Intelligent Operation Management System and Method for Medical Devices

Abstract

The present invention provides an intelligent operation management system and method for medical devices. The system includes an operation management middleware, a data collection module, a load intelligent analysis module, a health intelligent evaluation module, a node object generation module, and an intelligent operation management module. The data collection module collects the operation data and historical usage data of the target medical device. The load intelligent analysis module outputs load data through a load prediction model. The health intelligent evaluation module outputs a health evaluation result through a health evaluation model. The node object generation module generates a storage object result based on the health evaluation result. The intelligent operation management module stores the storage object result as a node in a preset operation management tree to perform intelligent operation management on the target medical device. The present invention intelligently outputs a health evaluation result through a load prediction model and a health evaluation model, and then manages the health evaluation result through an operation management tree, improving the efficiency and accuracy of the operation management of medical devices.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular, to an intelligent operation management system for medical devices. Background Art

[0002] The existing operation management methods for medical devices mainly rely on manual recording methods, that is, relevant information such as the maintenance status, usage status, and health status of medical devices is recorded and stored in a simple spreadsheet manner manually. However, with the increasing complexity of the management of medical devices, the manual recording method may lead to errors in the recording results, resulting in low accuracy of the operation management of medical devices. Further, when users need to query relevant information about medical devices, they need to obtain the relevant information by querying the form, resulting in low information query efficiency of medical devices, and thus low efficiency of the operation management of medical devices. Summary of the Invention

[0003] The present invention provides an intelligent operation management system and method for medical devices, aiming to improve the efficiency and accuracy of the operation management of medical devices.

[0004] In a first aspect, the present invention provides an intelligent operation management system for medical devices, including an operation management middle platform, a data acquisition module, a load intelligent analysis module, a health intelligent evaluation module, a node object generation module, and an intelligent operation management module; the operation management middle platform is respectively connected to the data acquisition module, the load intelligent analysis module, the health intelligent evaluation module, the node object generation module, and the intelligent operation management module, and stores and manages the data of each module;

[0005] The data acquisition module is used to acquire the operation data and historical usage data of the target medical device; the operation data includes the temperature data, pressure data, and operation duration data of the target medical device, and the historical usage data includes the historical maintenance data and usage frequency data of the target medical device;

[0006] The load intelligent analysis module is used to input the temperature data, the pressure data, and the operation duration data into a load prediction model to obtain the load data output by the load prediction model; wherein, the load prediction model is trained based on preset load data and its corresponding sample temperature data, sample pressure data, and sample duration data;

[0007] A health intelligent evaluation module, configured to input the load data, the historical maintenance data, and the usage frequency data into a health evaluation model, and obtain a health evaluation result output by the health evaluation model; wherein, the health evaluation model is trained based on preset health results and their corresponding preset load data, sample historical maintenance data, and sample usage frequency data;

[0008] A node object generation module, configured to generate a storage object result based on the health evaluation result;

[0009] An intelligent operation management module, configured to store the storage object result as a node in a preset operation management tree, so as to implement intelligent operation management of the target medical device.

[0010] In a second aspect, the present invention further provides an intelligent operation management method for medical devices, which is applied to the intelligent operation management system for medical devices described in the first aspect. The intelligent operation management method for medical devices includes:

[0011] Collect operation data and historical usage data of a target medical device; the operation data includes temperature data, pressure data, and operation duration data of the target medical device, and the historical usage data includes historical maintenance data and usage frequency data of the target medical device;

[0012] Input the temperature data, the pressure data, and the operation duration data into a load prediction model, and obtain load data output by the load prediction model; the load prediction model is trained based on preset load data and their corresponding sample temperature data, sample pressure data, and sample duration data;

[0013] Input the load data, the historical maintenance data, and the usage frequency data into a health evaluation model, and obtain a health evaluation result output by the health evaluation model; wherein, the health evaluation model is trained based on preset health results and their corresponding preset load data, sample historical maintenance data, and sample usage frequency data;

[0014] Generate a storage object result based on the health evaluation result;

[0015] Store the storage object result as a node in a preset operation management tree, so as to implement intelligent operation management of the target medical device.

[0016] According to the intelligent operation management method for medical devices provided by the embodiments of the present invention, the load prediction model includes a load factor prediction layer, a response load prediction layer, and a load calculation layer; the load calculation layer is respectively connected to the load factor prediction layer and the response load prediction layer;

[0017] Inputting the temperature data, the pressure data, and the operation duration data into a load prediction model to obtain load data output by the load prediction model, includes:

[0018] Inputting the temperature data and the pressure data into the load factor prediction layer to obtain a temperature load factor and a pressure load factor output by the load factor prediction layer;

[0019] Inputting the temperature data, the pressure data, and the operation duration data into the response load prediction layer to obtain a load prediction value output by the response load prediction layer;

[0020] Inputting the temperature load factor, the pressure load factor, and the load prediction value into the load calculation layer to obtain load data output by the load calculation layer.

[0021] According to the intelligent operation management method for medical devices provided by an embodiment of the present invention, the objective function of the load prediction model is:

[0022] ;

[0023] ;

[0024] ;

[0025] ;

[0026] Wherein, represents the objective function of the load prediction model, represents the preset load data, represents the sample temperature data, represents the sample pressure data, represents the sample duration data, represents the preset weight coefficient, represents the temperature operation function, represents the temperature weight decay parameter, ||·|| represents the calculation of the two-norm, represents the pressure operation function, represents the pressure weight decay parameter, represents the preset smoothing parameter, represents the duration operation function, represents the base of the exponential function, c represents the duration weight decay parameter.

