Intelligent detection method and system for concentricity of medical wires and cables

Abstract

The invention relates to the technical field of physical parameter measurement methods of electric wires and cables, and discloses an intelligent detection method and system for concentricity of a medical electric wire and cable, and the method comprises the steps: obtaining a cross-sectional image and a nominal radius of the medical electric wire and cable; performing image conversion according to the section image to obtain a geometric image, and performing construction operation on the geometric image to obtain geometric data; performing extraction operation on the geometric data to obtain an edge radius; performing calculation operation according to the edge radius and the nominal radius to obtain an actual radius; performing concentricity calculation according to the actual radius and the nominal radius to obtain concentricity data; performing correction calculation according to the concentricity data to obtain a correction loss parameter; judging whether the corrected loss parameter is greater than a preset minimum transmission threshold, and if yes, performing alarm operation; and if not, the alarm operation is not carried out. According to the method, the medical wire and cable concentricity measurement accuracy can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of physical parameter measurement methods of wires and cables, and in particular to an intelligent detection method and system for the concentricity of medical wires and cables. Background Art

[0002] At present, medical wires and cables are important carriers for transmitting medical information in the medical field. In order to ensure the reliability of signal transmission, copper core insulated cables with specific signal characteristic impedance (such as 75 ohms) are selected to achieve the best signal transmission performance. The conductor of medical wires and cables consists of a core wire and an insulation layer, and the shape and concentricity of the core wire have a direct impact on the characteristic impedance. Poor core coaxiality will lead to impedance discontinuity, which will cause reflection and scattering of the transmitted signal, causing signal distortion and reducing the accuracy of medical diagnosis. Therefore, medical wires and cables must meet certain concentricity standards.

[0003] In one prior art, the traditional concentricity test method relies on instrument measurement, requires a standard part as a reference, and determines the quality of the concentricity by calculating the relative error rate.

[0004] However, as the instrument is used for a longer time, the measurement error will increase, resulting in low measurement accuracy for medical wires and cables. Summary of the invention

[0005] The present invention provides an intelligent detection method and system for the concentricity of medical wires and cables, so as to improve the measurement accuracy of the concentricity of medical wires and cables.

[0006] In the first aspect, in order to solve the above technical problems, the present invention provides an intelligent detection method for the concentricity of medical wires and cables, comprising: Obtain cross-sectional images and nominal radius of medical wires and cables; Performing image conversion according to the cross-sectional image to obtain a geometric image, and performing a construction operation on the geometric image to obtain geometric data; Performing an extraction operation on the geometric data to obtain an edge radius; Performing a calculation operation according to the edge radius and the nominal radius to obtain an actual radius; Calculating the concentricity according to the actual radius and the nominal radius to obtain concentricity data; Perform correction calculation according to the concentricity data to obtain a corrected loss parameter; It is determined whether the modified loss parameter is greater than a preset minimum transmission threshold value, and if so, an alarm operation is performed; if not, no alarm operation is performed.

[0007] In an optional implementation, obtaining the nominal radius includes: The inner diameter of the insulation layer of the wire and cable and the diameter of the cable core are obtained from the initial data of the system, and the nominal radius is obtained by subtracting the diameter of the cable core from the inner diameter of the insulation layer of the wire and cable.

[0008] In an optional implementation, performing image conversion according to the cross-sectional image to obtain a geometric image, and performing a construction operation on the geometric image to obtain geometric data includes: According to the cross-sectional image of the medical wire and cable, performing image conversion on the cross-sectional image of the medical wire and cable to obtain a binary image; Processing the binary image with pixel grayscale values ​​to obtain a geometric image; Classify the pixel grayscale values ​​of the geometric image to obtain an insulating layer, an inner lining layer, a core wire and a conductor shielding layer; According to the insulating layer, the lining layer, the core wire and the conductor shielding layer, each layer structure of the insulating layer, the lining layer, the core wire and the conductor shielding layer is analyzed to obtain geometric data.

[0009] In an optional implementation, performing a calculation operation based on the edge radius and the nominal radius to obtain an actual radius includes: The actual radius is calculated by the following formula, The total cross-sectional area of ​​each core wire is calculated by the following formula: in, is the actual radius, is the total cross-sectional area of ​​each core wire, is the number of cores, is the nominal radius, is the edge radius, is the number of central pixels, is the number of edge pixels.

[0010] In an optional implementation, the extracting operation on the geometric data to obtain the edge radius includes: Performing area segmentation on the geometric data to obtain single core line data; performing binary image data extraction on the single core line data to obtain a center radius, a center pixel number and a total pixel number; calculating the center radius, the center pixel number and the total pixel number to obtain an edge radius; The edge radius is calculated by the following formula, in, is the edge radius, is the center radius, is the number of central pixels, is the total number of pixels.

[0011] In an optional implementation, the concentricity calculation is performed according to the actual radius and the nominal radius to obtain the concentricity data, including: The concentricity data is calculated by the following formula, in, is the concentricity data, is the actual radius, For the The actual radius of the core wire, is the number of cores, is the nominal radius.

