An Insulation Test Method for Single-Phase Smart Energy Meters

Abstract

This invention relates to the field of electricity meter testing technology, solving the technical problem of misjudgment of leakage current caused by metal debris on the surface of terminals during insulation testing, and particularly to an insulation testing method for single-phase smart electricity meters. This invention captures point cloud data by projecting structured stripe light onto the surface of the terminals; projects a low-density grating onto the surface of the terminals to generate a basic curvature map, marking high-risk areas; dynamically adjusts the grating period based on the basic value, and simultaneously acquires multi-channel polarization images and calculates the Stokes vector. This invention uses curvature adaptive polarization light field technology to reduce the error in normal vector calculation in areas with curvature radii less than a critical value, eliminating signal attenuation in the ultraviolet / near-infrared channels due to overcompensation, and ensuring optical measurement accuracy on extreme curved surfaces; and combines tomographic confocal scanning with deconvolution reconstruction algorithms to penetrate multiple layers of stacked metal debris, solving the problem of missed detection of bottom-layer debris caused by the shading effect in traditional optical systems.

Description

Technical Field

[0001] This invention relates to the field of electricity meter testing technology, and in particular to an insulation testing method for a single-phase smart electricity meter. Background Technology

[0002] Insulation testing of single-phase smart meters is a procedure used to evaluate whether the insulation performance between the internal circuitry of the meter and external accessible metal parts (such as terminals, junction boxes, etc.), as well as between different energized circuits (such as phase and neutral wires, phase and ground wires, etc.), meets the standard requirements. Before testing the meter, the physical contact points of the high-voltage or grounding terminals need to be cleaned, especially the terminals, which undergo irreversible electrochemical reactions in the environmental medium, resulting in the formation of an oxide layer on their surface. This oxide layer can create false conductive paths during insulation testing, causing the measurement results to deviate from the actual insulation condition. Therefore, before insulation testing, a precision eraser or fiberglass pen is used to wipe the surface of the terminals to remove the oxide layer.

[0003] When wiping the surface of the terminals, manual operation cannot precisely control the wiping force. This microscopic cutting action causes fine metal chips to detach from the oxide layer on the terminal surface. Because the oxide layer on the terminal surface has hybrid insulating and semiconductor properties, these metal chips scatter within the terminal gaps, forming bridging channels that can lead to leakage and incorrect insulation test results, misclassifying qualified products as unqualified. Furthermore, in high-voltage environments during insulation testing, the high voltage creates a high-intensity electric field around the metal chips. This causes the originally electrically neutral metal chips to accumulate charge through induction or contact, generating electrostatic attraction. Under the influence of this electrostatic attraction, they migrate, resulting in dynamic errors in the data correction strategy for the distribution of metal chip positions. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an insulation testing method for single-phase smart energy meters. This method solves the technical problem of misjudging leakage current caused by metal debris on the surface of the terminals during insulation testing of single-phase smart energy meters. It achieves the goal of quantifying the quantity, size, and location of metal debris on the terminal surface using a vision system, establishing an equivalent conductivity model of the debris on the terminal surface, correcting the measured resistance value in insulation testing, and thus avoiding the misjudgment of qualified energy meters as unqualified.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an insulation testing method for a single-phase smart energy meter, the method comprising the following steps:

[0006] Structured stripe light is projected onto the surface of the terminal block to capture point cloud data;

[0007] A low-density grating is projected onto the surface of the terminal block to generate a basic curvature map, marking high-risk areas. The grating period is dynamically adjusted based on the basic value, multi-channel polarization images are acquired simultaneously, and Stokes vectors are calculated. Optical distortion is corrected by combining the surface normal vector compensation. Tomographic confocal scanning is performed based on the basic curvature map, and spectral wavelengths are switched according to curvature partitions to generate a four-dimensional tensor of spectral data. The corrected polarization images and the four-dimensional tensor of spectral data are fused, and the volume data of metal chips is reconstructed through three-dimensional Fourier deconvolution.

[0008] The risk index is calculated by identifying the corrected three-dimensional coordinates of metal chips and the equivalent conductive area, and constructing a metal chip matrix on the surface of the terminal block. Then, a spatial admittance matrix is ​​established based on the metal chip matrix, and the Poisson equation is solved to simulate the potential distribution and predict the displacement of metal chips. The spatial admittance matrix is ​​updated by the displacement of metal chips, and the node current distribution is calculated by combining the node potential vector.

[0009] Then, extract the metal chip branch current that forms a path between the two measured terminals from the node current vector, and sum them to obtain the spurious leakage current component; by subtracting the spurious leakage current component from the measured total leakage current, calculate the true leakage current that corrects the measured insulation resistance value.

[0010] Furthermore, the method of correcting optical distortion by combining surface normal vector compensation is as follows:

[0011] The surface gradient is calculated based on the point cloud data of the terminal block surface, and a compensation vector is generated, i.e.:

[0012]

[0013] In the formula, The normal vector compensation value is δ, which is a modifier. α is the basic normal vector; α is the curvature and error conversion factor; unit(...) is the vector normalization operator; It is a radial unit vector; κ represents the rate of change of curvature along the radial direction; r is the radial coordinate; κ is the curvature value; z is the depth direction.

