients, and the sum is calculated to obtain the target initial electrical performance index. The weighted calculation results are added together to obtain the target initial electrical performance index. Taking into account factors such as the target static voltage, target insulation resistance, and target conductivity, an index reflecting the electrical performance of the target bus duct is obtained. The index can be used to evaluate the performance status of the bus duct and provide a basis for subsequent maintenance and decision-making.

[0026] Step S200: When the target initial electrical performance index reaches the electrical performance index limit, activating the second detection component.

[0027] Specifically, the target initial electrical performance index is monitored. When it is detected that the target initial electrical performance index has reached the preset electrical performance index limit, the system will issue an instruction to activate the second detection component. When the second detection component is activated, the relevant circuit or signal path is connected to provide the second detection component with the energy and control signal required for operation, and further conduct more in-depth measurement or analysis of certain specific electrical parameters of the target bus duct, and feed back the detected data to the system in real time so that the system can grasp the relevant situation in a timely manner.

[0028] In one possible implementation, when the target initial electrical performance index reaches the electrical performance index limit, the second detection component is activated, and step S200 further includes the second detection component including a digital storage oscilloscope and a handheld infrared thermal imager. Specifically, the data involved in the digital storage oscilloscope and the data involved in the handheld infrared thermal imager in the second detection component are distinguished and sorted, and the data of the digital storage oscilloscope and the data of the handheld infrared thermal imager are recorded to form digital storage oscilloscope recorded data and handheld infrared thermal imager recorded data. The digital storage oscilloscope recorded data is a record of the relevant electrical signals obtained by the digital storage oscilloscope when detecting the target bus duct; the handheld infrared thermal imager recorded data is a record of the temperature distribution image, etc. formed when the handheld infrared thermal imager detects the surface temperature of the target bus duct.

[0029] The method further includes step S210, wherein the target waveform of the target bus duct is detected by the digital storage oscilloscope, and the target waveform includes a target voltage waveform and a target current waveform. Specifically, the digital storage oscilloscope is correctly connected to the target bus duct, ensuring that the probe of the oscilloscope is in close contact with the measuring point of the bus duct to ensure accurate signal transmission, the digital storage oscilloscope is turned on and relevant parameters are set, and according to the characteristics of the target bus duct and the expected measurement range, the vertical sensitivity, horizontal time base and other parameters of the oscilloscope are adjusted to obtain a clear and appropriately proportional waveform display, and the digital storage oscilloscope begins to monitor the target bus duct in real time, receives the voltage and current signals from the bus duct through the probe, and converts these signals into electrical signals for processing and storage. During the monitoring process, the digital storage oscilloscope is used to monitor the target bus duct in real time, receives the voltage and current signals from the bus duct through the probe, and converts these signals into electrical signals for processing and storage. The digital storage oscilloscope continuously collects the instantaneous values ​​of voltage and current and arranges them in chronological order to form the target voltage waveform and target current waveform. As time goes by, the oscilloscope continuously records and updates the target waveform, and displays the target voltage waveform and target current waveform on the screen in real time so that the operator can intuitively observe the changes in voltage and current. During the entire detection process, the digital storage oscilloscope collects and processes data with high precision and high speed to ensure that accurate target waveforms are obtained. The target waveform data can provide an important basis for evaluating the electrical performance of the target bus duct and fault diagnosis.

[0030] In step S220, the target temperature time series of the target bus duct is detected by the handheld infrared thermal imager. Specifically, the relevant parameters of the handheld infrared thermal imager, such as the temperature measurement range and resolution, are appropriately adjusted based on factors such as the approximate temperature range of the target bus duct. The lens of the handheld infrared thermal imager is aimed at the target bus duct while maintaining an appropriate distance and angle. The infrared rays emitted from the surface of the target bus duct are continuously received and converted into temperature data. The temperature data of each part of the target bus duct is recorded in chronological order to form a target temperature time series. The detected target temperature time series is stored. The target temperature time series data can be used to evaluate the temperature changes of the target bus duct, whether there are abnormally high temperature areas, etc., providing an important basis for judging the operating status and potential problems of the bus duct.

