A medical equipment maintenance service management system and method

Abstract

The application discloses a kind of medical equipment maintenance service management system and method for realizing intelligentization, belong to data management technical field.The application is by using correlation network to carry out node correlation to each level medical equipment, sets up data log library in each node, sets up data drainage port, utilizes port to carry out data drainage and obtains the equipment data of corresponding node;With the running time period and maintenance time period of equipment as table header index, the equipment data in corresponding period is classified to build corresponding running period data table and maintenance data table;The running life curve and maintenance wear curve of equipment are fitted using running period data table and maintenance data table;The running state of current medical equipment is comprehensively analyzed in combination with running life curve and maintenance wear curve, the health condition and real-time value of current medical equipment are judged, the correlation network is dynamically handled by layering;For analysis feedback result, medical equipment is maintained pertinently.

Description

Technical Field

[0001] This invention relates to the field of data management technology, specifically to an intelligent medical equipment maintenance service management system and method. Background Technology

[0002] Medical equipment maintenance service management refers to the systematic maintenance and upkeep activities of medical equipment used in medical institutions to ensure the normal operation of the equipment, extend its service life, protect patient safety, and improve the quality of medical services; it covers the entire life cycle from equipment procurement, installation, daily use, periodic inspection, fault repair to final disposal.

[0003] Currently, hospital medical equipment is characterized by its large quantity, intricacy, and fragility, making it highly dependent on equipment maintenance. However, in the current environment, most hospitals still rely on maintenance personnel to conduct regular inspections and maintenance of each piece of medical equipment. This method cannot conduct real-time inspections of all equipment or quickly grasp the health status of the equipment. Therefore, it is not only inefficient but also prone to ineffective maintenance. This results in a waste of human and material resources and the potential for untimely or missed inspections of medical equipment. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent medical equipment maintenance service management system and method to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A method for intelligent medical equipment maintenance service management, comprising the following steps:

[0007] S100. By using a service association network to associate nodes of medical equipment at all levels in the hospital, a maintenance and management equipment network is constructed. By setting up a data log library at each node and setting up a data diversion port, the data is comprehensively diverted through the port to obtain the equipment data of the corresponding node in the log library.

[0008] S200: Using the equipment's operating time cycle and maintenance time cycle as table header indexes respectively, the equipment data within the corresponding cycle is categorized and corresponding operating cycle data tables and maintenance data tables are constructed during data retrieval; the generated operating cycle data tables and maintenance data tables are used to fit the operating life curve and maintenance wear curve of the equipment within the current life cycle respectively.

[0009] S300 combines the operating life curve and maintenance wear curve of the corresponding medical equipment to comprehensively analyze the current operating status of the medical equipment, judge the current health status and real-time value of the medical equipment, and provide comprehensive feedback based on the judgment results to dynamically divide the associated network into layers.

[0010] S400: Based on the analysis feedback results, perform targeted maintenance operations on the medical equipment.

[0011] The steps of S100 are as follows: First, by associating medical devices at all levels in the hospital using a service association network to build a maintenance and management equipment network. Second, by setting up data log databases at each node and data diversion ports to comprehensively divert data, the specific steps for obtaining device data for the corresponding nodes in the log database are as follows:

[0012] S101. By expanding the hospital's local area network, a data service association subnet is constructed between various medical devices; in the data service association subnet, each medical device is treated as a node of the same level, and a dense network is formed through data interconnection between the nodes; corresponding node identity labels are assigned to each medical device in the association network.

[0013] S102. In the associated subnet, an independent data log library is allocated to each node;

[0014] S103. Data diversion ports are built into each device node of the associated network, and data at the node is diverted and retrieved through the diversion ports; the data diversion is achieved by packaging data from a single node or multiple nodes and outputting the data through the associated network using a planned route.

[0015] S200 uses the equipment's operating time cycle and maintenance time cycle as table header indexes, respectively, and categorizes the equipment data within the corresponding cycle to construct corresponding operating cycle data tables and maintenance data tables during data retrieval. The specific steps for fitting the operating life curve and maintenance wear curve of the equipment within the current life cycle using the generated operating cycle data tables and maintenance data tables are as follows:

[0016] S201. Within the working cycle of the medical equipment corresponding to the node, the equipment operation data of the current working cycle is recorded by generating a cycle operation data record form; within the equipment maintenance cycle, the equipment maintenance data of the corresponding cycle is recorded by generating a corresponding cycle maintenance data form; in the data log library, by allocating feature storage subspaces, the equipment operation data and maintenance data are used as first-level header indexes for comprehensive classification and retrieval of the corresponding data; in the comprehensive classification and retrieval of data, the equipment operation cycle or maintenance cycle is used as a second-level sub-header index for retrieving the medical equipment operation data or maintenance data of the target cycle;

[0017] S202. Generate a temporary periodic data list by taking the primary header index as the main feature label, the secondary header index as the secondary feature label, and the corresponding retrieved equipment data as the body data from the retrieved equipment data for each period of the retrieved equipment. By sequentially cross-splitting the multi-period data of a single device according to the operating cycle and maintenance cycle, construct the full-cycle operating life data and maintenance wear data of the current corresponding single device.

[0018] S203. Using curve fitting to a large model, the full-cycle operating life data and maintenance wear data of the corresponding equipment are input into the model, and a coordinate system is constructed at the output end to output the full-cycle operating life curve and maintenance wear data of the corresponding equipment respectively; by spatial mapping of the two curves, the two curves are merged and mapped to the same spatial coordinate system.