[0014] The final true normal vector is generated based on the normal vector compensation and the original optical normal vector, i.e.:

[0015]

[0016] In the formula, S is the true normal vector; k is the parameter index of the Stokes component; S k Stokes components; is the polarization basis vector.

[0017] Furthermore, the method of switching spectral wavelengths according to curvature partitions is as follows:

[0018] Activate a single wavelength in the low curvature region;

[0019] In the medium curvature region, complementary dual-wavelength combinations are employed;

[0020] Triggering three-wavelength synergy in high curvature regions.

[0021] Furthermore, the method for reconstructing the volume data of metal micro-chips using three-dimensional Fourier deconvolution is as follows:

[0022] An enhanced dataset for calculating high-risk areas based on terminal block surface curvature values;

[0023] The polarization compensation angle at each coordinate point on the terminal block surface is calculated based on the true normal vector. An inverse compensation matrix is ​​constructed based on the polarization compensation angle. The original polarization image is then depolarized. Finally, the polarization image is convolved with the inverse compensation matrix.

[0024]

[0025] In the formula, I corr (x,y) represents the corrected image; G UV The UV gain factor; Conv(...) is the matrix convolution operation; I raw This is the original polarization image; It is the inverse compensation matrix;

[0026] The corrected image and the four-dimensional tensor of the spectral data are fused along the depth direction to form an image sequence with depth information. Then, the image sequence is subjected to tonal deconvolution processing, i.e.:

[0027]

[0028] in,

[0029] In the formula, V vol For reconstructed metal chip volume data; F{...} denotes the three-dimensional Fourier transform operator; F -1 [...] indicates the inverse Fourier transform; H * (w) is the curvature-adaptive hybrid transfer function; w is the spatial frequency vector; OTF(w) is the optical transfer function; CTF(w) is the confocal transfer function; D(x,y,z) is the image sequence with depth information; κ th This is the risk threshold.

[0030] Furthermore, the method for identifying the corrected three-dimensional coordinates of the metal chips is as follows:

[0031] The reflectance of ultraviolet and near-infrared light at each coordinate point on the surface of the terminal block is calculated. The material of the terminal block surface is classified according to the reflectance, including metal chips, mixed chips, and non-metal residues. Metal weight, non-metal weight, and mixed weight are calculated based on the reflectance. Then, the multispectral information is fused into the final fused image based on the three weights.

[0032] A partition mapping function is established based on the basic curvature diagram of the terminal block surface, i.e.

[0033] Ω i ={(x,y)|κ th,i ≤κ(x,y)<κ th,i +Δκ max}

[0034] In the formula, Ω i For the i-th curvature partition, κ th,i Let Δκ be the reference curvature threshold for the i-th curvature partition. max The maximum curvature difference threshold is κ(x,y); κ(x,y) is the basic curvature map.

[0035] Each curvature partition is then independently segmented into watersheds, and morphological filtering is used for denoising to generate a binary label matrix for metal chips. Based on a three-dimensional weighted average, the geometric center of each connected component is calculated as the coordinate point to generate the precise three-dimensional coordinates r of each metal chip on the terminal surface. obs The precise three-dimensional coordinates are corrected using curvature gradients, i.e.:

[0036]

[0037] In the formula, r real The coordinates are the three-dimensional coordinates after correction for metal chips; τ is the correction coefficient used to adjust the compensation intensity of the curvature gradient. This represents the depth gradient vector at the precise three-dimensional coordinates of the metal chip.

[0038] Furthermore, the method for identifying the equivalent conductive area is as follows:

[0039] The light intensity distribution along the depth direction z is extracted from the reconstructed metal chip volume data, and the perpendicular projection component of the metal chip is calculated. Then, normal vector tilt compensation is introduced to generate the equivalent conductive area of ​​the metal chip, i.e.:

[0040]

[0041] In the formula, A eff V(z) represents the equivalent conductive area of ​​the metal chips; V(z) represents the light intensity distribution along the depth direction z; max(V(z)) represents the peak light intensity on the surface of the terminal block, z maxThe depth of the peak light intensity location; τ′ is the tilt amplification factor; cos -1 (...) is the inverse cosine function; It is the unit vector along the absolute optical axis.

[0042] Furthermore, the formula for calculating the three-dimensional risk index is as follows:

[0043]

[0044] In the formula, R risk,i Let A be the risk index for the i-th metal chip; eff,i d represents the equivalent conductive area of ​​the i-th metal chip; i Let be the distance from the i-th metal chip to the nearest conductor; where:

[0045]

[0046] In the formula, f(R) u / ir ) is the mapping function from micro-chip material to weight.

[0047] Furthermore, the elements of the spatial admittance matrix satisfy:

[0048]

[0049] In the formula, y i,j d represents matrix elements; ij R is the distance between the i-th and j-th metal chips; ele radius of electric field, A eff,i Let A be the equivalent conductive area of ​​the i-th metal chip. eff,j Let be the equivalent conductive area of ​​the j-th metal chip.