[0027] According to the intelligent operation management method for medical devices provided by an embodiment of the present invention, the health assessment model includes a life prediction layer and a health assessment layer;

[0028] Inputting the load data, the historical maintenance data, and the usage frequency data into the health assessment model to obtain the health assessment result output by the health assessment model, includes:

[0029] Inputting the historical maintenance data and the usage frequency data into the life prediction layer to obtain the life prediction result output by the life prediction layer;

[0030] Inputting the load data and the life prediction result into the health assessment layer to obtain the health assessment result output by the health assessment layer.

[0031] According to the intelligent operation management method for medical devices provided by the embodiment of the present invention, the objective function of the health assessment model is:

[0032] ;

[0033] ;

[0034] ;

[0035] Wherein, represents the objective function of the health assessment model, represents the preset health result, represents the weight of the life prediction layer, represents the weight of the health assessment layer, represents the preset load data, represents the sample historical maintenance data, represents the sample usage frequency data, represents the maintenance operation function, represents the frequency operation function.

[0036] According to the intelligent operation management method for medical devices provided by the embodiment of the present invention, the device information of the target medical device includes the device registration number, the device model number, and the device manufacturer number; the types of medical devices include diagnostic device types, treatment device types, and monitoring device types;

[0037] Generating the storage object result based on the health assessment result, includes:

[0038] Obtaining the device information and the type of medical device of the target medical device;

[0039] Determining the coding method of the target medical device based on the type of medical device;

[0040] Encoding the device registration number, the device model number, and the device manufacturer number based on the coding method to obtain the coding information of the target medical device;

[0041] Generate the storage object result based on the encoded information and the health assessment result.

[0042] According to the intelligent operation management method for medical devices provided by the embodiments of the present invention, the storing the storage object result as a node in a preset operation management tree to implement intelligent operation management of the target medical device includes:

[0043] Determine a root node, a parent node, and a child node based on the storage object result;

[0044] Based on the root node, the parent node, and the child node, determine a target storage link of the storage object result in the operation management tree;

[0045] Store the storage object result in the operation management tree through the target storage link to implement intelligent operation management of the target medical device.

[0046] In a third aspect, the present invention further provides an electronic device, including: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby implementing the intelligent operation management method for medical devices as described in any one of the above.

[0047] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, in which a computer software program is stored, and when the computer software program is executed by a processor, the intelligent operation management method for medical devices as described in any one of the above is implemented.

[0048] In a fifth aspect, the present invention further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the intelligent operation management method for medical devices as described in any one of the above is implemented.

[0049] The intelligent operation management system for medical devices provided by the embodiments of the present invention, through the load prediction model in the load intelligent analysis module and the health assessment model in the health intelligent assessment module, combines the operation data and the historical usage data to intelligently output the health assessment result, without the need for manual recording, avoiding the situation of recording errors, thereby improving the accuracy of the operation management of medical devices. At the same time, storing the health assessment result as a node in the operation management tree, when it is necessary to query the relevant information of the medical device, the health assessment result of the medical device can be queried only through the node in the operation management tree, without the need to query in the form of a spreadsheet, which speeds up the query efficiency of the relevant information of the medical device and improves the operation management efficiency of the medical device. Description of the Drawings

[0050] Figure 1It is a schematic structural diagram of the intelligent operation management system for medical devices provided by the present invention;

[0051] Figure 2 It is a schematic flow diagram of the intelligent operation management method for medical devices provided by the present invention;

[0052] Figure 3 It is one of the schematic structural diagrams of the operation management tree provided by the present invention;

[0053] Figure 4 It is the second schematic structural diagram of the operation management tree provided by the present invention;

[0054] Figure 5 It is a schematic structural diagram of the electronic device provided by the present invention. Specific embodiments

[0055] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0056] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless otherwise specifically defined.

[0057] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily construed as being more preferred or having more advantages than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those skilled in the art can recognize that the present invention can be implemented without these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope that conforms to the principles and features disclosed in the present invention.

[0058] Optionally, refer to Figure 1 as shown in Figure 1It is a schematic structural diagram of the intelligent operation management system for medical devices provided by the present invention. The intelligent operation management system for medical devices includes an operation management middleware, a data acquisition module, a load intelligent analysis module, a health intelligent assessment module, a node object generation module, and an intelligent operation management module. Among them, in the embodiment of the present invention, the operation management middleware is respectively connected to the data acquisition module, the load intelligent analysis module, the health intelligent assessment module, the node object generation module, and the intelligent operation management module, and stores and manages the data of each module.

[0059] Optionally, the data acquisition module can be understood as data acquisition equipment, such as sensors, which can be installed on medical devices. Therefore, the data acquisition module can collect the operation data and historical usage data of the target medical device. Among them, the operation data includes the temperature data, pressure data, and operation duration data of the target medical device, and the historical usage data includes the historical maintenance data and usage frequency data of the target medical device.

[0060] Optionally, the load intelligent analysis module inputs the temperature data, pressure data, and operation duration data into the load prediction model to obtain the load data output by the load prediction model. The load prediction model is trained based on the preset load data and its corresponding sample temperature data, sample pressure data, and sample duration data.

