An intelligent biopsy needle system and method with real-time breakage early warning monitoring

Abstract

The application provides a kind of intelligent biopsy needle system and method with real-time fracture early warning monitoring, system includes intelligent biopsy needle, data analysis and early warning host and man-machine interaction unit;Intelligent biopsy needle includes needle body, handle, signal processing and transmission module and two axial interval mechanical sensing groups, respectively collect axial, radial strain and convert into mechanical data;Data analysis and early warning host stores needle body geometry, material and fatigue S-N curve parameters, according to the axial strain of first sensing group axial force is solved, and based on the difference of two groups of radial strain, material Young's modulus, section inertia moment and axial spacing is solved combined bending moment;Equivalent stress time series is calculated again, and fatigue damage accumulation result is obtained by cycle counting and average stress correction, and comprehensive fracture risk coefficient is generated in combination with equivalent stress and change rate;Man-machine interaction unit then outputs early warning information representing the fracture risk of needle body accordingly;The application can solve complex stress in real time and warn fracture risk.

Description

Technical Field

[0001] This invention relates to the field of medical devices and intelligent safety monitoring technology, and in particular to an intelligent puncture needle system and method for monitoring the force on the needle body and providing early warning of breakage risk for biopsy puncture needles. Background Technology

[0002] Biopsy needles are commonly used medical devices for tissue sampling, typically consisting of an external guide tube and an internal cutting needle to perform puncture and sampling. Due to their small diameter and long length, the needles are easily subjected to axial propulsion force, lateral resistance from the tissue, and bending moment simultaneously during the puncture process.

[0003] Currently, the stress state of biopsy needles relies primarily on the operator's experience and tactile judgment during use. When the needle passes through fibrotic tissue, tough capsules, hardened tissue, or microcalcifications, the insertion resistance may suddenly increase, and the needle may also bend and deform due to localized obstruction. If these abnormal stresses are not identified in time, stress concentration can easily occur on the needle surface, thereby increasing the risk of breakage.

[0004] Currently, some puncture devices are capable of sensing puncture force or monitoring needle morphology, but they usually focus on overall advance resistance, needle tip force, or puncture path control. They are unable to calculate the axial force and bending load on the needle body based on the strain differences at different axial positions of the needle body, and they are also unable to accurately reflect the combined force state of the needle body during the actual puncture process.

[0005] Furthermore, needle fracture does not originate from a single overload. Repeated advancement, retraction, and angle adjustments during puncture subject the needle to cyclic loads. Even if a single stress does not reach the material's ultimate strength, fatigue damage can gradually accumulate. Existing puncture force monitoring methods mostly provide alerts based on real-time force values ​​or fixed thresholds, lacking a fracture risk assessment mechanism that combines real-time stress, needle material ultimate performance, and fatigue life characteristics. This makes it difficult to provide timely warnings of potential fracture risks caused by repeated bending and fatigue damage.

[0006] Therefore, there is an urgent need for an intelligent biopsy needle system and method with real-time fracture early warning monitoring, which can calculate the composite stress of the biopsy needle in real time and output a needle fracture risk warning based on the cumulative fatigue damage. Summary of the Invention

[0007] In view of the above-mentioned defects in the prior art, the purpose of this invention is to provide an intelligent biopsy needle system and method with real-time fracture early warning monitoring, which aims to solve the technical problem that existing biopsy needles are unable to identify the risk of fracture due to combined stress and fatigue accumulation in real time.

[0008] To achieve the above objectives, in a first aspect, the present invention provides an intelligent biopsy needle system with real-time fracture early warning monitoring, comprising an intelligent biopsy needle, a data analysis and early warning host, and a human-computer interaction unit; The intelligent biopsy needle includes a needle body, a needle handle, a mechanical sensing module, and a signal processing and transmission module. The needle body includes an outer guide tube and an inner cutting needle. The mechanical sensing module includes a first sensing group and a second sensing group arranged at intervals along the axial direction of the needle body. The first sensing group is located at a distance from the end of the needle handle. The second sensor group is located at a distance from the needle handle connection end and collects axial and radial strain data. Radial strain was collected at the location, 0 < < <Effective length of needle body; Signal processing and transmission module, used to condition and convert each strain signal into analog and digital data to obtain real-time mechanical data; The data analysis and early warning host is used to store the needle's geometric parameters, material parameters, static ultimate strength, and fatigue SN curve parameters. It calculates the axial force based on the axial strain of the first sensor group, and calculates the resultant bending moment based on the radial strain difference collected by the first and second sensor groups, the Young's modulus of the needle material, the moment of inertia of the needle section, and the axial distance between the first and second sensor groups. The host is also used to calculate the equivalent stress of the needle based on the axial force and resultant bending moment, and to form an equivalent stress time series. It performs cyclic counting and average stress correction on the equivalent stress time series, calculates the cumulative fatigue damage result, and generates a comprehensive fracture risk coefficient based on the equivalent stress, the cumulative fatigue damage result, and the rate of change of the equivalent stress. The human-computer interaction unit is used to output early warning information that only represents the risk of needle body fracture based on the comprehensive fracture risk coefficient.

[0009] As a further improvement to the above technical solution, the first sensing group and the second sensing group are disposed in the groove of the tube wall of the external guide needle tube and / or the internal cutting needle. The groove is filled with medical encapsulation material, and the outer surface after encapsulation is flush with or lower than the outer surface of the corresponding needle body component. The depth of the groove is less than half the thickness of the corresponding tube wall, and the length of the groove extending along the needle body axis is greater than the length of the corresponding sensor group.

[0010] As a further improvement to the above technical solution, the first sensing group includes an axial strain detection channel and a radial strain detection channel, and the second sensing group includes at least a radial strain detection channel.

[0011] As a further improvement to the above technical solution, the signal processing and transmission module is disposed inside the needle handle, and includes a signal amplification circuit, a filtering circuit, an analog-to-digital converter, a microprocessor, and a wireless transmission unit. The needle handle is also equipped with a miniature vibration motor connected to a microprocessor. The miniature vibration motor outputs different vibration prompts according to the warning level corresponding to the comprehensive fracture risk coefficient.

[0012] As a further improvement to the above technical solution, the needle body geometric parameters include the outer diameter of the needle body, the inner diameter of the needle body, the cross-sectional area, the moment of inertia of the cross section, and the bending section modulus; the material parameters include Young's modulus and mechanical solution coefficients obtained from calibration experiments.