[0050] Furthermore, the method for predicting the displacement of metal chips is as follows:

[0051] The charge density is weighted based on the risk index of metal debris, i.e.:

[0052]

[0053] In the formula, ρ( x,y,z ) represents the charge density. It is the Dirac function;

[0054] The potential distribution Φ(x,y,z) on the surface of the terminal block is solved based on the Poisson equation, and the calculation formula is as follows:

[0055]

[0056] In the formula, ε is the Laplace operator for electric potential; ε is the dielectric constant of the medium;

[0057] Then, the electric field intensity vector is obtained using the potential gradient. The displacement of the metal chips is calculated using the following formula:

[0058]

[0059] In the formula, ψ is the position change vector of the metal chip; ψ is the mobility coefficient of the metal chip; Δt is the time step of the electric field. This represents the electric field intensity vector at the i-th metal chip.

[0060] Furthermore, the method for calculating the node current distribution is as follows:

[0061] Calculate the potential value at the location of each metal chip, and arrange the potential values ​​at all locations of the metal chips in order to form a nodal potential vector. The calculation formula is:

[0062]

[0063] Y is the node current vector, and Y′ is the new spatial admittance matrix.

[0064] By employing the above technical solution, the present invention provides an insulation testing method for a single-phase smart energy meter, which has at least the following beneficial effects:

[0065] 1. This invention utilizes curvature adaptive polarization optical field technology to reduce the error in normal vector calculation in regions with curvature radii less than a critical value (such as arc-shaped press-fit structures), eliminates the signal attenuation of ultraviolet / near-infrared channels caused by overcompensation, and ensures the optical measurement accuracy on extreme curved surfaces. Furthermore, tomographic confocal scanning combined with deconvolution reconstruction algorithms can penetrate multiple layers of stacked metal debris, solving the problem of missed detection of bottom-layer debris caused by the shading effect in traditional optical systems.

[0066] 2. This invention accurately calculates the equivalent conductive area by integrating data on the volume, spatial distribution, and surface morphology (normal vector tilt angle compensation) of metal chips; it constructs a risk index model based on three-dimensional coordinates and conductive area, significantly reducing the misjudgment rate of insulation testing to below the industrial threshold, and achieving accurate quantification of the electrical risks of metal chips.

[0067] 3. In this invention, by establishing a spatial admittance matrix and solving the Poisson equation, the displacement of metal chips under a high-voltage electric field is simulated, the chip position is dynamically updated, and the spurious leakage current component is calculated, thereby achieving accurate correction of the measured total leakage current. Attached Figure Description

[0068] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0069] Figure 1 This is a simplified schematic diagram of the method flow of the present invention. Detailed Implementation

[0070] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0071] This embodiment proposes an insulation testing method for single-phase smart energy meters. By quantifying the quantity, size, and location of metal debris on the surface of the terminals using a vision system, an equivalent conductivity model of the debris on the terminal surface is established. This corrects the measured resistance value in the insulation test, thereby avoiding the misclassification of qualified energy meters as unqualified. Figure 1 As shown, the method includes the following steps:

[0072] After grinding the terminal block surface, loosely attached particles are removed using mechanical vibration. A high-resolution industrial camera is then used to capture surface images of the terminal blocks, and a multi-band ring light source is used to highlight the characteristics of metal micro-shavings, such as a combination of white light (surface morphology) and ultraviolet light (enhanced metal reflection). Specifically, the method for correcting the measured resistance value is as follows:

[0073] First, structured striped light is projected onto the surface of the terminal block, and surface images of the terminal block are captured from different angles using an industrial camera (e.g., an elevation angle of 15° can capture the groove shadow features of the terminal block surface caused by grinding, and a depression angle of 60° can capture the protruding contours of metal chips on the terminal block surface). These images are then combined to generate point cloud data of the terminal block surface. All bumps and depressions, including scratches, metal chips, and grooves, are marked on the point cloud data.

[0074] Simultaneously, a multispectral light source is projected onto the surface of the terminal block to separate features, generating a multispectral image of the terminal block surface. The multispectral light source includes white light, ultraviolet light, and near-infrared light. White light suppresses specular reflection on the terminal block surface; ultraviolet light activates the fluorescence of metal debris on the terminal block surface, enhancing metal reflection signals; and near-infrared light penetrates the electrolytic film and abrasive residue on the terminal block surface. The surface normal vector is calculated in real-time using the point cloud data of the terminal block, and the incident angle of the multispectral light source and the direction of the polarizer are dynamically adjusted to eliminate surface reflection interference, ensuring clear imaging of metal debris and generating a multispectral image of the terminal block surface. A global coordinate system is established on the terminal block surface, and the multispectral image and point cloud data are fused and aligned to the global coordinate system based on coordinate mapping relationships to generate a fused feature map.

