Method for determining volume resistivity of insulating material in cable and electronic equipment

Abstract

The invention provides a method for determining the volume resistivity of an insulating material in a cable and electronic equipment, and the method comprises the steps: obtaining the near infrared spectrums of a plurality of target samples, and obtaining a plurality of pieces of spectral data; acquiring the volume resistivity of each target sample to obtain a plurality of volume resistivity values; according to each piece of spectral data and each volume resistivity value, a basic database corresponding to each piece of spectral data is determined from each piece of spectral data, and the basic database represents the spectral data corresponding to the wavelength related to the change of the volume resistivity value; establishing a regression model according to each basic database and each volume resistivity value; determining the volume resistivity of the to-be-measured sample by using the regression model; and according to the volume resistivity of the to-be-tested sample, determining the aging condition of the to-be-tested cable. According to the invention, the problems of long time consumption and low accuracy during detection of the volume resistivity of the polypropylene cable insulating material in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the technical field of power cables, and in particular to a method for determining the volume resistivity of an insulating material in a cable, a computer-readable storage medium, a computer program product, and an electronic device. Background Art

[0002] High Voltage Direct Current (HVDC) cables, due to their high efficiency and low losses in long-distance, high-capacity power transmission, have become a key component of modern power systems. They are particularly suitable for scenarios such as offshore wind power grid integration, cross-regional grid interconnection, and urban underground power transmission. Currently, HVDC cables primarily use cross-linked polyethylene (XLPE) as an insulation material, but the presence of its byproducts exacerbates the problem of space charge accumulation. Modified polypropylene (PP), with its excellent corona resistance and low dielectric loss, is gradually becoming an emerging alternative material. With the large-scale integration of renewable energy and the popularization of flexible DC technology, polypropylene HVDC cables will evolve towards higher voltage levels, lightweight, and intelligent design.

[0003] With the rapid expansion of power cable networks, the number of cable suppliers has surged, but overall quality control in the industry faces severe challenges. Some manufacturers have engaged in irregular operations, including failing to strictly adhere to production process standards, using equipment that does not meet technical specifications, experiencing large fluctuations in product quality, and even systemic quality defects. These issues not only affect the reliability of cable products but also pose a potential threat to the safe and stable operation of urban power grids. The industry urgently needs to pay close attention and implement effective measures to regulate them. Testing of polypropylene (PP) cable insulation materials currently focuses on electrical, mechanical, and aging properties, with a focus on evaluating dielectric strength (typically ≥30kV / mm), dielectric loss (tanδ), and space charge distribution (via electroacoustic pulse or thermal stimulation current). Mechanical property testing includes tensile strength (ASTM D638) and low-temperature impact resistance (ISO 180). Thermal properties are analyzed using differential scanning calorimetry (DSC) and heat deflection temperature (HDT). Current cable quality spot checks face multiple practical challenges. Traditional testing methods suffer from technical bottlenecks such as long testing cycles, low sampling efficiency, and severe wear and tear on testing equipment. Furthermore, they require high technical capabilities and equipment from local organizations, making regular independent testing difficult. This situation prevents comprehensive quality screening of incoming cables, creating a significant regulatory gap in supply chain quality control and posing a potential threat to the safe operation of power grids.

[0004] Currently, the assessment of the aging state of polypropylene cable insulation materials still relies primarily on qualitative analysis methods, and a systematic, accurate testing and quality assessment standard system has yet to be established. In the field of power cable technology, Fourier transform infrared spectroscopy (FTIR) is widely used in domestic and international research to study the aging characteristics of insulation materials. However, due to technical limitations such as the complex light source system and bulky optical components of traditional spectrometers, portable on-site testing applications are difficult to implement. Consequently, this technology is currently limited to testing, verification, and auxiliary analysis in laboratory environments. Summary of the Invention

[0005] The main purpose of the present application is to provide a method for determining the volume resistivity of an insulating material in a cable, a computer-readable storage medium, a computer program product, and an electronic device, so as to at least solve the problem in the prior art of time-consuming and low-accuracy detection of the volume resistivity of polypropylene cable insulating materials.

[0006] To achieve the above-mentioned purpose, according to one aspect of the present application, a method for determining the volume resistivity of an insulating material in a cable is provided, comprising: obtaining near-infrared spectra of a plurality of target samples to obtain a plurality of spectral data, wherein the target samples are insulating materials in a target cable; obtaining the volume resistivity of each of the target samples to obtain a plurality of volume resistivity values; determining a basic database corresponding to each of the spectral data from each of the spectral data based on each of the spectral data and each of the volume resistivity values, wherein the basic database represents the spectral data corresponding to a wavelength associated with a change in the volume resistivity value; establishing a regression model based on each of the basic databases and each of the volume resistivity values; determining the volume resistivity of a sample to be tested using the regression model, wherein the sample to be tested is the insulating material in the cable to be tested; and determining the aging condition of the cable to be tested based on the volume resistivity of the sample to be tested.

[0007] Optionally, after obtaining multiple target samples and obtaining the near-infrared spectrum of each target sample, before determining the corresponding basic database from each spectral data based on each spectral data and each volume resistivity value, the method also includes: preprocessing each spectral data, and the preprocessing includes smoothing processing and second-order derivative processing.

