Cable end face multilevel defect detection method based on multi-wavelength laser scanning

Abstract

According to the cable end face multi-level defect detection method based on multi-wavelength laser scanning, defect information of different parts of the cable end face can be efficiently and accurately detected by combining laser scanning of different wavelengths, and a more reliable technical guarantee is provided for safe operation of a power system; a cable end face multi-level defect detection method based on multi-wavelength laser scanning comprises the following steps: step 1, scanning a cable end face through a multi-wavelength laser scanning device to obtain original three-dimensional point cloud data of different levels including a metal wire, a sheath, an insulating layer and the like; 2, Gaussian filtering is carried out on the obtained original three-dimensional point cloud data, scanning noise is eliminated, and three-dimensional point cloud data is generated; step 3, extracting multi-scale defect features; and 4, judging the position, the size, the depth, the type and the potential risk level of the defect in combination with the reflection characteristics and the geometric characteristics, and generating a defect assessment report.

Description

Technical Field

[0001] The present invention relates to the technical field of cable detection, and particularly to a multi-level defect detection method for cable end faces based on multi-wavelength laser scanning. Background Art

[0002] Cables in the power system are widely used in fields such as high-voltage power transmission, power distribution, and communication. With the increase in power load and the extension of operation years, the health status of cables plays a crucial role in the safe operation of the power system. During long-term operation, cables are often affected by external environments (such as temperature changes, mechanical friction, corrosion, etc.), as well as internal problems such as aging, wear, and cracks, resulting in a decline in the performance of cables such as insulation and conductivity. In severe cases, it may even lead to power system failures, endangering the stability and safety of power supply.

[0003] To ensure the normal operation of the power system, it is particularly important to detect the health status of cables in a timely and accurate manner. Traditional cable defect detection technologies usually include manual inspection, infrared thermal imaging, ultrasonic detection, X-ray imaging, etc. These methods have their own advantages and disadvantages. For example: Manual inspection has low efficiency, high cost, and is greatly affected by human factors; Infrared thermal imaging can only detect the temperature changes on the cable surface and cannot penetrate deep into the cable; Ultrasonic detection is applicable to certain materials, but has limited detection effects on cable surface damage; X-ray imaging. Although it can identify deeper defects, the equipment is expensive and the operation is complex.

[0004] Although these methods have achieved certain effects in some applications, they usually can only identify the damage on the cable surface or local areas and are difficult to comprehensively evaluate the health status of cables. There are obvious limitations especially in the detection of deep internal defects or multi-level structural defects in cables. Therefore, traditional technologies cannot meet the requirements of the power industry for high efficiency, high precision, and comprehensiveness in cable health detection.

[0005] In recent years, with the progress of laser scanning technology, cable defect detection methods based on laser scanning have begun to be widely used. Traditional three-dimensional laser scanning technology usually relies on a single wavelength laser (such as visible light, near-infrared, etc.) for scanning, obtains the reflection signal through the interaction between the laser and the cable surface, and obtains the three-dimensional point cloud data of the cable through computer processing. These scanning data can provide high-precision information on the cable surface topography and provide basic data support for defect detection.

[0006] However, the single wavelength laser scanning technology has significant deficiencies, which are mainly reflected in the following aspects:

[0007] (1)Poor adaptability to different materials: The composition of a cable usually includes multiple layers of different materials such as metal wires, insulating sheaths, and insulating layers. The surface characteristics (such as reflectivity, absorbency) of different materials vary greatly. When scanning with a single-wavelength laser, it is impossible to effectively distinguish the reflection characteristics between different layers of the cable. For example, metal wires usually have strong reflection to near-infrared light, while the insulating sheath or insulating layer may be more sensitive to visible light. Using a single-wavelength laser often leads to distortion of the scanned data, making it difficult to accurately obtain the details of each layer. Especially at the material interface, misjudgment or information loss is likely to occur.

[0008] (2)Lack of depth information: Single-wavelength laser scanning can only provide the surface or relatively shallow reflection data of the cable, making it difficult to deeply identify the defects inside the cable. For the metal wire part of the cable, defects such as surface cracks and corrosion may penetrate to deeper layers, but a single-wavelength laser usually cannot provide sufficient depth information. Therefore, when detecting deep-seated defects inside the cable (such as metal corrosion, wire breakage, insulation layer peeling, etc.), the detection accuracy and reliability of single-wavelength scanning are relatively low.

