A method and system for monitoring the service life of a medical device

Abstract

The present invention relates to the technical field of medical device life monitoring, and specifically to a method and system for monitoring the service life of medical devices; the method includes: constructing a basic attribute file of medical devices, recording the operation behaviors of medical devices and generating an operation behavior time series path vector; constructing a path enhancement factor based on the operation behavior time series path vector, and introducing the path enhancement factor into the Miner model to analyze the differences in the damage to medical devices caused by different operation behavior sequences; using the Paris model as the core evaluation framework to dynamically calculate the crack evolution trend and real-time update the life state of medical devices; and displaying the life state, risk warning and usage suggestions of medical devices in a visual manner on the user interface. The present invention constructs a path enhancement factor, combines the Miner model and the Paris model, comprehensively considers the different damage effects of operation behavior sequences on medical devices, and accurately analyzes and monitors the service life of medical devices.

Description

Technical Field

[0001] The present invention relates to the technical field of medical device life monitoring, and particularly relates to a method and system for monitoring the service life of medical devices. Background Art

[0002] Medical devices are widely used in modern surgical, laparoscopic, interventional and other clinical operations. Especially high-complexity devices such as electric laparoscopic surgical instruments and energy platform instruments, due to their repeated use, frequent sterilization, and repeated wiping, their structural integrity and service life directly affect patient safety and surgical outcomes. Establishing a scientific, dynamic, and traceable medical device life monitoring mechanism has become a key technical issue in medical equipment management and intelligent operation and maintenance;

[0003] The current mainstream medical device life monitoring methods mainly conduct passive failure analysis based on the cycle-threshold method, the Miner fatigue accumulation model, and the basic crack growth Paris crack growth model. Moreover, the traditional Miner cumulative fatigue model defaults that the contribution of each operation to fatigue damage is fixed, ignoring the differences in the actual structural stress response and material damage degree caused by the execution order of different operation behaviors, and the traditional Paris crack growth model also does not consider the different effects of different operation types on crack evolution, which will lead to inaccurate medical device life monitoring and the problem that the early or delayed scrapping of medical devices increases potential medical risks. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for monitoring the service life of medical devices to solve the problems in the above background art that the traditional Miner cumulative fatigue model ignores the differences in the actual structural stress response and material damage degree caused by the execution order of different operation behaviors, and the traditional Paris crack growth model also does not consider the different effects of different operation types on crack evolution, which will lead to inaccurate medical device life monitoring and the problem that the early or delayed scrapping of medical devices increases potential medical risks;

[0005] To achieve the above purpose, the technical solution of the present invention: A method for monitoring the service life of medical devices includes:

[0006] S1. Construct a basic attribute file of the medical device, and record the operation behaviors of the medical device in real time through a sensing device and generate an operation behavior time sequence path vector;

[0007] S2. Based on the operation behavior time sequence path vector, combine the current operation behavior and historical operation behaviors of the medical device to construct a path enhancement factor, and introduce the path enhancement factor into the Miner cumulative fatigue model to analyze the damage differences of the combined medical device caused by different operation behavior sequences;

[0008] S3. Use the Paris crack growth model as the core evaluation framework, embed the path enhancement factor into the crack growth rate function, dynamically calculate the crack evolution trend, and update the life state of the medical device in real time;

[0009] S4. Display the evaluated life state of the medical device, as well as the corresponding risk warnings and usage suggestions, in a visual manner on the user interface.

[0010] Preferably, the content data of the basic attribute file of the medical device includes: material type, geometric structure parameters, critical crack length, initial crack state, and a set of traceable behavior identifiers;

[0011] Among them, the set of traceable behavior identifiers includes usage behavior , sterilization behavior and wiping behavior of the three operating behaviors, which are used to generate the operating behavior time sequence path vector of each medical device.

[0012] Preferably, the operating behavior time sequence path vector is an ordered symbol sequence used to record the operating behavior identifiers and their time order experienced by the medical device during its usage cycle;

[0013] The specific method for generating the operating behavior time sequence path vector is as follows:

[0014] Arrange and record the operating behavior identifiers of the medical device in chronological order, and arrange them in sequence to form the operating behavior time sequence path vector of the medical device .