[0019] The S300 comprehensively analyzes the current operating status of the medical equipment by combining the operating life curve and maintenance wear curve of the corresponding medical equipment, judges the current health status and real-time value of the medical equipment, and provides comprehensive feedback based on the judgment results. The specific steps for the dynamic layering processing of the associated network are as follows:

[0020] S301. In the spatial coordinate system, the total service life coordinate axis of the equipment, constructed by the intersection of the operating cycle and the maintenance cycle, is used as the correlation axis of the two curves. By extending the coordinate points of the two curves on the correlation axis, the corresponding curve values ​​are obtained on different two-dimensional surfaces. The health status data area of ​​the current medical equipment at the current time point within its service life is constructed by connecting the curve values ​​of the corresponding coordinate points of the two curves. Since the operating life curve and the maintenance wear curve describe the state of the equipment during its use, the area constructed by the two curves in the spatial coordinate system can represent the current health status of the equipment.

[0021] S302. In a spatial coordinate system, the health values ​​of the equipment during its usage cycle are analyzed using a dynamic simulation method. Using the time point on the associated axis as the translation point, the points corresponding to the curve values ​​on the connecting lines are translated to obtain multiple health status data regions within the equipment's usage cycle. The health value of each region representing the medical equipment at the current time point is calculated using the formula Hel. n =(Lf n *Wt n ) / 2; where Hel n Lf represents the device health value at each time point. n Wt represents the equipment lifespan curve value at each time point. n This represents the equipment maintenance and wear curve value at each time point, where n is the time point on the associated axis.

[0022] S303. By incorporating the current operating life curve and maintenance wear curve of the medical equipment into the equipment fault analysis model, the corresponding equipment fault state function is obtained, the function expression of which is:

[0023] Where F(t) is the failure value of the medical equipment during its service life, δ rd Let δ be the random failure coefficient. ls Let t be the wear and tear failure coefficient, and t be the real-time usage time of the medical equipment. Since there are two types of failures during the use of medical equipment, one is random failure and the other is wear and tear failure. Since these two types of failures exist simultaneously under any environment and conditions, the failure value of the medical equipment at any point in time is always two.

[0024] Analyze the current health status of medical equipment, if (HEL) in -Hel n )≤F(t) rd If F(t) is true, then the health status of the device at this point in time is determined to be healthy; if F(t) is true... rd <(HEL in -Hel n )≤F(t) ls If the device's health status is slightly compromised at this point in time, then it is determined that the device is only slightly damaged; if (HEL) in -Hel n )>F(t) ls If the device's health status at this point in time is determined to be severely damaged, then n is considered to be equal to t, and F(t) is the device's health status at this point in time. rd Let F(t) be a random fault value. ls For wear and tear fault values, HEL in Initialize the health values ​​for each medical device; since random faults are accidental, their fault values ​​are generally less than those for wear and tear faults.

[0025] S304. Dynamically hierarchically divide the maintenance network based on the health status and value of medical equipment; assess the real-time residual value of each medical device using the following formula: Where VALL represents the current real-time remaining value of the medical equipment, V(pt) represents the initial value of the medical equipment, V(ma) represents the value per unit time after equipment maintenance, and T represents the initial estimated usable time of the equipment; the health value Hel of the equipment is used to measure the remaining value of the equipment. n The real-time health value ratio of each medical device is calculated independently using the real-time residual value (VALL), and the calculation formula is as follows: Where n and t take equal values; according to the calculation results, if HV≥HV midThen, determine that the network node corresponding to the current medical device is a first-level network node; if HV <HV mid If the current medical device is identified as a secondary network node, then the associated network node is determined to be a secondary network node. By dynamically partitioning the associated network based on primary and secondary network nodes respectively, a primary node network layer and a secondary node network layer are constructed. Among these, HV... mid The system pre-sets comparison parameters; since the health value and remaining value of the equipment change dynamically in real time, the corresponding node level also changes dynamically, and therefore the nodes in the network layer it constructs are dynamic; when calculating the impact of the equipment's health value ratio on the maintenance plan, since the equipment with the higher value should be maintained first when the health values ​​of the equipment are equal, the calculated health value ratio shows that the equipment with the higher value is lower; similarly, when the equipment values ​​are equal, the equipment with the lower health value is maintained first; therefore, the system uses 1 minus its health value ratio to obtain its corresponding complement as the judgment object.

[0026] The specific steps for S400 to perform targeted maintenance operations on medical equipment based on the analysis feedback results are as follows:

[0027] S401. The dynamic network layer partitioning results and the real-time health values ​​of the corresponding node devices are demonstrated on various medical devices using an association network.

[0028] S402. Relevant personnel obtain the current node level and health value of the medical equipment, and perform maintenance operations on the medical equipment according to the node level and health value; they prioritize maintaining the health values ​​of each device node in the primary network from low to high; and secondly, they maintain the health values ​​of each device node in the secondary network from low to high.

[0029] An intelligent medical equipment maintenance service management system, the system comprising a multi-device data acquisition module, a data preprocessing module, a multi-data comprehensive analysis module, and a device status feedback module;

[0030] The multi-device data acquisition module connects medical devices at all levels in the hospital through a service association network to construct a maintenance and management device network. It sets up data log libraries at each node and data flow ports to comprehensively import data from the corresponding nodes in the log libraries. The data preprocessing module uses the device's operating time cycle and maintenance time cycle as table header indexes. During data retrieval, it categorizes the device data within the corresponding cycle to construct corresponding operating cycle data tables and maintenance data tables. These tables are then used to fit the operating life curve and maintenance wear curve of the device within its current life cycle. The multi-data comprehensive analysis module combines the operating life curve and maintenance wear curve of the corresponding medical device to comprehensively analyze its current operating status, determine its health condition and real-time value, and provide comprehensive feedback based on the results. It also dynamically stratifies the association network. The device status feedback module performs targeted maintenance operations on the medical devices based on the analysis feedback results.