[0075] Then, 3D morphology analysis is used to remove scratches on the surface of the terminals, ultraviolet-infrared reflectance spectra are used to distinguish materials, and the dimensions are calibrated by surface tilt angle to achieve accurate identification of metal chips and accurate measurement of geometric parameters, providing reliable data input for correcting measured resistance values.

[0076] It should be noted that during the dynamic adjustment of the incident angle of the multispectral light source and the direction of the polarizer, the terminals of high-voltage, high-current energy meters generally adopt an arc-shaped crimp structure to withstand the high current electrodynamic force. This leads to a sharp curvature change on the terminal surface when calculating the surface normal vector in real time based on the 3D point cloud data of the terminal. The standard normal vector formula deviates significantly at the point of curvature abrupt change due to phase calculation errors, resulting in polarization angle compensation errors and abnormal ultraviolet and near-infrared channel signals (a sharp drop in ultraviolet reflectivity and Fresnel transmission artifacts in near-infrared), causing the failure to identify metal debris. In particular, when polishing the oxide layer on the surface of terminals with arc-shaped crimp structures, the bending stress in the arc area of ​​the terminal surface causes greater internal stress when debris is generated, resulting in more irregularly shaped (such as curled or hooked) metal debris. These metal debris are large in size and irregular in shape, causing large metal debris (especially curled ones) to accumulate in the arc concave surface, forming a stacked area that obscures the lower layer of metal debris, making it impossible for the optical detection system to identify them. This leads to significant errors in the identification of the number, size, and location of metal debris. To address this issue, a combined approach of curvature-adaptive polarization modulation and tomographic confocal scanning is employed to overcome the limitations imposed by optical distortion and metal debris stacking on high-curvature terminal surfaces. This enables panoramic, high-precision identification of metal debris on terminal surfaces with extreme geometries, providing a reliable data foundation for insulation performance evaluation. A more specific implementation method is as follows:

[0077] A low-density grating (e.g., period 200 μm) is projected onto the surface of the terminal block, and the basic curvature map κ(x,y) of the terminal block surface is obtained by phase-shift interference, where κ(x,y) represents the curvature value κ at coordinates (x,y) on the terminal block surface.

[0078] Based on the fundamental curvature map of the terminal block surface, the projected grating period is dynamically calculated and adjusted. The grating period is adjusted to 20 μm in the low curvature region (κ < 1), to 20 - 8(κ - 1) μm in the medium curvature region (1 < κ < 2), and to 4 μm in the high curvature region (κ > 1). By segmenting the curvature regions of the terminal block surface, the surface geometry changes drastically in the high curvature regions, requiring a smaller grating period to capture details. Dynamically adjusting the grating period ensures that the fringe deformation accurately reflects the surface morphology of the terminal block.

[0079] Four polarization camera channels (0°, 45°, 90°, 135°) are used to simultaneously expose polarization-modulated structured light reflected from the terminal block surface. The exposure time is inversely proportional to the curvature of the terminal block surface; higher curvature areas require shorter exposure times to avoid saturation. Then, the Stokes component is calculated from the polarization images acquired by each polarization camera channel, expressed as:

[0080]

[0081] In the formula, S0 is the sum of the unpolarized components; S1 is the degree of linear polarization of the 0° directional polarization camera channel; S2 is the degree of linear polarization of the 45° directional polarization camera channel; I0 is the light intensity collected by the 0° directional polarization camera channel, which is the reflection from the metal body; I 45 The light intensity collected by the 45° directional polarization camera channel reflects the characteristics of the oxide layer; I 90 The light intensity collected by the 90° directional polarization camera channel reflects the surface geometry; I 135 The light intensity collected by the 135° directional polarization camera channel is due to microstructure scattering. Applying the Mueller matrix transformation to the Stokes vector yields a corrected Stokes vector, which eliminates polarization measurement errors caused by surface reflection characteristics.

[0082] The surface gradient is calculated based on the point cloud data of the terminal block surface, and a compensation vector is generated, i.e.:

[0083]

[0084] In the formula, δ is the normal vector compensation value, representing the polarization measurement error correction value; δ is a modifier. α is the basic normal vector calculated from point cloud data for the terminal block surface; α is the curvature to error conversion factor, a dimensionless coefficient obtained based on experimental data calibration; unit(...) is the vector normalization operator used to normalize vectors; r is the radial coordinate. It is a radial unit vector, obtained based on the surface gradient; is the radial second derivative, representing the rate of change of curvature along the radial direction; z is the depth direction.

[0085] Then, based on the normal vector compensation and the original optical normal vector, the final true normal vector is generated, that is:

[0086]

[0087] In the formula, As the true normal vector, by fusing confocal geometric measurements and polarization optical data, interference caused by the curved surface of the terminal block is eliminated, solving the problem of metal chip identification on strongly reflective curved surfaces; k is the parameter index of the Stokes component, [0,1,2] corresponding to S0, S1, and S2 respectively; S k Stokes components; is the polarization basis vector.