[0013] As a further improvement to the above technical solution, the data analysis and early warning host calculates the axial force and resultant bending moment according to the following relationship: ; ; in, It is an axial force. For axial force calibration coefficient, The axial strain collected by the first sensor group For the resultant bending moment, Let be the Young's modulus of the needle material. Let the moment of inertia of the needle body cross section be . Radial strain collected by the first sensor group Radial strain collected by the second sensor group. The axial distance between the first sensor group and the second sensor group; The data analysis and early warning host calculates the equivalent stress according to the following relationship: ; ; ; in, For axial stress, For bending stress, The cross-sectional area of ​​the needle body. The section modulus is the bending section modulus. For a moment Equivalent stress; When the needle body is a circular tube structure, the cross-sectional area, moment of inertia, and section modulus are determined according to the following relationships: ; ; ; in, The outer diameter of the needle. The inner diameter of the needle body. Let the moment of inertia of the cross section be... This is the section modulus for bending resistance.

[0014] As a further improvement to the above technical solution, the cycle counting involves counting rainflows on the equivalent stress time series to obtain the stress amplitude of the stress cycle. and average stress ; The average stress The correction uses Goodman correction, and the fatigue damage accumulation result is the fatigue cumulative damage degree. The cumulative fatigue damage is calculated according to the following relationship: ; ; ; in, This is the corrected equivalent stress amplitude. The static ultimate strength of the needle material. and For fatigue SN curve parameters, Let be the number of occurrences of the i-th type of stress cycle. Let i be the number of fracture cycles corresponding to the i-th type of stress cycle. This represents the cumulative fatigue damage.

[0015] As a further improvement to the above technical solution, the data analysis and early warning host generates a comprehensive fracture risk coefficient according to the following relationship: ; ; ; in, As a component of instantaneous overload risk, The stress change rate impact factor, Let be the normalized constant of the rate of change of stress. , , These are the weighting coefficients, and ; For equivalent stress, The static ultimate strength of the needle material. This refers to the cumulative fatigue damage. The comprehensive fracture risk coefficient; For equivalent stress The derivative with respect to time.

[0016] As a further improvement to the above technical solution, the human-machine interaction unit includes a display screen and an audible and visual alarm. The display screen is used to display the force waveform of the needle body and / or the comprehensive fracture risk coefficient, and the audible and visual alarm is used to output the comprehensive fracture risk coefficient. Corresponding graded early warning signals; when At that time, the human-computer interaction unit outputs a level one warning signal; when At that time, the human-computer interaction unit outputs a level-two warning signal; when At that time, the human-computer interaction unit outputs a three-level warning signal.

[0017] As a further improvement to the above technical solution, the data analysis and early warning host is also used to record the axial strain, radial strain, axial force, resultant bending moment, equivalent stress, fatigue cumulative damage degree and comprehensive fracture risk coefficient at the time of triggering the early warning.

[0018] In a second aspect, the present invention also provides a method for monitoring the risk of needle breakage for biopsy puncture needles, executed by the intelligent biopsy puncture needle system described in the first aspect, the steps of which include: The axial strain and radial strain output from the first sensor group and the radial strain output from the second sensor group are collected. The acquired strain signals are conditioned and converted from analog to digital to obtain real-time mechanical data; The axial force is calculated based on the axial strain of the first sensor group, and the resultant bending moment is calculated based on the radial strain difference between the first and second sensor groups, the Young's modulus of elasticity of the needle material, the moment of inertia of the needle section, and the axial distance between the first and second sensor groups. The equivalent stress of the needle body is calculated based on the axial force and the resultant bending moment, and an equivalent stress time series is formed. The equivalent stress time series is subjected to cycle counting and average stress correction to calculate the cumulative fatigue damage result. A comprehensive fracture risk coefficient is generated based on the equivalent stress, the cumulative fatigue damage result, and the equivalent stress change rate. Based on the comprehensive fracture risk coefficient, early warning information on needle body fracture risk is output.

[0019] As a further improvement to the above technical solution, during the cycle counting process, the equivalent stress data that has not formed a complete stress cycle is cached, and the occurrence count of the corresponding stress cycle category is updated after a complete stress cycle is identified.

[0020] As a further improvement to the above technical solution, before generating the comprehensive fracture risk coefficient, a sliding filter is applied to the equivalent stress time series to reduce the impact of single-point noise on the stress change rate calculation results.

[0021] As a further improvement to the above technical solution, when outputting the needle body fracture risk warning information, a risk event record containing sampling time, axial force, resultant bending moment, equivalent stress, fatigue cumulative damage degree and comprehensive fracture risk coefficient is generated simultaneously.

[0022] Because the present invention adopts the above technical solutions, the beneficial effects of the present invention are as follows: This invention provides an intelligent biopsy needle system with real-time fracture warning monitoring. Addressing the problem of difficulty in real-time identification of complex stress states and fatigue-accumulated fracture risks during biopsy needle puncture, this system uses sensor arrays placed at different positions along the needle's axial direction to collect and process needle strain signals. Furthermore, by combining needle geometric parameters, material parameters, static ultimate strength, and fatigue SN curve parameters, the system performs real-time assessment of the needle's stress state and fracture risk. Therefore, this invention can objectively monitor and warn of needle fracture risks without altering the basic usage of biopsy needles, improving the safety of instrument use during biopsy punctures. Specifically, it has at least the following beneficial effects:

[0023] First, the mechanical sensing module of this invention includes a first sensing group and a second sensing group arranged at intervals along the needle body axis. The first sensing group is located at a distance L1 from the needle handle connection end and is used to collect axial strain and radial strain. The second sensing group is located at a distance L2 from the needle handle connection end and is used to collect radial strain. Because the first sensing group is closer to the operator's force application end, it can better reflect the axial propulsion force on the needle body; the second sensing group is located in the middle section of the needle body and can better reflect the local lateral resistance experienced by the needle body in the tissue. Through the cooperation of the above two sets of sensing signals, this invention can avoid relying solely on the single needle insertion resistance to determine the force state of the needle body, making the needle body force monitoring results closer to the combined force situation in the actual puncture process.

[0024] Second, this invention calculates the axial force based on the axial strain collected by the first sensor group, and calculates the combined bending moment based on the radial strain difference collected by the first and second sensor groups, the Young's modulus of the needle material, the moment of inertia of the needle section, and the axial distance between the two sensor groups. In this way, the system can convert the deformation differences at different axial positions of the needle into mechanical quantities characterizing the bending load, thereby simultaneously obtaining information on axial force and bending force. Compared to methods that only detect overall thrust or needle tip resistance, this invention can more accurately reflect the bending risk of the needle due to local obstruction, path deviation, or lateral tissue constraints.