[0031] In step S230, the target voltage waveform, the target current waveform, and the target temperature time series constitute the target real-time operating status information. Specifically, the target voltage waveform and target current waveform of the target bus duct are detected by a digital storage oscilloscope, and the relevant parameters are set to obtain clear and appropriately proportioned waveforms. The target temperature time series of the target bus duct is detected using a handheld infrared thermal imager, and the instrument parameters are adjusted according to factors such as the temperature range. The temperature data is accurately scanned and recorded, and the obtained target voltage waveform, target current waveform, and target temperature time series are summarized and integrated. The summarized information together constitutes the target real-time operating status information, which reflects the important status characteristics of the target bus duct at a specific moment, such as voltage, current, and temperature. The target real-time operating status information can be used to further analyze and evaluate the operating status, performance, and potential problems of the target bus duct.

[0032] Step S300: Perform dynamic operation detection on the target bus duct through the second detection component to obtain target real-time operation status information.

[0033] Specifically, prepare the second detection component, ensure that it is in normal working condition and the relevant parameters have been appropriately set according to actual needs, connect the second detection component to the target bus duct or place it in a suitable detection position, start the second detection component, and let it perform dynamic operation detection on the target bus duct. The second detection component will collect relevant information of the target bus duct in real time during operation, such as voltage, current, temperature and other specific data. The second detection component continuously performs detection and data collection, organizes and records the data in chronological order, and forms the target real-time operation status information. The second detection component stores the complete target real-time operation status information to provide important data support for evaluating the operating status, performance and potential problems of the target bus duct.

[0034] Step S400: Analyze the target real-time operation status information through an edge data processor to obtain a real-time status feature set, wherein the edge data processor is communicatively connected to the second detection component.

[0035] Specifically, ensure that a stable communication connection is established between the edge data processor and the second detection component so that data can be received smoothly. The second detection component transmits the detected target real-time operation status information to the edge data processor in real time. The edge data processor analyzes the received target real-time operation status information, and uses specific algorithms and logic to process information such as voltage waveforms, current waveforms, and temperature time series to extract key features in the target real-time operation status information, such as the voltage fluctuation range, current change trend, temperature peak, etc. The key features are integrated to form a real-time status feature set. The feature set summarizes the various current status features of the target bus duct. The edge data processor completes the analysis of the target real-time operation status information and obtains a complete real-time status feature set, which provides an important basis and foundation for subsequent further evaluation, judgment, and decision-making.

[0036] In one possible implementation, the target real-time operation status information is analyzed by an edge data processor to obtain a real-time status feature set, wherein the edge data processor is in communication with the second detection component, and step S400 further includes step S410, wherein the waveform processing layer in the edge data processor collects features of the target voltage waveform to obtain target waveform component features of the target voltage component. Specifically, the waveform processing layer in the edge data processor receives and processes the target voltage waveform, analyzes the shape, amplitude, frequency, etc. of the waveform, identifies and marks key nodes on the waveform, such as peaks, troughs, turning points, etc., extracts and records relevant information of the key nodes, including specific values, time points of occurrence, etc., collects various features reflected by the target voltage component on the waveform, and forms target waveform component features of the target voltage component. The feature set summarizes important feature information related to the target voltage component in the target voltage waveform, which can be used for subsequent further analysis and application.

[0037] Step S420, the target temperature time series is collected through the timing processing layer in the edge data processor to obtain the target temperature characteristics. Specifically, the timing processing layer in the edge data processor establishes a connection with the data channel of the target temperature time series, analyzes the data in the target temperature time series one by one, pays attention to the trend of temperature change over time, such as the stage of temperature rise, fall or stability, identifies the temperature peak, valley and temperature fluctuation within a specific time period, extracts and quantifies the obtained characteristics, records the slope, change rate and other information of the temperature change trend, and accurately records the specific values ​​and corresponding time points of the peak and valley values, collects various key characteristics of the target temperature time series, such as the main temperature change mode, special temperature nodes, etc., integrates the collected characteristics to form the target temperature characteristics, reflects the core characteristics of the target temperature time series, and provides an important basis for the subsequent evaluation and decision-making of the bus duct temperature status.