[0015] Preferably, in S2, based on the operating behavior time sequence path vector, combined with the current operating behavior and historical operating behavior of the medical device, a path enhancement factor is constructed. The specific method is as follows:

[0016] S2.1. Set the latest operating behavior in the operating behavior time sequence path vector as the current operating behavior, and the remaining operating behaviors as historical operating behaviors;

[0017] S2.2. Construct an operating behavior sequence influence matrix , which is used to quantify the influence of the execution order of each operating behavior on the service life of the medical device;

[0018] S2.3. Define the path enhancement factor based on the operating behavior sequence influence matrix:

[0019] ;

[0020] Among them, is the path enhancement factor; is the total number of times of the current operating behavior; is the operation behavior index; is the time decay weight; is the th operation behavior; is the th operation behavior;

[0021] Preferably, the path enhancement factor is a dynamic adjustment factor used to quantify the non-linear influence degree of the sequential combination of all operation behaviors of the medical device during its entire life cycle on its service life.

[0022] Preferably, introducing the path enhancement factor into the Miner cumulative fatigue model to analyze the damage differences of the medical device under different operation behavior sequences is as follows:

[0023] ;

[0024] Among them, is the total fatigue damage value; is the path enhancement factor; is the th operation behavior upper limit of fatigue life under standard stress conditions.

[0025] Preferably, the crack growth rate function is the basic form of the Paris crack growth model, which is used to describe the propagation trend of microcracks under alternating stress;

[0026] Embedding the path enhancement factor into the crack growth rate function is as follows:

[0027] ;

[0028] Among them, is the crack length after the th operation behavior; is the cumulative fatigue loading cycle number; is the crack growth rate; is the material constant of the Paris crack growth model; is the material exponent of the Paris crack growth model; is the th operation behavior, the amplitude of the stress intensity factor borne by the medical device structure; is the enhanced stress intensity factor.

[0029] Preferably, the method for dynamically calculating the crack evolution trend and real-time updating the life state of the medical device is as follows:

[0030] Update the current main crack length recursively according to the fatigue cycle increment, and calculate the life state index of the medical device based on the current main crack length and the set failure critical length;

[0031] Among them, the fatigue cycle increment is the degree of fatigue driving equivalently caused by the operating behavior of the medical device, and is used to map the discrete operating behavior into the equivalent fatigue driving degree.

[0032] Preferably, in S4, the evaluated life state of the medical device and the corresponding risk warnings and usage suggestions are visually displayed in the user interface, specifically as follows:

[0033] The life state of the medical device includes the current main crack length, the total fatigue damage value, the life state index of the medical device, the path enhancement factor, and the crack growth rate;

[0034] The risk warnings and usage suggestions are as follows:

[0035] If the life state index of the medical device is greater than or equal to 0.5 and the crack growth rate is stable, the medical device can be used normally;

[0036] If the life state index of the medical device is greater than or equal to 0.2, less than 0.5, and the crack growth rate is gradually increasing, the service life of the medical device is about to reach the limit;

[0037] If the life state index of the medical device is less than 0.2, the service life of the medical device has reached the upper limit.

[0038] On the other hand, the present invention provides a medical device service life monitoring system, including a memory, a processor, and a computer program stored in the memory and operable on the processor. The processor executes the computer program to implement the above-mentioned medical device service life monitoring method.

[0039] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0040] 1. In the present invention, the operation behavior time sequence path vector of the medical device in the whole use cycle is constructed, the sequential combination relationship between the current behavior and the historical behavior is analyzed to construct the path enhancement factor, and in combination with the Miner cumulative fatigue model, the fatigue accumulation method is realized from the operation times drive to the sequential behavior structure drive, improving the monitoring accuracy of the service life of the medical device;

[0041] 2. In the present invention, the path enhancement factor is embedded into the Paris crack growth model, so that the crack growth rate is not only affected by the material and stress intensity, but also dynamically adjusted based on the operation behavior sequence, reducing the waste caused by misjudgment of the medical device and improving the life cycle management level of hospital medical equipment. Description of the Drawings

[0042] Figure 1 This is a flowchart of an embodiment proposed by the present invention. Detailed Description of the Invention

[0043] Embodiment 1, as Figure 1 shown, a method for monitoring the service life of a medical device proposed by the present invention, and its specific implementation steps are as follows:

[0044] S1. Construct a basic attribute file of the medical device, and use a sensing device to record the operation behavior of the medical device in real time and generate an operation behavior time sequence path vector;

[0045] The content data of the basic attribute file of the medical device includes: material type, geometric structure parameters, critical crack length, initial crack state, and a traceable behavior identification set;

[0046] Among them, the traceable behavior identification set includes usage behavior , sterilization behavior and wiping behavior of three operation behaviors, which are used to generate the operation behavior time sequence path vector of each medical device.