[0025] Third, the data analysis and early warning host of this invention stores the needle's geometric parameters, material parameters, static ultimate strength, and fatigue SN curve parameters, and calculates the equivalent stress of the needle based on axial force and resultant bending moment. Since the equivalent stress comprehensively reflects the stress level at the needle's critical section caused by the combined action of axial and bending loads, its calculation results can be correlated with the needle's material strength and fatigue life parameters. Therefore, the fracture risk assessment of this invention no longer relies solely on empirical thresholds or real-time force thresholds, but can be evaluated by combining the needle's structural dimensions and material properties, providing a clearer basis for early warning.

[0026] Fourth, this invention performs cyclic counting and average stress correction on the equivalent stress time series, and calculates the cumulative fatigue damage. During biopsy puncture, the needle often undergoes repeated stress processes such as advancement, retraction, rotation, or angle fine-tuning. Even if the stress at a certain moment does not reach the static ultimate strength of the material, long-term or repeated cyclic loading may still cause fatigue damage accumulation. By performing fatigue analysis on the equivalent stress time series, this invention can identify the risk of fatigue fracture caused by repeated bending or repeated loading, making up for the shortcomings of existing puncture force monitoring methods that mainly focus on instantaneous overload and are difficult to reflect the risk of fatigue accumulation in a timely manner.

[0027] Fifth, this invention generates a comprehensive fracture risk coefficient based on equivalent stress, cumulative fatigue damage, and the rate of change of equivalent stress. The equivalent stress reflects the current stress intensity of the needle body, the cumulative fatigue damage reflects the degree of material damage caused by historical cyclic loading, and the rate of change of equivalent stress reflects the stress abrupt change when the needle body encounters sudden resistance or impact. By comprehensively evaluating these factors, this invention can simultaneously consider instantaneous overload risk, fatigue accumulation risk, and sudden impact risk, making the needle body fracture risk assessment more complete.

[0028] Sixth, this invention outputs early warning information based on a comprehensive fracture risk coefficient through a human-computer interaction unit, and this early warning information is only used to characterize the fracture risk of the needle itself. Therefore, this invention can provide operators with needle safety status alerts, facilitating timely awareness of abnormal needle stress or increased fatigue risk, thereby reducing the possibility of fracture due to needle overload bending or fatigue accumulation. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0030] Figure 1 This is a schematic block diagram of an intelligent biopsy needle system with real-time fracture early warning monitoring disclosed in this invention. Figure 2 This is a schematic diagram of the intelligent biopsy needle structure disclosed in this invention; Figure 3 This is a flowchart illustrating a method for monitoring the risk of needle breakage in a biopsy puncture needle, as disclosed in this invention. Figure 4 This is a schematic diagram of the process for generating the comprehensive fracture risk coefficient disclosed in this invention.

[0031] Figure label: 1. Needle body; 11. Outer guide needle tube; 12. Inner cutting needle; 13. Groove; 2. Needle handle; 3. Mechanical sensing module; 31. First sensing group; 32. Second sensing group; 4. Signal processing and transmission module.

[0032] The realization of the objective, functional characteristics and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] It should be noted that the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0035] Example 1

[0036] See Figure 1 and Figure 2 To address the problem that existing biopsy needles cannot identify the combined stress state of the needle body 1 and the risk of fatigue-accumulated fracture in real time, this invention provides an intelligent biopsy needle system with real-time fracture early warning monitoring. The system is used to monitor and warn of the stress state and fracture risk of the biopsy needle itself. The output information is only used to characterize the safety state of the needle body 1 and is not used to form a disease diagnosis conclusion or to determine a treatment plan.

[0037] Specifically, the intelligent biopsy needle system includes an intelligent biopsy needle, a data analysis and early warning host, and a human-machine interaction unit. The intelligent biopsy needle includes a needle body 1, a needle handle 2, a mechanical sensing module 3, and a signal processing and transmission module 4. The needle body 1 includes an outer guide tube 11 and an inner cutting needle 12. The outer guide tube 11 is used to establish a puncture channel, and the inner cutting needle 12 is used to cooperate with the outer guide tube 11 to complete the sampling action. Since both the outer guide tube 11 and the inner cutting needle 12 may be subjected to axial propulsion force and bending load during the puncture process, this embodiment uses the needle body 1 as a force monitoring object, and its force changes are collected and analyzed in real time.

[0038] The mechanical sensing module 3 includes a first sensing group 31 and a second sensing group 32 arranged at intervals along the axial direction of the needle body 1. The first sensing group 31 is located at a distance L1 from the connecting end of the needle handle 2 and is used to collect the axial and radial strain of the proximal end of the needle body 1. The second sensing group 32 is located at a distance L2 from the connecting end of the needle handle 2 and is used to collect the radial strain of the middle section of the needle body 1, where 0 < L1 < L2 < the effective length of the needle body 1. The proximal position is closer to the operator's force application end, and its axial strain can better reflect the active propulsion force on the needle body 1; the middle position is more likely to reflect the local lateral resistance encountered by the needle body 1 after entering the tissue. By combining the strain information from the two axial positions, the system can avoid judging the state of the needle body 1 based solely on single-point force or overall thrust, enabling the system to more accurately reflect the combined force situation during the puncture process.

[0039] The signal processing and transmission module 4 is located within the needle handle 2. It amplifies, filters, and performs analog-to-digital conversion on the strain signals output from the first sensor group 31 and the second sensor group 32 to obtain real-time mechanical data, and then transmits the real-time mechanical data to the data analysis and early warning host. By performing preliminary signal conditioning within the needle handle 2, interference from weak strain signals during transmission can be reduced, providing a more stable data foundation for subsequent mechanical calculations.

[0040] The data analysis and early warning host stores the geometric parameters, material parameters, static ultimate strength, and fatigue SN curve parameters of needle body 1. The geometric parameters of needle body 1 include its outer diameter, inner diameter, cross-sectional area, moment of inertia, and flexural section modulus; the material parameters include Young's modulus and other mechanical parameters related to the material of needle body 1. By combining real-time acquired data with the structure and material properties of needle body 1, the system can perform risk calculations based on the specific specifications of needle body 1, rather than relying solely on fixed empirical thresholds.

[0041] The data analysis and early warning host is also used to calculate the axial force of the needle body 1 based on the axial strain collected by the first sensor group 31, and to calculate the resultant bending moment based on the radial strain difference collected by the first sensor group 31 and the second sensor group 32, the Young's modulus of the needle body 1 material, the moment of inertia of the needle body 1 section, and the axial distance between the two sensor groups. Thus, the axial force characterizes the force caused by the operator's advance and the resistance from the needle tip, while the resultant bending moment characterizes the bending load on the needle body 1 caused by local lateral resistance, tissue constraints, or path deviation. The combination of these two methods provides a more complete reflection of the actual stress state of the needle body 1 than simply detecting the needle insertion resistance alone.