[0061] Optionally, the health intelligent assessment module inputs the load data, historical maintenance data, and usage frequency data into the health assessment model to obtain the health assessment result output by the health assessment model. Among them, the health assessment model is trained based on the preset health result and its corresponding preset load data, sample historical maintenance data, and sample usage frequency data.

[0062] Optionally, the node object generation module generates a storage object result according to the health assessment result. Specifically, it obtains the device information and medical device type of the target medical device, and generates a storage object result according to the device information and medical device type.

[0063] Optionally, the intelligent operation management module stores the storage object result as a node in the preset operation management tree to realize the intelligent operation management of the target medical device. Specifically, it determines the root node, parent node, and child node according to the storage object result, and then determines the storage path of the storage object result in the operation management tree according to the root node, parent node, and child node.

[0064] The intelligent operation management system for medical devices provided by the embodiments of the present invention uses the load prediction model in the load intelligent analysis module and the health assessment model in the health intelligent assessment module to intelligently output the health assessment results by combining the operation data and historical usage data, without the need for manual recording, avoiding the occurrence of recording errors, thereby improving the accuracy of the operation management of medical devices. At the same time, the health assessment results are stored as nodes in the operation management tree. When it is necessary to query the relevant information of the medical device, the health assessment results of the medical device can be queried only through the nodes in the operation management tree, without the need to query through the spreadsheet method, which speeds up the query efficiency of the relevant information of the medical device and improves the operation management efficiency of the medical device.

[0065] Optionally, referring to Figure 2 , Figure 2 is a schematic flowchart of the intelligent operation management method for medical devices provided by the present invention. In the embodiments of the present invention, the execution subject of the intelligent operation management method for medical devices is the intelligent operation management system. Therefore, the intelligent operation management method for medical devices includes:

[0066] Step 10, collect the operation data and historical usage data of the target medical device.

[0067] Among them, sensors are installed in each medical device, and the sensors can collect the operation data of the medical device. The sensors are connected and communicate with the intelligent operation management system through wired communication or wireless communication.

[0068] Optionally, the intelligent operation management system can collect the operation data of the target medical device through the sensors. Among them, the sensors can include temperature sensors, pressure sensors, and operation duration sensors. Therefore, the intelligent operation management system can collect the temperature data, pressure data, and operation duration data of the target medical device through the sensors, and the target medical device is any medical device.

[0069] Optionally, the intelligent operation management system can call the usage record form of the target medical device, and obtain the historical usage data of the target medical device through the usage record form. Among them, the historical usage data includes the historical maintenance data and usage frequency data of the target medical device.

[0070] Step 20, input the temperature data, pressure data, and operation duration data into the load prediction model to obtain the load data output by the load prediction model.

[0071] In an embodiment of the present invention, a load prediction model and a health assessment model are trained in the intelligent operation management system. Among them, the load prediction model is trained based on preset load data and its corresponding sample temperature data, sample pressure data, and sample duration data, and the health assessment model is trained based on preset health results and their corresponding preset load data, sample historical maintenance data, and sample usage frequency data.

[0072] Optionally, the intelligent operation management system inputs the temperature data, pressure data, and operation duration data into the load prediction model, and the load prediction model processes the temperature data, pressure data, and operation duration data to obtain the load data output by the load prediction model.

[0073] Step 30: Input the load data, historical maintenance data, and usage frequency data into the health assessment model to obtain the health assessment result output by the health assessment model.

[0074] Optionally, the intelligent operation management system inputs the load data, historical maintenance data, and usage frequency data into the health assessment model, and the health assessment model processes the load data, historical maintenance data, and usage frequency data to obtain the health assessment result output by the health assessment model.

[0075] Step 40: Generate a storage object result based on the health assessment result.

[0076] Step 50: Store the storage object result as a node in a preset operation management tree to implement intelligent operation management of the target medical device.

[0077] Optionally, the intelligent operation management system generates a storage object result according to the health assessment result, specifically: obtaining the device information and medical device type of the target medical device, and generating a storage object result according to the device information and medical device type. The intelligent operation management system stores the storage object result as a node in a preset operation management tree to implement intelligent operation management of the target medical device, specifically: determining the root node, parent node, and child node according to the storage object result, and then determining the storage path of the storage object result in the operation management tree according to the root node, parent node, and child node.

[0078] In the embodiment of the present invention, through the load prediction model and the health assessment model, combined with the operation data and the historical usage data, the health assessment result is intelligently output, without manual recording, avoiding the situation of recording errors, thereby improving the accuracy of the operation management of medical devices. At the same time, the health assessment result is stored as a node in the operation management tree. When it is necessary to query the relevant information of the medical device, the health assessment result of the medical device can be queried only through the node in the operation management tree, without querying in the way of spreadsheets, which speeds up the query efficiency of the relevant information of the medical device and improves the operation management efficiency of the medical device.

[0079] In one embodiment, the load prediction model includes a load factor prediction layer, a response load prediction layer, and a load calculation layer. Among them, the load calculation layer is respectively connected to the load factor prediction layer and the response load prediction layer. Among them, the role of the load factor prediction layer is to match the corresponding temperature load factor and pressure load factor by calling the factor matching function and the load factor matching table. The role of the response load prediction layer is to match the corresponding load prediction value by calling the load prediction table. The role of the load calculation layer is to calculate the load prediction value by calling the load calculation function. Therefore, in step 20, the temperature data, pressure data, and operation duration data are input into the load prediction model, and the load data output by the load prediction model is obtained, including:

[0080] Step 201, input the temperature data and pressure data into the load factor prediction layer, and obtain the temperature load factor and pressure load factor output by the load factor prediction layer.