[0009] (3)Greatly affected by environmental factors: The effect of laser scanning technology is often greatly affected by environmental conditions (such as temperature, humidity, etc.). The penetration power, reflectivity, and other characteristics of lasers with different wavelengths are also different in different environments. Single-wavelength laser scanning cannot stably obtain high-quality reflection data under various complex environmental conditions, thus affecting the accuracy of defect identification.

[0010] In summary, the existing cable defect detection methods based on single-wavelength laser scanning have obvious deficiencies in dealing with the multi-layer structure of cables and the reflection characteristics of different materials, and it is difficult to accurately and comprehensively identify potential defects in cables. Therefore, the present invention proposes a multi-wavelength laser scanning-based method for detecting multi-level defects on the end face of cables. Summary of the Invention

[0011] The present invention aims at the technical defects of the existing technology and discloses a multi-wavelength laser scanning-based method for detecting multi-level defects on the end face of cables. By combining laser scans of different wavelengths, it can efficiently and accurately detect defect information at different parts of the cable end face, providing a more reliable technical guarantee for the safe operation of the power system.

[0012] The present invention provides the following technical solutions:

[0013] A multi-wavelength laser scanning-based method for detecting multi-level defects on the end face of cables, the method comprising the following steps:

[0014] Step 1: Scan the end face of the cable with a multi-wavelength laser scanning device to obtain the original three-dimensional point cloud data including different layers such as metal wires, sheaths, and insulating layers;

[0015] Step 2: Perform Gaussian filtering on the acquired original three-dimensional point cloud data, eliminate scanning noise, and generate three-dimensional point cloud data;

[0016] Step 3: Extract multi-scale defect features;

[0017] Reflection feature: According to the reflection characteristics of different wavelengths, analyze the reflection information of each layer of the cable, and extract surface defect features of different parts such as metal wires, sheaths, and insulation layers;

[0018] Geometric feature: Extract features from the three-dimensional point cloud data of different parts, and automatically detect defects such as cracks, corrosion, wear, and insulation layer peeling that may exist on the cable end face;

[0019] Step 4: Combine the reflection feature and the geometric feature to judge the position, size, depth, type, and potential risk level of the defect, and generate a defect assessment report.

[0020] Furthermore,

[0021] The multi-wavelength laser scanning device includes at least one of the following laser sources: visible light band, near-infrared band, short-wave infrared band. Distinguish the structural layers according to the spectral reflectance differences of different materials, that is:

[0022] (1),

[0023] where R(λ) is the reflectance at the wavelength, I r reflected light intensity, I 0 incident light intensity.

[0024] Furthermore,

[0025] The original three-dimensional point cloud data in Step 1 is scanned by a three-dimensional scanning device to obtain the three-dimensional coordinates (x, y, z) and reflection intensity of the cable end face, and construct a multi-modal data set D = {(x i , y i , z i , I λi )}, where λ i is the i-th wavelength.

[0026] Furthermore,

[0027] The three-dimensional point cloud data , where σ is the standard deviation of the filtering kernel, which is adaptively adjusted according to the surface roughness of the cable, and σ = 0.5 - 1.0 mm.

[0028] Furthermore,

[0029] For the reflection feature, statistically analyze the variance of the reflection intensity at different wavelengths ;

[0030] The geometric features reflect the unevenness of the cable surface by calculating the local curvature. to reflect the unevenness of the cable surface.

[0031] Furthermore,

[0032] the depth of the defect , where c is the speed of light.

[0033] Furthermore,

[0034] The risk level constructs a risk index R based on the curvature and the reflection variance, and its calculation formula is:

[0035] (2),

[0036] where α and β are weight coefficients, α = 0.6, β = 0.4, C max , σ max is the maximum threshold allowed by the cable specification, and when the risk coefficient exceeds the specified threshold, an alarm is triggered.

[0037] Furthermore,

[0038] The multi-wavelength laser scanning device includes a tunable wavelength laser, a laser receiver, and a scanning device, which can perform scans at different wavelengths, scan different parts of the cable respectively, and generate corresponding original three-dimensional point cloud data.

[0039] A computer-readable medium stores a computer program thereon, and when the program is executed by a processor, it implements a multi-level defect detection method for the cable end face based on multi-wavelength laser scanning.