[0012] In an optional implementation, performing correction calculation according to the concentricity data to obtain a corrected loss parameter includes: The modified loss parameter is calculated by the following formula, The corrected concentricity parameter is calculated by the following formula: in, To correct the loss parameters, For the The loss factor of the core wire, is the number of cores, is the maximum allowable value of concentricity, is the concentricity data, To correct the concentricity parameters, correct the concentricity parameters Range is 0 to 1.

[0013] In a second aspect, the present invention provides an intelligent detection system for the concentricity of medical wires and cables, comprising: An image acquisition module, used to obtain cross-sectional images and nominal radius of medical wires and cables; An image processing module, used for performing image conversion according to the cross-sectional image to obtain a geometric image, and performing a construction operation on the geometric image to obtain geometric data; A data processing module, used for extracting the geometric data to obtain an edge radius; A parameter calculation module, used for performing a calculation operation according to the edge radius and the nominal radius to obtain an actual radius; A parameter implantation module, used for performing concentricity calculation according to the actual radius and the nominal radius to obtain concentricity data; A correction calculation module, used to perform correction calculation according to the concentricity data to obtain a correction loss parameter; The result output module is used to determine whether the modified loss parameter is greater than a preset minimum transmission threshold. If so, an alarm operation is performed; if not, no alarm operation is performed.

[0014] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the intelligent detection method for the concentricity of medical wires and cables as described in any one of the above.

[0015] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned intelligent detection methods for the concentricity of medical wires and cables.

[0016] Compared with the prior art, the present invention has the following beneficial effects: the present invention discloses an intelligent detection method for the concentricity of medical wires and cables, comprising obtaining a cross-sectional image and a nominal radius of the medical wires and cables; performing image conversion according to the cross-sectional image to obtain a geometric image, performing a construction operation on the geometric image to obtain geometric data; performing an extraction operation on the geometric data to obtain an edge radius; performing a calculation operation according to the edge radius and the nominal radius to obtain an actual radius; performing a concentricity calculation according to the actual radius and the nominal radius to obtain concentricity data; performing a correction calculation according to the concentricity data to obtain a corrected loss parameter; judging whether the corrected loss parameter is greater than a preset minimum transmission threshold, and if so, performing an alarm operation; if not, not performing an alarm operation.

[0017] The present invention obtains a cross-sectional image of a medical wire and cable, measures the nominal diameter and core wire radius of the medical wire and cable, and uses a concentricity formula algorithm to analyze the data to obtain a concentricity parameter, and then compares the corrected loss parameter with the minimum transmission requirement to ensure that the medical wire and cable meet the minimum transmission requirement. In the present invention, the image and the concentricity formula are used for calculation and analysis, and no instrumentation is required for measurement, which can reduce measurement errors and thus improve measurement accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic flow chart of an intelligent detection method for concentricity of medical wires and cables provided by a first embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of an intelligent detection system for the concentricity of medical wires and cables provided in the second embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Reference Figure 1 The first embodiment of the present invention provides an intelligent detection method for the concentricity of medical wires and cables, comprising the following steps: S11, obtaining a cross-sectional image and a nominal radius of the medical wire and cable; S12, performing image conversion according to the cross-sectional image to obtain a geometric image, and performing a construction operation on the geometric image to obtain geometric data; S13, extracting the geometric data to obtain an edge radius; S14, according to the edge radius and the nominal radius, a calculation operation is performed to obtain an actual radius; S15, performing concentricity calculation according to the actual radius and the nominal radius to obtain concentricity data; S16, performing correction calculation according to the concentricity data to obtain a corrected loss parameter; S17, determining whether the corrected loss parameter is greater than a preset minimum transmission threshold, if so, performing an alarm operation; if not, not performing an alarm operation.

[0021] In step S11, it is necessary to obtain a cross-sectional image and a nominal radius of the medical wire and cable.

[0022] In this embodiment, a high-precision camera, such as a CCD camera or a digital microscope, is used to photograph the cross section of the medical wire and cable to obtain a clear image. Ensure that the cable cross section is flat and perpendicular to the shooting plane to obtain an accurate cross-sectional image of the medical wire and cable. The image has sufficient resolution and contrast so that the various layers of the cable structure can be accurately identified and analyzed in subsequent steps.

[0023] In one implementation, obtaining the nominal radius includes: The inner diameter of the insulation layer of the wire and cable and the diameter of the cable core are obtained from the initial data of the system, and the nominal radius is obtained by subtracting the diameter of the cable core from the inner diameter of the insulation layer of the wire and cable.

[0024] It should be noted that the nominal radius is an important parameter in the design and manufacture of medical wires and cables. It refers to the radius of the cable core under ideal or standard conditions. In concentricity detection, the nominal radius is used to compare with the actual measured radius to calculate the concentricity deviation. If the cable structure is relatively simple, the nominal radius can be given directly. For more complex structures, it is necessary to find the inner diameter of the insulation layer and the outer diameter of the core wire, and the nominal radius is calculated by the difference between the two. There is a certain difference between the nominal radius and the actual radius. The actual radius is the radius value actually measured by the object, while the nominal radius is a reference value given by the manufacturer. Due to tolerances, material changes and other factors in the manufacturing process, the actual radius will fluctuate within a certain range. Therefore, the nominal radius is regarded as an approximate value or average value. In some cases, manufacturers provide a tolerance range for the actual radius to indicate the difference between the actual radius and the nominal radius. For example, if the nominal radius is 0.5 mm and the tolerance range is ±0.05 mm, then the minimum actual radius is 0.5 mm - 0.05 mm = 0.45 mm; the maximum actual radius is 0.5 mm + 0.05 mm = 0.55 mm. Manufacturers allow the actual radius of the cable to vary between 0.45 mm and 0.55 mm to accommodate minor differences in the production process.

