Medical equipment management system

Abstract

The invention relates to the technical field of medical equipment information management and maintenance guarantee, and discloses a medical equipment management system which comprises a basic information management module, a dynamic monitoring and calculation module and an intelligent decision and resource control module. According to the system, firstly, a multi-dimensional attribute model containing clinical risk indexes is constructed, and the accumulated equivalent damage degree of equipment is calculated based on real-time working condition data by using a nonlinear damage model; meanwhile, the inventory state and the purchase cycle of the spare parts are monitored, a floating maintenance threshold value is dynamically generated in combination with the clinical risk index, and when the accumulated equivalent damage degree meets the early warning condition, the system automatically executes soft locking operation for the associated spare parts. According to the method, the physical loss of the equipment can be accurately evaluated, the maintenance opportunity is adaptively adjusted according to the supply chain state, the buffer time is reserved in advance in the inventory shortage or high-risk scene, intelligent optimal configuration of medical maintenance resources is realized, and the risk of non-planned shutdown of the equipment is effectively reduced.

Description

Technical Field

[0001] This invention relates to the field of medical device information management and maintenance support technology, specifically a medical device management system. Background Technology

[0002] With the rapid development of modern medical technology, medical equipment has become a core element in clinical diagnosis and treatment, and its operational reliability is directly related to medical quality and patient safety. Currently, medical institutions mainly adopt a preventive maintenance strategy based on fixed cycles, that is, strictly following the maintenance schedule recommended by the equipment manufacturer.

[0003] In existing technologies, some medical institutions have begun to introduce digital tools, such as using the OM04IB database for equipment registration or building simple equipment management applications using low-code development platforms (such as FineReport, JianDaoYun, and H3yun). However, most of these applications currently only achieve the function of "paperless recording," that is, static storage and display of information through simple form entry and spreadsheets. This superficial application based on general tools ignores the differences in operating loads of equipment in actual clinical applications and lacks the ability to conduct in-depth quantitative analysis of equipment physical wear and tear. The system often cannot detect whether the equipment is in a high-load continuous operation state or a low-frequency standby state, causing high-load equipment to suddenly fail due to accumulated damage exceeding limits before the scheduled maintenance cycle.

[0004] Furthermore, existing medical equipment maintenance management systems are often disconnected from supply chain inventory management systems. Maintenance decisions are frequently based solely on the physical condition of the equipment, failing to adequately consider the real-time availability of critical spare parts and the logistical efficiency of the supply chain. When spare parts inventory is low or procurement lead times are long, if the system mechanically triggers maintenance requests based on fixed physical lifespan thresholds, it is highly susceptible to prolonged downtime due to spare parts shortages when equipment fails or replacement parts are needed, severely impacting the continuity of clinical operations. More critically, current technology lacks a cross-dimensional intelligent resource locking mechanism. When scarce spare parts face competition from multiple devices, it cannot prioritize allocation based on the clinical risk level of the equipment, making it difficult to prevent routine maintenance of low-risk equipment from consuming critical last-ditch inventory.

[0005] Therefore, how to embed deep intelligent decision-making algorithms and condition damage models on the basis of general low-code development platforms and standard information bases to achieve intelligent optimization of medical maintenance resources is an urgent problem to be solved in this field. Summary of the Invention

[0006] The purpose of this application is to provide a medical device management system that aims to improve the existing maintenance model, which suffers from delayed or excessive equipment maintenance due to neglecting the differences in actual operating conditions and loads, the risk of downtime due to lack of material supply due to failure to consider the status of the supply chain, and the inability to prioritize and lock in scarce maintenance resources based on clinical risks.

[0007] The first aspect of the present invention provides a medical device management system, which includes a basic information management module, a dynamic monitoring and calculation module, and an intelligent decision-making and resource control module.

[0008] The basic information management module is used to construct the data foundation for medical devices, storing the static attributes and clinical risk index of the devices. The clinical risk index is a quantitative indicator reflecting the importance of the device in clinical practice. This index is calculated based on parameters such as the department to which the device belongs, its dependence on life support, and whether there is a redundant backup status, using preset weighting rules.

