PVC cable main insulation material decomposition gas online detection method based on miniature gas sensor

Abstract

The invention is suitable for the technical field of gas detection, and provides a PVC cable main insulation material decomposition gas online detection method based on a miniature gas sensor, and the method comprises the following steps: carrying out in-situ gas sampling on a running PVC insulation cable, and obtaining a sample gas flow containing decomposition gas; inputting the sample gas stream into a micro gas sensor array, the sensor array comprising a plurality of corrosion resistant micro sensors for HCl, CO, and organic volatiles, respectively; acquiring a multi-channel concentration signal output by the sensor array, wherein the multi-channel concentration signal comprises a real-time concentration value of characteristic gas; environment interference compensation processing is carried out on the multichannel concentration signals, and multi-source data fusion processing is carried out in combination with cable operation parameters; and generating an insulation state evaluation result and an early warning signal according to the fused data. The problems that the insulation state cannot be monitored in situ in real time, measurement is not accurate under strong electromagnetic interference, and the false alarm rate of a single gas threshold value is high are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas detection, in particular to an online detection method for decomposition gas of a PVC cable main insulation material based on a micro gas sensor. Background Art

[0002] Polyvinyl chloride (PVC) is widely used as the primary insulation material for cables in medium and low voltage distribution networks. Over long-term operation, PVC insulation decomposes and ages due to electrothermal stress, releasing characteristic gases such as HCl, CO, and styrene. The concentration and ratio of these gases accurately reflect the degree of insulation degradation and are key indicators for predicting cable failures. Currently, mainstream detection methods include offline chromatography and infrared spectroscopy. These methods periodically collect cable gas chamber samples or cut insulation fragments, and then analyze the decomposition gas composition in the laboratory. Although this method has high accuracy, it suffers from significant hysteresis and cannot locate localized defects.

[0003] Online detection technologies have emerged in recent years to address this timeliness issue, such as deploying fixed gas sensors at cable joints. However, existing technologies suffer from three major flaws: First, traditional sensors are large and power-hungry, making dense deployment difficult and leading to blind spots. Second, they fail to account for interference from strong electromagnetic fields in cables and high-humidity, oily environments, leading to significant measurement drift. For example, HCl corrosion can cause metal oxide sensors to fail. Third, they rely on a single gas concentration threshold for alarms, failing to integrate diagnostic parameters such as cable load current and operating temperature, resulting in a high false alarm rate.

[0004] In view of this, an online detection method for decomposition gas of PVC cable main insulation material based on micro gas sensor is proposed. Summary of the Invention

[0005] The present invention provides an online detection method for decomposition gas of the main insulating material of PVC cable based on a micro gas sensor, which is used to solve the problems of being unable to monitor the insulation status in real time, measurement inaccuracy under strong electromagnetic interference, and high false alarm rate of single gas threshold.

[0006] The present invention provides an online detection method for decomposition gas of the main insulating material of a PVC cable based on a micro gas sensor, comprising:

[0007] Conduct in-situ gas sampling on running PVC insulated cables to obtain sample gas streams containing decomposition gases;

[0008] Inputting the sample gas flow into a micro gas sensor array, wherein the sensor array comprises a plurality of corrosion-resistant micro sensors for HCl, CO and organic volatiles respectively;

[0009] Acquire a multi-channel concentration signal output by the sensor array, wherein the multi-channel concentration signal includes a real-time concentration value of a characteristic gas;

[0010] Performing environmental interference compensation processing on the multi-channel concentration signal and performing multi-source data fusion processing in combination with cable operation parameters;

[0011] Generate insulation status assessment results and early warning signals based on the fused data.

[0012] Furthermore, the micro gas sensor array comprises a plurality of corrosion-resistant micro sensors for HCl, CO and organic volatiles, specifically including:

[0013] A first micro sensor based on a metal oxide semiconductor is provided to detect the concentration of HCl gas;

[0014] providing a second microsensor based on noble metal-doped carbon nanotubes for detecting CO gas concentration;

[0015] providing a third microsensor based on a polymer molecularly imprinted membrane for detecting the concentration of volatile organic compounds;

[0016] The first micro sensor, the second micro sensor and the third micro sensor are integrated into the same MEMS chip, and the surface is covered with an oleophobic and hydrophobic corrosion-resistant packaging layer.

