Medical instrument service life monitoring method and system

Abstract

The invention relates to the technical field of medical instrument service life monitoring, in particular to a medical instrument service life monitoring method and system. The method comprises the following steps: constructing a basic attribute file of a medical device, recording an operation behavior of the medical device, and generating an operation behavior time sequence path vector; constructing a path enhancement factor based on the operation behavior time sequence path vector, introducing the path enhancement factor into a Miner model, and analyzing different operation behavior sequence involution medical instrument damage differences; a Paris model is used as a core evaluation framework, the crack evolution trend is dynamically calculated, and the service life state of the medical instrument is updated in real time; and displaying the service life state, the risk early warning and the use suggestion of the medical instrument on a user interface in a visual mode. According to the method, the path enhancement factor is constructed, the Miner model and the Paris model are combined, different damage influences of the operation behavior sequence on the medical instrument are comprehensively considered, and the service life of the medical instrument is accurately analyzed and monitored.

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 for highly complex 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; The current mainstream medical device life monitoring methods mainly conduct passive failure analysis based on the cycle-threshold method, Miner fatigue cumulative 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 of increased potential medical risks due to early or delayed scrapping of medical devices. Summary of the Invention

[0003] 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 of increased potential medical risks due to early or delayed scrapping of medical devices; To achieve the above purpose, the technical solution of the present invention: A method for monitoring the service life of medical devices includes: S1. Construct a basic attribute file of the medical device, and use sensing devices to record the operation behaviors of the medical device in real time and generate an operation behavior time series path vector; S2. Based on the operation behavior time series 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; 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 medical device life status in real time; S4. Visualize the evaluated life status of the medical device, along with the corresponding risk warnings and usage suggestions, on the user interface.

[0004] 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; 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 operating behavior time - sequence path vector for each medical device.

[0005] 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; The specific method for generating the operating behavior time - sequence path vector is as follows: Arrange and record the operating behavior identifiers of the medical device in chronological order, and sequentially form the operating behavior time - sequence path vector of the medical device. .

[0006] Preferably, in S2, based on the operating behavior time - sequence path vector, combined with the current and historical operating behaviors of the medical device, a path enhancement factor is constructed. The specific method is as follows: 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; 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; S2.3. Define the path enhancement factor based on the operating behavior sequence influence matrix : ; Among them, is the path enhancement factor; is the total number of times of the current operating behavior; is the operating behavior index; is the time - decay weight; is the th operating behavior; is the th operating behavior; Preferably, the path enhancement factor is a dynamic adjustment factor used to quantify the non - linear influence degree of all operating behavior sequence combinations on the service life of the medical device during its entire life cycle.

[0007] Preferably, the path enhancement factor is introduced into the Miner cumulative fatigue model to analyze the differences in the damage to the medical device under different operation behavior sequences, as follows: ; wherein, 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.

[0008] 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; The path enhancement factor is embedded into the crack growth rate function, as follows: ; wherein, 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 amplitude of the stress intensity factor borne by the medical device structure under the th operation behavior; is the enhanced stress intensity factor.

[0009] Preferably, the dynamic calculation of the crack evolution trend and the real-time update of the medical device life state are carried out as follows: Recursively update the current main crack length according to the fatigue cycle increment, and calculate the medical device life state index according to the current main crack length and the set failure critical length; wherein, the fatigue cycle increment is the fatigue driving degree equivalently caused by the operation behavior received by the medical device, and is used to map the discrete operation behavior into the equivalent fatigue driving degree.

[0010] Preferably, in S4, the evaluated medical device life state and the corresponding risk warnings and usage suggestions are visually displayed in the user interface, as follows: The medical device life state includes the current main crack length, the total fatigue damage value, the medical device life state index, the path enhancement factor, and the crack growth rate; The risk warnings and usage suggestions are as follows: If the 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; 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, 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, the service life of the medical device has reached its upper limit.

[0011] On the other hand, the present invention provides a monitoring system for the service life of a medical device, including 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 method for monitoring the service life of a medical device.

