A puncture needle tube early warning system and method based on electromagnetic signals

Abstract

The present invention relates to the technical field of early warning for puncture needles, and discloses a puncture needle early warning system and method based on electromagnetic signals, the method comprising: obtaining user information data, displacement data of the puncture needle in the urethra, and primary resistance change data and secondary resistance change data; obtaining preset resistance data and preset displacement data of the puncture needle in the urethra based on the user information data and a pre-configured resistance model; determining whether the operation is abnormal based on the relationship between the primary resistance change data and the secondary resistance change data and the preset resistance data, or the relationship between the displacement data and the preset displacement data, wherein: if it is determined that the operation is abnormal, an alarm is performed based on an audible and visual alarm device, and the puncture operation is terminated. The present invention achieves real-time identification and intelligent early warning of abnormal puncture conditions by integrating user individual information, electromagnetic displacement monitoring, and resistance data comparison, thereby significantly improving the accuracy and safety of the puncture operation.

Description

Technical Field

[0001] The present invention relates to the technical field of early warning of puncture needle tubes, and in particular to a puncture needle tube early warning system and method based on electromagnetic signals. Background Art

[0002] Urethral puncture is a common interventional technique in urological clinical practice, often used for procedures such as catheter implantation, tissue biopsy, or local treatment. Existing technologies rely primarily on doctors visually observing the advancement of the puncture needle using an endoscope or ultrasound device to determine whether it has entered the urethra or target area. However, this method is subject to certain subjectivity and delays, and is unable to quantitatively reflect the specific position of the needle in the tissue and changes in resistance in real time, resulting in limited puncture accuracy and an increased risk of mispuncture, overpuncture, or missing the target area.

[0003] Some current research attempts to incorporate sensor devices, such as force sensors or displacement sensors, to assist in monitoring relevant physical parameters during the puncture process. However, these approaches suffer from common issues such as low sensor data accuracy, slow dynamic response, and a lack of integration with individual physiological characteristics, making them difficult to meet the clinical need for precise puncture control in complex physiological structures. Furthermore, most existing systems lack intelligent recognition and alarm mechanisms for abnormal conditions, making it impossible to promptly identify abnormal tissue resistance or deviation from the path during the puncture process, thereby failing to effectively avoid tissue damage or intraoperative errors.

[0004] Therefore, there is an urgent need to invent an early warning technology for puncture needles to solve the problems of low sensing accuracy and slow response speed of the existing puncture monitoring system, as well as the lack of adaptation to individual physiological characteristics and intelligent recognition of abnormal conditions, making it difficult to achieve precise control and real-time early warning of the puncture process in complex tissue structures. Summary of the Invention

[0005] In view of this, the present invention proposes a puncture needle tube warning system and method based on electromagnetic signals, aiming to solve the problems of low sensing accuracy and slow response speed of the puncture monitoring system in current technology, lack of adaptation to individual physiological characteristics and intelligent recognition of abnormal conditions, and difficulty in achieving precise control and real-time warning of the puncture process in complex tissue structures.

[0006] The present invention proposes a puncture needle tube early warning method based on electromagnetic signals, comprising:

[0007] Obtaining user information data, displacement data of the puncture needle tube in the urethra, and primary resistance change data and secondary resistance change data;

[0008] According to the user information data and the pre-configured resistance model, the preset resistance data and the preset displacement data of the puncture needle tube in the urethra are obtained;

[0009] Whether the operation is abnormal is determined based on the relationship between the primary resistance change data and the secondary resistance change data and the preset resistance data, or the relationship between the displacement data and the preset displacement data, wherein:

[0010] If it is determined that the operation is abnormal, an alarm is issued based on the sound and light alarm device and the puncture operation is terminated.

[0011] Furthermore, when obtaining user information data, it includes:

[0012] Acquiring user age information, a user's urethra surface image, and / or a urethra fluoroscopic image;

[0013] Performing image preprocessing on the urethra surface image and / or the urethra fluoroscopic image, wherein the image preprocessing includes denoising, grayscale processing, contrast enhancement processing, image edge detection, and image segmentation processing;

[0014] Extracting image features from the urethra surface image and / or the urethra fluoroscopic image after image preprocessing, wherein the image features include image feature data of urethra contour edge, texture distribution, grayscale gradient change and image contrast;

[0015] Based on the image features and the user's age information, the user's urethral wall thickness and tissue softness are determined.