[0009] The dynamic monitoring and calculation module is used to acquire equipment operation data in real time and quantify the physical wear and tear of the equipment. Specifically, this module collects operating condition data of the medical equipment during operation and uses a nonlinear damage model to calculate the cumulative equivalent damage. To overcome the shortcomings of traditional time-based methods that cannot reflect the impact of high-load operation on the accelerated wear and tear of equipment lifespan, this module executes a nonlinear operating condition weighted algorithm through a damage calculation engine.

[0010] In its implementation, the damage calculation engine divides the device's runtime into continuous time windows and calculates the time up to the current moment according to the following formula. Cumulative equivalent damage

[0011] ;

[0012] in, Indicates the first The duration of each data collection window Indicates within the time window Average operating load within, Indicates the rated standard load of the equipment. This represents the nonlinear damage factor. The formula introduces an exponential term. The load ratio is converted into a nonlinear damage increment, thereby accurately characterizing the actual physical life consumption under different operating conditions.

[0013] The intelligent decision-making and resource control module is used to dynamically adjust maintenance strategies and control resources. This module monitors the real-time inventory status and procurement cycle of spare parts associated with medical equipment, and dynamically generates floating maintenance thresholds based on the aforementioned clinical risk index. Internally, this module includes an inventory monitoring unit for calculating spare parts scarcity coefficients. The calculation logic is as follows:

[0014] ;

[0015] in, Indicates real-time inventory quantity. Indicates the safety stock level. This indicates the average lead time for procurement. This represents the baseline procurement cycle. When inventory is sufficient, the coefficient remains at the baseline value of 1; when inventory is insufficient, the coefficient increases as the procurement cycle lengthens to reflect the time cost risk of obtaining spare parts.

[0016] Furthermore, this module utilizes a threshold generation unit to construct a decay model coupling basic risk and inventory, and calculates the floating maintenance threshold according to the following formula.

[0017] ;

[0018] in, This indicates the maximum design lifespan of key components of the equipment. This represents the preset threshold sensitivity coefficient. Represents the natural logarithm function. This represents the clinical risk index. The innovative principle of this model lies in: as the clinical risk index... or spare parts scarcity coefficient The increase of , the calculated floating maintenance threshold It will be significantly lower than the design maximum lifespan. This allows for a safety buffer period before the physical lifespan is exhausted, which can be used to offset the risk of potential logistical delays or sudden failures.

[0019] The intelligent decision-making and resource control module ultimately compares the calculated cumulative equivalent damage level with the floating maintenance threshold. When the cumulative equivalent damage level reaches the product of the floating maintenance threshold and the preset early warning judgment factor, the system determines that the early warning condition is met and executes a resource locking operation for the associated spare parts. This operation modifies the status identifier field of the spare parts from an available state to a soft-locked state at the database level and binds the spare parts to the unique identification code of the high-risk medical equipment to prevent other low-priority needs from occupying this critical resource.

[0020] A second aspect of the present invention provides a method for managing medical devices, the method comprising the following steps:

[0021] First, a multidimensional attribute data model for medical devices is constructed, which covers the static attributes of the devices and the quantified clinical risk index.

[0022] Secondly, operating condition data of the medical equipment during operation is collected, and the cumulative equivalent damage degree of the equipment is calculated using a nonlinear damage model. This step, by collecting the average operating load within different time windows and combining it with a nonlinear damage factor, converts the load stress into an equivalent life loss value, thereby achieving precise quantification of the actual health status of the equipment.

[0023] Simultaneously, the system monitors the real-time inventory status and procurement cycle of spare parts associated with the medical device, and calculates the spare parts scarcity coefficient. Combining the clinical risk index and the spare parts scarcity coefficient, the system dynamically generates a floating maintenance threshold using a logarithmic decay model. This step, through mathematical modeling, establishes a functional relationship between supply chain capabilities and maintenance decision-making criteria, enabling the maintenance threshold to automatically decrease when spare parts are scarce or clinical risks are high, thereby triggering maintenance plans in advance.