[0008] Optionally, according to each of the spectral data and each of the volume resistivity values, a corresponding basic database is determined from each of the spectral data, including: according to each of the spectral data and each of the volume resistivity values, using an interval combination optimization method to determine a wavelength interval that is strongly correlated with a change in the volume resistivity value, the interval combination optimization method including an optimization algorithm and / or a random forest algorithm; according to the wavelength interval, the corresponding basic database is determined from each of the spectral data.

[0009] Optionally, a regression model is established based on each of the basic databases and each of the volume resistivity values, including: organizing each of the basic databases into a matrix form to obtain a near-infrared spectral feature matrix, wherein in the near-infrared spectral feature matrix, each row represents the basic database of a target sample, and each column corresponds to the spectral intensity value of a wavelength point in the basic database; organizing each of the volume resistivity values ​​into a matrix form to obtain an attribute matrix; and establishing the regression model based on the near-infrared spectral feature matrix and the attribute matrix using the principal component analysis regression method.

[0010] Optionally, using the regression model to determine the volume resistivity of the sample to be tested includes: acquiring a near-infrared spectrum of the sample to be tested to obtain target spectrum data; inputting the target spectrum data into the regression model to obtain the volume resistivity of the sample to be tested.

[0011] Optionally, the insulating material includes polypropylene, the target sample is a sheet sample with uniform thickness, and obtaining multiple target samples includes: obtaining multiple initial samples, and performing a thermal oxidation aging test on each of the initial samples to obtain multiple target samples.

[0012] Optionally, determining the aging condition of the cable to be tested based on the volume resistivity of the sample to be tested includes: when the volume resistivity of the sample to be tested is less than a predetermined volume resistivity, determining that the cable to be tested is severely aged, and replacing the cable to be tested.

[0013] According to another aspect of the present application, a computer-readable storage medium is provided, which includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute any one of the methods for determining the volume resistivity of the insulating material in the cable.

[0014] According to another aspect of the present application, a computer program product is provided, comprising computer instructions, which, when executed by a processor, implement any one of the methods for determining the volume resistivity of an insulating material in a cable.

[0015] According to another aspect of the present application, an electronic device is provided, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for determining the volume resistivity of the insulating material in the cable according to any one of the above methods.

[0016] By applying the technical solution of the present application, first, the near-infrared spectra of multiple target samples are obtained to obtain multiple spectral data, and the volume resistivity of each target sample is obtained to obtain multiple volume resistivity data. Then, based on each spectral data and each volume resistivity value, the basic database corresponding to each spectral data is determined from each spectral data, and a regression model is established based on each basic database and each volume resistivity value. The regression model is then used to determine the volume resistivity of the sample to be tested. Finally, based on the volume resistivity of the sample to be tested, the aging condition of the cable to be tested is determined. Compared with the existing technology that detects the volume resistivity of polypropylene cable insulation materials, which is time-consuming and has low accuracy, the present application can establish a basic database by collecting near-infrared spectra and corresponding volume resistivities of multiple target samples. This database determines the wavelength range most relevant to volume resistivity changes. The wavelength optimization process essentially identifies which spectral features are most valuable for predicting volume resistivity, thereby reducing unnecessary data dimensions in subsequent model establishment and improving the model's processing speed and prediction accuracy. Based on the basic database and volume resistivity values, a regression model is established. This model can capture the complex relationship between spectral data and volume resistivity values. Once the regression model is established, this model can be used to quickly predict the volume resistivity of the sample to be tested, reducing the steps required for long waiting times and complex equipment operation in traditional methods. Finally, based on the predicted volume resistivity of the sample to be tested, the known trends of aging and volume resistivity changes can be combined to determine the degree of cable aging. Through rapid spectral analysis, wavelength optimization, and regression models, the present application ensures short detection time and low detection costs, as well as high detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings that constitute part of this application are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for determining the volume resistivity of an insulating material in a cable provided in an embodiment of the present application is shown;

[0019] Figure 2A schematic flow chart of a method for determining the volume resistivity of an insulating material in a cable according to an embodiment of the present application is shown;

[0020] Figure 3 The figure shows a near-infrared spectrum of a target sample provided in an embodiment of the present application within a wavelength range of 1650 to 1800 nm;

[0021] Figure 4 A method for Figure 3 Near-infrared spectrum after taking the second-order derivative of the mid-near-infrared spectrum;

[0022] Figure 5 A schematic diagram of wavelength optimization for near-infrared spectrum provided in accordance with an embodiment of the present application is shown;

[0023] Figure 6 A schematic diagram showing measured values ​​and predicted values ​​of a target sample provided according to an embodiment of the present application is shown;

[0024] Figure 7 A schematic diagram showing a comparison between the measured value and the predicted value of a target sample in a prediction set provided according to an embodiment of the present application is shown;

[0025] Figure 8 A schematic diagram showing the trap energy level and trap density of a target sample after an isothermal surface potential decay experiment is performed on the target sample according to an embodiment of the present application;

[0026] Figure 9 A schematic diagram showing the correlation between the trap energy level and the near-infrared spectrum of a target sample provided according to an embodiment of the present application is shown.

[0027] The above drawings include the following reference numerals:

[0028] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. DETAILED DESCRIPTION

[0029] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0030] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] As introduced in the background technology, the existing technology has the problems of long time consumption and low accuracy when detecting the volume resistivity of polypropylene cable insulation materials. To solve the above problems, the embodiments of the present application provide a method for determining the volume resistivity of the insulation material in the cable, a computer-readable storage medium, a computer program product and an electronic device.