[0025] In step S12, image conversion is performed according to the cross-sectional image to obtain a geometric image, and a construction operation is performed on the geometric image to obtain geometric data, including: According to the cross-sectional image of the medical wire and cable, performing image conversion on the cross-sectional image of the medical wire and cable to obtain a binary image; Processing the binary image with pixel grayscale values ​​to obtain a geometric image; Classify the pixel grayscale values ​​of the geometric image to obtain an insulating layer, an inner lining layer, a core wire and a conductor shielding layer; According to the insulating layer, the lining layer, the core wire and the conductor shielding layer, each layer structure of the insulating layer, the lining layer, the core wire and the conductor shielding layer is analyzed to obtain geometric data.

[0026] Specifically, the cross-sectional image of the medical wire and cable is first converted into an image. This conversion process mainly converts the original image into a binary image, that is, the pixels on the image are divided into two categories, black and white in this embodiment, to facilitate subsequent pixel gray value processing and geometric structure recognition.

[0027] It should be noted that in the field of image processing, grayscale refers to the brightness information of each pixel in the image, which is represented by an integer value between 0 and 255. For the cross-sectional image of medical wires and cables, since it contains multiple materials such as insulation layer, lining layer, core wire and wire shielding layer, these materials often present different grayscale values ​​on the image. Therefore, through grayscale processing, different parts in the image can be more clearly distinguished, providing an accurate basis for subsequent structural analysis and concentricity calculation. Before grayscale processing, the original image needs to be preprocessed, including denoising, contrast enhancement and other operations. These operations help to eliminate interference information in the image and improve the clarity and recognizability of the image. The preprocessed color image is converted into a grayscale image, that is, a geometric image. This step can be achieved by calculating the average value of the red, green and blue channels of each pixel. Optionally, after obtaining the grayscale image, the grayscale value needs to be adjusted to meet different analysis requirements. For example, the distribution range of grayscale values ​​can be adjusted through linear transformation, nonlinear transformation and other methods to make the image visually clearer while retaining sufficient detail information.

[0028] After obtaining the geometric image, the image can be further classified by pixel grayscale values. The purpose of this step is to distinguish different parts of the image, such as the insulation layer, lining layer, core wire and wire shielding layer, according to the grayscale values. By setting appropriate thresholds or using clustering algorithms, the image can be segmented into multiple regions, each corresponding to a material or structure. For example, if the grayscale values ​​of the insulation layer in the image are mainly concentrated in 50-100, the lining layer is 100-150, the core wire is 150-200, and the wire shielding layer is 200-255, multiple thresholds, such as 100, 150, and 200, can be set to distinguish these different layers. In this way, each threshold divides the image into regions corresponding to different materials or structures. Alternatively, using K-means clustering, the image pixels can be divided into several clusters, each cluster representing a material or structure. By calculating the cluster center, the grayscale value range of different materials or structures can be determined.

[0029] Optionally, after grayscale classification, the image can also be subjected to detail enhancement processing. The purpose of this step is to improve the clarity of edges and textures in the image so that geometric data can be more accurately extracted in subsequent structural analysis.

[0030] Finally, according to the classified geometric images, the geometric data of each layer structure is analyzed. This step involves measuring and calculating the size, shape, position and other geometric features of the insulation layer, lining layer, core wire and conductor shielding layer to obtain geometric data for subsequent concentricity calculation. Specifically, Canny edge detection and Sobel operator are used to identify the boundaries of each layer structure; the Hough transform algorithm is used to extract the outline of the boundary to form a closed curve; the image is divided into different areas, each area corresponds to a specific layer structure; the length and width of each layer structure are measured, and the angle between the layer structures is measured using an angle measurement tool; the center point position of each layer structure is determined, and the offset between the core wire center and the cable geometric center is calculated; all measured and calculated quantities are summarized to obtain geometric data.

[0031] In step S13, the geometric data is subjected to an extraction operation to obtain an edge radius, including: Performing area segmentation on the geometric data to obtain single core line data; performing binary image data extraction on the single core line data to obtain a center radius, a center pixel number and a total pixel number; calculating the center radius, the center pixel number and the total pixel number to obtain an edge radius; The edge radius is calculated by the following formula, in, is the edge radius, is the center radius, is the number of central pixels, is the total number of pixels.

[0032] It should be noted that the center radius is the distance from the center point of the image to the center of the core line. By detecting the edges and extracting the contours of the geometric data, the center of mass of the core line is found to determine the size of the center radius and obtain the center radius. The number of center pixels is the number of pixels detected in the center area of ​​the core line, and the total number of pixels is the total number of pixels in the entire core line area. This value provides the size information of the entire core line area. Use the functions in the OpenCV library of the image processing software Python to count the number of pixels in the center area and the entire core line area to obtain the number of center pixels and the total number of pixels. The edge radius is the farthest distance from the edge of the core line to its geometric center.