[0024] Finally, the cumulative equivalent damage calculated in real time is compared with the floating maintenance threshold. When the cumulative equivalent damage meets the warning conditions set based on the floating maintenance threshold, the resource locking logic is triggered, a soft locking operation is performed on the associated spare parts in the database, and a corresponding maintenance work order is generated.

[0025] This invention, through the aforementioned technical solution, deeply couples parameters from three dimensions: the physical damage characteristics of the equipment, its clinical importance, and the status of the supply chain logistics. The effects are twofold: firstly, it improves the accuracy of lifespan prediction through nonlinear operating condition weighting; secondly, it achieves proactive prevention through a floating threshold mechanism, effectively avoiding unplanned downtime of high-risk equipment due to spare parts shortages, thereby improving the overall management efficiency and operational reliability of medical equipment.

[0026] In summary, this application includes at least one of the following beneficial technical effects:

[0027] 1. This invention collects real-time operating conditions through a dynamic monitoring and calculation module and applies a nonlinear damage model to convert the operating status of the equipment under different loads into cumulative equivalent damage. This method overcomes the shortcomings of traditional periodic maintenance that ignores the differences in actual usage intensity, and can accurately reflect the actual physical wear and tear of core components. It not only prevents sudden failures of high-load equipment due to delayed maintenance, but also avoids over-maintenance of low-load equipment, thus improving the management accuracy of the entire equipment life cycle.

[0028] 2. This invention utilizes an intelligent decision-making and resource control module to dynamically generate floating maintenance thresholds by combining clinical risk index and spare parts inventory status. When spare parts are scarce or clinical dependence is high, the maintenance trigger threshold is automatically lowered. This mechanism creatively maps the supply chain status to maintenance decision parameters, reserving a safe buffer period for critical equipment to cope with procurement delays when inventory is insufficient, effectively avoiding the risk of medical business interruption caused by downtime due to lack of materials.

[0029] 3. This invention establishes a direct closed loop from fault warning to resource control by comparing the cumulative equivalent damage degree with the floating maintenance threshold and performing a spare parts soft locking operation. This ensures that when inventory is critical, scarce maintenance resources can be locked and allocated to the highest risk equipment that is about to reach its life limit, preventing routine requests from low-priority equipment from seizing critical resources and achieving the global optimal allocation of medical asset maintenance resources. Attached Figure Description

[0030] Figure 1 This is a functional architecture block diagram of the medical device system of the present invention;

[0031] Figure 2 This is a flowchart of the medical device management method of the present invention;

[0032] Figure 3 This is a logic diagram illustrating the dynamic adjustment principle of the floating maintenance threshold of the present invention. Detailed Implementation

[0033] The following is in conjunction with the appendix Figure 1 -Appendix Figure 3 This application will be described in further detail below.

[0034] Please see the appendix Figure 1 -Appendix Figure 3 This invention provides a medical device management system, including a basic information management module, a dynamic monitoring and calculation module, and an intelligent decision-making and resource control module.

[0035] The medical device management system described in this embodiment is built and configured visually based on a low-code development platform (such as h3yun). The system's underlying data relies on the OM04IB standard information database for structured storage and flow. The system's functional modules (basic information management, dynamic monitoring and calculation, intelligent decision-making and resource control) are constructed using visual components of the low-code platform (such as form designers, process engines, and logic orchestration nodes). Business flow rules are configured through drag-and-drop functionality, and core computational logic is embedded in the background scripts.

[0036] The system is deployed on one or more servers, which communicate with the hospital information system (HIS), equipment department management terminals, and the medical equipment itself via an internal local area network (LAN) or wireless network. Data collection may include: automatically acquiring equipment operation logs through an application programming interface (API), periodically parsing equipment operation record files stored in a specific path through a log capture program, or manual data entry through wired or wireless terminal devices deployed at nurse stations and equipment departments.