[0033] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0034] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a method for determining the volume resistivity of an insulating material in a cable according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown) a processor 102 (the processor 102 may include but is not limited to a microprocessor MCU or a programmable logic device FPGA and other processing devices) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input and output device 108 for communication functions. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the mobile terminal. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0035] The memory 104 can be used to store computer programs, such as software programs and modules for application software, such as the computer program corresponding to the method for determining the volume resistivity of the insulating material in the cable in the embodiment of the present invention. The processor 102 executes the computer programs stored in the memory 104 to perform various functional applications and data processing, thereby implementing the above-mentioned method. The memory 104 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof. The transmission device 106 is used to receive or transmit data via a network. Specific examples of such networks may include a wireless network provided by the mobile terminal's telecommunications provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0036] In this embodiment, a method for determining the volume resistivity of the insulating material in a cable running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0037] Figure 2 This is a flow chart of a method for determining the volume resistivity of an insulating material in a cable according to an embodiment of the present application.

[0038] like Figure 2 As shown, the method includes the following steps:

[0039] Step S201, acquiring near-infrared spectra of a plurality of target samples to obtain a plurality of spectral data, wherein the target samples are insulating materials in a target cable;

[0040] Specifically, the test wavelength of the near infrared spectrum is 1650nm-1800nm.

[0041] Step S202, obtaining the volume resistivity of each of the target samples to obtain a plurality of volume resistivity values;

[0042] Specifically, the volume resistivity test process is as follows: the volume resistivity of the target sample is measured according to IEC standard 62631-3-1:2016. The test uses a 6517B electrometer and a three-electrode system. The test voltage is 1kV and the polarization time is 10min. The steady-state leakage current is measured to calculate the volume resistivity of the insulating material.

[0043] Step S203, determining a basic database corresponding to each spectral data from each spectral data according to each spectral data and each volume resistivity value, wherein the basic database represents the spectral data corresponding to a wavelength associated with a change in the volume resistivity value;

[0044] Step S204: establishing a regression model based on the above basic databases and the above volume resistivity values;

[0045] Step S205, using the above regression model, determining the volume resistivity of the sample to be tested, where the sample to be tested is the above insulating material in the cable to be tested;

[0046] Step S206: determining the aging condition of the cable to be tested according to the volume resistivity of the sample to be tested.

[0047] Through the above embodiment, first, the near-infrared spectra of multiple target samples are obtained to obtain multiple spectral data, and the volume resistivity of each target sample is obtained to obtain multiple volume resistivity data. Then, based on each spectral data and each volume resistivity value, the basic database corresponding to each spectral data is determined from each spectral data, and a regression model is established based on each basic database and each volume resistivity value. The regression model is then used to determine the volume resistivity of the sample to be tested. Finally, based on the volume resistivity of the sample to be tested, the aging condition of the cable to be tested is determined. Compared with the existing technology that detects the volume resistivity of polypropylene cable insulation materials, which is time-consuming and has low accuracy, the present application can establish a basic database by collecting near-infrared spectra and corresponding volume resistivities of multiple target samples. This database determines the wavelength range most relevant to volume resistivity changes. The wavelength optimization process essentially identifies which spectral features are most valuable for predicting volume resistivity, thereby reducing unnecessary data dimensions in subsequent model establishment and improving the model's processing speed and prediction accuracy. Based on the basic database and volume resistivity values, a regression model is established. This model can capture the complex relationship between spectral data and volume resistivity values. Once the regression model is established, this model can be used to quickly predict the volume resistivity of the sample to be tested, reducing the steps required for long waiting times and complex equipment operation in traditional methods. Finally, based on the predicted volume resistivity of the sample to be tested, the known trends of aging and volume resistivity changes can be combined to determine the degree of cable aging. Through rapid spectral analysis, wavelength optimization, and regression models, the present application ensures short detection time and low detection costs, as well as high detection accuracy.

[0048] Specifically, near-infrared spectroscopy analysis can be completed in seconds without destroying the sample or requiring complex sample pre-treatment. This means that spectral data for a large amount of insulating materials can be acquired in a very short time, significantly improving the speed and efficiency of detection compared to traditional, time-consuming and potentially sample-destructive detection methods.

[0049] Specifically, the reliability of near-infrared prediction is verified: isothermal surface potential decay experiments are carried out on 4 target samples to obtain the trap characteristics of the target samples, revealing the relationship between the volume resistivity and the microstructure represented by the selected wavelength in the near-infrared spectrum, that is, establishing a correlation between volume resistivity and microstructure, and then verifying the reliability of near-infrared spectrum prediction.

[0050] It should be noted that near-infrared molecular spectroscopy can identify and determine the components, specific molecular configurations, and functional groups of polymer raw materials such as rubber and plastics, thereby inferring the macroscopic properties of the raw materials. Near-infrared spectroscopy is fast, efficient, and easy to operate. Changes in the microstructure and macroscopic mechanical properties of insulating materials after thermal oxidation aging will also cause changes in the intensity of characteristic bands in their near-infrared spectra. The changes in the intensity of the near-infrared spectra are related to the different degrees of aging. This shows that there is a correlation between the near-infrared spectra of power cable and accessory insulation materials and their microscopic molecular states. A quantitative function relationship can be established to predict their macroscopic properties based on the near-infrared spectral intensity. By testing the near-infrared spectra of polypropylene insulation materials, it is possible not only to evaluate the aging state of the cable but also to predict the macroscopic properties of the cable insulation. Therefore, it is reasonable and feasible to predict the volume resistivity of polypropylene cable insulation materials using near-infrared spectroscopy.