[0012] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects: 1. In the present invention, an operation behavior time-sequence path vector of the medical device in the entire usage cycle is constructed, the sequential combination relationship between the current behavior and the historical behavior is analyzed to construct a path enhancement factor, and in combination with the Miner cumulative fatigue model, the fatigue accumulation method is realized from operation frequency drive to sequential behavior structure drive, improving the monitoring accuracy of the service life of the medical device; 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 materials 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 management level of the hospital medical equipment life cycle. Description of the Drawings

[0013] Figure 1 It is a flowchart of an embodiment proposed by the present invention. Detailed Embodiments

[0014] Embodiment 1, as Figure 1 shown, a method for monitoring the service life of a medical device proposed by the present invention, the specific implementation steps are as follows: S1. Construct a basic attribute file of the medical device, and record the operation behavior of the medical device in real time through a sensing device to generate an operation behavior time-sequence path vector; 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; Among them, the traceable behavior identification set includes three operation behaviors of usage behavior , sterilization behavior and wiping behavior to generate an operation behavior time-sequence path vector for each medical device.

[0015] In this embodiment, represents the code for the usage behavior of medical devices; represents the code for the sterilization behavior of medical devices; represents the code for the wiping behavior of medical devices; The operation behavior of medical devices includes usage behavior , sterilization behavior and wiping behavior ; The operation behavior time-sequence path vector is an ordered symbol sequence used to record the operation behavior identifiers and their time sequence experienced by medical devices during the usage cycle; The specific method for generating the operation behavior time-sequence path vector is as follows: Arrange and record the operation behavior identifiers of medical devices in chronological order, and sequentially arrange them to form the operation behavior time-sequence path vector of the medical device ; 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: ; This operation behavior time-sequence path vector represents that its behavior sequence is: the sequential behaviors of usage, sterilization, wiping, usage, and sterilization, which is used to identify the relationship between the behavior sequence and fatigue damage, not only recording the total number of operations, but also considering the damage to medical devices caused by the restriction of the behavior combination effect.

[0016] 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; 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: 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; 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 medical devices; In this embodiment, the construction of the operation behavior sequence influence matrix is specifically as follows: ; Among them, is the operation behavior sequence influence matrix; is the operation behavior index; is the The secondary operation behavior; For the secondary operation behavior; As follows: Sterilize immediately after use U→M, indicating thermal shock after high stress, Is 1.4; Wipe immediately after heat sterilization M→A, Is 1.3; Sterilize after wiping A→M, and the operation sequence is relatively reasonable, Is 0.9; The conventional operation sequence M→U after sterilization and then use, Is 1.0; 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; For the secondary operation behavior; For the secondary operation behavior; 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 medical devices during their entire life cycle.

[0017] 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 sequence path vector, constructs a sequence sensitivity mapping relationship, and assigns fatigue gain or inhibition weights to different behavior combinations, thereby forming a dynamic correction coefficient for the contribution of single fatigue damage; the path enhancement factor is introduced into the traditional Miner cumulative fatigue model to replace the mechanism where the single 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, thereby constructing an improved Miner cumulative fatigue model with "behavior sequence dependence" and "cumulative sensitivity".

[0018] Introducing the path enhancement factor into the Miner cumulative fatigue model to analyze the damage differences of different operation behavior sequences to the combined medical devices is specifically as follows: ; Among them, Is the total fatigue damage value; is the path enhancement factor; is the th operation behavior the upper limit of fatigue life under standard stress conditions; In this embodiment, the total fatigue damage value If it is greater than or equal to 1, it means that the medical device reaches the failure threshold, the life reaches the upper limit, and it needs to be manually scrapped and destroyed.

[0019] 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; The crack propagation 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; Embedding the path enhancement factor into the crack propagation rate function is specifically as follows: ; where 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.