[0042] After obtaining the axial force and resultant bending moment, the data analysis and early warning host further calculates the equivalent stress of needle body 1 and generates an equivalent stress time series. The equivalent stress is used to characterize the combined stress level at the critical section of needle body 1 caused by the combined action of axial and bending loads. Since the equivalent stress is related to the geometric dimensions and material parameters of needle body 1, it can establish a correspondence with the static ultimate strength and fatigue life parameters of needle body 1, providing a clear mechanical basis for fracture risk assessment.

[0043] During puncture, the needle 1 is often subjected to repeated loads due to operations such as advancement, retraction, rotation, or angle fine-tuning. Even if the equivalent stress at a certain moment does not reach the static ultimate strength, repeated bending or repeated loading can still cause fatigue damage accumulation. Therefore, the data analysis and early warning host performs cyclic counting on the equivalent stress time series, extracts the amplitude and average stress of the stress cycles, corrects the average stress, and then calculates the fatigue damage accumulation result by combining the fatigue SN curve parameters of the needle 1. Through this processing, the system can continuously assess the risk of fatigue fracture, making up for the inability to identify fatigue accumulation risk when only monitoring instantaneous overload.

[0044] Furthermore, the data analysis and early warning host generates a comprehensive fracture risk coefficient based on equivalent stress, cumulative fatigue damage results, and the rate of change of equivalent stress. Here, equivalent stress reflects the current stress intensity, cumulative fatigue damage results reflect the degree of damage caused by historical cyclic loading, and the rate of change of equivalent stress reflects the stress mutation when the needle 1 encounters sudden resistance or impact. By comprehensively evaluating the above factors, the system can simultaneously consider instantaneous overload risk, fatigue accumulation risk, and sudden impact risk, thus making the assessment of the fracture risk of the needle 1 more complete.

[0045] The human-machine interface unit outputs warning information based on the comprehensive fracture risk coefficient. The warning information can be output using one or more methods, including display, audio-visual prompts, or vibration alerts. For example, when the comprehensive fracture risk coefficient is in a low range, the human-machine interface unit outputs a prompt signal; when the comprehensive fracture risk coefficient continues to rise, a higher-level warning signal is output. This warning information is only used to indicate the fracture risk status of the biopsy needle body 1 itself and does not involve the judgment of the nature of the lesion, the conclusion of the disease diagnosis, or the determination of the treatment plan.

[0046] Through the above structure and processing flow, this embodiment can acquire strain information at different positions of the needle body 1 in real time without changing the basic usage of the biopsy needle, and convert it into axial force, resultant bending moment, equivalent stress, and fatigue damage accumulation results. Therefore, the system can comprehensively assess the instantaneous overload risk and fatigue cumulative fracture risk of the needle body 1, providing the operator with safety status alerts for the needle body 1 and reducing the possibility of fracture due to abnormal stress or fatigue damage accumulation.

[0047] In a preferred embodiment, the first sensor group 31 and the second sensor group 32 are disposed within the groove 13 of the tube wall of the outer guide needle tube 11 and / or the inner cutting needle 12. Specifically, the groove 13 can be machined along the axial direction of the needle body 1 at a predetermined position of the outer guide needle tube 11 or the inner cutting needle 12, and the corresponding sensor group is embedded in the groove 13. The groove 13 is filled with medical encapsulation material so that the encapsulated outer surface is flush with or lower than the outer surface of the corresponding needle body 1 component.

[0048] With the above configuration, the sensor assembly can be positioned close to the stress-bearing area of ​​the needle body 1, facilitating direct sensing of the axial and radial strain of the needle body 1 during puncture. Simultaneously, the sensor assembly does not protrude significantly from the outer surface of the needle body 1, reducing the impact of the sensor mounting structure on the shape of the needle body 1, insertion resistance, and tissue contact state, allowing the needle body 1 to achieve stress monitoring while maintaining its basic puncture structure. After the medical encapsulation material is filled into the groove 13, it also provides fixation and isolation protection for the sensor assembly, reducing the impact of body fluids, irrigation fluids, or mechanical friction on the sensor assembly and its lead connection points, thus improving the stability of strain signal acquisition.

[0049] Furthermore, the depth of the groove 13 is less than half the thickness of the corresponding tube wall, and the length of the groove 13 extending axially along the needle body 1 is greater than the length of the corresponding sensor group. Limiting the depth of the groove 13 to less than half the thickness of the tube wall can avoid significantly weakening the tube wall strength of the outer guide needle tube 11 or the inner cutting needle 12 due to excessive groove depth, and reduce the possibility of forming a local weak area at the groove 13; setting the axial length of the groove 13 to be greater than the length of the corresponding sensor group can reserve space for sensor group installation, encapsulation material filling and lead wire transition, so that the sensor group has a more reliable installation position in the groove 13, and reduces local stress concentration at the edge of the sensor group caused by assembly compression or insufficient encapsulation.

[0050] In a preferred embodiment, the first sensor group 31 includes an axial strain detection channel and a radial strain detection channel, and the second sensor group 32 includes at least a radial strain detection channel. The first sensor group 31 is located at the proximal end of the needle body 1, close to the connecting end of the needle handle 2. Its axial strain detection channel is used to collect the strain change of the needle body 1 in the advancing direction, and its radial strain detection channel is used to collect the radial strain change caused by bending at the proximal end of the needle body 1. The second sensor group 32 is located in the middle section of the needle body 1, and its radial strain detection channel is used to collect the radial strain change caused by local lateral resistance or bending deformation in the middle section of the needle body 1.

[0051] In use, the axial strain detection channel of the first sensor group 31 outputs a proximal axial strain signal, which serves as the data basis for calculating the axial force of the needle body 1. The radial strain detection channels of the first sensor group 31 and the second sensor group 32 output proximal radial strain signals and mid-section radial strain signals, respectively. The data analysis and early warning host calculates the resultant bending moment of the needle body 1 based on the difference between the two signals. Thus, the system can obtain strain information related to axial thrust load and bending load separately, avoiding the need to judge the stress state of the needle body 1 based solely on a single thrust signal.