[0038] In step S430, the target waveform component features and the target temperature features are combined into the real-time state feature set. Specifically, the waveform processing layer in the edge data processor collects features of the target voltage waveform to obtain the target waveform component features. The timing processing layer collects features of the target temperature time series to obtain the target temperature features. The features of these two parts are combined, and the voltage waveform-related feature information contained in the target waveform component features, such as specific voltage fluctuation patterns and key voltage values, is matched and integrated with the temperature change features in the target temperature features, such as temperature fluctuation range and peak temperature time. This ensures that each feature is accurately placed in the corresponding position in the real-time state feature set. This involves arranging them according to a specific logical order or classification method. With the orderly combination of all target waveform component features and target temperature features, a complete real-time state feature set is finally formed. This set covers the key features of the target bus duct in terms of voltage and temperature, accurately and comprehensively reflects the current real-time state of the bus duct, and provides an important and rich data foundation for subsequent analysis, evaluation, and decision-making.

[0039] In one possible implementation, the waveform processing layer in the edge data processor collects features of the target voltage waveform to obtain target waveform component features of the target voltage component. Step S410 further includes step S411, in which the waveform processing layer performs modal decomposition on the target voltage waveform to obtain a target voltage decomposition result, wherein the target voltage decomposition result includes multiple voltage components. Specifically, the waveform processing layer prepares to process the target voltage waveform. The waveform processing layer imports the data of the target voltage waveform into a modal decomposition calculation model. The model will perform a detailed analysis and disassembly of the target voltage waveform according to the set rules and methods. During the decomposition process, the model will gradually decompose the target voltage waveform into multiple different parts, each of which represents a specific voltage component. These voltage components have their own unique characteristics, such as frequency and amplitude. The model continuously extracts and separates these voltage components, and organizes and summarizes them into target voltage decomposition results. The results contain multiple different voltage components, each with a clear definition and attributes. The voltage components together constitute a detailed decomposition and description of the target voltage waveform. The waveform processing layer outputs the target voltage decomposition results for subsequent further analysis and utilization of the multiple voltage components obtained by these decompositions to obtain more in-depth information and characteristics about the target voltage waveform.

[0040] In step S412, a component weight evaluation function is introduced to evaluate and screen the multiple voltage components to obtain the target voltage component, which refers to the voltage component with the largest weight among the multiple voltage components. Specifically, the multiple voltage components obtained are input into the component weight evaluation function one by one, and each voltage component is analyzed. During the evaluation process, various attributes of the voltage component may be considered, such as amplitude, frequency of occurrence, degree of influence on the overall waveform, and other factors. The evaluation function calculates a corresponding weight value for each voltage component. As all voltage components are evaluated, their corresponding weight values ​​are obtained. These weight values ​​are compared and screened, and the voltage component with the largest weight value is found among the numerous voltage components, and is determined as the target voltage component. The target voltage component is considered to have the most critical and important position in the current situation. The target voltage component obtained after evaluation and screening from the multiple voltage components represents the most representative or influential voltage component under specific conditions, providing an important basis for subsequent further analysis and decision-making.

[0041] In a possible implementation, step S410 further includes step S413, wherein the waveform processing layer collects features of the target current waveform to obtain target current component features of the target current component. Specifically, the waveform processing layer establishes a connection with the data of the target current waveform and begins to receive relevant information, analyzes the features of the target current waveform, such as the shape of the waveform, the change in amplitude, the period, etc., and uses a special feature extraction technology to identify and mark key feature points on the waveform, such as the peak value, valley value, zero crossing point, etc. of the current. For these identified feature points, relevant feature information is further extracted, such as the specific value of the peak value, the depth of the valley value, the time interval between adjacent zero crossing points, etc. The feature information reflects the specific performance of the target current component on the waveform. As the analysis continues to deepen, various feature information of the target current component is continuously collected and accumulated, including the trend of current change, the law of fluctuation, etc. All the collected target current component feature information is sorted and summarized to form the target current component features of the target current component.