[0051] In one optional solution, after acquiring multiple target samples and obtaining near-infrared spectra for each target sample, the method further includes preprocessing each spectral data, including smoothing and second-order derivative processing, before determining a corresponding basic database from each spectral data based on the spectral data and the volume resistivity values. In this embodiment, smoothing effectively suppresses random noise in the spectral data, resulting in a smoother spectral curve and clearer characteristic peaks; second-order derivative processing focuses on highlighting subtle variations in the spectrum, particularly those characteristic wavelengths closely related to volume resistivity.

[0052] In an exemplary embodiment, according to each of the above-mentioned spectral data and each of the above-mentioned volume resistivity values, a corresponding basic database is determined from each of the above-mentioned spectral data, including: according to each of the above-mentioned spectral data and each of the above-mentioned volume resistivity values, using an interval combination optimization method to determine a wavelength interval that is strongly correlated with the change in the above-mentioned volume resistivity value, the above-mentioned interval combination optimization method includes an optimization algorithm and / or a random forest algorithm; according to the above-mentioned wavelength interval, the corresponding above-mentioned basic database is determined from each of the above-mentioned spectral data. In this embodiment, using the interval combination optimization method, in particular the random forest algorithm or the optimization algorithm, the wavelength interval that is most correlated with the change in volume resistivity can be effectively identified from a large amount of spectral data, which means that the spectral features with the most valuable information for prediction are extracted. These features can more accurately reflect the electrical properties and aging state of the material. By screening out the wavelength interval that is strongly correlated with the volume resistivity, the prediction accuracy of the subsequent model can be significantly improved; the wavelength interval determined by the interval combination optimization method only includes key wavelengths that have an important contribution to the prediction of volume resistivity, which not only reduces the amount of data and reduces the computational cost, but also accelerates the model training process and improves the efficiency of the overall prediction process.

[0053] In other embodiments, a regression model is established based on each of the above-mentioned basic databases and each of the above-mentioned volume resistivity values, including: arranging each of the above-mentioned basic databases into a matrix form to obtain a near-infrared spectral feature matrix, wherein in the above-mentioned near-infrared spectral feature matrix, each row represents the above-mentioned basic database of the target sample, and each column corresponds to the spectral intensity value of a wavelength point in the above-mentioned basic database; arranging each of the above-mentioned volume resistivity values ​​into a matrix form to obtain an attribute matrix; and establishing the above-mentioned regression model using the principal component analysis regression method based on the above-mentioned near-infrared spectral feature matrix and the above-mentioned attribute matrix. In this embodiment, arranging the data into a matrix form realizes the structuring and standardization of the data. The matrix form facilitates subsequent mathematical operations and statistical analysis, ensuring the consistency and comparability of the data at different processing stages; the principal component analysis regression method is an efficient dimensionality reduction technology that can extract the principal component that best represents the data change trend from the original near-infrared spectral feature matrix. Through principal component analysis, the data dimension can be reduced, redundant information can be removed, and the spectral features most relevant to the volume resistivity change can be retained. In this way, during model training, the algorithm can focus more on key data, further improve the training efficiency of the model, and further enhance the prediction ability and accuracy of the model.

[0054] Specifically, the partial least squares regression algorithm may also be used to establish a regression model, and this application does not impose any specific limitation on this.

[0055] According to some exemplary embodiments of the present application, the volume resistivity of the sample to be tested is determined using the above-mentioned regression model, including: obtaining the near-infrared spectrum of the sample to be tested to obtain target spectrum data; and inputting the above-mentioned target spectrum data into the above-mentioned regression model to obtain the volume resistivity of the sample to be tested. In this embodiment, first, the near-infrared spectrum data of the sample to be tested is obtained, and then these data are input into the regression model. The model will predict the volume resistivity of the sample to be tested based on the relationship between the spectral characteristics and the volume resistivity established during training. This not only avoids direct destructive testing of the cable, but also can quickly and accurately evaluate the insulation performance of the cable, which is of great significance to the safe operation of the power system. Through model prediction, signs of aging of the cable insulation material can be discovered in time, and corresponding maintenance measures can be taken to prevent the occurrence of power accidents.

[0056] According to other exemplary embodiments of the present application, the insulating material comprises polypropylene, the target sample is a sheet sample of uniform thickness, and obtaining multiple target samples includes obtaining multiple initial samples and performing a thermal oxidative aging test on each of the initial samples to obtain the multiple target samples. In this embodiment, obtaining and thermal oxidative aging multiple initial samples ensures that the target samples encompass the characteristics of the polypropylene insulation material at different aging levels, thereby enhancing the diversity and representativeness of the data.

[0057] In some other optional solutions of the present application, the aging condition of the cable to be tested is determined based on the volume resistivity of the sample to be tested, including: if the volume resistivity of the sample to be tested is less than a predetermined volume resistivity, determining that the cable to be tested is severely aged and replacing the cable to be tested. In this embodiment, the volume resistivity is quickly predicted by near-infrared spectroscopy, and the insulation aging condition of the cable can be monitored in real time. Once the volume resistivity is found to be lower than a preset critical value, it indicates that the aging of the cable has reached a level that requires attention or treatment. This allows maintenance personnel to respond immediately and arrange necessary inspections or replacements, avoiding potential failure risks.