[0047] In this embodiment, represents the encoding of the usage behavior of the medical device; represents the encoding of the sterilization behavior of the medical device; represents the encoding of the wiping behavior of the medical device; the operation behavior of the medical device includes usage behavior , sterilization behavior and wiping behavior ;

[0048] The operation behavior time sequence path vector is an ordered symbol sequence used to record the operation behavior identifications and their time sequences experienced by the medical device during its service life;

[0049] The specific method for generating the operation behavior time sequence path vector is as follows:

[0050] Arrange and record the operation behavior identifications of the medical device in chronological order, and arrange them in sequence to form the operation behavior time sequence path vector of the medical device ;

[0051] In this embodiment, after a medical device is first used, it undergoes high-temperature sterilization and alcohol wiping, and then is used and sterilized again. Its operation behavior time sequence path vector is: ; The operation behavior time - sequence path vector represents its behavior sequence as: sequential behaviors of use, sterilization, wiping, use, and sterilization, which is used to identify the relationship between the behavior sequence and fatigue damage. It not only records the total number of operations but also considers the damage to medical devices caused by the combined effect of behavior combinations.

[0052] S2. Based on the operation behavior time - sequence path vector, combined with the current operation behavior and historical operation behavior of the medical device, construct a path enhancement factor, and introduce the path enhancement factor into the Miner cumulative fatigue model to analyze the differences in damage to the medical device caused by different operation behavior sequences;

[0053] In embodiment S2, based on the operation behavior time - sequence path vector, combined with the current operation behavior and historical operation behavior of the medical device, construct a path enhancement factor. The specific method is as follows:

[0054] S2.1. Set the latest operation behavior in the operation behavior time - sequence path vector as the current operation behavior, and the remaining operation behaviors as historical operation behaviors;

[0055] S2.2. Construct an operation behavior sequence influence matrix , which is used to quantify the influence of the execution sequence of each operation behavior on the service life of the medical device;

[0056] In this embodiment, the operation behavior sequence influence matrix is constructed as follows:

[0057] ;

[0058] Among them, is the operation behavior sequence influence matrix; is the operation behavior index; is the th operation behavior; is the th operation behavior;

[0059] As follows:

[0060] Sterilizing immediately after use, U→M, represents a thermal shock after high stress, is 1.4;

[0061] Wiping immediately after thermal sterilization, M→A, is 1.3;

[0062] Sterilizing after wiping, A→M, the operation sequence is relatively reasonable, is 0.9;

[0063] The conventional operation sequence of using after sterilization, M→U, is 1.0;

[0064] S2.3. Define the path enhancement factor based on the operation behavior sequence influence matrix :

[0065] ;

[0066] Wherein, is the path enhancement factor; is the total number of current operation behaviors; is the operation behavior index; is the time decay weight; is the th operation behavior; is the th operation behavior;

[0067] The path enhancement factor is a dynamic adjustment factor used to quantify the non - linear influence degree of all operation behavior sequence combinations of medical devices on their service life during the whole life cycle.

[0068] In this embodiment, the path enhancement factor analyzes the sequence relationship between the current operation behavior and various historical behaviors in the operation behavior time - series path vector, constructs a sequence - sensitivity mapping relationship, assigns fatigue gain or inhibition weights to different behavior combinations, so as to form a dynamic correction coefficient for the contribution of single - time fatigue damage; the path enhancement factor is introduced into the traditional Miner cumulative fatigue model to replace the mechanism that the single - time fatigue damage in the Miner cumulative fatigue model is a fixed value, so that the damage increment caused by each operation behavior is not only related to the operation type, but also related to its relative position and combination mode in the complete behavior path, thus constructing an improved Miner cumulative fatigue model with "behavior sequence dependence" and "cumulative sensitivity".

[0069] The introduction of the path enhancement factor into the Miner cumulative fatigue model to analyze the damage difference of medical devices caused by different operation behavior sequences is as follows:

[0070] ;

[0071] Wherein, is the total fatigue damage value; is the path enhancement factor; is the th operation behavior under the standard stress condition, the upper limit of fatigue life;

[0072] In this embodiment, for the total fatigue damage value if it is greater than or equal to 1, it means that the medical device reaches the failure threshold, its life reaches the upper limit, and it needs to be manually scrapped and destroyed.