[0081] Optionally, the intelligent operation management system inputs the temperature data and pressure data into the load factor prediction layer. In the load factor prediction layer, the load factor matching is performed by calling the temperature factor matching function and the pressure factor matching function, and the temperature load factor and pressure load factor output by the load factor prediction layer are obtained.

[0082] The expression of the temperature factor matching function is:

[0083]

[0084] Among them, represents the temperature load factor, represents the load factor corresponding to the range where the temperature data T is located, represents the load factor corresponding to the remainder obtained by taking the temperature data T modulo 10.

[0085] The expression of the pressure factor matching function is:

[0086]

[0087] Among them, represents the pressure load factor, It represents the load factor corresponding to the range where the pressure data P is located, and represents the load factor corresponding to the remainder obtained by taking the pressure data P modulo 20.

[0088] In one embodiment, the load factor prediction layer can call the load factor matching table, and perform load factor matching by combining the temperature factor matching function and the pressure factor matching function with the load factor matching table to obtain the temperature load factor corresponding to the temperature data and the pressure load factor corresponding to the pressure data.

[0089] In a specific embodiment, the temperature load factor matching table is shown in Table 1, and the pressure load factor matching table is shown in Table 2. The temperature data T is 45°C (degrees Celsius), and the pressure data P is 114 Pa (Pascals). T = 45°C is in the range of 40 ≤ T < 50. The load factor corresponding to the range where the temperature data T is located is a5. The remainder obtained by taking T = 45°C modulo 10 is 5, and the load factor corresponding to the remainder obtained by taking the temperature data T modulo 10 is b2. Therefore, the temperature load factor obtained by matching through the temperature factor matching function combined with the temperature load factor matching table is a5 + b2. P = 114 Pa is in the range of 100 ≤ P < 120. The load factor corresponding to the range where the pressure data P is located is c4. The remainder obtained by taking P = 114 Pa modulo 20 is 14, and the load factor corresponding to the remainder obtained by taking the pressure data P modulo 20 is d3. Therefore, the pressure load factor obtained by matching through the pressure factor matching function combined with the pressure load factor matching table is c4 + d3.

[0090] It should be noted that in the embodiments of the present invention, the purpose of setting the load factor corresponding to the range and the load factor corresponding to the remainder is to reduce errors and improve the accuracy of the load factor.

[0091] Table 1 Temperature Load Factor Matching Table

[0092]

[0093] Table 2 Pressure Load Factor Matching Table

[0094]

[0095] Step 202: Input the temperature data, pressure data, and operation duration data into the response load prediction layer to obtain the load prediction value output by the response load prediction layer.

[0096] Optionally, the intelligent operation management system inputs the temperature data, pressure data, and operation duration data into the response load prediction layer, and the load prediction layer performs matching by calling the load prediction table to obtain the load prediction value output by the response load prediction layer.

[0097] In one embodiment, the load prediction table is shown in Table 3. It should be noted that Table 3 only shows a part of the data, not all of the data. The temperature data T is 40°C, the pressure data P is 100 Pa, the operation duration data t is 5 hours, and the matched load prediction value is Q4.

[0098] Table 3 Load Prediction Table

[0099]

[0100] Step 203, input the temperature load factor, pressure load factor, and load prediction value into the load calculation layer to obtain the load data output by the load calculation layer.

[0101] Optionally, the intelligent operation management system inputs the temperature load factor, pressure load factor, and load prediction value into the load calculation layer, and the load calculation layer calls the load calculation function to calculate in combination with the temperature load factor, pressure load factor, and load prediction value to obtain the load data output by the load calculation layer.

[0102] The load calculation function can be expressed as:

[0103] Among them, represents the load prediction value, represents the base of the exponential function, represents the load prediction value, represents the temperature load factor, represents the pressure load factor.

[0104] In a specific embodiment, the temperature data T is 45°C, the pressure data P is 110 Pa, and the operation duration data t is 5 hours. Therefore, the load prediction value is Q4, the temperature load factor is a5 + b2, and the pressure load factor is c4 + d2. Therefore, the load prediction value .

[0105] In one embodiment, the objective function of the load prediction model in the embodiment of the present invention is:

[0106] ;

[0107] ;

[0108] ;

[0109] ;

[0110] Among them, represents the objective function of the load prediction model, represents the preset load data, represents the sample temperature data, represents the sample pressure data, represents the sample duration data, represents the preset weight coefficient, represents the temperature operation function, represents the temperature weight decay parameter, ||·|| represents the calculation of the two-norm, represents the pressure operation function, represents the pressure weight decay parameter, represents the preset smoothing parameter, represents the duration operation function, represents the base of the exponential function, c represents the duration weight decay parameter.

[0111] Therefore, it can be understood that during the process of training the load prediction model, multiple model trainings are required. After multiple model trainings, the result of tends infinitely to and That is, when the difference between

[0112] is less than or equal to the preset value, it indicates that the training of the load prediction model is completed. Among them, the preset values are such as 0.01, 0.05, 0.1, etc., which are set according to the required accuracy.

[0113] In one embodiment, the health assessment model includes a life prediction layer and a health assessment layer. Among them, the function of the life prediction layer is to predict the corresponding life prediction result by calling the life prediction function. The function of the health assessment layer is to match the corresponding health assessment result by calling the preset health score mapping table.