[0016] Furthermore, when determining the thickness of the user's urethra wall and the softness of the tissue based on the image features and the user's age information, the following steps are included:

[0017] Calculate the urethra wall thickness based on the urethra contour edge information in the image features;

[0018] Based on the texture distribution and grayscale gradient changes in image features and combined with the user's age information, the softness of urethral tissue is evaluated.

[0019] Furthermore, when obtaining preset resistance data and preset displacement data of the puncture needle tube in the urethra based on the user information data and the pre-configured resistance model, the method includes:

[0020] Establishing user puncture characteristic data based on user's age information, urethral wall thickness and tissue softness;

[0021] Matching is performed between the puncture characteristic data and the resistance model, and the preset resistance data and preset displacement data of the puncture needle tube in the urethra are determined based on the matching results:

[0022] When the puncture characteristic data matches any characteristic data in the resistance model, the puncture resistance data and puncture displacement data corresponding to the characteristic data are determined to be the preset resistance data and preset displacement data of the puncture needle tube in the urethra;

[0023] When the puncture feature data does not match the feature data in the resistance model, the feature vectors between the feature data and the feature data close to the puncture feature data are obtained. Based on the relationship between the feature vectors and the feature data close to the puncture feature data, the preset resistance data and preset displacement data of the puncture needle tube in the urethra are determined.

[0024] Furthermore, when determining the preset resistance data and the preset displacement data of the puncture needle tube in the urethra based on the relationship between the characteristic vector and the characteristic data close to the puncture characteristic data, the method includes:

[0025] Obtaining a puncture feature vector between the puncture feature data and the adjacent feature data, and obtaining a vector ratio between the puncture feature vector and the feature vector;

[0026] The adjustment coefficient is determined according to the relationship between the vector difference and the pre-configured first preset vector ratio and the second preset vector ratio:

[0027] When the vector ratio is lower than the first preset vector ratio, the adjustment coefficient is determined to be L1;

[0028] When the vector ratio is higher than or equal to the first preset vector ratio and the vector ratio is lower than the second preset vector ratio, it is determined to be adjusted to L2;

[0029] When the vector ratio is higher than or equal to the second preset vector ratio, it is determined that the adjustment system is L3;

[0030] wherein the first preset vector ratio is lower than the second preset vector ratio, and L1<1<L2<L3;

[0031] When the adjustment system is determined to be Li, i=1,2,3, the characteristic data close to the puncture characteristic data, the corresponding puncture resistance data and puncture displacement data are adjusted according to the adjustment coefficient Li, and the adjusted puncture resistance data and puncture displacement data are determined as the preset resistance data and preset displacement data of the puncture needle tube in the urethra.

[0032] Furthermore, pre-configured resistance models include:

[0033] Obtain data on urethral thickness, tissue softness of each tissue part, and puncture needle tube puncture resistance change data and puncture displacement depth during each advancement for users of different ages during urethral puncture;

[0034] A resistance correlation formula is established based on the age of each user, urethral thickness data during urethral puncture, tissue softness of the tissue part, puncture needle tube puncture resistance change data during advancement, and puncture displacement depth;

[0035] Obtain the Euclidean distance between each resistance correlation equation and establish a distance matrix;

[0036] According to the distance matrix, iteratively cluster the resistance correlation formulas and obtain the clustered resistance correlation formulas;

[0037] According to the magnitude relationship between each resistance correlation formula, a linear axis between each resistance correlation formula is established according to the magnitude relationship;

[0038] A resistance model is established based on the linear axes between the resistance correlation equations.

[0039] Furthermore, determining whether the operation is abnormal based on the relationship between the primary resistance change data, the secondary resistance change data and the preset resistance data, or the relationship between the displacement data and the preset displacement data includes:

[0040] Determine whether the operation is abnormal based on the relationship between the primary resistance change data and the preset resistance data:

[0041] When the primary resistance change data is lower than or equal to the preset resistance data, it is determined that there is no abnormality in the operation, and based on the relationship between the displacement data and the preset displacement data, and the relationship between the secondary resistance change data and the preset resistance data, it is determined whether the operation is abnormal;

[0042] When the primary resistance change data is higher than the preset resistance data, it is determined that there is an abnormality in the operation.

[0043] Furthermore, determining whether an operation is abnormal based on the relationship between the displacement data and the preset displacement data includes:

[0044] When the displacement data exceeds the preset displacement data, it is determined that the operation is abnormal;

[0045] When the displacement data does not exceed the preset displacement data, it is determined whether the operation is abnormal based on the relationship between the secondary resistance change data and the preset resistance data.