[0033] in the formula It directly reflects the center radius of the core wire and is the basic value for calculation. It is an adjustment for the edge effect of the core. It takes into account the difference in pixel distribution between the core center area and the entire core area. This difference value represents the difference in the number of pixels between the center area and the entire core area, reflecting the sparseness of the core edge. By multiplying this difference by the center radius and dividing by the total number of pixels, an adjustment value related to the sparseness of the core edge is obtained. The larger this value is, the sparser the core edge is, and a larger radius is required to cover the entire core area.

[0034] In step S14, the geometric data is extracted to obtain an edge radius; and a calculation operation is performed based on the edge radius and the nominal radius to obtain an actual radius, including: The actual radius is calculated by the following formula, The total cross-sectional area of ​​each core wire is calculated by the following formula: in, is the actual radius, is the total cross-sectional area of ​​each core wire, is the number of cores, is the nominal radius, is the edge radius, is the number of central pixels, is the number of edge pixels.

[0035] It should be noted that in the cross-sectional total area calculation formula, the product of the number of central pixels and the nominal radius in the numerator represents the contribution of the central area to the area; the product of the number of edge pixels and the edge radius represents the contribution of the edge area to the area; the sum of these two parts is divided by the number of core lines to obtain the average comprehensive radius related value of each core line, and then the square sum is multiplied by Get the total area.

[0036] In the actual radius formula, Transform to get The area formula of a circle is a basic formula based on geometric principles. In actual detection, considering the influence of the number of central pixels, the formula is modified to obtain It is reasonable to introduce the number of central pixels. Because when calculating the radius through image analysis, the central pixel area often plays an important role in determining the center and radius. The number of central pixels reflects the weight of the central area when calculating the radius. If the number of central pixels is large, it means that the central area contributes more when calculating the actual radius, which is in line with the law of determining the radius of circular objects in image processing.

[0037] In another embodiment, the actual radius of the cable is obtained by image recognition and algorithm calculation, including: First, identify the core line boundary in the cross-sectional image, and obtain the position data of the core line boundary between the cable boundaries. Then, according to the algorithm of the actual pixel size of the platform on the X-axis, obtain the position coordinate data of the core line. According to the position coordinate data, obtain the actual length of the corresponding core line, and then according to the nominal radius of the corresponding core line, obtain the actual radius.

[0038] Use edge detection algorithm to identify the core line boundary. Use contour extraction algorithm to obtain the precise boundary of the core line. The algorithm identifies the edge by calculating the gradient or edge strength of each pixel in the image. Based on edge detection, use contour extraction algorithm to obtain the precise boundary of the core line. Contour extraction algorithm can identify continuous edge points in the image and connect them into complete contour lines. For example, Hough transform and gradient-based contour extraction. These algorithms can connect edge points into continuous contour lines based on the position and direction information of edge points, thereby obtaining the precise boundary of the core line.

[0039] After the core wire boundary is identified, the pixel coordinates of the core wire boundary and the cable boundary in the image are recorded. According to the pixel coordinates of the core wire boundary and the cable boundary, the relative position data of the core wire in the cable can be calculated. These position data include the offset of the core wire in the cable.

[0040] Specifically, w -C cw ; h -C ch , where C w and C h are the width and height coordinates of the center point of the core line, C cw and C ch are the width and height coordinates of the cable center point, and are the horizontal offset and vertical offset respectively. The total offset is .

[0041] Based on the total offset, the actual radius is calculated by the following formula, in, is the actual radius, is the nominal radius and D is the total offset.

[0042] In step S15, concentricity calculation is performed according to the actual radius and the nominal radius to obtain concentricity data, including: The concentricity data is calculated by the following formula, in, is the concentricity data, is the actual radius, For the The actual radius of the core wire, is the number of cores, is the nominal radius.

[0043] It should be noted that the concentricity data is a key indicator to measure the concentricity of medical wires and cables. It reflects the degree of deviation of the cable core wire relative to its design center. The better the concentricity, the more stable the transmission performance of the cable and the smaller the signal distortion. Concentricity refers to the degree of concentricity inside the cable, that is, the deviation of the center axis between the conductor and the shielding layer and the outer sheath protective layer. Ideally, the conductor, shielding layer, and outer sheath protective layer of the cable should be completely arranged around a common center axis to form a perfect concentric structure. However, in the actual manufacturing process, due to the influence of various factors, there will be a certain deviation in the various parts of the cable, which is the concentricity problem.

[0044] In step S16, a correction calculation is performed according to the concentricity data to obtain a corrected loss parameter, including: The modified loss parameter is calculated by the following formula, The corrected concentricity parameter is calculated by the following formula: in, To correct the loss parameters, For the The loss factor of the core wire, is the number of cores, is the maximum allowable value of concentricity, is the concentricity data, To correct the concentricity parameters, correct the concentricity parameters Range is 0 to 1.