[0037] The basic information management module is configured to build and maintain a multi-dimensional attribute database for medical devices. Operators create basic device profiles in the OM04IB information database through an information entry page configured on the low-code platform. The entered information includes static attributes such as product manufacturer, model, unique device identifier, serial number, and activation date.

[0038] This module further includes an equipment ledger submodule and a risk assessment submodule. The equipment ledger submodule stores the static attributes of each medical device, including but not limited to the device's unique identifier, model number, manufacturer information, and activation date. The risk assessment submodule stores and manages the clinical risk index for each device, i.e. This clinical risk index The equipment is quantitatively set according to pre-defined business rules, such as the department where the equipment is deployed (e.g., emergency department, intensive care unit) and the functions of the equipment (e.g., life support, diagnostic imaging).

[0039] The dynamic monitoring and calculation module is configured to collect equipment operating data and quantify physical damage. This module utilizes a low-code platform's data integration interface (API) or log capture plugin to obtain data about the equipment within a preset time window. Operating data (average operating load) ).

[0040] This module includes a damage calculation engine, which is a computational logic script deployed on the backend of a low-code platform. Its core function is to convert the collected raw working condition data into a standardized cumulative equivalent damage degree based on a nonlinear damage accumulation model. .

[0041] The damage calculation engine performs calculations according to the following formula (which is written as the platform's calculation rules):

[0042] ;

[0043] in:

[0044] Indicates the time up to the current moment. The cumulative equivalent damage;

[0045] Indicates the first The duration of each data collection window;

[0046] Indicates within the time window Average operating load of devices collected internally;

[0047] This indicates the rated standard load for this type of equipment; this value is a preset baseline value.

[0048] The nonlinear damage factor is a preset constant used to characterize the degree of nonlinearity of the load's impact on equipment wear.

[0049] The intelligent decision-making and resource control module is configured to generate dynamic maintenance strategies and execute resource allocation based on damage calculation results and external constraints. This module further includes an inventory monitoring unit, a threshold generation unit, and a resource locking unit.

[0050] Inventory monitoring unit: Used to obtain key spare parts associated with each device in real time. Inventory quantity and the average lead time for procurement of this spare part .

[0051] Threshold generation unit: Used to dynamically calculate the unique floating maintenance threshold for each device. The calculation process first obtains spare parts information provided by the inventory monitoring unit and then calculates the spare parts scarcity coefficient.

[0052] ;

[0053] in:

[0054] Indicates the scarcity of spare parts;

[0055] express Real-time inventory quantity at any given moment;

[0056] This indicates the preset safety stock level;

[0057] This indicates the average lead time for spare parts procurement.

[0058] This indicates the standardized benchmark procurement cycle.

[0059] Subsequently, the threshold generation unit combines the spare parts scarcity coefficient. and clinical risk index provided by the basic information management module Calculate the floating maintenance threshold :

[0060] ;

[0061] in:

[0062] This represents the dynamically calculated floating maintenance threshold.

[0063] Indicates the maximum design life of key components of the equipment;

[0064] This represents the threshold sensitivity coefficient, which is a preset adjustment constant;

[0065] This represents the natural logarithm function.

[0066] The resource locking unit is used to reserve spare parts under specific conditions. This is based on the cumulative equivalent damage calculated by the dynamic monitoring and calculation module. Meets preset warning conditions (e.g., reaches a floating maintenance threshold). When the capacity reaches 90%, the unit will be activated and execute a soft-lock command for the corresponding spare parts, thereby ensuring that high-risk equipment has priority access to scarce resources.

[0067] This invention also provides a medical device management method, which is executed based on an application environment built on a low-code development platform (such as h3yun) and relies on the OM04IB standard information database as underlying data support, and includes the following steps:

[0068] S10: Construct a multi-dimensional attribute data model for medical equipment. Operators can use the visual input interface configured on the low-code platform to create an archive in the OM04IB standard information database that includes equipment physical life parameters, clinical risk index, and related key spare parts inventory status information.