[0042] Step S414: The mean of the target current component characteristic and the target voltage component characteristic of the target voltage component is used as the target waveform component characteristic. Specifically, the target current component characteristic and the target voltage component characteristic are clearly obtained, the mean of the target voltage component characteristic is calculated, the target current component characteristic is associated with the calculated mean, and the result of this association is defined as the target waveform component characteristic, that is, the target waveform component characteristic is equal to the combination of the mean of the target current component characteristic and the target voltage component characteristic. In this way, we obtain the target waveform component characteristic that comprehensively considers the current and voltage characteristics, which provides an important data foundation for further analysis and application.

[0043] In one possible implementation, a component weight evaluation function is introduced to evaluate and screen the multiple voltage components to obtain the target voltage component, where the target voltage component refers to the voltage component with the largest weight among the multiple voltage components. Step S412 further includes step S4121. The component weight evaluation function is expressed as follows:

[0044]

[0045] Wherein, w(x) represents the weight of the xth modal component in the multiple voltage components, amount(x) represents the characteristic information of the xth modal component, amount(X) represents the total characteristic information of the target voltage waveform, cor(x) represents the correlation coefficient between the xth modal component and the target voltage waveform, α and β are the first coefficient and the second coefficient respectively, and α+β=1. Specifically, the expression of the component weight evaluation function is clarified x represents the xth modal component among multiple voltage components. For each voltage component, amount(X) is calculated, that is, the amount of characteristic information contained in the xth modal component is determined, which involves analyzing and quantifying the specific characteristics of the modal component. To determine amount(X), that is, the total characteristic information of the entire target voltage waveform, it is necessary to comprehensively consider all relevant characteristic information of the target voltage waveform to obtain a total amount, and calculate cor(x), that is, to find the correlation coefficient value between the xth modal component and the target voltage waveform, which reflects the degree of correlation between the modal component and the overall waveform. The calculated values ​​of amount(X), amount(X) and cor(x) are substituted into the component weight evaluation function, and according to the given first coefficient α and second coefficient β (and satisfying α+β=1), they are respectively calculated. Finally, these two values ​​are added together to obtain w(x), the weight of the x-th modal component. By repeating this process for all voltage components, the weight of each modal component can be obtained, thus completing the weight evaluation of multiple voltage components and screening the target voltage component. Throughout this process, the values ​​of α and β have a significant impact on the weight calculation and need to be reasonably set according to the specific situation and needs.

[0046] In one possible implementation, the target temperature time series is subjected to feature collection by the timing processing layer in the edge data processor to obtain target temperature features, and step S420 further includes step S421, in which the target temperature time series is subjected to time domain feature collection by the timing processing layer to obtain target temperature time domain features. Specifically, the timing processing layer processes the target temperature time series, obtains data of the target temperature time series, and extracts time domain features, such as calculating the time interval between two adjacent temperature values, calculating the rate of change of the temperature value, selecting two adjacent temperature values ​​divided by the time interval, calculating the maximum and minimum temperature values ​​within a period of time, and calculating the mean temperature. Through calculation and analysis, the target temperature time domain features are gradually collected, including time interval features, rate of change features, maximum value features, mean features, etc., which describe the characteristic performance of the target temperature in the time domain and provide basic data and key information for subsequent further processing and analysis.

[0047] Step S422, the target temperature spectrum obtained by converting the target temperature time series is collected through the time series processing layer to obtain the target temperature frequency domain characteristics. Specifically, the time series processing layer obtains the data of the target temperature time series, which is a series of temperature values ​​that change with time. The target temperature time series is converted into a target temperature spectrum through fast Fourier transform. Fast Fourier transform is a common method for converting time domain signals into frequency domain signals. The frequency domain characteristics of the target temperature spectrum are collected, including calculating the amplitude, frequency, phase and other characteristics of the spectrum. When calculating the frequency domain characteristics, some simple calculation processes are involved. For example, the amplitude of the calculated spectrum can be obtained by squaring the spectrum value. The frequency domain characteristics are analyzed and processed to extract more meaningful information, organize the data, and obtain the target temperature frequency domain characteristics.