[0058] In actual application, those skilled in the art may set the predetermined volume resistivity according to empirical values, or obtain it through multiple experiments, and this application does not impose any specific restrictions on this.

[0059] The present application is described in further detail below with reference to specific embodiments.

[0060] Example 1:

[0061] This example uses 110kV domestically produced polypropylene cable insulation as an example. Sheet samples were processed into uniform thickness and subjected to 48 days of accelerated thermal oxidative aging at 135°C. Near-infrared spectra were measured, with 20 data sets used as modeling sets and 5 data sets used as prediction sets. This example uses a UV-3600 near-infrared / visible / ultraviolet spectrophotometer, with the target sample placed in the sample chamber and operating in transmission mode with a resolution of 0.1nm.

[0062] The specific steps include:

[0063] (1) Prepare target samples: Prepare polypropylene cable insulation material into sheet samples with uniform thickness of 1 mm. The samples are subjected to accelerated thermal oxidative aging test at 135°C for 48 days and divided into modeling set and prediction set;

[0064] (2) Near-infrared spectrum test: First, the background spectrum without the target sample is detected by the UV-3600 near-infrared / visible / ultraviolet spectrophotometer, and then the near-infrared spectrum of the target sample in the wavelength range of 1650nm to 1800nm ​​is tested by the UV-3600 near-infrared / visible / ultraviolet spectrophotometer. The near-infrared spectrum of the model set is as follows: Figure 3 shown.

[0065] (3) Spectral preprocessing: The spectrum data of the PP aging sample is smoothed and second-order derivative processed to eliminate the influence of the miscellaneous peaks and baseline drift, and highlight the absorption peak at the characteristic wavelength. The spectrum after second-order derivative processing is as follows Figure 4As shown, the figure contains the near-infrared spectrum curves of 20 polypropylene cable insulation material samples after second-order derivative processing;

[0066] (4) Volume resistivity test: The volume resistivity of the target sample is measured according to IEC standard 62631-3-1:2016. The test uses a 6517B electrometer and a three-electrode system. The test voltage is 1kV, the polarization time is 10min, and the steady-state leakage current is measured to calculate the volume resistivity of the insulating material. According to the formula The volume resistivity of the polypropylene cable insulation material was calculated, where U is the voltage applied to the insulation material, I is the leakage current, S is the electrode area, and h is the thickness of the target sample. The volume resistivity test results of the modeling set and the prediction set are shown in Table 1.

[0067] Table 1 Volume resistivity test results of modeling set and prediction set

[0068]

[0069]

[0070] (5) Spectral data wavelength optimization: The RF (Random Forest) algorithm is used to optimize the wavelength of the spectral data, and the wavelength points that are strongly correlated with the volume resistivity value change during the aging process of the PP sample are selected. The program running results are as follows: Figure 5 As shown;

[0071] (6) Establish a partial least squares regression model: The correlation model between the near-infrared spectral feature matrix and the attribute matrix is ​​established by partial least squares regression method. According to the spectrum data (i.e. basic database) optimized by RF algorithm and the volume resistivity value during PP aging, a regression model is constructed. The program running results are as follows: Figure 6 As shown;

[0072] (7) Verify the accuracy of the model: Collect the near-infrared spectrum of the PP sample of the prediction set, pre-process the spectral data, and input it into the regression model to obtain the volume resistivity of the prediction set. The results are as follows: Figure 7 As shown in Table 2, in Table 2, the measured value represents the volume resistivity of the target sample actually measured, the predicted value represents the volume resistivity of the target sample predicted by the regression model, the error range represents the difference between the measured value and the predicted value, and the prediction error represents the ratio of the error range to the measured value;

[0073] Table 2 Prediction results of volume resistivity of samples predicted by regression model

[0074]

[0075]

[0076] (8) Verify the reliability of near-infrared prediction: Select 4 target samples and perform isothermal surface potential decay experiments on these 4 target samples. Apply a voltage of -10kV. Based on the surface potential decay curve, the trap energy level and trap density of the target samples can be calculated, as shown in Table 3 and Figure 8 As shown, the correlation between the microstructure characterized by near-infrared spectroscopy and volume resistivity is revealed, as Figure 9 As shown, the feasibility of near-infrared prediction is verified. Figure 9 It can be seen that with the increase of aging time, the deep trap energy level gradually decreases, the carrier capture ability gradually decreases, and the volume resistivity gradually increases. In the near-infrared spectrum, the second-order derivative intensity value at 1730nm gradually decreases with the increase of aging time, and is positively correlated with the change law of the corresponding deep trap energy level. The decrease in volume resistivity is regulated by the deep trap energy level, so the near-infrared spectrum can accurately predict the volume resistivity.