[0017] Furthermore, the performing environmental interference compensation processing on the multi-channel concentration signal specifically includes:

[0018] Obtain real-time temperature and humidity data collected by the ambient temperature and humidity sensor;

[0019] Calculate the concentration compensation factor in the current environment based on the pre-calibrated temperature and humidity sensor baseline mapping table;

[0020] The multi-channel concentration signal is dynamically corrected according to the concentration compensation factor; and electromagnetic shielding is applied to the sensor output to suppress cable electromagnetic field interference.

[0021] Furthermore, the multi-source data fusion processing based on the cable operation parameters specifically includes:

[0022] Synchronously collect cable load current, operating temperature and historical degradation data;

[0023] The corrected multi-channel concentration signal and the load current, operating temperature and historical degradation data are input into a pre-trained insulation state assessment model.

[0024] Furthermore, the insulation status assessment model is constructed using a random forest algorithm to output a fusion evaluation value and a confidence index.

[0025] Furthermore, the generation of insulation status assessment results and warning signals based on the fused data specifically includes:

[0026] When the fusion evaluation value exceeds the first threshold, a mild degradation warning signal is generated;

[0027] When the HCl concentration change rate exceeds a preset slope and the fusion evaluation value exceeds a second threshold, an accelerated degradation warning signal is generated;

[0028] When the ratio of the organic volatile compound concentration to the CO concentration exceeds the critical value and the confidence index is greater than the preset ratio value, a partial discharge fault alarm is generated.

[0029] Furthermore, the method further comprises a calibration step:

[0030] Providing a reference gas source, wherein the reference gas source comprises a mixed gas of HCl, CO, and organic volatiles of known concentrations;

[0031] Performing a first calibration test on a reference gas source by using the micro gas sensor array to obtain first calibration data;

[0032] After moving the reference gas source along a preset path, performing a second calibration test to obtain second calibration data;

[0033] A sensor response correction factor is generated based on the first calibration data and the second calibration data.

[0034] Furthermore, the reference gas source includes a through-type porous release structure, and the porous release structure has a rotationally asymmetric characteristic point; the preset path is an arc path rotated 90° around the rotation axis; the time interval between the first calibration data and the second calibration data is less than the gas diffusion characteristic time.

[0035] Furthermore, the in-situ gas sampling of the running PVC insulated cable specifically includes:

[0036] The sampling probe is driven by the moving module to move along the cable axis, with the moving direction forming an acute angle with the normal of the cable surface; three sampling points are periodically switched within the moving plane to form a triangular sampling path;

[0037] Among them, the first sampling point is located at the cable joint, the second sampling point is located at the stress cone position, and the third sampling point is located at the heat-prone area of ​​the cable body.

[0038] Furthermore, the method further comprises:

[0039] A height detection module is set to monitor the distance between the sampling probe and the cable surface in real time. When the distance exceeds a threshold, the Z-axis coordinate of the mobile module is adjusted through feedback control. The ratio of the spacing between each point in the triangular sampling path to the cable diameter ranges from 0.8 to 1.2.

[0040] It can be seen from the above technical solutions that the present invention has the following advantages:

[0041] The online detection method for decomposition gas of the main insulating material of a PVC cable provided by the technical solution of the present invention adopts in-situ gas sampling to obtain decomposition gas samples of the operating cable, synchronously detects the concentrations of HCl, CO and organic volatiles through a corrosion-resistant microsensor array, and performs environmental interference compensation and cable operation parameter fusion processing on the multi-channel concentration signals. Thus, real-time online evaluation and early warning of the insulation status are achieved, thereby solving technical problems such as the inability to monitor the insulation status in real time in-situ, measurement inaccuracy under strong electromagnetic interference, and high false alarm rate of single gas thresholds. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The figure is a flow chart of an embodiment of a method for online detection of decomposition gas of the main insulating material of a PVC cable based on a micro gas sensor in the present invention. DETAILED DESCRIPTION

[0043] The terms "first", "second", "third", "fourth", etc. (if any) in the specification 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 interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "corresponding to" and any variations thereof 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.