[0069] S20 collects equipment operation data in real time and converts the weighted operating conditions under different loads into cumulative equivalent damage degree through the nonlinear damage accumulation model logic node configured in the platform backend.

[0070] S30. Calculate the spare parts scarcity coefficient based on the real-time inventory status and average procurement cycle of the associated spare parts, and dynamically calculate the floating maintenance threshold of the equipment in conjunction with the clinical risk index.

[0071] S40, compare the cumulative equivalent damage degree with the floating maintenance threshold, and when the early warning condition is met, perform a soft locking operation on the spare parts and automatically generate a maintenance work order to trigger the early warning response.

[0072] In the process of constructing the multidimensional attribute data model (S10) of medical devices, the specific operations performed by the system are broken down into the following sub-steps:

[0073] S101, the system establishes a basic profile of the equipment in the OM04IB standard information database through a data interface or a manual input terminal on a low-code platform. This basic profile includes the equipment's static physical attributes, specifically covering the equipment's unique identifier (ID), manufacturer name, equipment model and specifications, factory serial number, and rated design parameters. The rated design parameters include the equipment's rated standard load. (For example, the rated heat capacity of the CT tube, the rated speed of the centrifuge, or the rated flow rate of the dialysis pump) and the maximum design life of critical components. .

[0074] S102, the system generates a clinical risk index for each device based on the clinical business rule base preset in the platform's logical nodes. The data is then written back to the OM04IB database. This quantification process is not arbitrarily assigned but executed based on a weighted scoring algorithm. The system obtains the departmental attribute of the equipment (e.g., emergency department weight is 1.5, general outpatient department weight is 1.0), the equipment's dependence on life support (e.g., ventilator weight is 2.0, electrocardiograph weight is 1.0), and whether the equipment has redundant backups (no backup single unit weight is 1.5, hot backup weight is 1.0). The system multiplies or adds the weights of the above items to obtain the final result. Value. In this embodiment, the value is set. The range of values ​​is That is, limiting the clinical risk index through algorithmic logic. The minimum value is 1.0 (corresponding to low-risk, non-life support devices with backups), which serves as the benchmark for mathematical operations.

[0075] S103, the system establishes a mapping table between equipment and critical spare parts in the OM04IB information database. This mapping table not only records the correspondence between equipment IDs and spare part IDs, but also synchronizes the current inventory quantity of spare parts from the supply chain management subsystem in real time. Safety stock level and the average lead time for procurement calculated based on historical procurement data.

[0076] In the process of real-time acquisition of equipment operation data and calculation of cumulative equivalent damage (S20), the system adopts the platform's built-in nonlinear operating condition weighted algorithm to accurately reflect physical losses, specifically including the following sub-steps:

[0077] S201, the system divides the device's operating timeline into continuous time windows. The length of this time window can be set according to the characteristics of the device (e.g., set to a check process every time the device is powered on, or a fixed duration of 1 hour).

[0078] S202, the system collects data for each time window. The internal operating data is used to calculate the average operating load. For fluctuating loads, the system uses the root mean square (RMS) algorithm to process the raw sampled data to obtain an equivalent average value, ensuring the representativeness of the load data.

[0079] S203, the system calls the damage calculation engine script deployed on the low-code platform backend to calculate the damage up to the current time according to the following formula. Cumulative equivalent damage

[0080] ;

[0081] in:

[0082] Representing the The duration of each time window;

[0083] This represents the average load within the window;

[0084] The rated load baseline for the device stored in the OM04IB library.

[0085] parameter This is a nonlinear damage factor used to characterize the accelerating effect of load stress on lifespan loss. Different preset parameters are used for equipment with different physical characteristics. Value: For components where heat effects dominate losses (such as X-ray tubes), set... (For example, taking 2.0) means that overload operation will lead to exponentially increasing lifespan loss; for components dominated by pure mechanical wear, a setting can be made. Approaching 1.0. By introducing this factor, this method achieves in-depth operating condition analysis on a general platform, overcoming the technical deficiency of traditional recording methods that cannot distinguish the differences in the impact of low-load and high-load operation on equipment lifespan.