[0077] Table 3 Calculation results of deep and shallow trap energy levels of four samples

[0078] Sample Deep trap level (eV) Shallow trap level (eV) R1 1.151 0.931 R6 1.149 0.973 R11 1.109 0.944 R16 1.082 0.998

[0079] In summary, this application uses a sheet sample with uniform thickness and smooth surface as the target sample, uses a UV-3600 near-infrared / visible / ultraviolet spectrophotometer to test the near-infrared spectrum of the target sample, and combines the variation law of volume resistivity with the peak height value of each characteristic functional group to construct a database of near-infrared spectral characteristics and macroscopic properties of samples with different structural parameters, studies the correlation and action coefficient between the principal component spatial parameters of the near-infrared characteristic spectrum and the principal component spatial parameters of the macroscopic performance in the database, establishes a matrix relationship between the near-infrared spectrum and the volume resistivity, and realizes the prediction of volume resistivity based on the near-infrared spectrum. This application realizes rapid automatic scanning detection of cable insulation material cross-sections and dielectric property evaluation by constructing a correlation between the near-infrared spectrum of polypropylene cable insulation material and the volume resistivity of the insulation material, establishes a portable, non-destructive means for rapid detection of insulation material performance, speeds up detection, improves detection efficiency, reduces human resources, and realizes instant, rapid and full coverage detection of the material quality of the core components of the incoming cables and accessories.

[0080] Specifically, Figure 3 In the figure, the horizontal axis represents the wavelength, the vertical axis represents the spectral intensity, and the different colors of the curve represent different target samples. Figure 4 For Figure 3 Schematic diagram of the spectrum after taking the second-order derivative of the spectral data in .

[0081] Specifically, the 10 wavelength points with the highest correlation with the predicted variable volume resistivity in the PP near-infrared spectrum are obtained through wavelength optimization, such as Figure 5 As shown, the selected wavelength points are 1655nm, 1663nm, 1664nm, 1714nm, 1722nm, 1752nm, 1753nm, 1758nm, 1770nm, and 1772nm. Figure 5 In (a), the horizontal axis represents the variable and the vertical axis represents the probability of selection; Figure 5 In (b), the horizontal axis represents the wavelength, and the vertical axis represents the spectral intensity value after the second-order derivative (i.e. Figure 4 The blue curve represents the spectral data after the second-order derivative of the near-infrared spectrum of the target sample, and the orange boxes represent the 10 wavelength points with the highest correlation with the predicted variable volume resistivity after wavelength optimization.

[0082] Specifically, Figure 6 The horizontal axis represents the volume resistivity of the target sample obtained by actual testing, the vertical axis represents the volume resistivity of the target sample predicted by the regression model, the gray circle represents the target sample in the modeling set, and the red circle represents the target sample in the prediction set. Figure 6 It can be seen that the measured value is not much different from the predicted value, and the slope is approximately 1.

[0083] Specifically, Figure 7 Schematic diagram showing the comparison between the measured and predicted values ​​of the volume resistivity of the five target samples in the prediction set. Figure 7 In the figure, the horizontal axis represents different target samples, the vertical axis represents the volume resistivity value, the yellow bar graph represents the predicted value predicted by the regression model, and the purple bar graph represents the actual measured value.

[0084] Specifically, Figure 8 Schematic diagram showing the trap energy level and trap density of the target sample after the isothermal surface potential decay experiment. Figure 8 In the middle, the horizontal axis E T represents the trap energy level, the ordinate represents the trap density, and the different colors of the curve represent different target samples. Figure 8 (a) is a schematic diagram of shallow trap energy levels. Figure 8 (b) Schematic diagram of deep trap energy levels.

[0085] Specifically, Figure 9 Schematic diagram of the correlation between trap energy levels and near-infrared spectra. Figure 9 In the figure, blue represents the shallow trap energy level of the target sample, red represents the deep trap energy level of the target sample, and the horizontal axis represents the spectral intensity value after the second-order derivative at a wavelength of 1730 nm (i.e. Figure 4), the ordinate represents the deep trap energy level or the shallow trap energy level.

[0086] An embodiment of the present invention provides a computer-readable storage medium, which includes a stored program. When the program is run, the device where the computer-readable storage medium is located is controlled to execute the method for determining the volume resistivity of the insulating material in the cable.

[0087] Specifically, the method for determining the volume resistivity of the insulating material in the cable includes:

[0088] Step S201, acquiring near-infrared spectra of a plurality of target samples to obtain a plurality of spectral data, wherein the target samples are insulating materials in a target cable;

[0089] Step S202, obtaining the volume resistivity of each of the target samples to obtain a plurality of volume resistivity values;

[0090] Step S203, determining a basic database corresponding to each spectral data from each spectral data according to each spectral data and each volume resistivity value, wherein the basic database represents the spectral data corresponding to a wavelength associated with a change in the volume resistivity value;

[0091] Step S204: establishing a regression model based on the above basic databases and the above volume resistivity values;

[0092] Step S205, using the above regression model, determining the volume resistivity of the sample to be tested, where the sample to be tested is the above insulating material in the cable to be tested;

[0093] Step S206: determining the aging condition of the cable to be tested according to the volume resistivity of the sample to be tested.

[0094] Optionally, after obtaining multiple target samples and obtaining the near-infrared spectrum of each of the above target samples, before determining the corresponding basic database from each of the above spectral data based on each of the above spectral data and each of the above volume resistivity values, the above method also includes: preprocessing each of the above spectral data, and the above preprocessing includes smoothing processing and second-order derivative processing.

[0095] Optionally, according to each of the above-mentioned spectral data and each of the above-mentioned volume resistivity values, a corresponding basic database is determined from each of the above-mentioned spectral data, including: according to each of the above-mentioned spectral data and each of the above-mentioned volume resistivity values, using an interval combination optimization method to determine a wavelength interval that is strongly correlated with the change of the above-mentioned volume resistivity value, the above-mentioned interval combination optimization method including an optimization algorithm and / or a random forest algorithm; according to the above-mentioned wavelength interval, the corresponding basic database is determined from each of the above-mentioned spectral data.