[0031] The multi-device data acquisition module includes an association network construction unit, a data log library allocation unit, and a data diversion port. The association network construction unit extends the hospital's local area network to construct a data service association subnet between various medical devices. Within this subnet, each medical device is treated as a peer-to-peer node, and a dense network is formed through data interconnection between these nodes. Each medical device is assigned a corresponding node identity label within the association network. The data log library allocation unit allocates an independent data log library to each node within the association subnet. The data diversion port, by embedding data diversion ports at each device node in the association network, diverts and retrieves data from the nodes. Data diversion involves packaging data from one or more nodes and outputting the data via a planned route through the association network.

[0032] The data preprocessing module includes a periodic data record unit generation unit, a periodic data processing unit, and a curve fitting unit. The periodic data record unit generation unit records the equipment operation data for the current work cycle by generating a periodic operation data record form within the corresponding medical equipment work cycle. Within the equipment maintenance cycle, it records the equipment maintenance data for the corresponding cycle by generating a corresponding periodic maintenance data form. In the data log library, by allocating a feature storage subspace, the system uses the equipment operation data and maintenance data as primary header indexes for comprehensive classification and retrieval of the corresponding data. In the comprehensive classification and retrieval of data, the system uses the equipment operation cycle or maintenance cycle as a secondary sub-header index for retrieving the medical equipment operation data or maintenance data for the target cycle. The periodic data processing unit retrieves the periodic data from each device, using the primary header index as the main feature label, the secondary header index as the secondary feature label, and the corresponding retrieved device data as the body data to generate a temporary periodic data list. By sequentially cross-splitting the multi-period data of a single device according to the operating cycle and maintenance cycle, it constructs the full-cycle operating life data and maintenance wear data for the current single device. The curve fitting unit utilizes a large curve fitting model, inputting the full-cycle operating life data and maintenance wear data of the corresponding device into the model, and constructing a coordinate system at the output end to output the full-cycle operating life curve and maintenance wear data of the corresponding device, respectively. Through spatial mapping of the two curves, they are merged and mapped to the same spatial coordinate system.

[0033] The multi-data comprehensive analysis module includes a curve data comprehensive analysis unit, an equipment health status analysis unit, an equipment real-time value analysis unit, and a relational network hierarchy partitioning unit. In the spatial coordinate system, the curve data comprehensive analysis unit uses the total equipment usage cycle coordinate axis (constructed by the intersection of the operating cycle and maintenance cycle) as the relational axis for two curves. By extending the coordinate points of the two curves on the relational axis, corresponding curve values ​​are obtained on different two-dimensional surfaces. The curve values ​​of the corresponding coordinate points of the two curves are used as endpoints to connect and construct the health status data region of the current time point within the current usage cycle of the medical equipment. The equipment health status analysis unit analyzes the health values ​​of the equipment's usage cycle using dynamic simulation in the spatial coordinate system. Using the time point on the relational axis as the translation point, the corresponding curve values ​​of the connecting lines on the curves are maintained. The system performs translation to acquire multiple health status data regions within the equipment's usage cycle; calculates the health value of each region representing the medical equipment at the current time point; obtains the corresponding equipment's fault status function by introducing the current medical equipment's operating life curve and maintenance wear curve into the equipment fault analysis model; analyzes the current medical equipment's health status by combining the current medical equipment's health value and fault data; the equipment real-time value analysis unit dynamically divides the maintenance association network by combining the medical equipment's health status and value; evaluates the real-time residual value of each medical equipment; the association network hierarchy division unit independently calculates the real-time health value ratio of each medical equipment by numerically calculating the health value and real-time residual value of the equipment, and divides the node level of each equipment and constructs the corresponding hierarchical network based on the calculation results.

[0034] The device status feedback module includes a comprehensive analysis feedback unit and a device maintenance planning unit. The comprehensive analysis feedback unit demonstrates the dynamic network layer partitioning results and the real-time health values ​​of the corresponding node devices on each medical device using the associated network. The device maintenance planning unit helps relevant personnel obtain the current node level and health value of the medical device and performs maintenance operations on the medical device according to the node level and health value. It prioritizes maintaining the health values ​​of each device node in the primary network from low to high, and then maintains the health values ​​of each device node in the secondary network from low to high.

[0035] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention collects and analyzes data from medical devices through multiple modules, completing the construction of a network of interconnected medical devices. By analyzing the real-time periodic data of each medical device, its real-time health status and value are analyzed. Based on the analysis results, the network nodes are classified and corresponding network hierarchies are constructed. This invention can adaptively perform maintenance and inspection on each device in the interconnected network within a group of medical devices. By combining the health status and remaining value of each device, the maintenance and inspection levels of the devices are classified, and maintenance plans are formulated based on the levels. This invention can help maintenance personnel perform accurate target detection and status display in the maintenance and inspection of a large number of medical devices, and through the interconnected network, it can help maintenance personnel control the status of all device nodes in the entire interconnected network at each node. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0037] Figure 1 This is a schematic diagram of the structure of an intelligent medical equipment maintenance service management system according to the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0039] Please see Figure 1 The present invention provides the following technical solution:

[0040] A method for intelligent medical equipment maintenance service management, comprising the following steps:

[0041] S100. By using a service association network to associate nodes of medical equipment at all levels in the hospital, a maintenance and management equipment network is constructed. By setting up a data log library at each node and setting up a data diversion port, the data is comprehensively diverted through the port to obtain the equipment data of the corresponding node in the log library.

[0042] S200: Using the equipment's operating time cycle and maintenance time cycle as table header indexes respectively, the equipment data within the corresponding cycle is categorized and corresponding operating cycle data tables and maintenance data tables are constructed during data retrieval; the generated operating cycle data tables and maintenance data tables are used to fit the operating life curve and maintenance wear curve of the equipment within the current life cycle respectively.

[0043] S300 combines the operating life curve and maintenance wear curve of the corresponding medical equipment to comprehensively analyze the current operating status of the medical equipment, judge the current health status and real-time value of the medical equipment, and provide comprehensive feedback based on the judgment results to dynamically divide the associated network into layers.