[0052] During biopsy puncture, the needle body 1 is subjected to both axial thrust applied by the operator and bending deformation due to uneven tissue density, needle tip path deviation, or obstruction by locally hardened tissue. The aforementioned channel configuration provides a relatively complete data source for calculating the combined forces acting on the needle body 1. When the second sensor group 32 only has a radial strain detection channel, it can meet the basic requirements for calculating the combined bending moment. It should be noted that when further improving monitoring redundancy is required, the second sensor group 32 can also be equipped with an axial strain detection channel for auxiliary verification of the proximal axial strain signal.

[0053] In a preferred embodiment, the signal processing and transmission module 4 is disposed within the needle handle 2, and includes a signal amplification circuit, a filtering circuit, an analog-to-digital converter, a microprocessor, and a wireless transmission unit. The strain signals output by the first sensor group 31 and the second sensor group 32 are sequentially amplified, filtered, and converted from analog to digital to form digitized real-time mechanical data, which is then transmitted by the microprocessor to the data analysis and early warning host via the wireless transmission unit.

[0054] By integrating the signal processing and transmission module 4 into the needle handle 2, the connection path between the sensing group and the front-end processing circuit can be shortened, reducing the attenuation and interference of weak strain signals during transmission, while avoiding external wiring affecting the grip and operation of the puncture needle. The real-time mechanical data, after amplification, filtering, and analog-to-digital conversion, can provide a relatively stable data foundation for subsequent calculations of axial force, resultant bending moment, equivalent stress, and comprehensive fracture risk coefficient.

[0055] Furthermore, a miniature vibration motor connected to a microprocessor is installed inside the needle handle 2. The microprocessor controls the miniature vibration motor to output different vibration prompts based on the warning level corresponding to the comprehensive fracture risk coefficient. Thus, even when the operator's attention is focused on the puncture operation or image observation, changes in the fracture risk level of the needle body 1 can still be perceived through touch, improving the perceptibility of the warning information.

[0056] In a preferred embodiment, the data analysis and early warning host calculates the axial force and resultant bending moment of the needle body 1 based on the strain signals collected by the first sensor group 31 and the second sensor group 32. The axial strain collected by the first sensor group 31 is used to characterize the propulsion load on the proximal end of the needle body 1, and the difference in radial strain collected by the first sensor group 31 and the second sensor group 32 is used to characterize the difference in bending deformation of the needle body 1 at different positions along the axial direction.

[0057] Axial force reflects the operator's active pushing force and the frontal resistance encountered when the needle tip penetrates the tissue, serving as a fundamental indicator for determining whether needle body 1 is subjected to excessive pressure load. However, the stress state faced by biopsy needles during puncture is far more complex than simple tension and compression: due to the uneven density of human tissue and the possibility of the needle tip deviating from the expected path, needle body 1 also experiences significant bending loads. Axial force alone cannot describe this bending effect; therefore, the difference in radial strain between the two sets of sensors is needed to further calculate the resultant bending moment on needle body 1. A larger resultant bending moment indicates more severe bending deformation of needle body 1 within the tissue, and a corresponding increase in local stress on the surface of needle body 1. Therefore, the data analysis and early warning host calculates the axial force and resultant bending moment according to the following relationship: ; ; in, It is an axial force. For axial force calibration coefficient, The axial strain collected by the first sensor group 31; For the resultant bending moment, Let be the Young's modulus of the needle body material. Let be the moment of inertia of section 1 of the needle body. The radial strain collected by the first sensor group 31 The radial strain collected by the second sensor group 32 This refers to the axial distance between the first sensor group 31 and the second sensor group 32. Through the above calculation, the strain signals collected by the sensor groups can be converted into axial force and bending load, so that the stress state of the needle body 1 is no longer characterized by a single propulsion force, which is helpful in identifying the bending risk caused by local resistance, tissue constraints or deflection of the needle body 1 during puncture.

[0058] After obtaining the axial force and resultant bending moment, the data analysis and early warning host further calculates the axial stress, bending stress, and equivalent stress of the needle body 1. The specific relationships are as follows: ; ; in, For axial stress, For bending stress, Let be the cross-sectional area of ​​needle body 1. This is the section modulus for bending resistance.

[0059] Preferably, when the needle body 1 is a circular tube structure, the cross-sectional area, moment of inertia, and section modulus can be determined according to the following relationship: ; ; ; in, The outer diameter of the needle body is 1. The inner diameter of needle body 1 Let the moment of inertia of the cross section be... The bending section modulus is used. By introducing the outer diameter, inner diameter, moment of inertia, and bending section modulus of needle body 1, the equivalent stress calculation can be matched with the specific structural dimensions of needle body 1, avoiding the judgment of the fracture risk of needle body 1 based solely on empirical force thresholds.

[0060] The strain signal acquired by the sensor is transformed into two physically distinct stress components—axial normal stress and bending normal stress—using the aforementioned formulas for calculating axial and bending stress. However, the actual stress at the most critical point on needle body 1 is the superposition of these two stresses, rather than their independent stresses. Therefore, this invention employs the Von Mises yield criterion to address this issue. For a slender rod like a biopsy needle, the most critical point is typically located on the surface of needle body 1, where axial and bending stresses superimpose in the same direction. Therefore, this invention uses a conservative additive method to calculate the equivalent stress. The specific calculation formula is as follows: ; For a moment The equivalent stress is used to characterize the comprehensive stress level of the needle body 1 under the combined action of axial load and bending load, and serves as the basis for subsequent overload risk assessment and fatigue damage calculation.

[0061] The equivalent stress at a given instant can be obtained using the above formula. It reflects the instantaneous force intensity experienced by needle body 1 at the current moment. If this value continuously exceeds the ultimate strength of the material... Needle 1 will experience a one-time overload fracture. However, a more common and insidious risk in clinical practice is fatigue fracture: during repeated bending, advancing, and retracting, even if the force applied to needle 1 is far below its ultimate strength each time, the internal material will suffer microscopic damage due to the accumulation of stress cycles. When the damage accumulates to a certain extent, needle 1 will suddenly fracture without any obvious warning. Therefore, relying solely on instantaneous stress for early warning is far from sufficient.

[0062] Based on this, the present invention further introduces a time-series-based fatigue cumulative damage model, which expands the early warning dimension from "instantaneous" to "full life cycle", enabling a forward-looking judgment of fatigue fracture risk.

[0063] In this embodiment, see Figure 4 After obtaining the equivalent stress of needle 1, the data analysis and early warning host continuously generates the equivalent stress time series according to the predetermined sampling frequency. The equivalent stress time series is used to reflect the force changes of the needle 1 during puncture. Since this time series is a continuously changing waveform, it cannot be directly used for fatigue damage calculation. The data analysis and early warning host performs load spectrum analysis on the equivalent stress time series to identify stress cycles formed by the needle 1 during advancement, retraction, fine-tuning, or obstructed bending. Through this processing, the continuous force waveform can be converted into cyclic load data that can be used for fatigue analysis, providing a basis for subsequent fatigue damage calculation.