[0046] Furthermore, when determining whether the operation is abnormal based on the relationship between the secondary resistance change data and the preset resistance data, the method includes:

[0047] When the secondary resistance change data is lower than the preset resistance data, it is determined that there is no abnormality in the operation;

[0048] When the secondary resistance change data is higher than or equal to the preset resistance data, it is determined that there is an abnormality in the operation.

[0049] Compared with the existing technology, the beneficial effect of the present invention is that by comparing and analyzing the actual puncture data with the pre-established resistance model and displacement model, it is possible to determine whether the puncture path and depth are in a normal state. Compared with the existing puncture methods that rely solely on the doctor's experience or visual assistance, this method greatly improves the objectivity and controllability of the operation process. In addition, the present invention adopts a combination of electromagnetic sensing and mechanical sensing to achieve high-precision monitoring of the puncture needle advancement process. Among them, the electromagnetic induction module can obtain the spatial displacement information of the needle in the urethra in real time, while the resistance sensor can dynamically capture the mechanical feedback generated by the tissue on the puncture needle. By fusing these two types of sensor data and combining it with the big data-driven resistance trend model, the present invention can intelligently identify and match the primary resistance (such as penetrating the urethral wall) and secondary resistance (such as entering the target tissue) that appear during the puncture process. It is worth emphasizing that when the system detects an abnormal deviation between the current resistance data and the preset model (for example, the resistance is too high, the duration is abnormal, or the key resistance stage is missing), the sound and light alarm mechanism can be immediately triggered to remind the doctor that there is a risk in the current puncture state, and automatically terminate the puncture operation when necessary to avoid further tissue damage or operational errors. This intelligent early warning mechanism significantly enhances the system's safety protection capabilities, helps to reduce medical risks, and ensures patient safety. Finally, on the basis of improving puncture accuracy, the present invention also has good adaptability and scalability. By continuously accumulating historical data during the operation and optimizing the resistance model, the system can achieve self-learning and intelligent evolution, thereby better adapting to the personalized needs of patients of different age groups and different anatomical structures, and further improving the individual adaptability and clinical universality of the puncture operation.

[0050] On the other hand, the present application also provides a puncture needle tube early warning system based on electromagnetic signals, comprising:

[0051] an acquisition module configured to acquire user information data, displacement data of the puncture needle tube in the urethra, and primary resistance change data and secondary resistance change data;

[0052] An analysis module is electrically connected to the acquisition module, and is configured to obtain preset resistance data and preset displacement data of the puncture needle tube in the urethra based on the user information data and a pre-configured resistance model; the analysis module is further configured to determine whether the operation is abnormal based on the relationship between the primary resistance change data and the secondary resistance change data and the preset resistance data, or the relationship between the displacement data and the preset displacement data;

[0053] The sound and light alarm module is electrically connected to the analysis module and the puncture needle tube respectively. The sound and light alarm module is configured to perform an alarm process and terminate the puncture operation when the analysis module determines that the operation is abnormal.

[0054] It is understandable that the electromagnetic signal-based puncture needle tube warning system and method in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0056] Figure 1 A flowchart of a puncture needle tube early warning method based on electromagnetic signals provided by an embodiment of the present invention;

[0057] Figure 2 This is a functional block diagram of a puncture needle tube early warning system based on electromagnetic signals provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0059] like Figure 1 As shown, in some embodiments of the present application, this embodiment provides a puncture needle tube early warning method based on electromagnetic signals, including:

[0060] Step S100: acquiring user information data, displacement data of the puncture needle tube in the urethra, and primary resistance change data and secondary resistance change data.

[0061] Specifically, when obtaining user information data, it includes: obtaining the user's age information, the user's urethral surface image and / or urethral fluoroscopic image; performing image preprocessing on the urethral surface image and / or urethral fluoroscopic image, wherein the image preprocessing includes denoising processing, grayscale processing, contrast enhancement processing, image edge detection and image segmentation processing; extracting image features from the urethral surface image and / or urethral fluoroscopic image after image preprocessing, wherein the image features include image feature data of urethral contour edge, texture distribution, grayscale gradient change and image contrast; and determining the user's urethral wall thickness and tissue softness based on the image features and the user's age information.

[0062] Specifically, when determining the user's urethral wall thickness and tissue softness based on image features and the user's age information, it includes: calculating the urethral wall thickness based on the urethra contour edge information in the image features; and evaluating the softness of the urethral tissue based on the texture distribution and grayscale gradient changes in the image features, combined with the user's age information.