[0044] S400: Based on the analysis feedback results, perform targeted maintenance operations on the medical equipment.

[0045] The steps of S100 are as follows: First, by associating medical devices at all levels in the hospital using a service association network to build a maintenance and management equipment network. Second, by setting up data log databases at each node and data diversion ports to comprehensively divert data, the specific steps for obtaining device data for the corresponding nodes in the log database are as follows:

[0046] S101. By expanding the hospital's local area network, a data service association subnet is constructed between various medical devices; in the data service association subnet, each medical device is treated as a node of the same level, and a dense network is formed through data interconnection between the nodes; corresponding node identity labels are assigned to each medical device in the association network.

[0047] S102. In the associated subnet, an independent data log library is allocated to each node;

[0048] S103. Data diversion ports are built into each device node of the associated network, and data at the node is diverted and retrieved through the diversion ports; the data diversion is achieved by packaging data from a single node or multiple nodes and outputting the data through the associated network using a planned route.

[0049] S200 uses the equipment's operating time cycle and maintenance time cycle as table header indexes, respectively, and categorizes the equipment data within the corresponding cycle to construct corresponding operating cycle data tables and maintenance data tables during data retrieval. The specific steps for fitting the operating life curve and maintenance wear curve of the equipment within the current life cycle using the generated operating cycle data tables and maintenance data tables are as follows:

[0050] S201. Within the working cycle of the medical equipment corresponding to the node, the equipment operation data of the current working cycle is recorded by generating a cycle operation data record form; within the equipment maintenance cycle, the equipment maintenance data of the corresponding cycle is recorded by generating a corresponding cycle maintenance data form; in the data log library, by allocating feature storage subspaces, the equipment operation data and maintenance data are used as first-level header indexes for comprehensive classification and retrieval of the corresponding data; in the comprehensive classification and retrieval of data, the equipment operation cycle or maintenance cycle is used as a second-level sub-header index for retrieving the medical equipment operation data or maintenance data of the target cycle;

[0051] S202. Generate a temporary periodic data list by taking the primary header index as the main feature label, the secondary header index as the secondary feature label, and the corresponding retrieved equipment data as the body data from the retrieved equipment data for each period of the retrieved equipment. By sequentially cross-splitting the multi-period data of a single device according to the operating cycle and maintenance cycle, construct the full-cycle operating life data and maintenance wear data of the current corresponding single device.

[0052] S203. Using curve fitting to a large model, the full-cycle operating life data and maintenance wear data of the corresponding equipment are input into the model, and a coordinate system is constructed at the output end to output the full-cycle operating life curve and maintenance wear data of the corresponding equipment respectively; by spatial mapping of the two curves, the two curves are merged and mapped to the same spatial coordinate system.

[0053] The S300 comprehensively analyzes the current operating status of the medical equipment by combining the operating life curve and maintenance wear curve of the corresponding medical equipment, judges the current health status and real-time value of the medical equipment, and provides comprehensive feedback based on the judgment results. The specific steps for the dynamic layering processing of the associated network are as follows:

[0054] S301. In the spatial coordinate system, the total service life coordinate axis of the equipment, which is constructed by the intersection of the operation cycle and the maintenance cycle, is used as the correlation axis of the two curves. By extending the coordinate points of the two curves on the correlation axis, the corresponding curve values ​​are obtained on different two-dimensional surfaces. The health status data area of ​​the current medical equipment at the current time point within the service life is constructed by connecting the curve values ​​of the corresponding coordinate points of the two curves as endpoints.

[0055] S302. In a spatial coordinate system, the health values ​​of the equipment during its usage cycle are analyzed using a dynamic simulation method. Using the time point on the associated axis as the translation point, the points corresponding to the curve values ​​on the connecting lines are translated to obtain multiple health status data regions within the equipment's usage cycle. The health value of each region representing the medical equipment at the current time point is calculated using the formula Hel. n =(Lf n *Wt n) / 2; where Hel n Lf represents the device health value at each time point. n Wt represents the equipment lifespan curve value at each time point. n This represents the equipment maintenance and wear curve value at each time point, where n is the time point on the associated axis.

[0056] S303. By incorporating the current operating life curve and maintenance wear curve of the medical equipment into the equipment fault analysis model, the corresponding equipment fault state function is obtained, the function expression of which is:

[0057] Where F(t) is the failure value of the medical equipment during its service life, δ rd Let δ be the random failure coefficient. ls The wear and tear failure coefficient is denoted by t, and t is the real-time usage time of the medical equipment.

[0058] Analyze the current health status of medical equipment, if (HEL) in -Hel n )≤F(t) rd If F(t) is true, then the health status of the device at this point in time is determined to be healthy; if F(t) is true... rd <(HEL in -Hel n )≤F(t) ls If the device's health status is slightly compromised at this point in time, then it is determined that the device is only slightly damaged; if (HEL) in -Hel n )>F(t) ls If the device's health status at this point in time is determined to be severely damaged, then n is considered to be equal to t, and F(t) is the device's health status at this point in time. rd Let F(t) be a random fault value. ls For wear and tear fault values, HEL in Initialize the health values ​​of each medical device;

[0059] S304. Dynamically hierarchically divide the maintenance network based on the health status and value of medical equipment; assess the real-time residual value of each medical device using the following formula: Where VALL represents the current real-time remaining value of the medical equipment, V(pt) represents the initial value of the medical equipment, V(ma) represents the value per unit time after equipment maintenance, and T represents the initial estimated usable time of the equipment; the health value Hel of the equipment is used to measure the remaining value of the equipment. n The real-time health value ratio of each medical device is calculated independently using the real-time residual value (VALL), and the calculation formula is as follows: Where n and t take equal values; according to the calculation results, if HV≥HV midThen, determine that the network node corresponding to the current medical device is a first-level network node; if HV <HV mid If the current medical device is identified as a secondary network node, then the associated network node is determined to be a secondary network node. By dynamically partitioning the associated network based on primary and secondary network nodes respectively, a primary node network layer and a secondary node network layer are constructed. Among these, HV... mid Set up comparison parameters for the system.