[0088] The tomographic confocal scanning range and variable step size are calculated based on the curvature map of the terminal block surface to match the thickness of the metal chip stack on the terminal block surface, maximizing penetration depth while ensuring accuracy. Wavelengths are switched based on the curvature region of the terminal block surface to enhance surface details and improve deep penetration of the metal chip stack. Different spectral wavelengths for tomographic confocal scanning are implemented based on the curvature region of the terminal block surface; for low curvature regions, a single wavelength (visible spectral region) is activated to prioritize rapid scanning efficiency; for medium curvature regions, a complementary dual-wavelength combination (visible light + near-infrared) is enabled to construct a spectral contrast channel; for high curvature regions, a three-wavelength synergy (ultraviolet + visible light + infrared) is triggered to form a broadband penetration scheme, establishing a feature mapping between wavelength and depth, and eliminating masking artifacts through spectral difference. Pulsed lasers are emitted onto the surface of the terminal block and photon arrival times are recorded to generate depth-photon curves. Different levels of metal chip signals are accurately separated by optical path difference. Finally, a four-dimensional tensor T(x,y,z,λ) containing the depth attenuation curve is generated. UV ); where z is the depth direction, λ UV For spectral channels.

[0089] The surface curvature value of the terminal block exceeds the preset risk threshold κ. thThe region is defined as a high-risk region; the scanning density coefficient β = min(3, 1 + 2κ) is increased based on the curvature value of the high-risk region to enhance the scanning density. The optimal incident angle of the multispectral light source, the additional compensation angle of the polarizer direction, and the ultraviolet gain factor are calculated based on the surface curvature value of the terminal block, and an enhanced dataset for the high-risk region is constructed; that is:

[0090]

[0091] In the formula, θ opt α is the optimal incident angle for the multispectral light source; arcsin(...) is the arcsine function; NA is the numerical aperture, representing the light-gathering capability of the optical lens; α′ is the material compensation coefficient, determined by fitting experimental data; γ is the curvature correction term. 25° is the compensation angle increment in the polarizer direction, used to solve the polarization distortion problem during curved surface reflection; 25° is the maximum polarization offset of the copper terminal at the limiting curvature; 0.35 is the attenuation coefficient, a constant representing the coupling characteristics of a material and wavelength; G UV η is the ultraviolet gain factor; η is the basic compensation coefficient; α″ is the curvature sensitivity factor.

[0092] The polarization compensation angle at each coordinate point on the terminal block surface is calculated based on the actual normal vector, i.e.:

[0093]

[0094] In the formula, The polarization compensation angle represents the angle between the incident light and the normal to the surface, and determines the amount of change in polarization state. Let be the direction vector of the incident light.

[0095] Then, an inverse compensation matrix is ​​constructed based on the polarization compensation angle. This matrix contains information about the polarization state change of the polarized light after reflection from the terminal surface. The matrix expression is as follows:

[0096]

[0097] In the formula, This is the inverse compensation matrix, used to eliminate polarization distortion caused by curvature; It is a secant function. Used to compensate for the optical path difference caused by the tilt of the normal; It is the tangent function. It is the cosecant function, used to determine the cross-polarization component compensation intensity; Polarization state rotation used to correct reflections from curved surfaces.

[0098] The original polarization image is depolarized by convolving it with the inverse compensation matrix, i.e.:

[0099]

[0100] In the formula, I corr (x,y) represents the corrected image, the output result after eliminating polarization distortion; Conv(...) is a matrix convolution operation used to apply the inverse compensation matrix to each coordinate point; I raw This is the original polarization image.

[0101] The corrected image and the four-dimensional tensor of the spectral data are fused along the depth direction to form an image sequence with depth information, i.e.:

[0102]

[0103] In the formula, D(x,y,z) is an image sequence with depth information; It is a depth-direction convolution.

[0104] Next, the image sequence is subjected to tomographic deconvolution processing, that is:

[0105]

[0106] in,

[0107] In the formula, V vol The reconstructed metal chip volume data represents the true structure after optical distortion removal; F{...} denotes the three-dimensional Fourier transform operator, which converts the spatial domain occlusion data into a frequency domain representation by performing a three-dimensional Fourier transform on the image sequence; F -1 [...] indicates the inverse Fourier transform; H * (w) is the curvature-adaptive hybrid transfer function used to solve the optical distortion problem in surface detection; w is the spatial frequency vector; OTF(w) is the optical transfer function used to correct lateral blur and diffraction effects; CTF(w) is the confocal transfer function used to enhance depth direction resolution.

[0108] The filtering parameters for each coordinate position are calculated based on the basic curvature map of the terminal block surface, and a three-dimensional Gaussian kernel function is generated in real time. The three-dimensional Gaussian kernel function is used to perform a three-dimensional convolution operation on the reconstructed metal chip volume data, and the optimized metal chip volume data is output.