[0063] It can be understood that by combining image processing with individualized data, the structural characteristics and tissue properties of the urethra can be accurately analyzed, thereby providing personalized physiological parameter support for the puncture process. Specifically, by first collecting surface images and / or fluoroscopic images of the user's urethra, various image preprocessing techniques such as denoising, grayscale conversion, contrast enhancement, edge detection, and image segmentation are used to improve image quality and the discernibility of key structures, providing an accurate data foundation for subsequent feature extraction. Secondly, by extracting key features from the image, such as the urethral contour edge, texture distribution, grayscale gradient changes, and image contrast, the urethral morphology and internal tissue structure can be accurately portrayed. The urethral contour edge information reflects the morphology and thickness variations of the urethral wall, while the texture distribution and grayscale gradient reveal subtle structural differences in the tissue. Together, these features provide rich visual information for assessing tissue flexibility. Finally, the extracted image features are combined with the user's age information to calculate and assess urethral wall thickness and tissue flexibility using an algorithmic model. Age, as a key physiological parameter affecting tissue elasticity and thickness, can effectively assist in interpreting image features, improving the accuracy and individual adaptability of the assessment. The principle of this technology is to start from multimodal images and individual characteristics, and use the fusion of image processing and physiological parameters to provide a scientific basis for the personalized configuration of puncture paths and resistance models, thereby improving the accuracy and safety of the puncture process.

[0064] It can be seen that image preprocessing operations (such as denoising, grayscale conversion, contrast enhancement, edge detection, and image segmentation) can significantly improve image clarity and feature recognizability, effectively reducing recognition errors caused by poor image quality and ensuring the accuracy and stability of subsequent image feature extraction. This process improves adaptability to complex anatomical structures and enhances the robustness of overall detection. Secondly, during the image feature extraction stage, multi-dimensional information such as urethral contour edges, texture distribution, grayscale gradient changes, and image contrast is considered, thereby achieving in-depth perception of urethral wall morphology and tissue state. This method not only has high local resolution but also accurately assesses urethral wall thickness and tissue softness by jointly analyzing image features with physiological indicators (such as age). Furthermore, customized interpretation of image features based on user age effectively enhances the model's ability to discern differences in tissue properties across age groups, making the assessment results more clinically relevant. For example, elderly users may have tissue calcification or atrophy, while younger users may have more flexible tissue. This method can effectively quantify and reflect these individual differences, providing a basis for personalized puncture path planning.

[0065] Step S200: Acquire preset resistance data and preset displacement data of the puncture needle tube in the urethra according to the user information data and the pre-configured resistance model.

[0066] Specifically, when obtaining the preset resistance data and preset displacement data of the puncture needle tube located in the urethra based on the user information data and the pre-configured resistance model, it includes: establishing the user's puncture feature data based on the user's age information, urethral wall thickness and tissue softness; matching the puncture feature data with the resistance model, and determining the preset resistance data and preset displacement data of the puncture needle tube located in the urethra based on the matching result: when the puncture feature data is consistent with any feature data in the resistance model, the puncture resistance data and puncture displacement data corresponding to the feature data are determined to be the preset resistance data and preset displacement data of the puncture needle tube located in the urethra; when the puncture feature data is inconsistent with each feature data in the resistance model, the feature vector between each feature data and the feature data close to the puncture feature data are obtained, and the preset resistance data and preset displacement data of the puncture needle tube located in the urethra are determined based on the relationship between the feature vector and the feature data close to the puncture feature data.

[0067] Specifically, when determining the preset resistance data and preset displacement data of the puncture needle tube in the urethra based on the relationship between the characteristic vector and the characteristic data close to the puncture characteristic data, the method includes: obtaining the puncture characteristic vector between the puncture characteristic data and the characteristic data close to it, and obtaining the vector ratio between the puncture characteristic vector and the characteristic vector; determining the adjustment coefficient based on the relationship between the vector difference and the pre-configured first preset vector ratio and the second preset vector ratio: when the vector ratio is lower than the first preset vector ratio, the adjustment coefficient is determined to be L1; when the vector ratio is higher than or equal to the first preset vector ratio, and is directed to When the quantity ratio is lower than the second preset vector ratio, it is determined to be adjusted to L2; when the vector ratio is higher than or equal to the second preset vector ratio, the adjustment system is determined to be L3; wherein, the first preset vector ratio is lower than the second preset vector ratio, and L1<1<L2<L3; when the adjustment system is determined to be Li, i=1,2,3, the characteristic data close to the puncture characteristic data, the corresponding puncture resistance data and puncture displacement data are adjusted according to the adjustment coefficient Li, and the adjusted puncture resistance data and puncture displacement data are determined as the preset resistance data and preset displacement data of the puncture needle tube located in the urethra.