[0060] The specific steps for S400 to perform targeted maintenance on medical equipment based on the analysis feedback results are as follows:

[0061] S401. The dynamic network layer partitioning results and the real-time health values ​​of the corresponding node devices are demonstrated on various medical devices using an association network.

[0062] S402. Relevant personnel obtain the current node level and health value of the medical equipment, and perform maintenance operations on the medical equipment according to the node level and health value; they prioritize maintaining the health values ​​of each device node in the primary network from low to high; and secondly, they maintain the health values ​​of each device node in the secondary network from low to high.

[0063] An intelligent medical equipment maintenance service management system, the system comprising a multi-device data acquisition module, a data preprocessing module, a multi-data comprehensive analysis module, and a device status feedback module;

[0064] The multi-device data acquisition module connects medical devices at all levels in the hospital through a service association network to construct a maintenance and management device network. It sets up data log libraries at each node and data flow ports to comprehensively import data from the corresponding nodes in the log libraries. The data preprocessing module uses the device's operating time cycle and maintenance time cycle as table header indexes. During data retrieval, it categorizes the device data within the corresponding cycle to construct corresponding operating cycle data tables and maintenance data tables. These tables are then used to fit the operating life curve and maintenance wear curve of the device within its current life cycle. The multi-data comprehensive analysis module combines the operating life curve and maintenance wear curve of the corresponding medical device to comprehensively analyze its current operating status, determine its health condition and real-time value, and provide comprehensive feedback based on the results. It also dynamically stratifies the association network. The device status feedback module performs targeted maintenance operations on the medical devices based on the analysis feedback results.

[0065] The multi-device data acquisition module includes an association network construction unit, a data log library allocation unit, and a data diversion port. The association network construction unit extends the hospital's local area network to construct a data service association subnet between various medical devices. Within this subnet, each medical device is treated as a peer-to-peer node, and a dense network is formed through data interconnection between these nodes. Each medical device is assigned a corresponding node identity label within the association network. The data log library allocation unit allocates an independent data log library to each node within the association subnet. The data diversion port, by embedding data diversion ports at each device node in the association network, diverts and retrieves data from the nodes. Data diversion involves packaging data from one or more nodes and outputting the data via a planned route through the association network.

[0066] The data preprocessing module includes a periodic data record unit generation unit, a periodic data processing unit, and a curve fitting unit. The periodic data record unit generation unit records the equipment operation data for the current work cycle by generating a periodic operation data record form within the corresponding medical equipment work cycle. Within the equipment maintenance cycle, it records the equipment maintenance data for the corresponding cycle by generating a corresponding periodic maintenance data form. In the data log library, by allocating a feature storage subspace, the system uses the equipment operation data and maintenance data as primary header indexes for comprehensive classification and retrieval of the corresponding data. In the comprehensive classification and retrieval of data, the system uses the equipment operation cycle or maintenance cycle as a secondary sub-header index for retrieving the medical equipment operation data or maintenance data for the target cycle. The periodic data processing unit retrieves the periodic data from each device, using the primary header index as the main feature label, the secondary header index as the secondary feature label, and the corresponding retrieved device data as the body data to generate a temporary periodic data list. By sequentially cross-splitting the multi-period data of a single device according to the operating cycle and maintenance cycle, it constructs the full-cycle operating life data and maintenance wear data for the current single device. The curve fitting unit utilizes a large curve fitting model, inputting the full-cycle operating life data and maintenance wear data of the corresponding device into the model, and constructing a coordinate system at the output end to output the full-cycle operating life curve and maintenance wear data of the corresponding device, respectively. Through spatial mapping of the two curves, they are merged and mapped to the same spatial coordinate system.

[0067] The multi-data comprehensive analysis module includes a curve data comprehensive analysis unit, an equipment health status analysis unit, an equipment real-time value analysis unit, and a relational network hierarchy partitioning unit. In the spatial coordinate system, the curve data comprehensive analysis unit uses the total equipment usage cycle coordinate axis (constructed by the intersection of the operating cycle and maintenance cycle) as the relational axis for two curves. By extending the coordinate points of the two curves on the relational axis, corresponding curve values ​​are obtained on different two-dimensional surfaces. The curve values ​​of the corresponding coordinate points of the two curves are used as endpoints to connect and construct the health status data region of the current time point within the current usage cycle of the medical equipment. The equipment health status analysis unit analyzes the health values ​​of the equipment's usage cycle using dynamic simulation in the spatial coordinate system. Using the time point on the relational axis as the translation point, the corresponding curve values ​​of the connecting lines on the curves are maintained. The system performs translation to acquire multiple health status data regions within the equipment's usage cycle; calculates the health value of each region representing the medical equipment at the current time point; obtains the corresponding equipment's fault status function by introducing the current medical equipment's operating life curve and maintenance wear curve into the equipment fault analysis model; analyzes the current medical equipment's health status by combining the current medical equipment's health value and fault data; the equipment real-time value analysis unit dynamically divides the maintenance association network by combining the medical equipment's health status and value; evaluates the real-time residual value of each medical equipment; the association network hierarchy division unit independently calculates the real-time health value ratio of each medical equipment by numerically calculating the health value and real-time residual value of the equipment, and divides the node level of each equipment and constructs the corresponding hierarchical network based on the calculation results.

[0068] The device status feedback module includes a comprehensive analysis feedback unit and a device maintenance planning unit. The comprehensive analysis feedback unit demonstrates the dynamic network layer partitioning results and the real-time health values ​​of the corresponding node devices on each medical device using the associated network. The device maintenance planning unit helps relevant personnel obtain the current node level and health value of the medical device and performs maintenance operations on the medical device according to the node level and health value. It prioritizes maintaining the health values ​​of each device node in the primary network from low to high, and then maintains the health values ​​of each device node in the secondary network from low to high.

