A medical device management system and method

Abstract

The application provides a medical equipment management system and method, the system comprises: an acquisition module for acquiring monitoring data and fault data of medical equipment; a classification module for aggregating the fault data and transmitting the fault data to a preset classification model for processing to obtain a classification result corresponding to the fault; a construction module for performing supervised learning training on the monitoring data and the classification result by using a neural network to construct a monitoring model; and a processing module for: transmitting the monitoring data to the monitoring model for processing to obtain a corresponding monitoring result; judging whether the corresponding medical equipment is normal and available according to the monitoring result; if the judgment is no, generating corresponding prompt information and issuing the prompt information; the effect is that the system realizes the operation management of the medical equipment in actual use, can timely judge whether the corresponding medical equipment is normal and available, guarantees the availability, and improves the practicality and timeliness of the medical equipment management scheme.

Description

Technical Field

[0001] This invention relates to the field of equipment management technology, specifically to a medical equipment management system and method. Background Technology

[0002] Medical devices refer to instruments, equipment, appliances, materials, or other articles used alone or in combination on the human body. Medical devices are mainly divided into three categories: diagnostic equipment, therapeutic equipment, and auxiliary equipment. However, due to the special nature of medical device applications, malfunctions can affect the availability of the equipment.

[0003] Currently, most existing medical equipment management solutions focus on the management of medical equipment entering, leaving, repairing, and purchasing, lacking operational management in actual use. To ensure availability, most rely on maintenance personnel to conduct regular inspections of each piece of medical equipment. However, this approach is not only somewhat outdated, but this manual management method is also time-consuming and labor-intensive for the large number of various equipment in hospitals, making it difficult to meet the current needs of intelligent management. Summary of the Invention

[0004] In view of the technical deficiencies mentioned in the background art, the purpose of this invention is to provide a medical device management system and method.

[0005] To achieve the above objectives, in a first aspect, embodiments of the present invention provide a medical device management system, the system comprising:

[0006] The acquisition module is used to acquire monitoring data and fault data of medical equipment; wherein, both the monitoring data and fault data are time-stamped.

[0007] The classification module is used to summarize the acquired fault data and send it to the preset classification model for processing to obtain the classification result of the corresponding fault.

[0008] A construction module is used to train a monitoring model by using a neural network to supervise the learning of the monitoring data and the classification results.

[0009] Processing module, used for:

[0010] The monitoring data is transmitted to the monitoring model for processing to obtain the corresponding monitoring results;

[0011] Based on the monitoring results, determine whether the corresponding medical equipment is normal and usable; if not, generate and send out the corresponding prompt message.

[0012] As a specific implementation of this application, the classification model employs a genetic algorithm incorporated into the K-means algorithm for processing fault data, specifically including:

[0013] First, determine whether the selected cluster center data have high similarity. If they do, process them according to the genetic algorithm process.

[0014] Then it checks whether the value is less than the set threshold; if so, the result is displayed as the corresponding classification result.

[0015] As a specific implementation of this application, during supervised learning training, the neural network adopts a BP neural network; and the collected monitoring data is used as input, the corresponding fault classification results are used as output, and a mapping relationship is established between them to train the network parameters.

[0016] In a preferred implementation of this application, the processing module is further configured to:

[0017] Based on the monitoring results, early warning conditions associated with the fault are derived;

[0018] Pre-arrange maintenance operations related to the derived early warning conditions; wherein, the maintenance operations include generating early warning information and generating maintenance tasks.

[0019] In a preferred implementation of this application, the processing module further obtains the working time and usage frequency of the corresponding medical equipment based on the monitoring data, and generates a corresponding procurement plan by combining the preset equipment availability cycle and monitoring results.

[0020] Secondly, embodiments of the present invention also provide a medical device management method, applied to a medical device management system as described in the first aspect, the method comprising:

[0021] Acquire monitoring data and fault data of medical equipment; wherein, both monitoring data and fault data are time-stamped;

[0022] The acquired fault data is aggregated and transmitted to a preset classification model for processing to obtain the classification results of the corresponding faults.