[0040] The present invention discloses a multi-level defect detection method for the cable end face based on multi-wavelength laser scanning. This method uses multi-wavelength laser scanning technology and combines deep learning algorithms to perform efficient and accurate detection on the cable end face, and is particularly suitable for defect detection of multi-level structures such as metal wires, sheaths, and insulating layers on the cable end face. It has the following advantages:

[0041] (1) Multi-wavelength laser scanning: Use a multi-wavelength laser scanning device to scan the cable end face, and extract the morphological features of each layer such as metal wires, sheaths, and coatings from the laser reflection intensity data at different wavelengths; Lasers with different wavelengths can match the reflection characteristics of different materials, so as to obtain more comprehensive surface information;

[0042] (2) Data preprocessing: Perform noise removal, normalization, and depth information extraction on the obtained point cloud data to ensure data stability and accuracy for subsequent analysis;

[0043] (3)Defect feature extraction: Combining reflection features and geometric features, extract defect features at each level, and automatically detect defects such as cracks, corrosion, wear, and coating peeling.

[0044] (4)Defect assessment: Generate a cable health status report based on the detected defect features, and output the defect location, depth, type, and potential risk level.

[0045] Through the above technical solutions, the present invention can accurately identify multi-level defect conditions on the cable end face, especially showing extremely high detection accuracy when facing cables with different material structures. Brief Description of the Drawings

[0046] Figure 1 It is a simplified schematic diagram of the cable structure;

[0047] Figure 2 It is a distribution diagram of the laser reflection intensity and the cable radius at different wavelengths;

[0048] Figure 3 It is a schematic diagram of cable defect detection;

[0049] Figure 4 It is a schematic diagram of the multi-wavelength switching three-dimensional laser scanning device module. Detailed Embodiment

[0050] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0051] The core principle of the present invention is to obtain the reflection intensity data of each layer of material on the cable end face through multi-wavelength laser scanning. Since different materials have different reflection characteristics for lasers of different wavelengths, by analyzing the changes in reflection intensity at different wavelengths, the distribution of different materials on the cable end face and the location and type of defects can be inferred.

[0052] The laser scanning principle is based on the interaction between the laser beam and the object surface. When the laser irradiates the cable end face, part of the laser is reflected back by the surface, and the reflection intensity is closely related to the refractive index of the material. Different materials have different reflection characteristics for lasers of different wavelengths. Generally, metal materials have a strong reflection ability for near-infrared lasers, while the insulating layer has a stronger reflection ability for visible light and short-wave infrared lasers. Therefore, using multi-wavelength lasers can more accurately obtain the reflection information of different layers of materials on the cable end face. Through the reflection intensity at different positions and different wavelengths, the three-dimensional spatial distribution of the materials can be obtained, and by comparing with a normal cable, the fault detection information can be obtained.

[0053] According to Fresnel's law, the relationship between the reflection intensity R(λ) and the refractive index of the material can be described by the following formula:

[0054]

[0055] where n(λ) is the refractive index of the medium, and the refractive index is different at different wavelengths λ. n 0 is the refractive index of air, approximately 1. The refractive indices of different materials (such as metals, insulating layers) are different at different wavelengths. Therefore, light of different wavelengths scanning the cable can obtain different intensity spatial distributions. Thus, the reflection intensity information of different layers of the cable can be obtained by multi-wavelength laser scanning.

[0056] More importantly, different materials have different absorption and reflection of light of different wavelengths. According to the different reflection characteristics of the materials, the reflection intensity R(λ) of the laser will show different distribution patterns at different wavelengths. For example, metals have a strong reflection ability for near-infrared light (NIR), while the reflection of visible light is relatively weak. Insulating layers or insulating materials have a strong reflection in the visible light band, while the reflection of near-infrared or short-wave infrared light is weak. And the sheath material has a stronger reflection for short-wave infrared. The reflection intensities Rmetal(λ) and Rcoating(λ) of metal wires and plastic insulating layers at different wavelengths can be expressed as follows:

[0057]

[0058]