[0068] Specifically, the pre-configured resistance model includes: obtaining the urethral thickness data of each urethra, the tissue softness of each tissue part, and the puncture needle tube puncture resistance change data and puncture displacement depth when users of different ages perform urethral puncture; establishing a resistance correlation formula based on the age of each user, the urethral thickness data during urethral puncture, the tissue softness of each tissue part, the puncture needle tube puncture resistance change data and puncture displacement depth when advancing; obtaining the Euclidean distance between each resistance correlation formula and establishing a distance matrix; iteratively clustering each resistance correlation formula based on the distance matrix and obtaining each resistance correlation formula after clustering; establishing a linear axis between each resistance correlation formula based on the size relationship between each resistance correlation formula and the size relationship; establishing a resistance model based on the linear axis between each resistance correlation formula.

[0069] As can be understood, user-specific puncture feature data is constructed by collecting key physiological parameters such as the user's age, urethral wall thickness, and tissue flexibility. This feature data reflects individual physiological structural differences and tissue mechanical properties, providing a quantitative representation of the actual environment within the user's urethra. Age, a key factor influencing tissue elasticity and thickness, combined with urethral wall morphology and texture features obtained through image processing, can effectively infer urethral wall thickness and tissue flexibility, providing a critical reference for subsequent resistance prediction. Secondly, feature matching is performed using a pre-established resistance model. This model is based on a large amount of clinical puncture data from users of different ages, encompassing puncture resistance curves and corresponding displacement information under conditions of varying urethral wall thickness and tissue flexibility. During the matching process, if the user's puncture feature data highly matches a typical feature in the model, the resistance and displacement data corresponding to that feature are directly used to ensure computational efficiency and accuracy. If there is a discrepancy, the ratio of the user's puncture feature vector to the similar feature vector in the model is calculated, and mathematical operations in vector space are used to quantitatively reflect the similarity between the two. Based on the magnitude of the vector ratio, multiple preset threshold ranges are set, each corresponding to a different adjustment coefficient. The adjustment coefficient reflects the scaling of the preset resistance and displacement values, ensuring that the preset data better reflects individual user differences. For example, when the vector ratio is low, indicating a significant divergence between the user's characteristics and the model's, the adjustment coefficient is set to less than 1, reducing the preset resistance and displacement values. Conversely, when the vector ratio is high, an adjustment coefficient greater than 1 is used to moderately scale the preset parameters, thereby achieving dynamic adaptive adjustment of the data. This mechanism effectively avoids bias in the preset data due to physiological differences, improving the accuracy and safety of puncture predictions. Finally, the resistance model is constructed based on a complex multidimensional data processing pipeline. Urethral puncture data from users of different age groups is collected, including urethral wall thickness, tissue softness, and resistance changes and needle advancement depth measured during the puncture process. A distance matrix is ​​constructed by calculating the Euclidean distance between each resistance correlation. An iterative clustering algorithm is then used to group similar resistance curves and extract representative resistance patterns. Based on these clustering results, linear axes of the resistance correlations are established, leading to a resistance model with strong generalization capabilities. This model can reflect the diversity and complexity of resistance changes, effectively simulating and predicting puncture processes for different users.

[0070] As can be seen, by combining the user's age, urethral wall thickness, and tissue softness, precise and personalized puncture feature data is established, enabling personalized presets for resistance and displacement during the puncture process. This matching mechanism, based on individual characteristics and a preconfigured resistance model, effectively improves the accuracy of the preset resistance and displacement data, thereby enhancing the safety and reliability of the puncture procedure. Furthermore, by comparing and dynamically adjusting the puncture feature data with multiple feature data in the resistance model, the system can flexibly adapt to user physiological differences. A multi-level adjustment coefficient fine-tunes the preset data based on vector ratios, ensuring reasonable and realistic resistance and displacement estimates even when the match is not completely consistent, significantly reducing the risk of misjudgment and the probability of procedural errors. Finally, the pre-built resistance model, based on extensive clinical data and using clustering and linear axis modeling, can reflect the resistance variation patterns under different age and tissue conditions. This model provides a scientific basis for puncture early warning, enabling effective coverage of complex individual differences in practical applications, further enhancing precise control and intelligent early warning capabilities during the puncture process, and helping to improve surgical success rates and patient safety.

[0071] Step S300: Determine whether the operation is abnormal based on the relationship between the primary resistance change data, the secondary resistance change data and the preset resistance data, or the relationship between the displacement data and the preset displacement data.