[0023] The monitoring data and the classification results are trained using a neural network under supervised learning to construct a monitoring model;

[0024] The monitoring data is transmitted to the monitoring model for processing to obtain the corresponding monitoring results;

[0025] Based on the monitoring results, determine whether the corresponding medical equipment is normal and usable; if not, generate and send out the corresponding prompt message.

[0026] As a specific implementation of this application, the classification model employs a genetic algorithm incorporated into the K-means algorithm for processing fault data, specifically including:

[0027] First, determine whether the selected cluster center data have high similarity. If they do, process them according to the genetic algorithm process.

[0028] Then it checks whether the value is less than the set threshold; if so, the result is displayed as the corresponding classification result.

[0029] As a preferred implementation of this application, the method further includes:

[0030] Based on the monitoring results, early warning conditions associated with the fault are derived;

[0031] Pre-arrange maintenance operations related to the derived early warning conditions; wherein, the maintenance operations include generating early warning information and generating maintenance tasks.

[0032] As a preferred implementation of this application, the working time and usage frequency of the corresponding medical equipment are obtained based on the monitoring data, and a corresponding procurement plan is generated by combining the preset equipment availability cycle and monitoring results.

[0033] The technical solution provided by this invention first processes the summarized fault data using a classification model, thereby more accurately mining the fault information corresponding to medical equipment, and then constructs a monitoring model based on this, establishing a mapping relationship between monitoring data and faults. This enables the system to realize the operation management of medical equipment in actual use, and by utilizing the system's automatic processing, it can promptly determine whether the corresponding medical equipment is normal and usable, ensuring availability while improving the practicality and timeliness of the medical equipment management solution, and meeting the current intelligent management needs. Attached Figure Description

[0034] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below.

[0035] Figure 1 This is a schematic diagram of a medical device management system provided in an embodiment of the present invention;

[0036] Figure 2 This is a flowchart of a medical device management method provided in an embodiment of the present invention. Detailed Implementation

[0037] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0038] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate for the embodiments of this application described herein.

[0039] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0040] Throughout this specification, references to "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination.

[0041] It should be noted that, unless otherwise stated, the technical terms used in this embodiment have the common meaning as understood in the relevant technical field.

[0042] Please refer to Figure 1 The present invention provides a medical device management system, the device comprising:

[0043] The acquisition module is used to acquire monitoring data and fault data of medical equipment; wherein, both the monitoring data and fault data are time-stamped.

[0044] The classification module is used to summarize the acquired fault data and send it to the preset classification model for processing to obtain the classification results of the corresponding faults; the data is then summarized to form the corresponding dataset.

[0045] A construction module is used to train a monitoring model by using a neural network to supervise the learning of the monitoring data and the classification results.

[0046] Processing module, used for:

[0047] The monitoring data is transmitted to the monitoring model for processing to obtain the corresponding monitoring results;

[0048] Based on the monitoring results, determine whether the corresponding medical equipment is normal and usable; if the determination is not, generate the corresponding prompt message and send it out.

[0049] During implementation, the monitoring data and fault data are correlated through time stamps; the monitoring data includes various operating parameters and status data;

[0050] To more accurately uncover the fault information corresponding to medical equipment, and to better provide an early warning mechanism for the occurrence of equipment failures;

[0051] The classification model employs a genetic algorithm incorporated into the K-means algorithm for processing fault data, specifically including:

[0052] First, determine whether the selected cluster center data have high similarity. If they do, process them according to the genetic algorithm process.

[0053] Then it checks whether the value is less than the set threshold; if so, the result is displayed as the corresponding classification result.

[0054] It should be noted that both the K-means algorithm and the genetic algorithm are mature algorithms, and their processes will not be elaborated here. By introducing the genetic algorithm, additional similarity and threshold judgments are added. If the judgment is negative when the value is less than the set threshold, the data center is reselected, and the cluster center point is judged again to see if it meets the set threshold. At the same time, the introduced genetic algorithm can reduce the defect of the K-means algorithm being easily affected by the initial center position, thus making the clustering effect better.

[0055] In this embodiment, during supervised learning training, the neural network adopts a backpropagation (BP) neural network; and the collected monitoring data is used as input, the corresponding fault classification results are used as output, and a mapping relationship is established between them to train the network parameters.