[0073] S3. Use the Paris crack growth model as the core evaluation framework, embed the path enhancement factor into the crack growth rate function, dynamically calculate the crack evolution trend, and update the life state of the medical device in real time;

[0074] The crack growth rate function is the basic form of the Paris crack growth model, which is used to describe the propagation trend of microcracks under alternating stress;

[0075] Embedding the path enhancement factor into the crack growth rate function is specifically as follows:

[0076] ;

[0077] where, is the crack length after the th operation; is the cumulative fatigue loading cycle number; is the crack growth rate; is the material constant of the Paris crack growth model; is the material exponent of the Paris crack growth model; is the stress intensity factor amplitude borne by the medical device structure under the th operation; is the enhanced stress intensity factor.

[0078] In this embodiment, the material constant of the Paris crack growth model is related to the material used in the device, surface treatment, temperature environment, etc., and determines the proportionality coefficient of crack propagation; the material exponent of the Paris crack growth model represents the sensitivity of crack growth to changes in the stress intensity factor, and its value is 3;

[0079] The traditional Paris crack growth model is as follows:

[0080] ;

[0081] where, is the stress intensity factor, which represents the actual loading response ability of the crack tip region of the material structure. This value usually comes from hard physical factors such as mechanical loading amplitude, geometric structure, and boundary conditions; however, in medical devices, under the same loading conditions, different operation paths, such as whether it has just undergone sterilization, whether the surface has just been wiped, whether it has been continuously used without sufficient cooling, etc., will cause changes in the local stress state, residual thermal stress, and surface vulnerability of the crack tip region. Therefore, without modifying the stress intensity factor of the external force, a "crack tip stress amplification effect" multiplier caused by the behavior path is constructed, which is the path enhancement factor .

[0082] Dynamically calculate the crack evolution trend and update the life status of medical devices in real time. The specific method is as follows:

[0083] Recursively update the current main crack length according to the fatigue cycle increment, and calculate the life status index of the medical device based on the current main crack length and the set failure critical length;

[0084] Among them, the fatigue cycle increment is the degree of fatigue driving equivalently caused by the operating behavior of the medical device, and is used to map discrete operating behaviors into the equivalent fatigue driving degree.

[0085] In this embodiment, the current main crack length is recursively updated according to the fatigue cycle increment Specifically as follows:

[0086] ;

[0087] Among them, is the current main crack length; is the crack length under the th operating behavior; is the fatigue cycle increment;

[0088] The specific calculation method of the life status index of the medical device is as follows:

[0089] ;

[0090] Among them, is the life status index of the medical device under the th operating behavior; is the failure critical length set artificially according to the conditions of the medical device;

[0091] The fatigue cycle increment can specify the operation behavior mapping value according to the empirical operation type or fit the equivalent cyclic effect of the operation behavior on fatigue growth based on the historical statistical samples of device damage to form a mapping table or learning model; the fatigue cycle increment is a variable that realizes the evolution from the operation behavior to the crack structure, and converts non-standard behavior events into the standard physical quantity input of structural fatigue, so as to realize the unified structural life calculation logic under the influence of different behaviors.

[0092] S4. Display the evaluated life status of the medical device, the corresponding risk warnings and usage suggestions in a visual manner on the user interface;

[0093] In embodiment S4, display the evaluated life status of the medical device, the corresponding risk warnings and usage suggestions in a visual manner on the user interface, specifically as follows:

[0094] The service life status of a medical device includes the current main crack length, the total fatigue damage value, the service life status index of the medical device, the path enhancement factor, and the crack growth rate;

[0095] The risk warning and usage suggestions are as follows:

[0096] If the service life status index of the medical device is greater than or equal to 0.5 and the crack growth rate is stable, the medical device can be used normally;

[0097] If the service life status index of the medical device is greater than or equal to 0.2, less than 0.5, and the crack growth rate is gradually increasing, the service life of the medical device is about to reach its limit;

[0098] If the service life status index of the medical device is less than 0.2, the service life of the medical device has reached its upper limit.

[0099] Embodiment 2. A service life monitoring system for a medical device proposed by the present invention is applied to a service life monitoring method for a medical device proposed in Embodiment 1. It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the above-mentioned service life monitoring method for a medical device.

[0100] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made without departing from the spirit of the present invention within the knowledge scope of those skilled in the art.