[0044] Example 1

[0045] The implementation method in this embodiment can be implemented in the system, can be implemented in the server, and can also be implemented in the terminal, and the specific implementation is not clearly limited. The following is an introduction to the online detection method of the decomposition gas of the main insulating material of the PVC cable based on the micro gas sensor in this application from the perspective of system implementation. Figure 1 The method provided in the embodiment of the present application includes the following steps:

[0046] S1. Perform in-situ gas sampling on a running PVC insulated cable to obtain a sample gas flow containing decomposition gases;

[0047] In this embodiment, in-situ gas sampling is implemented based on a triangular path scanning mechanism, as follows:

[0048] 1. The sampling probe is driven by the moving module to move along the cable axis, with the moving direction forming an acute angle with the normal direction of the cable surface; three sampling points are periodically switched within the moving plane to form a triangular sampling path;

[0049] Among them, the first sampling point is located at the cable joint, the second sampling point is located at the stress cone position, and the third sampling point is located at the heat-prone area of ​​the cable body.

[0050] A corrosion-resistant sampling probe made of 316L stainless steel and coated with polytetrafluoroethylene is driven by a mobile module to move along the cable axis. The movement direction maintains an acute angle of 15°-45° with the normal to the cable surface to avoid mechanical interference. Within the movement plane perpendicular to the axis, the probe periodically switches between three sampling points to form a closed triangular path. The first sampling point locates the cable joint sealing interface, the second sampling point aligns with the stress cone semi-conductive layer transition zone, and the third sampling point covers the area with the maximum temperature gradient in the cable body. The dwell time at each sampling point is 10±2 seconds, the probe is 1.5mm from the insulation surface, and gas is extracted at a micro-flow rate of 0.8-1.2mL / min. The ratio of the triangular path side length to the cable diameter is 0.8-1.2, ensuring the simultaneous capture of local hotspot decomposition gases (HCl peak response time ≤15 seconds) within a single cycle (≤40 seconds) and suppressing concentration homogenization caused by gas mixing, thereby resolving monitoring blind spots and spatial positioning failures.

[0051] S2. Inputting the sample gas flow into a micro gas sensor array, the sensor array comprises a plurality of corrosion-resistant micro sensors for HCl, CO and organic volatiles, respectively;

[0052] In this example, the sensor array is integrated into a corrosion-resistant architecture, including:

[0053] A first micro sensor based on a metal oxide semiconductor is provided to detect the concentration of HCl gas;

[0054] providing a second microsensor based on noble metal-doped carbon nanotubes for detecting CO gas concentration;

[0055] providing a third microsensor based on a polymer molecularly imprinted membrane for detecting the concentration of volatile organic compounds;

[0056] The first micro sensor, the second micro sensor and the third micro sensor are integrated into the same MEMS chip, and the surface is covered with an oleophobic and hydrophobic corrosion-resistant packaging layer.

[0057] Through MEMS micromachining technology, the 2Three sets of sensor units are integrated on the silicon chip. The first unit uses SnO2-WO3 heterojunction metal oxide semiconductor (film thickness 200±10nm), whose surface oxygen vacancies preferentially adsorb HCl molecules, resulting in a linear decrease in resistivity (detection limit 0.5ppm). The second unit uses Pt@SWCNT noble metal-doped single-walled carbon nanotubes as the sensitive material (Pt loading 5wt%), and the oxidation reaction of CO molecules at the Pt catalytic sites produces a conductivity jump (response time <10s). The third unit uses a methacrylate-based molecularly imprinted polymer membrane (thickness 50μm), whose pre-constructed cavities specifically capture organic volatiles such as styrene and toluene (selectivity coefficient K>100). A polytetrafluoroethylene-silica composite hydrophobic layer (contact angle >150°) is co-deposited on the surface of the three units. This encapsulation layer can block the penetration of more than 90% of large molecular pollutants in the oily cable environment, and inhibits the corrosion of HCl gas on metal oxides through the dense SiO2 structure (porosity <0.1%). The baseline drift rate of the sensor array is controlled within ±2% under a strong electromagnetic field of 10kV / m, thus solving the problem of measurement inaccuracy caused by environmental interference.