[0064] Specifically, the data analysis and early warning host... Rainflow counting was performed to obtain the stress amplitude for each stress cycle. and average stress The stress amplitude characterizes the strength of the corresponding cyclic load, while the mean stress characterizes the stress bias state of that cycle. During biopsy puncture, the load on the needle body 1 is usually not ideally symmetrical; for example, it is compressed during advancement and unloaded during retraction or angle adjustment. If the stress amplitude is directly substituted into the fatigue SN curve, the fatigue life calculation may deviate from the actual stress state. Therefore, this embodiment further corrects the influence of the mean stress.

[0065] In a preferred embodiment, the data analysis and early warning host uses Goodman correction to calculate the corrected equivalent stress amplitude: ; in, This is the equivalent stress amplitude after mean stress correction. The stress cycle amplitude is obtained from rainflow counting. The average stress corresponding to the stress cycle. Let be the static ultimate strength of the needle body 1 material. Through the above correction, the asymmetric stress cycle can be converted into an equivalent cyclic load that can be used for fatigue life calculation, making the fatigue damage calculation more consistent with the stress situation of the needle body 1 during the actual puncture process.

[0066] The data analysis and early warning host pre-stores the fatigue SN curve parameters of the needle body material. For the first... For stress-like cycles, the data analysis and early warning host calculates the number of fracture cycles corresponding to this type of cycle based on the corrected equivalent stress amplitude: ; in, For the first The number of fracture cycles corresponding to stress-like cycles. and The parameters of the SN curve are obtained from fatigue tests or calibrations of needle body 1; C is a material constant, obtained from fatigue test fitting. The material constant, obtained from fatigue experiments, is dimensionless and typically ranges from 3 to 12 for metallic materials. By combining real-time load cycles with the fatigue life parameters of the needle body 1 material, the system can avoid judging fracture risk solely based on the magnitude of instantaneous force, thereby quantifying material damage caused by repeated bending or repeated loading.

[0067] Furthermore, the data analysis and early warning host uses Miner's linear cumulative damage rule to calculate the cumulative fatigue damage degree: ; in, Deadline The cumulative fatigue damage degree is dimensionless and ranges from 0 to 1. When it reaches 1, it indicates that the fatigue life has been exhausted. For the first The number of times stress-like cycles have occurred. For the first The number of fracture cycles corresponding to the stress cycle is calculated. This process ensures that the impact of each stress cycle on the needle body 1 material is included in the cumulative damage result, enabling the system to continuously track the fatigue state of the needle body 1. For slender metal instruments such as biopsy needles, even if the current equivalent stress has not reached the static ultimate strength, repeated bending and multiple loading can still cause fatigue damage accumulation; this implementation method can identify such potential risks.

[0068] After obtaining the cumulative fatigue damage, the data analysis and early warning system further generates a comprehensive fracture risk coefficient. Specifically, it first calculates the instantaneous overload risk component based on the ratio of the current equivalent stress to the static ultimate strength of the needle body material: ;in, As a component of instantaneous overload risk, The equivalent stress at the current moment, This represents the static ultimate strength of the needle body 1 material. This component reflects the degree to which the needle body 1 is currently subjected to stress close to the material's limit; its square form makes the risk changes in high-stress areas more apparent.

[0069] Meanwhile, the data analysis and early warning system calculates the impact sensitivity factor based on the equivalent stress change rate: ; in, The stress change rate impact factor is dimensionless and usually takes the value of 0 to 1. It is used to capture sudden stress increases, such as needle tip impact on calcification. The derivative of equivalent stress with respect to time is the rate of change of stress, expressed in Pa / s, and is calculated from the difference between two adjacent sampling points. The stress change rate normalization constant is set based on typical clinical impact rates, mapping the change rate to a reasonable range. An empirical value of 10 is used. 7 ~10 8 Pa / s. During puncture, if the needle tip suddenly encounters a tough capsule, hardened area, or calcification, the equivalent stress may change rapidly within a short period. By introducing this impact-sensitive factor, the system can respond to sudden load changes, rather than only alerting the system after the absolute value of the equivalent stress has reached a high level.

[0070] The comprehensive fracture risk coefficient is generated according to the following relationship: ; in, For a moment The comprehensive fracture risk coefficient, , , Let be the weight coefficient, and satisfy... Preferred =0.5 (Instantaneous overload dominates), =0.4 (fatigue accumulation is the second most important factor). =0.1 (impact assist), which can be optimized and adjusted based on subsequent clinical trials; among which, Reflecting the current instantaneous overload risk, Reflects the cumulative fatigue damage caused by historical cyclic loading. It reflects sudden impacts or rapid changes in stress. Through the integration of the above three factors, the data analysis and early warning host can simultaneously consider the impact of current stress, historical fatigue, and sudden load changes on the fracture risk of needle body 1, so that fracture risk assessment is not limited to a single threshold judgment.

[0071] In a preferred embodiment, the human-machine interface unit includes a display screen and an audible and visual alarm. The display screen is communicatively connected to the data analysis and early warning host and is used to display the stress waveform of the needle body 1, the comprehensive fracture risk coefficient R(t), and the corresponding risk level information. The stress waveform of the needle body 1 may include a curve showing the equivalent stress changing over time, or it may include information on changes in axial force, resultant bending moment, or cumulative fatigue damage. Through this display method, the operator can intuitively understand the changing trend of the stress state of the needle body 1, avoiding the need to judge whether the needle body 1 is under abnormal stress based solely on touch.

[0072] The audible and visual alarm is used to output graded warning signals based on the comprehensive fracture risk coefficient R(t). When 0.3 ≤ R(t) < 0.6, the human-machine interface unit outputs a level 1 warning signal; when 0.6 ≤ R(t) < 0.8, the human-machine interface unit outputs a level 2 warning signal; and when R(t) ≥ 0.8, the human-machine interface unit outputs a level 3 warning signal. Different levels of warning signals can be distinguished by different colors, sound frequencies, or durations, so that the operator can promptly identify changes in the fracture risk level of needle body 1.

[0073] Since the comprehensive fracture risk coefficient R(t) simultaneously considers equivalent stress, cumulative fatigue damage, and the rate of change of equivalent stress, the above-mentioned graded early warning method can convert continuously changing risk assessment results into intuitive prompts, thus taking into account the fracture risk of needle body 1 caused by instantaneous overload, fatigue accumulation, and sudden impact. The early warning signal is only used to characterize the safety status of the biopsy puncture needle body 1 itself and is not used to output disease diagnosis conclusions or treatment plans.