[0059] where Ametal(λ) and Acoating(λ) are the reflection coefficients of the metal and the insulating layer respectively; αmetal(λ) and αcoating(λ) are the absorption coefficients of the metal and the insulating layer for light of different wavelengths; d is the distance between the laser and the material surface, representing the skin depth of laser scanning. By analyzing the changes in the reflection intensity at different wavelengths, the distribution of different layers (such as metal wires, insulating layers, sheaths, etc.) in the cable can be identified. Taking Figure 1 the simplified cable structure shown as an example, when the end face of the cable is scanned with visible light (wavelength range 400 - 700 nm), the distribution of its reflection intensity and radius is as shown in Figure 2 (a), when the end face of the cable is scanned with near-infrared (wavelength range 700 - 1500 nm), the distribution of its reflection intensity and radius is as shown in Figure 2 (b), when the end face of the cable is scanned with short-wave infrared (wavelength range 1500 - 2500 nm), the distribution of its reflection intensity and radius is as shown in Figure 2As shown in (c). Metals have a higher reflectivity to near-infrared light, the insulating layer has a higher reflectivity to visible light, and the sheath has a higher reflectivity to short-wave infrared light. By analyzing the reflection distributions of the three types of lasers, the material distribution of the cable can be obtained, and fault detection can be achieved by comparing with a normal cable.

[0060] Moreover, the three-dimensional point cloud data obtained by laser scanning usually includes position coordinates (x, y, z) and reflection intensity information. On this basis, combined with laser ranging technology, the distance from the object surface to the laser source can be obtained through the time-of-flight principle of the laser, thereby obtaining depth information. The relationship between the flight time t of the laser after reflection on the object surface and the distance d can be expressed by the following formula:

[0061]

[0062] where c is the speed of light. By measuring the reflection intensity and flight time of lasers with different wavelengths, the depth and reflection intensity information of the cable surface can be obtained simultaneously, so as to comprehensively reflect the defect situation of the cable.

[0063] Defects (such as cracks, corrosion, insulation layer peeling) usually cause significant changes in the reflection intensity of the cable surface. For example: cracks will cause uneven distribution of local reflection intensity, and the change in the reflection angle of the laser beam at the crack leads to a decrease in reflection intensity, as Figure 3 shown. Corrosion will increase the surface roughness, thereby increasing the scattering of the reflected light, causing changes in the reflection intensity, and at the same time, the reflection time will also be extended. Insulation layer peeling will significantly reduce the reflection intensity in the insulation layer area and extend the reflection time, especially at the place where the insulation layer is peeled. By analyzing the changes in reflection intensity and flight time (i.e., depth), it is possible to accurately identify whether there are defects on the cable surface and determine the type and location of the defects.

[0064] Using the reflection intensity data obtained by multi-wavelength laser scanning, by comparing the changes in reflection intensity at different wavelengths, the reflection intensity characteristics of each point are extracted. If there are significant changes in the reflection intensity at a certain position (such as a significant decrease or uneven distribution of reflection intensity at different wavelengths), it can be judged that there is a defect at that position.

[0065] Automatically identify the type of defect. For example, by analyzing the spatial distribution of reflection intensity, depth information, and the change pattern of reflection, the classification model can classify the defect types into cracks, corrosion, insulation layer peeling, etc.

[0066] The present invention discloses a multi-level defect detection method for the cable end face based on multi-wavelength laser scanning, and the method includes the following steps:

[0067] Step 1: Scan the cable end face with a multi-wavelength laser scanning device to obtain the original three-dimensional point cloud data including different layers such as metal wires, sheaths, and insulation layers;

[0068] Step 2: Perform Gaussian filtering on the obtained original three-dimensional point cloud data and eliminate scanning noise to generate three-dimensional point cloud data;

[0069] Step 3: Extraction of multi-scale defect features;

[0070] Reflection feature: According to the reflection characteristics of different wavelengths, analyze the reflection information of each layer of the cable, and extract the surface defect features of different parts such as metal wires, sheaths, and insulation layers;

[0071] Geometric feature: Extract features from the three-dimensional point cloud data of different parts to automatically detect possible defects such as cracks, corrosion, wear, and insulation layer peeling on the cable end face;

[0072] Step 4: Combine the reflection features and geometric features to judge the position, size, depth, type, and potential risk level of the defect, and generate a defect assessment report.

[0073] Furthermore, the multi-wavelength laser scanning device includes at least one of the following laser sources: visible light band, near-infrared band, short-wave infrared band. The structural layers are distinguished according to the spectral reflectance differences of different materials, that is:

[0074] (1),

[0075] where R(λ) is the reflectance at the wavelength, I r reflected light intensity, I 0 incident light intensity.