[0069] In the embodiment:

[0070] A hospital has implemented an intelligent medical equipment maintenance service management system to manage and maintain various medical devices. Firstly, the hospital's local area network (LAN) is expanded to construct a data service association subnet connecting the various medical devices. Within this subnet, each medical device is treated as a peer-to-peer node, and a dense network is formed through data interconnection between these nodes. Each medical device is assigned a corresponding node identity label within the association network. An independent data log database is allocated to each node within the association subnet. Data flow ports are built into each device node in the association network, allowing data to be retrieved and fed back from the nodes.

[0071] Within the working cycle of the medical equipment corresponding to a node, the equipment operation data for the current working cycle is recorded by generating a cycle operation data record form; within the equipment maintenance cycle, the equipment maintenance data for the corresponding cycle is recorded by generating a corresponding cycle maintenance data form; in the data log library, by allocating feature storage subspaces, the equipment operation data and maintenance data are used as first-level header indexes for comprehensive classification and retrieval of the corresponding data; in the comprehensive classification and retrieval of data, the equipment operation cycle or maintenance cycle is used as a second-level sub-header index for retrieving the medical equipment operation data or maintenance data for the target cycle; the retrieved equipment data for each cycle is then sorted by first-level... The header index serves as the primary feature label, the secondary header index as the secondary feature label, and the corresponding retrieved device data as the body data to generate a temporary periodic data list. By sequentially cross-stitching multi-period data from a single device according to the operating cycle and maintenance cycle, the full-cycle operating life data and maintenance wear data for the current single device are constructed. A large model is then fitted with curves. The full-cycle operating life data and maintenance wear data of the corresponding device are input into the model, and a coordinate system is constructed at the output end to output the full-cycle operating life curve and maintenance wear data for the corresponding device, respectively. Finally, the two curves are spatially mapped and merged into the same spatial coordinate system.

[0072] In a spatial coordinate system, the total service life axis of the equipment, constructed by the intersection of the operating cycle and the maintenance cycle, serves as the correlation axis between two curves. By extending the coordinate points of the two curves on the correlation axis, corresponding curve values ​​are obtained on different two-dimensional surfaces. Using the curve values ​​of the corresponding coordinate points of the two curves as endpoints, a health status data region for the current time point within the current service life of the medical equipment is constructed. In the spatial coordinate system, the health values ​​of the equipment's service life are analyzed using dynamic simulation. Using the time point on the correlation axis as the translation point, the points corresponding to the curve values ​​of the connecting lines are translated to obtain multiple health status data regions within the equipment's service life. The health value of the medical equipment at the current time point is calculated for each region, using the formula Hel. n =(Lf n *Wtn ) / 2; By incorporating the current operating life curve and maintenance wear curve of the medical equipment into the equipment fault analysis model, the corresponding fault state function of the equipment is obtained, and its function expression is: Analyze the current health status of medical equipment; for example, at a certain moment, the health value of a certain medical device is 60; its corresponding random failure value is 20, and its wear and tear failure value is 70; calculate the current failure value of the device (HEL). in -Hel n The calculated result is 40; where HEL in If F(t) is 100, then... rd <(HEL in -Hel n )≤F(t) ls If the health status of the equipment at this point in time is slightly deteriorated, then the maintenance network is dynamically hierarchically divided based on the health status and value of the medical equipment; the real-time residual value of each medical device is assessed using the following formula:

[0073] If the current real-time remaining value of the device is 90, then the device's health value (Hel) will be monitored. n The real-time health value ratio of each medical device is calculated independently using the real-time residual value (VALL), and the calculation formula is as follows: The calculated result is 0.33; due to HV <HV mid HV mid If the value is 0.5, then the current device node is a secondary node and is assigned to the secondary node network layer.

[0074] The dynamic network layer partitioning results and the real-time health values ​​of corresponding node devices are demonstrated on various medical devices using an interconnected network. Personnel obtain the current node level and health value of each medical device and perform maintenance operations based on these values. Maintenance is prioritized for each device node in the primary network, from low to high health values; secondly, maintenance is performed for each device node in the secondary network, also from low to high health values.

[0075] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0076] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent medical equipment maintenance service management, characterized in that: The method includes the following steps: S100. By using a network of connections to link medical equipment at all levels in the hospital, a maintenance and management equipment network is constructed. By setting up a data log library at each node and setting up a data flow port, the data is comprehensively flowed through the port to obtain the equipment data of the corresponding node in the log library. S200: Using the equipment's operating time cycle and maintenance time cycle as table header indexes respectively, the equipment data within the corresponding cycle is categorized and corresponding operating cycle data tables and maintenance data tables are constructed during data retrieval; the generated operating cycle data tables and maintenance data tables are used to fit the operating life curve and maintenance wear curve of the equipment within the current life cycle respectively. S300 combines the operating life curve and maintenance wear curve of the corresponding medical equipment to comprehensively analyze the current operating status of the medical equipment, judge the current health status and real-time value of the medical equipment, and provide comprehensive feedback based on the judgment results to dynamically divide the associated network into layers. S400: Based on the analysis feedback results, perform targeted maintenance operations on the medical equipment.

2. The method for intelligent medical equipment maintenance service management according to claim 1, characterized in that: The steps of S100 are as follows: First, by associating medical devices at all levels in the hospital using a network of connections, a maintenance and management equipment network is constructed. Second, by setting up data log databases at each node and data diversion ports, data is comprehensively diverted through these ports to obtain the device data for the corresponding node in the log database. S101. By expanding the hospital's local area network, a data service association subnet is constructed between various medical devices; in the data service association subnet, each medical device is treated as a node of the same level, and a dense network is formed through data interconnection between the nodes; corresponding node identity labels are assigned to each medical device in the association network. S102. In the associated subnet, an independent data log library is allocated to each node; S103. Data diversion ports are built into each device node of the associated network, and data at the node is diverted and retrieved through the diversion ports; the data diversion is achieved by packaging data from a single node or multiple nodes and outputting the data through the associated network using a planned route.