[0048] In step S423, the target temperature time domain feature and the target temperature frequency domain feature constitute the target temperature feature. Specifically, the time domain data of the target temperature is obtained through relevant equipment or sensors. The data reflects the change of temperature over time. The time domain data is analyzed and processed to extract the target temperature time domain features, including the mean, maximum, minimum, and change rate of the temperature. The time domain data is converted into frequency domain data using fast Fourier transform to obtain the target temperature spectrum. In the frequency domain data, the amplitude, frequency and other characteristics of the spectrum can be analyzed to obtain the frequency domain characteristics of the target temperature. The target temperature time domain features and frequency domain features are combined to form the target temperature features. The features can more comprehensively describe the characteristics of the target temperature and provide a basis for subsequent analysis and application. After fast Fourier transforming the time domain data, the frequency domain data is obtained. By analyzing the amplitude and frequency of the frequency domain data, the frequency domain features can be obtained. The time domain features and the frequency domain features are combined to obtain the target temperature features.

[0049] Step S500: input the real-time state feature set into an electrical performance evaluation model to obtain a target real-time electrical performance index. The electrical performance evaluation model is an intelligent model trained based on the principle of a neural network.

[0050] Specifically, a real-time status feature set is prepared, which includes various characteristic data related to electrical performance, such as voltage value, current value, power factor, frequency, etc. The data of these real-time status feature sets are passed as input to the electrical performance evaluation model. The electrical performance evaluation model is an intelligent model trained based on the principle of neural network. Within the model, the input data will be calculated and processed at various levels of the neural network. The calculation of the first layer of the neural network is a weighted summation of the input features. The calculation results will be further processed and transmitted in subsequent network levels, and the data will be adjusted and transformed through operations such as activation functions. After calculation and processing by the entire neural network, the target real-time electrical performance index is finally output. The index comprehensively considers the real-time status characteristics of all inputs and is an evaluation result obtained through model training and learning, reflecting the performance status of the current electrical system. By continuously inputting new real-time status feature sets into the model, changes in electrical performance can be monitored and evaluated in real time, providing a basis for relevant decision-making and maintenance.

[0051] Step S600: When the target real-time electrical performance index is not within the electrical performance index limit, stop inspection of the target bus duct.

[0052] Specifically, by inputting the real-time state feature set into the electrical performance evaluation model, a target real-time electrical performance index is obtained. This target real-time electrical performance index is then compared with a set electrical performance index limit. The limit can be a specific numerical range. If the index is not within this limit, it indicates that the target real-time electrical performance index is not within the electrical performance index limit, and the target busbar needs to be shut down for inspection. This involves a series of operations, such as stopping the operation of the busbar and turning off related circuit switches. Then, the busbar is comprehensively inspected and maintained to find the cause of the abnormal electrical performance index. This may include checking for loose line connections, equipment faults, overloads, or short circuits. After the problem is identified and resolved, the busbar is restarted to restore its normal operation. Through this process, electrical performance problems of the busbar can be discovered and addressed in a timely manner, ensuring the safe and stable operation of the system.

[0053] In the embodiment of the present application, a first detection component is used to detect the initial electrical performance index of the target bus duct according to a predetermined static strategy. When the index reaches the limit, the second detection component is activated, which performs dynamic operation detection to obtain real-time operation status information. The edge data processor analyzes the real-time status feature set of the information and inputs it into the real-time electrical performance index of the electrical performance evaluation model (the model is based on neural network training). If the real-time index does not reach the limit, the inspection of the target bus duct is stopped, thereby realizing comprehensive, real-time, accurate and intelligent detection and evaluation of the electrical performance of the ship bus duct, and improving the safety and reliability of the ship's electrical system.

[0054] In the above, refer to Figure 1 A method for detecting the electrical performance of a ship bus duct according to an embodiment of the present invention is described in detail. Figure 2 A ship bus duct electrical performance detection system according to an embodiment of the present invention is described.