[0109] Utilizing the multispectral information of the terminal block surface, including: ultraviolet band reflectance intensity I u Reflectance intensity I in the near-infrared band ir and the reflection intensity I in the visible light band v Then calculate the reflectance R of ultraviolet light and near-infrared light at each coordinate point on the surface of the terminal block. u / ir ,Right now: The material of the terminal block surface is classified according to reflectance, including metal debris, mixed debris, and non-metallic residue. Metal weights, non-metal weights, and mixed weights are dynamically calculated based on reflectance, i.e.:

[0110]

[0111] ω d =1-ω m

[0112]

[0113] In the formula, ω m For the metal weight, ω d For non-metallic weights, ω c For mixed weights; b is the slope coefficient, μ is the decision boundary point used to distinguish between metals and non-metals; R ref The reference point reflects the reflectance under ideal mixing conditions, based on the statistical mean of typical mixed materials; S is the normalization factor.

[0114] Then, based on three weights, the multispectral information is fused into the final fused image F, that is:

[0115] F(x,y,z)=ω m ·I v +ω c ·I u +ω d ·I ir

[0116] A partition mapping function is established based on the basic curvature diagram of the terminal block surface, i.e.

[0117] Ω i ={(x,y)|κ th,i ≤κ(x,y)<κ th,i +Δκ max}

[0118] In the formula, Ω i For the i-th curvature partition, κ th,i Let Δκ be the reference curvature threshold for the i-th curvature partition. max This is the threshold for the maximum curvature difference.

[0119] Each curvature region is then independently segmented using watershed techniques, and morphological filtering is applied for denoising to generate a binary label matrix for metal chips. This divides the originally adhered areas into independent parts, where each label represents a different individual metal chip. Consecutive pixel regions with the same label value in the binary label matrix are identified; each connected region is considered an independent metal chip. The geometric center of each connected region is calculated using a 3D weighted average as the coordinate point, generating the precise 3D coordinates r of each metal chip on the terminal block surface. obs Furthermore, by correcting the precise three-dimensional coordinates through curvature gradient, the projection errors caused by three-dimensional distortion and metal chip tilting in the curved surface region are resolved, accurately quantifying the equivalent conductive area of ​​the metal chips under actual working conditions. That is:

[0120]

[0121] In the formula, r real κ represents the corrected three-dimensional coordinates of the metal chip; τ represents the curvature value at the precise three-dimensional coordinates of the metal chip; and τ is the correction coefficient used to adjust the compensation intensity of the curvature gradient, which was determined experimentally. This represents the depth gradient vector at the precise three-dimensional coordinates of the metal chip.

[0122] Then, the light intensity distribution along the depth direction z is extracted from the reconstructed metal chip volume data, and the vertical projection component of the metal chip is calculated. Then, normal vector tilt angle compensation is introduced to generate the equivalent conductive area of ​​the metal chip, that is:

[0123]

[0124] In the formula, A eff V(z) represents the equivalent conductive area of ​​the metal chips; V(z) represents the light intensity distribution along the depth direction z; max(V(z)) represents the peak light intensity on the surface of the terminal block, z max The depth of the peak light intensity location, compared to the other two, reflects the maximum projected density of metal chips in the vertical direction; τ′ is the tilt amplification factor; cos -1 (...) is the inverse cosine function; It is the unit vector along the absolute optical axis.

[0125] Finally, based on the equivalent conductive area of ​​the metal debris on the terminal block surface, the corrected three-dimensional coordinates, and the reflectance ratio of ultraviolet and near-infrared light, the three-dimensional risk index of the metal debris is calculated to quantify the electrical short-circuit risk that metal debris on the curved surface of the terminal block may cause. The formula for calculating the three-dimensional risk index is:

[0126]

[0127] In the formula, R risk,iLet A be the risk index for the i-th metal chip; eff,i d represents the equivalent conductive area of ​​the i-th metal chip; i f(R) is the distance from the i-th metal chip to the nearest conductor, calculated based on point cloud data of the terminal block surface; u / ir Let ) be the mapping function from micro-chip material to weights, expressed as:

[0128]

[0129] Finally, the matrix of metal chips on the surface of the output terminals, namely:

[0130]

[0131] In the formula, M is the metal chip matrix, and m is the number of metal chips.

[0132] The method for correcting the measured resistance value is as follows:

[0133] The equivalent conductivity of each metal chip is calculated based on the metal chip matrix. The calculation formula is as follows:

[0134]

[0135] In the formula, g i Let f(R) be the equivalent conductivity of the i-th metal chip; σ be the conductivity; f(R) be the equivalent conductivity of the i-th metal chip. risk,i Let be the mapping function from the risk index of the i-th metal chip to the conductivity weight, used to nonlinearly transform the risk index of the metal chip into the conductivity weight, and its expression is:

[0136]

[0137] In the formula, exp(...) is an exponential function, and R... risk,min R is the minimum risk index in the metal chip matrix. risk,max This represents the highest risk index in the metal chip matrix.

[0138] Establish the spatial admittance matrix Y of all metal chips on the surface of the terminal block, where the matrix elements y i,j Express the mutual differentiation between the i-th metal chip and the j-th metal chip, that is:

[0139]

[0140] In the formula, d ij R is the distance between the i-th and j-th metal chips. ele The radius of the electric field: when the distance between two metal chips is smaller than the radius of the electric field, a conductive circuit will be formed.