[0086] In the process of dynamically calculating the floating maintenance threshold (S30) of the equipment, the system achieves adaptive adjustment of the maintenance strategy by coupling the supply chain status with clinical risks, specifically including the following sub-steps:

[0087] S301, the system calculates the spare parts scarcity coefficient based on the latest inventory data. This coefficient reflects the time cost risk of obtaining spare parts, and its calculation logic is as follows:

[0088] ;

[0089] When real-time inventory Below safe water level At that time, the system introduced the lead time for spare parts procurement. Compared with standard reference period The ratio of makes It becomes a value greater than 1. The longer the procurement cycle, the greater the scarcity coefficient.

[0090] Step S302: The system combines the clinical risk index from the OM04IB library. Spare parts scarcity coefficient The floating maintenance threshold is generated based on the following attenuation model. :

[0091] ;

[0092] in,

[0093] Maximum physical lifespan;

[0094] This is the threshold sensitivity coefficient (e.g., 0.1).

[0095] It is the natural logarithm function.

[0096] Based on S102 The minimum value is set to 1.0, and in S301 Setting the value to 1.0 when inventory is sufficient yields the following result: ≥1. According to the properties of logarithmic functions, when the independent variable is greater than or equal to 1, ≥0. The constraint is determined. ≤1, which is the calculated floating maintenance threshold. Always less than or equal to the equipment's maximum design life. This logically eliminates the abnormal situation of the maintenance threshold increasing in the opposite direction, and strictly meets the early warning purpose of preventive maintenance.

[0097] During the process of performing a soft lock operation on spare parts and triggering an early warning response (S40), the system establishes a closed loop from diagnostic analysis to work order generation, which specifically includes the following sub-steps:

[0098] S401, The system compares the cumulative equivalent damage in real time. With floating maintenance threshold The system sets early warning judgment factors. (e.g., 0.9). When detected At that time, the equipment is determined to have entered a high-risk warning period.

[0099] S402: Once a high-risk warning period begins, the system immediately queries the associated spare parts inventory. If the inventory is in a critical state (e.g., only one piece remains), the system performs an atomic operation in the database, changing the status field of the spare parts record from Available to Soft-Locked, and writing the lock ownership field to the ID of the current high-risk device. This ensures that, even without physical inventory movement, logical locking at the data level prevents routine maintenance requests from other low-risk devices from preempting the critical spare parts, thus achieving intelligent resource scheduling based on risk level.

[0100] S403, when Ultimately reach or exceed When the low-code platform automatically triggers the workflow, it generates a mandatory maintenance work order with the highest priority, and automatically associates the locked spare parts information with the equipment basic information in the OM04IB library, and pushes the work order to the engineer to perform on-site maintenance.

Claims

1. A medical device management system, characterized in that, include: The basic information management module is configured to store the static attributes and clinical risk index of medical devices. The dynamic monitoring and calculation module is configured to collect operating condition data of the medical device during operation and calculate the cumulative equivalent damage degree of the medical device based on a nonlinear damage model. The intelligent decision-making and resource control module is configured to monitor the real-time inventory status and procurement cycle of spare parts associated with the medical equipment, and dynamically generate floating maintenance thresholds in conjunction with the clinical risk index. The intelligent decision-making and resource control module is further configured to compare the cumulative equivalent damage degree with the floating maintenance threshold, and when the cumulative equivalent damage degree meets the early warning conditions set based on the floating maintenance threshold, to perform a resource locking operation on the spare part.

2. The medical device management system according to claim 1, characterized in that, The basic information management module further includes a risk assessment submodule, which is configured to generate the clinical risk index based on preset weighting rules. The weighting rules are based on the departmental attributes of the medical device, its life support dependence, and the device's redundancy backup status for numerical calculation.