[0055] A ship bus duct electrical performance testing system according to an embodiment of the present invention is designed to address the technical issues of incomplete, inaccurate, real-time, and insufficiently intelligent testing of ship bus duct electrical performance in the prior art. This system achieves comprehensive, real-time, accurate, and intelligent testing and evaluation of ship bus duct electrical performance, thereby improving the safety and reliability of ship electrical systems. The system comprises an electrical performance index acquisition module 10, a detection component activation module 20, an operating status information acquisition module 30, a real-time status feature set acquisition module 40, an electrical performance index acquisition module 50, and a shutdown processing module 60.

[0056] The electrical performance index acquisition module 10 is used for the first detection component to perform electrical performance detection on the target bus duct based on a predetermined static detection strategy to obtain a target initial electrical performance index;

[0057] The detection component activation module 20 is configured to activate the second detection component when the target initial electrical performance index reaches the electrical performance index limit;

[0058] The operation status information acquisition module 30 is used to perform dynamic operation detection on the target bus duct through the second detection component to obtain target real-time operation status information;

[0059] The real-time status feature set acquisition module 40 is configured to analyze the target real-time operation status information through an edge data processor to obtain a real-time status feature set, wherein the edge data processor is in communication with the second detection component;

[0060] The electrical performance index acquisition module 50 is used to input the real-time state feature set into the electrical performance evaluation model to obtain the target real-time electrical performance index. The electrical performance evaluation model is an intelligent model trained based on the neural network principle.

[0061] The inspection stop processing module 60 is configured to perform an inspection stop processing on the target bus duct when the target real-time electrical performance index is not within the electrical performance index limit.

[0062] The specific configuration of the electrical performance index acquisition module 10 will be described in detail below. As described above, the first detection component performs an electrical performance test on the target bus duct based on a predetermined static detection strategy to obtain a target initial electrical performance index. The electrical performance index acquisition module 10 may further include: a power disconnection unit configured to disconnect the power supply to the target bus duct before performing the electrical performance test on the target bus duct, according to the predetermined static detection strategy.

[0063] Among them, the electrical performance index acquisition module 10 can further include: a first detection component including a unit for the first detection component including a non-contact voltage detector, a digital insulation resistance meter and a digital multimeter; a target static voltage acquisition unit for obtaining the target static voltage of the target bus duct through the non-contact voltage detector; a target insulation resistance acquisition unit for obtaining the target insulation resistance of the target bus duct through the digital insulation resistance meter; a target conductivity unit for obtaining the target conductivity of the target bus duct through the digital multimeter; a target initial electrical performance index acquisition unit for performing weighted calculation on the normalized target static voltage, the target insulation resistance and the target conductivity when the target static voltage meets the static limit constraint to obtain the target initial electrical performance index.

[0064] The specific configuration of the detection component activation module 20 will be described in detail below. As described above, when the target initial electrical performance index reaches the electrical performance index limit, the second detection component is activated. The detection component activation module 20 may further include: a second detection component including a unit for detecting that the second detection component includes a digital storage oscilloscope and a handheld infrared thermal imager; a target waveform acquisition unit for detecting and obtaining the target waveform of the target bus duct through the digital storage oscilloscope, wherein the target waveform includes a target voltage waveform and a target current waveform; a target temperature time series acquisition unit for detecting and obtaining the target temperature time series of the target bus duct through the handheld infrared thermal imager; and a target real-time operation status information composition unit for composing the target real-time operation status information using the target voltage waveform, the target current waveform, and the target temperature time series.

[0065] The specific configuration of the real-time state feature set acquisition module 40 will be described in detail below. As described above, the target real-time operation state information is analyzed by the edge data processor to obtain the real-time state feature set, wherein the edge data processor is in communication with the second detection component. The real-time state feature set acquisition module 40 may further include: a target waveform component feature acquisition unit for collecting features of the target voltage waveform through the waveform processing layer in the edge data processor to obtain target waveform component features of the target voltage component; a target temperature feature acquisition unit for collecting features of the target temperature time series through the timing processing layer in the edge data processor to obtain target temperature features; and a real-time state feature set acquisition unit for combining the target waveform component features with the target temperature features to form the real-time state feature set.