[0072] Specifically, when determining whether the operation is abnormal based on the relationship between the primary resistance change data, the secondary resistance change data and the preset resistance data, or the relationship between the displacement data and the preset displacement data, it includes: determining whether the operation is abnormal based on the relationship between the primary resistance change data and the preset resistance data: when the primary resistance change data is lower than or equal to the preset resistance data, it is determined that there is no abnormality in the operation, and based on the relationship between the displacement data and the preset displacement data, and the relationship between the secondary resistance change data and the preset resistance data, it is determined whether the operation is abnormal; when the primary resistance change data is higher than the preset resistance data, it is determined that there is an abnormality in the operation.

[0073] Specifically, based on the relationship between the displacement data and the preset displacement data, determining whether the operation is abnormal includes: when the displacement data exceeds the preset displacement data, determining that the operation is abnormal; when the displacement data does not exceed the preset displacement data, determining whether the operation is abnormal based on the relationship between the secondary resistance change data and the preset resistance data.

[0074] Specifically, based on the relationship between the secondary resistance change data and the preset resistance data, determining whether there is an abnormality in the operation includes: when the secondary resistance change data is lower than the preset resistance data, determining that there is no abnormality in the operation; when the secondary resistance change data is higher than or equal to the preset resistance data, determining that there is an abnormality in the operation.

[0075] As can be understood, by comparing multiple sets of key data collected in real time during the puncture process with preset standards, a multi-level, multi-dimensional anomaly identification mechanism has been established. Its core principle is to use primary resistance change data as the first judgment threshold. If this resistance data does not exceed the preset range, displacement data and secondary resistance change data are further introduced to assist in the judgment, achieving a step-by-step judgment process from "coarse screening" to "fine judgment." This method not only improves response speed but also significantly enhances the accuracy and robustness of judgment. In the first step, the primary resistance change data generated during the advancement of the puncture needle is acquired in real time and compared with the preset resistance data established based on the user's individual characteristics. If this data does not exceed the safety threshold, that is, is lower than or equal to the preset resistance data, the current puncture environment is preliminarily considered to be normal. This design effectively avoids false alarms due to minor fluctuations while ensuring sensitive response to sudden high resistance conditions. If the primary resistance change data is within the safe range, the difference between the current displacement data and the personalized preset displacement data is further evaluated. This step effectively monitors the puncture path and depth control. When the actual displacement exceeds the preset upper limit, it may indicate that the puncture has exceeded the safe physiological zone, for example, crossing the target tissue boundary or entering a high-risk tissue area. This triggers an abnormality detection and a timely alarm, preventing further operation. If the displacement data does not exceed the preset range, the secondary resistance change data is introduced for final verification. The secondary resistance reflects the stability and continuity of the resistance change trend during the puncture process and is a more refined dynamic signal judgment criterion. When the secondary resistance change data is significantly higher than the preset resistance data, it may indicate a sudden change in tissue structure, the needle encountering high-density tissue, or mechanical jamming during the puncture process, triggering a final abnormality detection. This layer of judgment compensates for the blind spot in detecting transient changes and local fluctuations in resistance, effectively improving comprehensive recognition capabilities. In summary, this technical solution achieves dynamic monitoring of the puncture process and high-precision anomaly identification through the combined judgment of three-dimensional parameters (primary resistance, secondary resistance, and displacement). It has the technical advantages of high recognition sensitivity, low false positive rate, and strong adaptability. Especially in complex clinical environments or those with significant individual differences, this method can significantly improve the safety and accuracy of puncture operations, effectively reduce the incidence of intraoperative complications, and has good application prospects and clinical practical value.

[0076] Step S400: If it is determined that the operation is abnormal, an alarm is issued based on the sound and light alarm device, and the puncture operation is terminated.