[0074] In a preferred embodiment, the data analysis and early warning host also has a risk event recording function. When the comprehensive fracture risk coefficient reaches the preset early warning condition, the data analysis and early warning host records the axial strain, radial strain, axial force, resultant bending moment, equivalent stress, fatigue cumulative damage degree, and comprehensive fracture risk coefficient at the corresponding time, and stores the above data in correspondence with the trigger time and early warning level to form a risk event record.

[0075] The above settings preserve the stress state of needle 1 and the basis for risk assessment when an early warning is triggered. Axial strain and radial strain reflect the original deformation of needle 1; axial force, resultant bending moment, and equivalent stress reflect the combined stress state of needle 1; and cumulative fatigue damage and comprehensive fracture risk coefficient reflect the fatigue damage and comprehensive fracture risk level of needle 1. This facilitates subsequent tracing and verification of the cause of the early warning, providing a data foundation for puncture needle safety records, equipment status analysis, and parameter calibration. This recorded information is only used for the safety status analysis of the biopsy puncture needle itself and does not involve disease diagnosis conclusions or treatment plans.

[0076] Example 2

[0077] See Figure 3 The present invention also provides a method for monitoring the breakage risk of the needle body 1 of a biopsy puncture needle. The method is executed by the intelligent biopsy puncture needle system provided in Example 1 and is used to monitor the stress state and breakage risk of the biopsy puncture needle body 1 itself, but is not used to output disease diagnosis conclusions or treatment plans.

[0078] Specifically, the system first acquires the axial and radial strain output by the first sensor group 31, and then acquires the radial strain output by the second sensor group 32. The first sensor group 31 is located at the proximal end of the needle body 1; its axial strain reflects the axial propulsive load on the needle body 1, and its radial strain reflects the proximal bending deformation. The second sensor group 32 is located in the middle section of the needle body 1; its radial strain reflects the bending deformation of the middle section of the needle body 1 after being subjected to local lateral resistance. Through the above acquisition method, strain information at different axial positions of the needle body 1 can be obtained, providing a data basis for identifying the combined stress state of the needle body 1.

[0079] Subsequently, the system conditions and converts the acquired strain signals to analog-to-digital data to obtain real-time mechanical data. Signal conditioning may include amplification and filtering to reduce interference from weak strain signals during transmission and acquisition; the digitized data generated after analog-to-digital conversion facilitates subsequent mechanical calculations by the data analysis and early warning host. This improves the data stability of axial force, resultant bending moment, and equivalent stress calculations.

[0080] After obtaining real-time mechanical data, the data analysis and early warning host calculates the axial force of the needle body 1 based on the axial strain of the first sensor group 31, and calculates the combined bending moment based on the radial strain difference collected by the first sensor group 31 and the second sensor group 32, the Young's modulus of the needle body 1 material, the moment of inertia of the needle body 1 section, and the axial distance between the two sensor groups. Through this step, the system can convert the strain signal of the needle body 1 into axial load and bending load with clear mechanical meaning, avoiding the judgment of the safety status of the needle body 1 based solely on the single propulsion resistance, and helping to identify bending risks caused by lateral tissue constraints, path deviation, or local hardening areas during puncture.

[0081] Furthermore, the data analysis and early warning host calculates the equivalent stress of needle body 1 based on the axial force and the resultant bending moment, and forms an equivalent stress time series according to the sampling time. The equivalent stress is used to characterize the comprehensive stress level of needle body 1 under the combined action of axial load and bending load. Its time series can reflect the stress changes of needle body 1 during continuous puncture, providing a basis for instantaneous overload judgment and fatigue damage analysis.

[0082] For the resulting equivalent stress time series, the data analysis and early warning host performs cycle counting and mean stress correction to calculate the cumulative fatigue damage. Specifically, rainflow counting can be used to identify complete stress cycles and obtain the amplitude and mean stress of each cycle. Then, based on the mean stress correction result and the fatigue SN curve parameters of needle 1, the fatigue damage caused to needle 1 by the corresponding stress cycle is calculated, and the cumulative fatigue damage result is obtained. Through this processing, the system can incorporate repeated stress processes such as propulsion, retraction, and angle fine-tuning into the risk assessment, making up for the inadequacy of relying solely on instantaneous stress thresholds to reflect the cumulative fatigue risk.

[0083] During the cyclic counting process, equivalent stress data that has not yet formed a complete stress cycle are cached by the data analysis and early warning host. Once subsequent data meets the criteria for identifying a complete stress cycle, the occurrence count of the corresponding stress cycle category is updated. This method avoids directly including unclosed stress fluctuations in fatigue damage, thus making the fatigue accumulation results more consistent with the requirements of cyclic load analysis.

[0084] Before generating the comprehensive fracture risk coefficient, the data analysis and early warning host can also perform sliding filtering on the equivalent stress time series. Sliding filtering can suppress the impact of abnormal fluctuations at individual sampling points on the stress change rate calculation results, reduce the possibility of misjudgment due to instantaneous noise, and make the subsequent impact risk assessment based on the equivalent stress change rate more stable.

[0085] Subsequently, the data analysis and early warning host generates a comprehensive fracture risk coefficient based on equivalent stress, cumulative fatigue damage, and the rate of change of equivalent stress. The equivalent stress reflects the current stress intensity of needle 1, the cumulative fatigue damage reflects the damage level caused by historical cyclic loading, and the rate of change of equivalent stress reflects the rapid stress change of needle 1 when it encounters sudden resistance. By combining these factors, the system can simultaneously consider the impact of instantaneous overload, fatigue accumulation, and sudden impact on the fracture risk of needle 1, making the risk assessment results more complete.

[0086] Finally, the system outputs a fracture risk warning for needle 1 based on the comprehensive fracture risk coefficient. This warning can be displayed, presented visually and audibly, or vibrated to indicate changes in the safety status of needle 1. Simultaneously, the system generates a risk event record, which includes sampling time, axial force, resultant bending moment, equivalent stress, cumulative fatigue damage, and the comprehensive fracture risk coefficient. Recording key data at the time of warning triggering facilitates subsequent tracing and verification of the warning cause and provides a data foundation for equipment status analysis and parameter calibration. The aforementioned warning information and risk event record are used for the safety monitoring of the biopsy needle itself and do not involve disease diagnosis or treatment plans.