3. The medical device management system according to claim 1, characterized in that, The dynamic monitoring and calculation module includes a damage calculation engine, which is configured to calculate the cumulative equivalent damage degree through a nonlinear operating condition weighted algorithm. The algorithm introduces a nonlinear damage factor to convert the ratio of the collected average operating load to the rated standard load into the damage increment per unit time.

4. A medical device management system according to claim 3, characterized in that, The damage calculation engine performs the calculation logic for the cumulative equivalent damage, including: Divide the equipment's operating time into continuous time windows; For each time window, calculate the ratio of the average operating load within that window to the rated standard load; The ratio is exponentially calculated using the nonlinear damage factor to obtain the load acceleration effect coefficient. Multiply the load acceleration effect coefficient by the corresponding time window duration to obtain the damage increment within the window. The cumulative equivalent damage is obtained by summing the damage increments over all time windows.

5. A medical device management system according to claim 1, characterized in that, The intelligent decision-making and resource control module includes an inventory monitoring unit, which is configured to calculate the spare parts scarcity coefficient. When the real-time inventory quantity of the spare parts is greater than or equal to the preset safety stock level, the spare parts scarcity coefficient is set as a baseline value. When the real-time inventory quantity of the spare parts is less than the safety stock level, the inventory monitoring unit calculates a spare parts scarcity coefficient that is greater than the benchmark value based on the ratio of the average procurement lead time of the spare parts to the benchmark procurement cycle.

6. A medical device management system according to claim 5, characterized in that, When the real-time inventory quantity is less than the safety stock level, the logic by which the inventory monitoring unit calculates the spare parts scarcity coefficient includes: Obtain the average lead time for procurement of the spare parts; Calculate the ratio of the average procurement lead time to the benchmark procurement cycle; Add the ratio to the value 1, and use the sum as the spare parts scarcity coefficient.

7. A medical device management system according to claim 1, characterized in that, The intelligent decision-making and resource control module includes a threshold generation unit, which is configured to construct a decay model based on the coupling of risk and inventory. The attenuation model is used to reduce the floating maintenance threshold according to the logarithmic attenuation law when the clinical risk index increases or the spare parts scarcity coefficient increases, so as to reserve a safe buffer period to deal with procurement delays before the equipment reaches its maximum physical life.

8. A medical device management system according to claim 1, characterized in that, The logic for the threshold generation unit to calculate the floating maintenance threshold includes: Calculate the product of the clinical risk index and the spare parts scarcity coefficient; Perform a natural logarithm operation on the product, and multiply the result by a preset threshold sensitivity coefficient to obtain the attenuation factor; Calculate the difference between the value 1 and the attenuation factor; The floating maintenance threshold is obtained by multiplying the difference by the maximum design life of the key components of the equipment.

9. A medical device management system according to claim 1, characterized in that, The intelligent decision-making and resource control module includes a resource locking unit, which is configured as follows: Set a warning judgment factor less than 1; When the cumulative equivalent damage level is detected to reach the product of the floating maintenance threshold and the early warning determination factor, it is determined that the early warning condition is met. In the database, the status identifier field of the spare part is changed from available to soft locked, and the spare part is bound to the unique identification code of the medical device.

10. A medical device management method, applied to a medical device management system according to any one of claims 1-9, characterized in that, Includes the following steps: Construct a multidimensional attribute data model for medical devices, the model including the static attributes and clinical risk index of the devices; The operating condition data of the medical device during operation are collected, and the cumulative equivalent damage degree of the medical device is calculated based on a nonlinear damage model. Monitor the real-time inventory status and procurement cycle of spare parts associated with the medical equipment, and dynamically generate floating maintenance thresholds in conjunction with the clinical risk index; The cumulative equivalent damage is compared with the floating maintenance threshold. When the cumulative equivalent damage meets the early warning conditions set based on the floating maintenance threshold, a resource locking operation is performed on the spare part.

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