[0077] In the above-mentioned embodiment, actual puncture data is compared and analyzed with pre-established resistance and displacement models to determine whether the puncture path and depth are normal. Compared to existing puncture methods that rely solely on physician experience or visual guidance, this method significantly improves the objectivity and controllability of the procedure. Furthermore, the present invention utilizes a combination of electromagnetic and mechanical sensing to achieve high-precision monitoring of the puncture needle's advancement process. The electromagnetic sensing module captures real-time spatial displacement information of the needle within the urethra, while the resistance sensor dynamically captures the mechanical feedback exerted by the tissue on the puncture needle. By integrating these two types of sensor data with a big data-driven resistance trend model, the present invention intelligently identifies and analyzes primary resistance (e.g., penetration of the urethral wall) and secondary resistance (e.g., entry into the target tissue) during the puncture process. It is worth noting that when the system detects an abnormal deviation between the current resistance data and the pre-established model (e.g., excessively high resistance, abnormal duration, or missing critical resistance phases), it immediately triggers an audible and visual alarm mechanism, alerting the physician to the risk of the current puncture condition and, if necessary, automatically terminating the puncture procedure to avoid further tissue damage or procedural errors. This intelligent early warning mechanism significantly enhances the system's safety and protection capabilities, helping to reduce medical risks and ensure patient safety. Finally, while improving puncture accuracy, this invention also possesses excellent adaptability and scalability. By continuously accumulating historical data from surgical procedures and optimizing the resistance model, the system can achieve self-learning and intelligent evolution, thereby better adapting to the personalized needs of patients of different age groups and anatomical structures, further improving the individual adaptability and clinical universality of puncture operations.

[0078] In another preferred embodiment based on the above embodiment, Figure 2 As shown, this embodiment provides a puncture needle tube warning system based on electromagnetic signals, including: an acquisition module, an analysis module and an audio-visual alarm module.

[0079] Specifically, the acquisition module is configured to obtain user information data, displacement data of the puncture needle in the urethra, and primary resistance change data and secondary resistance change data; the analysis module is electrically connected to the acquisition module, and the analysis module is configured to obtain the preset resistance data and preset displacement data of the puncture needle in the urethra based on the user information data and the pre-configured resistance model; the analysis module is also configured to determine whether the operation is abnormal based on the relationship between the primary resistance change data and the secondary resistance change data and the preset resistance data, or the relationship between the displacement data and the preset displacement data; the sound and light alarm module is electrically connected to the analysis module and the puncture needle, respectively, and the sound and light alarm module is configured to perform alarm processing and terminate the puncture operation when the analysis module determines that the operation is abnormal.

[0080] It is understandable that the electromagnetic signal-based puncture needle tube warning system and method in the above-mentioned embodiments of the present invention have the same beneficial effects and will not be described in detail.

[0081] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0083] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0084] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0085] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A puncture needle tube early warning method based on electromagnetic signals, characterized in that: include: Obtaining user information data, displacement data of the puncture needle tube in the urethra, and primary resistance change data and secondary resistance change data; According to the user information data and the pre-configured resistance model, the preset resistance data and the preset displacement data of the puncture needle tube in the urethra are obtained; Whether the operation is abnormal is determined based on the relationship between the primary resistance change data and the secondary resistance change data and the preset resistance data, or the relationship between the displacement data and the preset displacement data, wherein: If it is determined that the operation is abnormal, an alarm is issued based on the sound and light alarm device, and the puncture operation is terminated; When obtaining preset resistance data and preset displacement data of the puncture needle tube in the urethra based on user information data and a pre-configured resistance model, the method includes: Establishing user puncture characteristic data based on user's age information, urethral wall thickness and tissue softness; Matching is performed between the puncture characteristic data and the resistance model, and the preset resistance data and preset displacement data of the puncture needle tube in the urethra are determined based on the matching results: When the puncture characteristic data matches any characteristic data in the resistance model, the puncture resistance data and puncture displacement data corresponding to the characteristic data are determined to be the preset resistance data and preset displacement data of the puncture needle tube in the urethra; When the puncture feature data does not match each feature data in the resistance model, the feature vectors between each feature data and feature data close to the puncture feature data are obtained, and the preset resistance data and preset displacement data of the puncture needle tube in the urethra are determined based on the relationship between the feature vectors and the feature data close to the puncture feature data; Determining the preset resistance data and the preset displacement data of the puncture needle tube in the urethra based on the relationship between the characteristic vector and the characteristic data close to the puncture characteristic data includes: Obtaining a puncture feature vector between the puncture feature data and the adjacent feature data, and obtaining a vector ratio between the puncture feature vector and the feature vector; The adjustment coefficient is determined according to the relationship between the vector difference and the pre-configured first preset vector ratio and the second preset vector ratio: When the vector ratio is lower than the first preset vector ratio, the adjustment coefficient is determined to be L1; When the vector ratio is higher than or equal to the first preset vector ratio and the vector ratio is lower than the second preset vector ratio, it is determined to be adjusted to L2; When the vector ratio is higher than or equal to the second preset vector ratio, it is determined that the adjustment system is L3; wherein the first preset vector ratio is lower than the second preset vector ratio, and L1<1<L2<L3; When the adjustment system is determined to be Li, i=1, 2, 3, the puncture characteristic data, the corresponding puncture resistance data and puncture displacement data are adjusted according to the adjustment coefficient Li, and the adjusted puncture resistance data and puncture displacement data are determined as the preset resistance data and preset displacement data of the puncture needle tube in the urethra; Pre-configured resistance models include: Obtain data on urethral thickness, tissue softness of each tissue part, and puncture needle tube puncture resistance change data and puncture displacement depth during each advancement for users of different ages during urethral puncture; A resistance correlation formula is established based on the age of each user, urethral thickness data during urethral puncture, tissue softness of the tissue part, puncture needle tube puncture resistance change data during advancement, and puncture displacement depth; Obtain the Euclidean distance between each resistance correlation equation and establish a distance matrix; According to the distance matrix, iteratively cluster the resistance correlation formulas and obtain the clustered resistance correlation formulas; According to the magnitude relationship between each resistance correlation formula, a linear axis between each resistance correlation formula is established according to the magnitude relationship; A resistance model is established based on the linear axes between the resistance correlation equations.