[0114] Therefore, in step 30, the load data, historical maintenance data, and usage frequency data are input into the health assessment model, and the health assessment result output by the health assessment model is obtained, including:

[0115] Step 301, input the historical maintenance data and usage frequency data into the life prediction layer, and obtain the life prediction result output by the life prediction layer.

[0116] Optionally, the intelligent operation management system inputs the historical maintenance data and usage frequency data into the life prediction layer, and calculates through the life prediction function in the life prediction layer in combination with the historical maintenance data and usage frequency data to obtain the life prediction result output by the life prediction layer.

[0117] The life prediction function can be expressed as:

[0118] ;

[0119] ;

[0120] Wherein, L represents the life prediction result (in hours); represents the historical maintenance data, i.e., the historical number of maintenance times; represents the service life restored after each maintenance, i.e., the number of hours extended by the maintenance; represents the usage frequency data, i.e., the number of uses or hours per unit time; represents the influence factor of each unit of usage frequency on the life, such as the life reduction caused by overloading; C represents the correction coefficient for adjusting the influence of maintenance on the usage frequency; D represents the depreciation factor, represents the base of the exponential function, represents the decay rate, t represents the usage duration data; E represents the environmental factor, U represents the usage condition factor. In a specific embodiment, the environmental factor E is 0.5 - 0.8, and the usage condition factor U is 0.8 - 1.2.

[0121] Step 302, input the load data and the life prediction result into the health assessment layer to obtain the health assessment result output by the health assessment layer.

[0122] Optionally, the intelligent operation management system inputs the load data and the life prediction result into the health assessment layer, and the health assessment layer matches the load data and the life prediction result in a preset health score mapping table to obtain a health score. In an embodiment, some data in the health score mapping table are, for example: {(load data F1, life prediction result L1, health score s1), (load data F2, life prediction result L2, health score s2), (load data F3, life prediction result L3, health score s3)}. When the load data is F1 and the life prediction result is L1, the health score is s1.

[0123] Furthermore, the health assessment layer determines the range where the health score is located, and outputs the health assessment result according to the range where it is located, so as to obtain the health assessment result output by the health assessment layer.

[0124] In an embodiment, if the health score is greater than 0.8, the output health assessment result is a healthy state; if the health score is between 0.5 and 0.79, the output health assessment result is a state that needs attention; if the health score is less than 0.5, the output health assessment result is an unhealthy state.

[0125] In an embodiment, the objective function of the health assessment model in the embodiments of the present invention is:

[0126] ;

[0127] ;

[0128] ;

[0129] Among them, represents the objective function of the health assessment model, represents the preset health result, represents the weight of the life prediction layer, represents the weight of the health assessment layer, represents the preset load data, represents the sample historical maintenance data, represents the sample usage frequency data, represents the maintenance operation function, represents the frequency operation function.

[0130] Therefore, it can be understood that during the process of training the health assessment model, multiple model trainings are required, so that after multiple model trainings, the result of tends infinitely to and That is, when the difference between

[0131] is less than or equal to the preset value, it indicates that the training of the health assessment model is completed. Among them, the preset values are such as 0.01, 0.05, 0.1, etc., and are set according to the required accuracy.

[0132] In one embodiment, step 40 generates a storage object result based on the health assessment result, including:

[0133] Step 401, obtaining the device information and medical device type of the target medical device;

[0134] Step 402, determining the coding method of the target medical device based on the medical device type;

[0135] Step 403, encoding the device registration number, device model number, and device manufacturer number based on the coding method to obtain the coding information of the target medical device;

[0136] Step 404, generating a storage object result based on the coding information and the health assessment result.

[0137] Optionally, the intelligent operation management system obtains the device information and medical device type of the target medical device. The device information of the target medical device in the embodiments of the present invention includes the device registration number, device model number, and device manufacturer number. The medical device type includes a diagnostic device type, a treatment device type, and a monitoring device type.

[0138] Further, the intelligent operation management system matches according to the medical device type and a preset coding mapping table to obtain the coding method of the target medical device. The coding method is the arrangement method of the device registration number, device model number, and device manufacturer number. In one embodiment, the coding mapping table can be {(medical device type: diagnostic device type; coding method: device registration number - device model number - device manufacturer number), (medical device type: diagnostic device type; coding method: device registration number - device manufacturer number - device model number), (medical device type: treatment device type; coding method: device model number - device registration number - device manufacturer number), (medical device type: treatment device type; coding method: device model number - device manufacturer number - device registration number), (medical device type: monitoring device type; coding method: device manufacturer number - device registration number - device model number), (medical device type: monitoring device type; coding method: device manufacturer number - device model number - device registration number)}. It should be noted that for the fault tolerance of coding, each medical device type corresponds to two coding methods, and the embodiments of the present invention randomly select the coding method for each medical device type.

[0139] Further, the intelligent operation management system encodes the device registration number, device model number, and device manufacturer number according to the coding method to obtain the coding information of the target medical device.

[0140] Further, the intelligent operation management system synthesizes with the coding information as the prefix information and the health assessment result as the suffix information to generate a storage object result. In one embodiment, the medical device type of the target medical device W1 is the diagnostic device type, the device registration number is N1, the device model number is N2, the device manufacturer number is N3, and the health assessment result is the health status. Therefore, the storage object result of the target medical device W1 is N1 - N2 - N3 - health status, or N1 - N3 - N2 - health status.