[0045] In this embodiment, for the modified concentricity parameter formula, the normalization of concentricity is achieved by calculating the ratio of the actual concentricity to the maximum allowable concentricity, so that the concentricity of different cables can be compared under the same standard. The calculation method of the modified concentricity parameter allows the maximum allowable value of concentricity to be adjusted according to different application scenarios and quality requirements, thereby flexibly adapting to different technical standards.

[0046] For the modified loss parameter formula, the modified loss parameter takes into account the loss of all cores and provides an overall loss assessment. The loss coefficient of each core reflects the additional loss of the core due to poor concentricity. By multiplying it with the modified concentricity parameter, it can more accurately reflect the contribution of each core to the total loss.

[0047] It should be noted that the corrected loss parameter is a quantitative index calculated by comprehensively considering the concentricity data of medical wires and cables, the number of cores, the loss coefficient of each core, and the maximum allowable value of concentricity. It reflects the additional loss caused by poor concentricity of the cable during signal transmission.

[0048] In another embodiment, the modified loss parameter can also be calculated by the following formula: in, To correct the loss parameters, is the length of the cable, Loss coefficient, is the concentricity data, It is the radius corresponding to the maximum actual deviation of the core line.

[0049] It should be noted that cable length is one of the key factors affecting signal transmission loss. The longer the signal is transmitted in the cable, the greater the loss. This is because the signal encounters resistance and dielectric loss during transmission, and these losses are proportional to the cable length. The cable length can be obtained by physical measurement or from the cable specification. Is a dimensionless coefficient used to correct the calculation of signal transmission loss. It takes into account the impact of the specific application scenario and material properties of the cable on signal loss. The loss factor is an empirical coefficient that can be adjusted according to the type, material and use environment of the cable. For example, in high-frequency applications, will be larger to account for skin effects and dielectric losses. Determined by experimental measurement or based on historical data of the cable. In some cases, it can also be derived by theoretical calculation. Indicates the radius corresponding to the maximum actual deviation of the core wire in the cable. It is the distance from the center of the cable to the farthest core wire. It is another important factor affecting signal transmission loss. This means that the core is further away from the center, which increases the signal's propagation path in the cable, thereby increasing losses.

[0050] In step S17, it is determined whether the modified loss parameter is greater than a preset minimum transmission threshold value. If so, an alarm operation is performed; if not, no alarm operation is performed.

[0051] Specifically, it is necessary to set a preset minimum transmission threshold T based on the application scenario of the cable, the frequency and amplitude of the transmitted signal, and other factors. This threshold is the maximum loss allowed by the cable during signal transmission. Assuming there is a medical wire and cable, according to the method in S16, the corrected loss parameter is calculated .

[0052] In a specific embodiment, if the calculated is 0.05. Next, the calculated Compare with the preset minimum transmission threshold T. Assume T is set to 0.03.

[0053] if >T, which means that the additional loss caused by poor concentricity exceeds the maximum allowed value. In this case, the system will perform alarm operations. Alarm operations include: Trigger visual or audible alarms to notify operators to check cables; automatically record substandard cable batches and related data for further analysis; trigger automatic production line stop to prevent substandard products from continuing to be produced.

[0054] if ≤T, which means the cable loss is within the acceptable range and the system will not perform alarm operation. The cable can continue to be used in its intended medical device.

[0055] It should be noted that the minimum transmission threshold refers to the maximum loss value allowed by the cable concentricity under the premise of ensuring the quality of signal transmission. This threshold is determined based on factors such as the application scenario of the cable, the frequency and amplitude of the transmitted signal, and is used to determine whether the cable meets the transmission requirements.

[0056] The following describes the working process of the present invention using a common scenario as an example. Figure 2 , which is Figure 1 Schematic diagram of the working scenario of the method.

[0057] In the medical field, electrocardiogram monitoring equipment is an important tool for diagnosing heart disease. The electrocardiogram monitoring equipment transmits the patient's cardiac electrical signals to the monitor through wires and cables, and the doctor makes a diagnosis based on the waveform displayed by the monitor. However, the concentricity problem of wires and cables causes signal transmission distortion, which in turn affects the accuracy of diagnosis. The traditional concentricity test method relies on instrument measurement, and requires reference to standard parts to determine the quality of concentricity by calculating the relative error rate. However, as the use time increases, the measurement error of the instrument will increase, resulting in low measurement accuracy for medical wires and cables. In addition, the traditional method is inefficient and cannot quickly adapt to the detection needs of a large number of cables on the production line. Therefore, the present invention proposes an intelligent concentricity detection method for medical wires and cables, which does not require the use of high-precision measuring instruments and improves the accuracy of detecting and measuring the concentricity parameters of medical cables.

[0058] ECG monitoring equipment consists of three parts: electrodes, wires and cables, and monitors. The electrodes are attached to the patient's skin to collect cardiac electrical signals; the wires and cables transmit the signals from the electrodes to the monitor; the monitor is responsible for displaying and analyzing the waveforms. In this scenario, the concentricity problem of the wires and cables stems from errors in the manufacturing process, such as uneven distribution between the conductor and the insulation layer. This uneven distribution will cause the signal to attenuate, distort or interfere during transmission, thereby affecting the accuracy of the ECG.