[0058] S3 obtains the multi-channel concentration signal output by the sensor array, the multi-channel concentration signal includes the real-time concentration value of the characteristic gas;

[0059] Concentration quantification is achieved through a three-channel synchronous signal conversion circuit: the first channel collects the resistance change rate ΔR / R0 of the metal oxide semiconductor sensor (range 0-50%), based on the pre-stored HCl concentration-resistivity mapping table (fitting equation: C_HCl = 3.21×(ΔR / R0) 2 -0.87, R 2 >0.99) is converted into HCl concentration value; the second channel captures the conductivity jump time τ of noble metal-doped carbon nanotubes (range 5-100ms), and uses the CO catalytic oxidation kinetic model (τ -1 =0.18×C_CO^{0.5}) to invert the CO concentration; the third channel records the mass frequency shift Δf of the molecular imprinted membrane (resolution 0.1 Hz), according to the Sauerbrey equation (Δf = -2.26×10 -6 f0 2 The three-channel signal is synchronously sampled at 0.1 second intervals and converted by a 24-bit ADC to form a time-stamped concentration data stream (HCl / CO / VOC). Its real-time performance is guaranteed by FPGA hardware logic (delay <1ms), and differential transmission (common mode rejection ratio >100dB) in strong electromagnetic fields ensures that the concentration drift rate is less than ±0.5ppm, thus eliminating the problem of misjudgment caused by signal distortion.

[0060] S4. Perform environmental interference compensation processing on the multi-channel concentration signals and perform multi-source data fusion processing in combination with the cable operation parameters;

[0061] The multi-channel concentration signal is processed for environmental interference compensation, which specifically includes the following steps:

[0062] 1. Obtain real-time temperature and humidity data collected by the ambient temperature and humidity sensor;

[0063] 2. Calculate the concentration compensation factor for the current environment based on the pre-calibrated temperature and humidity sensor baseline mapping table;

[0064] 3. Dynamically correct the multi-channel concentration signal according to the concentration compensation factor; and apply electromagnetic shielding to the sensor output to suppress cable electromagnetic field interference.

[0065] Combined with the cable operation parameters, multi-source data fusion processing is carried out, including:

[0066] 1.Synchronously collect cable load current, operating temperature and historical degradation data;

[0067] 2. Input the corrected multi-channel concentration signal, load current, operating temperature and historical degradation data into the pre-trained insulation status assessment model.

[0068] Among them, the insulation status assessment model is constructed using the random forest algorithm to output fusion evaluation values ​​and confidence indicators.

[0069] First, real-time environmental data is acquired through temperature and humidity sensors placed closely against the sensor array. Based on a pre-calibrated temperature and humidity-sensor baseline mapping table, for example, the HCl sensor baseline drift δ = 0.12 × ΔT - 0.05 × ΔH at 25°C / 50% RH is calculated to dynamically correct the multi-channel signal. Simultaneously, a double-layer electromagnetic shielding (inner μ metal / outer copper mesh, attenuation > 80dB) is employed to suppress interference from the 10kA / m cable magnetic field. The corrected concentration signal, along with the simultaneously collected cable load current I (range 0-1000A), operating temperature T_cable (±1°C), and historical degradation data H (degradation index 0-10), was then fed into a pre-trained random forest assessment model. This model, constructed from 500 decision trees, takes as input the feature vector [X] = [C_HCl, C_CO, C_VOC, I, T_cable, H]. The model optimizes weights by splitting nodes using the Gini coefficient, outputting a fused evaluation value F (range 0-1, with F > 0.7 providing a warning) and a confidence index C. The model was trained using a ten-year dataset of faulty cables (including 327 insulation failures), achieving an AUC of 0.98 on the test set, significantly reducing false alarm rates in high-interference environments.

[0070] S5. Generate insulation status assessment results and early warning signals based on the fused data.

[0071] 1. When the fusion evaluation value exceeds the first threshold, a mild degradation warning signal is generated;

[0072] 2. When the HCl concentration change rate exceeds the preset slope and the fusion evaluation value exceeds the second threshold, an accelerated degradation warning signal is generated;

[0073] 3. When the ratio of organic volatile compound concentration to CO concentration exceeds the critical value and the confidence index is greater than the preset ratio value, a partial discharge fault alarm is generated.

[0074] This embodiment implements real-time diagnosis of insulation status based on a multi-level early warning strategy: when the fusion evaluation value F output by the random forest model exceeds 0.7 (the first threshold), the PVC insulation is judged to have entered the mild degradation stage, a yellow early warning signal is triggered, and the weekly inspection mode is initiated; when the HCl concentration change rate dC_HCl / dt exceeds 0.5ppm / min (the preset slope) and F exceeds 0.85 (the second threshold), it indicates accelerated thermal aging, triggering an orange early warning signal and automatically increasing the sampling frequency to 1 time / minute; when the ratio of the organic volatile compound concentration C_VOC to the CO concentration C_CO (C_VOC / C_CO) exceeds 3.5 (the critical value) and the confidence index C exceeds 90%, it indicates that partial discharge has caused high-temperature cracking (the styrene / CO generation ratio increases), triggering a red fault alarm and triggering the circuit breaker.