[0066] Among them, the target voltage waveform is characterized by collecting features through the waveform processing layer in the edge data processor to obtain the target waveform component features of the target voltage component, and the target waveform component feature acquisition unit may further include: a target voltage decomposition result acquisition subunit is used to perform modal decomposition on the target voltage waveform through the waveform processing layer to obtain a target voltage decomposition result, and the target voltage decomposition result includes multiple voltage components; a target voltage component acquisition subunit is used to introduce a component weight evaluation function to evaluate and screen the multiple voltage components to obtain the target voltage component, and the target voltage component refers to the voltage component with the largest weight among the multiple voltage components.

[0067] Among them, the target waveform component feature acquisition unit can further include: a target current component feature acquisition subunit for collecting features of the target current waveform through the waveform processing layer to obtain the target current component feature of the target current component; a target waveform component feature acquisition subunit for taking the average of the target current component feature and the target voltage component feature of the target voltage component as the target waveform component feature.

[0068] Among them, the target temperature time series is characterized by being collected through the timing processing layer in the edge data processor to obtain the target temperature characteristics, and the target temperature characteristic acquisition unit may further include: a target temperature time domain characteristic acquisition subunit for collecting the time domain characteristics of the target temperature time series through the timing processing layer to obtain the target temperature time domain characteristics; a target temperature frequency domain characteristic acquisition subunit for collecting the frequency domain characteristics of the target temperature spectrum obtained by converting the target temperature time series through the timing processing layer to obtain the target temperature frequency domain characteristics; the target temperature characteristic acquisition subunit is used to combine the target temperature time domain characteristics and the target temperature frequency domain characteristics to form the target temperature characteristics.

[0069] Among them, a component weight evaluation function is introduced to evaluate and screen the multiple voltage components to obtain the target voltage component, where the target voltage component refers to the voltage component with the largest weight among the multiple voltage components. The target voltage component acquisition subunit may further include: a weight evaluation function microunit for the component weight evaluation function having the following expression:

[0070]

[0071] Among them, w(x) represents the weight of the x-th modal component among the multiple voltage components, amount(x) represents the characteristic information amount of the x-th modal component, amount(X) represents the total characteristic information amount of the target voltage waveform, cor(x) represents the correlation coefficient value between the x-th modal component and the target voltage waveform, α and β are the first coefficient and the second coefficient respectively, and α+β=1.

[0072] A ship bus duct electrical performance detection system provided by an embodiment of the present invention can execute a ship bus duct electrical performance detection method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects of the execution method.

[0073] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0074] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for detecting the electrical performance of a ship bus duct, characterized in that: include: The first detection component performs electrical performance detection on the target bus duct based on a predetermined static detection strategy to obtain a target initial electrical performance index; When the target initial electrical performance index reaches the electrical performance index limit, activating the second detection component; Performing dynamic operation detection on the target bus duct by the second detection component to obtain target real-time operation status information; Analyzing the target real-time operation status information through an edge data processor to obtain a real-time status feature set, wherein the edge data processor is communicatively connected with the second detection component; Inputting the real-time state feature set into an electrical performance evaluation model to obtain a target real-time electrical performance index, wherein the electrical performance evaluation model is an intelligent model trained based on the principle of neural network; When the target real-time electrical performance index is not within the electrical performance index limit, the target bus duct is stopped for inspection.

2. According to claim 1, a method for detecting electrical performance of a ship bus duct is characterized in that: The predetermined static detection strategy refers to disconnecting the power supply of the target bus duct before performing electrical performance detection on the target bus duct.

3. According to claim 1, a method for detecting electrical performance of a ship bus duct is characterized in that: The first detection component includes a non-contact voltage detector, a digital insulation resistance meter and a digital multimeter, including: The target static voltage of the target bus duct is obtained by detecting the non-contact voltage detector; The target insulation resistance of the target bus duct is obtained by detecting with the digital insulation resistance meter; Detecting the target conductivity of the target bus duct by the digital multimeter; When the target static voltage meets the static limit constraint, a weighted calculation is performed on the normalized target static voltage, the target insulation resistance and the target conductivity to obtain the target initial electrical performance index.