[0076] Furthermore, the original three-dimensional point cloud data in Step 1 scans the three-dimensional coordinates (x, y, z) and reflection intensity of the cable end face through a three-dimensional scanning device to construct a multi-modal data set D = {(x i , y i , z i , I λi )}, where λ i is the i-th wavelength.

[0077] Furthermore, the three-dimensional point cloud data , where σ is the standard deviation of the filtering kernel, which is adaptively adjusted according to the surface roughness of the cable, σ = 0.5 ∼ 1.0 mm.

[0078] Furthermore, for the reflection feature, the variance of the reflection intensity at different wavelengths is statistically calculated ;

[0079] The geometric feature is obtained by calculating the local curvature to reflect the unevenness of the cable surface.

[0080] Further, the depth of the defect , where c is the speed of light.

[0081] Further, the risk level constructs a risk index R based on the curvature and the reflection variance, and its calculation formula is:

[0082] (2),

[0083] where α and β are weight coefficients, α = 0.6, β = 0.4, C max , σ max is the maximum threshold allowed by the cable specification, and when the risk coefficient exceeds the specified threshold, an alarm is triggered.

[0084] Further, the multi-wavelength laser scanning device includes a tunable wavelength laser, a laser receiver, and a scanning device, which can perform scans at different wavelengths, scan different parts of the cable respectively, and generate corresponding original three-dimensional point cloud data.

[0085] A computer-readable medium stores a computer program thereon, and when the program is executed by a processor, it implements a method for multi-level defect detection of a cable end face based on multi-wavelength laser scanning

[0086] The above method for multi-level defect detection of a cable end face based on multi-wavelength laser scanning is used to perform multi-level defect detection of the cable. The specific on-site detection steps are as follows:

[0087] (1) Scanning parameter setting: As Figure 4 shown, the scanning device has a visible light band, a near-infrared band, and a short-wave infrared band. A three-band laser source of visible light (532 nm), near-infrared (1064 nm), and short-wave infrared (1550 nm) is used to distinguish the structural layers by using the spectral reflectance differences of different materials;

[0088] Laser wavelength switching sequence: 532 nm → 1064 nm → 1550 nm;

[0089] Scanning inclination: perpendicular to the cable end face (θ = 0°);

[0090] Scanning range: covering the cable end face diameter (50 mm).

[0091] (2) Data acquisition:

[0092] Each wavelength scan generates an independent point cloud data set, which contains coordinates (x, y, z) and reflection intensity I λ ; An example of the original data is shown in Table 1 (partial point cloud):

[0093] Table 1

[0094]

[0095] (3)Data preprocessing

[0096] 1)Gaussian filtering:

[0097] The standard deviation of the filtering kernel is taken as σ = 0.8 mm;

[0098] The signal-to-noise ratio (SNR) of the point cloud after processing is increased from 28 dB to 35 dB.

[0099] 2)Depth information extraction:

[0100] The defect depth is calculated according to the formula d = 1 / 2c⋅Δt;

[0101] Example: At a certain crack, Δt = 133 ps and the depth d = 0.12 mm.

[0102] (4)Defect feature extraction and classification

[0103] Feature calculation: Local curvature: C = 0.15 mm −1 (Crack area), variance of reflection intensity: σ I 2 = 420 (corrosion area)

[0104] Risk assessment and report generation

[0105] 1)Risk index calculation:

[0106] R = 0.6×0.15 / 0.2 + 0.4×420 / 600 = 0.73; Risk level: High risk (threshold R≥0.5).

[0107] 2)Report output:

[0108] a) Defect location: Metal wire (crack), sheath (corrosion), insulation layer (peeling);

[0109] b) Depth: 0.12 mm, 0.08 mm, 0.25 mm;

[0110] c) Suggested measures: When the risk coefficient exceeds the specified threshold, such as 0.2, trigger an alarm, replace the insulation layer, and locally repair the metal wire.

[0111] In summary, the present invention proposes a multi-level defect detection method for cable end faces based on multi-wavelength laser scanning. Compared with single-wavelength laser scanning, multi-wavelength laser scanning has obvious advantages:

[0112] (1)Improve adaptability to different materials: By combining lasers of multiple wavelengths (such as visible light, near-infrared, short-wave infrared, etc.), more accurate reflection signals can be obtained for different levels of materials such as metal wires, insulating sheaths, and insulating layers. For example, the metal part has a strong reflection to near-infrared laser, while the insulating layer is more sensitive to visible light or short-wave infrared light.