[0075] Furthermore, the detection method further comprises a calibration step:

[0076] 1. Provide a reference gas source containing a mixture of HCl, CO, and organic volatiles with known concentrations;

[0077] 2. Performing a first calibration test on the reference gas source using the micro gas sensor array to obtain first calibration data;

[0078] 3. After moving the reference gas source along a preset path, perform a second calibration test to obtain second calibration data;

[0079] 4. Generate a sensor response correction coefficient based on the first calibration data and the second calibration data.

[0080] Among them, the reference gas source includes a through-type porous release structure, the porous release structure has a rotationally asymmetric characteristic point; the preset path is an arc path rotated 90° around the rotation axis; the time interval between the first calibration data and the second calibration data is less than the gas diffusion characteristic time.

[0081] Specifically, the reference gas is an N2 balance gas containing known concentrations of HCl 50±0.5ppm, CO 30±0.3ppm, and styrene 20±0.2ppm. The traceability reference is provided by a reference gas source, and its through-type porous release structure is designed with rotationally asymmetric characteristic points, such as an eccentric hexagonal hole array with an eccentricity of ≥1.5mm, which is used for spatial orientation calibration. When performing calibration, the gas source is first detected in the initial position (rotation angle θ=0°), and the original outputs V1_HCl, V1_CO, and V1_VOC of the sensor array are recorded; then the gas source is driven to rotate 90° around the rotation axis. The system moves along an arc path (arc length r = 10 cm, angular velocity ω = 5° / s), completes the second detection at the θ = 90° position (time interval Δt < 0.5τ, τ = 2.3s is the gas diffusion characteristic time), and obtains the outputs V2_HCl, V2_CO, and V2_VOC. Based on the measurement deviation of the two-body posture, the response correction coefficient k_i = 1 + α·(ΔV_i / V_ref) is calculated, where i is the gas type and α = 0.82 is the material attenuation factor, thereby solving the measurement inaccuracy problem caused by environmental interference.

[0082] In addition, the detection method is also equipped with a height detection module to monitor the distance between the sampling probe and the cable surface in real time; when the distance exceeds a threshold, the Z-axis coordinate of the mobile module is adjusted through feedback control; the ratio of the spacing between each point in the triangular sampling path to the cable diameter ranges from 0.8 to 1.2.

[0083] The vertical distance h between the sampling probe and the cable surface is monitored in real time by the laser triangulation module. When h>1.5±0.1mm (threshold), the feedback control circuit drives the Z-axis piezoelectric ceramic actuator of the mobile module (displacement resolution 0.1μm) to adjust h back to the optimal value of 1.5mm within 10ms to avoid gas diffusion dilution caused by increased distance or mechanical collision caused by reduced distance. At the same time, the distance d between the three vertices of the triangular sampling path satisfies d / D=0.8-1.2, where D is the cable diameter. This ratio range has been verified by finite element thermal field simulation: when D=50mm, d=40-60mm can cover more than 90% of local hot spots (thermal gradient>5℃ / cm 2 ), and the total path length L = 3d matches the gas diffusion time constant τ (τ∝L / D), ensuring that the spatial decomposition gas concentration extremes (HCl spatial variation coefficient <8%) are captured within a single sampling cycle (40 seconds).

[0084] It is understandable that those skilled in the art can, under the guidance of the above embodiments, combine various implementation methods in the above embodiments to obtain technical solutions of multiple implementation methods.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An online detection method for decomposition gas of PVC cable main insulation material based on micro gas sensor, characterized in that: include: Conduct in-situ gas sampling on running PVC insulated cables to obtain sample gas streams containing decomposition gases; Inputting the sample gas flow into a micro gas sensor array, wherein the sensor array comprises a plurality of corrosion-resistant micro sensors for HCl, CO and organic volatiles respectively; Acquire a multi-channel concentration signal output by the sensor array, wherein the multi-channel concentration signal includes a real-time concentration value of a characteristic gas; Performing environmental interference compensation processing on the multi-channel concentration signal and performing multi-source data fusion processing in combination with cable operation parameters; Generate insulation status assessment results and early warning signals based on the fused data.