[0059] First, the wires and cables of the ECG monitoring equipment to be tested are fixed on a dedicated testing platform. The platform is equipped with a high-resolution camera that can capture clear images of the cable cross section. In order to obtain more accurate measurement results, the cable should be pre-processed before fixing, such as cleaning and removing surface impurities. After the camera is started, it will automatically capture the image of the cable cross section and transmit it to the computer in real time for subsequent processing. At the same time, the inner diameter of the insulation layer and the diameter of the cable core of the wire and cable are obtained from the initial data of the system, and the nominal radius is calculated. In the captured cable cross-section image, the outer diameter of the cable is about 5mm. The inner diameter of the insulation layer (4mm) and the diameter of the cable core (3mm) of the wire and cable are obtained from the initial data of the system, so the nominal radius is calculated to be 0.5mm.

[0060] After the computer receives the image of the cable cross section, it will perform image preprocessing. This includes steps such as image enhancement, denoising and edge detection, which are designed to improve the clarity and contrast of the image so that the characteristic information of the cable can be extracted more accurately. In the feature extraction stage, the computer will use image processing algorithms to identify key parameters such as the nominal diameter of the cable, the radius of the core wire, and the thickness of the insulation layer. The acquired cross-sectional image of the medical wire and cable is converted into an image, and the pixels on the image are divided into black and white by setting a suitable threshold to obtain a binary image. The binary image is processed with pixel grayscale values, and the average value of the red, green and blue channels of each pixel is calculated, and it is converted into a grayscale image, that is, a geometric image. According to the differences in grayscale values ​​of the cable structures, such as the insulation layer, lining layer, core wire and conductor shielding layer, set appropriate thresholds, such as 50-100 grayscale values ​​for the insulation layer, 100-150 for the lining layer, 150-200 for the core wire, and 200-255 for the conductor shielding layer, or use clustering algorithms, such as K-means clustering, to classify the pixel grayscale values ​​of the geometric image and distinguish the structures of each layer. Use Canny edge detection and Sobel operator to identify the boundaries of each layer structure, and the Hough transform algorithm to extract the boundary contour to form a closed curve. Measure the length, width, and angle of each layer structure, determine the center point position, calculate the offset between the core wire center and the cable geometric center, and summarize the geometric data.

[0061] The geometric data is processed by area segmentation method, and the single core line data is separated according to the geometric shape and position relationship of each layer structure. Through area segmentation, the characteristics of each core line can be analyzed separately, and the interference of other layer structures can be eliminated to prepare for the accurate calculation of the edge radius. Binary image data is extracted from the single core line data. Through image processing software, such as the counting function in Python's OpenCV library, the distance from the center point of the image to the center of the core line, that is, the center radius (0.4mm), the number of pixels in the center area of ​​the core line, that is, the number of center pixels (50) and the total number of pixels in the entire core line area, that is, the total number of pixels (100) are obtained. Finally, the edge radius (0.2mm) is obtained by substituting the above parameters into the edge radius calculation formula.

[0062] Based on the edge radius and the nominal radius, the computer will calculate the actual radius through a formula, including calculating the total cross-sectional area of ​​each core wire, and then calculating the actual radius through a formula. (1), Number of edge pixels (30), substituting the edge radius (0.2 mm), the number of center pixels (50), and the nominal radius (0.5 mm) calculated in the above steps into the total cross-sectional area formula, the total cross-sectional area is obtained. 529.88mm 2 Substituting the total cross-sectional area into the actual radius formula, the actual radius value is 13.06 mm.

[0063] Based on the concentricity parameter formula, the concentricity parameter is calculated to be 0.5. Assuming the maximum concentricity parameter is 1, the corrected concentricity parameter can be calculated to be 0.5. The corrected concentricity parameter is corrected and calculated to obtain a corrected loss parameter of 0.1.

[0064] According to the application scenario of the ECG monitoring equipment, the transmission signal frequency and amplitude and other factors, the preset minimum transmission threshold T=0.05 is set, and the calculated corrected loss parameter is compared with the minimum transmission threshold. The minimum transmission threshold is the maximum loss value allowed for the cable concentricity under the premise of ensuring the signal transmission quality. It is set according to the equipment requirements and is used to determine whether the cable meets the transmission requirements. If >T, which means that the additional loss caused by poor concentricity exceeds the maximum allowed value. In this case, the system will perform alarm operations. Alarm operations include: Trigger visual or audible alarms to notify operators to check cables; automatically record substandard cable batches and related data for further analysis; trigger automatic production line stop to prevent substandard products from continuing to be produced.

[0065] if ≤T, which means the cable loss is within the acceptable range and the system will not perform alarm operation. The cable can continue to be used in its intended medical device.

[0066] In this embodiment, >T, which means that the additional loss caused by poor concentricity exceeds the maximum allowable value, and the system will issue an alarm.