3. The method for intelligent medical equipment maintenance service management according to claim 2, characterized in that: S200 uses the equipment's operating time cycle and maintenance time cycle as table header indexes, respectively, and categorizes the equipment data within the corresponding cycle to construct corresponding operating cycle data tables and maintenance data tables during data retrieval. The specific steps for fitting the operating life curve and maintenance wear curve of the equipment within the current life cycle using the generated operating cycle data tables and maintenance data tables are as follows: S201. Within the working cycle of the medical equipment corresponding to the node, the equipment operation data of the current working cycle is recorded by generating a cycle operation data record form; within the equipment maintenance cycle, the equipment maintenance data of the corresponding cycle is recorded by generating a corresponding cycle maintenance data form; in the data log library, by allocating feature storage subspaces, the equipment operation data and maintenance data are used as first-level header indexes for comprehensive classification and retrieval of the corresponding data; in the comprehensive classification and retrieval of data, the equipment operation cycle or maintenance cycle is used as a second-level sub-header index for retrieving the medical equipment operation data or maintenance data of the target cycle; S202. Generate a temporary periodic data list by taking the primary header index as the main feature label, the secondary header index as the secondary feature label, and the corresponding retrieved equipment data as the body data from the retrieved equipment data for each period of the retrieved equipment. By sequentially cross-splitting the multi-period data of a single device according to the operating cycle and maintenance cycle, construct the full-cycle operating life data and maintenance wear data of the current corresponding single device. S203. Using curve fitting to a large model, the full-cycle operating life data and maintenance wear data of the corresponding equipment are input into the model, and a coordinate system is constructed at the output end to output the full-cycle operating life curve and maintenance wear data of the corresponding equipment respectively; by spatial mapping of the two curves, the two curves are merged and mapped to the same spatial coordinate system.

4. The intelligent medical equipment maintenance service management method according to claim 3, characterized in that: The S300 comprehensively analyzes the current operating status of the medical equipment by combining the operating life curve and maintenance wear curve of the corresponding medical equipment, judges the current health status and real-time value of the medical equipment, and provides comprehensive feedback based on the judgment results. The specific steps for the dynamic layering processing of the associated network are as follows: S301. In the spatial coordinate system, the total service life coordinate axis of the equipment, which is constructed by the intersection of the operation cycle and the maintenance cycle, is used as the correlation axis of the two curves. By extending the coordinate points of the two curves on the correlation axis, the corresponding curve values ​​are obtained on different two-dimensional surfaces. The health status data area of ​​the current medical equipment at the current time point within the service life is constructed by connecting the curve values ​​of the corresponding coordinate points of the two curves as endpoints. S302. In the spatial coordinate system, the health values ​​of the equipment during its service life are analyzed using dynamic simulation. Using the time point on the associated axis as the translation point, the points corresponding to the curve values ​​of the connecting lines on the curve are translated to obtain multiple health status data areas within the equipment's service life. Calculate the health value of representative medical devices in each region at the current time point using the formula Hel. n =(Lf n *Wt n ) / 2; where Hel n Lf represents the device health value at each time point. n Wt represents the equipment lifespan curve value at each time point. n This represents the equipment maintenance and wear curve value at each time point, where n is the time point on the associated axis. S303. By incorporating the current operating life curve and maintenance wear curve of the medical equipment into the equipment fault analysis model, the corresponding equipment fault state function is obtained, the function expression of which is: Where F(t) is the failure value of the medical equipment during its service life, δ rd Let δ be the random failure coefficient. ls The wear and tear factor is t, and the real-time usage time of the medical equipment is t. Analyze the current health status of medical equipment, if (HEL) in -Hel n )≤F(t) rd If F(t) is true, then the health status of the device at this point in time is determined to be healthy; if F(t) is true... rd <(HEL in -Hel n )≤F(t) ls If the device's health status is slightly compromised at this point in time, then it is determined that the device is only slightly damaged; if (HEL) in -Hel n )>F(t) ls If the device's health status at this point in time is determined to be severely damaged, then n is considered to be equal to t, and F(t) is the device's health status at this point in time. rd Let F(t) be a random fault value. ls For wear and tear fault values, HEL in Initialize the health values ​​of each medical device; S304. Dynamically hierarchically divide the maintenance network based on the health status and value of medical equipment; assess the real-time residual value of each medical device using the following formula: Where VALL represents the current real-time remaining value of the medical equipment, V(pt) represents the initial value of the medical equipment, V(ma) represents the value per unit time after equipment maintenance, and T represents the initial estimated usable time of the equipment; the health value Hel of the equipment is used to measure the remaining value of the equipment. n The real-time health value ratio of each medical device is calculated independently using the real-time residual value (VALL), and the calculation formula is as follows: Where n and t take equal values; according to the calculation results, if HV≥HV mid Then, determine that the network node corresponding to the current medical device is a first-level network node; if HV <HV mid If the current medical device is identified as a secondary network node, then the associated network node is determined to be a secondary network node. By dynamically partitioning the associated network based on primary and secondary network nodes respectively, a primary node network layer and a secondary node network layer are constructed. Among these, HV... mid Preset comparison parameters for the system.

5. The intelligent medical equipment maintenance service management method according to claim 4, characterized in that: The specific steps for S400 to perform targeted maintenance operations on medical equipment based on the analysis feedback results are as follows: S401. The dynamic network layer partitioning results and the real-time health values ​​of the corresponding node devices are demonstrated on various medical devices using an association network. S402. Relevant personnel obtain the current node level and health value of the medical equipment, and perform maintenance operations on the medical equipment according to the node level and health value; they prioritize maintenance of the health values ​​of each device node in the primary network from low to high. Secondly, the health values ​​of each device node in the secondary network are maintained from low to high.