[0056] A BP neural network is a multilayer feedforward neural network. The BP algorithm includes two processes: forward propagation of information and backward propagation of error.

[0057] During forward propagation, the input information is passed from the input layer to the output layer. The error between the expected output vector and the actual output vector is calculated. If the required accuracy is not achieved, backward propagation is performed. The weights and thresholds of neurons in each layer are adjusted so that the actual output continuously approaches the expected output until the error meets the accuracy requirements.

[0058] Meanwhile, to achieve more specific monitoring results, the monitoring model includes a main monitoring model and multiple sub-monitoring models in application.

[0059] That is, first, the main monitoring model is used to perform main classification monitoring, and then based on the obtained main classifications, the corresponding sub-monitoring models are used to obtain more specific results.

[0060] For example, if the main monitoring model determines that the fault is overheating, the corresponding sub-monitoring model can further process the overheating fault to determine the specific overheating of the power supply, motor, probe, and thermal protector.

[0061] The above solution first processes the aggregated fault data using a classification model to more accurately extract the fault information corresponding to the medical equipment, and then uses this to build a monitoring model to establish a mapping relationship between monitoring data and faults. This enables the system to realize the operation and management of medical equipment in actual use, and through the system's automatic processing, it can promptly determine whether the corresponding medical equipment is normal and usable, ensuring availability while improving the practicality and timeliness of the medical equipment management solution, and meeting the current intelligent management needs.

[0062] Furthermore, to improve the level of intelligent management and reduce corresponding manual management methods, in another embodiment, based on the above technical solution, the processing module is further used for:

[0063] Based on the monitoring results, early warning conditions associated with the fault are derived;

[0064] Pre-arrange maintenance operations related to the derived early warning conditions; wherein, the maintenance operations include generating early warning information and generating maintenance tasks.

[0065] Specifically, the monitoring results include corresponding early warning results. The actual monitoring results are used to automatically plan future equipment maintenance, and preventive maintenance operations are performed to promptly issue early warnings and generate maintenance tasks before failures occur, thereby improving the preventiveness and effectiveness of maintenance.

[0066] Furthermore, in the embodiment, the processing module also obtains the working time and usage frequency of the corresponding medical equipment based on the monitoring data, and generates a corresponding procurement plan by combining the preset equipment availability cycle and monitoring results.

[0067] Through the above processing, the system can formulate corresponding procurement plans in advance, shorten the downtime caused by equipment failures and procurement, and further improve the level of intelligent management while ensuring availability.

[0068] Reference Figure 2Based on the same inventive concept, this invention also provides a medical device management method, applied to the medical device management system described above, the method comprising:

[0069] S101, acquire monitoring data and fault data of medical equipment; wherein, the monitoring data and fault data are both time-stamped;

[0070] S102, the acquired fault data is summarized and transmitted to a preset classification model for processing to obtain the classification result of the corresponding fault.

[0071] S103, The monitoring data and the classification results are trained using a neural network under supervised learning to construct a monitoring model;

[0072] S104, The monitoring data is transmitted to the monitoring model for processing to obtain the corresponding monitoring results;

[0073] S105, determine whether the corresponding medical device is normal and usable based on the monitoring results; if the determination is not, generate the corresponding prompt information and send it out.

[0074] Furthermore, the classification model employs a genetic algorithm incorporated into the K-means algorithm for processing fault data, specifically including:

[0075] First, determine whether the selected cluster center data have high similarity. If they do, process them according to the genetic algorithm process.

[0076] Then it checks whether the value is less than the set threshold; if so, the result is displayed as the corresponding classification result.

[0077] Furthermore, the method also includes:

[0078] Based on the monitoring results, early warning conditions associated with the fault are derived;

[0079] Pre-arrange maintenance operations related to the derived early warning conditions; wherein, the maintenance operations include generating early warning information and generating maintenance tasks.

[0080] Furthermore, the method also includes:

[0081] Based on the monitoring data, the working time and usage frequency of the corresponding medical equipment are obtained, and a corresponding procurement plan is generated by combining the preset equipment availability cycle and monitoring results.

[0082] It should be noted that for a more detailed description of the workflow of the method embodiments, please refer to the aforementioned system embodiments section, which will not be repeated here.