[0067] In summary, the present invention discloses an intelligent detection method for the concentricity of medical wires and cables, including obtaining a cross-sectional image and a nominal radius of the medical wires and cables; performing image conversion according to the cross-sectional image to obtain a geometric image, performing a construction operation on the geometric image to obtain geometric data; performing an extraction operation on the geometric data to obtain an edge radius; performing a calculation operation according to the edge radius and the nominal radius to obtain an actual radius; performing a concentricity calculation according to the actual radius and the nominal radius to obtain concentricity data; performing a correction calculation according to the concentricity data to obtain a corrected loss parameter; judging whether the corrected loss parameter is greater than a preset minimum transmission threshold, if so, performing an alarm operation; if not, not performing an alarm operation. The present invention obtains a cross-sectional image of the medical wires and cables, measures the nominal diameter and core wire radius of the medical wires and cables, uses a concentricity formula algorithm to analyze the data to obtain a concentricity parameter, and then compares the corrected loss parameter with the minimum transmission requirement to ensure that the medical wires and cables meet the minimum transmission requirement. In the present invention, calculation and analysis are performed through images and concentricity formulas, and there is no need to use instruments for measurement, which can reduce measurement errors and thus improve measurement accuracy.

[0068] Reference Figure 2 The second embodiment of the present invention provides an intelligent detection system for the concentricity of medical wires and cables, comprising: An image acquisition module, used to obtain cross-sectional images and nominal radius of medical wires and cables; An image processing module, used for performing image conversion according to the cross-sectional image to obtain a geometric image, and performing a construction operation on the geometric image to obtain geometric data; A data processing module, used for extracting the geometric data to obtain an edge radius; A parameter calculation module, used for performing a calculation operation according to the edge radius and the nominal radius to obtain an actual radius; A parameter implantation module, used for performing concentricity calculation according to the actual radius and the nominal radius to obtain concentricity data; A correction calculation module, used to perform correction calculation according to the concentricity data to obtain a correction loss parameter; The result output module is used to determine whether the modified loss parameter is greater than a preset minimum transmission threshold. If so, an alarm operation is performed; if not, no alarm operation is performed.

[0069] Preferably, the image acquisition module is specifically used to obtain the cross-sectional image and nominal radius of the medical wire and cable, including: The inner diameter of the insulation layer of the wire and cable and the diameter of the cable core are obtained from the initial data of the system, and the nominal radius is obtained by subtracting the diameter of the cable core from the inner diameter of the insulation layer of the wire and cable.

[0070] Preferably, the image processing module is specifically used for performing image conversion on the cross-sectional image to obtain a geometric image, and performing a construction operation on the geometric image to obtain geometric data, including: According to the cross-sectional image of the medical wire and cable, performing image conversion on the cross-sectional image of the medical wire and cable to obtain a binary image; Processing the binary image with pixel grayscale values ​​to obtain a geometric image; Classify the pixel grayscale values ​​of the geometric image to obtain an insulating layer, an inner lining layer, a core wire and a conductor shielding layer; According to the insulating layer, the lining layer, the core wire and the conductor shielding layer, each layer structure of the insulating layer, the lining layer, the core wire and the conductor shielding layer is analyzed to obtain geometric data.

[0071] Preferably, the data processing module is specifically used to extract the geometric data to obtain the edge radius, including: Performing area segmentation on the geometric data to obtain single core line data; performing binary image data extraction on the single core line data to obtain a center radius, a center pixel number and a total pixel number; calculating the center radius, the center pixel number and the total pixel number to obtain an edge radius; The edge radius is calculated by the following formula, in, is the edge radius, is the center radius, is the number of central pixels, is the total number of pixels.

[0072] Preferably, the parameter calculation module is specifically used to perform a calculation operation according to the edge radius and the nominal radius to obtain an actual radius, including: The actual radius is calculated by the following formula, The total cross-sectional area of ​​each core wire is calculated by the following formula: in, is the actual radius, is the total cross-sectional area of ​​each core wire, is the number of cores, is the nominal radius, is the edge radius, is the number of central pixels, is the number of edge pixels.

[0073] Preferably, the parameter implantation module is specifically used to perform concentricity calculation according to the actual radius and the nominal radius to obtain concentricity data, including: The concentricity data is calculated by the following formula, in, is the concentricity data, is the actual radius, For the The actual radius of the core wire, is the number of cores, is the nominal radius.

[0074] Preferably, the correction calculation module is specifically used to perform correction calculation according to the concentricity data to obtain the corrected loss parameter, including: The modified loss parameter is calculated by the following formula, The modified concentricity parameter is calculated by the following formula, in, To correct the loss parameters, For the The loss factor of the core wire, is the number of cores, is the maximum allowable value of concentricity, is the concentricity data, To correct the concentricity parameters, correct the concentricity parameters Range is 0 to 1.

[0075] It should be noted that the intelligent detection system for the concentricity of medical wires and cables provided in an embodiment of the present invention is used to execute all the process steps of the intelligent detection method for the concentricity of medical wires and cables in the above-mentioned embodiment. The working principles and beneficial effects of the two correspond one to one, and thus will not be repeated here.

[0076] The embodiment of the present invention also provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a data acquisition program. When the processor executes the computer program, the steps of the above-mentioned intelligent detection method for concentricity of each medical wire and cable are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above-mentioned device embodiments are realized, such as the data processing module.