Claims

1. A method for monitoring the service life of a medical device, characterized in that, It includes the following steps: S1. Build the basic attribute file of the medical device, and record the operation behavior of the medical device in real time through the sensing device to generate the operation behavior time series path vector; S2. Based on the operation behavior time series path vector, combined with the current operation behavior and historical operation behavior of the medical device, build a path enhancement factor, and introduce the path enhancement factor into the Miner cumulative fatigue model to analyze the damage differences of the medical device under different operation behavior sequences; S3. Use the Paris crack growth model as the core evaluation framework, embed the path enhancement factor into the crack propagation rate function, dynamically calculate the crack evolution trend and update the life state of the medical device in real time; S4. Display the evaluated life state of the medical device and the corresponding risk warnings and usage suggestions in a visual manner on the user interface; In S2, based on the operation behavior time series path vector, combined with the current operation behavior and historical operation behavior of the medical device, build a path enhancement factor. The specific method is as follows: S2.

1. Set the latest operation behavior in the operation behavior time series path vector as the current operation behavior, and the remaining operation behaviors as the historical operation behaviors; S2.

2. Construct the operation behavior sequence impact matrix , which is used to quantify the impact of the execution order of each operation behavior on the service life of medical devices; S2.

3. Define the path enhancement factor based on the operation behavior sequence influence matrix : ； Among them, is the path enhancement factor; is the total number of current operation behaviors; is the operation behavior index; is the time decay weight; is the th operation behavior; is the th operation behavior; The introduction of the path enhancement factor into the Miner cumulative fatigue model to analyze the damage differences of the medical device under different operation behavior sequences is as follows: ； Among them, is the total fatigue damage value; is the path enhancement factor; is the th operation behavior upper limit of fatigue life under standard stress conditions.

2. The method for monitoring the service life of a medical device according to claim 1, characterized in that: The content data of the basic attribute file of the medical device includes: material type, geometric structure parameters, critical crack length, initial crack state, and traceable behavior identifier set; Among them, the set of traceable behavior identifiers includes usage behavior , sterilization behavior and wiping behavior These three operating behaviors are used to generate the timing path vector of the operating behaviors of each medical device.

3. The method for monitoring the service life of a medical device according to claim 2, wherein: The operation behavior time series path vector is an ordered symbol sequence used to record the operation behavior identifiers and their time sequences experienced by the medical device during the usage cycle; The specific method for generating the operation behavior time series path vector is as follows: Arrange the operation behavior identifiers of the medical device in chronological order and sequentially form the operation behavior time sequence path vector of the medical device .

4. The method for monitoring the service life of a medical device according to claim 3, wherein: The path enhancement factor is a dynamic adjustment factor used to quantify the non-linear influence degree of all operation behavior sequence combinations on the service life of the medical device during the whole life cycle.

5. The method for monitoring the service life of a medical device according to claim 4, wherein: The crack propagation rate function is the basic form of the Paris crack growth model, which is used to describe the propagation trend of micro-cracks under alternating stress; Embed the path enhancement factor into the crack propagation rate function as follows: ； Among them, is the crack length after the th operation behavior; is the cumulative fatigue loading cycle number; is the crack growth rate; is the material constant of the Paris crack growth model; is the material exponent of the Paris crack growth model; is the stress intensity factor amplitude borne by the medical device structure under the th operation behavior; is the enhanced stress intensity factor.

6. The method for monitoring the service life of a medical device according to claim 5, wherein: The specific method for dynamically calculating the crack evolution trend and updating the life state of the medical device in real time is as follows: Recursively update the current main crack length according to the fatigue cycle increment, and calculate the life state index of the medical device according to the current main crack length and the set failure critical length; Among them, the fatigue cycle increment is the fatigue driving degree equivalently caused by the operation behavior received by the medical device, which is used to map the discrete operation behavior into the equivalent fatigue driving degree.

7. The method for monitoring the service life of a medical device according to claim 6, characterized in that: In S4, display the evaluated life state of the medical device and the corresponding risk warnings and usage suggestions in a visual manner on the user interface as follows: The life state of the medical device includes the current main crack length, total fatigue damage value, life state index of the medical device, path enhancement factor, and crack growth rate; The risk warnings and usage suggestions are as follows: If the life state index of the medical device is greater than or equal to 0.5 and the crack growth rate is stable, the medical device can be used normally; If the life status index of the medical device is greater than or equal to 0.2, less than 0.5, and the crack growth rate is gradually increasing, then the service life of the medical device is about to reach its limit; If the life status index of the medical device is less than 0.2, then the service life of the medical device has reached its upper limit.

8. A medical device service life monitoring system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the method for monitoring the service life of a medical device according to any one of claims 1-7.

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