[0141] The embodiments of the present invention generate storage object results for the device information and health assessment results of the target medical device in different coding methods according to the medical device type of the target medical device, so as to store the health assessment results of each medical device type in a diversified manner, avoiding storing the health assessment results of multiple medical device types in a single way. Therefore, when querying data, the health assessment results of different medical device types can be quickly queried, improving the efficiency of the operation management of medical devices.

[0142] In one embodiment, the operation management tree can be understood as an N-ary tree. A simple N-ary tree such as a DOM tree. The nodes of the operation management tree in the embodiments of the present invention can be mainly classified into a root node, multiple parent nodes under the root node, multiple child nodes under the parent nodes, and multiple leaf nodes under the child nodes. Among them, the leaf nodes can be understood as attribute nodes for storing attribute information. In the embodiments of the present invention, the leaf nodes are used to store health assessment results.

[0143] For the operation management tree, in a specific embodiment, the root node A branches into two parent nodes, namely parent node a1 and parent node a2. Parent node a1 branches into two child nodes, namely child node a3 and child node a4. Child node a3 and child node a4 are similar, so a similarity connection is established between them. The leaf nodes of child node a3 and child node a4 are different. The leaf node of child node a3 is a7, and the leaf node of child node a4 is a8. Parent node a2 branches into two child nodes, namely child node a5 and child node a6. Child node a5 and child node a6 are different. The leaf node of child node a5 is a9, and the leaf node of child node a6 is a10. Leaf node a9 and leaf node a10 are similar, so a similarity connection is established between them. For details, refer to Figure 3 shown. Therefore, step 50 stores the storage object result as a node into the preset operation management tree to realize intelligent operation management of the target medical device, including:

[0144] Step 501, determining the root node, parent nodes, and child nodes based on the storage object result;

[0145] Step 502, determining the target storage link of the storage object result in the operation management tree based on the root node, parent nodes, and child nodes;

[0146] Step 503, storing the storage object result into the operation management tree through the target storage link to realize intelligent operation management of the target medical device.

[0147] Optionally, the intelligent operation management system determines the root node, parent nodes, and child nodes according to the storage object result. Continuing the above embodiment, the storage object result of the target medical device W1 is N1-N2-N3-health status. Therefore, it can be determined that the root node of the target medical device W1 is N1, the parent node is N2, the child node is N3, and the leaf node is health status.

[0148] Furthermore, the intelligent operation management system obtains the first text of each root node in the operation management tree and the second text of the root node in the storage object result.

[0149] Furthermore, the intelligent operation management system obtains the number of consecutive identical words between the first text and the second text with a length greater than the preset length, as well as the maximum length of consecutive identical words, where the preset length is set according to requirements, such as 2, 3, 4, etc.

[0150] Furthermore, the intelligent operation management system calculates the similarity between the first text and the second text according to the number of consecutive identical words with a length greater than the preset length, the maximum length, the total length of the first text, and the total length of the second text. The similarity between the first text and the second text can be understood as the similarity between the root node in the storage object result and any root node in the operation management tree. The specific calculation formula is as follows:

[0151]

[0152] where, represents the similarity between the first text and the second text, represents the first text, r represents the second text, represents the number of consecutive identical words with a length greater than the preset length, represents the maximum length, represents the total length of the first text, represents the total length of the second text.

[0153] Furthermore, the intelligent operation management system determines the root node with the maximum similarity to the root node in the storage object result in the operation management tree as the target root node, that is, the parent node of the storage object result is classified under the branch of this target root node.

[0154] Continuing the above embodiment, the storage object result of the target medical device W1 is N1 - N2 - N3 - health status. The similarity between the root node N1 and the root node A in the operation management tree is the largest. Therefore, the root node A is the target root node, and the parent node N2 is classified under the root node A.

[0155] It should be noted that if there are multiple calculation results with the same maximum similarity, any root node corresponding to the maximum similarity in the operation management tree is selected as the target root node.

[0156] Similarly, the intelligent operation management system determines the target parent node and the target child node in the operation management tree according to the above algorithm, establishes a similarity connection between the parent node in the storage object result and the target parent node, and establishes a similarity connection between the child node in the storage object result and the target child node. In addition, a similarity connection is established between the target child node with the same leaf node in the operation management tree and the storage object result, obtaining the target storage link of the storage object result in the operation management tree.

[0157] In one embodiment, the storage object result of the target medical device W1 is N1 - N2 - N3 - health status. After similarity calculation, the similarity between the root node N1 and the root node A is greater than the similarity between the root node B. Therefore, the parent node N2 is classified into the root node A. Further, after similarity calculation, the similarity between the parent node N2 and the parent node a2 is greater than the similarity between the parent node a1, the similarity between the child node N3 and the child node a6 is greater than the similarity between the child node a5, the leaf node a9 is in an unhealthy state, and the leaf node a10 is in a healthy state. Therefore, after storing the storage object result of the target medical device W1 into the operation management tree, as shown in Figure 4 shown Figure 4 is the second structural schematic diagram of the operation management tree provided by the present invention.

[0158] Further, the intelligent operation management system stores the storage object result into the operation management tree through the target storage link to realize the intelligent operation management of the target medical device.