[0096] Optionally, a regression model is established based on each of the above-mentioned basic databases and each of the above-mentioned volume resistivity values, including: organizing each of the above-mentioned basic databases into a matrix form to obtain a near-infrared spectral feature matrix, wherein in the above-mentioned near-infrared spectral feature matrix, each row represents the above-mentioned basic database of one of the above-mentioned target samples, and each column corresponds to the spectral intensity value of a wavelength point in the above-mentioned basic database; organizing each of the above-mentioned volume resistivity values ​​into a matrix form to obtain an attribute matrix; and establishing the above-mentioned regression model based on the above-mentioned near-infrared spectral feature matrix and the above-mentioned attribute matrix using the principal component analysis regression method.

[0097] Optionally, the volume resistivity of the sample to be tested is determined using the above regression model, including: obtaining the near-infrared spectrum of the above sample to be tested to obtain target spectrum data; inputting the above target spectrum data into the above regression model to obtain the volume resistivity of the above sample to be tested.

[0098] Optionally, the insulating material includes polypropylene, the target sample is a sheet sample with uniform thickness, and obtaining multiple target samples includes: obtaining multiple initial samples, and performing a thermal oxidation aging test on each of the initial samples to obtain multiple target samples.

[0099] Optionally, the aging condition of the cable to be tested is determined based on the volume resistivity of the sample to be tested, including: when the volume resistivity of the sample to be tested is less than a predetermined volume resistivity, determining that the cable to be tested is severely aged, and replacing the cable to be tested.

[0100] The present application also provides a computer program product, including computer instructions, which, when executed by a processor, implement at least the following method steps: step S201, obtaining near-infrared spectra of multiple target samples to obtain multiple spectral data, where the target samples are insulating materials in a target cable; step S202, obtaining the volume resistivity of each of the target samples to obtain multiple volume resistivity values; step S203, determining a basic database corresponding to each of the spectral data from each of the spectral data based on each of the spectral data and each of the volume resistivity values, where the basic database represents the spectral data corresponding to wavelengths associated with changes in the volume resistivity values; step S204, establishing a regression model based on each of the basic databases and each of the volume resistivity values; step S205, determining the volume resistivity of the sample to be tested using the regression model, where the sample to be tested is the insulating material in the cable to be tested; step S206, determining the aging condition of the cable to be tested based on the volume resistivity of the sample to be tested.

[0101] Optionally, after obtaining multiple target samples and obtaining the near-infrared spectrum of each of the above target samples, before determining the corresponding basic database from each of the above spectral data based on each of the above spectral data and each of the above volume resistivity values, the above method also includes: preprocessing each of the above spectral data, and the above preprocessing includes smoothing processing and second-order derivative processing.

[0102] Optionally, according to each of the above-mentioned spectral data and each of the above-mentioned volume resistivity values, a corresponding basic database is determined from each of the above-mentioned spectral data, including: according to each of the above-mentioned spectral data and each of the above-mentioned volume resistivity values, using an interval combination optimization method to determine a wavelength interval that is strongly correlated with the change of the above-mentioned volume resistivity value, the above-mentioned interval combination optimization method including an optimization algorithm and / or a random forest algorithm; according to the above-mentioned wavelength interval, the corresponding basic database is determined from each of the above-mentioned spectral data.

[0103] Optionally, a regression model is established based on each of the above-mentioned basic databases and each of the above-mentioned volume resistivity values, including: organizing each of the above-mentioned basic databases into a matrix form to obtain a near-infrared spectral feature matrix, wherein in the above-mentioned near-infrared spectral feature matrix, each row represents the above-mentioned basic database of one of the above-mentioned target samples, and each column corresponds to the spectral intensity value of a wavelength point in the above-mentioned basic database; organizing each of the above-mentioned volume resistivity values ​​into a matrix form to obtain an attribute matrix; and establishing the above-mentioned regression model based on the above-mentioned near-infrared spectral feature matrix and the above-mentioned attribute matrix using the principal component analysis regression method.

[0104] Optionally, the volume resistivity of the sample to be tested is determined using the above regression model, including: obtaining the near-infrared spectrum of the above sample to be tested to obtain target spectrum data; inputting the above target spectrum data into the above regression model to obtain the volume resistivity of the above sample to be tested.

[0105] Optionally, the insulating material includes polypropylene, the target sample is a sheet sample with uniform thickness, and obtaining multiple target samples includes: obtaining multiple initial samples, and performing a thermal oxidation aging test on each of the initial samples to obtain multiple target samples.

[0106] Optionally, the aging condition of the cable to be tested is determined based on the volume resistivity of the sample to be tested, including: when the volume resistivity of the sample to be tested is less than a predetermined volume resistivity, determining that the cable to be tested is severely aged, and replacing the cable to be tested.

[0107] An embodiment of the present application also provides an electronic device, comprising: one or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for executing any one of the above-mentioned methods for determining the volume resistivity of the insulating material in the cable.