[0087] The above are merely preferred embodiments of the present invention and do not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct or indirect applications in other related technical fields, are within the patent protection scope of the present invention.

Claims

1. An intelligent biopsy puncture needle system with real-time fracture early warning monitoring, characterized in that, Includes intelligent biopsy needles, data analysis and early warning host, and human-computer interaction unit; The intelligent biopsy needle includes a needle body, a needle handle, a mechanical sensing module, and a signal processing and transmission module. The needle body includes an outer guide tube and an inner cutting needle. The mechanical sensing module includes a first sensing group and a second sensing group arranged at intervals along the axial direction of the needle body. The first sensing group is located at a distance from the end of the needle handle. The second sensor group is located at a distance from the needle handle connection end and is used to collect axial and radial strain. It is used to collect radial strain, 0 < < <Effective length of the needle body; The signal processing and transmission module is used to condition and convert each strain signal into an analog-to-digital converter to obtain real-time mechanical data; The data analysis and early warning host is used to store the needle's geometric parameters, material parameters, static ultimate strength and fatigue SN curve parameters, and calculate the axial force based on the axial strain of the first sensor group, and calculate the resultant bending moment based on the radial strain difference value collected by the first and second sensor groups, the Young's elastic modulus of the needle material, the moment of inertia of the needle section and the axial distance between the first and second sensor groups. The data analysis and early warning host is also used to calculate the equivalent stress of the needle body based on the axial force and the resultant bending moment, and to generate an equivalent stress time series; to perform cycle counting and mean stress correction on the equivalent stress time series, and to calculate the cumulative fatigue damage result, which is the cumulative fatigue damage degree. The comprehensive fracture risk coefficient is generated according to the following relationship. : ； ； ； in, As a component of instantaneous overload risk, The stress change rate impact factor, Let be the normalized constant of the rate of change of stress. , , These are the weighting coefficients, and ; For equivalent stress, The static ultimate strength of the needle material; For equivalent stress The derivative with respect to time; The human-computer interaction unit is used to output early warning information that only represents the risk of needle body fracture based on the comprehensive fracture risk coefficient.

2. The intelligent biopsy needle system with real-time fracture early warning monitoring according to claim 1, characterized in that, The first and second sensor groups are disposed in the grooves of the tube wall of the external guide needle tube and / or the internal cutting needle. The grooves are filled with medical encapsulation material, and the outer surface after encapsulation is flush with or lower than the outer surface of the corresponding needle body component. The depth of the groove is less than half the thickness of the corresponding tube wall, and the length of the groove extending along the needle body axis is greater than the length of the corresponding sensor group.

3. The intelligent biopsy puncture needle system with real-time fracture early warning monitoring according to claim 1, characterized in that, The first sensing group includes an axial strain detection channel and a radial strain detection channel, and the second sensing group includes at least a radial strain detection channel.

4. The intelligent biopsy puncture needle system with real-time fracture early warning monitoring according to claim 1, characterized in that, The signal processing and transmission module is located inside the needle handle and includes a signal amplification circuit, a filtering circuit, an analog-to-digital converter, a microprocessor, and a wireless transmission unit. The needle handle is also equipped with a miniature vibration motor connected to a microprocessor. The miniature vibration motor outputs different vibration prompts according to the warning level corresponding to the comprehensive fracture risk coefficient.

5. The intelligent biopsy puncture needle system with real-time fracture early warning monitoring according to claim 1, characterized in that, The needle's geometric parameters include its outer diameter, inner diameter, cross-sectional area, moment of inertia, and section modulus; the material parameters include Young's modulus and mechanical solution coefficients obtained from calibration experiments.

6. The intelligent biopsy puncture needle system with real-time fracture early warning monitoring according to claim 1, characterized in that, The data analysis and early warning host calculates the axial force and resultant bending moment according to the following relationship: ； ； in, It is an axial force. For axial force calibration coefficient, The axial strain collected by the first sensor group For the resultant bending moment, Let be the Young's modulus of the needle material. Let the moment of inertia of the needle body cross section be . Radial strain collected by the first sensor group Radial strain collected by the second sensor group. The axial distance between the first sensor group and the second sensor group; The data analysis and early warning host calculates the equivalent stress according to the following relationship: ； ； ； in, For axial stress, For bending stress, The cross-sectional area of ​​the needle body is... The section modulus is the bending section modulus. For a moment The equivalent stress.

7. The intelligent biopsy needle system with real-time fracture early warning monitoring according to claim 1, characterized in that, The cycle counting involves counting rainflows on the equivalent stress time series to obtain the stress amplitude of the stress cycle. and average stress ; The average stress The correction was performed using the Goodman correction, and the cumulative fatigue damage was calculated according to the following relationship. : ； ； ； in, This is the corrected equivalent stress amplitude. The static ultimate strength of the needle material. and For fatigue SN curve parameters, Let be the number of occurrences of the i-th type of stress cycle. Let i be the number of fracture cycles corresponding to the i-th type of stress cycle. This represents the stress cycle amplitude extracted using the rainflow counting method.

8. The intelligent biopsy puncture needle system with real-time fracture early warning monitoring according to claim 1, characterized in that, The human-machine interface unit includes a display screen and an audible and visual alarm. The display screen is used to show the force waveform of the needle body and / or the comprehensive fracture risk coefficient, and the audible and visual alarm is used to output the comprehensive fracture risk coefficient. Corresponding graded early warning signals; when At that time, the human-computer interaction unit outputs a level one warning signal; when At that time, the human-computer interaction unit outputs a level-two warning signal; when At that time, the human-computer interaction unit outputs a three-level warning signal.

9. A method for monitoring the risk of needle breakage in a biopsy needle, executed by the intelligent biopsy needle system according to any one of claims 1-8, characterized in that, The steps include: The axial strain and radial strain output from the first sensor group and the radial strain output from the second sensor group are collected. The acquired strain signals are conditioned and converted from analog to digital to obtain real-time mechanical data; The axial force is calculated based on the axial strain of the first sensor group, and the resultant bending moment is calculated based on the radial strain difference between the first and second sensor groups, the Young's modulus of elasticity of the needle material, the moment of inertia of the needle section, and the axial distance between the first and second sensor groups. The equivalent stress of the needle body is calculated based on the axial force and the resultant bending moment, and an equivalent stress time series is formed. The equivalent stress time series is subjected to cycle counting and average stress correction to calculate the cumulative fatigue damage result. A comprehensive fracture risk coefficient is generated based on the equivalent stress, the cumulative fatigue damage result, and the equivalent stress change rate. Based on the comprehensive fracture risk coefficient, early warning information on needle body fracture risk is output.

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