[0020] 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 proportional coefficient of crack propagation; the material exponent of the Paris crack growth model represents the sensitivity of crack growth to the change of the stress intensity factor, and the value is 3; The traditional Paris crack growth model is as follows: ; 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. Then, without modifying the stress intensity factor of the external force a "crack tip stress amplification effect" multiplier caused by a behavior path is constructed, which is the path enhancement factor .

[0021] The method for dynamically calculating the crack evolution trend and real-time updating the life state of the medical device 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 based on the current main crack length and the set failure critical length; Among them, the fatigue cycle increment is the degree of fatigue drive equivalently caused by the operation behavior of the medical device, and is used to map discrete operation behaviors into the equivalent fatigue drive degree.

[0022] In this embodiment, the current main crack length is recursively updated according to the fatigue cycle increment Specifically as follows: ; Among them, is the current main crack length; is the crack length under the th operation behavior; is the fatigue cycle increment; The specific calculation method of the medical device life state index is as follows: ; Among them, is the medical device life state index under the th operation behavior; is the failure critical length set artificially according to the medical device conditions; 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 medical device damage statistical samples 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.

[0023] S4. Visualize the evaluated lifespan status of the medical device, along with the corresponding risk warnings and usage suggestions, on the user interface. In Embodiment S4, the evaluated lifespan status of the medical device, along with the corresponding risk warnings and usage suggestions, is visualized on the user interface as follows: The lifespan status of the medical device includes the current main crack length, total fatigue damage value, medical device lifespan status index, path enhancement factor, and crack growth rate. The risk warnings and usage suggestions are as follows: If the medical device lifespan status index is greater than or equal to 0.5 and the crack growth rate is stable, the medical device can be used normally. If the medical device lifespan status index is greater than or equal to 0.2 and 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. If the medical device lifespan status index is less than 0.2, the service life of the medical device has reached its upper limit.

[0024] Embodiment 2. A medical device service life monitoring system proposed by the present invention is applied to a medical device service life monitoring method 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 medical device service life monitoring method.

[0025] The above has described in detail the embodiments of the present invention with reference to the accompanying drawings. However, the present invention is not limited to this. 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 and generate the operation behavior time series path vector; S2. Based on the operation behavior time series path vector, combine the current operation behavior and historical operation behavior of the medical device to build a path enhancement factor, and introduce the path enhancement factor into the Miner cumulative fatigue model to analyze the damage difference 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 growth rate function, dynamically calculate the crack evolution trend and update the life state of the medical device in real time; S4. Visualize the evaluated life state of the medical device and the corresponding risk warnings and usage suggestions in the user interface.

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 identification set; Among them, the set of traceable behavior identifiers includes usage behavior , sterilization behavior and wiping behavior These three operation behaviors are used to generate the operation behavior time sequence path vector 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 identification and its time sequence experienced by the medical device during the usage cycle; The specific method for generating the operation behavior time series path vector is as follows: Record the operation behavior identifiers of the medical device in chronological order, and arrange them sequentially to 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: In S2, based on the operation behavior time series path vector, combine the current operation behavior and historical operation behavior of the medical device to 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 historical operation behaviors; S2.

2. Construct the operation behavior sequence impact matrix , which is used to quantify the impact of the execution sequence 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.

5. The method for monitoring the service life of a medical device according to claim 4, characterized in that: 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.

6. The method for monitoring the service life of a medical device according to claim 4, characterized in that: The introduction of the path enhancement factor into the Miner cumulative fatigue model to analyze the damage difference 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.

7. The method for monitoring the service life of a medical device according to claim 6, characterized in that: The crack growth 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; Embedding the path enhancement factor into the crack growth rate function is 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.

8. The method for monitoring the service life of a medical device according to claim 7, characterized in that: 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 discrete operation behaviors into equivalent fatigue driving degrees.

9. The method for monitoring the service life of a medical device according to claim 8, characterized in that: In S4, visualizing the evaluated life state of the medical device and the corresponding risk warnings and usage suggestions in the user interface is 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.

10. 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 1-9.

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