2. The puncture needle tube early warning method based on electromagnetic signals according to claim 1, characterized in that: When obtaining user information data, including: Acquiring user age information, a user's urethra surface image, and / or a urethra fluoroscopic image; Performing image preprocessing on the urethra surface image and / or the urethra fluoroscopic image, wherein the image preprocessing includes denoising, grayscale processing, contrast enhancement processing, image edge detection, and image segmentation processing; Extracting image features from the urethra surface image and / or the urethra fluoroscopic image after image preprocessing, wherein the image features include image feature data of urethra contour edge, texture distribution, grayscale gradient change and image contrast; Based on the image features and the user's age information, the user's urethral wall thickness and tissue softness are determined.

3. The puncture needle tube early warning method based on electromagnetic signals according to claim 2, characterized in that: When determining the user's urethral wall thickness and tissue softness based on image features and the user's age information, the following steps are included: Calculate the urethra wall thickness based on the urethra contour edge information in the image features; Based on the texture distribution and grayscale gradient changes in image features and combined with the user's age information, the softness of urethral tissue is evaluated.

4. The puncture needle tube early warning method based on electromagnetic signals according to claim 1, characterized in that: When determining whether the operation is abnormal based on the relationship between the primary resistance change data, the secondary resistance change data and the preset resistance data, or the relationship between the displacement data and the preset displacement data, the following steps are included: Determine whether the operation is abnormal based on the relationship between the primary resistance change data and the preset resistance data: When the primary resistance change data is lower than or equal to the preset resistance data, it is determined that there is no abnormality in the operation, and based on the relationship between the displacement data and the preset displacement data, and the relationship between the secondary resistance change data and the preset resistance data, it is determined whether the operation is abnormal; When the primary resistance change data is higher than the preset resistance data, it is determined that there is an abnormality in the operation.

5. The puncture needle tube early warning method based on electromagnetic signals according to claim 4, characterized in that: Determining whether an operation is abnormal based on the relationship between the displacement data and the preset displacement data includes: When the displacement data exceeds the preset displacement data, it is determined that the operation is abnormal; When the displacement data does not exceed the preset displacement data, it is determined whether the operation is abnormal based on the relationship between the secondary resistance change data and the preset resistance data.

6. The puncture needle tube early warning method based on electromagnetic signals according to claim 5, characterized in that: When determining whether there is an abnormality in operation based on the relationship between the secondary resistance change data and the preset resistance data, the following steps are included: When the secondary resistance change data is lower than the preset resistance data, it is determined that there is no abnormality in the operation; When the secondary resistance change data is higher than or equal to the preset resistance data, it is determined that there is an abnormality in the operation.

7. A puncture needle tube early warning system based on electromagnetic signals, equipped with a puncture needle tube early warning method based on electromagnetic signals according to any one of claims 1 to 6, characterized in that: include: an acquisition module configured to acquire user information data, displacement data of the puncture needle tube in the urethra, and primary resistance change data and secondary resistance change data; An analysis module is electrically connected to the acquisition module, and is configured to obtain preset resistance data and preset displacement data of the puncture needle tube in the urethra based on the user information data and a pre-configured resistance model; The analysis module is further configured to determine whether the operation is abnormal based on a relationship between the primary resistance change data and the secondary resistance change data and the preset resistance data, or a relationship between the displacement data and the preset displacement data; The sound and light alarm module is electrically connected to the analysis module and the puncture needle tube respectively. The sound and light alarm module is configured to perform an alarm process and terminate the puncture operation when the analysis module determines that the operation is abnormal.

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