[0113] (2)Enhance the acquisition of depth information: Multi-wavelength laser scanning can effectively scan the surface and internal levels of the cable at different wavelengths, accurately obtain the depth information of each level, and thus effectively identify deep defects inside the cable, such as metal corrosion, wire breakage, etc.

[0114] (3)Improve environmental adaptability: Multi-wavelength laser scanning can obtain more stable and accurate reflection data under different environmental conditions, reduce the influence of environmental changes on the detection results, and improve the reliability of detection.

[0115] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those of ordinary skill in the art, various changes can be made without departing from the spirit of the present invention. These changes involve related technologies well-known to those skilled in the art, and all fall within the protection scope of this invention patent.

[0116] Many other changes and modifications can be made without departing from the concept and scope of the present invention. It should be understood that the present invention is not limited to specific embodiments, and the scope of the present invention is defined by the appended claims.

Claims

1. A cable end face multi-level defect detection method based on multi-wavelength laser scanning, characterized in that: The method comprises the following steps: Step 1: Scan the cable end face with a multi-wavelength laser scanning device to obtain original three-dimensional point cloud data of different layers including metal conductors, sheaths, insulation layers, etc. Step 2: Perform Gaussian filtering on the acquired original 3D point cloud data and eliminate scanning noise to generate 3D point cloud data; Step 3: Extraction of multi-scale defect features; Reflection characteristics: According to the reflection characteristics of different wavelengths, the reflection information of each layer of the cable is analyzed to extract the surface defect characteristics of different parts such as metal conductors, sheaths, and insulation layers; Geometric features: Extract features from 3D point cloud data of different parts to automatically detect possible defects such as cracks, corrosion, wear, and insulation peeling on the cable end face; Step 4: Combine the reflection characteristics and geometric characteristics to determine the location, size, depth, type and potential risk level of the defect, and generate a defect assessment report.

2. The cable end face multi-level defect detection method based on multi-wavelength laser scanning according to claim 1 is characterized in that: The multi-wavelength laser scanning device includes at least one of the following laser sources: visible light band, near infrared band, short-wave infrared band, and distinguishes structural layers according to the difference in spectral reflectivity of different materials, that is: （1）， Where R(λ) is the reflectivity at wavelength, I r I0 is the reflected light intensity, I0 is the incident light intensity.

3. The cable end face multi-level defect detection method based on multi-wavelength laser scanning according to claim 2 is characterized in that: The original three-dimensional point cloud data in step 1 is scanned by a three-dimensional scanning device to obtain the three-dimensional coordinates (x, y, z) and reflection intensity of the cable end face, and a multimodal data set D = {(x i ,y i ,z i ,I λi )}, where λ i is the ith wavelength.

4. The cable end face multi-level defect detection method based on multi-wavelength laser scanning according to claim 3 is characterized in that: The three-dimensional point cloud data , where σ is the standard deviation of the filter kernel, which is adaptively adjusted according to the cable surface roughness, σ = 0.5 ∼ 1.0 mm.

5. The cable end face multi-level defect detection method based on multi-wavelength laser scanning according to any one of claims 1 to 4, characterized in that: The reflection characteristics are used to calculate the reflection intensity variance at different wavelengths. ; The geometric features are characterized by calculating the local curvature To reflect the convexity and concavity of the cable surface.

6. The cable end face multi-level defect detection method based on multi-wavelength laser scanning according to claim 5 is characterized in that: The depth of the defect , where c is the speed of light.

7. The cable end face multi-level defect detection method based on multi-wavelength laser scanning according to claim 6 is characterized in that: The risk level constructs a risk index R based on the curvature and reflection variance, and its calculation formula is: （2）， Among them, α, β are weight coefficients, α=0.6, β=0.4, C max , σ max It is the maximum threshold allowed by the cable specification. When the risk factor exceeds the specified threshold, an alarm is triggered.

8. The cable end face multi-level defect detection method based on multi-wavelength laser scanning according to claim 6 is characterized in that: The multi-wavelength laser scanning device includes an adjustable wavelength laser, a laser receiver and a scanning device, and can scan at different wavelengths, respectively scan different parts of the cable, and generate corresponding original three-dimensional point cloud data.

9. A computer readable medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for detecting multi-level defects on a cable end face based on multi-wavelength laser scanning as described in any one of claims 1 to 8 is implemented.

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