4. According to claim 1, a method for detecting electrical performance of a ship bus duct is characterized in that: The second detection component includes a digital storage oscilloscope and a handheld infrared thermal imager, including: The target waveform of the target bus duct is obtained by detecting with the digital storage oscilloscope, wherein the target waveform includes a target voltage waveform and a target current waveform; The target temperature time sequence of the target bus duct is obtained by detecting with the handheld infrared thermal imager; The target voltage waveform, the target current waveform and the target temperature timing constitute the target real-time operation status information.

5. A method for detecting electrical performance of a ship bus duct according to claim 4, characterized in that: include: Collecting features of the target voltage waveform through a waveform processing layer in the edge data processor to obtain target waveform component features of the target voltage component; Collecting the characteristics of the target temperature time series through the time series processing layer in the edge data processor to obtain the target temperature characteristics; The target waveform component feature and the target temperature feature constitute the real-time state feature set.

6. A method for detecting electrical performance of a ship bus duct according to claim 5, characterized in that: include: Performing modal decomposition on the target voltage waveform through the waveform processing layer to obtain a target voltage decomposition result, wherein the target voltage decomposition result includes multiple voltage components; A component weight evaluation function is introduced to evaluate and screen the multiple voltage components to obtain the target voltage component, where the target voltage component refers to the voltage component with the largest weight among the multiple voltage components.

7. A method for detecting electrical performance of a ship bus duct according to claim 6, characterized in that: The expression of the component weight evaluation function is as follows: Among them, w(x) represents the weight of the x-th modal component among the multiple voltage components, amount(x) represents the characteristic information amount of the x-th modal component, amount(X) represents the total characteristic information amount of the target voltage waveform, cor(x) represents the correlation coefficient value between the x-th modal component and the target voltage waveform, α and β are the first coefficient and the second coefficient respectively, and α+β=1.

8. A method for detecting electrical performance of a ship bus duct according to claim 6, characterized in that: include: Collecting the characteristics of the target current waveform through the waveform processing layer to obtain the target current component characteristics of the target current component; An average value of the target current component characteristic and the target voltage component characteristic of the target voltage component is used as the target waveform component characteristic.

9. A method for detecting electrical performance of a ship bus duct according to claim 5, characterized in that: include: Collecting the time domain features of the target temperature time series through the time series processing layer to obtain the target temperature time domain features; The target temperature frequency spectrum obtained by converting the target temperature time series is collected through the time series processing layer to obtain the target temperature frequency domain features; The target temperature time domain feature and the target temperature frequency domain feature constitute the target temperature feature.

10. A ship bus duct electrical performance detection system, characterized in that: The system is used to implement a method for detecting electrical performance of a ship bus duct according to any one of claims 1 to 9, and the system comprises: An electrical performance index acquisition module, wherein the electrical performance index acquisition module is used for the first detection component to perform electrical performance detection on the target bus duct based on a predetermined static detection strategy to obtain a target initial electrical performance index; a detection component activation module, the detection component activation module being used to activate the second detection component when the target initial electrical performance index reaches the electrical performance index limit; An operation status information acquisition module, the operation status information acquisition module is used to perform dynamic operation detection on the target bus duct through the second detection component to obtain target real-time operation status information; A real-time status feature set acquisition module, the real-time status feature set acquisition module is used to analyze the target real-time operation status information through an edge data processor to obtain a real-time status feature set, wherein the edge data processor is communicatively connected with the second detection component; An electrical performance index acquisition module, the electrical performance index acquisition module is used to input the real-time state feature set into an electrical performance evaluation model to obtain a target real-time electrical performance index, the electrical performance evaluation model is an intelligent model trained based on the neural network principle; A stop inspection processing module is used to perform a stop inspection process on the target bus duct when the target real-time electrical performance index is not within the electrical performance index limit.

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