[0159] In the embodiment of the present invention, the health assessment result is stored as a node into the operation management tree. When it is necessary to query the relevant information of the medical device, the health assessment result of the medical device can be queried only through the nodes in the operation management tree, without querying through the way of spreadsheets, which speeds up the query efficiency of the relevant information of the medical device and improves the operation management efficiency of the medical device.

[0160] Figure 5 illustrates a schematic physical structure diagram of an electronic device, as shown in Figure 5 shown. The electronic device may include: a processor 510, a communication interface 520, a memory 530, and a communication bus 550. Among them, the processor 510, the communication interface 520, and the memory 530 complete mutual communication through the communication bus 550. The processor 510 can call the logical instructions in the memory 530 to execute the intelligent operation management method for medical devices, and the method includes:

[0161] Collect the operation data and historical usage data of the target medical device; the operation data includes the temperature data, pressure data, and operation duration data of the target medical device, and the historical usage data includes the historical maintenance data and usage frequency data of the target medical device;

[0162] Input the temperature data, pressure data, and operation duration data into the load prediction model to obtain the load data output by the load prediction model; the load prediction model is trained based on the preset load data and its corresponding sample temperature data, sample pressure data, and sample duration data;

[0163] Input the load data, historical maintenance data, and usage frequency data into the health assessment model to obtain the health assessment result output by the health assessment model. Among them, the health assessment model is trained based on the preset health results and their corresponding preset load data, sample historical maintenance data, and sample usage frequency data.

[0164] Generate a storage object result based on the health assessment result.

[0165] Store the storage object result as a node in the preset operation management tree to achieve intelligent operation management of the target medical device.

[0166] In addition, when the logical instructions in the above-mentioned memory 530 can be implemented in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0167] On the other hand, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is used to execute the intelligent operation management method for medical devices provided by the above-mentioned various methods. The method includes:

[0168] Collect the operation data and historical usage data of the target medical device. The operation data includes the temperature data, pressure data, and operation duration data of the target medical device, and the historical usage data includes the historical maintenance data and usage frequency data of the target medical device.

[0169] Input the temperature data, pressure data, and operation duration data into the load prediction model to obtain the load data output by the load prediction model. The load prediction model is trained based on the preset load data and its corresponding sample temperature data, sample pressure data, and sample duration data.

[0170] Input the load data, historical maintenance data, and usage frequency data into the health assessment model to obtain the health assessment result output by the health assessment model. Among them, the health assessment model is trained based on the preset health results and their corresponding preset load data, sample historical maintenance data, and sample usage frequency data.

[0171] Generate a storage object result based on the health assessment result.

[0172] Store the storage object result as a node in the preset operation management tree to achieve intelligent operation management of the target medical device.

[0173] On the other hand, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is configured to execute the intelligent operation management method for medical devices provided above. The method includes:

[0174] Collect the operation data and historical usage data of the target medical device. The operation data includes the temperature data, pressure data, and operation duration data of the target medical device, and the historical usage data includes the historical maintenance data and usage frequency data of the target medical device.

[0175] Input the temperature data, pressure data, and operation duration data into the load prediction model to obtain the load data output by the load prediction model. The load prediction model is trained based on the preset load data and their corresponding sample temperature data, sample pressure data, and sample duration data.

[0176] Input the load data, historical maintenance data, and usage frequency data into the health assessment model to obtain the health assessment result output by the health assessment model. Among them, the health assessment model is trained based on the preset health results and their corresponding preset load data, sample historical maintenance data, and sample usage frequency data.

[0177] Generate a storage object result based on the health assessment result.

[0178] Store the storage object result as a node in the preset operation management tree to achieve intelligent operation management of the target medical device.

[0179] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0180] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent operation management system for medical devices, characterized in that, It includes an operation management middleware, a data collection module, a load intelligent analysis module, a health intelligent assessment module, a node object generation module, and an intelligent operation management module; the operation management middleware is respectively connected to the data collection module, the load intelligent analysis module, the health intelligent assessment module, the node object generation module, and the intelligent operation management module, and stores and manages the data of each module; The data collection module is used to collect the operation data and historical usage data of the target medical device; the operation data includes the temperature data, pressure data, and operation duration data of the target medical device, and the historical usage data includes the historical maintenance data and usage frequency data of the target medical device; The load intelligent analysis module is used to input the temperature data, the pressure data, and the operation duration data into a load prediction model to obtain the load data output by the load prediction model; wherein, the load prediction model is trained based on preset load data and its corresponding sample temperature data, sample pressure data, and sample duration data; The health intelligent assessment module is used to input the load data, the historical maintenance data, and the usage frequency data into a health assessment model to obtain the health assessment result output by the health assessment model; wherein, the health assessment model is trained based on preset health results and their corresponding preset load data, sample historical maintenance data, and sample usage frequency data; The node object generation module is used to generate a storage object result based on the health assessment result; The intelligent operation management module is used to store the storage object result as a node in a preset operation management tree to realize the intelligent operation management of the target medical device; Among them, the objective function of the load prediction model is: ； ； ； ； Among them, represents the objective function of the load forecasting model, represents the preset load data, represents the sample temperature data, represents the sample pressure data, represents the sample duration data, represents the preset weight coefficient, represents the temperature operation function, represents the temperature weight decay parameter, ||·|| represents the calculation of the second norm, represents the pressure operation function, represents the pressure weight decay parameter, represents the preset smoothing parameter, represents the duration operation function, represents the base of the exponential function, c represents the duration weight decay parameter; The objective function of the health assessment model is: ； ； ； Among them, represents the objective function of the health assessment model, represents the preset health result, represents the weight of the lifespan prediction layer, represents the weight of the health assessment layer, represents the preset load data, represents the sample historical maintenance data, represents the sample usage frequency data, represents the maintenance operation function, represents the frequency operation function; The device information of the target medical device includes the device registration number, device model, and device manufacturer number; the medical device types include diagnostic device types, treatment device types, and monitoring device types; The generating the storage object result based on the health assessment result includes: Obtaining the device information and medical device type of the target medical device; Determining the coding method of the target medical device based on the medical device type; Encoding the device registration number, the device model, and the device manufacturer number based on the coding method to obtain the coding information of the target medical device; Generating the storage object result based on the coding information and the health assessment result; The storing the storage object result as a node in a preset operation management tree to realize the intelligent operation management of the target medical device includes: Determining the root node, parent node, and child node based on the storage object result; Determining the target storage link of the storage object result in the operation management tree based on the root node, the parent node, and the child node; Storing the storage object result in the operation management tree through the target storage link to realize the intelligent operation management of the target medical device.