[0141] Each metal chip in the metal chip matrix is ​​considered as a point charge source, and the charge density is calculated by weighting based on the risk index of the metal chips, i.e.:

[0142]

[0143] In the formula, ρ (x,y,z) For charge density, Let be the Dirac function, used to mathematically describe the location of discrete metal chips in three-dimensional space; the position vector of any point on the surface of the terminal block is defined if and only if... The position vector of the i-th metal chip When they overlap, In all other locations,

[0144] When applying high voltage for insulation testing of an electricity meter, the potential distribution Φ(x,y,z) on the surface of the terminals is solved based on the Poisson equation. The calculation formula is as follows:

[0145]

[0146] In the formula, ε is the Laplace operator for electric potential, representing the degree of difference between the electric potential at a point in space and the surrounding electric potential, used to describe the non-uniformity of the electric field distribution; ε is the dielectric constant of the medium, reflecting the response characteristics of the insulating material to the electric field. For each metal chip, the potential value at its location is taken. Arrange the potential values ​​at all locations of the metal chips in order into a column vector, i.e., the nodal potential vector. This reflects the specific potential experienced by each metal chip under the test voltage.

[0147] Then, the electric field intensity vector is obtained using the potential gradient. The displacement of metal chips under high voltage caused by the Coulomb force is calculated using the electric field intensity vector. The calculation formula is as follows:

[0148]

[0149] In the formula, ψ is the position change vector of the metal chip, reflecting the displacement vector of the metal chip in time Δt under the action of the electric field; ψ is the mobility coefficient of the metal chip, reflecting the moving speed of the metal chip under unit electric field strength, which is determined based on experimental data; Δt is the time step of the electric field, used to consider the time increment of the dynamic migration of the metal chip. This represents the electric field intensity vector at the i-th metal chip.

[0150] The spatial admittance matrix is ​​recalculated using the position change vector of the metal chips to correct for the conduction path changes caused by the displacement of the metal chips, resulting in a new spatial admittance matrix Y′. The node current distribution is then calculated using the new spatial admittance matrix and the node potential vector. The calculation formula is as follows:

[0151]

[0152] In the formula, Let be the node current vector, representing the total current flowing into the node, which is the algebraic sum of the currents through the mutual conduction channels between the metal chip and all adjacent metal chips.

[0153] Next, the metal chip branch current that forms a path between the two measured terminals (such as the high-voltage terminal and the reference ground) is extracted from the node current vector, and summed to obtain the total chip current, i.e., the spurious leakage current component. The true leakage current is obtained by subtracting the spurious leakage current component from the measured total leakage current, and then the corrected insulation resistance is calculated.

[0154] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0155] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. Since the above embodiments are substantially similar to the method embodiments, their descriptions are relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0156] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An insulation testing method for a single-phase smart energy meter, characterized in that, The method includes the following steps: Structured stripe light is projected onto the surface of the terminal block to capture point cloud data; A low-density grating is projected onto the surface of the terminal block to generate a basic curvature map, marking high-risk areas. The grating period is dynamically adjusted based on the basic value, multi-channel polarization images are acquired simultaneously, and Stokes vectors are calculated. Optical distortion is corrected by combining the surface normal vector compensation. Tomographic confocal scanning is performed based on the basic curvature map, and spectral wavelengths are switched according to curvature partitions to generate a four-dimensional tensor of spectral data. The corrected polarization images and the four-dimensional tensor of spectral data are fused, and the volume data of metal chips is reconstructed through three-dimensional Fourier deconvolution. The risk index is calculated by identifying the corrected three-dimensional coordinates of metal chips and the equivalent conductive area, and constructing a metal chip matrix on the surface of the terminal block. Then, a spatial admittance matrix is ​​established based on the metal chip matrix, and the Poisson equation is solved to simulate the potential distribution and predict the displacement of metal chips. The spatial admittance matrix is ​​updated by the displacement of metal chips, and the node current distribution is calculated by combining the node potential vector. Then, extract the metal chip branch current that forms a path between the two measured terminals from the node current vector, and sum them to obtain the spurious leakage current component; by subtracting the spurious leakage current component from the measured total leakage current, calculate the true leakage current that corrects the measured insulation resistance value.

2. The insulation testing method according to claim 1, characterized in that, The method for correcting optical distortion by combining surface normal vector compensation is as follows: The surface gradient is calculated based on the point cloud data of the terminal block surface, and a compensation vector is generated, i.e.: ； In the formula, For normal vector compensation, For modifiers, The basic normal vector; This is the curvature to error conversion factor; For vector normalization operators; It is a radial unit vector; is the rate of change of curvature along the radial direction; r is the radial coordinate; z is the curvature value; z is the depth direction; The final true normal vector is generated based on the normal vector compensation and the original optical normal vector, i.e.: ； In the formula, is the true normal vector; k is the parameter index of the Stokes component; Stokes components; is the polarization basis vector.