6. A smart medical equipment maintenance service management system, characterized in that: The system includes a multi-device data acquisition module, a data preprocessing module, a multi-data comprehensive analysis module, and a device status feedback module; The multi-device data acquisition module connects medical devices at all levels in the hospital through a service association network to construct a maintenance and management device network. It sets up data log libraries at each node and data flow ports to comprehensively import data from the corresponding nodes in the log libraries. The data preprocessing module uses the device's operating time cycle and maintenance time cycle as table header indexes. During data retrieval, it categorizes the device data within the corresponding cycle to construct corresponding operating cycle data tables and maintenance data tables. These tables are then used to fit the operating life curve and maintenance wear curve of the device within its current life cycle. The multi-data comprehensive analysis module combines the operating life curve and maintenance wear curve of the corresponding medical device to comprehensively analyze its current operating status, determine its health condition and real-time value, and provide comprehensive feedback based on the results. It also dynamically stratifies the association network. The device status feedback module performs targeted maintenance operations on the medical devices based on the analysis feedback results.

7. The intelligent medical equipment maintenance service management system according to claim 6, characterized in that: The multi-device data acquisition module includes an association network construction unit, a data log library allocation unit, and a data diversion port. The association network construction unit extends the hospital's local area network to construct a data service association subnet between various medical devices. Within this subnet, each medical device is treated as a peer-to-peer node, and a dense network is formed through data interconnection between these nodes. Each medical device is assigned a corresponding node identity label within the association network. The data log library allocation unit allocates an independent data log library to each node within the association subnet. The data diversion port, by embedding data diversion ports at each device node in the association network, diverts and retrieves data from the nodes. Data diversion involves packaging data from one or more nodes and outputting the data via a planned route through the association network.

8. The intelligent medical equipment maintenance service management system according to claim 7, characterized in that: The data preprocessing module includes a periodic data record unit generation unit, a periodic data processing unit, and a curve fitting unit. The periodic data record unit generation unit records the equipment operation data for the current work cycle by generating a periodic operation data record form within the corresponding medical equipment work cycle. Within the equipment maintenance cycle, it records the equipment maintenance data for the corresponding cycle by generating a corresponding periodic maintenance data form. In the data log library, by allocating a feature storage subspace, the system uses the equipment operation data and maintenance data as primary header indexes for comprehensive classification and retrieval of the corresponding data. In the comprehensive classification and retrieval of data, the system uses the equipment operation cycle or maintenance cycle as a secondary sub-header index for retrieving the medical equipment operation data or maintenance data for the target cycle. The periodic data processing unit retrieves the periodic data from each device, using the primary header index as the main feature label, the secondary header index as the secondary feature label, and the corresponding retrieved device data as the body data to generate a temporary periodic data list. By sequentially cross-splitting the multi-period data of a single device according to the operating cycle and maintenance cycle, it constructs the full-cycle operating life data and maintenance wear data for the current single device. The curve fitting unit utilizes a large curve fitting model, inputting the full-cycle operating life data and maintenance wear data of the corresponding device into the model, and constructing a coordinate system at the output end to output the full-cycle operating life curve and maintenance wear data of the corresponding device, respectively. Through spatial mapping of the two curves, they are merged and mapped to the same spatial coordinate system.

9. The intelligent medical equipment maintenance service management system according to claim 8, characterized in that: The multi-data comprehensive analysis module includes a curve data comprehensive analysis unit, an equipment health status analysis unit, an equipment real-time value analysis unit, and a relational network hierarchy division unit. In the spatial coordinate system, the curve data comprehensive analysis unit uses the total equipment usage cycle coordinate axis, constructed by the intersection of the operating cycle and maintenance cycle, as the correlation axis between two curves. By extending the coordinate points of the two curves on the correlation axis, corresponding curve values ​​are obtained on different two-dimensional surfaces. The curve values ​​of the corresponding coordinate points of the two curves are used as endpoints to connect and construct the health status data region of the current medical equipment at the current time point within its usage cycle. The equipment health status analysis unit analyzes the health values ​​of the equipment's usage cycle using a dynamic simulation method in the spatial coordinate system. Using the time point on the correlation axis as the translation point, the points corresponding to the curve values ​​of the connecting lines on the curve are translated to obtain multiple health status data areas within the equipment's usage cycle; the health value of the medical device at the current time point is calculated for each area. By incorporating the current operating life curve and maintenance wear curve of medical equipment into the equipment failure analysis model, the failure state function of the corresponding equipment is obtained; the health status of the current medical equipment is analyzed by combining the current health value and failure data; the real-time value analysis unit of the equipment dynamically divides the maintenance association network according to the health status and value of the medical equipment; the real-time residual value of each medical equipment is evaluated; the association network hierarchy division unit independently calculates the real-time health value ratio of each medical equipment by numerically calculating the health value and real-time residual value of the equipment, and divides the node level of each equipment according to the calculation results and constructs the corresponding hierarchical network.

10. A medical equipment maintenance service management system for realizing intelligence according to claim 9, characterized in that: The device status feedback module includes a comprehensive analysis feedback unit and a device maintenance planning unit. The comprehensive analysis feedback unit demonstrates the dynamic network layer partitioning results and the real-time health values ​​of the corresponding node devices on each medical device using an associated network. The device maintenance planning unit helps relevant personnel obtain the current node level and health value of the medical device and perform maintenance operations on the medical device based on the node level and health value. It prioritizes maintaining the health values ​​of each device node in the primary network from low to high; Secondly, the health values ​​of each device node in the secondary network are maintained from low to high.

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