2. An intelligent operation management method for medical devices, which is applied to the intelligent operation management system for medical devices as described in Claim 1, and is characterized in that The intelligent operation management method for medical devices includes: Collect the operation data and historical usage data of the target medical device; the operation data includes the temperature data, pressure data, and operation duration data of the target medical device, and the historical usage data includes the historical maintenance data and usage frequency data of the target medical device; Input the temperature data, the pressure data, and the operation duration data into a load prediction model to obtain the load data output by the load prediction model; the load prediction model is trained based on preset load data and its corresponding sample temperature data, sample pressure data, and sample duration data; Input the load data, the historical maintenance data, and the usage frequency data into a health assessment model to obtain the health assessment result output by the health assessment model; wherein, the health assessment model is trained based on preset health results and their corresponding preset load data, sample historical maintenance data, and sample usage frequency data; Generate a storage object result based on the health assessment result; Store the storage object result as a node in a preset operation management tree to implement intelligent operation management of the target medical device; Wherein, the objective function of the load prediction model is: ； ； ； ； Among them, represents the objective function of the load prediction model, represents the preset load data, represents the sample temperature data, represents the sample pressure data, represents the sample duration data, represents the preset weight coefficient, represents the temperature operation function, represents the temperature weight decay parameter, ||·|| represents the calculation of the two-norm, represents the pressure operation function, represents the pressure weight decay parameter, represents the preset smoothing parameter, represents the duration operation function, represents the base of the exponential function, c represents the duration weight decay parameter; The objective function of the health assessment model is: ； ； ； Among them, represents the objective function of the health assessment model, represents the preset health outcome, represents the weight of the life prediction layer, represents the weight of the health assessment layer, represents the preset load data, represents the sample historical maintenance data, represents the sample usage frequency data, represents the maintenance operation function, represents the frequency operation function; The device information of the target medical device includes the device registration number, device model number, and device manufacturer number; the medical device types include diagnostic device types, treatment device types, and monitoring device types; The generating the storage object result based on the health assessment result includes: Obtain the device information and medical device type of the target medical device; Determine the coding method of the target medical device based on the medical device type; Encode the device registration number, the device model number, and the device manufacturer number based on the coding method to obtain the coding information of the target medical device; Generate the storage object result based on the coding information and the health assessment result; The storing the storage object result as a node in a preset operation management tree to implement intelligent operation management of the target medical device includes: Determine the root node, parent node, and child node based on the storage object result; Determine the target storage link of the storage object result in the operation management tree based on the root node, the parent node, and the child node; Store the storage object result in the operation management tree through the target storage link to implement intelligent operation management of the target medical device.

3. The intelligent operation management method for medical devices according to claim 2, wherein The load prediction model includes a load factor prediction layer, a response load prediction layer, and a load calculation layer; the load calculation layer is respectively connected to the load factor prediction layer and the response load prediction layer; The inputting the temperature data, the pressure data, and the operation duration data into a load prediction model to obtain the load data output by the load prediction model includes: Input the temperature data and pressure data into the load factor prediction layer to obtain the temperature load factor and pressure load factor output by the load factor prediction layer; Input the temperature data, the pressure data, and the operation duration data into the response load prediction layer to obtain the load prediction value output by the response load prediction layer; Input the temperature load factor, the pressure load factor, and the load prediction value into the load calculation layer to obtain the load data output by the load calculation layer.

4. The intelligent operation management method for medical devices according to claim 2, wherein The health assessment model includes a life prediction layer and a health assessment layer; Inputting the load data, the historical maintenance data, and the usage frequency data into the health assessment model to obtain the health assessment result output by the health assessment model includes: Input the historical maintenance data and the usage frequency data into the life prediction layer to obtain the life prediction result output by the life prediction layer; Input the load data and the life prediction result into the health assessment layer to obtain the health assessment result output by the health assessment layer.

5. An electronic device, comprising: A memory and a processor, characterized in that a computer software program is stored on the memory, and when the processor reads and executes the computer software program, the intelligent operation management method for medical devices according to any one of claims 2 to 4 is implemented.

6. A non-transitory computer-readable storage medium, characterized in that, A computer software program is stored in the storage medium, and when the computer software program is executed by a processor, the intelligent operation management method for medical devices according to any one of claims 2 to 4 is implemented.

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