[0108] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, can be centralized on a single computing device, or can be distributed across a network of multiple computing devices. They can be implemented using program code executable by the computing device, and thus, can be stored in a storage device and executed by the computing device. In some cases, the steps shown or described herein can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0109] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0113] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0114] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0115] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0116] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0117] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0118] In the method for determining the volume resistivity of the insulating material in the cable of the present application, first, near-infrared spectra of multiple target samples are obtained to obtain multiple spectral data, and the volume resistivity of each target sample is obtained to obtain multiple volume resistivity data. Then, based on each spectral data and each volume resistivity value, the basic database corresponding to each spectral data is determined from each spectral data, and a regression model is established based on each basic database and each volume resistivity value. The regression model is then used to determine the volume resistivity of the sample to be tested. Finally, based on the volume resistivity of the sample to be tested, the aging condition of the cable to be tested is determined. Compared with the existing technology that detects the volume resistivity of polypropylene cable insulation materials, which is time-consuming and has low accuracy, the present application can establish a basic database by collecting near-infrared spectra and corresponding volume resistivities of multiple target samples. This database determines the wavelength range most relevant to volume resistivity changes. The wavelength optimization process essentially identifies which spectral features are most valuable for predicting volume resistivity, thereby reducing unnecessary data dimensions in subsequent model establishment and improving the model's processing speed and prediction accuracy. Based on the basic database and volume resistivity values, a regression model is established. This model can capture the complex relationship between spectral data and volume resistivity values. Once the regression model is established, this model can be used to quickly predict the volume resistivity of the sample to be tested, reducing the steps required for long waiting times and complex equipment operation in traditional methods. Finally, based on the predicted volume resistivity of the sample to be tested, the known trends of aging and volume resistivity changes can be combined to determine the degree of cable aging. Through rapid spectral analysis, wavelength optimization, and regression models, the present application ensures short detection time and low detection costs, as well as high detection accuracy.

[0119] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for determining the volume resistivity of an insulating material in a cable, characterized in that: include: Acquire near-infrared spectra of a plurality of target samples to obtain a plurality of spectral data, wherein the target samples are insulating materials in a target cable; Obtaining the volume resistivity of each target sample to obtain a plurality of volume resistivity values; Determining, from each of the spectral data and each of the volume resistivity values, a basic database corresponding to each of the spectral data, wherein the basic database represents the spectral data corresponding to a wavelength associated with a change in the volume resistivity value; Establishing a regression model based on the basic databases and the volume resistivity values; Determining the volume resistivity of a sample to be tested using the regression model, where the sample to be tested is the insulating material in the cable to be tested; The aging condition of the cable to be tested is determined according to the volume resistivity of the sample to be tested.

2. The method for determining the volume resistivity of the insulating material in the cable according to claim 1, characterized in that: After obtaining a plurality of target samples and obtaining a near-infrared spectrum of each target sample, and before determining a corresponding basic database from each spectral data according to each spectral data and each volume resistivity value, the method further includes: The spectral data are preprocessed, and the preprocessing includes smoothing processing and second-order derivative processing.

3. The method for determining the volume resistivity of the insulating material in the cable according to claim 1, characterized in that: Determining a corresponding basic database from each of the spectral data according to each of the spectral data and each of the volume resistivity values ​​includes: Determining, based on each of the spectral data and each of the volume resistivity values, a wavelength interval that is strongly correlated with a change in the volume resistivity value using an interval combination optimization method, wherein the interval combination optimization method includes an optimization algorithm and / or a random forest algorithm; According to the wavelength range, the corresponding basic database is determined from each of the spectral data.

4. The method for determining the volume resistivity of the insulating material in the cable according to claim 1, characterized in that: A regression model is established based on the basic databases and the volume resistivity values, including: Arranging each of the basic databases into a matrix form to obtain a near-infrared spectrum feature matrix, wherein each row in the near-infrared spectrum feature matrix represents the basic database of one of the target samples, and each column corresponds to the spectral intensity value of a wavelength point in the basic database; Arranging the volume resistivity values ​​into a matrix form to obtain a property matrix; The regression model is established based on the near-infrared spectral feature matrix and the attribute matrix using principal component analysis regression method.

5. The method for determining the volume resistivity of the insulating material in the cable according to claim 1, characterized in that: Using the regression model, the volume resistivity of the sample to be tested is determined, including: Acquire the near infrared spectrum of the sample to be tested to obtain target spectrum data; The target spectrum data is input into the regression model to obtain the volume resistivity of the sample to be tested.

6. The method for determining the volume resistivity of the insulating material in the cable according to claim 1, characterized in that: The insulating material includes polypropylene, and the target sample is a sheet sample with uniform thickness. Acquiring multiple target samples includes: A plurality of initial samples are obtained, and a thermal oxidative aging experiment is performed on each of the initial samples to obtain a plurality of target samples.

7. The method for determining the volume resistivity of the insulating material in the cable according to claim 6, characterized in that: Determining the aging condition of the cable to be tested according to the volume resistivity of the sample to be tested, including: When the volume resistivity of the sample to be tested is less than a predetermined volume resistivity, it is determined that the cable to be tested is severely aged, and the cable to be tested is replaced.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for determining the volume resistivity of the insulating material in the cable according to any one of claims 1 to 7.

9. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the method for determining the volume resistivity of the insulating material in the cable according to any one of claims 1 to 7 is implemented.

10. An electronic device, characterized in that: include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and are configured to be executed by the one or more processors, and the one or more programs include a method for determining the volume resistivity of the insulating material in the cable according to any one of claims 1 to 7.

Images