[0083] The entire solution first uses a classification model to process the aggregated fault data, thereby more accurately identifying the fault information corresponding to the medical equipment. Based on this, a monitoring model is built to establish a mapping relationship between monitoring data and faults. This enables the system to realize the operation and management of medical equipment in actual use. Furthermore, by utilizing the system's automatic processing, it can promptly determine whether the corresponding medical equipment is normal and usable, ensuring availability while improving the practicality and timeliness of the medical equipment management solution, thus meeting the current intelligent management needs.

[0084] This invention also provides another medical device management system, including a memory and a processor;

[0085] The memory is used to store executable instructions;

[0086] The processor is used to execute executable instructions stored in the memory to implement a medical device management method as described above.

[0087] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can also be implemented in other ways. The system embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0088] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0089] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the 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 to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. It should be noted that, in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0090] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A medical device management system, characterized in that, The system includes: The acquisition module is used to acquire monitoring data and fault data of medical equipment; wherein, both the monitoring data and fault data are time-stamped. The classification module is used to summarize the acquired fault data and send it to the preset classification model for processing to obtain the classification result of the corresponding fault. A construction module is used to train a monitoring model by using a neural network to supervise the learning of the monitoring data and the classification results. Processing module, used for: The monitoring data is transmitted to the monitoring model for processing to obtain the corresponding monitoring results; Based on the monitoring results, determine whether the corresponding medical equipment is normal and usable; if the determination is not, generate the corresponding prompt message and send it out.

2. The medical device management system as described in claim 1, characterized in that, The classification model employs a genetic algorithm incorporated into the K-means algorithm for processing fault data, specifically including: First, determine whether the selected cluster center data have high similarity. If they do, process them according to the genetic algorithm process. Then it checks whether the value is less than the set threshold; if so, the result is displayed as the corresponding classification result.

3. A medical device management system as described in claim 2, characterized in that, During supervised learning training, the neural network adopts a backpropagation (BP) neural network; and the collected monitoring data is used as input, the corresponding fault classification results are used as output, and a mapping relationship is established between them to train the network parameters.

4. A medical device management system as described in claim 3, characterized in that, The processing module is also used for: Based on the monitoring results, early warning conditions associated with the fault are derived; Pre-arrange maintenance operations related to the derived early warning conditions; wherein, the maintenance operations include generating early warning information and generating maintenance tasks.

5. A medical device management system as described in any one of claims 1 to 4, characterized in that, The processing module also obtains the working time and usage frequency of the corresponding medical equipment based on the monitoring data, and generates a corresponding procurement plan by combining the preset equipment availability cycle and monitoring results.

6. A method for managing medical devices, characterized in that, Applied to a medical device management system as described in claim 1, the method includes: Acquire monitoring data and fault data of medical equipment; wherein, both monitoring data and fault data are time-stamped; The acquired fault data is aggregated and transmitted to a preset classification model for processing to obtain the classification results of the corresponding faults. The monitoring data and the classification results are trained using a neural network under supervised learning to construct a monitoring model; The monitoring data is transmitted to the monitoring model for processing to obtain the corresponding monitoring results; Based on the monitoring results, determine whether the corresponding medical equipment is normal and usable; if the determination is not, generate the corresponding prompt message and send it out.

7. A medical device management method as described in claim 6, characterized in that, The classification model employs a genetic algorithm incorporated into the K-means algorithm for processing fault data, specifically including: First, determine whether the selected cluster center data have high similarity. If they do, process them according to the genetic algorithm process. Then it checks whether the value is less than the set threshold; if so, the result is displayed as the corresponding classification result.

8. A medical device management method as described in claim 7, characterized in that, The method further includes: Based on the monitoring results, early warning conditions associated with the fault are derived; Pre-arrange maintenance operations related to the derived early warning conditions; wherein, the maintenance operations include generating early warning information and generating maintenance tasks.

9. A medical device management method as described in claim 7 or 8, characterized in that, The method further includes: Based on the monitoring data, the working time and usage frequency of the corresponding medical equipment are obtained, and a corresponding procurement plan is generated by combining the preset equipment availability cycle and monitoring results.

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