2. The online detection method for decomposition gas of PVC cable main insulation material based on micro gas sensor according to claim 1 is characterized in that: The micro gas sensor array comprises a plurality of corrosion-resistant micro sensors for HCl, CO and organic volatiles, specifically including: A first micro sensor based on a metal oxide semiconductor is provided to detect the concentration of HCl gas; providing a second microsensor based on noble metal-doped carbon nanotubes for detecting CO gas concentration; providing a third microsensor based on a polymer molecularly imprinted membrane for detecting the concentration of volatile organic compounds; The first micro sensor, the second micro sensor and the third micro sensor are integrated into the same MEMS chip, and the surface is covered with an oleophobic and hydrophobic corrosion-resistant packaging layer.

3. The online detection method for decomposition gas of PVC cable main insulation material based on micro gas sensor according to claim 1 is characterized in that: The performing environmental interference compensation processing on the multi-channel concentration signal specifically includes: Obtain real-time temperature and humidity data collected by the ambient temperature and humidity sensor; Calculate the concentration compensation factor in the current environment based on the pre-calibrated temperature and humidity sensor baseline mapping table; The multi-channel concentration signal is dynamically corrected according to the concentration compensation factor; and electromagnetic shielding is applied to the sensor output to suppress cable electromagnetic field interference.

4. The online detection method for decomposition gas of PVC cable main insulation material based on micro gas sensor according to claim 1 is characterized in that: The multi-source data fusion processing based on the cable operation parameters specifically includes: Synchronously collect cable load current, operating temperature and historical degradation data; The corrected multi-channel concentration signal and the load current, operating temperature and historical degradation data are input into a pre-trained insulation state assessment model.

5. The online detection method for decomposition gas of PVC cable main insulation material based on micro gas sensor according to claim 4 is characterized in that: The insulation status assessment model is constructed using a random forest algorithm and outputs a fusion evaluation value and a confidence index.

6. The online detection method for decomposition gas of PVC cable main insulation material based on micro gas sensor according to claim 1 is characterized in that: The generating of insulation status assessment results and early warning signals based on the fused data specifically includes: When the fusion evaluation value exceeds the first threshold, a mild degradation warning signal is generated; When the HCl concentration change rate exceeds a preset slope and the fusion evaluation value exceeds a second threshold, an accelerated degradation warning signal is generated; When the ratio of the organic volatile compound concentration to the CO concentration exceeds the critical value and the confidence index is greater than the preset ratio value, a partial discharge fault alarm is generated.

7. The online detection method for decomposition gas of PVC cable main insulation material based on micro gas sensor according to claim 1 is characterized in that: The method further comprises a calibration step: Providing a reference gas source, wherein the reference gas source comprises a mixed gas of HCl, CO, and organic volatiles of known concentrations; Performing a first calibration test on a reference gas source by using the micro gas sensor array to obtain first calibration data; After moving the reference gas source along a preset path, performing a second calibration test to obtain second calibration data; A sensor response correction factor is generated based on the first calibration data and the second calibration data.

8. The method for online detection of decomposition gas of the main insulating material of PVC cable based on a micro gas sensor according to claim 7, characterized in that: The reference gas source includes a through-type porous release structure, and the porous release structure has a rotationally asymmetric characteristic point; the preset path is an arc path rotated 90° around the rotation axis; the time interval between the first calibration data and the second calibration data is less than the gas diffusion characteristic time.

9. The online detection method for decomposition gas of PVC cable main insulation material based on micro gas sensor according to claim 1 is characterized in that: The in-situ gas sampling of the running PVC insulated cable specifically includes: The sampling probe is driven by the moving module to move along the cable axis, with the moving direction forming an acute angle with the normal direction of the cable surface; three sampling points are periodically switched within the moving plane to form a triangular sampling path; Among them, the first sampling point is located at the cable joint, the second sampling point is located at the stress cone position, and the third sampling point is located at the heat-prone area of ​​the cable body.

10. The online detection method for decomposition gas of PVC cable main insulation material based on micro gas sensor according to claim 9 is characterized in that: The method further comprises: A height detection module is set to monitor the distance between the sampling probe and the cable surface in real time. When the distance exceeds a threshold, the Z-axis coordinate of the mobile module is adjusted through feedback control. The ratio of the spacing between each point in the triangular sampling path to the cable diameter ranges from 0.8 to 1.2.

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