[0077] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, which are used to describe the execution process of the computer program in the electronic device.

[0078] The electronic device may be a computing device such as a desktop computer, a notebook, a PDA, and a smart tablet. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. The electronic device may include more or fewer components than the above components, or may combine certain components, or different components. For example, the electronic device may also include input and output devices, network access devices, buses, etc.

[0079] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, and uses various interfaces and lines to connect various parts of the entire electronic device.

[0080] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the electronic device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0081] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0082] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0083] The specific embodiments described above further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent detection method for the concentricity of medical wires and cables, characterized in that: Executed by a computer, including: Obtain cross-sectional images and nominal radius of medical wires and cables; Performing image conversion according to the cross-sectional image to obtain a geometric image, and performing a construction operation on the geometric image to obtain geometric data; Performing an extraction operation on the geometric data to obtain an edge radius; Performing a calculation operation according to the edge radius and the nominal radius to obtain an actual radius; Calculating the concentricity according to the actual radius and the nominal radius to obtain concentricity data; Perform correction calculation according to the concentricity data to obtain a corrected loss parameter; It is determined whether the modified loss parameter is greater than a preset minimum transmission threshold value. If so, an alarm operation is performed; if not, no alarm operation is performed.

2. The intelligent detection method for concentricity of medical wires and cables according to claim 1, characterized in that: Get the nominal radius, including: The inner diameter of the insulation layer of the wire and cable and the diameter of the cable core are obtained from the initial data of the system, and the nominal radius is obtained by subtracting the diameter of the cable core from the inner diameter of the insulation layer of the wire and cable.

3. The intelligent detection method for concentricity of medical wires and cables according to claim 1, characterized in that: The step of performing image conversion according to the cross-sectional image to obtain a geometric image and performing a construction operation on the geometric image to obtain geometric data includes: According to the cross-sectional image of the medical wire and cable, performing image conversion on the cross-sectional image of the medical wire and cable to obtain a binary image; Processing the binary image with pixel grayscale values ​​to obtain a geometric image; Classify the geometric image by pixel gray value to obtain an insulating layer, an inner lining layer, a core wire and a conductor shielding layer; According to the insulating layer, the lining layer, the core wire and the conductor shielding layer, each layer structure of the insulating layer, the lining layer, the core wire and the conductor shielding layer is analyzed to obtain geometric data.

4. The intelligent detection method for concentricity of medical wires and cables according to claim 1, characterized in that: The step of performing a calculation operation according to the edge radius and the nominal radius to obtain an actual radius includes: The actual radius is calculated by the following formula, The total cross-sectional area of ​​each core wire is calculated by the following formula: in, is the actual radius, is the total cross-sectional area of ​​each core wire, is the number of cores, is the nominal radius, is the edge radius, is the number of central pixels, is the number of edge pixels.

5. The intelligent detection method for concentricity of medical wires and cables according to claim 1, characterized in that: The step of extracting the geometric data to obtain the edge radius includes: Performing area segmentation on the geometric data to obtain single core line data; performing binary image data extraction on the single core line data to obtain a center radius, a center pixel number and a total pixel number; calculating the center radius, the center pixel number and the total pixel number to obtain an edge radius; The edge radius is calculated by the following formula, in, is the edge radius, is the center radius, is the number of central pixels, is the total number of pixels.

6. The intelligent detection method for concentricity of medical wires and cables according to claim 1, characterized in that: The concentricity calculation is performed according to the actual radius and the nominal radius to obtain the concentricity data, including: The concentricity data is calculated by the following formula, in, is the concentricity data, is the actual radius, For the The actual radius of the core wire, is the number of cores, is the nominal radius.

7. The intelligent detection method for concentricity of medical wires and cables according to claim 1, characterized in that: The method of performing correction calculation according to the concentricity data to obtain the corrected loss parameter includes: The modified loss parameter is calculated by the following formula, The corrected concentricity parameter is calculated by the following formula: in, To correct the loss parameters, For the The loss factor of the core wire, is the number of cores, is the maximum allowable value of concentricity, is the concentricity data, To correct the concentricity parameters, correct the concentricity parameters Range is 0 to 1.

8. An intelligent detection system for the concentricity of medical wires and cables, characterized in that: include: An image acquisition module, used to obtain cross-sectional images and nominal radius of medical wires and cables; An image processing module, used for performing image conversion according to the cross-sectional image to obtain a geometric image, and performing a construction operation on the geometric image to obtain geometric data; A data processing module, used for extracting the geometric data to obtain an edge radius; A parameter calculation module, used for performing a calculation operation according to the edge radius and the nominal radius to obtain an actual radius; A parameter implantation module, used for performing concentricity calculation according to the actual radius and the nominal radius to obtain concentricity data; A correction calculation module, used to perform correction calculation according to the concentricity data to obtain a correction loss parameter; The result output module is used to determine whether the modified loss parameter is greater than a preset minimum transmission threshold. If so, an alarm operation is performed; if not, no alarm operation is performed.

9. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the intelligent detection method for the concentricity of the medical wire and cable as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the intelligent detection method for the concentricity of the medical wire and cable as described in any one of claims 1 to 7.

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