3. The insulation testing method according to claim 1, characterized in that, The method of switching spectral wavelengths according to curvature partitioning is as follows: Activate a single wavelength in the low curvature region; In the medium curvature region, complementary dual-wavelength combinations are employed; Triggering three-wavelength synergy in high curvature regions.

4. The insulation testing method according to claim 1, characterized in that, The method for reconstructing the volume data of metal chips using three-dimensional Fourier deconvolution is as follows: An enhanced dataset for calculating high-risk areas based on terminal block surface curvature values; The polarization compensation angle at each coordinate point on the terminal block surface is calculated based on the true normal vector. An inverse compensation matrix is ​​constructed based on the polarization compensation angle. The original polarization image is then depolarized. Finally, the polarization image is convolved with the inverse compensation matrix. ； In the formula, The corrected image; Ultraviolet gain factor; This refers to matrix convolution operations. This is the original polarization image; It is the inverse compensation matrix; The corrected image and the four-dimensional tensor of the spectral data are fused along the depth direction to form an image sequence with depth information. Then, the image sequence is subjected to tonal deconvolution processing, i.e.: ； in, ; In the formula, For reconstructed metal chip volume data; Represents the three-dimensional Fourier transform operator; Indicates the inverse Fourier transform; is the curvature-adaptive hybrid transfer function; w is the spatial frequency vector; Optical transfer function; For confocal transfer functions; It is an image sequence with depth information; The curvature value, This is the risk threshold.

5. The insulation testing method according to claim 1, characterized in that, The method for identifying the corrected three-dimensional coordinates of metal chips is as follows: The reflectance of ultraviolet and near-infrared light at each coordinate point on the surface of the terminal block is calculated. The material of the terminal block surface is classified according to the reflectance, including metal chips, mixed chips, and non-metal residues. Metal weight, non-metal weight, and mixed weight are calculated based on the reflectance. Then, the multispectral information is fused into the final fused image based on the three weights. A partition mapping function is established based on the basic curvature diagram of the terminal block surface, i.e. ； In the formula, For the i-th curvature partition, Let be the baseline curvature threshold for the i-th curvature partition. The threshold for the maximum curvature difference; Based on the curvature diagram; Each curvature partition is then independently segmented into watersheds, and morphological filtering is used for denoising to generate a binary label matrix for metal chips. Based on a three-dimensional weighted average, the geometric center of each connected component is calculated as the coordinate point to generate the precise three-dimensional coordinates of each metal chip on the terminal block surface. The precise three-dimensional coordinates are corrected using curvature gradients, i.e.: ； In the formula, The three-dimensional coordinates of the metal chips after correction; The correction coefficient is used to adjust the compensation intensity of the curvature gradient; This represents the depth gradient vector at the precise three-dimensional coordinates of the metal chip.

6. The insulation testing method according to claim 1, characterized in that, The method for identifying the equivalent conductive area is as follows: The light intensity distribution along the depth direction z is extracted from the reconstructed metal chip volume data, and the perpendicular projection component of the metal chip is calculated. Then, normal vector tilt compensation is introduced to generate the equivalent conductive area of ​​the metal chip, i.e.: ； In the formula, This represents the equivalent conductive area of ​​the metal chips. The light intensity distribution along the depth direction z; The peak light intensity on the surface of the terminal block. The depth of the peak light intensity location; This is the tilt angle magnification factor; It is the inverse cosine function; It is the unit vector along the absolute optical axis.

7. The insulation testing method according to claim 1, characterized in that, The formula for calculating the three-dimensional risk index is as follows: ； In the formula, The risk index for the i-th metal chip; Let be the equivalent conductive area of ​​the i-th metal chip; Let be the distance from the i-th metal chip to the nearest conductor; where: ； In the formula, This is the mapping function from micro-chip material to weights.

8. The insulation testing method according to claim 1, characterized in that, The elements of the spatial admittance matrix satisfy: ； In the formula, For matrix elements; Let be the distance between the i-th metal chip and the j-th metal chip; radius of electric field effect Let be the equivalent conductive area of ​​the i-th metal chip. Let be the equivalent conductive area of ​​the j-th metal chip. is the conductivity.

9. The insulation testing method according to claim 1, characterized in that, The method for predicting the displacement of metal chips is as follows: The charge density is weighted based on the risk index of metal debris, i.e.: ； In the formula, For charge density, It is the Dirac function; Solving the potential distribution on the surface of the terminal block based on the Poisson equation The calculation formula is: ； In the formula, The Laplace operator for electric potential; Dielectric constant of the medium; Then, the electric field intensity vector is obtained using the potential gradient. And calculate the displacement of the metal chips, using the following formula: ； In the formula, This is the vector representing the positional change of the metal chip. The migration coefficient of metal chips; The time step of the electric field; This represents the electric field intensity vector at the i-th metal chip.

10. The insulation testing method according to claim 1, characterized in that, The method for calculating the current distribution at the computing nodes is as follows: Calculate the potential value at the location of each metal chip, and arrange the potential values ​​at all locations of the metal chips in order to form a nodal potential vector. The calculation formula is: ； For node current vectors, This is the admittance matrix for the new space.

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